From da7accbc1f1f84bdc9702503efd2b053db499121 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Fri, 17 Nov 2017 18:02:10 -0800 Subject: [PATCH 001/174] counts objects and says a description using osx say --- environment.yml | 2 +- object_detection/data/mscoco_label_map.pbtxt | 2 +- .../object_detection_tutorial.ipynb | 121 ++++++++---------- object_detection/utils/visualization_utils.py | 56 +++++++- 4 files changed, 104 insertions(+), 77 deletions(-) diff --git a/environment.yml b/environment.yml index 1b88164..7cbd262 100644 --- a/environment.yml +++ b/environment.yml @@ -1,4 +1,4 @@ -name: object-detection +name: ai channels: !!python/tuple - menpo - defaults diff --git a/object_detection/data/mscoco_label_map.pbtxt b/object_detection/data/mscoco_label_map.pbtxt index 1f4872b..c8a4d57 100644 --- a/object_detection/data/mscoco_label_map.pbtxt +++ b/object_detection/data/mscoco_label_map.pbtxt @@ -311,7 +311,7 @@ item { item { name: "/m/07c52" id: 72 - display_name: "tv" + display_name: "monitor" } item { name: "/m/01c648" diff --git a/object_detection/object_detection_tutorial.ipynb b/object_detection/object_detection_tutorial.ipynb index 655c1d5..3eff70c 100644 --- a/object_detection/object_detection_tutorial.ipynb +++ b/object_detection/object_detection_tutorial.ipynb @@ -17,9 +17,8 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 1, "metadata": { - "collapsed": true, "scrolled": true }, "outputs": [], @@ -47,10 +46,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "# This is needed to display the images.\n", @@ -62,30 +59,25 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, + "execution_count": 3, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['',\n", - " '/Users/datitran/anaconda/envs/kaggle/lib/python35.zip',\n", - " '/Users/datitran/anaconda/envs/kaggle/lib/python3.5',\n", - " '/Users/datitran/anaconda/envs/kaggle/lib/python3.5/plat-darwin',\n", - " '/Users/datitran/anaconda/envs/kaggle/lib/python3.5/lib-dynload',\n", - " '/Users/datitran/anaconda/envs/kaggle/lib/python3.5/site-packages',\n", - " '/Users/datitran/anaconda/envs/kaggle/lib/python3.5/site-packages/xgboost-0.6-py3.5.egg',\n", - " '/Users/datitran/anaconda/envs/kaggle/lib/python3.5/site-packages/IPython/extensions',\n", - " '/Users/datitran/.ipython',\n", - " '..',\n", - " '..',\n", - " '..',\n", + " '/Users/hobs/anaconda3/envs/ai/lib/python35.zip',\n", + " '/Users/hobs/anaconda3/envs/ai/lib/python3.5',\n", + " '/Users/hobs/anaconda3/envs/ai/lib/python3.5/plat-darwin',\n", + " '/Users/hobs/anaconda3/envs/ai/lib/python3.5/lib-dynload',\n", + " '/Users/hobs/anaconda3/envs/ai/lib/python3.5/site-packages',\n", + " '/Users/hobs/anaconda3/envs/ai/lib/python3.5/site-packages/setuptools-27.2.0-py3.5.egg',\n", + " '/Users/hobs/anaconda3/envs/ai/lib/python3.5/site-packages/IPython/extensions',\n", + " '/Users/hobs/.ipython',\n", " '..']" ] }, - "execution_count": 17, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -104,24 +96,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "ename": "ImportError", - "evalue": "No module named 'object_detection.protos'; 'object_detection' is not a package", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mlabel_map_util\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mvisualization_utils\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mvis_util\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/datitran/Desktop/tensorflow-api/models/object_detection/utils/label_map_util.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotobuf\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtext_format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mobject_detection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotos\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstring_int_label_map_pb2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mImportError\u001b[0m: No module named 'object_detection.protos'; 'object_detection' is not a package" - ] - } - ], + "execution_count": 4, + "metadata": {}, + "outputs": [], "source": [ "from utils import label_map_util\n", "\n", @@ -148,10 +125,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 5, + "metadata": {}, "outputs": [], "source": [ "# What model to download.\n", @@ -177,10 +152,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "opener = urllib.request.URLopener()\n", @@ -201,10 +174,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ "detection_graph = tf.Graph()\n", @@ -226,10 +197,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ "label_map = label_map_util.load_labelmap(PATH_TO_LABELS)\n", @@ -246,10 +215,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ "def load_image_into_numpy_array(image):\n", @@ -267,10 +234,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 14, + "metadata": {}, "outputs": [], "source": [ "# For the sake of simplicity we will use only 2 images:\n", @@ -286,12 +251,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { - "collapsed": false, "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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8aMJrL59ybzTmx166z2unt5hMRhyOBeOENHJEW0LePofxUsphH6OQaNA4cHb+\nB5y3C37tG9/iS1+9oO+U0E6oRnO8VWKEbujohg2xFohw/3TOq7du8SOvvcaDowNeOT1gMq2p6hz6\nN2IYuiHDILdN9DGoGpIajLgSii9h5Ty5CpsUCoMipJR1u9dR9q0cwX/HpZX1mgbROvOAYkluyCxS\nH7AxIH2inhrG1lFpjtSs/IKmMjhrqajywSqGZIU+5L4TK4Tk80FQGZw1xBJPs7bG9z1IPryiQtJE\nI4mIEogkyfyrSUokMZDyIRzIyS0xgmRgbRGcQDA5xJ/Z45jZ15HD+7iTaqiCmiEDInU7xloEJKXd\n37P+cSCKUIX8mpEcUxdj8NHnEDs5KVhSxEqOBAF4LepfiZSXSiJWjhRtddckxYkhOcX5xNw2XLQ9\nMhkxHzleefEFJCWqOuvzVAQrAdV8gOSbGLCZnVRTGFvNwCpuB9sUGnubA7AFRSgp5JDj9gfJ0gi/\nZdadQRIMw4BLoKZHjBBDlhQ0XkgGpO0xlwvCaoUh4oxhXFWEpAwxu57rMDBpaqwb5fXFQBWVH/30\np7l69A4HI8P66pyztqci4bsl49FdLi8H7GjKw/ce0daWZxdLBMe4qonRY8Tg1QABSevsSCUlDfnA\nnNkGlQEp63oIHkSojMmRCypULNjMahpXYWLEAsl4ggaszbIYa13R/iuKQ5NCGR8fEj55giT6kSWi\nEBQj1c6BqKqaEEIOA8s1u7111NbLNfWBA4FEKBIvLXkSFJAeGdXTnG8zrtCYWHjFR8U2FU8vzrG2\nwUpN8oKTLIEJmrIWXiMV9U7aYEuSVdCUE9tCQkVYDWu895w9esZq3fLe+QVvvfuIi6tLrJthXJV1\n02nANTWNVsS6zjkIkuehq8BhmLm6nB/QVQmNCdNnFs9OslC1MiV53eU+iSmRBPwQEVflpEENYLJc\nT0R3TLrVCCnLm0QTduwxJCZjS9XUjMYWsQ6fDHKg3MVRe+FMArZTLtsNBKGeHOGt0PlQIpo5wc04\nz/wEGltxODnA1TPCqqVS5d69u3TLC8xGaG0iyMCLt445mnpk2rCoHUKkYsTB1DGuJ6TJKQ9XK9zm\nkkprXrw15lgsz84Gbt05wdaRFxkzOTwgMWO4vOL2uGJ6IhxPH9Ac3eF9NcwmFZezCXUa88mXX2d2\neIe3n52DWTNzjnG3ZNRMWMiKR4sVr7z4gBkdB02Fd3Pe3yTS+RUjv6EaRe7fvYUbLGGjHB9U3D0e\n82DSoGYzarzVAAAgAElEQVTMVbdhONugrmYynnF7OmWk0LoJbz97yud/8fMMMXAyOWR2NOFjn/gU\nt0/vcfeVBzx45T7TyYi+C1g3p6kOGPrIxidScsSodN0GqyHnhoWKANTTxKipGbkNQ1KcKDF5Qkgg\nkdG4oW1bJAmzcc4/aYeIhkCqDEmzIy8pUYtlcjDL+1o5Q4O0WRJSZBl/IkDyP5cJ9H2L9w3OTZFM\nPSEpEqPPCy0HnJGUKwiklIiaw8UiOUOzEiHEgfGopta8qIwxGSgbQ/SByTTricVZpMpVGkQTdZ0T\n+Now0CTlcrnEx8B0MmIybmhcZmqNMTgjxJDBVdLrxBnvPTEmnCvMUPndNnRmxWDIFQ+YNqyvBoJk\nL9day2Q0JsSc9GdNZqP64NFYKlmk4vGHlCePhRS2XpGh7QaOb9/CNpHjY8ev/Oov8Btf/k0+9VOn\n/OhnfprGnbK6dHz1t89Yn32Tj/+ll2jXG+oqV87ofb/Tp334JyAKRwdzXriXeO9yyXnX8vTRGRdn\nz7g7v4tWFU5MTugaEquVZ1l3LDc946llPBnn0C8+J8KoIxEzc1g8yygefER7zzAMPLtacLbqOLts\nOVu0rDeeoU+ZKYyQfA7r1LVl3Ix4YT7ndDrj9PCAg1FNU1lUAqnoDDUpGhLW1Dl0XzR0qsrQtQTf\n8uzykieLBe+fLem9EJNFqhrF0HZrujjg+4Fh8NS1YTJyvHzvLi/dusWd4wOOplPs1sMthTKuSdw8\nTwS7mzciBiNgiuZ3+3r5y04fSmEEb7KGW/tgeHxnKQNR1ci2bINKrlWwZRiNQGUc1ahBbG6HLVrM\nlBKxwLAcjs5VW262IXv9pRpGaUdSnw/6EorPWuIMVlUoIbfMCEc0a5a3CUslATCRD2EpulEBJCSk\nVJNRk19PKUeW2PYzRYJzg3G/2VY+0H+2aIOvtdPmQ1pOU5hxIwIxP8OW+d+C8y1AEhHEuZ32OKtN\ntix3ZhOtzU76ZDpi3FTXbSWAbrflm3MhZkAoWRsqFHlK2Rfj9eN8iIHe3heKNg/gufHKDPn2jgFL\nCtlR2+ahx+gpsTO8q9B6TCR35U7moVn/a8uYDcOA2Y69wsFshtOOSTOibVucG4EfmM1mDMPAZDrm\n/PycxWLBet3SdR2qhhgCzm2jgLmV12OqRaOuu6he1Gu5QvFirpeD3Mjd14QrCXcuOTQX1sjjkLZV\nLNxuXlbmes0OwWcWO+YKBEakVPko99my7cZQuTyeO620MTQjWwA4GM2MmH4X9ZSkfJ8YIxqy1MEP\nAyRP9JmBrZ3dJSxv2ezc3msNvGz7K+Xx1lIJKA6efuhZ9wObYWDT9fR+yOdNTFiJGKs3nFDdsXjO\n5SRb5/K6yVpUIaDZ2Uo50oCCDQpWMe666sVufaqisYiRUpZlxCIbsjbLX7bvzzrUCleYfYWdzvQ6\nmmSworv8H1XFx0AlMKqqXIEpKdYYFAuSQTJJqcUycmCtwbmci4HmShnOGSyOtiQVhhDouh51G9pg\nCbGjcgErOVmt6zqGvt2NOz4iTUUUAyFiiNCkkmfCLuvcxEzCUQgTUxlGdUVqsjQthNw3oesYjDAm\n0oxr5kdz1irEwdOljrEDMxLqpsE4S92McXagHzzDkMAaAonBR9q2J5riZAn44NkMHd14jLMu68Wt\noZ5MsQqTw1tEjXzjG9/m62++zStPX+HoeEYtyqSZIgTWm0ucq5g0lhASMQY0GoIYiIkQA8k4nEqu\nohKzFGMrWUpFurUd95z0XqqFBcAIYbu/xQQqu6oq1tqdnr+xFAVBBsnuT4Am+Z/LkkaObs2YTWv6\ndsnRwTFJI2gg9B2ihsZV+UAMkbZrcwcKfPFn/2O++P/lpq//0bT9v33nH9NbT0g5/Lrpe2KClIQh\n5s3Uh5zdHj0YaUkHnvHIkcIaNxsTfcD7nlFzzNiMuAodKWT96WqzBuBisyYm6GOk7QLBK6NqzMxM\nmDYjqtO7fO1rX+Nf+omf5NM/9Um++utf4j/5kZ/j9gGM4wWvvXbJf/U3/m2+8Mv/jNBWvPvo2/w3\n/93/wCuvvMKf++mfYDxy+MUl4jJLFWMB+JqIW92ceE5vzTg9OcKvl9yaT5k0ljefnPGNt77O8Z1T\nXrn7Am8/fMTKWo6rMSZ1/B4XHB1FXqkaahtxtkNQohkBgi3MoA9DZpOvFqyfXXDe9Xz57bd562nH\nm28vWbSR1Tqz/lJ0w0ZgPpnwkRdOOb17hz/zwi2OJiNeuDOnahKmiliXk8UM1wegiMGGDNT6ONAN\nPevFGRfLBf/0d97g4dmKb10ENpuayjXYZkSIkcVmhY8em2A2MnzqlRe5ezTjhz/2Ee7NRtw7PmLc\nWBBPIGTNnhSQJVuZQLWTPohw4/B/HmxB1pduj15jTQlHfif7ziBZM0olHz6FRQwZ9sZygngSM03c\nOj3GvPUeySYEiw6JFEHLwRNDzBulPp8QJJI3uITgrIIkQupwVVNAtmI1s3B9yOX8ksnX3Za4Eilh\n2HT9JMFmRs0gWFXsEBHyYbVlsSJg4tYhVTC50oHItTRgu8FuK1ncDD1vzZZN0yAQE6qJqiS4bss2\nptqUmndbhyWPhLWuhAjZbc43JQBqQI0pYMLhQ2DcVCCJ11+6z+mtYzT2GZQYwZhtBY6S+a03yp5l\nujs7EEUfrAX4FX9o57jfNLONNKSc/JKSoMmA9oXtze3NkbwKP3R57KSUiWyXLILFGlj4lo14vJIr\nh5QQuUgAhdo5HBaRRFU3GAKHswmnR3Nm6jiYjLk6ewppxKwZcXw0p+03vPDyC/yjf/IbPL644NnZ\nks4HpuMZrd+gWu9Ab47KRUydu2MLApMGJDWl9GaWUqlCIOuA0RxJEJPTDBsiTixChYhD1RLxBaQ5\nMAZnxziTS342TZN1wH6ga4cyJlBKzxBj2OVYbOeMcw5fJE+1zeNYOVvKY2YhaUoWDYrYa/nLLkfD\nGYzNlZiC93TdBu8jNgnqobu6Ik7G1HbKkDwhBILeDD8n1GQ9/s6taEFDIHQDQ0g8vTxn07a8c3HO\nZtPx7YfvcbFY0odIY3uaBiZVAespohqoKot1NbUzWZdtQ147RUokKVGT97ZUZeckaSAFcE3z4fkZ\nBT/0aIqEFHM+QSVlDUMQJYWQ9fKqmCpHC4iUJMts3nskWWLMbHOyEZMiLhfQYd4mDoBxjCz6lr5b\nsBwM81ARgyG1LUO8omksoQpgM8kmxmNGiTuzO+ArlmcLUlzliHGX6LtLrlqPrTuqMax1hJkYmsZQ\nWYeEXCFjPK1xzYhhJIy7noMIJmc8oCXyYBFcyE4GMRFdxDXCqBGGDQQdCH7N0LXMjcGRciWk0QhT\nO5LJ+Gjje6Z2xGw+5v6rL7I2Dl08Y9kuebpsqcyUcx/oVmBcjxuNkNrxuO0563oWvWcZhORzwubZ\nuuNZ23PRRzqvtHbg/sl95gcHDO2G/+dXvsyvfuGXOTwa8TP/8s/w6uuv84nPfAqfEnFZnIpksG4E\nrsYPiaq2GOeItmFQYfAeq4lAYtMNTMgRhliS8rJsKe/nh9Oq5LyUn5CICn1SYvSIKHUhIGrTlH3O\ngK2gGX9oDn43+5MBkkuYz9cVNUryASOKNRQwFDHGXoeLQu5w5/74m++cKwexUDUNrc8hAin6MUSw\nVY2Pia5vcU2k7QcqC1hH7UYkN9D1LZvNilund2k3PdFYQlC6oWzGtmh7MLlUSjKIOEQsvt9wdPeE\n9y+f8Gu/+iv86//hX+HO7Jg/+Pr/zuFhxfrhbzO91/D6q3c4Oa5Ih0ccn8w5e+8x33z4Pie//wY/\n8dkfButIkvWy2yxU2f69AIAUPbU13L99AvWIZ4tLhhD4zYfnPJPHvHL8gNl4RgiJi4s1TuBi1pA2\nPbe6gYORI1qBFHIofccUJmxKDGlg2HQ8vVjwdNPx8OkFj8+VZZcYfC7nlROqFKuJ8bjh9sGYF28d\n8eDWEcdHE6ZNhTaCdwlrlOQ/DCCNdYXhUkKKtEPPe+fnnF1d8fBsxZNlT9QDqrrCiGPwniH0eO9R\nhaaqOJk2vHzniPtHM+7ORxxPKkY1VK4cykWHqCYzrVuNb4pb7evzuuLvLEEt7K/mclr6Xb//5zuD\nZLtjIyG3Ktd7ziyyorJNIoTZZMpoVGNWOTKx66utjt8P+BioXfW8DrSws6q6eyZVRc01cDOQmclC\nvZoCkIMozpjM/oWIkqiMw5gb194+oSq2MIpJ2NXWtR9gpbblA2+yyB9klJ9re3lGKxnI5rC73end\ntp+PplRqKQzrFlhr2mpI839GHCENz7edwqq5DBZEwSkcTCfFYfYkiUhVZcAatzWsE1vdvNFUVJpF\ngqGlnngBztt23iBOd88ctzRjkW5k5r9UNyiJVwnFp4jr1wTviZroQ5/HnMi6J0txlguGdg1SFact\nzw8jhmRilqMkobYus8oI92/fxYaB2bRBY8TVI9abDcfzI2KMpWSbsNysWXc9UbcOXnnuLTNbxsxa\nu5OMZHI8P+cwDGDNTuefNJdPTOU9lm0N7bTb07YaZW7oGp9j41OOAm71jDGlnYOyzaH44Lq8qZFX\ncv1752zRUWfNpSQQ50g+OzZy475KZmsbl3NYMoefdkNrbYWmwDAMuQRb9ARNBE07qQ/G7EiPXQQq\nZcY2pYT3kcF7uran6zzrzcCq7Vi3Hb1XYsx9F/WavbNFfW0wiM37GmJKbXWgRH1nrkFNjrj0mp1r\nX5JMtw7ETWfVx0jwOTrsYyj3kPKsNyoilfwDjR6D0IwyebY9R67XOmANlTF5zqTExAr1qGHmLGNr\nmY4aNr0FydHIqrKY6Kj7rCeubGbHU0o4KzSNZTKaQhxhlx3Gj4hdy2YY6PyGVeuZTAPJgVQNVjSP\ndfl+gm3UxUfFx1zuNEnCx1zVw6dEjEJQgy+kgwI+hlxGMkbafkMfE824wsfE2eKKcWVg6piEmGe4\nrVl0SyQEmqFD/ICaCqzFxxyF9hjUOaSZ0qaOqzZR+RZpIled56qPLDYDaj3OrLEpA9Bl7xm8MvjE\nqvM8eXqeq4WhzCYHSOi4PFvyy5//R8wPv8TPXF7y2kde5bW7L6MKi3VPCD2WnEw3qma4kosWVSFq\nmesw+IhziW4YMLuIgxQHuOwB26gGWU+uMezmekqBGLcElUULkXJTv/y92B8/yiSHSp48uyJ6uPvq\nfRo3Zt0/o5I8TWLvwcDk7pj5dMrV4pw+Blz9gQzFn/95+Jt/8/9fY37+57+395X7RB8KWBecNVRx\nRFxnj6cfBmpjGdUj2hTojUWictWBpoFkDzEkZvMpDmGxWrK5fUzdWIYhEiNshg7vPct2Q9t7Nv2A\nTwnUIjKimhgkrJnWnp/+sc/wtd/4Hf6Df/Un+Tf+rX8T75/yv/29X+Av/Ll/jV/6h/8Xn/+VN1h2\nIJMZd+894N/7a3+Zz33uc/yvf+9/5v/48u/wqY++ysz4MoG2xb+3fxfU5mSXnshLrzzgqO0IYeD0\n5JhAxdtnZ3zt936dj73+L+DslEeP32XVb2BkOPHK1NW0kwZ3Ms/F8Qv7pjoQg6fbLNj0A99+8oQv\nv/kW5xvPw0ctF4sBP5iSwJa/XGViDZPZmNdfvsPLd474oZNjTg+mPDg8xDhBJOL7gNgcwUqqDAKK\nQaxjGFbUSSBEnq2ueHx5zpd+71ucrda8c+5JMmaUZkgT6fzA1eIC73PZLwO8cnqb08OGH3xwi7tH\nM+4fT5iMFTU9HtCSaT74TARWlcGVpJJczSGDxm2SlzFCTB8Ghc6VMFc5hFNJcPmgfbckL1MOL93W\nqBZBokVTwkjCaKktKZZbB1PunRzyzuMVBKWq6xzmDamAxlx+bnu/m2WnjPUFp1TkL66B3AVFKKWK\nRqHSmAlYm3WipITThCsVJFQNlWpOikmS9cqlJp86wZbEHbQk1iVIpt8BEU0us11cs2k3wTMFWGcn\npTgnugU8OcxnjclVMmL+Mpm6JILFKlPXGhLbr5jIvXEjQSXlyjvoda3cpFkSYJOWqIwl+I6RHfPg\n3i1ODicYYt7Ud4A7HxYZaG3voiVRrQDDqKB1Zq5LcX9SyuDkhuQgpYSErbOQk51jygktGnJd3z4F\nfAys+w4xic0m0HulHSAkpd+0nPeO6FtYX/DovMenUr5Kc95jnm85U92HiHEVoe/4+J96nU+8+iou\nrmmMYbPZMIRIXefkpU274vDklK/81u9ztVrz9OKSVJhcP3SlVvM1+DFGcM4RuJbhXP9eStmp/GUQ\nWfGrpUhd7lPVXN0lkSVUUVJOpjU5ymGtZIaLHMYdVw2urrHOEdoNPuaKDqI5sRbNSaLbuWaM2UUT\nUsryk5RiTlqNgZSEppnQhx5rDTZ/GwURU75cJZeEc85SN46msjih1OfO5BGuJqrnbLWgWU2oY0cX\ne0RKKcUir7B2QiqRiehzu/uiQd4sOjrvee/8Mn+Z0NMLNv1A6IXGNPnLu1yDc/Wu1Ja1Fokml2NU\ndkC2kuysToxjZmteqOe4oAwkHtOxUs8yDpnxK3LJbf+EENj0kWXfkxK0fY8ScbrGGEPdOOra7RIq\nq6rK1YwUprNx/t6E6XjXvlTItMEJ06ahqix1N/BgfoAdJ+omUlth5WA+dkyaSGU7UtrgJGaW3whW\nco3/tosY70nSMR8dYJ0l2AYhEtpE6CN9UvpNpDG5BBx1TxpaLtpnXK1a+rbD+MhaA87H/5e9N4u1\nLVvvu37faOacq9l7n32aqnOq6twq39bXXeDajlHAIaAQJRAcgZEAWSJ5CC80kRBCIF544o2HQJTk\nAR4IOMJSIhIcYpkuJk5EHAyOb8x1e/tb7el2s5o552h5+MZae1fduva1FImyuEPaWrtZe625Zje+\n8f/+DZItl3lmqntOyoLcrbhKmRe7SMiRy7LHnghdFLY5s5sy19PI5XbPZS6ciOViXyBmNjkSjKFL\nht0kbK8rT6Y98/Y53zVY5Bx2U2XOBdstCPOGPZY7J3fIu8B71xPPrie+aibsaqBEYZYV19Fw8WLm\n2a5Z+6VCyIHOLvHtep/Cht1GBa++qq7H1MI8LQkZ/os//1MUI/zI576fV199lX/8c9/H2cmC5cqT\ncuDcLHG2UHKg5EwOM9I7KIXdpGFxMc4svW9CT0vXDdRauU5bqhGW6xW97zDeqId/1o6pBsKocDrk\nGwG7EXVe+nbHR6JIBhiGgRACm82G3hZOVyeMpjAsPRfjDrDMqTLOmcViQbiaMIfG8weL48PPv9Pj\nh43D37/V+MDf45TxtiOHmVXnMcuBjdMJ8nSxVi6TwMnJilkEcDjriTWTO0vYFNb9wOlyYJ5H3ru4\n4GThmedIzIkpZL1xbBPb3cx2b7jeBxaLE07WayaTedQvWBvL5z7zCX70uz/FbAOb977Gb/7WF3ny\ntT0/9MN/kH/qj/wYP/H3f4X/+zd+i7/yv/89nrz9gv/mz/55vuvhG/wbf+Y/4Vc///P89b/0X/HH\n/7GPM213nLgFc4yMrhC8ZTEpom2tQ2olx5luccr3fOp1Xnr2gnk/ct8afuEr7/Irv/4r3Ln/kNce\nPiDut7z31guuVhOnd1/iYpeZxhc8Or/D0gvzPFLKHihcX17ylat3+OLXL/n8l6+5nCLbXDjJXttO\nRakszsLZ2ZqXz8/4/gfn3F10PDo/ZTkMjGmHSbR0oMIUI7VYplLZWsBUdruneOco44bdvOc3373g\nzefXfOGdHc6ucPKaops8YbsJ7Pcj5A5nBD84Fsslrz54hddfvsud0xOWS0stW7ZhRYmxcT1VUJi8\ncvRKzso9RJg6wVZto0qpSBYEQyRgpPlFFtFOgWsFt1QsmZziTQelITwHzvgBKYJWxCJk0ck6HSPu\nBC+WIkKaM944YigYIoNbsh7WuAILERBHIhFqxYto2mLOSD4spFRUAYeHJkBEwHu6kJW7JwVnLFYq\ng9N2eE5VE6yMw4hVBJn2mhQKhc72rb2rgqZSApWDDZt2amqFzh+oCTRuszvagx32RWzHxTmLmKq2\nU2KhmKM2LssNx7arzWu5VvpuoFbBTJGCkFrRLEUt76qpCIaMNN9ctVHL9eDtrAVF8YlaDOthxW4z\nIQTu9msGGYgxUkzRc9c4Sm7+wXIT+BGVkwJWF63BgpdErFERbGMQClPjrZbUEKykARopJeZ5BCnE\nOCufsvbEVLjaTUwpMc2ZmBPPRmE/w3avKO60WTGnC2qtLNw9NtvAnPXccyIqPBNPNY5dmKhisU4t\n1D77mU9Sr3Y8evWctHlGnQP5OnG6HMhTYHl2wtsvnvC1J2/x5CJQksfFHdbqwrhaf9yHauIgWPWY\nO0CIeuyNwa8GpmmiCKSok+yAaU4jGRFDRo+xdRPZdlhR/rgtQnbN5YLms28EBwzOknLCm0IxlRRV\n52Lswa85U4tpXbeGSiflkee2OJ9G1V+IycgUMRhq5kgHg4xrASYCeOtYSq/uIKJeupMRQhU6MSyW\n93nyIhHclu5+gAniNLPyPb3vcGKow0gME5RMbxw5JZ5fbsglst3s2YfI1VSJxTDGwpSBfonLBu/Q\nrp8YEKOLDlGnjVjyMcQFC6dpwInwar/gjut4bE8pNZCcJW70WpGusI8Tk2lCq5yJJTOGiTRF5hTI\nxTDPSb3IbcEYDS0qMQAF6QwyTXhr8VaQnWCdsO5XDMbS5Y6C53q+QOqSsobZLbB+wdmpg7znxHRI\nMVx7z66Dl++f8PBkzcnUM+WJ2UeMS7jhLlMWivd6vDc75jN1EdmMhSKWpV+z7s8Y3Amlbgl54p3r\nHTFBPitsw445BV5cvyCFiF13PLCJ5xdP2JHYh8LDZaGeL9nuJza7LU/2EVcy8SXHI9YMxZDihif7\nLVfZ8GxrkR62aebrzwKb6+d88vF9uBe52sPF5ZYnu5E3n12R7j/Ez44SIsHCXeN5Lzjk9AEf//Sn\neXu54b2vJp5dXjO9dcnLj865++guYQ648Ro/J6acCaaycBFS5jpOQOFhvyQZy5npmFLiYtzSz47M\ngos5cHd1ymc//gme7yZ+4beecvH3fwP7V3+G9WD57s884PHHXuFf/Vf+BZaypjMDrnbkyRCygp++\nH9jPhjlZxvgUIz1GenxU2mEuKvz3xtKvHRghG3Cp4MQ2D3Tl78VJawdaeInvbsKafqfx0SiSRYNB\nHBzbMaX9rhRVyC7Ww3HleZyImq3H73r8Tmjz71Qow7HQzrUqVzNnpLojV/P40US9+Zyt9CFQk0BK\nOCoSE75Z3znnqFXN9YPVFkvJqBp0DljriM2Uv/cdOQWuri4YuoGTl+6yXC7V+9h5ihTW6zU/9EM/\nxOnZCT//8z/PH/kX/3mchY+9+pA/9RP/Ol/8tS/y3/2ln+Iv/ud/lj/4Yz/O649f41/+8T/BW7/4\ntxi6jqFqoeEXA2GeiEZTmQ4K7SKCo7LwHeuh5/Rkzfp6RKjEeWa32xDjXTo/cPH8OWOYefbiEnt+\nwtRXnm43rJ1CgWHekePEi4sLnl5ueHa5YY5qTea9WkBlo+iKFRi84fxkyelKRZWr1aD7D6Wy3G67\nxZhIVAJwuZ+pNTPNW8Awbi/Ybre8/XTDk6uReYJqC8bO1CLEmIlxVpvbNid3fsFqdUo/DBjvmEth\nDFCJMFvlENdm+UVrH1XoOl3QOTEYUZV3bRy/rOaplAKHNIcDIhUzlHLw64UUi/poy8GGrbS2+wEp\nbCfegSbQ9kM90GaMtgBr0fjTbAxxnhFjcN7guxsrMuV1NcpNaS4Zt9qah/Nb29DKFatZKSGuCXic\naOGgbVpNNmyGdxoNK3DwWdOV/u3XLyrihcbLvbldvY/ze6B63KJPfPDxg9SJemzRy/v9hw8o7Qde\n+30chg8MY0xbsDT7O2fJomIcDUHR1wpB43NLVq7rsFhipNNjn1TTUFFHCcQcm+ul3LY5K0hT4mcq\n8eCJfItyMqfGM24YQkqJjC4UduNIrZr6FmNkphBT4XofCbEwhsI0Ja5myxQN+1knFVPNMdFNFzYO\n75V2Y6p2RUx7/5gSfe8ZdyP3Hr5EyZmlV5Rvl9TBoFtZVssF1akv7ovLDdOcmWMglaxUgXbyKd+4\nHbdbx+WABt2m1KSWvnVAc7Ut20Q75cBHL8djessn5tZ5dNMpsR+COFXR8/pgana45tThr5JM8+UW\npVpJ46MfqWW3hIeHzyIi+gKl0QxMc5bpO4RCbpxy9WPOVKNz4W4/wfUGcJQxU3MmLtcMncYoGzqk\nqmh9X0ZyiFzsdtRcmKeRKRTiXMlFWLiBWmdy0hAVSj36yh68aA8C1uZsiKWqtR4JXwUfAx2C7aA7\n6cnW0qcOFxOhVEJMpMYnjgf3j6A5ApSiGt9ysK671Qlp3rq1dbWKCLktfkxxKD1Dv1TPlBESUpWW\nQnbqEGQ1cbYkQ5WMsZZV37FwzQUEIWbVPaSscdwhF0JKhBIJjf4ZcyIy61zuhKXvwHeEMBOz4fnl\nDspAZiCI2tWmEIixkGIlxMqYE2NIbCSwmxMhVuaQ2YUZWwrbKbAPmRrSseZJsTCFxDxlxjkyzZnd\nPnC92bMbJ5AVznfsdiPzFI+0qlR0Pum6jr7v6YeBfjHQ2fGo1TDOMpwsuHd+F7PZsvU7dUEpiVIq\nw2KpnT7Xk3JQN5KloxwSEE0l1UhKgVwTKcx0znJ+smJjMjVb4rhjThOf/4dv8sUvvyDXwmuvvcL3\nfs9nuLe+w+OXXme/08Vtis2JrFgcK2LOIBGGZvVrBk3m22wAWAwdhyCclJSHpZ8/k1I53vdzSR/W\njP2W4yNRJAvQWYvkwjjObB0sTwas71n4gTurE0yvUYTjNDFOmRALfX9r4joUtbcfP4gu3/7+tyuU\nD889POeDP98a/+Fn/vDv7sP+Ix6/Dvztb/XHCnzyDVWu/LUvAh64q3979APwH/0Anwf6/+zP3fqn\nT+vzD+Pit3nzhLpg329fAD+uDxPw4gNP/39+uw8C789o/G3GFviV9v3PfXv/8o9sXLWvb/yjeLFD\n7TNhnuwAACAASURBVHUQ/8D7bQoyfKiz27dQwX9w/OWrX1GrpMbfyrQiwJZjQXBQYKd9wLgVi7MT\noFBzAPN+/8nSxJw3frn5yAfWCaZCzNoSLoqqOnOjNs7kFsssRwr17cJWC/GK87bxzJqPN16FVNUq\nfaaUYyGrLdhbuyzVo7Dj9ra7JsJLKVJaYIMxILhv4iqrNWA8FoW6r8qR63bbSeRI46hyLGaNUVpI\nrrpokMaPdE4FPJSA6x3d6RnZBHbzlhID3isqWTLY7lbaYKt2D9ZziIrvcuMu314glFIwtcUfl0wo\nld2sKXQhRS62E1OqbGflOF7uky7Eg6MWR6kdvTslZiFXi+s8GrAxoxTASm89/bTBmk4pIiko39wq\nNcRbLcwe33/A93/607hx5GRQwGM2QkwZf7rCeji5d5enlxu+9u5z3r0Yud6PGqzSkJ5SFM0VEYzV\nTsyh0DzSLG4duwMV5eCoUEshHc4VcRgxWN+uiZDU5UiUNpOoWOOUbuEMnXUsvYGsuoX9PLHd6wTe\nuZ4Dl592XR2sDFMpVGfUxaHq+V4ByfVo9TbXuVEyjFKfjKE3anWIVHzn6b1Srawqjqm50HuPc50G\n+QAvriNPw47Ns8YBN4b1lPBW90EOW7quI+TAHLbkOpHGEVsLXUmYYnCou0Atyg21yxuOdWmLjcP1\nVGulSMY0u0ZXDmBJZFWFB9mwtpbUTYSFJ5AZh4lJItttZMyVRKLERJhmTW+cZ2IopJy1A1F1P1V7\n6/rKuhCLc6Y6EA+5xqMg2kjEmsRgKyFkVgJT2tMVh8mC1I4iHdWMsFggsWO8mKjGcmI6VrajZpgT\n7LI6J53dhWiE53PmajOzn3ecPgr0JvEiTuzma+LmCS/dfZk37p8yj5VnVzP7KbJ2MF0VOmZiJ4Rp\nIsWRsIe88oToeb4dmWIllcJZdIwVptpzMW/ZbAJyNbM6q/hpIodITlCCkJNjDkKqA5dj5mqCaNYE\nVlyHzCZmUnUYVgzuHMNKQcfVktN751zPO8RZCuDFH3nXMhjuPbrP7//c5/jq22+x241EuyONM13v\neP1jnyRu9oypspuuMSGQTGGqCeO1w5emid4arqaRzYsnvD0lsvXY0zvcPT2h3HmFKpZ5DsRp5n/4\n2a8T469xsvqbnK09f/Sf/gP8s3/on+HunTv0IuSpMm035OVruMFgvWGMlZoSgwwY07GbttjO0w+O\n/bSnZEsOKm41jVI37kZSybqMKuD8h1umftj4SBTJ0NTyKVOqrgfFekoKnJ2c4t3A080OqGpIvR3Z\n726hyN8OMgy/bbH7oc//dn/+zvjO+AiOF9N4LJIP9m06yellf4jIjRF2IbJcVObqKdaSU8YbLYQp\npU2S9chfPorz6m0EThBryakSStb2NDdBHyqgOiDctfGKD0XuwcP3Ni9b49FvFgUHOsb7x0E4e1MU\nv7/o/WAR/MFx9DPmYHOn3sO30VluoX+H19Rf3yDVt39vjHp0GqFN4mBt48MKiHcUWxiJ2DxR0kxH\n1wrt96Pbx85ImNu+aw4KSEN5ys1zasXENbXCNhXmlNmEqEhWSlzuEyHBPqoIeEqeVKDSaWCEDFQT\nWonUSN8CXnqSTKSonGrr3bFI1PevzYRD6L0lzoHPfur38dKdU1zYcPf0hN24pVRhConzB2vC7gI3\n9Dx58XU248SL7Y5s2r5qBX+uWiAZY6m3jmyFY/iJuc3jFzmKQnPjIVY5pNW9vwOix5SbBZ7RxZQx\nipDaw6NvPOHcFmFUOuM0nbEq17/SbAGpFKkc/EIMtI5RPZ5nuhk3j2pfWDG956hsFbVqzFS1Oqvm\nuDjsrNMEQucJxRBmyzY6VosBUw117o7Xe5oLZqqEmAlhotQJ4oSlcmLU+tlTmoON1URCkaMQNH9A\n5KT3DxVTAs1mTUgxkjAE79mVyIvxmilZYsk83V9zHdLRcYSsyLGGN2naX4xRhVtGj4te+zdBT7ev\nVQ2GUa55zJkahd08sYqawpijUUuxeaZMk95rTM8UR1KZWfaAWOZYmEumc83T21hCEbahkOfELhSK\nhf1c2EyJq31mF8D0llRhlyqb3YRbZe6lxGaKXI0zuzlhz3pWfkkaE0nae9RCHCfm7Z4QItMY2MWZ\n5ckS3AIxCTGObIR9SGA8YhxWHK7RXPSr1/qoGsR0GNvhuxXW9RR2er+xqg0xzdquWoMpGqhG41wX\n6g0tzTS0xsGiH+icx1ohmYw1Bd95Xnn0iI2/Ynj+DlOeoUZCyXTLQTs/yYP11OZE4cSQUyDlwrh/\nhu0XlM5iXUsmdg4/fJbd/oIwv8ebb77LT//N/4N337viU5/4Lr7vM59gPfScn6wpdkfIlZwFa09w\nbk1wiQ5HypkQ1PGrtq5vrYWSTTMgKoyxkJplb6YyGP9N59a3Gh+JItkag7PKKzTNmN72PYPxnJ9k\nzs8cl+NXWJ2u2O8nCpZUhN307RtCA98pbr8z/n81fvVpocryVktcle7bvVoEiTXEnBSRjBbvRsat\nJdslMztcsRqVDVAFY3xLMztMnMLBBs0gFBGihWzaTTmikdSohVMyWhAc+NK1FN0up4WtdYdCr/kc\nW4+1TlE07elSawsyqVrAl1KOYSvKEdVi/kB7OjjhfDDZUdvoorZ4Wunr3wBnLSlXbhB0RZLFfDiS\nbK0Gplh7SDezGBtJCUitWDJCygtII9Z7bHdGKAOff2fHclmQGvE2QTE42xFruLWtjTbQ9mUuidL4\nzwXe99lKKaS4o1RhDJWQFFUqslBu8ZxIWcAoaiiu4nJR7Zk4nDOUMuCJUDJFEll0f1hvyTXjvMVa\npY3Uessb2lSlgzBzdjbwmU+8Qpcn7tw5ZdU5nj7fkmph5QfO/YJ0avnK19/lrafP+PqTJ1xNkc4v\nSaUQYmxxxoZD7LGI0eKxagDFh5kblZhau16dLA50I0XZaa3Y5ktfPTlVqs1I1xZzzmIFvBUWvWXo\nLFUc+zmQa8H3yuGeKaRSiTXhrFEKk1G+86FQJyX1za+2Laj0uigC5IRxcqQX1mpIpjlYiOB7T/WW\nru+BAkkLeJkDIc04q2K6mY5dWbCXM9LuBvEVEYyzQCbPhTwbStyR5hFnCs5WFoPDiaV2B5/uDsh4\n2xwxSqX69+/kQ1cloXTHlKIK6HBEJ/waCWpiuhq5nCK5Jsai0cUmaaR0mCdCCOznSbs7JRP0I+pC\nuXmJH8oYTYDUv1lx1FyZc0ScVZHfNJHLTKiZhd9RsqVbeu4Od3BRE1lP7w1sx8KTJwXTZzpn2SXL\ndoLJC6vTjmIMl2nmyWZLmve8Mle8h2mGaW+Z9z1jWOFlYD9adnvYzJ5VWRDoSdZrAm6ceF4Kj+7d\nxz3fMY1blt0Zc+lw0zVpY/A54iRhSbhhhe8G9hvl2JZaMTVwuhwwJeOdo/cDRgrWwWI14HvHlKLS\nSir0fqBzC6Rusa4wxp26xrRiMBLUIcsI1jvWd87oV0vG+pwoFbGaZumlELZ7xv2OQibkCHVmPaz4\n7Gc/zbO3nvLVp99gO1+z224oneN7fuAH2O0D85tvcXFVyPWaapxSJaqwnfbQn1JSxS0NtUYImbW1\nmHPDsFgR86ep9z/JKw/u8Q9+/df4X//O/4SZtgym8vs++9181/e9xvd+zxu8/PI5rzx4BHmLkRXG\nKg332fML5qnQdR376fp4TzwsjOdU270A5pC4vP49FiZyRIDqgUcaFRJ3jhwTvtOVVIyxGbIbfu3f\n/ff/v97s74zvjI/0+Ivf908A8C/93X9wdIPQeGlF6KooEoMRXDEwR1IoZDHqfZuUG6iWS/q9s5bU\nOGjADVVXtOBMVLIRzKHoyCra0ZS9Cgek6gPgrk7sN04U+tKN6yvNA/nWG95GTrvOc3CYKFkRg/fb\nQd0UycbcpofIBzfjOI48UQ4o87fmJB+ec1wAmPfb0R3ew7CgUDG1RxiI0fP0RWY1q4uPt8qdHLzS\nlfR9NRZeRLClaTZqJVc5LnTKLYJdKZVQZsAwRdU0YBxilYIRi5BzxWJBPEYiGQ1uQiKUSsk9pja+\nbhOilVJwLTHzYCuiC5Qbrt+hgMq1sF6ekuJIR2E9LLi8VN6WFcdqGJg2O07v3+XFN74BxhBSpF94\nauO2H6JpnbkJBrCiNn16/HVbbp8vSnVxt4ri0jQsYOyN08nxHEAoTaDpbnUNDsEvSjMQQmpIZ84I\nmnyZqp7PqYWYVFB0rt4g24cIcyPSLDV1f4o1xw7PzflFi8YuWNPcShofOueMKaXxgjOxpCNKjnGI\n6XD9Gt8SYjWIoQKWYCsGC1LpzACyVa6qrQSjV0Bt+yMi5Pa5O6NhGPOtsKzD0P2UG69YzxlJFSxc\nVk3ELFmYcJTSxKtUcozYWskpkVPzkG8CyVoNWSoHRYS99Z61VhpNW1MgcybXfOzW5JwZoxBiJrZo\n60EcBkucJ8hJOy6oXiJlwRpBQ3scl9srNqcBGVQsn2ppoluPiMWKornWdFAdtZhG6zIYGZhCPgZ9\n1arf+25geXqGHYUxBmzukDxTykia95QsVKLSunzje9fSAq7AtU5GTipWNsYQQiCXqEFEViO4D12k\n5XLJYrGg5Mg87dvfVTdgraXSvN5bx+Nw/86t01VNc3Ux9ug+oteJiqQ7b/GdbU4jHdaKAhfWc/f+\nPez1RP/0glzU2SYJYCy9X9BXIcmSki0xKHBhCqwXCxKX2vUpA50bWJ2d0z+/x+mZ4eQ8s7l8wpe+\n8jZf+PpX+I3f/AKPHp7wIz/4Pbzy8FVe//gP4qunXy5ICfa7QMmGMc7H+4ccup7Yth/0/Nnsf88V\nyQbrHeIKxjV7jgyLkxW279iHkd7APG7o+p5q8+/8ot8Z3xnfGQDE2WvbvxaKCMWeKTpbCq7xjqda\n6MVgzBJkhZiZ1LxJnTXaKpbS6ADlGJJyaMv2doG1WhS4xsW1KTRvcxXaiLHNyQKOyVLopAqGkpRq\n4ESRrZgVreucp1SdDLvatxZ3RkxWl5AgiKMJSCrOFCbhKPI1mOZpbBAplKKJYFUy6kfrcOVGmFqc\nwbMkV8BooXSgFrhmbwccAw2yd01M2cRaKaEhcBbvVKkdc6KrI6UzRKks+hX9+i6T9Oy3hZoHrO3V\nAmxU4cmh8AS94bdwOGzjrhocVSAZp/7JYnUh1MpyjTu3gCdHtX5z4uj6g3VfacJSwXS9FmnOYcqo\nLctWxFkSQ2+INWtCVxGwS/zJkjDPuH3UmOysiP6rj+/xqdcfM29fsF6viUnYzpFhYTm7c4JgKcnw\n9sULfvVL3+BiN5Kipc4wdIKX5qXe2B4Gg1RNjYz1RjDRVdECqyU7HqR4VvT5xhhdmClfQ4sLtGgw\nCKVmfN+1wr8tmGJUykc/KPpfMrVaskC2lRhGaoG6i3hb6Uzjy+KIVUNvjgUsAsZSJFGr5eB3HeZJ\nz5Os117vNShFUtPnAOsq9EXoaqaKsJs1ZENMxduqgQiushoMvV8xuPuMdYttgroqkA3YUBCf1Q8d\nPX5ObuwaSxZS9BjfUcpMopBURdqK+0OMn6UeC6uqdo1WUcmaC7lx/puOlFwKsUZNMx21rb9PE1MM\n5KhlhxSDLcprTpIw4snNMcVYQyqxLVY8VTS0RpJBjEeMoaLe+u6w8O/W7PuKL4VUMpNEVm4NJfPs\nyTvs5h1RHFP1pKzWkiI9vhuovSd4gWqIQcAvwfYsTE9Me/rzBSVvCflQ2Cecr1TZ8+juQG8NGxRY\nCPuRVx48JKaKf/keuQTe/cZbXO0u+cGPfxyycMdkTLVczte8XAN2fM4sW/bsmkTF4I1n1Q/M11eE\nOmNNJkyVykAxAcrMYAxTyfjeEUqgVMHZXmk3JbFcOfrB4IxlvU+MMQMF8QMpWvI0Ml3NuGzpXWY5\nWKa653p3zRgC0xxJcY8bhO18xeV+w1QKU87UVLHe8frDc9Idz5ffes5u2GKM0G0SLgRkaejMgJQJ\na3vmuCEliG7NZc4sph4QnGRO14Lk55Qc6Jd3sKs7nN15jXl/wanr+a33NnzhGyN/43/5GbzJPDgv\nPH78mB/743+MBw8ekNPEarXi3uldcuwIqR4pKlcbdeZZrU+Vv5y/TWEPH5Ei+YDypJToTpYMXeMW\n0aIIATGGNM1YcXj7kdjs74zvjN8TQyfnQpFWXIlVdwl74D8WfNFJUbxTXuaBp8kNknpoBYo5hCTU\nIye3JCVWWCMNOeKIrNVSlLNWoJrajN9vniNiqUp9xlTRABdjSHVSu7cDVQQNZkAOrhcNvU4JZ1Xo\nZYyhGsGkiEagKwXAtIjew//Xaqhora7OVqb5amoBmYyiWdK20RxinG9xpo/BE1XN6qnqblwbq7nC\n0S/XVHWryAXdF77DdD3WDModzQeetyDijyFFN8dQqCU0RK3RDkQwxWiLGnPY2uNxu83tPozb/HFF\nWBqftmrgArUci2xuIXm1CgYVVBmqxjkDtWaSVKypeK8o0yv373L3ZMnCFoaFJ9TA9fU1/WB4/Y3H\nXF5cs1qv+dpbb3K127OZA9Y54Js5qEc++yGA4yjyLG0Pt+dVLcygqDf4cVdoVPaRW36gIog5LtSU\nKtKOpVrMoO4xB9cS0xZcTahXK0VZ60gVwGLEozaM6O9F/49qtIdSKkjW0IrOULMhBA2tGYbhm1yR\nDrHPB85o4eCpbluKn8ea5pFtLc5XbLY6odfWWjZgvKdERXPLobuRKtA6AbYSKdiqLOqcda86p9t/\nDKGRW92VxmfNTXAs1IaC33x29QcPxGSYp4mUVEBZi9XFFscmBVi9Lgrm5j1079H2vh6vwxkiAtYq\nWn5wXFIBBYiQrbC3SttwQw8pE8aJMCeKTJDvgHisN5hgsHITHHTwb5YC1uhiPaVE4qaz8D6EG7Wv\ndc7ddCJEhZlZKq7rMI06U6yjW99hgWV+fsEwDHTznrjbMO0uSXMmjHtyiTgDIs3FpKHtIq5d+0rP\n0YV7wlg5Os0czqODu4t3DisKcsyix6nWFqpTKjVlva82rjn2JjQnpQRVfeOt0deIUa0pOz+0Qnki\nk4kIgcqUCnGOLDrPcHZKt+iY9zOII1W95ETscSG5GNTlqXMdq8WSOGVCiljnyVUdzofVkv02s1rd\nY7UCbzpqCkzpbX7jS2/zF/7Ln+Tho/t872c/xcsvv8wb9x/w6OFrOOOQEqjZUCjsxz1jGlmdrEm/\nC2e0j0S1aUR442OPub58QUwTCeHJ82dsp5HdfmS73+GHnu2T54SQmNOHv86P/8x/DcDy5BRb7ZEn\n9+67b+Oc4/Err3L/wRmxcbpyhhdXF5R284sxMs8Razy7SZGX692WF5cXvPb6x3i4WvDi+VM+/cbr\n3L1zyrOn7+IXGm+43Y2McyDGTHeyYrvdMu4nuk79n+c58uz5czabHdtdwLueBy+/wv17S1YLj+RE\nTIXz+4/Y7bd85atfZRgWXG12pFTYbSc2my2+7xER7ty9y6uPP8Ybr79KGffkFOgkE6eR3Hmollo6\nSjakVJEHKz7/+c+zG6/4kR9+nX/uR3+QP/3v/ATPv/Ym95YP+Ks/+Vd47733+KF/8vfz1//7v8Nv\nfnWgW54Q5sp6OOGt8TmlFIaux1qrnqthS56uCeMV77z7dbb7HdHAcnmH5fKU07MzjEWN+K3VKF44\nRt6OU4u+LYnabpxjhTBlfAsC6HvhxPVcPXkHqZHtdssUCqerM3zf8+DRyyyGE5arB1jrGHEYEuP1\nC148fYsQZnbbt8lzxNke3y0w/ZrdfKWWOzFzvRmVA2t6nB04OXuF9eouL9+7z3LQSeGQCjQnjX7t\nvT0WbtKKOG8sxWqJEY1oOuDt87wVYL4JUtSaSO2K9O/tcmzt2VIKvfnmdLvxVmLVYbT0YH7mD3/2\nmy+Mw4SPpoVJ0e/bVEqtGW86CgXxjmGxYNwaqAcDdqVZGKsResd0LQ5CG5A2mdcq2LZdkVZoNi50\nqVV9K+WmiMmobVetgrcO73t8K3ilaPz4EflzQs7hWErVWrUeNNpGq80DNxelD5iqYiwRjkmAh7RB\nLYYtYoWSZnJnWtsZXKk3RQkHhbTSRIwx7UYvjf8szNOBP9wW9NZyKANM286SMgyeUgyu6/HLU/zi\nhBh04uv7g/e1VdoZFY7UAj3uvXFHhxLdPuWCy639Uatp4khDaTY1YrT1ekDWoRwnqVx02zXMpOAp\nOPlmmopktXiSlojlEGoM5DCTS8Ia4e7pKXfPz/nUo5cZDNw9XbBaLXj7nfc4PT1l3F400VLgvau3\neLLZcxUTu5BI1ZFTE8HdOt+PHNt6Uzgetiul1LZdJ3BbKrNVi8ob/Zsc6RO37dacc2DAew32OVi9\ndS2tz4mDIoQYyHimKTDFRE7aEnd9B6gtZQwV6o1doVTl7R8YCpWKOwQbNIuyqPWHWrGF2AqTyGo5\nMPTdsegZ56lx7T0VRy66T5xVS7NarHLz66Re5FVFbZmbAjLnTIpRRXJVk/RSLYwxHdvt1Iht/H0R\nYUpRBXrtPmOsNG69etSWqneQhF7X/a0FR6Yyh8Bm3uo2BOX1TylTECL5lmWlPi6sPy40KboIs60L\nYyxY2wItRINbitFbpxSLE8P5y3c5X68ZvKOIUYFYBbNa4JKwDzMynLB2M6erJXNqAU6msPAD3lpK\nzEedBMA8R3a1HENMYppYLHo641gul8z7GWd7qA7vepz1VDQZ1XSZyI5SB/q+JzrPVYx0Z/dZes/V\n1Za5FPZikP01+a1INj3LOLGb9qx6z+AL2KntL6Fki+sqxo2UsqAUIZdArfFIP1IwQ3DG0lmHM5Yc\nI/vdjtR5pcpV6EUVDjkmxBpSyXSrgW61aLz9RA6RmjK9dSxdp2FprlkrWkPG4nrDLIWrOLKLkZhg\nN0/cefgS91//GGXOZDcTTEfYz8Sg9LEDOLLf7plyZLFcYq3l6tkLrPe4ThCXkSKkUBnOoabKuJ/B\nLuhWp9w9/S4Axt01711kPv/XfhmoLFzi7vmS1WrJ2ekaZ+zRI/7s/JTXPvYaJ6erb54nv8X4SBTJ\nUBk6R1wMzFdbYhRO1yf6wWJgv99jfQ9GSEVjFT9slFKoAiEkJGfu3DllmqbGU7slMjquGtUto9aq\nPKOcscarZU4I5FoZBg2RICcWp2tOc2AOI/PscQZyDHrhp5kYE1NI+LKiFggpg4lgHcZXnOswZqYW\n/Z1zHVOcMJIpYSZV6Mcd+RjWAIpiJOY5AoJxjkLler/FPX2Xz372U9AtGK+35HnHGCfmCBVLyo6c\nCzFV9tsXPL77Br/6xX/IF77wBf7P/+2X+Lm/8ZNM+5l/60//GR6/8phf+Lu/yL/97/0HLOxr/Mf/\n6X/LNkw8OP84ly8m+rNlQxk0Ga5isWagsKdUS248rowBYxWZO9hztRX+YeJNWb0MseptnFIkZ13F\nJyvkMDM4y2Aqa7HYeeTc7vA1c+/Esp8rWSa8KQz5iqFm7tDRyUD1quq+9Buy2TDLyE481TqkPyX7\ngRlPic/ZXo+kDLVqK77zS7phxXJ9l8WwovOW2hLijuWo0Qv8iCIiRw5iPuZZHKJgBfKNgMA246qQ\nD+pfRbBMK7ZDrjfvgU5EMd84F5jG5b1xlrjFm71VMH/TkBZx3MBhc0gw42AzZjCNN2yM+np31lFy\n0hYtrUASqChdAWj6/bZtthV25vB7fVQEQjEhU8txctQNUO5k8Q3dMIYskbFx4tbWKX+zFY3WGEpD\nF/RlGoWi8VDTceItSAsakapilYMrgTNeKSPc8Ihp+zIXLYAKmo6nxaju2yKaZHZ73yv6XaAmxKpM\nrIoGldR6iJG/hepWg7OqTneuQ8QSG5pobdunCjWTzS1rt4OdWf4gQtyw8VKbrd6hqLbvOzegHgWM\nh98fis1UrQrdsM1ezTaKQSvi2ucw9WafZfT9emdJ5tCGV4HRsu/ofUcJI65aiBVrPTlE1ssVKUS6\nruPdr32D5yPERrGxrU9w6BzenCK30eybL/1UrXguB46xbletei4fF2hy45nMrfPvwMVV5klt57HF\nGXNE19Sz32pqZ0MaOfxP44McuJvKSTdaoZdb+04EoVAblzinSrEqMlWeeNbuTblJ6ztsq3P2eCyK\nQJgTYgvDcODmW6zpcK7TBMaWqkmtx4XpESFtKGkSQymZOQRoi0dMwla1dTNISyms9H5oRfJtVL+d\nV6L2awcRaaa9V62MYWaeNEzF0XjmJaKJp7p4PNwLcuPJcljQGC1gD+ismIxz2unpqsU4q3xwqorN\nrONssWDpOroMgUKVTDHKv0eUkrBNiaUxmG5BLbmhqkrXOAiRj+CDQG50rwPYpjQQKEfU32CMO/pI\nHxdwNeE7i0WFpOTU7oeixb21zCJchcxFivSLjlItXaycWsuUE8Z2SE3UOFNiomZIES2G7cF20lBr\nbvfoGxQcNDwjJU0DPnxN5uZ6ckavuIhSU2JKem+yjpDicXFcSlGk2liMKH1hGAa6blAP7W5BNaL2\nfTnhqq7+7tw55dXXXuPi+YYQr7Cuhww2q8d/TAUxiVwyuWSSn8lxVOMNaDaOmpoXK4zjFm8XUCJI\nRUxlSiiHmh7nhIcvf5K+73l+8Sa7lEh7CC1QyDev6ifP3uLLX3vCZnPFtzs+EkWyoC2Nvu+xnWcK\nE/lCC+Szs3MK8N6772Ccxzhp2e7fPFLWm9oYrhlMx7179+j7NmGXwjAMDWYvxBgw0rHofEMlKmQV\nuPSux6+a6n8TefjSXcb9Ne+8K3zmk59g8+Qtxv2Oe3fO2MWZXAz7aWSz3zFOgX0IhBAIWY3/13dO\n6KpQiyUX4fnliMsVYz3b8TnXVzPLvsdYx3tP36Zznmma2G53hKQ321QyWWAz7lRBHeB63vHLX/gC\nf/RH/xjTcsebX/oigxP2kyUVT2IJztOvey4uLlinNT/8ue/nl770i3z8k6f81i/9Gvce3OXP/YW/\nzW58grGFn/rLP82/9if/Tf7En7rgp3/2f+YbT55wfvY63lyqpcvB03OxJI+e7bxlqoVYZpKJ7QH+\nCAAAIABJREFU5NpTWvFyEPZ0zpNLYpc19SwljSetqRVYVVG9agynbofvRt5YDby0crx+f0mue+74\nBywtnN+5rxGd2ajFvgRqKaztls7MDFaFEPGhJX3mMSJC2G0Y94mnm8LlfuIr777L/zV2OLFaONkF\nw+KEfrHm5PQu53dfxiI4G/Gt5VaqCk5whm4YMK2FextJnkUjaEXQdnQV5FbggdSCIO+zPi6lNPGM\nuSU6srrSLmjRZW9ij3UhpxSCw6i1Yj5M6t9GJmGttuUP+I8ciyqhZoszllAyYg2LxYJpsWCeA6Vo\nS70UvUZLyji/uCnCDgV857XFWsrxMddCbx1OnAoBC+SQWuwrrf2bKV5toKqFagUnlZI1pGfoei1U\ncqbrOkRUHJNbLWAbehuLeiAbp/ZGS7fU6/pwb0CgFErWDoV16usL4PKC4oymNVVhMA47J51ApLbU\nMU15M8ZosX8rmKQftF0bcgtpaTBmBbwoCmWtJVWhc56hX9HZharwO4exQqlNbEKHMY7SyJ213tA6\niu2RWpFYoCr/O9lKsZrIpiEVhtQCbWo7R8QarPEcuOSH806f4/QOXA90lxar2+7Myh4QnFGHklgL\nrup7LrAkoDZ06e7pCQ8fvMRut+fMe86GU66vr8mTAoSPHj3ixYtnrM7P2Yx73vzGlUbHi8EqO515\nno+hBx+0bDsgyAdEOZdbSLcI3jlmJ5SYkdYFUNTV3nJESTdFttzaDwf0+kjbcVgsuRry4bi3e5Q0\nJFWD6CrGJ0QKvhzsB4VaDKlRfxOx0X4q3vWsl2umGm6lQDbhVknv+8y6IcI8zcQs6vNstRizTvnJ\nOUGUSjCFKgWTCqVZXUUKfe+JcNx36lWtCCJjZIhN0CRW901M1Fyg0wSzoVPApusz0oot4zuwDhEo\nxZByIs0TuWgkeywaClOqCu+dEptI6L3Z02sR2tZDFV0sGStgHINxdNbgvQoORTLOq7BsEA/W6Hlo\nLUvfs+h7Hp6esxwW3Fms2YfAJiZm61qR57gOia89fYJJhZOXPkUqSs/JJWLMcBPgYvR8qRQNOAuV\neR6ZkgJm3ntyyMdF52KxIOfKPAf1m09BKTReWHiDCYkyBQyWvlswloCtntE5vr4feTEl3njjdV5a\nneAuLnG1sp5nnk8Jpi2TJOZ9pjAwTxmRinWJed8W02U+LiCmSV1DblMtvLHUXEhzoHQDBQ206pya\nIdTOkUUXYOvlilU3qF1kLVgx1FwwHjrnGccdOc6sTtasT0+5cB7xC4wfEEm4mvHzyMLAeuh4/Pgx\nD+5Zvmy/wZvvvMmcK8tObQrHea9WkqWnlMRme8l7T6AXjy2ezhhymqkYHIKhYylrhImYJ1IU8vAC\nxDKXCbED/WKB7zyvP3xMiVuMVO6fPWDRL7iY95SizhbjOCMSeIe/9y3nzNvjI1Ekq7dSofeVVd/R\nu57nF9dIEeK0Y+GEcT/h1gMSIcb6oS8zF4e4Bfv9RD9oMdZ7qxw7U1jYJV2dqRTmHEilkM1AlUpK\nI9UkrKnkGrHeklJkGJYwBlKBQZY4LDlXLi8vOV13LLoFTy6vqRiSWGo/cD0KOXucFaZQ+NjpuaLX\nY2a9jpyudlQxlJyVp5UzZU70nccPC673KpzZzROx9IjpqOIRMcQQqAa86anF8JU33+VL732Fj7/2\nMc7u3+Hq2QvkpFJy1ptTiJzVHnNywhz2lFL4zMuPSCmx+vSn+Oo7b/Gpe49Z+Qe4rudn/9Yv8+N/\ncskf+MHvY7p8h//x575EuQPxxZ6aCyerU51U4sxU9iTZUYxa+djqyb7iyJiUyLMWPtnP2uIUnQir\naYVla0PO4yVSZ85PFixN5MHDgccr4e4gPH7JsJwjZ2dnjLs98/49Tr1n0Wnx4L1HBFK60Amv9cpO\nVyoQEhHM2QpvO0ryTHPm6+8u+di7O37znR1Pt4U3NwXbd5w/eowxhjtLgzUFW2esdEBhn7YY4zjx\n51S061BLwnlNAZz21wyLMw4Cq9LcFmp6vxVNrWq3U8iNn+qQogbn2TTIumTl+okibOSG0gIWS2p8\nyJvLx2Dzh18TAEsEl9X7N4pp1BblvirSJsxFSbiVpCIo6ygpHlHHWIvG0naWUOIxTe/Y2q4ea8Dk\nQCXTWcdsKr5W5YXVQi9CbIvcEtV72TlPuU70XY/LFhMK3lukCs7KETl2naWQsdJTS9LfF4g1MwyG\nEgKUgkSNKY2diqaOKvqmAjdOERiqchiNVYSanDEZihQmAribLDZbijp01EQtqonIxhDmogWUzlf0\n1mO8HvcxJYSMa1zwiIUcEP8xcKcaO109U50xWdvp4BS1ruo+obSQFnEuAk3IVP0NsmqtcoWlCIlM\nJmGoILwvDCWSFOms5RgsE3PCWxWNhVoQqxHbN5TkG5eJWDL7qOepKRNmCmTrGUtBrAoVV4sBbyy2\nTjy4f5/r6yea5re74OG9M0KNDPfO+fLb7/FiM2FKjwm7hlA2Z5CltntDUdHXUNGQAqdopemUHlEo\nrDqDSUqTK9ayc5U+OfYlgNMgDokFWxy3vZSNVFIO+K6AE+2oiEPEMgNSBIslANFGXKqYkrGi97OC\nkIOm2znvSKkgksFlTDH4Fuc+SkW8B7GQM82xlnncUW3FC+SS6YzBGiFW9SDPxjFVFZUyG0q1GFPw\nNkNJ5BSxcgfXdWwlkYnkMmMFsqlkn8kKpTBNgrU9dIXrnSNkDe3yYpCkwSc4LWKnNOtCMmV8bBSQ\ntcH7jqFm9XoGpqnixZCzOmdU60kyqtNJzEjO1CwQCs46kmloOYYSBV9npMVca6fGIMXS2Yoh49GO\nTq4ZI4bed3QNqbXVUnPFSuP7dzCaiEmRO/05JkRKCBQpuEEoeUOIwsIU0sWexWogTwkrnhwrnV0S\nbGGTZvI+E3OglKCHLG4pxbIeFuw21yykp+4qsRTSuIU4kaMGoowxMRZIdsF+vqTPA6Z4tvNI7YTN\n7gXGBAavqH9JsEiZVU68fPaA0+WCF8+3dNZAf87S7zGXL4AFo+8YL/csZGS5WmFkoQ4TMdHVgVoj\nPlmGRmccY2JTR7rOMXQLOuMZ/AlT3TDbniA9Q/f/UvcmvbZtWX7Xb8xiFXuf6t776heviIwMZ2Q6\n7UxwGlnCNhZG8B2gRZMG9BCmg+ggEDKGb2Ah2TTcoEETCYGRsUincYEtO+SMKl+84r53q1Ptvdda\nsxg0xlx7n/vihrEQjfCSrt65952zz95rzbXmGP/xL3qWDLHfENPMgBJlIMzKve8ZNBIQ5svIfrlj\no4Wdwn0O+JQZ8gEfAgN36AJLhhoLu1KMCy6FTagUr2y3kftlIkZP2s9GodsIc1lMq5EhLZHD4pnW\npgjFN4/jbojUnZB0Z7x+X3BVqPOAeGEcIqUmDvXe6LqywaknRsduzMzdHlyAItzf7ymSCZt/yYR7\nAE+//ponj7acbzdMS6KmxJImSrZ0qa7ruL2/p+8HHhYID4+UZlwpjL0n5Tuub8wmpagHjRTnmFIF\nLZYWJY5l37wac0ZUjqOTlGws2I89IU5MS2JKEy9evsSHjpwTr252dD5xd33H3WHicLunOs+8mF2N\nusDhcGB3f2Dc9HS9sD3r2GwDuUKIBb+H65sD12nHZpvYnDtUhVI9uAGpETQwDmekklmyspTMbrJg\ngbjd8r/+rb/N17/xkt/7rd/m7PEV/uvn7JfE589e4XxPGHt854hhYKkVug7xnr/w536PDz98n//u\nL/83fP97v8328i3+/u//Q/69f/8/5W/8T/8171zB//4//31e/OiHXL3/XkNvLBrZ0A8hhM74WKEj\n5YVYFN8oAUUL4hxpZ4Ku4JQgyui98cxcxunC5kI573vefzLynW7Lxei4CJlOMmey59CP7OZEGDf0\nvYlcQtvsQ+PxjlvjNGmw3x18R0qGzAQnaDYu68XFwA+uPuVP/Y6nht9lLvCPfvyC5y9mfrwTDvuZ\n2/kGDRumcMXukOm6SBceARWSgja+q48IhuLF7UWbqpUmbAHjpP5i8WqjJPv/QjXuH7D+1Le++xf+\n5aG5/1p8//PYFkXccVS/ijAUo3IUrZSqoIEiGdRQkpwzqeRWqJv9lriTYM3xkHICS3OysELM0C2c\nN/9iBK0WNW+FglClHrmmfd9bIl7jC1tErdI1PvZDpM9EWByRRNXCPFeiN0GTtKJqRc1qs846cmy1\ncVZlHa2a5WTobBOuUk1V/4bzOAwDtVaWVMhVCTGYE0ebDRQ9ORuI644TBqNLeOIYGS8viNtzNEaW\nLMeC3Ubz1SgEsoqDrPi1UTfU9EBo1vi6RTOoRVTTKCiFB0K19VDjKprbhSGeK4f64fGQklH15Ahd\nUUPtnd3Lru9QMdstr5n3Hj/m+598xLYf2bbn6O2cuNvtubq44PGjd1CpvLp+yc1ux5fPvublrqM6\nJXpP31A/LfWEnFeju6ypWSGEI1XLBW9CvUZrEhFcqVAszNsmNLY2O4RQoeYFAfrmqV1I7Vw6vFsT\nGgsheJBkdCMt3O1nu+ZqDQ8qhGBF4knMadHTVEGCcUJDNIs+XKT4dg+oHOOr1+Mo0FSj5AVnFJiS\nMznRJimJGCNjH/FdR6VQNVNZjJeaZpJrEbw1Nc56QXOg5EROM1pmXM32DCst4l4gLflIDQEBF1lW\n3vF+T4kZL0b1C2IBIzUXas7UlNCU0CVDbVH3xcRgWYTaJI44xXtzHgnVnnSKNDgeqnPU4CzUxDV6\n1XrPNi69xVM3S8AQjEZVCpozX+5vyV3Ho/4SrcoYAvNScd7Wz93NNcPoGbuO7WYwzU4LNQlq/F0Z\nOkrKNgd1zuKra2lcbOyavCbuM3rQ2bghpZmcZoKzsJcjn9+J0T68UNxpIphzPlnaDpHu4oJrUdK8\n50Bi6Db4nVLvd3i3ZyMdbp7oxhFXE/NSWXymrqmofaQ6YS4W921TS6iamUtlrgvzoTDvD+Sy+kob\n194momoe4THgC+RaOUwLeZ5xQ0c82+KrmE6l2DX2PjL2IzlXDocDy7LgxVNKZrs5Zxy3PHvxkt1+\nz3k/4itkV+w+qBWpSsqJZUlHHcXZ2Zbb23sOhwNDZ/qrb549M1S8s8ms0WUz09T4zR5A6UZzkNnn\nxHmMQKVOFarDGDSV7Wag1o5pvv8lu+UvHr8SRbKipGT2QUPXcXd3R04H0jyTc7DUmGrjBBCi69/4\nOoKhBGfbEZdmkEIppirXKlzf3jN2FdFKdYKKsixtNFnMl9CJjb6maQLn6IKcxntNGSrekapyt9sz\ndNXEhLPlpAcXUZ1YlaiWaJboS0/KM6oF5xWTNyyt2AgsuiC5knZ7IpFUAI0436MSQS0xp2BWOAq4\nNhpfqvDVNy+4+e6Od588Jj6/pnfK1fnAs+t7tv1jbp8/5/LiCUpAcmFednz51Vf8id/5bd754F1u\nDjuq73FXj/npi2+4PnzJ7F/xg998h7/9+z/C61vU2jig/qS2Rh1Cu0bYZyhtI6sU42X5dSQ+gVZ8\ncAzOc+6V3ivvb8+4GoW3L3vO5kwfnKHR1fxJa3dGzpk4RrrWwAQ9idfWEaz3noS5L/gYkRaYoPmA\n9x05F5a053aa0E7Znp2xHQZ+57uPmT4Q3r2JXF8f+L9/8or7PFP0MTWaLRfF0NfQaA2lFJzzVONE\nID6gzZ5KS0PkHnB2Hx7+DaDvL+trf2H8+oZ/N/T6lyPJFZulm+ocXF0pIA88UNVCMl7jfBpRkFXp\nL7KSrutRDEX7hENcY5+bE0BW8I2S8QCZlCP/7/Rvx1FwKccNauVPfrtIdiJN3GXhIs55QvD4lUtY\njTt9dG94wM2tjXNsThGVUgtKItCd6BPuIQ/4IZ/4YSLgykMVGwfzekG60lBE1viP9l68b44fhsxW\nddZsAc6dftfpzyoQtP+uokOlFfqo+QpL8ymujXS+XtOHx2tU8NPvevj1a9/+rZ8/CuDqyiVV4jAS\nusjYZa4uzzjrAkEz55utXVOEVApD5zk/v+Dl9XP+6Odf8Pz+nlItldHL6oRwet/SGrCihVJXulF7\nv6ubQeNf4wRp6sygpgWIzWfYntWmBTDqRUCkiejErou38Qmi5qDgHMSA6UTU1iO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2AAAg\nAElEQVQXFFzFhYh3Aa8OvMNjmgYvEBCij7b+JZJr4797NZTe2/NmLXaXWXGuZ8nJ/I+d4IaIRscy\nL9RaiS2kIjTLq4pZxZWaLBpYHaUhfi4InfNHTn0phVQrrmvewXNEajUKoijn/SOcBjQXlmlnhf/O\nxuxa7VwmzSCVWZWAsskLvmZi9BRnAnhbz8nG4zUTi6fvN3hvbGKX7T1OraihGuikYiDFssZEJ5tl\nrTSNiLMxd3vGalEkBbx65hZpfZxulWyj/Io9p1vzG4cW5JEyXTBRmujKXU84J+RSqZo5E6NKHTBA\na+gGCBXpz9B5Zrm9ZvPkCW+/8ym7w8GmTKHy8uUr/LbjrYsryznQwoubF7z9+BHb0bObKjEr9XDA\nvXcJtXB3tyP0I/X+jk0XkQKH+5mUKne7iRA6Oo30ceDGT+SxZ/aVMQglVUpZ6LqBu/0d3egJY8R1\nniXtWZaJWhKVLdoX5pz5o5uJNN+yqfc8efKE2+So845P4kgcOioOTYVdmpnyDhdhWWa2m97oDPcT\nfuhxuwVXZoZhZDu+xf7umlCN17vXSuwGnASWZaHPEA97XCxcjBvOk3J9/ZJUD+yXQhfPEX1K6DsO\nXnm139NpoDs7Q3VHvznny6++4euvnpKrkF3ExYDPM06U4mGRmW3suS2Jd7/zAU8urghT5vPdlzgJ\njJ25fG36jutX963GCxYg1QpgHJQOxCux84h6gnpyNoT5TjLXr57z6Oqcs40jdkLnN9zP/5LFUtdS\ncEUYhoHN1SX3y0Q3DmgVHj16hHjP7f2NZZOn1TP4F4/tdkvwwrLMZmM1mzo2duaTen9/jxPzSb2/\nv2flZA7DYK9dK7vdjlIKc7KuJOVCUYvfzNkU9fOSOUwLL549x48bzrZXoMrVVSR2jnlWchZynsjN\nBxQgsaemzGEuLAmgZ1oiuQrj2cZQCt+zObtguz1nGAaGbmy2Uxar+vjxY84uzxBXuLm54efffM71\ny1f8/u//PhLEbJaK8pOf/ISLYeTD99+nfO89br/4ksdnW27uJpa0Q0IhupFf//j7/I9//W9wXl/w\nm3/sU8r8Cl8XPnzvN4ixg74wXo4shxvmV9ecjWeMY2Q+3HA1VJZ8x7zbMc97dsuBKZ4Ra+Wt84E/\n98k7fDh6vvukMsaJ7BPqKpvQsXWOECO0FCAamla1klXwXWTYbOn7nn0tSLfBuM/e/EVbkVSLjYC7\nbmCIkXPN5nH9IHnIrK8q2iIeTMxlzQ+lmFLdOe73k6UJdZ1xinMmd3cMG8+fef8Jf1o+5O/+oy/4\n2R99RnzWM9PxKp2RVRi7C5C3SCkRpVJEcDlQBhOfuZJa8VmgutM4U9dxOGQnx6Jk/eNPU3MDUXT9\nFCeqhXE132Af1Q5NC7mlLqHmW7r6WFVVpFqgS/AZJZHSTMozm1RsnC2Kl9oEEg1Nc+ZL65pTiXoF\nbWElYr6a4pL51uJImHBsDVxYeaAU4/kJtum+5prxps+zapxKpWJxv4saSk1Dnoq2orZWVhqJcw7B\nWwDBinZhaFQUaa+nrUgylDmXQs0FHyM+BvOobYUbcuIVBiOuMi8JnND3AzXNpCWRKjaOp6PURK0n\n9ErEmnJDdOz8naJB2seVFbXXk2cWD5BkWYk2DdlHcQ94elUVp9KS09rJkxPy7dv1WFFileaZLA+s\nC1mpEIXQOrhSlUErT64ueWuz4czDk/NLzsYtN19/Q9d1vHdxzjtP3iLVwpdffsYXXz/n+uaeJRXC\nMDCDuTDkAo2fTJAjWm0TiBMfVNq6UDEO7VHMWR8IFUtbC5jDD6p4Z3QWp81LmdqW/2nSoa2ZktAQ\ndIFazP2hsnLD/VFwtE5HygMe/WG/N8edJhRNdTG6nxPi0NuEA6Xve5ZlaecXcrY7enCB1XnmqLHI\nC4Knjy2IKZkvbkozKpByME4vC4tWfLN4LFQWTQT1rfkxMEpqRZfluH5zLUwU8Obe5MURNTa01Rw9\nXGgJbECpGVfafmapOuZGUsy/f27P3d4LTmaqakPw7V3aZODhNMM8kXOtUKzRXaexZLGpsfPWuqsV\nReI9iFGktqEjhoG3fc+VesRZ8tvd4YbLs3P6cctU4OVuos6ZYXPHo0ePWJblSLsaJRBX4aIT/GZg\nuDijlMLd7Q5cpOvH5kpk3zfPM9M8M5494n46sNRMkUppIwI3dGj0pGrUilCwfW8cybM2vnpD7X1o\n9oT1mHiIM9qYNF3T7f0dP3n2lN+Il/zWJ59Q5gU/ZZ6kZxSETUNZw7RwfXtHkA3beI5PHp1gmHvu\n655DSZDhMFnk9KLC7WHhkJS4uUC6LQd1LAiT2qSz217Sn18Sc6TrjB53t9tZMEn17O4TWgPzUghx\nQBQO+0wtB5QO7zvSkqlHrZJaQEpRHl2M0A3UfEBkSzfaVCYE85+W4Hnx+ecMg+U0BNdi50Wo0Roi\nEROK+twIjA7m/YHQd8ShJ6XEsxfPyWXLduxJaeL8fPOLe8svOX4limRHM6t2gYqjiI0cVQxhFr92\n5EoS25Tf+DrOEaPDS6XMmUeXV+RaefHZlxSF/TQhkhE8AwNzPvlUhhCY55mUHwR5iCctM3NO0EYk\nOWdT4qojZUWKI/gerYlhGPABdrsdOZc2mWqoj3cNnYBaBCEy9BdoGEATXbSuL/gOiaHxsG30EWyg\nT1EIGF/q6vwKp47r3S2HwwGthc8++wxV5fEH7+K18s2XT/EiPPnoAz4Rx88++4LL7cjz5ztyWXgr\nDJRpx/c++oS//t//NT768C007bncbri8vGLotmR3RxeFsf2+w854bX0Q+nrAlwPL7prDbiKlhUUd\nb40D751t+e7lyLu98phXUB1ZAhIiIgO+oWWoTdqqmmJ2IJJLNgqKiIFioUd9wPeGCnsfEcrRV7jW\nig/B1kmpbfx1UkR/O6Ag54rU6SQQo1psa01WuDkBTJSTZMYVTzk8Zzx/zO/+4C3eeyT843/8kle7\nGb9X5tIRXM9d+xz4QPQREd8MuWwN+LqGeljBqmJ157qc16JwfbcmsmtG+w+OdUy2fv2mkfm37y/f\nxusOo16ANmoBVGzkuh4rfaEXE9eURjlSY6tyFHGx0gAwelKxsfHKy9VaUO8MGa+eKtCsT41ykUFr\n4RgQauTS1z4fcGwEjp9ZHYglh0GlqqO2BCfzC24C22+dr3VTXIvkIx0BsYZhpUfIyXHARJFmk3fi\ngZsifxUXrs8nX08/V1twhKHl3nyfpY3Uc6ZKskTGEIy6sVJf7BMf+cItNPpY/J6udWuQXLsSK9+Y\n19eCg2NBclxjD87t6rdtRbop9r/1EifKS7HQbd/oGI/Oz3h0PtCLowOomf39/fE99tF0Bdc3N3z9\n/BUvb+5JaoXGsmScNF/walaBzj0kIq3PTjmtSVmR8XXU/eY1sjbbtmc4pPG2T+vn9XWumKjIObNb\nNFqPHsfsrPSgb723X7j3ijU52poQ8dagp1rweXVMOL3Hh+fWvm7OHd6+liYkBD02LbHzR5RfV0oF\ngaqJKpYWZ6NoK0ZyWpsJtedqqZC1UaEsDrqoIbqhcbopbd0JBCcEhOSMO1up9GqUqpWwriIs1TQH\nh/LA37sJyY7nyNvUQ9ak61YEagsOWYXY659jomKjXJRSqK6N4sWa++CtgRgRulyQnCzxUhStloyq\nfU8Vx9Nnr9gOjzjbXrA/zCxLpmSla2mqtVaSFjKV2iZgtkasWLci2uFdgKqkbH7C4m0SU7RRf4L5\niefmdGNCQE7c7AfPlvV5tH6+1JwjxnEk9oG4VLbbc5bljjCeEzcX5DBy2C02HdgOpKVw9+IZU4aB\nyuBAQoSuY1JlqYqKWXziVpG5XYOsRnfLal8vpZDUfOXnUkGqBXHFjvmQyeUktI4x0IW+8cPNGlfF\no9UbEJhnajFBpoXxtMa0UVUqVnsN6thNB9I007eQEnGO+8Pe6GjBN9MDC4Naw5eWlKGdQ4fgUjpq\nDI7Pe+epvjKnhTl1XJ5vzUf+l1Ah33T8ShTJOBvfzot1N86PdkMq7Pf7YxpRcJ6CEevfdKSUGIae\n7XZkaQty7HsePzrn1c0tS04s88QwbNhst0dR1CoUSimRl9lEYIstiN00cZgmQj+00blwd2+2cYlI\nqFjakfWE1i3NUAnUAqkIUzJFf/IbNLeQESJj/5hwfs48z8QY6fuevu+JzjOOZn8Sot1EXbNByi07\n/cnlE87Hc0pWDocD9/s7vvjsc755/jV/ZvyzfO+jj/nJD/8ZX37zD/jtUPmtDz7ge/4DvvnmJZ07\n4zAl8u5zPvn0I77+8XOu4pb9q5nHmyeMH56T9/dsR8/jJ4/xQcnOnCa2m0u6EHj+7CVKZeyFi6Fn\n+fIp86x88PaGf/Wjt/nwvOM7/Z4zschUUce2e4TzkXlKLGA2N13k/OzSknXKguZV2Q59v6HfjISc\n6YYeXMB1AcEfuVHizIZnng/MyZALS3Ry5IYqd1hQR2iesPMyk8pE17X447U4dQEpSr7fQy1ohKId\nYYiIFqaXL9j053z6aMMnf/6Mm/uJ2zmw31eePZv4Z7s7nl1PzKXjdh4hDGi1jtVsz5r6Nrz+sFx5\n76vwat3EgzZRX+OHllYs+QcbybpZ11KPG+m3j05p/FjBeWfFFiaYaKAQMQbc+vCqpRWLa3tm3Lxa\nFK2KH04otxp1vxVoVmyrahMrdUcuoYjZeUkup/fuhCSVPth1OSqk21RhRT7XQsYQd2mx1bXx4k10\nZHTb1pBwOg8Pmwn7Wo6b0roxDd3AKpKj0VxWNMOvfNBjytbrr7ter+A80hm1aplmmyDREEot5FJx\n0bPG1hrVpoltpLktnFgUJnZstJS15On19B7e1BitzYtvC0gfFvpqoSaGTgknUeCxlTgVku19mFCt\nvUbjMGouzNOBPjq+/8c+odfMkzjz+GwA77m93zEtM10IXGy2aKn86I9+yrNXEy/vFvZzJangnTkz\n0JqZdhWPzcvDJgesCJHG41asoPFiTfNa1DtnXFJLEGxUElGci3j0+P9sXVVU42vns9aKCxYrr64t\nBCenokaP38xqjvewqHMIXhzzbFMq30WcgBQTkPfONDW+QlkbBOH4WqUmqhkx28ShRVCrQi1NNBpX\nDntnPuEpmQ+3zubOUZzFzqt5qjcT83b/e0QqybXmtn1Oqe0vwZxCcjZnIkSPn/koDG4uFiJWUFuY\njxjVR40Ws3JO0YxvSKlrxbAKqLP9Vr2zRsKZBuj4XJC1IF/XtCMV4zDLNFsR7DLUQi4dqoFh09N5\n0MOeLnZUqZxtBvLtNbvdAXGOBWU3zUy5kIpSKzgXoAskmgNFNc2QiLmuhDiQyx27/UwVZUknFwvV\ngkZl7Afubg+UnEjLZNPqJVHW16k2kXAxGApKaTSZmb43cWouiVQyh3lmn2YKmSlNzCmzPb+g1AM3\ndzdsNhtc3HCo93z19DO2YeasH3mUFd3PXPUBZmGJZjl7fXjFPt2hLoObCQidRJgrOifu72/bs8Ao\nbN47puVArmZ5q2RcMHeMKSlzquSijfLmOD/bEIKl8VWppLLQ+8icMjot+K4nJaxlU23X3qhMFY7h\nQZIr6TARzy+IXYeLwQJp5krsOu6ubwxljx1+s6HUxDId2sR5NNFsNTcR0czQ9bb3tOnki7t7DvPM\ndrT3W+v8C8/PX3b8ahTJaHsYmSWbKHTjYCKPttAskcgehKsN17ePXBaWRSiDa/yp+mAUV17b6Ixr\n6I5pR8e88vY9y2JRjdM8s5sOhFrpXWAYNuRkfJYC7Pf35LwgEhsNpFKya8/X3saE1XLpQ+jwvsLG\n1L2xN1Pv6Iz8H2OkC5GgHochcbkaR9c1hKNaO0Z0kegiV1dXPL55hErlbndDmhI//9GP2IaBDz7+\nhJevvuGP/vAPOXeV737wKXnOpMNLkitIt3DYfU3QxP3NLduLc87PHjH4Hj3fc/VICGIWWb4LxBCM\nj+gd9JEpZ4azc76zueL5fuKrZ8/5+MnI++eeJ4My+IqQOISBzgXGYF7GSZONsw3CAQxViC6i3tH3\nI0uuhL5j3JyR5x0+eMQLuYn8aIifaiGlhVJnTAHv2g1YEJvKtYJoAV0FX5ElT0fcrjThlVbwap6q\ntiHbZpqnTDcaGluTxYPXIJxt4HwD+Szw0aNL3t1n/vBnL3l+V/jD/cE46K48GNXayNC502jPRFb2\nPtyDukfU/l4bP7QawHNUi38bSV5H0m86gl8FqM3vs2DIo8iR6+uabYA2viBUkjoT2jiLkW2SDrtj\n1Yqa1UvXia0LLS2FEOx8Y6h8aCh6KTYydV4oTtHgjiiOcipEjoSBByjo+nlX31xVQ2fMf/T02dcg\nl7Xge9PxELVam1+zhTTs1rXpQ9bawoi+5fzBgwJJm+K9qa3tGWLFVirJ1lXo25q1omU9PKei9PSe\nv0W5QI9jxfWcrDW61W927asanzwYlo6XFiHd+AO1xR6vr6FtYRm6vJ7rU5G6Fo4iwlwLoc09jK7U\nMWwG3LxrfumOXU7MZSaEwHa7xXvP/nDgm+fPeH69sF+qobJYepzUYkK8xi0qDsL6weQ0qVjR75UG\ngQLemZOFyJFX7cSs1VTktfNp19emNwLQEFfauvdOjGvb7qWVky1ixZuWfCr8aPepnNbRMcr4gQXa\n0bmgVpz4k1d3LhQxusq30en1tZzj6MHMGnW9ItfNSUJ9b81TUQgOrU18qtJG93bP6grbqtFoRMy1\nYKWrOGfWebVWisvN4k5Z7UmX9nrreXTizGpPXGtqPF4dydso3DXaQlDBEXEiDGJAz4LZf9X1+Xdc\nh0a59N6fziUgzWPd1kzFi+JytvuqAQZzzbgES1AIgl8qvmZqcfSu4lmoy8QyH6hq1KnSKE6rL/ch\nLUxpaZMkofeRTTSQqiktUBETkAnk2kRyuq41c44JYtpDj9LHzvy4G7JfHEhsos96mhQBjfpzmkyo\nqgnovLe0u8HRHQYoE1omez0VbqeJZ/MNH70z8v3zS0I4IF3Ae9DdDq8Zt79FpzvcrKxWfAFve0Ay\nrQg148gMnaMLWNJdyTgqiNJFRwyOWteY9tMadyhOCqUsiChZC9sQKSWRktFxVsFeXcWgeEOoVTns\nLaFQC0yHRC1GocjLQmhpyH3fU7dbswbO+SjSD5j+pOaE+mDPBifEJoZf9/d1epNS4ubmhu12w7A9\nNcj/b8evRJFs6TrKXDLdEEEy7777LmVJxAat301QbjPBxderiQdHTpX97sDF2UDWxHYTubu75tnz\np0xzJqjnfplY6oHdNNM5YTMEqAspCbXA9c0OVTgbLtCiLLsCxfPq1Q3bizNUOsJwxjJn+tFxuDvw\n8uXEZhNwBFQd93lrD9/ejOGRlp6lnr7vGcYnzPNMNwyEKsR+wEfrtkPfIS6zlAQ4gp54k6vK14Sw\nZv79pHuC1l/j8tUVP/7yZ9zvdvzsJ1/wsy+f8hf+4l/gu9/5mKc//EP+t7/7z9j+2S3vf/o+C0L3\nzVMuvbK/+ZrbV1/Re8evffghmxA55Int22/Rjz3397fUwx3T3jjjt/sdh7lwfvEIyi2HF3fs5oXf\n+5MfE+7f4qIsXMRCXw9M8wxjR+evCAFSB653DGFLrRC65iigEbLDuYEaM+qdqatRtC64EBr30+GW\n1G6wDqQiZWGaDkiZ2fSRXKwzDyKUlPC1EtQxJ0V9JfaBYRsp0nG/2wFweXmJx1EaVzg5808VVaJm\nnGQ4OOPm+sZlG86QKBzywcaSceCjqyf84NN3SUvl//rh1/zs61t++CXmbRoec1d2SMwcFk+g0suE\naiG7juo8LmPIrjM/WBpyuwZFuIaIa60ENcRQgoPgjs3km47g+zY1MY/I0oop5zzaVNeS4Kyf2R8O\nuLRHVZh7e12XKkOIFsuLOWO4Np4rrSBLVn1QxVuKJI7Y1PKq0FeLda6t2JntxrfGaKXMdD2okBRw\nHYEFVVBvccAZIYowu4QEQxKLKqE4NJh7h9FrAiHa65OMj57V4UOHFivuo7NI6+A8ZbbNzibNhmmG\n4JoANDBlo+xEp6SS6LoNCkyTFYSpCDJbc28OBAteArhMLnuzDQuejY/cL0L0ld4vJDpCOI0G1Xgo\nVkyTQSKooNlZ+mhYxXXNeQQ90mhEhWXlebI3mpa2iZv31HKDiCfLAEWM/gLUttmvh4glHe5dba4B\nJqDUg0d9YsqJzfnI5ZkQSyJ0njgOMHaU5wvbcIa7PGPoR/aHmRf7e55fz3z22T2HDG7TM0gg383U\nMVPbmq44Si5oMa4tYbXvE3M6aE1LLRVofrMugJifMsXEkzoE07jUwrY1hbQCFazB6ogoSheFPiiq\n2ZoXTZTqcNXjqoVYaW0UgYaQ5pzt+au+uY0EUlbwQq4zPmVi7KnFCgBraDq8E3KZUQ80j+ylafL6\n2JmbjwakFZbOJXJJUEZLq+vsw0yzjZ2H7kCuwiQ9uQoSe9yS2z1ogkVfCr4Ii5oT0uwFLx2HUnCh\nCRZrhTJRK+RJSa6YtaUCRXGFBkpUXAh0fSRMRk/sBRyFWpXeG+VpakCWL4oulYAjOuNN32qi4Nh7\n61ZKssJ40w3cB8sI9M6x2QzUXEgy08UeckFyQ31bI7ohUjzcl8KrnDnbH3DjFe9tLxnEIYd79k+/\nwj16F1lmXF649JF+E9j2gZfXN4jCtCQedWc8Gi94pi+5n6amRwKVnnnas4u37MOOgSeM1eP2Ezko\nhwBDHPE6UtMtQxgpaaY/67jcblhaCExtQUtelGU3M00L2cMSMmW0acVye2CiknvPOI48Ekd2Z0x1\nh+RrSDuGzcAwjhAcc050m3N+/uUf0p+f8+rRwCyFTRHeebzlSr/CR4d8/YJ8c0cZz/ECvYezTcSl\niX6euTrv+PqrzOAj2z4idTaTEa1stj031895Mg6co7wqhaoJnw74mqjnAxo69rNx79PhwEClF48v\nNrG1eVGlpmI8/2i1UMzQqTKXGUolKdzme/r5jmG7YX93T5lMABvGkTF2lK43br1E9vNMXzvKXFiG\nCXBoErwEVMy2McaAD6Ylu+xHVAuvrm+Zlpl3u3f+hevTX4kiGZqIoHGQhmGwcW0wLtaJaxaO9kD/\nvCNnM7Qfuo6D2zNNC4f9ggk/LJVl9ThteTrktHJYlWVZiH5qv8/+PcZIzsVSilq3HGPEn3lc7IwP\n3WJgh1oRH3AhID5Y4pkqQS3SVp0ppLsQretpY6cT8uEsGUsN/XBv+MhHpKK9t/Ptlu12S66VaZ9g\nKXz+058TUuXJe++y+eprfvzjH6MVHj16CznsGHYL97tbdvs9IXrC2NF1HfMukYsSnWMuyrSfcGNn\nKYS+R/rK7u6O7izw+OoR/WHPMu+JZMZO6CNEZwlNeMcwDITAkXdmPCyzfhIRYogNQQngVysrOx9p\nngldd0ToSjUeuXeeWmzkZqOeprIurcNtY3IRwRXw3kQnOUNXHJ0PlGid6uFwoAuRYTy3aUJOxvXU\nJmxT10bDHFE/VyzGNzQbMHUeFzLRKbF3fPejC8aLjn294/rmGbdzpeSIpwUGcHKtQNVse1h9H61A\n8Uf+74kDiRiKQxvDmxr+lFL2pkOANWQDXf1BOSJq6vTIrz3+jCjNTOFYYOeGcrgWMnJch3L6Pea0\nYO99NeFHjNbR6JXmPvBgPR+RbJqsqpjH8psGRg8RUItQ1uYd3OgoatcphBNatY7hta5f27/XWklY\nkpzRu7SN80/318rnXFFWMNROBdaYiqy5+d42nN0dZ9ltk+CIKL32GeQkOnPy+k1++r0ntE3LieOp\nulpz6RFlPf2coY+1IdIVULV0yHbhjz+7TtpP153mNGNrS7NSRO15VA1d6kJHlErvwKv5D2vJIEpa\nZt5+6y0uz8748c9+ylcvXzHlzGLeaO29NwcVFbQCpSDtXoUH53H9PPr69Vjv79fOZ1tfOedWZLom\nFJPjpGY9LHWxIZh2ghuY7bHlK8c1UGs+8uyPNBCR5vTRRMdi9mFlnQQcz2W7R5yskLj5L7uCHvUq\n8uB62ti7rsi32acgRw9vfQ1p1YYAVzF+p2BhKSfKyomihNo6NHZ2MLedsk6NvMVtt5Neq01CACiL\nxaQHITT6z1ILFIheCKI4p3hxrQGx9xecUJ1nzRzNYqz6QjWxKi1NsTXIQxWCcwQ8sQDiER8Jjbcs\n3iYG2ZtOw3jJ9vOuqln1VZhyQcUaZS9KXiwGuQO2wTF6c8pZvaklWJO/ZKOzDLEj90PzSX59MrCu\nXesf1qfdqclf0zZPXObTNOZI73KWZleKuQl1Ib62Ztbf+TBs5CHX/iFVLKVEF3qGOBBjz8FN3E53\nSCr0q6PtNEMLfMlOMWs4m5QVgcN+Zp5nwFIXa1FSKaRSmye4aYAcvtEx/GuTpq4LR63WOpUHzP7u\nAcff+wDYtGu12JTmIe+cPUnzMvP8q6955613iM5TtRxTTFf3s9KEnCEE0iGhzgxRPUJuVKqlLsZ/\nb+YAzjl8CNRqrlnzUrm92/MvevxKFMkmuDPLpN1uR07WXXixCN2+77m6vOT27oZajJLxpiPGHieF\nZbZUGR89vgYOtzt2+4Xx4i3GMdqNvmTEO2pQRheNXlEcRT03dzOHOTGOW5Zi8H4MHTTv4pQVrWZN\nN54PLNOMCx3DYN6AvjcrK0PZQMXoF70fjou+72NDiB241e3AHsrBd3jf20ZXzbhcWvTi+rC2/UFx\nLnBxcWXkflFu7m75yc9/Qj7s+fE/+SGff/45f/rf/vN876OP+OpnP+SbZzf8+X/j3+TTX/+E/pln\nf7hl++gRF4/P2Ty54MXXX5NVOdwduP/6Fc9ePMd7z/c+fo++hTZ0HQxdJKXEoyFyHnq+efYFl5st\nVz6x7b0Vvt2IeE/ohBjNlaJoJeXaCmUjxxnvFsQpnQ+GItSEFzF0qJpozMaQliYlvjT0VHGO4yh2\nFe0BJ1pOVdQr891ELgsxmODD+EmBu7s7C7oIjhB7AhZpmfLSojYh+GC/tzmX5wcQQbQAACAASURB\nVPtDExB4G/nonjzdMolZ0Xz06IIPn0R+88NHfPX0Ff/kp6/4p087Drrltti1z86BOHy1qM/auLlV\naxvHtc3+WFS5o4jIvHkBjKsZwi/3fTzG3rKOlMVQ+LbmHELyaonbtM0uCN2SjYfnDYFe1AaxUfvW\nPDRO8sMC3q2pTDYKDU2Vr85Gz6EVzKvVnVmLgQvmSatYYASl8g/+k//s/8PT5P+/49/5q/8t4sTi\nxmu1aGEcqY3ftW12h0YZCo0k4lwg1I5Cwktn6H325OZcINLEV2LTK2k9h1Fw1mJQqGY23R7+WJww\nNARTjO/6LQGKiFCqo+Ba+qdSVMi6Aa2EQrv2uSGE/hcYKZ2Y8IlG2egcZBcp08TYed7Zeq6iciUL\nIQhXXcf1zUtySSy18OjiAlXlR19+yecvrvnR0xtmFWoIlCXhSiaKENXcHDxCtDdP5s0MmZU7bfHJ\n9t9UswEKyNEfuuRCF2NrPJoHbPPd9c39xBohE2PJYs8Qv9qVJ4+Taq45jbcd4ykefW3oavP4dWvD\n5DzSmn3vvN0TjVZQHZRmE5i0Ii5awbHy4Ivpb7KfWUrFazQaQzDwBWdNI9V85EPwVPXmYJELqYkT\nXY5mgfj/sPcmMZdl253Xb+3mnHPv/ZpoMyJfZr6+sY1tSmVbVWCVKEGBkBjChAFzJCZMLEaMGKGS\nmCKExIQ5sxITBGXRWBQUlKv8jB/vPfs1kV10X3ebc85uFoO1z/2+yJfp9yyqpISKnUpFRGZ8tz1n\n77X+6994RUkopYmmTDSea2gxZNZUlJJQDdDCIbKo7UM5mSWgVLrYWzGD7Q+HycKwZlGiwKn3ODGO\neRBHly0ZzVUYa0dBOVQLejpQyCIk73AVus42AR+ELldWmx7vLcrahUD0K6gGmLhgfP8qs+13DRSY\nm41jFze4bs1FNjT69OweT/xMSpVNiDyOkZnEI5Q4HqjjgSntKZqRGMwxpX3HZZzJ02zi2+ZIY57X\nmSQzGoTDPJOqJ2uHYJziOSdyBedjK3C5BXdaQ7LPM2NudAEVNrF/A+BIycLNQghHCuhSHA/DwGaz\naf7exn0Poafv1qhGdnPlpy9fcbje82Bz4OtP3+PpakP0A/NhhAAb71iJoDGzk4Nd38HscmMYgMBu\nzKQMKZkd4jpuCGpJt/ZZWMO2WvX0Q8d4sPeTVCnimGpukzlpPwN9bwBezTN2Zmnb5xM527RXakC2\nM7v62s7VZvWbcsaL0VFdBVxgGAZuitECXQWnFZeqgSZemafpOHkvpeDEhL4qA6V4Xt98Phvh89aX\nokhWlHEcccOt8tyEGc4MrLvbDvquKv2zy5KwrMNzphDAIUxzZrsfWZ1a4hLVbHd869hzgmJzbrQ6\nSq6MqihTQ97M+9Y2UiGGDifa0rE8LkRi7ImtIFhe41xbTCTNqcOHI+l/iaz+BX5jMzO3jb91W1Zq\nw52/d4uSeevWgnD/7JwYI68vX7IvMM57xu2WH/7wx2yefJ2vvP8ezz55wY9+/EPWX3+Pbh6p80xB\niZ0d5rvDlqnAXgduxsT1oRAc/Mn3/28ePX2Pp+89pQvBxj/BM129Akmc9p5VJ7hSLP3KB0IfEd+Z\nMCM4XLRDMStoUWIwYUlRS1SqqPntijAdzL/6/PSMku1Al+CsE6ZS8giqeB8shlVNLLYo5ssRJYFG\na2uqcWliJCF0kaG3WMw5J6PAdHYDGkJjqIU4G7dKbfZfdtG+UXxqTqiaddycC4fDBa7r6eM5752f\noV/bU6JycZj58aeFhJC1N8SE28hj39AXqqVK0TaKu+haLbDI6XzjX919LZ9dWQ2lthfamFq68Dub\nZRqOXIXFWxypdohTmz+H8ddKe98LKlnrguIJqLbYaKi5HAMu4KiBsoPjM1OR2iywGsHGTPC/BOuI\nbDpFcHa/I5RmDVZyK5Rb8VQa+r9YFLnavGXdLRp6KyB0tj99FhksBa2C8819gGVztxFoe2G2ZyjH\nQvA4aQBDsI+foUPVkC/fmm45Rqb/4kxOgNIoBdSG6itogEBlFQInUVi7iquJ9WptTe00U0tlPXR2\n/9bMLiW240SpkETa92+IKc2DOsiCUJkTxCJk/CySvHwfd7n4RVsQjrNmWxRiQwCrLjxuw/K13V/O\nLai9pdgVrLkptTaEsTbusR4FtQtCeBe5lubEErwBHFrf5NSK3J4D9pk7lkhyJ5hoLWvj+dp7Kqrk\nasWpX5ogZ1SdzsvRAUi04NzQuivrVG2qdItc6iKUohjFSZp+QMFpc5zx0SzaWkIfTayLVtN1qFD8\nMs2qBhCoIcRVLV56bo/ZizbrPVrcNGQxdG8Gkta2rwC17QPNRlIdTKp4Xw24kkrwhQEoWmxPb3vS\nMqU5nn9tkBW6Huc9Y7G/MoRACYrHgLbT1ZqpJkKeIU04TdRiYSzTODIQj9+tj4H1ZtPuq3L8d/k+\nDfVsdg1LsbdcF95BQy/dguRzq6VK1YI9ajXXk4UHvdzDSwG8iMqXdTvB8IyjgYTLdAHspZgji7Cv\nhbzd8agq1Xc4n3FupvOFdXD0UilpJueZcdqTG5+41kpO9bgnLGeoeEeuxdB37gTrOEf0jhIdvouk\nkskYN5vmgW2CVsx1yC3nnP3eIS3Fz868rhsIDfwC6LtIwFxO5qNoWY6f5TAM1mQ3O9NlOne7n96i\n+LmkJt4Pf+nU9fPWl6JIds64uqXMvHz5ki56G2SEQD40VBmh6oR4rHj9nNX3vRVRUrne7rmeR27G\nPTOOGiLb7Z6wPkOrsr0ZSR5WmzUXlzd03YCIIxfBh4H9bBZNq25N8JYUJQQO+8LJ6SlD3xNiD1Lp\neksVwpuX8UYsFtXP2TxtvVEJYmhhCS3lryx+kP7WN1e8J1QLZbBN3dA7uPWUvLuKKl4FJ57H9x5x\n76wQUD599ZKffvgzDvstz//4R/yP5x/zb/5r/wrvf+MbPP/Jz/izq+f8egfp6prD9sBqteL83inP\nHFy/vOT1tKE/vY9K5mq3Y38z8o9/8n1Ozv6cR+cbfufXv8o7J8p0+QkiM6f3etw8mWm5A+8i625t\nHrOrDucg9h6RnlrcEQ2iYlzM5qzgMJQgL04NVZkniw+tKlQdAWVwxj0sal1ryomUZoIXus5CSKbG\n3ytqHDMvkEvh5nJHxHNyfsZuTqzPTulq4erimryHeZjouoHY98jKxnOaLYrZ+2gKdk3UZljupFBl\n5pDskCy1MO52hn77HcFlnqw966+ec3Xo0cMl18nxkymQJBi3stwewLcH9WfG/nJbNGQxrqgrS/jH\nFxfJe81oveV4RqKh986QSguHCEQdgEQtgZIdfbdGSqGMowU9OGfe1Bg1YSlqFzHiUjgoSkmJ4EEX\nLloTqxRnvGubsNAK7WxjOpug4lsD+cb6gz+Av/t3f8lO8ius5XG+6Nc7f09/zXhri+vIVG3UmVpY\nhB1ut6lrbZ7N4AJJZ5BC9Pa+c5pxrrNJhb8VjTgX2vey2Mo1ioQ6844FGzWLEkM9hsxYAQSuNgqa\napvoN4snhFys0K4CwZs9klej9kQRlpH6XcxBxNIuU0NXOzFqwk2tbDrHg3XkLFY2dUaGwPreGR/9\n+Cd0YUDrxIN7D3m1u+TF5RUvLq95/uqaIW4Yq0Uei3eor4xaGIpH3RKagiHcdwrkZS1UF9sz7v6r\n5AXdFTtMOx+P99AycvXOGsnFOipEE6T64vHeRN4lTS1u+PYAtYYNQosKX4oYEaGWGa/tDmiN57Gx\nUKPt1WzvxKvx+FXFGsLQMbmCrxVXl5GzA+kQWYo1o5aJX4EzqyxRj5ZMjJGxRjoRfC7k6kgV+rjQ\nQwA83kUcqYmYDaGtZGrzTh5LJosyi3lLd7XQYwBHrgXVypxMcNuLnWFelLo3bUFxMDmYRUk+Ez3G\nL62FHCwVUXFMrk2ICviqxNToXL2JuKurlOg4kPBF8A5KUpIfbTzvIqWCiw7novHPkzUuDiU6oUq2\nSY1zSAxkB7vhhM1m4LDd069OiFqQtKPOHnSmF+E0dLyzOad3ndF1tLLXzOSUTaMHlWpUjBg9dQs+\nB+o4ExXWfqHeFIoWqvOEJvrjDg1jKeo68SSMsjJTKbFx7du9vlAL1uv1sZhbGq/FAevqyhDg/X5P\ncRnXKUMQhiB0baI4hhX+9DFy9pj9zSXZVeT6FfdixB9Gth99jOz3KCOxM83Fat0RYiWWRKyZedqT\n64T0whyrXVuqzPPEPI9spLI5WbE/3DT7OrsfNTjmxXQgeCiFqgdMEN2a9GINcQgdwTtSaxr2JbNZ\nD4wpc7pZsQqBD59/Qipq+QW45v5SWcceJ8q6N1D1Vd6TELxybFiPTeOYKWR8l5EQ8PL/MeGeqaUD\nosp0uEarJ4b+yCs1kYC39DssAejzlrQxpFbBh8hhtnjU3eHAOGV6X3BtDJJLxlVhhbMoYzy+eTXH\n2CNpsczxCEIfHH7VGU+ndXShCcoWBbF1gb5xpW5fl7vdQt/oGpfxnbYbCpaOEeuaaY4F+tn3eOex\nRYzXpcXsThTO1yfMtfDxxQtKnglzZndzzY//4s/57vd+g+gDu6sLdp1S5+nW2qgUSsrtO4Fpn9oo\npbCfCvjIi4s911d7vvHuORuUUGeQhJaIasL5jUXySvMoZUH/CyklvMd4aqqkamP80NwJnCiSId1B\nbHa7Hd73xjsV2ghakd7iY2suuNDdjrR8vKVBHMU2CzWBdpPbGC70HRVlfWbBLTfcMM8zcyq4E0fo\nIs4H0MKSlLd4DpcWnOFZXDAUF63g9+rp1z0lTdAph901/ezxeU3MHSe9FYouGSJblwKH2/HcEqqx\ncCMXjqp1yh7FNqxciyXMtQLj81amebfWZa7fPHXVt4LLCsGqgpeIqoW0+HgnvKLY9QhqXGCWIIb2\nmuqdYr7dC9HZ66feImZLKX8XTb6drLzJATyuP/iDN9/Q8uelqL37//+qhfRn//6dx67/5X9qr6Xd\n86XZDhZdEKSGGCtHNxLBRs0LN9ioDW/6v97lyS7FtsE35mxgBaBdb7pcFyIN0W5or9jvFw7ggjQ5\n56gOHC0uvfGwlQyuGhjXXudxInV3f8GcBoxbaehUEUNigwgxGD/fqflD+xjYHg48ur/CF6OKPb++\n4NlHHzJnc3Yoc0Gi+RtL416Xxu3M1dBOHwJ/GcBzNzDkyPWkUX7uIEallCMSdtd95Giz2DiuThUk\nEJp3b6kOqS0yndumlKUI/gw6FcX2bbsv1Chzx9dodKhalyJ5odRYh+OW91MrUiwkRUWPwTuIUJmP\nz33nUyDGyNBFpskih50zzQRggIoubjceEYdTG9cHlGzVBSoWOZJqIZMpzWFEVXFi/GozKoMYu2Pw\nBdhzSbHGrWE3iAhzPiDNmi/VyqQmPq5SzNYOjtZ8XfNltqht8+8N4qy4AaKK+cMHbZ9LJWslumgW\nc3dcRG41G6VR8kIT2XpktcZJz3y9RUpBSyE0n17VxqWXzuzYdCLPyfjlMUBYJjz1+Nk756ipYjpG\n42EHf3tdHAGNO+4dd3nFzlkssqPxir1YEcmb+91np8XLn2OMlhshI0d3DvM8wjv7zmou1JwYa6FI\nQOLAWC0joksJryaiy+MBKRkfLOXQLu/mBpZL48EbZYco4A3cK5KP72fRGI3jyDRN5hOdb/nSFhPt\n27ncLpdGa9Jim6YTm97nYmCKHzpSNccqaTRcuAUWq0qjHxVWccBh2q5ighOzKPX+6A6TUrK6IylI\npRqBGfmFGdoXry9FkexEWPeeIIGPLi7Y7hMPH50wj4n7Jx3OVTrXESUYv2XYfO7j1ApRPJIrl2XH\nq4sbppTx7oQgExfTnidxwGUFH5iccHm9Z73aEDsHLtCvzphmx2a9wnvPMKxZQiu6zqzbVqs1MfRm\nZ+Znajaj+64J+BRHdTam8NrspJzgxEYpq6G5NXiHiBHm5ci5gwWXUHIbTbSRoSqO0NCntilTiMGc\nD6RxJcPpI1bDOXnKPF+/4MPnH8Oc+Uf/5Pv89KPn/Bt/829wNhWe/fgfsj1U7t37JsENHKbMq5sL\nXu33vLjZofGGy/2ecdo38aHj7NEZT++v+MoQCZNFX66C49FgzUXvK6ED1zlKsE2X1Ej9zptAx2PO\nJWEFqqRpTwgOHxw1mcfo4jG5HWdW6w1dHynZvBFD9MwuU6pSJsd63bEOge20twZoSpyePELEs91u\nKaVSykQYjOd0OASmcs3+J3/BuuvpENb3znj3ax9QSuHnP/85Ly9ecqYTD995QpkrYy04HPvJaBn9\naoXFouY2kvZEwA3RDkN1iOvwaeT+yT3GOVHkQIwTXz3Z8+rQ8Wx7ynWqoAnPxM51pNoTyoRHmeIZ\nDhM6KA0trJmpjd2dWlgFAnXZhT5n+dIEJ23T7SUbUlksWa06j6uZbZkpecYPG4YyUZIhFt4bjzbX\nQq2FzvXghKz1yJ3TNiZfxodx3aFFyKnYNR0c6RiOsDSOUKWy0p6S7CCsArHmowXcG0jvL/vzPw2k\n+c7z6rcf2++zjRpzDdYciblfhEaLITQkvRXMBUVKNEs4SVZQd57gjNoiGgky4Fy/sGlaGEpDiQW0\nOjq1mGqvCWo1ZLCh7E5Nj1B0xvuuFdV26bmGRpclQAB3tPGq3hDoW9/S3Ow1bX8JzqY9qzaxqrES\nBIbtyDtfe4/zVSJOH7FeeZ6cDVy+eIb4wsjMo/e/wnbKfPTxBS8ubvjk9WuuDom5GArae6FSyKLU\n6C3N0N9Sz2q1w1hVIRun2KtDxHFYQIBGrZiz7ZvOGeIuDiQ2MZ8qvuvIWOBTsPOTGK0YC3SA4GWx\n8TKkVr1nTuk2RQ9hEDVnkVyOaXDq3NEvdwFKaq6EMBii1nilywGvKVNLYugHBEFjoJ/t0Dab4dYA\nmyk2iMO5NU6Mp+9FGPNMcBBXniknShFS8sR4jo8Q8ogdC8HCqkQMrChCL95oHijVBeZo4tuV9Eb5\naiPoUPeoFCZfiOmErgrJ1ya4TahWkih9AEei96Y1SVVx1TO3xDkVsXumcbmXe1lWhsgP2TQI+5xQ\nFXIC3yWUiDphFSO9b57QCJ1XYt+YFj6ZDV40+sblPJNq4cEqsOnWXM/mCBOZKWNG7t9n6yM/u7jk\n/snAI7/m/ha+4+9z4kZ2MRP8vhX+O/b7Lf36nGlUdn7PXCpu17EqG2Qzcn0TuCqJOVSmtKN3HaLZ\ngFFnCLxUK9ZDCWhxaB5Zh45eLSn3cnfJIY+shg2rfk2dlHSopNmxPyTO7wWk79FUKbuZLInL/Z6+\nDyieiuMwJ5z3DMOKB+cPqBOQTPNTUHqvrPoJerjMmZfsmLZ7zruJrz/5CmfhASs3sS6B58BlGdnN\ne+pc2UvhYndD9CaIxzumUvGxR8aKxkAKns2w4Wp3zZxBisPPwrpCVzK+Jrrg2c6JSSspSQOaBKmO\nyq3FZkojAN55orNgNnHKfk6sz085Ozvjar5EcXjxaFK0KJPO0IWj7eTT+6ekMZFcZZwsdTcrxBCQ\nLiNq99OuFvb6q1MuvhRF8pKgFHqLEJxLoeRsqkU1FMuLdZiz3np9fnblUi2RDUeRwG6cGedKwVOk\nQ9PIOM6IOrp+aMVoZ0KX6ggE00aFSGh8wb5fEULXUFAbqy4opSnlPQsDQpqxehHztDVa9CIUuV2/\nqJq14vjYPTY3jMUfU1mKJHeHRNjoGITGWa548Yhrvw/C+fl9slaurrdcpQNaKhfPX/Dxx5/gV9DF\nFaGN2zfDhiABH1b0m8h2v2c3Hthvd9S5EqQQXOT9Rw94/9GGda/0VVnHyCZ4hr4j+I5VDHRrE6Zo\nqRRSixmtlOKQmnDRRpxOTJykqtRsSUfkBRG1w3HxF+1CNFHAYUeZCz52hrZ6Z76wVenEW7KOFqZp\nbEzuQvQeEeN41QDxpKcWz8vnW+Y5c14qKWVwni523HvwkHB9Q5oyNzc3R6WzOHPqWA5RA5AsMlVT\novqKVBOE+s68rdN+IviBIJU6T1CETQeHrKzczOiESd+MmnYSkEVtvPBQ71wvbvE+ZeHnR/SX8Kze\n4HM2PrGIIcGuOQ94PKg1MThpEzGl2ITr+BqPXLM74SW3KClHv+a0UCbcm1zSu/xOERN34Ba+9+K6\n8Kt3+v+v1meL6zvotIgJcEptr9cZP7s2NL1UM4wT19n+7xq3vlYsiKXxiZ3gCe0zv1Wq1yNdo6nG\nBUP6qqHVTlrAhrSS7c7P2v5gFoHBe7PwyhnvhSigi1MKchRIosb/NF8fK9KcvImp1FrJitEixMTH\nIAzRMURH54QuRFZdb49dIVUT/5yennLDDR8+f86nFxeW9OXM23wJMkENSfVq6HgVQyl9Q9cXUL1o\no/F43/ju9vreQMzv3BrLf6PeTlQWdG9SQxCjVlytx8mGtuZGgVBturN8FnYNAnnxtuZ4fTsEDfb6\n2weLaiV2HlJtzKmFgylU9UjjVIo3Yd9MOj7m8uZca5Rgoc5g+gm7Ia2ObghZ0YY6AyoOF4zSZjZy\ny4fSdBp6+znXoMewmeCbP/LCofeR4gKJbNeqQnC9oYn2YSDYNW4Nm02gOpQU/dHVoaq9ppD1qOFY\nPjtQi7luDYJN5TKiKwIB7wKdQEdh4wwoWXmj/TiEPpgTx+iEGUFDx1wLMZjzitcO0UqQRO8MbKni\nuE5KqI5zPBqND392usHnHSFN+OroaqVHwPvmmtDj8HfcKVpzXzKpWCaCi6GFr9wiybFrtLtsnGYT\n3xnabTzchsQuNUQGrdm8hWsixM1Rv7Q8bimFLg744BAtR+pDDB39UjfNI8YtzqwGT9dF0yK4yGGb\nGceClMTsIrN4JhS/9pztA24OxN0BdaVpRKrVMQLO9/jQYyVuPdqgRB/wmKAyayVptnAoPErAdg9P\nkNssglyXBMOGtKuiLe0yOE8fO7Y7y6Ew1w3avbHc53qsGUpKaM1UFfro6cMKFUeM3r6TWtveaBMt\nUbNwvIv6/ypL/ip/+Z/VOtms9G//7ne5d3rKs2fP2E8T3/za10ErJ8HQqvuPn/I//9H/xjQXbrLn\n//oP/sNfeJx/9+//PXrvCLXyOr3g008vmHLh9es9qVS6uuJwGMmp8vD9D3Bdz0kw7s8wWBxizplx\n3FMrdF3H40dP8D6y3+8ZDzfE2NN1HU5alHXNRz5p9NZzVN+8R++MWqCh3J8dozQx2WLXpM48OjnG\nxYKN+f3xML07WhdMaHEksAO7yXxuq4tM48wnz1/w+vpjnj/7c8ZSuLfZ8J0n91jffMrp6Rn3Hr7L\nb//eX+OQ97x4veVimvjTiz3Pnj/n4x//lFXveP/klHfP7/O9bzxg0xdO9y85O3GcD5F15zk9saCQ\nIA7f28XcDSuG/oRdo8fUbCjNej0wDANz4wxTZ1NVa6HD/I+7LrIYjtda6YJtypoNqQma6LqBrrMN\nApmIMXBoN1xR42aamC9SBeYmcKlOGF99yquPr5j2M8P6hHeePmbzzj2GYc35+X28OF69uuD1zSuG\n2PHgwQO0VNI00fc94ziS80zJxvUULewxJXwIZv+Xa8FPs3llHvbEfiBX4fJq4nJ0/PHLE17Naz4d\nN8wVphqo0tOVEamFHWuE+Y0CV1XxztBZ1KHNiig1+sN/969+5xfui7/z3//JG1SMcLQGW8IrWuqV\nJigjr579E8abV9S0N2FPKuSqR5eFky4eCzUnJmw6eiS0ABaAfIffuYwdF3rAUiQ755Cx4LxH+2CF\nUeNofv8/+o9/yc5xZ/3TRpLfrrfr7Xq73q4v3XrnP/+vqRVqERBFnE05TP/teHy2oVutyFqZq3IY\nR8o0m16gNvFnF7j44T/+h6r6u7/s+b4USLJrxd+imBQRM1AXG+lqtbEWDZVdRG6/sIp116JivJuU\n0GKjYu8cw3DKYVZqyQiB6AJ9HFpXZ4iFFzN3XyzZ5jTSi7Ba9aCJsNjyNO6XW/zH4I0C2Iww5DhJ\nEzV3hVvcYOFovslFOv5J3bFIbMQvjjiHOrs4xLiliiASKHXhalvPF8ShXceDe/eRznHz6lMkzY3L\nKAybDeIjKR8oLnPIE+9+7WsMNzuu9AJfHa8+ecbhULh/3vHugzWnrhDSjnWAoVOGztEPkX7VE7pI\nFE/BbF6c92iArlnfVRFEknnc1moij3kETaZIHjokAwjeNd/JmkGVkjLq7HsKHkoWtvsDMWVTAgtM\neSLN9h2c33vAYZoZx5HNxuMIRHHHVLVwtmG/q0zzlpvtyNk4c0ahziOH/TXDakO/GdjUDdP+wOFw\nYD2sbi3mQvPIHQ9tTG2cMOZsgRXOE8XhVxvm/R7VvX2/TvFBCT7T60ysHldOkCLG8wNMCAbmXdFu\nUb2DxpTcroTSyL3FXBS+AHz9bOcsjT7kUOMni3FXgxpn0ZUmrKDyRg/dRlTLvXH36YxmZHQMe76K\nuu7N5y+VKovP5u1rc9r8ahdeszg+A6B/8fosP/nterverrfr7fr/7TJcSFvyVqORqZC1GH0wZXTQ\no81sypkyzcdpU3G3jjK/yvpSFMkLt2uxP+n7nldXlzy4d84QPTe7Pbs0oS23Pn7BaNlVR/Se3ntO\n4vukmwOlCuf9E4oK3b1AcoH9fiSGnl4G1p0JvbrOzK5d3+P9mpvcMsU9JJ2QhrrZB2/om/1ejz6T\nvtEkjtVDbXYn2oRN/k0kOYTAIY0mtljGowKu+fTeFsrN7ssZt7lWs9daRqXeB+OL6TKO7811IFuo\n5qoPTOFdPnj/m1zfXLCOyvmmI14e0DRzdv4AjYk/+fGf8tt//R2+853vcd5f8OfxZ2g48OGrT/jG\nk1O+drJmna+hjDw5XyEnhfWwYj0MnN6/R+w7hq5nTDc2WoodaCA2w/Vus0JL4ebqimlfmcatqVRX\nEQ8E59icr1EthKhM84FpStScQFvgC5ioM0bLABX7DHETw9Dh1MzNX724YFhtOF2fU4pZ+Q19jwBz\nPqCrjofvPGZ98ogXHz/n4uKK9aYQ+w4kU8n4zQnvrN6hpsyzZ8+4vrziqpctZAAAIABJREFUnUeP\nAOhPNwgrUkmk/QhOiKWyv77BI6ziI+JqIHUndBLxpZDzTAZO751T/ESoe7oZuuJB18wyWtOjaoJH\n6fDuTVP5EAKuNM56i+cGExF+0eq67vgYwLHgbsNaRCq9dPR0IMoudJTgOYzGXZSWeuZ1EVwWoz/d\nFaQ5Retigm+jrhLDUawj0Jw/6nGzAo6NpnloZnwM/NlfBUF+Wxy/XW/X2/V2/XOzXvz7/x4A7/8X\n/w1zmamiFArOR/p+xTzuudrt6E/WnD98RAiBcHYKpTKOI6pLiNyvtr4URbJI850UDxobyhs5bCc2\nYUUXAuNhRy2eznULu+EX1uzXOOfZ9Cvm+QU5eKZcWAVHkECelBB6XFCii5yuN2j0FDWRS/ThaFJ+\nOpghvVZMkSwOiRGpHhdC83j0SDm0Z68NCKt4WVnBq5Yw7wQIoMU4SAXj281TQXzEVMPNB1EVxPhq\nqnpU8vqWOKRFcd61sXVtvoKT8aXEVKLZg+RsISRO8EPgDEc9f0RJlWH3mnKdGfPEKqzZxA2dTERX\n+F/+0R+yH6/569/6m3itpP0lv/H1B5zJa3KyoJeTGAh+TefXhOBw0RN6xfeZXb5gvVrhXWcColrZ\njSPeOeZpD1UZyMzjRJhHE/RlE7ocrkcOVxfGbw5m6beWwLZxY7tu4LDbE1Coe4zfZs1VyUoWiMMK\nyZnpsCdL4WbcElcDtRZK3TJ0EdFKLjuGzSOcjsSHa25mx8urgfuPTok3N9z3HQdJzEHo+57T88fc\nXL1iN1+x3gzIbFGrp2dP0VPl+cuP6XQkdII42I03rIKjrhOsYNpX5u2W3jtcUt4ZVjw927Kfr3B+\nZ04fs+BlB11Aq9BPF5S6aK/MnF01U6vD3Edz4yA643R+wVpl45eZih5DbFtxvdx/NVbSuEfKSFW7\n/pw3vYC258/1djoSstI7s2uSEBjTvvGUW1y4EzTZtSqiuGCcTJ3MTWFJx6soPkSjdtaMpF8VQn67\n3q636+16u/55XZUDiCU1h9jhQ2CeR3adUA4VmWf2446KEr1nTIVSlCCRvgaufsXn+VIUySDMueIl\nIcETBZwESslMYyKXjN9OlKwc5j1eTj73UWLo6WLg5fU1KVWG9YZBoiXSFMg10Q9rvNuZh6aPrAYr\naFFFxSG+pTWRESwBCrf4bZo4IauhZkpG3PIRLkWHpyyInfgmRml2TpiAYgnOECdUaX6nC/zs7lAu\n5I5gRWgFt/233AQ/ogoSm0jEtwF9tZE1s/FAteIdvPvwIY+GDreNDBTmlPFEuvWa3baQR8//8U/+\nnB/+4Bn8Oxu+/bX3+W33mN2r5/gqRBc4cydsoufx2Yaw8qgm83Q+gC+Re8NAUAs4mYshwJvBoVLY\n3oxGgRkn8jQbPzxbOmKtmZOTEzyZ3dUVIsLmbMPTp0/R8Yp5nKkFpinh1CFUxmkyS6TNmi4YDWKa\nJrz3rNdrM6avt0jsPCdEK33f43PHvoCLPf6kcj6ccZ0e8PHuFbsgzK+vefwOTNkhrDndRKJ/wDyP\n7LeFvk/42DfrKsc7T58wXV9QokdqYcqJ/X7PvbMzRALEgTEVDuMM9Zp+teG9x+fUUPn02RYtGefv\no3imPKNA1wtJjRqhYBG1YkW4iXZCu3K+OGCH9nPhKIxqcc5iohJDgk3AN3SeXCrjuGeaJsaU2gME\naMl+S7y1NpGO5hYvG3x7LeYC4pwjzGaTtNgYKhVdTPKdHP1+l4S+X/BGfrverrfr7Xq73q7PWUVN\nA+ScpaDmlGxq2Y7CcRzJF2b/eHqyous6+rBqFpe/+lnzpSiSq8L+kIjBNZW7Y3cYqSlxMkRqDbx6\ntcOFHnImxPXnPo5zEcVTYo+mQtedgvcMwwaPxQdv5xldC6t+jQsBWpZ712xwtFRKqfQ+HnPurEhx\npFpaJKyzglqkebs2D9lW55ZsvrVL8lZt3qCLeCpjiJzxOKuplsX8dxF5k+0py2dkP5/ronxeVNVL\nprtZz7kQcORmCwZaE073SPbmfOnB9QE0c/X6ir5f8/DdJzx78VMuX13Sp57XH0380R/+IYdf+zq/\n9+iEr94fqARWvWcVQOtMnS/w2pnzCIKva0KBule0txF6HzwuePquWeBpZNoXLl7vGLc7LnfNHYBC\nzTMBxdWC7yJVhYsX1/TdCVWFacrMc+GwO1B7Zeijfd/qSHMlYAr/xQPTxUDf95ZypNZtTnNGi1le\ndWMhDg6/CuS0R13hW3/tbzBT+fEP/k9+8NMfcXKScB2kQ2ZYn7EaAjl1zLUyl2w+mWquBD4Ivl/h\nWkJdPRwotXLz8QtCF/HR4eOK/eEK1ZHQB77z9Ue886Tj5XjFxXbHz24i2XXgg00bKNgowyg8Cz/d\ne9/CQZr/qIP6l9zzRyeWpUjWRpRvF5c4K3i9L8x1h5IsOSrZVOWYOJbtV+m61kDWll4IQToWH2kc\ndN5RXMG7VtS3Yr80V5bFPSFXhax45wjxNlL889bv/mf/CVpodoJYQ9kivI92W61ZmJqFYGgUp2Xj\nXB5/iFasz01Brc1NYQmgGMSRmhe1etNDSDY9gg9mouZ8c6aotnfknI+fFa40J4CKEpDuhC70hKe/\nhetPESARGVr4BW3TDsse0oIMFks9AAlD24sSaKGUjCfgy0znKw9Wjj4IJ/lDovNsukDnlJM+si82\nxUnZ7JdSWKHO86cf7ZhmxYUzS82qhe32JR/+6Pt0knj3/gpHxkVT7J+ebvitX/8uXQysCAyrFX/x\ncsuHFzd8fO2ZZuWjZz/g5vIFjDtrUovgu2gCzybcHOeJZLJ3VIVOPF4CTqxxWz4Ip+b9nqj4lmCo\nYn+Wdnx5bzz3JcI3oybOyYXOBe6tB2RxenBYwwiE6Jr7A83v+DaKumVY2+df3J17BtvbXTzSho7f\nO4klPtupozbf76xQNFMEQtNV5NkCeRY6nYgwBI/vIniHBvNhjmK+1MFbkJHrzm1/8E8oYcXkVohC\n7ypJ+jcmNDaJbK+nTY4c0DmDZRaNwVH86+y9W5Rwo3iVWy4nmF+t1ohve0lyNhUaur4ltk0tFKq2\nyRNo8zkMtClUbNd2uvXc7eqKqSZzVGmvwy9NtKh5+QJyKPbd+ErRTNIMUvnN3/gt+j5Qy4457RmG\nwMm9FffYcHWY+Hje8u5mw3BQrsaZ3TSyzzecna55evaEV5d7Pnn1Ma8vP+RrT7/CyeoespvwUqnT\nFX0XWK/XfKqV8WbPzc0Np/fv8e63v87168zLy5fM88jZqufr73/A5uFDXn30ksPuhp9f/IwnTx7z\n5PwJLz+54MXVlm1O3F8Vvv7+V9innsuLaw7jjtcvP+Vb3/6Ap199nxefWDT2/vAJP//Zx/z+7/8t\nhn7D9//0R7y+es2rq5/z3W9+nQ/e/yavX+y5vr7mw49/wjjt+J3f/S6/+Wvf48++/wnXN4lRd6TL\nT7n3ja/xwQdf4/VfPOdnH37Ez3/yp4zphu/9xq/z+7//r/PsZy/4kx9+n9evXlB2l3z13af8rd/6\nPQ7Xe/bzNeN8xeurkaubA7/3t3+X2D/i7/+v/zuvri+52b4g6jW/+d1f49vv/0v84Ac/5pObT5mA\nqwu7R1K2e7O0ZNyQC7EPjJrIQKmO/c1oujFNbNYDT58+4fv/9r/1C+dBCHb/iwirfkDUMhDiMFDE\nwsRyNgrr9dWWvu/pm63gMAxfeM78wvP8yn/zn+HS2jY4ia24q8xNeGdG5I7DNFEUswTyn/+ypQqT\nZgqCk4j3AYmdRVYidDjGaihriB04Z1ZLzabJCOAtntGZ+M3iNJtdSUo2g3YmluJO9CtNBAU0gZpY\n9eL0aOO1hArgpJm6STOxaD7JR3He4nl6Wyw7CUcUUMz0tAmuCk48iDsGB9hraKNxsbSnkmezh5OK\nBrEEweV1Ovjk5cdc31zQF89mCHz7wZqnMXPmD2ycRTg7rUhNKNV8kMe58babYwFKyQVcwgWP1Ixm\noXMre65S0ZSZdxOH7YFaO2JneewaIyVnovdoVg7z3IR7pna0ItCcFpJrxlTOfHeLJkPOm11eCOZq\nsdi9EPyxeFp8TNdVqWWm1JngFeehCz2+7/jKd/5F0j7x6atnPH4QKK7SdTO4gASh84FURigFHzjy\ndBHzsHQYx15qpU6VkjISB3zfIbGjpkwlkJL5Oz4+N65u3Nsh64gIntSQ3nbx3GFRLTHTjluz+79k\n3b2ObGhi9107oKTVFLVM1DyZFFTkGPGtmtrBZ8W6qCHJ2l5XbWJah0OkNlesYtc+5egBbhD0naJP\nzZZqiSL2rZj/otXFgPTemjKkNRLOZKp33D/ACqc3Ajza/1s21rvhE0PsmIsJRJ1y1B0ELTb9cRbM\nQCg4pxZtLwLNcWZpQlwLlCjFmlEvipZCEY/D4V1AfMCFCNmaS1wT31ZLWVz8liu3k6LlPUzpgAN6\nqXS+InEiysQmKJtQebKurHvH44jZYdU9HmUdHWMZ2+cjKJ7klCKONE9Mk5D0QNGRcU5st58w7y4Y\nTtb06zPmeaaWkf1+ZsrKq+sDZ6crUp25mGauDpm5mLfvsOoY4sDoIjUILnuy0qzypNlVgvdmkFfl\njke8LPO21sI14MA1GlttRaZZR9pZcPe7rLUwhIAWmwI674ncWhMuNLcg/khPMvecOzfF8bpsBZ6+\n+f8W16Hbn73l+i+P5Zy9t2Xa55zRCJ2YnzMtndK5Zv9553mXxHAtBoasgt1wvoEiqRa0gu86a958\ntB7aFWpdLOHaZNT05e10Mp1DBSwHdhHPuuaUY25ILTTcrr9Gsbpr91icA+ePu07GkLokwWwR3RIB\nroT2GZaw0LzsrMe111T9UatQs0ekWCS3REMJXW6v0c7Eqop4S50V53A14KXaBDWEthnZ95C1UrRS\np5n9dsdF2fIgBDbulKvdJVeHHbv5Gomee31hdziwnWeu54lJPaEEggipHCAXnAjTNDHvRq52W17t\nDuhq4NE0o3TsDonr7ZZpmji7t6M7Oyfnys1h5Opmy/r8lM0wM42F/ZT58PULuDfwzn5kUs/1bs/H\nL1/w6csXPPzmB5yNM1NOOFeZ5mzanGpgzzgn9oeZy+sDuymRFKa5MM2FFxeXTHP77wXmKVmwRuco\nakAaEplLJRflelSj72lHTUKdC8JAKZ6X+5HzWZHVCZIcXTDv7f5yZiWBfHlNON3AYabsZm4ut3g/\nM2vAdStCt7I6BLM+ZBGGI0i0QJs6W0iI7eqmu9qFyYKHqp3xXzRhXAThtVaiN89/h4X52MXvlsMK\npZJzMdu9Fjb2q64vRZFctTLljPeBKrWFFsA8ZeY5N7N54eZ6x2ZzhvefHymolr9KloS4jn51SuzN\nR1DU0ZVqISI1k+Z8PEiVlibjjNsp3jE1q5BZzbNUpdI3tMrQ3qWQbSI7eycAFie6+PpVjlZwMZo3\nsDbKxJK0tqTzVUx1uRwQy68A85xvBVKAer09RLwdNLmYRbeV9QGcR1TwWpg18/r1K/K85xtfvc/T\nx0/R8dIOTGZSuea99875O//yt3nnbMNvvrti42bIz5nTnjCemKNGMEQi64zPA2Ho8MFRvDIz4Xyg\nlERJB/ZXV+Q0WUqTd0xzIU/K4fWWtB/pHp2zWnXM+yv62JPHA+NhJgw9VCHNLQVADflQVeYp40mU\nlHHBLvbOWQRrzplcZubZNphhvWIYBqZxZJ7n5vFo5H1/8wr3zkCplUFnppsLuprpTh7jzr/Cg9P3\n+eP/9r/ifH9AglJPWm69gnc9aUwUhdOzviUBzkaNYGdNWAh4oO8ju5TYpQn6Dg0dRT01DMwl023g\n3SdrDmnLw9nx+mZmLhskRKSA8+YwUZcCVWjOE2b/JnJb7P4yO0dpXpV2+BtCbSJBZRU9h8Oe/eHa\nBIY522F5B0HuWgqVYtesOEF9C8EoIKGphlssL86boFAxFMw5OikgS9qWPXZt17n7SzZEaIWAVrx6\nFCH4QPU2MlmK6+XnY+yXzeXWccaJebs6R5kTqkrfWwMd1VO9R1xLivSCU7Nb9OpAPRpvbEP3lryp\n6kHd0fHEAjGaBaEU1IklTVXzAlFxqA+Ij0QtVAKlpXOZl7IdHiKCb6iruZDYZ/NBrPhSuN9l1p1y\n0hdW/or7vRLrnvX4CacR8r2BWjPTuEeqsu485+4BaGnIoEdiRsXzzfUNMPD6aovrVnzwjW/xD37k\n+HvTTznMM+ugnG5OuXktdPcf8vLqgv/hf/oHvPv0Ed/61nv4MPDRyy1XO+g3D3FuoCNYaloIJKdo\ncfhcwHsOeW7fTzzGr6trSYytoKti15FpNOyad97jKsRg15HXymEub6BJ6/WaNZ40zQQc665v9oz2\n2OodTpTaUGPvIqXqsVEDcMFQW+cs9n2uGVdum6DQrCindJv8d7z/7kRMq9C8ri2hzuhN5rU7aaZT\nxalryK/FKSuYILxY8qiIILsZF5TubECCQGco2QF7rNktLjWFrOH2FGr3gwt2Plnhb/fGuIyanRUT\nqovtkl2Lc7UG0Ikj+cXqsd370ZC5RfikbW9IkwnI+4bQlTyj6QAiDXBqATYCWl1zfbIprlNPdgl1\nGecUJx5XPAdnhZSTYJMnVfAtpdWexIJTFFadJ3rHdjbB+/nDR/iYkW3h5vUlP52e81gdpyf3uJoL\nNyo8u7wm95F318r1duQmzexKpbv3kOjvcTG+Jo8ZuTlwMnSoEzZZOHQbEo7cDbgkpFRJWdmOCel7\n8rBiLKBhIPSn1NBB7HHDiqJ73HDC1fwxX+03rE7uk1NHDjfM/YZ9v2K33lh4V4Cu6+kOHf2woe8H\nFI+4ga4/p+qG4gbicE4Mlegr1Q2EVcSFe8CA0BM7ZXaT+S93G2K3QrVDXI92j4h6oO/O8TPIPpOu\nlHLwzLIiDSfo2UPm2bPfTdQcebC+z+MB8sfPef1qpjtkTrXjWs4Iwxn92RP69UP69Sv0tUM9xFWH\njBP5kK3xV7vPM+Y5vVr3iHhutgdW56cIG54+us/QRX7+k7/43PNgSjPBebQUDnMym1iEPCc0ZdvX\nvQWsVIamWzIgZL+fPvcxP299KYpkSiXvDoxF8UQ8nuA8Xbfm+eWe1WqFOM+qe4AnMnT95z5M6uzt\nBAZyPxN0jSPSxVUT0QlOM714XAxt3NsQmxCoIjYsVz36Fgs27tIsFFlCENqNGgLON0HSna7Gx/ax\nSjPLboedJ6BZrXB1AtGTSzlGA3ts0wpkKjY2rFjoQA21oVuu/bq0aJUxgXPKICB5pDglaMIVQ8I+\nudkx7wMlbRnH19xcjpwPlYdn93l5+YKNS/wLjx5wvgm8cx7ZDJUN1+i457A34ntZojDTjDaUC3dD\n8Ke42NGFlVEb0sScMykbP2jwPVdFYE7kmxEvgd0hoRLRaU8pM9E7clXGkoiu5+Y6sd1NDMOAZscc\nMnGzoqRMRdnPE30X0DSzCiuiLxSfcEEoqQkuXSAMG1zXEfKOlBXvA973lsI3rwiz8Zd9HUi5cHX9\njPefnJH7M/TRQ1bvfY+PPvkhT86F7eWnrM7uk7PHe/A+ISqk8WARpmvPkAvl5JRxt6XUjA9CKcLQ\nDRy2O1ICcR3rCDpnIo7OOR7dg+sb4ebqmuI3jHJCdhFfLkB64w6r+Qcj7khxcM6aiCrQ12qJZZ+z\n9BjFKtQqVGfXRQhrRCu+JPZ15JBntmlip5kclFSqoUOdb1QJW9HZtSdVaX46DKIwLdEVxjPOvTSn\nFijFigY/m71d9pNdR6Gjb0lpqaUtfuGqBa2R7Cw618wyIfTd8d5bKCQ525SjtqLZi6OUeiyq1ElD\niYWc7OfsH2s+klqgQhcHa0pqZqorK5irQ0vB14pzMIs11JMmqIXeO7RYkS3FJkkpbYkirFusay49\nce2QvOwbBudnaRQwUcp4ijDS9T9j1U18JUyshj1fOZ1ZdVZEdtNHDH7Adx4dBvZaiFuhFsfp5jGl\nhfek2YqZ0J1Yky2FGB0pRub5QH9WKOWK/W7id771Hv7mG7x4dc2siVonfn51oCC88/QRf/RnN/z4\nwys+ePxNHr3zgJNe0TpxmF5xenYflT0z1yAjEjyiAR+gSrEwCBVyKayykpyjekhF0ZzQFgtNzeAc\n0vuWBmfoc6mV/TjRb1a4pDBnVjgoNp6fJB8nSUkrPnhmb03hRgNSKi4vk55W0GJI1+JDL94xlUzR\nSujXRGeFvVINyS0KeZmeWeHnRBDX40WZUzry/QFmmTF5rZh3ehcoaU+QjloqzkccFuJRckGk0vnQ\nAJBCCZHeBbpilLFZeopfUcqmTSsrlQ3Bt1juavoBiygv+LLEdxuyG8tgqL1aAmRqVo+uudd0q4Fp\nOjCmkRA6RIS5WChU13XoVBt9ye51EZBWvJZs1p4OT3LW8MXa/OmNu8isBfEWOVxTJoglK6bakata\npLTAUK27UQra+GS+WJpdqgUfPWMpDKtIqtJ4ppXOO3rMVelK9uhqTbxSZLMmdbDxK8aDcv/kPo9O\nzskIPjrYC44NaS64s0zaHZgrvN7v+WAz8M7phlKU97qM//mnPH1wZpPd+QLSRCdmLEAqkBNFt+R6\nhZ8LMrdwEQJ13tPNmXVcIXT0Y+GkOOJuZBhnHnYdvWzI9ZrDfkIm4f+h7k16bduyO6/fmNVaa+9z\nzi1eFRmlIzNtY4GxEgnbCKWQ6CAhIdGlkR+ABuITkP38AvANaNJANDMlBAIJpXBi0jhsJ+FwVI7i\nFfeeYu9VzGLQGHPtfe6L+5ymF7ment59556zz95rzWLM//gXIdgBe5QRqQ2CMOcnXt5+xCGMIBsn\nPeNJtPMKh5FzGHB14cltDOfK/WbUmRQ86/kB3zZcW6kxk8ZAKyZUb7FQ88IHPnGUQKoQ1sqbunA+\nnZH1DR/c3jCQSF758BjxZUVvJr4ojxxdROKJWZ8Yjq8YHIR1Yw5w30Ek8ZWtNI4uWCcpJKuDRs8H\ng4U4ffujr7GumZ+19N7twDXrWhRv3RoFWqm44FgoEIQweNO3rVvXJVkQy016v67tfdevRZEsIjzO\nC7oV0uGGm+nAId2QUmHbNisuRIg3t4bafEWRXEsPn9BGrUIIHlDWde20g74BeRidBShsXZW/t510\npz+4a4teLjSIZ63ivV3X21Fm3dUQ3MV949I5VmtD126H5TuarM2EeM65i32cNov1bG1vaBuStP+z\nX3s9ZJ66vi8lFRFlqp6WCyUv1LZQTg/MbHz6199npKLDJ/zw55+TpkQKjtvkefWNDzkmhy8/w2V4\nnB3aAtpeIQSae0MI1qpp1VKEximxrIUtw1aEGAYOx5GQBkvgkUDbCjfnE1WV+3WmqOOcZ0P6gzl7\nWBiFI8Ujy5pZ1sqyVfygzOvGnBfWZWE9z8Q0kGI05NIJ3o+ImxCZ+iaXO7/WX/h1w2gBMDlnYoxs\n20YNynw6EZzjcHzNeEj85M//BS/vjqRvvab5id/7w/+I//V/+At+9tlbvv2y8FDfMkwvKfNsaX+i\nDEBtjXJuhDTgm9DaSOmiNcThx8TdOLAsGwXl8e09KTjePjxyVMeLl1/j9fHI090XVNd4c/8ZlQEd\nFM0N1d6uVEV75GkHfkwkqpC7A8j7rqy2gRldwvjOilDVOOGORt3OfP7ppzzdv4GSkeYYMO/lKuzk\nCht7vR1tY9CKvOy7nkCxlCqxsb0n0BnyqtQuUY19DvlqKLn2HvvfVCSrOHwIl3Z87fSInK9uMLXW\nfkiSy8G1v8teRNucaq2aK0rtxcNO08BbNHJrCN2esntQh2BRysu64EVwYgjytm3guMRFqwohBkMt\nVWlqrU7vjaO/I41NwLnFPru3wJsiBdXMy7byd15+yk1Y+TidGAbh9fxDc2OZH0nZcTzcIsdIKZm6\nZpyLxDDQpCDBkcaBnMFFd6EOxJQszrpZN+L2+PoyTy60jvYZv/t7XyfnTy42lls+8L/8H3/M47by\nB3//NT/55Rf80T//p/zmv/U7fPgbv8Xbv55pk+ehzsybkHNE2HCq+FYgJvaIvCYWQStUvHNXjUWz\n6GR7XoYcLEsP03ERFUsudOJAI6X7q7tOLWseoza0ap9XsINHsTS2QjF+b/BI8Hi/7xFcOPtr5x6n\nYIdtWqNJsE6OdjRYQfvhq+2IsdN+2MLW430YK9Sa2f3sa6to6Ruvc925xrz2nQuG+PaWcVNoIRkX\nPSbwAm5C3KHT9To9RW2vaOKQ4HCXMU9PxdMrJx8rLOyNGh9DxOa0zVPrQ/oYOq3QXfQlta/7XvxF\nByPO5uy22UGi0FCx35H81SedfS42A55oXbjb98vg9/22C+g7gEVf5y77nXaqhziQYOFbMTGXjeSM\n696kUfze3hJ8jDifKFWwlMhGpXaKpdE5W92Hp3ULvA8UX8nS2OJInW7R4TUlPTDejky3FoEc3jzQ\nTg/49YSenzh+8hFjmqiLY0wv+cXyGetSOE431FKMlldhiCO1gfjIuS5sWqnaSBK4kYEPDi/43H9B\n08rWlBod4ixsae9Yee+tbijVAKRnAU1TGi6Uw+YqQ0ocw0BUQSuoeEoITJJwQ+Aw2gEgr8WsO8eB\nshVIgVOrrFp5+7Dw5u0jn3/6c26PE3/4+3/A+rRw80FgmG44PnzGq7nySYT2+DkhP6FbZqmFWulW\nu4mlZOoGtEYJNmbX82yd4G1jbo3b4w0V5e3btzyent67H1jnvQOUzpFCgqAsrVzW2RhtHm+l9i6g\njePHx9NX7jNfvn4timRVy+lurlFTJeCYJBJiBIkXYUTtd0WfxeE+v0o/LdfaozN7OMNWLRbSN+lc\nXrm0i5zfBT/2vTtxwl2ypK+Fsj7bxN+3me9f21uFIntR3Vtyanw6T18gnECtl3YgWBvLObm0/8A8\nA/a3cinO+995Hy7xrKVlK6LEUJun9ZE1P7CWJx7nT7mdGjcu4OYVXQstCuM4kU/3xLpQcyU2m2xb\nU4SESkJbITizWUN2/p+yZcU3RaWQVfC+WgtkSKhWtqeZsm7U0xN495vGAAAgAElEQVSlbJzPZ5BA\nLg0foORK84prhVqFlhtrrl0Y1ziKxw8jZV6My3XauBknFNvkrYUdERcQn8xFoVrB1FrjfDYf5pt2\nQMTijwU7WU4vBh7ridDHX9PC6IR2+oIhKMVBGSY++c7f5c2Pv4f4akh/LQSB+bRZtu5Nwak3wafX\ny6HFWKGKi+Zw0rQRh8Th5sj9/SPSW/RaM1GEwTsOoXEblNRW5gY1HKzowrjOqLWqWtvblkp9hz7x\n/nlRxeG0oGrsxNI3bMWcT1QbNW8s6xO1bGaDWNt1kO3j70tjXfo4bvT44ma9cdGGICTVSwG6x5Ca\nL/J17nSqor3+s/jq912KoejGkZQrXeoZJ/T65y/9/z7f38N73rnE+593bcL+2nKJzL5+9j1EqNYe\np+4sonl/Pi3bAcQoK8bYzq0ytIaIbRhWdO+btB3ag1RE4NbPfJR+yQs/86ErjBqIPIIzZLdVD1qo\nRfCdk2nhM545nwFIZaXUwjQeiP3QH9NoKGs/vMRhJOdMXixJUkSIUcnzyno641phHCIvbxP/8A9/\nkzf3mX/x55/ht8TPfrjx4+//K15+8huM6cBaEk+nzLo1cjWqBNoIexIp5gwk3fXncj/7oQtt1mED\n2s6BbXSUUy5IZAPyVrunvAVMIUIVLoKy51d09vt24WhVcL3IeGc893lqlBnB90Ne7V+rqK07qpfx\n0ZRrfLtex+ZzvnJru9tQF46qdX8uAtbOo/ryum/3zHUusQn5xAeUftDqaDGi/TWeAzba76NFfu+H\nasEKf332uXHgnznk7PQREzoaP792GpFvjQuXkHZ5jPtcrP2zqzj2KaOdztLT1e1Q3ucA4romoplr\nkYAToz22y1jg8lrCLkZUzHXKoqxDCCZA9n2f9g6qWX6ZNkUQggkud1tKF0BCp4IF4za72PU/nqKF\ntW2cc2XVgPojp/YFWxY7ODUo5xkthdHD6IQX08TNOKBroHRRqjpLoh1cotVnB+/+TLbaupdWpw/q\nM40CUPbaB6OUVjUuP+JtbLfuDNSutYr3kSABUbvp2iyuXhQo9noFE56HEElpsBqoKlstVAdFFImJ\ntRa2pjydZ5atUXxidYnh9cfUds8yv6F5z+1h4sZvhLZSTve4shEFNvVsPTnW+2sMNyqmBfHOskFK\nQWpjW1a2mLi/v+fh9MTxeOT9ZfL1EhHTQSm4tXQgSIm19bkFrZXLmu7c3770/bUokmtT7k+zCRHS\nC1ISVh9IKTEeIzln5nkmJUs8C/H43tdp6mmtUFu3xxKsVQNWWIm7tNW0/97kB1vIqi1oyr7IGgpN\nR2eds9Y58KVN1gR+sK/7Vgyj17XE2kCdk4idZg0Jg9BbBpfCpxfPLlrW+EVY8aXieNdCLVu2uGsH\nVZTqC58tD9RaeJh/xHZ6w+QWvnOrHI8Dty4RNzi1yscvD3z46ghvf0ZjZtOGllskKHVYUFco7XOk\nKUM7Wpu4VEQcIQwsqyOlhHgoWSAXUnOkjlSs2WrWpcdPN3XkKpxX42q/DJFp9GgN1NyYl43iHBlH\n9Y14c8eqDSTi/MAwCTENhBgJQ78BMVBEOM0LVQtjGhhGz3RIxo1uJobLufT3nhmGiXUp0AU8rRVc\niLzwyk/+8k/44Ld/n/DqJdUP/L0/+I/5Xqv88kf/O9OQ8P6J2ylxuj+zBcfd3S1+GAg+kksjDcHa\npyFeQjWcag/06M9szrRcuE3Thb5zOIy8iAkdlI9iQfJIbgdKnSk+0DrNxpUMvnM4kQsqlHfY6D2X\nqjzb2ByuCU3cswK2cJ6/YF1OiGaSWGpl7UWmceafFTXOBBJ22DRMq7TO0u8Iv7SdH9zft3sWx/6s\nDqitsXnb4JNzz97nr16tc721o+fGV7yKioBr7PVeMHz5UNs9nH0w8Y+IkEu7pB02GlvZ7IDbnQui\n870gNnTZeMxQl60XyZlWbK46hFqMa+eEK3JchWtiKKAN1wRlQpvi28boN745LYy+8VH4KV/zP2Fy\nq9lYysQpesR7fDja+3aR4DKlKtoKw5AYxkQ43tmBMHi86wfNNePTCM5RRCAERJXzsuCc4+buzorn\nnPEyEEfhGF/gNVPrzP0Xv+Tm5sjLbwz81nf/Aadz5R/+4S/44z/5V/zFv/wjprtvcXt7ZHCRny6P\nUDJFIaviozPxsBqlRDsocCHwiDkf+I63AjTtnYmO9O5opIkiHes2k1I0/nhTJHhWrQTx3d7zOg58\nNVpMqxVxSpxsHxmSY49X32kDvhdqO1onapS3qnYgbb2grbqjw95oSXotUGvt3ZXu3hDiuwcsxTjW\nFzFTrx5N7N0nRxdyKxYIZeNR8N6Kdwne6HveNCmeQHk+sWQHdq6HCdmnsbO5s1uR7gU2zrojqs3m\nuFjhVmvtdBJnHVb21MxKbaUXHe5aoGP3LHfus29WnCcfu7DK1jzEEZJHt2KHa9+RceftYHQR9lz3\nPTcmSumc8uDwdSWlA6OPtLr1gzrkUhiHkSddaQSG4Zbj7Qf4kBAv+ORJTMR4g3cJIeAkktKI9wPa\nhGk6smXPNjzB4Q53eMEv5x9xPj3iZ+Xbr44M1THFA+nWcxM8d23FPd1TdOR0uqeS8V7QLAzH0Q5S\nKgQ8YRjJCtuSaSo07y3RtznWrdAQA/Ga4CXYSOjuOUYRCtyNBxIO7WNNpVOH1NGKofkpOOrTRqZZ\n97bCsqzkYhSbMB7x45FzbiyqnM4L2gRxgcPxli1Xcq0Mw8Cbt43z0pgOB25ffINFD3zvB3/N/PiW\nD2Pjm68+4u0v3rLJA3XO1Bnq1rqY06xbnapRWFUhdkOC3qVY5xmpnsM48vOf/5xSGtPtV1Ajuo2o\nyO5EY4eoGCPePwMtm1JbIfjIOI7M80ot+f2v+Z7r16JIVmxQaxVqbkj1iB/ARVRCf/AmVBDpFmvv\nfSG5trrUih/EIoJFvIkqGuC7m8WlPb0vYuY522EtuLAwfxVB/jJacUGdxFwBoPUF4/oaO9ZniyGo\nXGOtVfavG3/UXxLJDM2QppeW0+X1+oYrTWk0MhtVC+vpkZJnUtkYpDK1jUkbtyIcgyFb003kEJXJ\nN8gzTVeaOkqzVrKqoxZnohuEIjYEW+2neIVWCrVgKi4vFrstHiWDNLat0LbKuRS8BHIvkuelIXkz\nT+MQkVoom1Ky0pJn2Vam4w03t3c8Pp3tXopjPByILpBCJB7NOsuFLvjSApVuA9Vb3x58SJRsm2xK\nxq8LIZBDP4DURl4X8DBOA8tWWc6PDOMjLtwh0ytef+M3+MH3/mdqaaQhIlpZcqE2Zd5mkt+57HY4\nuyCNKK63N7QaFWd3dCjLRs6BeV0J69lU4Sw4hUMIjOpxeTHer3OoBGgNaUp2fWzJdVxVce8Ui88v\n3/mQ7vIebdOSTikQhVIzNa9IyRS1EB11phjfOdDXTsm1SJa+MPv9oOhsIRQn1LpZa1s6/7c1nIGu\n/WtqfMjLT/9r6BY8t7C7FsEll0uxsyPFWy2/UiSL2Ht4/jukowz713anBOnomzndGEKhvqN6/Xvo\nv882ri6ibDa/XPA7dwtt15+5aBf2zlG3ijvElRd+5mvDWw4uc5RHjk4ZgidNB9JwZH67dVFWMEuw\n3knBNUMTnSGHwzCgAikmQ/jFUVUIYgf1XKs9F+9o1QKPQkyoNjPs0UKpZtE1BnAxME4fIJLNQ3v7\njCHd8R/8wXf41jeO/E//7P8lIzy0J5IIUTLeNZbNBNhLbUzh2qKn04fEW49MnMc367x5MbpK3aln\nrtPJXLa54xUXzZ0nOtPEKx0t6s0PK+Z2bcnuDvKs0HJX/rGtsVcBXsBTELbSR6b3l46NUeB+dYyq\n7l3G69f217c9wearEzssiHMXsfbz79d2pdRZQSF4Kk4qwZkrnneN6GB57/zgV+aGqfy5iGMb4Lvj\nUm9yXi/f+eC7FYUI9RJm1e9Vbc/mlIMeM7/fZ6c2ryOe4jtqZ2W4HWDsJ9EGdV+La8OLR9R1FxSP\n193gkuvBEnAx4TA9CN1pw7uIV+OjmmPMdT1xzvIQYhzwPpBrvdg0uujta5s5Hhky79npl655nIt4\nF2ze5EJ1I7mjo5omQvQsy8wQIv4wETQjeabSCK5i6msYxxHzj/eGeuNIcejPyPaHXEyL5HykCpQG\nXis1Z6S07rylIL53KyF1n2B4Vlv0a3d3Ch2tLyg+RaNoVKBZZ4LOd15zYeuicOsMBMZhoNVKK5Wb\nw4Hbw8T9vWfwgSSRpyasrfG4FfI8c/fiyIt0JGsjJYfqYjXdcM0scKIIrgt35bLHee9pArejeRq/\nuX/A+Uj4G2xBReRSO7VmwnzpB1Z1xnV3YmFsPnTnr9aMlvW3vH4tiuQGrNVK1bwpy1rAGS8qjUfU\nrVQvSDMUyfmvEO51tE2icVxCF4k03fC+4rZIHQzV8dUTcLRQrX2j0fp6mO9i7ROejrYJkJy/2Oqg\nQtPulbm37foCFaTf1r4S1pqpKMldFyoAh/ZTdW8P9VZdLdV4hb3YEo+J27SZr6x0CzlVNAZDateN\nUu5Z55nH84/xeeYbsXA3VsZ1wxWIh9eU6jmv97x8eeA7HwqOMyEALSINmi7ktbLNiniHSwOgbL7z\nCbuPqGhB1XOMhuYETK2twLqaV2auhnAGXlrbSyY0AlHxIZHnExsT03jDOS+c/cC2Vp4eZr77rW/w\n4cs77k9vzBJuWRgOienFRPWerZiNkW8O502sEUOgbitrK7S6cTgmfBdRDTFdUMgUIiRliEdaa9zf\nv0FOhdv0CUHu+ezHf8GHfI30dWEcA9vH3+V7d7/JL//q/+bvfnzgrEeawLkUHs8LY4NxrLQYWE4z\nzkckDLScGULtJueOrRhi/fZx5TgEytpIofDTH32fr33zG5zrSqPxzSS4ZeFzd0dWIftI08IUFx6r\nMEqy9u4eSiNC0xXH+yd+cxYyY8hQtyhzVkh571i2E3p6IqgYV81BRpGy9M0+9EWoI8tecXicClN3\neFDfOmpmh5dlWazd11XyZl0oaNpbvcHs1rRRe1M5uGCo7Vdc25KpvvHy9sYKWQfjmMhr4HQ+940v\ngNOLCElELgX9EKIV8E3RYnxYmnI7HFnzRlVFvKdqxl8EfVCztXjbeAbnqBVEHX6I9jqSqOtKcFYY\nixiVojplLfZavgg328i4mgdujQmykm8dH7uVf0d/zoETL+TH+PyWV8cXqDbWrRJiQf1sXrulcnsz\nEWNEqbQ1M6WBeVs5LydC8kiG0IxXPY0jkiJuiM9QQSuQTaRmIhnBimTvIioVr7VzM+1nhlhwcWAU\noWoj1yeefn7P6xj5L/6Tfxv8gf/nz37IX/3wp/xp/YJaIPoXVF9onJj9yAFHUuvtrVRSCpTS7Czh\nrI2MuO7ctPvF9zHXizM/2EHXtdqfrflVa21o82hrpGhjKPfxK/6GpooLpv1Yt2p2bH4lEojiKGul\nlUIJiaqNNW/GQ26OqErOQgijHZqoiMZn6HKluU4JEMF5tV3Nb4bkakTVrEOjTxTvSZLItVDVETA/\n5L0lHGPEeStUfRpxMTH5wOiFzTtwjrB5aghIcKg0cvUg3TLNWSJsafCimcNIcY1AP8zWQADEG825\ndpvT5HaRemETRZ0yaLjc89YauWU7jPveSer7wV48O7Hn5YE7n+wA72wP9a27l7RGJF50CG4YCKVy\n8B0Q0+vBvvb9EW/0taoOF+zwF4NH6ohTxxBHhMbjyRNSoKDEBrWLKH3yBBFG9WzBsWRIzXE7TKQc\nefXqI36eZ7wkbo4vmFwgDIm2LLwaBg44ZoEZD37kIQaW1x9wiDf4p8+hLLiyEspK/uIz1mOgNqXc\nn4jV9utNMiqKnxJnVl5+9IEJyl00SG5byXkli2ORRnuaefXijl9gVIJBIg/rIz404pI5+kiOnun2\n1vzSxSxtYwy89APTKrx1Dq1KiontvHIIEZwiU8DXgrjERMQ7Rx48OozcSGSh0lR45W45pol7Picd\njnzz699ifvuWr714SUqwlQXnJ1Ru+NmyMmnktz58STi9ZnIbqn+KJDjVDW2N0Klry7big0dSwPvA\ntpmr1dEHvvWdbxlzYCu4kNi+IgRgGsZ3wI2LHmVboWbEBfRwpDrHON0xeI/mjRj037wiGTpXi3w9\n5TlLE7PTXecLXU7m70ebDE3q4oJeuF6R3St6cxXEaVcEuItgr8eZGQdRr8fs/SekOzu0/YvvubS3\nFZ+/S6fvnvJUrS20f6b2DM2QZ9+zf962v6+O4u7fJLXRakHLQl1nWj4R60qgEF33xPcO74WHh3um\nwy3f/Y1vMSVFZDOOX2vvsFn39jgiZqHVb8WX3785EmRLdEtXz9i9VfncQ3T/u7Z/dhp0Hltuaup2\niYA9OxVLzDHrM4uGHrsAz1Cd2lEpTE0dAzEEpFWa2gl662reXKwgOIRoCJNYQs/Oj41DorXGsp47\nsmcWRqHbmk3HA3cvXpKPNyYGKdkK7lI5n8/EaEEJZprvUOoF1VUFdQ7vnHGilC4msPRAERiOI+t5\nMWQ+N6KHlBQ9z1Q34VoFrda28+5ik+W5dh+o8hWM5P6sUNg9jPvpO/d7tWzP7XC6gFTpfDZ5xrTY\nOYjSqUHCnhRZ9MrhVTUEjk43+fJ7uXxf/1p4NlPqV00qrjSnUow377yQ141ar7xiVUXa9WC7zy3T\nAQjBv4ssm6ijhydgw6mUQkjxMnZLKSbskc1QIDWkMjpPC2YBdUG4VamiuNYuY92KUEfRMxIyGsw+\nUD18wsprFg7lnqQzMayEHn6TUrgU+6VUbg5GeQreisEQPNW3SwCK75xGFXAh4EPoh3NHkPBMzGo0\nGO9NiOi9Q7VRSr4cyncXkEs4yk5TE0P0vfdUKea9Xlac83z3ux/z+vXI//anf8l6yix5Y2uF8XDg\n/mnG+YCPHu3PQdV4riYau/JhbYh2fmof1btbw5Ue0e+t6FeO++frkUjnle+8d5GusSjGL1Xr+ZVy\nFYFexJuoCTwpFG2oVpITnBhdCGk9+dT/yu8XCReE+NpFkA6CdBqFGiWvtWvnY1+bdrFRCIEYhOiE\n5JQ1OhPq7eK5Zn+unSYkYt0/FUcTe95N1ICfHaaR/q/a+3f0LuxlXrtf2Wudu8rIkSvyrd27WHpR\n6xQCXXCM0nrnVugAMFcxnpM+f53peQCkdUpK52/Tf941MZdn5dm9sQK6dCqO9PTVJrsXt63LdsgX\ngotEn4ymiGdulazN7PLcs/rC3hgxRmKMhBSJyZxnhhCJPhB9YFF4mmd8zYxTBCeEzVxQDs7h0ojX\n1teqTO3rYi0C3rHVyloLOG80vWcj2sTFGecd67axlUzun8N5686UVinNHENCCPaMQzSNVkdZc54v\nri870hrE4aJxslHHmkufKzYurcldTeTcfchdd/ZSqvlG9zyDS/dNhJu7l6wlMS8rS8nk7mXdmtn7\nGV18JdfMeHQXpxOPvacxJtMv7Wu+vH9PeK5Jea4/Ka1i/k1KrqbRCl1smju3Xv5N4yTD803O1Psx\nHEA9rQaCT0QVgtt5w3+zuTQYbzBnU9x6l2yw+c7h7IubCkQN1lrWXlQDiLu2j5wJAkKItFIvvEIn\njkrohfTFxgKwtgZNCVdKlb2PvSDeB5STrpKWdwrfXSxxbduD0wlrgZX+q7qaNRfy8kBZ39JOP8Hr\nytf0xOAKR1VYMueaOaaB3/ydb/Pq1SvugiMvT5TzSidb0re+LhYQ4u7+0ScAKRrpvnPWWmvc3t5S\nu1BORIyfOa+XIuaSRFUBEUIK1FzY6oaLDnGR5hzzWtk0MhxfU84/Z2sbKo63pxM3d9OlCJimAecd\nrRRSsMK0FkNflzmzAENM+OBprbIupmjdi0DfTcRLKYQQWdcZkcBhuuF8PvP29IXZZZ0emd/+lPDy\njjVl0t3ER9/9TbaHXzCVLxiCZxZHiI55Plvc5ZCejQMTlAxDRDc7iIiP+G4X+OrVC+4//4zH5nh8\nWvhknHh6+0idV7anjb/z4R3jzchPf/bIqURcXs3GiUYePJK1M+a7sb4IBX/ZYL58NSrVdbqe7wcv\n1wVk1kZgW4tx/bReCgmVsReODSjgai8IDlf+F7YhKkY9sLalviOG272DobdOtVGrCYq896S9jq57\n1MT7r5QSKrCWjJZK8I6yLgjekFURci29jb9zUbonZ8clJcXLeN3Hw5rNq3lvwyeLBDPhEpgwKFq4\nS1DbgKVZ0aStEbynhUDL7bK2UEsXdIFqwQNPVUjbA+PNyF0cGL3y249/zIuQucufG8VhauDNmquJ\nwwdD97dSGFMixEjZMtu2MQwRfGBKI8N4MNFsHFi2hXEcCeNknvOdIhJCYBwPhFDJ3X8+TXKZu7Ef\njlWifabW2OqKqCUial/ivO/aDhcI3sO8Ucojt0k4vBb+m//qP+OP/viv+O/++z8iOscXbwN34xE8\nFK84Cl6VUs1SrFIpZe1Fj1wQxitcYOuwjam+5KqjtNyrgD7vgqAl96LQCuwggW0v/sV0HrnaYbU1\n402ryzTnTVRWrT3tMEqEz1CT7Rs55x7S1FDZbT3NNrDKs+JKzR1p9xYO3X5xd0IKO22oGSPYAAib\nL7H7ZNdakG73KM0ORsnDRKa1hTZVVowSJSiji6hrV+cHoOLJziE9gMVWgkbaeVpic/iig3EQu5e3\ndp7nngCpal7JIThEr0JI3w8t2ff5jNo9BI7Y4WMJ3SGnd1ZHrmvBXv4077rg3FBk24dMLGkOIJ1z\n2qQj4sZJji0yjSMtCloccQj4aKDNWosF2Yi3olYi8+lMaHAbBhsDW+apwROFOI2kYIVmKZWtu6RM\nw8AYDRU/BEdWoY6J1ClEZyd8sWTqtvD6w4/wPvC1ZeW8nVnXytAKY11NWN5m8y4/HPBh4jAe+Xx4\ngDUy3lmHLItS80oao43xptxMAy5GXEiXw8EUJ8RbkbypgTTTMDK6gEpgydnoHkGJQzN73a47cK0R\nvDLdRF5/+Iqbu1vOv/wUJ42b48A4eJwXhsE6HjibW0jhMEbGwaN1pjYDjKbDgPdHXhwPTC9f83A/\n89ly5m0+IaPn/GDi9tEFo4uEEW2NvFVCp02hyiElXt7c8umnn1LUkOda318kD5N5cpdi76+UYiCH\nKOqC2RuWgtTKMNyCCuuSCUPCh/fbyr3v+jUpkq+n231jvSA9nU1p3LB6afu+76q12kSPsVur+evC\n9Wz7vQh/tF24tlfEUy9cxL2Q9WLWTcaJNl5I62lb7Tny++z1v8x7vLjuPCt8TW3sL79XMfGI7QLG\nc7uwpp1Fqu5RojVbhPO8nDmfHyjLG+L2SHSVW59JXhlobK0R/ECMkZcv7zhMke30FvLSw2jM21YQ\nm8TP7r1DzF2h7QeBdzGbUgql2MbhpR9g2jX+eEdxaq29t2fFCjRcEIZhJITEWuxzNyrjYTArO+9o\ntO7qoLRa2UomudjFPxWaoTExRLQWK8pDIIWBupnlXvSuFypGT6CLMBVoar6lKQVCipRmPsx5eWQ7\nf0pdvw0BWguEwx1uvIHTG0II1C2D2Fjb6SW0a1ytjV3PplDKzlm1MTFOic+14eNA67zvm5uDGaNT\nEd1IoTCKcBOEU204FQrVOHK9K9JBXURAvHzVgbtHM3fJ6EUMY3zs4ARt5XKgMdBmR6WsMyOdNG8/\n2jn0YM+pCb5hz7a1y7/vzLUv/XdHeRvgVDv731DY51ZzX75EDHnpiS69vVgRrTi8FSpYZepd6vPQ\noXuB/J5DhPHa/Tu2StEHtGZkn6e9qyLNo85Ds3G88xuDi0Tnyc4+kyW40S35rOLzzqESqfNKXDOv\nh8gomVAe8NIsgS44avBkKdax2EyEMkwj3o/QUc7d1igEhwuDcbWx9c7HBJppXszGS60jV2vrgrIr\nOglcbL2ASyhKHMJl/Ysxdm7jjtxiUeMiaBupOFKMDAHI9lqvXky8+A9/lz/6lz/mT/7yc+osrOcF\nGU14KN4ziOs2dO+i+t73JK4K7MXlnoqqSt0F1qoGZoAhZ87WFW0FrddtTbkiTmC2Yo69w2WrX2kN\njbvA1NtYb51iUxst9fWjd7Xsvl3BAe+t8C1cHY2kH2D7VEO7EE/EkEwfhLJmo9vJ7rhyRW73zzjG\nQBgGhjQyhNa7N9Ako2Q2ZxSiKKZLoKfpoT3d0Dl8XyN6HWIR1WI87crVUMP39Mc9UMcQ5Ws38PJ5\nm7smqdkXaWLPyMO1+aT0A7f9j14cYp7tjXSRbesUjv2Z2eaI27tgzhmSbMR0iyvHUHPvO5+9d0Rq\nMaS/5YJga9whBCbnaNEzpUjogNA4RE7nBe8s3XAIgeD6od8LoQmxOutAtcJxiKyAOAuqipgbRpxG\nKo0WI34Y0BUkZI7GSUDKQvCKZ6OqcBwCg3eMyaHVrAyHwYSN0xBpdcV7c08JzrjHLWe0ZoKABM/t\nNDIkzxAj4xipVI5D4naYGLwn9c5GbV3EW6xILq3Y542R2Z2YxoEpBoJCoDL0VNkYo6G+uaDVkhuk\nVVL0jGNkGCLBKykKhxQIGjgETwwWkpP7IYNmzj9OPE3NZaphFpV2sPKUtpkz12HgtMys63rh7Yd/\nTTrec90JmLd9VkOU9w5na1DK3t3+G1/uV65fkyL5UhdSW+4nB9vQnTfhg6cnynmjTrz/NbTfAENO\nUkqX9qIQCa3Q+gan1VpPNW5WePa4USfakYLYfW6185D9xQpqbwfuogXdF7b9ffSCxe2+sarguq+r\nM+6sqlJa7bxphW7FZZ9h74NxKa5UWi8MN1qrlPWJlgvn5Q1vHn9GW+/5+lR4dTNy2E6ElpGi+Oq4\ne/kJH9wE2ApLe2AMFRcbW1YCztqgurcMr61wQw/EaBHsZGwQHMF7tm2zAtPti5SnPKtDWk+g2mNG\n7XChpDFy+/KWl+OtWSC1BVzhVN7igjLeHvFjwqkSxom1ziYeyBtFzT4nObUiQWxhIQSmIZLFkKLS\nFPA0dfhoDiZG6YDb45Ev3nzGNE3GsRbPMB0JYYUmrKdfkpmGIpUAACAASURBVNuJ4eNXfHDzXR5L\n4ZNv/33yw6e8+b9+RCsrISSUSilbt5s745oj3R3s75rQtBKnI65WDjHhxAqxZa6cHp/48Q9+ys04\n4dsXpI+FMCTCEDivZ1oOxHrLR1I4+42cIoLjgzAxynbhpO+blZPIxSfoS9cxRtu8n6mFhEjRyiiC\nW0vvENDT4bQfGLrwrNjP7R0ObUIRpXkYnCBeKN2reC/AtI+l53HNF8GadK9wINdqc0r7AfGrA/cA\n2EpmDL6jiRDEmNhtyyCWsLTPxZ1astsuisiFimUFVzV/19TREkD613eBVaM7urhAxlTZNsSNFtNE\nCU2JPkC/P2u/FzTMB7gVhqBIWLktn/NxdXxz3Zh4xA2Z5AouNqZhIqQI+cy6bbhg3Pt1K6TkcMFR\n/Y72ejQ4xmmiYVxw0cA0TTBYe97FdLmfaRj6IRZSHIjB7kXwAzlnE9H2FmRRJbgutMMcR3bqgYAd\nHkQ4eKMp7b7ZLgXiOHE+L3jd+Mf/9X/O977/C/7xP/kfWY53PAVldQ6KM+u6wYp3UGLai/Z8oXTQ\nf5cAXsOV3gWEFPFBoXq0Yp0DV6C3g8FRckNbxY+pj7XSD2gdkKk2RosagixOyGWhqDlhmB7Zsa4Z\nJ53DH63z0ppt4k7kEnGN3ylWHVToGpnozJ60NCV0JFlRYgo4lKDSDwHm+R1C6P8apcccMyqNShT7\n70fhnpMfcGnaZzOrM4TfHNIcrsHgKkMHPBqVgr0HoAtur91s5zp//6IjEIT8TpGsav7v0ulEu4vM\n1MERdcZlbg7OapztshfUvRrf5Oo8Yq9pokRlB476HttDi+w8ZH8O2gWqasW/D44hBVxTfK0M2HtL\nTQhROUQleEcogTvXmJOSXEaqBcpMSfmoGBAxUIjeMfnC5jcOQwOn3ATPB4OwuMwWrTOVhhvGFGhr\n4aCVdnOkSiG+vMGnkT//xWfU7cTtUAnSWO8/4yzCSwfZQ/QDg5w5hMAnU2J0Iyz31nWbH3EpspXM\nOp9xotzEyCE1NCovh0CtgvpGzLP5g+vMGIQbX3jpKp+EQCvK6AIbjaVnBAzDQDmdmUazw2O95xgK\nA5lBZ25kNd508IyHiVQbOZ/QbaW0hUhlnDyHMSBUYjtzEzaGpFQRXkfl40PiJ3mhzjOpJqiOIdnz\n11pokilSECeks6PklcfySKFy88kL/uwH32eeZw7jEV0zh/R+1HferuFT2g8OIkJS49xvORvPPgTy\n1izgywfrdKu89zXfd/3aFMnOY6cNd7XC2bkxoO+gnM99Vp9fdsOugQIX/ppeuV/QJ51BiWBE3wtC\n23OkbSPFNk1zmPibGsG/eqnb0cTrCUf1ytvZ368hJubK4XdO27O0pqtvq8UiazOOUisbpRae5ice\nTg+Qn8ih0VrAecy3d1O20piaw0vg7niD85W8vSW32hGYdikg+NJiuCMfzt7s9ev9fZV6fZ+ldKHh\nkC6q2pyzbcRyDYAASyochgGqMXhTFKozQc3bk3F8QwgoMI5mkVVWi5XO2fic43E0oV7NbOtm6u9k\n4iMztu/2dHhS4lKk7bHFtS+Sqkb4953f5VxEW2Nbn2h1hmob7c3tS+5ef8DnXshUboeB2jLn8xPj\nOFoXg93/Nlia1TOUTmXvdDg++OADlmXj+3/2A6ZgRcrpNF+4nziPdAvEOFeaVKoPRHWMPuK6LdSO\nhl3bvO8fi8n5C9K2c7gEh/jAEBxDCJdxpv3e1KZo6IV4bwdfkKMGbY9Fd4bo5pwvLf1rTHO5WkOJ\nXHi/v8JttynQfS6++hIxJwvtEbWtWVvXOcxuywlBjEu8b8i9l/3O73uOal8Syp5/TzXaiYrF9ZpC\n3pNVwfm+FrguntztjeSZP/pe9HsraJutb042Dn5jkkdSvmfgCRkijg2aicEiimrm8OoV02Hs49Xo\nKiHZva39sC/Bs64rx9sbjscjy2aHJ+l0iFIrNdtaNo7xQoV5fi+cC8QozPNCzjaHG2aTZsKpws7h\n3cfHjrDXcrIiMJj4DzG3mSm8oK73lPme3/7uJ/z+773in/3zB7bRU3TAYS17kR5fjhiNqotvDHT1\nl99pw0PeeUYWyNL6Pb8Ofuf3df/6HPb1iF2HcHlemMd6azStRimuvVhrO4c9IFI6N9J3rnaj5e3a\nQVSjGqjb+fzv7k/72mpzqdGkWZCUdza+GtB9q/c1IvmIc1dKkAVOGZIcEaCQxTplu5hWxOMwZFrp\nThFsODy+76Wq5iRxkeBgnaPn2pHLHvUlhO7yPfvn0u6o0P+8N0G7cYJ9XmcdlSbg1ewkq7uCStdb\npVdwpr+O7p0p7K3sHVpzB2kX1N45R/SOCowxQBckxgCxc9+zU2Kr1CGaDzxKDJ4YA04yVYRFIWqj\nm9zZnKTgne8osB2GVQQN1tFhqxad3mye+hQhCvdU8jqTtwdi2/DDSFPHlI4EgRYg+EqQgpaN1LsG\nAC+OB+bORd67lM45tvOZsi541xi8R+5uSQGCa8QgpGlkGyKj9/hmzlLRm4hzW5YLCGe2pIZQH6ZI\ncILXhtdGkGaFpfOM3kT5tRZ7bjXb93bwcMuLFcqGBBKDMAbB10xdN+q64prtNSrVLBiVPp7tWY9h\nQINwVrNuHG+PzH/9WY9rV8qWL7qXL19frvOuNoR6OcA6523t7ihy8O7ihf63vX5tiuTWF6etKWvd\nyGpqZOetjdMqxGm6bNrvu3yfNMF7fAqkvfXtG5tWzgXcWohATJaitOwx9r2o9W5PCepCAQkEHIGI\n00rOhpztzbrdgP+dVq4WamssvdidYuyDw3huEVNw11oJaT8JmU1Ta83iUPd8+55MJL7aKWybaW3j\nYX7LeT3zxWc/QOYHPpgC304j05rJKFtOzOvKhx+95O/9xpGbYyTJCbYVLRuq0NoGKoZ8d7WytARa\noc296BnwLrIJfZLRUfBu4NUcIZrfsCFwannptdGyUnNjZaU5oTZP6RZVY4iEA3jNpLqBDzw1x/b0\nxL/7u/8e6XjHeTmxSeYwJs4l4whsFr9oYQib0T1qqVSBRjEkzQnnZbaNzQkhOrxPPDw8gDp+/OMf\nM91EzudH48QGCyfx4tDoCFRGCTz89Vvubhe220B6u/DB3Tf4k+PE/JMfcvi6Oawcj0fwjjiOiFOW\nbaY0U/zZeIqIayzzI9NwsHjmvPF3PvqQD15OrPMT63rgfD5zeHnEkZjLjPiV1+lMri85tMQmiaAN\nl4Fu/4VTQi98DTx7f4l5SI615J4M5hGfKK3hQ0OlsG0LGrO1cWujiVKDtVV371ptaip1gJg5VmGc\noSXIwbxwnUp3YOjtewytlODJbUNRgk9WfPWEKC8OzYZ+rQM8p0V9+ao1M00TNW94cdZRodm86S31\ndW/Ht/0QVNHWOIzJ6DFiOKKRjBRSYqvbVflPQ6PQaqA5rOBwRgGYsoV1bK0hThkloc4KvVIsyQ2F\nQTyKbcyNipfKMSaOt/DN48LtduYuKiFA2U40F6juDskBGQLx+AIw5NGHgAQrNCQeQZXgI0PylHUh\nHm9YxbOtm7WRxZNLJefN7JN68tbTunI83kLzrEU5DEe8V1o707Ty6vXHqCqPj49m01eN+hLGI1UC\nmpdL6113e72wp1x6PLvISNFYCONLPIlt2/gv/9F/yr//+7/gn/y3/5ScPkKTcO/ecLPGbnXZaFpM\nWNY7EcuWKa2RxsnuhRp/9yLIVpDSo5B7wJFksfnV4XM7vLTOY9aOhwg4i3wu2oguYYcQ20x96vHd\nYoc/5xzSBnwQQ5a9PQuv06+s+7mLiVt/Px7rJDQE0WLjyztLtC6ZIIng7LC1VSihi1qDUTiGmAgp\n4py93r0K1IL3FuIUpsYLHFmOPDERpKPINKu8HYiOqNZLVyg6sQIdoHc5fS9+q6/9cCk0VmqrxF04\nh3WYEDiFra/1ncrV1MbI3sl1Qis9GKZ3p2xmGT1ma5a7Gbs1mTRlccXswESozsR5vqgJrZIDbdyM\nB/y6McTIVhaQxrLOqB7YlpXcKueycozRDjE68ZAXQoy8uBnJR+Hp/MDGCZwjDQM5rzQpZFfIoxBv\nJ5qXC+jhqlAOkWUILI8LT2riu1eHI5GN0jbWUGBbGbTgawUfGVxicQN/8mbmw7sX/IPbj/Glsc2z\nxXBTcMvnPGxf8Hb7gm0rxACH6YgEYT09cVpmWnA8bhsfhYBMr3jzxU9ZgbentzjO6Dgwr411U+bt\niSepTEeHvIo8/PzEE4WcN5anL0jTyJIzpyWbJ/LTG2pZeDWMuOHIZ48rTzPM28ZWHnk9Oj68+zrr\n242HkvnJzx9RX0ml8oEe2JbIp19Ufn5/5nx+IteN47e+wSu540dv/0++WN9wzgpBWbsuaA9qiVWo\nufJZe+D2cEc+Z16MN3xjuOOXIswqTMcbWsk8vX3zlXuCqrLMW183HKVkVJrxnKNRcRwm8vYuoSUT\ntf7/qnx/bYpk2dstHd3RJj1ZpycNOQExCoZ8xSlgP1XscYShFypVGjRh1nLhPvr+GsHvqIOdeHwX\nW2S1U/suarigfPty03mKO49x3ywMHTaulLZ6Qa6cOIJz73CTQ4r2amoLqaJUcUQ/Ibmi3aUBsQW6\n5cq6nallYTl9wbKeWecTd65yGz2HCCPCm6VwOleOxwMff/wxY0q4uuC80CSDZEOlu0Jmv52CUTq0\nU08MezR+qnMdJejG+U0rIfp3NokvK033TcRCPKJRBMRRq7IuGXdnvFgPBGm4NXMTEx9/+Josim8e\nlUbZTAQYnMd8MR1OAltZDZVyjlw2cILv8ZO1KGuuiDRCmghx4PbuJSLCsiyUurLzrE19DhJMXdtd\nltjySl5PtMOAc45hmMAl5rXQFuN57R0JEcGpcSJrNWRs5xi2fpqOcUAblGpK58PxyHw600ph2zIH\nHwlBcbnbmw2euFa8WpaanZTtbe8OIYamYB64X0W28ibG1EoXO3WRkFNDK7b5UrRekiiRK++5t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VNJhYj05TMMFIQ7TROgTku/F4K5ltmXFlo5VKi5DSQEojSy+ybjBSsQltTA2CTVDF2eTvpc9j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\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4fqBYS/GebU7Ew56NgbswYTuHNp64COJpSYGiIl6LllCXzPprr3MxelLcU0qkd55QNN5q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\nNXHd9XRdx2GaOCw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CUUq6XtIaaowsJbwiQIy5Q0+3WbFeLri++WMOhwVQLEvm6tkdS1go\nYUEhpqiapRA9mqeOngjrO9abs9PnWmNQImDG9R31Thiu1skyl0Im50RBvk+lYJt+MrYiuOYi2tRS\nySGRQyFV3QqRSqY1BbwTfXBKhCQNiQbVELqQ5oS6LO3e8r5ncDSzaDx1To/vw5jFEKBbvHeuBZsS\n++0dY5sihmXG2xaNTKVTkvDmFJIiWRSx1NY1V5jeNQ+F+Akq0mU+TjlSKVQlHdrBW5SBkBSmRKzS\nQmNynkMSA/NmlLCk5Iy8+xVUI53DimyMOmMptlBUwXhD1zlsM+3lkNHW0Y8r5rBQEKqB0CaMRMjb\nto7mz793jt6GWhENb7vrqlH0rhEfqlCJbGfxFFbZUEJie7iCujB2VjavKaNO5ufmETLH1L4oHdDn\nJmfHwxiDMlCUUJUslapEH1ybZLGUSlhkkkDVJySp3ANiCi/p3jTe9x6tLfMU6DrPNM3cPHsmkqXW\nlKFNS6WRKEV7pxU3d1uZeLexTY5SUK83G+zgyTUR48QSEuvxEqvPToErAEUZmSioSq2ZWqC6kSlX\nYeQjBJLOWS7tzOUjTfnp13n34z3/26//Cdn21CCBWrZYrNIEtRfjYj0SSqpMctqEWqFlAlEzQy9r\nd25spnSMt6dRq5qE8kWHt04mM0GeG1mvRPaVikxCu2Gk8z05Laf60ZjnJv5/huPPR5H8nCyhFolA\nPuLUcs7UlLEotJMTm1/Sgnedw1tPZ52Au9uNZdsD5m2g1EBlYYpbDtOOq7unzPs98bDl1YcXvPb4\nMedna0lrU1CtJkca71c0xB99+JSrZztyhKLF1AXyQGgQuD2CsqsV1s5w0XX0rrIZDZu1o6ZCLlp0\npVlQSdJZMqzXPXZZWJblFL0IjevYZBF9L0YGjEQiVzRr47Cd573dDcZ0dM6S4gIxYYq4n199+ID8\nTuTpBz/gH/1y4Ctf+QoPn3yJtP+Umg6UMFFLJJcgiyqikbKmorUhzAGydA2qFhNMRUgFpsrvkZXE\nECtroEouvLYGVUSicSyedbkfR0sxHdilhNG6mYQk+lYNGpyR5DQh20DjWQvGRzYvSusTRePIQwSY\npolaYVki+/2ezg+sRs88H3Da0HmPVhBTJuYJ3Ru0dYzrDUucSaqyKC8xLkZe/p9e7bnaTiQUt/sD\n3TjgkyWVQgqZQ1iwSrB0h3nL+szx2uNHPHlwyeHmGpWS5NI7y7ObZ1zdXHO+ueDTzz7h/HzD9Wef\ncrbSzOGSUg8oZbm72zEOZ6gBbJH7LOciDt6qTgSJLx69kgVEDCcGnV9jHM6x1bDsd+ToGTrLfn9L\nJdP1mloXapXXw/GFevxTt5dyLtJFVIrGkRX8UyVjjMMfOaOpoFpK33FkiNaUWggpsumEsqLsny5W\nv/ieGNcP+fv/3T/k2V/7OX72J/8i8/YZu89+iK6lGWrh+AJMKWHtSNWGJUZKLXRtLm2KIWYxCKVF\nroNqdIZV359Gc6rx25VSrNw9+QXuZSgVheuNXNOc8dYQm5lEa90CRWTsmI4mKSuTFulaGZacpLPW\nCtChX/G9P3mXu7s7fubrP8Wrr7/Bt3/31/nt3/kN/q1/72+wWRLXn95gzlfslhldK71x2BR4+t4P\nqbXy+huvUmvh7MlDwnRLDBJsYpTC5MxmFPqFd5ZQI9e7PcMrr/OlH/sq69e/zKIspevpnUe5noIl\nVTBVFlRSEcZ7kc3msWNQS4KS5FpYqNaAc6J595bhfMPFK49xOJ5NHdvplvfefcr11YFvfuvreG/Z\nH+745MNP6WyHMh1G1yY5MxhtyGXhtXfe4ezJq9weJorVHMLM5SheFD8O3Fx9RkyJi1Ec/l0qTBNM\nS0KpIoxt3wEW7R26VHwMJBTLfsduDmwPkVk70TNoRa2GUhVbJQXZPhxwZsTisFp09tbKz5dLxRgI\nKVOVYbudKCmi0oztPb3rWW3WhBCaZKlSyoy2lqHzUCqXLdV0ng4yqjeVoDN9Bq80Z8MgFA4ygxW6\nwpwyGSnOs1GEKSF+gIrWGWdHjLbkJIEaS0wsYcHaZmBEEedKoXBIEEKmpCzSFK3pvUL7jrgoAkUa\nXJPIOSoJ7Qzjqmee9hjAe+HlH+7knJrNWopF50Wy4jy9Vu1ZS9S2sRWqhDwjMUZQBWcHKeSwMrWy\nsjGptYMKpUTxmexndKcwumC8IuOJRrNrVKZSEr2XYmk6SHoeSpG0GG/t4NFREl+P75OcM6EEjGvp\nuKVIgJMRkkKhTS3cPa4U2rPBcSrsoVa6YWTeS9jMqu9xzhCjFHqbzWPqJIVtLJk5FVLOLMdUUyNx\n37539MNGivsUUKVijSbnyDTv8d5zdrEGI5v7wxQYzhxqzsQo75q+erQWv1DJC9b29K6Ta3q2wVrL\n3fZANp6PP/yQ3W7H+flDfvaNFT/7N7/Jdz6d+c77O67mRCwSXGP8KOZSZXBKzo81HbVErOnE3mGk\nIeNjEeyiERrSPgZcW8dKKe2Ze/GaYKdIwWDRhJxk1m1g0A0iXCs5FaKKkAMpiK485fjSpuwLP+fP\n/C//vzxqPY1dxZWsWkSodH1eZuM/7hSsUiylUIwAXfXphAVCWohxIS57Doc7bre3xMPMaGG1Gum6\n+05Top70iadOkhbhtzR1Tes2m5MO78U/l2558QpKEiwMmr4fyfEgI6KGfluW2CgR6tQ56fse20yI\nR8QZuvFfq3R9iVFYwdzDzxVGuu5G7rre9yzzxI+98xarwfGPf+332Jxf8vpbX2LZtp+/MXBr0+Ry\ndF5r/blNi2mjIt26IDq1zlgpGCsviGMH7qRVa0WW6AXLSad3LEhijMxG44vBVCTPrGmGC0IayTmf\nHLr52GFomu4T1o3m/P4Rov9CJS6Boo3IObJoC3OMTNOEcRlnNKm9kK3r0MqiGxZotR5Yn23Y7XZo\nDYfDjttDIhmRvqCFjNF5z5ISIQoGLcbIMh9k2qAUpnj2ux23t7d4Y9nt99TymP1hS54TH+8DGU+N\nWagCR3d8vZ+UiI7foF4it6CRPqqqGFVxtode0fcr0ryQUkaNTrrk7R6opTzHwf68Hg9UG/+JVObY\nxZfbrrbCWZ2CZKT107psDadE23SqIoB92qL4RS/C80dtEo/vv/sJv/1bv8svfOuvM28Fh6a0pSIb\nLDCoUk4SnqoNMUs3TWgt8m4xxuCHnhoPmCacrUm6iEKKiYB0Ho4TnZwzq1ES71KI4qzvPUY7tJWu\nsbWWeRFNoRj3ZGSrtSEF6fLKpk5R0r0USF7c9zppgM8++4ynT5/x1a9+lfc/eMSkJEWmAAAgAElE\nQVQfvv8eOWfOVmviEDj4wnxT2Qw98eoZ18+u0OfSxTTmDUIu+L4jJ0ctsoDrWiArrLGM5wNGKZac\nibXw9ltvs37wADuOhCKUivvz265DbbrVcryux+4xop08Tg+OneR272QFTmvwnn4coJuJFeYQ+eTj\nZ8xT5Rf/7W/QdZ7f+2e/w/txRqcsRJSS5MZs2ldF5fzRI3zfcXW7xWiN9w5jpON8xO9Vpcg5YYym\n77vTfQSKfFfE/W4UtvOoAnmpdMh16zOETmOsE1lXlZCNY1R7romYFmpusqNWvKgjW18XceprDdZz\nzF/VNZ6mhEddu/eeWCI5F3SVDnemkOYFP/TkmihJ5EAxRgiR6gzJNb1qTXQc8WtaNPrG8PH1Z3iz\norMdzkphklIhlIizis5aqLAsE1YJ65zq0NWiUCw5EmPGtm4rKYIXSpTyDqMh1kLYR7RSzWtQcN7g\nzUhqHWSloHMOhWa3RKZpYQkBZQ3n55ZDEqO6M4bed6fp31HTW6s8I6q0HAWlTtQlpaAG1bqPBqMt\n++nA5XBGbFPNisTHj05Tcm5b+Rbk1e7rXGWydAzUCiG0KHr5HhpYUsIU8RFQCk6BdshEq6rWPbUM\nw0oaejm19bxFJccoKMNhPOmRXdCnacLhIPXAWd+jTcYW2YymlMjhuHEwp3fGNCWs1VCFzDQOPap6\nusFzOByYY8DTY3uD0m2SgEgznHOkRQJPrNMss4x8bQ04owlpQWnDMKzknTRcYlnz7OYGdXPLm289\n5mtvX3K3nyl3BRcXKAlrN6ginG/XNj53d3ccDoLFtMaT6kKMBafE+5T4fI7Csd2imhLgRUecF4zt\nUKrJ3FKWsFXN56YlMUYczUfS0hP1j9A6f/H481EkN5PbyXdUoeYiRa/WJJXZ5QW1JOEdPjdS//y3\nMRTVcSgGpzIBI4zKktA1o4phd7Mjhh2fXr3Pbr4jpplSMqv1BZuzgbP1yNk4ggp0QAiJXluCLUzG\n0KuFSRlui2GJmkp87iQ2U44xsqjUxOAtjx90rLxi3Ves1oQsRIsSJ6HW6UpV8sL0gwfrRI+USovG\nNVgFphQ6fdQDCmqpooUckSfWT86IOeOsgRLRxnKYF2xVeH+G0rJgbjY9tT7hp7/1l7m+vuY3f/f3\n+dd+7pvsbz8l7BNTusOvpYhTu8KmH4llBipdZygt8U9bg3O2GYgqRSkhcFiN85Zpnila0VsZRw1t\n7FliwFkrRaI1hCLorlQyZg/Ga0yn0FbhOkffn6NWHt17cu7IWWF9DymTlwO5ZPKyA1VJWTYWXe/Q\nnSyU3jmoihwLYz/Q+YEyL4LlUSL+rypTcqG3mhICZQm4szOm+SDFVKfRg2VP5O5mS0mG4eyc4eEl\n5zjefe+7FDUTd5m1HwhUTCe6r8v1GZvxnEfnD0kpYPzA7TSxKwXUxLO7Lco4Pr2+Y9rLgl9T4un1\nFYd5i3/8OnazJtSC1oquenIVeU2mUrMiqJcb3rLWgBUNXS0y7jSOh5ev44wnxx1BV6xX1KLJS6Vq\nhx/EHEZIZARlVRSsk0VrGcMdX0Suc9RK05xplulA1Q7TOWo1xJzIuiKlQiWXKAuPqixKioi0iIHt\nZcdSZ1amowC/8e33mVLH+uKSni13n0VIiaz2lFzpraFow1wi1ntMqdSiyFpBVnizwuhMjLes7EhG\nfje3cix5RgfNMdCmaxzvMge6ceTucKDve+acBAOHbPRimPDGQs34Ng4uqeKMJ+VCCY2H3szEFehW\nHqU0SwzUINzr0TuG0bHdClv3Dz/+Abcs/Owv/gI/ebfnN//PX+eVN94kasPlK6+wz5HdIXIIM7r3\nPHr9TXrnGTZnmHkiznvOV2t28ZrbwxUlJTo/4Ddn5LsDUNlcvsb6tXP6V9+Bh69QXMeoNdr5puGd\n0MpKkEEqYsw1MuVS2gOmbVoV1nRk44lV6DCmLJCEFiOIgUK/fshytvDo0Vd5/zbyx+9+yo99+VUe\nvt63TWrA6EhXMq5mUjWoWKlagiZWD845f7Qmdz1XHwbifOAbX/4SqmZKrCx3E08evMLt1RXLErDO\nkqcDNWfGfiCkzNp7YjEsKTAYT66VYDM1RIwqjENHSJXr5UDMBW17rBsZxg2LzpRi8P6c3RxJJKY8\nsySHyrJAD6uRqDTWJVSJrF3HojRTdeQyc7iRCOZx6CAU8uyIRHQPcS6suoF+44WXPF8TUmJcXTCu\nz8kxUXNkShVbRW/98TKdii1tDd5YHqwHcqLJJxzad/RW3pF38xadwDmLGTUH1SKSUyWZTKoJU3vs\nYLGqokomp8Cym0i9k9F1SqzQmJUi5MLNIVHmQq6RVS8TwzCJL6Bi0EqxXTKHEMkKfE2Uu1uwBZU0\nnda8Po7Q1js3GCKFeZ4hVfYZVK30tuKVJueW4OpFWy6kn8r5qscvlZX1FGfY7nesx44SijQJqKQ4\nUXOiGn/yzuQlcXu9cHcTuBjXlBpPUipl3X2QEjINq0rkLboZv70WXFs4SKaDsRaFmIG11nSjyAkP\nhz3WKc4v1ihrmnGv0vc9Slm2MbKEfdvoe4y2LFUaaZL4l0kVVp0YknPJxJy5ub2TKbOxdMMZxsh7\nzDoNZY1Slc55bm9vmELA+4GaHWRH722btMuGeGzNAGel2fXKgwp1YLrx7HYHbp5OTOGOv/LjX2aO\ngcvvf8A/fXdGhXOe2kKtmXNvwY1Myx2lVkI8sITK+nxkXHvioVCyQuXSZBsQytHL4mSNe8macMgF\nUxOxZowznA2OedmDOmfJM1lVRgM5Zpx3dN6SS8FqxxL+/9ZJPmmMmnmoOY9BqBBWaXEnaknQ616y\nCxBVnXQvSjF0fkUC5iWK7ixtOUy3hOWOw3THHPaUKt2s3nkG34kj04hr/3mHpdxotPCRSC4QebFD\nstYKJeGMYnCWVecZbGmSBek0aZoGrXEUj92ZUqogftqYIedM33aP6sgpFvEoCs2Sgow0tPrcz3vs\nzHrXk1NAK4l31Y056pzj1dfewPYjv/fH3+PZ7YFVv0b3I2mZGIzBG01gD9pSj05fVYWkoCUIQbbx\nrUOoDVo/x9Vteu6jsQDudV4yXpTrGYNoU7W20oGpGqvkhWqU6CBdN6B8R6rISLDpvUOYiWHGcaRV\n3OtoT7rtWlHcfy60Lre+ZySXAnMS48c+zKANXbknEyjAGSsmSuskclcZHj16QpxFKtINmqgKUwiY\nzlNiQblKXGYOOD7++GNKFsRYqZU5TiSKBKTESGc885TISZGS4nYXORTLpfZQFN55dLlHsz2vFS5V\nxugvviGLjMBP+uDmCB86lL7g+nrCmErWltI6KSgjqVdV0VpQpHSvHz/d58/d8593gevTtT7+veiZ\nj0SIz/8Of5ZDlYr24LRiKpWr6zt+4ssP2YUrtNuR8wFVxfirlTj27aoj5YJ3AzFmipIxsnWc9PvK\nJQxiMLTOMUUZfx+nSs+HznzxZz5SMBYF8+HA0HmJB37OPf38PVc5plkK4H4YBpZlOU2rYlyaOdCd\n0JA5Fq6f3XB9dcubb77FPP8W19fXPHnrTTEOZXlnGNezXo90mw2rcUUuGu9H0uHAMgVJz7I9pSZy\nNaRYcONaxtjDGnd2gbaS+GlbJxZjUNqQ0oJylRQkZENXTQoFrSUBlGbcrRUwRrrVz113daL+yOjZ\nOUfqHKvzB2gzQIXLBw949uwZwzAwTaK57owQRUybCHbWUTV0q5FhXHOziBnY6dYMccKan5aFdd9h\nraM2qkNoshfvBAvZdR0ey7REMpqiE9p5CpHdNOP7Ddo6HEUCEKiEsIicoXG2lwwht3du0lgjYQpQ\nm4FUuoxaK0AQlJXMMAyUmqQzWiuhZKzrMcoJUk1rrLKsO0O2lpQ65mhO71MhPShUoyWgVdPXi5FJ\nJdm4WSsaeN1ijjXy7qy5yATnaGxq07yUK0aJMdcZjy3CYh86AwWmLHLHHKKcb1XBWLT2+KpY2SzJ\ncTGJpKPFLGsgt06zTBYzaNVSJ5vxuBEOUpR0qpiC+MW9baZARUmxBaOI5Cc1XTTILdi1+9ZohY2F\nUiCR8E4MinGCw34vndhUCFMU7bQxEqjV3o2ppaoeJwH1+OxrqQ+Oul75LFBWfDulGcJSURgFTh3P\nuXh3amqTcWNwpqXvZTERf47MxHFyG9vfa7zvCCGw3++b9KjHY+QZdLLBylmoWVrLhE+p+7Ww2izE\nJqvwaRTKCgav5HdSZcFgSceJpJIJgKzpcKE8dAOmN9Qbx/6zaxKB+bDFWs3P/oU3+PjDP+CH+3ex\n549JaiRmg3XS9Q4hMU3CzvaDxdpevEDN4ByWBY9moU0V27pi1EuaokaTW60o3tGKKopdmIk5yZTu\nlNsgwXEAlfwjJ/9fPP6lRbJS6kvAfw28gixt/2Wt9b9QSj0A/lvgHeBd4G/WWq/b1/xnwN9Gguj+\n41rr//wjP6PdAFpLQpECtK04Yxi9FK9nfU9vTXOMvvikeVVQOlNcwURLapHTLHuWsON69z5Xzz4m\nhZlp3jX0ksIZy6P1OQ9XZ9RamWPA9TJu0rVimsFiGDWx9OxSYVsykee6388dWknW+sNhxduvPuSN\nRxDmLU4lCS5ARh2+h1SiJP60zpJSMMVJWMjOsFI9q6EnLaERAdpNbxTKHKOspaguRYotYww5REqB\n8/NL5sNEroVYRUNljUY5y20MfOMvfZW3f/rn+ZVf+WUuVh0/8eYbnF08Id1+wn6ZsL1mX9JJOF5V\nM905RyGTW+FkrHSZULKjVQ2ir3KlqGMhem84lBQc1/57OiF1vBvouo6+d2Dg7GzD4nvc+SVm9Zjg\nPTln9tdXHEMfxEDVjEGtOPPen4riYRxkQ7FEDoeDfLbvyDmScqUsod3rYiyMWbAyKUWcsaIFDxGd\nZTFer1Zc724xXc+T117n4vIxf/S97/PDH7yH8j3TFDDFc7i+42LVsSx75ulTQsgoAytnWJ9t8OuR\nojVFG6YlMqfAq6+9xXsffoiumvX5K3zlJ7/B06dbStWsXEeeF2onG4FckTCJCtTEy3rJmtJixWXv\nqa3IaKZJsGbrzTmdnsn6QEmRUOW8LiVhlGZAFmcx5RXwXnjm0L5vOb107jeVkjpWvlBIp/r5QttU\nSM+NVV8qrEboEdOy5eLinGEJfPv3v8tbX/5FFrMm+xUog8qFrBZizKRSGIcVZY4o60ghysLsNanM\nxJIFT2UKd/sDSotzfIrpVDSAjHRjjLgko/zNuKLrOikEjKHkdNpsaa0/p1vOVSJoZUMoSZRH/b10\niTTTJB0U31lyiaKPXya88TjtuL6+pkb4/X/+h1xd7/k3/s2/xvsffcj5gweEWLl8+IhlWdg8ueTy\n8iF5GFk/fEydgpjf/C1liVIA+wuSTnTrkW4c2Jw/oipD1A63Oqc/O2/SHTHB5CSbJq1hnnaEZc+o\nxTRjux7jO6yxYBXGCMLsiGI7xpmL/lwMUjmLV2Bc9XB5ye1HnlB63nrnNd54+22urp9yls/5/p+8\nz35feP0VDymhK6y8peTIPgS+dHlO0pYlTJikWA0jOUSCMZxfnpN2O7bTzBwio+8lIt2IaSu3KJqx\nd1Ql49dYFQuJsfMsXcRQub25wvUbNkOH1YmC4m4fiHEhlRE1WvY5Sdy3MozDGustkUAtib6lzZmc\npUgiY3VlvfF0tsfqIti/TmKin91cc3Z2SYqReb/AUuirxnaWhw8vCSnz7GYiLjOpiMHPjp1MyooQ\nbIwx5O2+7UUUBs3QiWFT5QS5kJcF53tWfUt9a7i6uJ8pOdOPK1DSeRtrxBiN0xLh/HB9yafbQImR\nmhSqCAp1brr9s17jtIS76CSEDNsPjZogRkGj2qi/SZlyCqhUOF9dAjDPAecV1mlyMyhebDaMruP6\n6lOMMo3g16RdWlNKQhu556wqGGB80GMmQYDlkKEGUIm+F/OtMx6rNLv9DSklQkgoreidJWvhzve+\nxzjXwi+abKSlu2lV6LuOcNiyzJFsK/3GY50jVfEq5BQwCjy0AJrMMkc0hiWKmRgrpkFrm8E0JXKO\n9H2P8yPOSdPusI9tXRMTfS2G7a0gYvu+FxljrUz7GRUWrNV0vSTSlhSIppCKwbmOywePiDGzPwTI\nhhIjeY44o9H2GCTTMJVKAxW3JPY5YS8vuRzW2JphH7m7eopVlX6s/PVvvsKnZsX/8M8/4ibOGB6x\nhMikYZkjyyITxMN+kTUZ0MaimhRxKBq9cmQF+2VGK8XZsH7xeuBHqQHqTK2io89LYe4yMWUJbkpZ\nainupUDq/4VY6gT8p7XW31FKbYDfVkr9MvAfAv9rrfU/V0r9PeDvAX9XKfWTwN8C/hLwOvC/KKW+\nWgX18OJDKYz1iIagdaSs8G+ttTir6Y2jt7KQviR9F50r6ETIEWcE81ZrpZYdYbnj7u6Gw7SjNmdx\ng1HgtGHV9Qy+jYeSjKbKc90xpw1LPnCYJ3ZhOcVdvmw/ooF17zgbO5yO4mx3VlKNWkSltarxE6VA\n1kqBUeQkBr3aop2plXLsZqGE/dx0XtpK9nmpkFveuTGGqgUGKsxQjyKzhIO46I2kvJ0/fMScM6+9\n9gZf+do3+P4f/yFP58TKW8BTVcT4SkwVlxpN4nTNGq+zxiY5bW5j1bREz2F8jPk8M/ak+S4Z3TRr\nx8JJt91h1s0gZg39sGKKGeaIcRBLpgYZ9XZd1zilrROfn3dD35v4qKq9CANUzeXlJSlZ6RaoY5dQ\ndvMd5dQRr1m01uQimDjn0MPAPi4NxQfDauT1N19ju93y4dNr7g6B82EkK83+sAg1AkcgUzLMOTNz\ny6WT4IlK6547T0VzmMRg9Vd/6a/y+Me/wq/96rdJUVFDwhoZaxfqqZ6sCl4mR5bHSzW6gNw/miKR\nqWRyFZSOrc2NrRUlFZHOWNPuIdFsA6fr+kI0HJw6F0fj7fNFMnDS2urGgpH48/sJxI/kYuYWw60U\nRln+8I++z3sffo3HmwHVDWilpdCvQZ7d1uXFaEKM5FIRSINQR+QZ7KBoiWiPRSJpm+fmeQSj1pq8\nHOQMikpHtPFaiumsOPFdU8ynry1FOse2fdPnnfo5Z5ZlOd3/on8WVmpOmZwaQisLzghjORwOTU40\nsN1uefjoVarSxAzD5oJ+dUbpO7TyxJoJi6AulTU4banFo3JmOFtJxLQRpJvyK/A93TAQi2nXQbWN\nlWDTQpwpYSFWwVedOsRp1V56ot2l6eR57lrKORDij2hKK6b32H5DLIZUZRM0DO7Udeo71/THBYeh\n91Y2Z8qxeXjJnBJoSwoR1RmRIPQV1w2MVLZxERNXqVDyCc0WqdI9pBJLAmQEG5SipgWnC+vBEULC\nKDEQa2+pFZJWzEk0wDXNgGiAndEYW0AlYo5QMhqP14bBdqdpRGkMcY3CtzjkuARqyayGnnEYqH3P\nIWRSyczVokNlF7bUKqEedu3Y3h4IQdLmrO4Yho7DnWjolXVCGtIOY4TPbVTzGVTpLnotBJAYLUtJ\n0qiyHdHI+7vmyn5a0Lbiq4REqZJJ/j6V0GhNQZPIbLd7nNWsXI81EhJVkDV0mmYqYI0R7FkSr0Bu\nm0ttLTouGFvRVQOJUiFE1RJPOflanGlsnJJbLSDSQ63k2taaZXKgCvs547Wjc55oK9vtHlpKoDhM\npVmyWg3NZ7Rrk1zbPq82con4L6xzuPY/2pogsppe1iQqqURUgVW3kk5s8cK2F0+fmHLHjmkXKKni\nfc+c9icdvVKgUOQcmeeZGFWTYBiM9lijsLYTb0XzG6SUuN3eyZSz6ygIbeqLdA6jZDNWUkaZgrMw\ndpq8yPqTlSHXjDef9xSdePDWAQkVDjjXcX7RE3JHUAtKK0KcmPOBiwdnfOONSz6+iXzvg48Yzy+Z\nixUkmzLNXE+b1kOmUMt9I6f3HVMWRKY1Bm9fXPClkKiUk9YapdHeimFcVfFRGUNRGmUkM0O1JGD7\ncrLcnzr+pUVyrfUjECxxrXWrlPoD4A3g3wF+sf2z/wr4J8DfbX//92utC/B9pdQfA/8q8Osv+4wC\nKOdxWqQUpsBiJLO+95bOeQZn8V6yPNVLfupaIikZpmlLqjvCfqLkwG7/MYfplrtDYJ5nidXVVjrW\nFDpjORtWbMZVQwzBtCyNVFGbQ9fTOc13P/gBV3czsfDSzl3OMDjFw82GtTWk+Yq8TFi9YjV0WKx0\n3zRo62TklkvD4Rj84Hn26VNqrTw4vyDniNX2mIknnxETNSvRHR8OlFpxY0+pWXitKUMVMb6qmm49\nUE0DyDtLrx1vPfgKr735Dll7/pWf+9d568f+Av/gf/xvMCXz9ScPuFw/JOVrNBaT78M4aq2gFNbq\nxreubdwnRXtJCdOkDKo9rMfNwNF4KA+w3KlDN55CUnbRUKwiK8Gy2a7Hr9b8X3/0PfZ8wqtfHnhw\nuebh40cnhnZsdBHnHHMKMr7WlbFFmR4Oh5Pc4vgz3N5u8b5B348VZpPDdF2H1oqUw+dG7TEEYggS\nQtN5VnXDcj5xd3ON7w3rB2f83Ne/wdOrLb//L77LNgasX6GMIaTK+sE5SmW2289YlGhmnR0wyjD0\nPZuzC37w3vd5/UuvcnX9Gf/+f/A3+Me/8r8zqMTlk1d5+nRPilBNi30tbUOi9Mkg9cLnojb6Snva\nwjJDlVF/zpFQFINbEfyOlAM1RTKK0FKKvBa6i7eOfERwtbh0hYx53REWzH0RXfL9iOsoH4opYpQ+\nMUKPGDQQE+iPOmTBLIzDgBo8v/277/LGO7/F3/p3fwmz2wEG03WoWhidB2t4Ns2szx4QY2QcR1JJ\n5CJdFuM6UtbE4DCqUm0zKjYTqPf+tLFyzuH6/nMg+iOLuZTCNAUGp09GkeNxXGCMMeQln0Imjuab\nu+0NxhhSCpQi/z4E6Rg9vXkmiDk011d37KYdZ2drXn37Lfq+x/eGeZ6pSvPa62+CW5HQ/MQ7X5WA\ng6kQmbl8/QnPfvgBXd9zsT4jU3GDx3hDWBShKDYXr8jo3Xk6JNwGQCWZHsSwoMtMbwrh9pZcweZE\nCpJ4pVyPXVmsr7IYKU7FfymZrDLOdBzDXgwK7T1VO1L2hFi43d7RnY/c3V4xTwur1Vpip73BV4VW\n0PcDm/MVbuwJpTIMK26vrxnNBZNWMPQc5gnf93TDirgkSijoqrHOoXTFOstgNGpunTQ0ynWkHDCD\nQRXFo/MVDy4u+cEHn2JyZuzXdMPAue8JIXIVFdtlYdKFuERSNpRykFF8k4ZYNeKVpSSoEZQ3QGXa\nTSiEHiSEvNI2QwvT3Y7LB4/QF5V5t+XjuygTxV6Kw3TYoVH0fkPnhALybD7Q7y1+vaZkMQEaFCVn\n5mXBK402is4qjOvJWZFSQGXPOPaYxoUfBo2rmrkUSgkcdjv2VTSsF+sRpQw311timDFVup9qZchW\n052tKWHibn/LqutZXzzgentEaMp05e76jpwq42oF1kmh5xS99wxdJcw7VIX1ZqDWzN1+YbVZk5aF\nm5sbns2BjdeEEokqYL0D42UNKgGrNf1g2/nqiJ0jbRdyKNK5XWY661jCLHSLTqOJdE4IIZQqa5dp\nG5++a+uDk0aecYQyU0tm3fVYJTHPuu8xp5CkKnXI9gbnHOPZBrQWElCtaJPQGB4+fMhnnzzjcJgp\nOokEDpF79t3AZjNymHYy7YyGkjVKtUAkJe+dnCP7ULFeciSUkmfKGEuNcwuRyY2t7DDVSO1TKkpJ\nuqnvMtFYjBnYWnmfKBZqyZQseQ5ZNUlfd8ZoKjfPfkg2mu7yAW+9+oSPS+GTp9cEbbidA7ff/he8\n89arfP3NMy6i4iMF+zwwz21i5r1Ms5XBqcr+lEmwQmeZRJUi/HmlKst+/8L1wBZ5zyQFMUeWJBtE\ntwjezzjRVqdaxCieoeSARr9YAvCS4/+RJlkp9Q7wLeA3gFdaAQ3wMSLHACmg/+lzX/ZB+7svfq+/\nA/wdALQhVI9SYP1AQRF1pGrDrnhCVJTDRD8PZJPIdnnhz3dIjpqlGLidbpgPW8KyZ7f7jBQmOfFK\nk7EoZaU7q2Z0p7EdoCIqR3Q1DJ0FVVmiA2OIbitSjrBlXTzbRUgGNktZkIFWdeOBzeBZ9RMlTVQV\nGcc1veklWKPGplvu6bN0bEorKg/LjDEK5waUcRLBa7QYFxo6yxhDZwwpJfbbHd2wknHQXmQL5xeX\nDK4npkCqsyBt1IArBp0klQtjOX/lFWpn6dYDBcOTt77M3/6P/hOur674J//Tf8/7n33CL3z9S9Rl\nJikru9yS0Q2knlIUY08bYdRS0IvCKcEkYSzVQLWVUCvFGZQzhCLR3bW0EbXrqFoSEFd9xugBpcUo\nN/vKoXoO+QL8BTc3N+y2V1QUDx9cYqpQLuy4YUkZTKbkSIyVmoQn6juHs559CPihJ1W4vb3h4eUD\nQdzcbLHWcv7wAQD7wxYQdNH/zdybxMq6pelZz2r/LiJ2c/q8N/Nm3qqsyqTsKpdxumRVGbBs0Vge\nIcGARgwQE5gwYoCYIGSJkceWLWYY2ZZAAskIgRAgbChKrlLJmU5XVl+3O/eefu+I+LvVMfhWxD6Z\ndU+SAwb1S0fnaN97zt4R8f9rfev73vd5SRIgklQmZNHxJaUw2hLXRNaWYhref++b7K4f8b3v/4D9\ny1u+9eE3ePn6hum4h1y4f9Xw3nvv0W0GvveD3yanRO8duizokmlbz/H1U372g2/w27/3B/yr/9q/\nzNB4nn72EtdtWEqh9J55mjCImVPrgikrhZVsfnwXVquakJgkZaoUSEEATs4ZjnkVjVcpOKXQKWFx\ncmNXNnXJkozki5W2SI2YpspnTp2H0yHIQ40wFyh8KlliVJUET2QlWj7i2zryd78Gkyyu3TGPR6wN\ntA7+5//x1/grv/KXuH9vx3osHMYN81FQSbpoGlOIyxHre6aQRLNMIwdH51EqooaAGxwxG5aw4ooW\naUXl9KaYyTnQdlviOpPLQsqRxjtyTAy9JwdIcRHqDRZKZFkWSla0xjHd7FIzrdYAACAASURBVFHa\nE2MmxZVcw0QOpvoNqhtbZY3RjvkY2G4vCCEwLjPKwYPrLYNXrM8+wQwblrDl8uEV93f3mEKivXhE\nP2y5DZpkO67f67mXHvD6+Wfsrr5112mqY1RjDG7bYIyjOId2Fus6ijGEIFMfqzU6Q1Ertze33Cwz\n4XjDbmjZtTuMrZrrsKKWEVMy2VYCi6nJmGjaomDNlRYAmYT2DnNxxauQ+fzFxKd/9Bqz9Xz8+5/S\nq8ilSVz2PcYq8jpyWGf6rzzm8vFjjBlod1tefvE5lxc95ERYPO7lGxZjCY1hDJOYrlrRKpKTiN2K\nokSF8Z6QNToJVWhoG14eZkIUrOQaVq62G/bTwjwv+LZl6B1KB7aNph225H2m6QshL+xvF1xjcNYw\ndB39xZYUIjpHvPEsy4JxhjXDOo6YSZb0excXKKVoXc/+eODppwfarkdbw7ZppetOrli9iCKTskzS\nhkGS1dYYKMuC0UIAyUrhvcMpAE1SmhkpEojynLYaVFzYOYvvO14dpInUOEUwltZuydXb8vLwGmMU\n3aajZ0daF1IOjLe3aA3O7FDFEIpjv0C5CdzfOmIu7KdEsAaz6eQgaD2kzEUjHe8cMlMJXA1bmsqA\nnmNhYzxNKqAMcT5ijGLK4LQlJ8NyiPQJ+tbjd9tKoJkrMabQBUWMBZzBO837ux1zWAmrEa1vidIM\niQsxZvptS8kQC7WbLJPeeV5FUsSKXjLr4cgN43ky2l8PtG0rgR5ODMxtJ13R/f6AazzOZ0JaWWYx\n7Yc10fWeDkAPKJ2AIntOgVwCxmmabhCykrZM4yrEinEhZk0MgUY1lBjw3pBKJE4zxtkqO7S0bSeN\noZyZVkkCtU1DyAqjCr1uoYE1J7oLi29abl6OGOPY7uSQeppOr3FiDQXlhE2eD4ljuuHqgeLyvuWT\nzybUCKVvOL58Rrk48p2f+xr7tTB872P+KCZ+j0uK0eS2ZfEdbr6lVQbvWuK6MGpNmTOubTBZDH3W\n+S/dD3JvySkSV0EzmhQoYaTohq7xKGdZKYLRCwtqLXjrWUsm/eQ18k9eJCulNsB/C/zHpZTbtzfk\nUkpRSr27lfUlVynlbwF/C0DbpiicpB+hzk7YUjRr0YSYCWOm1YmiEtF8+Wx5XEMdpASWac/x8Jpl\nPrLMBzgVZlXHlJOcVowBrQrOIuMeI1nwYuyqpjpVpRBFM01TLQZkLFqlX3dGpDqKNRoxtBQ5JWpt\n8Fa0Ya6K8qloNZFEvKVHyjLGUoW7BTtFDBIVqrWMT7WCvEhhnKts4WSSM6f0n1LHfJVEUEohJ4t+\nK2ZWo87Ft2kGrq/v8Vtf+4AXHxVe7AMXw4BThXWZaIyhGxz7N6/RVowOJWViWMQ2qTUUhSQKngyF\nP2x8OnXXCiIbsd7doZAaQ47gfIMylnXW0im5vI/2F+yniRIn1CefkHLkp7/+AWldWaYZ27QUpGt9\nCjQ5FQOnzp2MCiX+N6T4lmEqMM/rGeeV3oo1ds7V8aDorQGUrazYnCSRqW+4Gu7JAabzxHWhtYb+\ncktcVhpl6DvH0HvuXYpZq4QozE+tIWkurraEtPL4yQN+8Rd/gWk+chhH1pjACN4oVtxTKbo2j39Y\nC/yOZ/cslTm9z0qdKNIFisKiSEp+10pTlGy0gGCcSq66eeGHq/rvpqrXz9yxg0MINE1DQcwqISWJ\nNVfcfd8aTKONQdVES601Tr+7m6xKIpVELAmtLP2m59Xrkd/6we/yl/6FDwnTiPaedrdjfv2KtjGE\nnFCh4BtLg4TPxJyJOcpBSoGuWuQlJMZ5Od8rJwPqedyo5TBL0RIOc0r2M45URAJSUmap4/MTHg+q\nvMrcGS3l/r8rGtd5Ed2qtWJEKYVUIKtMsYBWmJLxdWRqlUFrKwE2TUcME8NGUvGKbSGt9EPPOh/I\numGzGc7PwamzbYwhKnHeY0XakOtY/vRZntfrlGlci3aeKOcjMqK5LqVapnMmpCg/F8jGXxQCdFIy\nfNAWpQsaAyrT9j33Hz8io9HW8ebFDa9evGbT96cfi5ISGdEp9rsrtrsrQGOUrOPTNOG7QZK+Mizz\njE6WECrT3YlkJCPYsJOHUBUkspvMFKXLn40c4FIKqKi5GHq0VTx7+Ypx3GPalnGeCbSEkqlQf0yR\nDpk25ozNG8eRMM50vcNSsWFKs+sHQrbkuBLXwDhOtI3HNVYik7Wqo3eFSvH8WaFk7xJKRCUtqAwq\no1OSxk8WeNb5nlV3BnC0QhdYU8IaeTZyKVjvMNbSNbIXpJIxShNQZFPwtqWMYupb5oRyssfkctpv\nRHYnzR0HJbHGhcN+wjjL9W5D0Yrb/ZFpXQSzJre0TPhQxJRZg8g+JHymVOKDxnuLNRatNTHMMt3V\nSsy3OhPTwlZ3FAXTWoilEAw05vTzRYxx2NbicxbUZRH/ilEK6yylBJZ5knu0SomM8cI15s6gW6rU\n7fS1t6VT4rORz0Dj0EbXoA6paXQBlQpW18ZBnfmrAimLmVRri0KTQjkfsk9BJF3rKTlK6FUp6Kzr\n+i0ECLnHi7Cny50PSAyclkaJFMO+ZYQLcaEoVw/OFt8Ivm4cR4bB41zDzc0NTeMJYRaJjOGsUy45\nYvA477l/qYjzHuwFL189JSwjVs1svOabD1uymfl4H9DeQbGERePqpEpbU2khUms11pFWMSIq/+X7\nmtGakgULqpXUZGjqpLP+UjJkNU7WVW0NMZbzfvWTXD9RkayUckiB/HdKKf9d/fIXSqknpZSnSqkn\nwLP69U+Br77119+vX3v3v4+G5IWNpw3a6OqwrdxGVRhTYk2GojwpffkLjDGzhNfc3r7BqdccjzfE\ndUGpjDUwR5kSS+EoWrTGQWsM3hmc1iTq5qZEJ2m9oyjk95S5ebOvmCvR66VaMMjrkF+7Bh7tWq4G\nj1oDrXM0zqBVQZWMr/9mrEWtUjK+PzP9qrbxpHdc62KmEbpGrnGVp5z7tm3JFLbbbdVZnmQEd/rH\nEFeUErOQJI8NzOOEcQ2DtVAxOAuBaZn5V/7av8E6HvgHf+/v8Plh5cNHVzh3wTTdUNAsxtE1PVoH\n0XcepFuTlBIgfHWlStEU7+5ZVXWOueCdcEtNK987lczMTDO00FwyJc3zVzPztuPBBz+F316gP3vK\ntH/DFy++YF4mHl5estlsSCER4kSzkffucLuvkaK+Gu9kRD4MA69ubhm6Du89x/0BX4NZjofxLQqH\nPRfVIQRiCIKxa1uJ952PtN6xNp6SRAa0LCtf//rXePHiBZ99/DmNgddvXhPXgG57fv/3fhvrHUkH\nckisS5YNJloOJtEnmOeZ/+I//8/487/0Z/nf/vf/icNhTwgW9EzOFms1Ic710ZXDCMgCfDLP/eh1\nOhie/qyMBN8YJUVjSeCQw55Tck8XBau+45HmOhLW5u6+LDFK1wLDEqtD3FqWGFBWupFU02aMSTbg\n+jPl+pyhxEWfUiKsK9m6d64TzhmCgmbjSWsE62gaz9/9u/+AP/1z/wFky+WTr7HMew77iabt0aUy\nYNMiRJk1nhdloxVd2zCuKzEsqKLEJBul86e1JuSEqdHpGMBIQZtQhCRoKmtbrBUEXFhWShH+sVEa\nUwNS4hpISUbU3kriYymJaRHfhFX6vGlrK/GqqYhevuk8BRi6nqFvcG2L7wea+w/pHrxP8T1De43d\nXGFsy2IU2+Ee0zgyR8vu4QdCBsiZWLvI3XaLc47DcqgSCE1WipiENXpKtKQebvthR1pl/Lq56gTd\nlwulWOYgJp/Aig5R1h9rcF468coaUB5VqmcgiS/CGLj/lSf83He+w9/723+bNWlePX1FPM7ce7Qj\nTQe0gjmu9LuH7LqW7YOH6LZlnRcu9QWttSxTYCbh2ob9emRVb8R3oajrugelyNYQNdgim6u1lpZI\nVjAqeb+LEWNTa2ey0uw2W4ZLS4gj+3VlfywU5Xj5+kBGkWxHQRoDzmhB/UEd3cs9frtWAlFIGKXo\nm4HusiWFSAoLFuHcxjXgK1nEVoKCWheUtei2RWuDMnVtz6WaYzOmKIwuNH1PWFY0d4WRypBKIkc5\nkDlrUUXXYKoEjWVJmTiN2CL7VM7VbJgyeHlNm82OlAvjHJnGWWgj2tA2nRTkGDFRe0fre1qn8HUf\nSOssOtm80qlMIBNIkL1MDDF0m0vGZWKOiW0j8oHmQpJvx+mIUhIm07pGDP19IyEqRda09XiL0Y6w\nRGIqFBwqHvH9gNWKZQk8ffqUxjpQQgLqfAMqMy0L1nqGYWCeFqY11DVfcyj1kGM167qgrcMa8RZZ\nLdNcY+1Zq9z3vRh3Q0FbQEuSauNbvBHflaQpAqZKEaPkP9y8OaDUyPX1tWh+d5LEt6xSFDdO450m\nWkkLxWnWLLtALgmlNc6Kr8oZRciJ29tbjDF0Q3+WjxinSSmS0kosGdeIMXFZZ2wR9JprOvbHSV7T\nRoAGuhGT7nzYE3Oibxqs1uQQWULEkfnmV6/Zrxu89ozzxPJqpGkcX+9G/CPN8zbyZrklu2tcd834\nUpCUymm0gaZtyWuANQpOzxt09+Wd5FgPC9ZaCeRR4l/qvGE/zSKFseYu/rvWWyEEjsv0zn3mR6+f\nhG6hgP8K+GellL/x1n/6H4B/D/gv6+///Vtf/2+UUn8DMe59E/i1H/9NOAd+1O9JDSXG1IQoaywo\niQJV78jdzmkhrTNhuSUxS2cgZwmkoFSqXI21pBoYjMXbBqcdoMh1vCyFhXB8xeQncoCcT0Yzcy6O\n3y5NdIHWQuMEeK9KQqMpRVBmaEOpp2dlpFv8NgLubFQxcnruvBNkj7J3Jr+3iqEzWLzkM5bmlNpt\nnTmPjFIq0hmngIqUkqoRQhbTrCoqTSW0kqS7zfaS9z/4GT795COev164utyimCgxs718RKwPLySM\n85QoD2s5JZLU4YIy9u5kB/X3u7F7UQKkRwun1zQdq2lYcbDZcu/Re0wholPh0aNHvFGF6fiKEALP\nnz9Ha00/XBLSD5/wT7GvCsipoKyVbo/WvHj9im987QNijEzHUd7varqKKdROvD0fKpa5mnRODnIK\nKUQJlJhmMJreN0xtw7YfePDgHofDgT/46EBcVryS4i/kQtYrJStiUuSsabsO5xqM1fzZP/eLfPDh\nV9Em8ez5Z6Q405gNECEltBIDlKZ2NKnv81ud+h93nQyN1Vt3Z0JT0oGCUv8s97KCM3hdlbs/A2dS\niVIKE0XrdpKnkDK5VAwfkHKN3D0/urUD8xbN4tS5f9cVAG1a1nVGG81uN0BRvHnxhhevDvRdYWgH\ngjI0wwblDHoVUkNKolETSQPnbkqOQlVZK87JW0eMd0YhTjHdiNTIakVUBVUjV42S6HeLlaCFmDCY\n2qlS59veKEXMEaNslYfLfamyRHi707OdpU+Vapcul3xHiaAaH60jW0u326KajqgM1vXErEkJlJfD\nTMyFjKLrepbKbe16uX+VtoKO89JFiiGfGi8/dK+c7ystPoGcAt63KGNwuaCsuVs7zl21gi6RpCEb\nQZnhDBShCRUl6x5rIOMZLi5JSnNzs+eyj1xsNniric5itMXZFts29LsNGITfa5SsrQWcbeQzLiKN\nSctKVtA6K9hGLXSWogrUZE6thPAjt3oiV062QTT3xhhKSuz3t7TXot+fsmKeEkWL4Sui0M6RZwmI\nKqbccb5rt1MZiz7p8XMkzILiKwq8NRh07WQaGpUqTk28HtLwcFIcn+5BcvUBRMhiZDPWgu6kyVQ7\nySejtLEKkqyJVhu8MUJrikK60BicseeD8zLPDG2HKnA8HgnHiSVEsm7AiEzLaA0ln81XkPFeU6Jo\neovVlGTOsdzaujOeMueMWiOkwpoiuYjJLi+iVTSm3mspsYYguFQr+5gsWtJpPSHhTgFWBU1URZ5x\nWzDeoks6r3dd16GrNlrXg2hWnJtUxkgIScmwplylFLVjniSxVGt3xonFGIWNfRxxyZ/XwVPQjK1G\nsVxWFEL1AS3r3wk9WcQw55zBGMs8B2JcKUginsgs3qp1lHzW2tkasHHHlS+5cPKdnJsPzqGqFG6e\nZ4xxKKuwRbjJFk2pEeoGQ44ymUFb2r7j5uaGtGZ2bSPrcqld7jVJcmS9R0966BIiOS+0znG56QAJ\nRwlLpPWZTdvyQTL0eebzuCevQuU6TbNlQqBJKcsBDkSe+Y494TTdrdUcS4yomNBKiCZFKVSSdTVq\njc4FZxzFWMKPacb8se/zE/w/vwz8u8B3lVK/Wb/2nyLF8d9XSv37wB8B/2b9gP6pUurvA99HyBj/\n0Y8lWwCUU3KOqhG7Ga0SJSd0jDhjMaVhbpUYwmz7pf/M6xcfk8JMmPesSTA+WjkgEYO4T08FGmSs\nUmy6S4Z2izUtCokozWhiTtgMxkh+uZj32lqAKVJJGKuJOUuRgYzcUPDwauD+rqf3GlRD39RiwrUY\n58lFSSa6Nfi6oIEsIF3XYdIkbEdtsEqxTkeCloXMGhkQ2pO2r3Y7xREuRbKqXaCm9TLu0ScW5YKx\nSti9eSSGwHqSKrSyefZWRl+lwDjN/PK/+Jd5+fwZ3/uNX+N3P/4Dvnq/5/6uZ57f4LwXQ482mKYV\nanRZazIdmNpJ6NvmXLACZzNc2/QY51iytPid95RgyKbl5apIfuDBT/8CDy8veb6/pRhNowv+wX2m\n6TVhmfn+97/P008/4+d/4c/hfEsp5tw9jzHWLliHspq5ylDatmWz2fCD3/0d7l9ds9tJnv14OwOZ\n7XZLiCu3t7cMjT0btLz3NUEtM88jJsN26Njvb0AX4uFI7xrcg/tkA7sHF0xF88UnnxGCFGFLCCw1\nElRpR9t3/MI//0s8ePCAb//shwxDQ9NqXr9+zscff0QYb7m+3lG0YtyP5Bxx3kGpB6wTRcS+W5Ns\njKnHsoozU4m3RQ0qF7KRDnJUMk6MJPSJtXoa3RZZhhNZuieNJxaJTG2drwcSxW6QqN2FQmPFLEJ9\n6kqlU1gteLiU4rkbYI0Rw+k7rttpRc8KpYU5vi43mMbTmp7nL264uPRstztsN3D54AnLuGc+vJGN\nOEe27UBcR0KMQrUA5nlE++YcKR2THGxPo9PTJhVCoISIc5bGWFSRQjnlDEsipogtoLQiTgEUZ6yU\nGGcaCIGYghRVWQq9U2pk44UDX5SS5EXJ3sVkOchrrVlzoiyR7ByrVlxd3wM/0A+XoD1te422Dcqu\nhCDSoc5ZwrTQVCOeVhXNRjUSVc5qjBM5JZxtSLryRGuRbIxhLBG73aAL2KolZZXneK0u/1KTxZpG\npjDCfy4sGnq/EHORolFZjBfNYdKFx1/7Gj/17W+j37xhOn7Ow/uXLPMNloI3Pa63PHjvMWbwmM5g\nrKJvG8I4Mh1Hri8vUdky3i4om4jHlURhN/S0bUtrG0KJBL1SrMLoBl0Ux7Di4wJKuMsqQ06ZRSXM\ng5ZpXPjs8y9Y3xSUcjR+wLjMuGSmtBBR4BJLDOQsUiGtNc75c8GkCjCLWXO73Z6f0cM84duOsMwc\nxomu8SxxwTmhb4QoxVvS/nxIslqz8RprFIdSpBCPSvTsSDGZrMWoUnXuQrFRWguGzHsabxm6jmmZ\nuT3uSfOK6weGviN5Q58HvvjsKdu258Gj+5RYOEwjn718TgzQbQZYvGjzc6q4Q4NJC84oLJp4nJhI\nFN9KUa9ExqjqHqDzKUU3g84oE1kWjXYyFnfOoWNCe+kkL8t0ZrTnqMi+4JvASqRURjVeU4Ks98Ya\nSvX9iPTQcXl5yVEdWWsCnjMa5wU/9/LzZ4zjjDKaod9w7949xnHkeDyijeZwGAFN23SoRrTCRgka\nLYSFeZ7Z7XbntcJaS6hGPeM11hrBLIYkHWhnIWbm40LOE6uBth24f+8hIS6gAmFN3O5f4Zyv+xq8\n3kvUdEYmUbaRLANQjEcx8KItqZIhtLXSXXYO3zaEtRDmkdkqfGNwjWXTdyRjKCrh+hYVYV3UeZ8b\nx5FpmrDW4v2OdZlwtiNkxfH2yHZzwTyuNNbR+Za0Llj7Bzy4d4/txYZXN560LrycE1o7fvm9R4T3\nNf/Xs4WPDm+4ySu2sXhriPME0yyTrIrdXOdITF9eJHuruZ1ncgHbtOT1yDgtqEQ9XMmvUgo2JEgS\nZqWUYfn/EwFXSvmH/HCD4e3rL7/j7/x14K//xD+FKpCFVau1Fhe7Eh2WdScJWaJLgZQMa/nyEI+w\n3rDEiTVNuCSnTZE11PGG/HCAptCgrKNpwfmCNiftWmFZV5q2RaPIqzixeysGilOcpVFW+I7cdZK1\nkje0H1oa71DLRFlHbLPBGGBdxdntnUxuS0L5yn+djuQSZHyRa6wukTkFilcMThPjSmM9WhvWRWQV\nIQWUEr2SKQJ3t0rh24Y1RSKGdmhgCaSipeNBQTtLZxe8qmxEY8hElrHGN+oR5xRJL1w+vuAbf+bn\n8fcv+P0ffJdn68qm9TzsMt5fEqcRU0Y0Ea8cRjucMcQgurIaFC1kEqWwVjori9c4I7pehYPmHo1P\nfH6zcqM8u801vtkR9Y6SZ1RJ9Nc9R5UYdldM08Qnn33Bi8NTHr7/BY8fP2bjpUCzWjFPI1r1TGsl\nDFjLso70rSFrGTvdjLeYxmO9RXcySQh5IaWI1YakLEkVxulIkzXaNIQQaYoj5gVrCvd2A/P+Flcc\nT198we04iu6va3j/3kOuuw3j4cjt/gaVE5+9uWVNkV3fcf/BBfubz7l/3bHzmsYkpsMbNpueF5+/\nxLkGpXus2xDKjG8dZCMTjZKFD6oVRgT3X/54KQkIyAkBrhdF1ArnlfBzdQSkm6WNZz+NKGPpq0se\nBABi3uocK53IZcbQoIxD1e67YM9qxHeZKTExx0TXtHLALbl2LiNGKQbtSBpiSuANxb1bkxxDIQ25\n4k4VW9eglcE28A9/9bv82//Wv840veJrTx7wbHxDiYIayimRl4nlUEhRDmTz8SA6PeNwthCPBzbD\nlkU55tRQiuL17Q3eezbDjhQyzrTSQSuCHJrLkWgC5IxVlhQLFMuiI7YonLWkJUqHJWS8t2RnCMZQ\nslBtbIoYa1DZEKJotgMWUyKdcSL/ShEDqDVy78FDTF5Zl5GNuU/yA0pvsM0WrCXlmRQdqmiMKaAC\n2gesFhPgmjLeCDGoFAmxAIjMrCnSbgbSvJw1e13fixwsO4oTCUFeI8rIM5QpaC9FcVomnLFMzmB1\nplMGnSCMmbRR2CKc4FwCYc7o6Gh6MU195y/+Ct/7X/8Xwk2LyZoL3xCKorSKy12L6VuazhLKAipz\nxNDQsGYJJjjeHsEstNrxxY0UPNuHl3hvGMORlCKulfAQ68BYgzkUrN8KR36aGPqOg5kI60Kz3eK3\nO56NL7n5dGYGTDfQNZqYF5YyMR5GqAEY2sOUSx3F1+lY3TWNlohpp6qOco2SelckAbNxGkPikIWo\npI2RQ03MGBWlW5rkfnJOgqj6AEvMHNckfOaiWceJTd8zjbcUCs5ZKQycaDZDuaHEQlQbCdRpZC9M\n68KYk0QXuwZtWw6HA513eK252HSY9gmv97e8Ohx4srnPsSaVtoPIk6JLiKEnc9Vf8XC3I84rt/s3\nHKZXNL4hKmEglygHM9GPJjZdh1YTBkWaFnTf0vadTGyUGG0P4xGlDGrTE3NmXRKboac0nnFacHE5\n+3pUVizLgjcaYwKqFJblSHu5pVkLz58/w2wafNMSjObi6pp5nhmPE/v9HmsXKdRtK7KLHIlxYQ4z\njelxutC1ciC6fHBPIsLzIjKr7HBNg64TGKM9ihpu0hgiWrCbbUdrKhpwXVjCjLaV/OB7snMcXi1Q\nEkNTp9lFjMK2YmHFL9NIp7z3hBAJ60LnLNEJznSNJxOzo9k68vGEj5WDyjpFhl1Hppo/lTyfxmq2\n244YZ2KUg0ZMN6SYzpLO5A1zWsBkVg1GOyYCrb0gp0DfW3LWHPaWLhjCdEOMkfbimm/2hl2YODjN\nPjlu8oagMurwCt9dyoFDS5rwpsoyf/TajzegCvMsoS+tbyr3u0iEOhmr5XOYYkW22oLxjupo/Ymu\nPxGJewolvMNcZCQG5Cw3d0D0bk5nEoakRUv8Zdc070knwX0d2cScz0J7raAUgzGerh0YhoHLnWXo\neqwRLWvjPbqcurOZ1p/0LAvG3An1z6xIztMTrILGwtA26OpGds7RdqLbsgrgLlRDxquGnAwUBwVS\nFPoAKqMwhCD8wHmW4mENAuSHjGss3vhztvnbcpUQArmO4uZ5Ri8r1ma0dtJdAI5zxLTCyj2hb9qq\nzz11e08a3e3VBR/2P8v19TXL8cDnnz3lk5fP8Ci87rm32aFLZikLRhXmkslmwXQdJYTzz3bGwQFq\nBXShHR6wFsez15EvxondvUd88MHPsL16QLfZEJY3DBsxSs3zBGiu7j2gPe558vg9nj/9jP/n//xH\nPHn4iO/88l+gHXpKhjEG8rrQIh3UmE7jP8M8H3n/0RNubm6Y9gfpMPdiqAPEyMVyHsUpJQvvOs9V\n0y2f73g8cjgcKKXwbP+cV+Nr0TsqRZmlOzfNtzSbnvFNZLe75FuPH/PixQt+5qe/idaa66srPvj6\nV2l2nhhGLi62jOPIRx99RL99n8PtG0I84JRhmedqrDKULKYorRFO5DsuiS2t4z5VJTGpkIPmhIfT\nMaFTQSfholKUcMer5r6Uyt5EndmhStQZZBStuZuGnK6ii8gH6oKeFIKeOo03jT7rNyk/nNj3ZVfT\nOvEUVFRcJLHGQNaOf/Qbv8PP/Nx3+TPffsCLmxcMw8Dr8ch0OOJTEiPceqB4J3HkJZJCqVW0YuNb\n8rKwLgvJyFTk9IzOy1gZwQlrNdYJy9wojWt6yiqR5nmNKJXrATpCrvIlU/AOljWgnRjkilKUFKVw\nUuC0rjplKHHBVA2tMoV5lmTJ680OvEd5i7GWqJKsV95T6uEkZTE1ppSIYaWkSF4TyskB3HcNoJjr\nRlkqxaKzHkthqhp9Wzsw5ylVDZYpqhDJqArpF/NiwBqDqZ+zjg6sqxyEbgAAIABJREFUZkHQa8p6\njtMotJB1phsGMeXGiI3Cu/1TP/+nmT75iI//8cfoELCdoRsu8BeXdG2LdjKqbrLChIIjoxvFZrPh\n1efPuLm5waJ4cxSD6b2rKzabHu8KmblKBQrzLNO2ppHmxLofxdvRNnjveXSvYX8YeX0zkovh4faC\nhz+74+mLN4xhRMUVZ2TKcHl5xTEoOi+Gx9s3N3jr8NaJhOG0xotjVfTr2jC4hlhCZRxLqqh3jg+u\nnvDqcGRJganeA05ZjHVkVTiuGWWyoBiTIxaN8Z45BGyp3POU6LpB9oB1plFBusxayx6TDdpanDco\njxgUlRQUx3klHA4i2dGSXNaiYF3JzuKdY9cPfP7mJVaLpt+j0LlQDjOqBj19+vpT1vHIk+trdrsN\nrpX7Yg0rTdOyWDkUN8ahisEl8P2As5amabh9c8OsZ662vRymc+bRpRhPD/NC1zuc0szLka5XbDee\nVKcaoUo02qaRdbIYUoT9fiK+WRn3I7uLDVp5nj9/LRNXq3FNy843hJDY3x6Z1yjprlkmi6d/mzXJ\nAbFkVp1E02sNbS+Tmhgjxjl2u13Vv66VeiH1hVH6bOY1VqG1w3gr7/94YBxHuq5huLqSNTPEMzay\nadu7fdSYs5SiFFXN5Q7Kcl5HnXO09e/knHn16hX3799HA+PhlmkVg3V89RqMZthu6n0iAVHGGi6v\nduxvj6zrzJogrCL9TCHSNDshaoSFKYjJUitHmiGElZmF7fYejVUkC/OoWY4T6fiGh90FVw9bPtof\neHpcuSnitVBmR6RAjIQ6Zfwhycnb+0HTVcleqFQSaWFKcm/gRFnSWrPZbCilcJxG4pLZ7Xbv3Gd+\n9PoTUiRLb7eogtJKKBSx6iNPJj1ViCFSVCG9Q72RUqIocYiWt2JhTzeW6KkcznY0TS+xjr4uILnU\nIJOEUnVEUtJZ3yoPXyc/ipbTKicNXxa5lEIicxUSSQoFX/U6Wd3pxUrVVIvW2dZQCKEgl5Ip1cRT\nlEVpOcW1zVCJG7n+f0JfUEafSQNvEw5SSqIFq/pZbU86YUHAaSsYOPWWqF0CHfQPaZ5Pf05FXuR2\nd0XfbWmaLS8/3/D86SfcTqMEsOhCQ2J30Ut0aLKkvFLCqeuZxUhSCydve1LOrKtlTpo3Cxxtx3uP\n32d3/wHetSxhQbHStBa1atZqwCsKXNPx4MEDLIrP//APef78JZ9/9pRHTx5zu0x024GQIgOtUBOM\nmANTiJSQmOPIput58/qWkBYhMsTEnGVcZdQdW9k5YVSWKtmIQQ4qyypc5pgSc15Z1vUccZpS4eWr\nZ7hGioQn77/HZnvB8fY1zls224FSCk+ePObq6pKsU2VjJl68eEFWMMdEUyLLGjCuq1IfiSQ/D0eU\ndGTedYnUBdGM6lP8qaRBlVxfE/UAYwxWm3NyIXAudE8FsBR5pRpD7+6Tu+esfrHSZM50DUVFJN0V\nX6euRKnjY/0OvwFICIhBEevPkbU8i8UaEvDRJ5/xL/2Fb5LTkWJrMqTW6KRQSQpZVZ/3FKVjogqo\nuomllLDKnd9jW6UiJYoePFMoxRFjril6MtaOOZNCJodTXLDgmMREn0gxklXGuUFoEFk6yzlJZ9Uq\nJBGtAuBjSKwpoNwp6lZSIFORIJ0QI6WpoSrOSiKYEi1tVamTcxR9YZalLcWAahpM1SCHEEW29dY9\n8qPP/Nuac3Ik5kSOiTWsEnRUkPhXDY33tEpRciEFwWhGp9FFxr3rOGNKQqtC04J2FmwhrhOlZO7d\nu8fu3n0G2+AAbRSmaQVHB/RVTmRKFslHBlUP3CfJ2ZoSRTUM25btbjgXjf1mAHId4a7kuJK0xjUN\nyjlSiIQYmMOKKR5nLI21hJAJ88TprRHtpD7TEpZVzF0rYup0VRZTUiarjKpUCmUllDuFACnimlao\nTeuEcZ6UM0nDtBdjpDMWpUqV/0SUctUQnQmp+jhKqoE/lnVZqmSnRnNrh6TFCl1BqUpeyYlUFJF4\n9mpozZmkYTRy6IxR1sJiMTESUua4P7DGgHGeq/v3ePbpU6zRXNy7Ji4rloSzDtsK+/nN/habhPFs\nmzuijlKaeZmEh9w26JxrSmbG1U5w23dielUFJfJ+VA5yDy4jyvYMuw1KNYLK05qlsuNPl1JC4sFo\nTDUbU+R+Ph6PQKHxXaUajVXyJdriUxz97fFtI7dQkmw2pJiE924Vxt/tl2J05axLPhV4b+/JS524\nNU0D3OmbdeHcCT/JvDabDXENhBo4pM5korvsgWVeqi6cc4LtSZN8js2upCvtnWh1S6Fte8FQomph\nXD0nWkszohQUBmvFnKgX8MoxT5E3h1uUqibVUiRzIiM+m5zIszybKcyUHHBW0XXCry9rhBzo9Ypy\nnic9zEvkEBeK9SjXkeJ0fj+VEhP0l13rEs9rVFEFV/drnYEaR33aY07El9P69vZ69/91/YkokimZ\nuI6kKmq3RQseRmkMlhNqy+qFWCLMh3f8O7pGVIqG8A59JYWu0S3bzTVDv6Vtezl9qZkSFXFdJco5\nrmA0MUrU8emN9U2DVg6FqYVBXWTqt1ayx2ONRqeZgsG1IsqPMZIoNZDE4auByBlNKOrOYFZv/lgi\nMWaMzSgtsHR0JqVAqkgyrSwhiQv/hCg7mdKWEFljEKKG1hhrcJsLdJL0PVyHaVqa7SWu31KMOxdc\nP2qcMrVr54zgrVQ1GA27Lf3u21x/5avEuPLy2XP2tzfcvPiC58cVr4tosZXncnN9Ng9UvwEAny+F\naYrs5ze02yve++l/jse7gcePvyIjvJLZdj1vbm7OqKzTz3c4HMWB7xzvf/ghX//wG7x48YLf/Cff\nxfyz3+I7v/TnKUk6wtNxJnnpFoUU2e/3QKZp5NDz6NEjxnHk2RfPzgtYSInbdaHREkutkQ3J1g1l\nJmKdR2k5SLWdAPKnRcxrx+NIXCX2WBtL03kurh8wTRMXF1suLrY4J99rt9uwrjNxyTy4vsd3v/db\n/B+/+qvY+0+YJkNOCzEVpgzJOdEiVhg7SlCFKb+7CxtCIJb6fBiNs0Wwa2skqcQ4T3TWVCfJW4es\nVkbyMYiBLEstgOOPSyJOi87bBywxvRSMqYUMBeuNsFerXtwY2VTPh+F3KrvA2ESLY86p8pwNymou\nH2wJcc+v/9pv8p/8h/8Oy+0zPv74B8R5qlMcABmFF0STuy4TMUYxTLaWeRpr8EEmGUXX7yTgI0WU\nShgTsarBGohJNMXOSkHlvGYOCWM0pcjrimnCaIv3hnVNaKXJaWGJiSVErJZuYzERbCYR8K4erkgQ\nIilkjHN0RuKstXEcjxPN3tL7hnA80D94gm08OemK0DwlWSmZNBSYYyGTqlmvdvbr+220MHnk8Flk\nVFzNSW8jJZUzhGUVc7HSaKuZx0lkBSFK165uaOXwmoNK+KbD+o6m3ZKBeV3prGWaFjrbsi4Rp1eh\nXDx6yIMPvs7vFU/SCn/Z4i+3ZG2w3iCoM/kMVRGWdIpi3FNG0w49t7e36I3lW3/qp7je9cSwZw4z\nyyoGtsY5tIVpCcQlAQubvsdqw+0ygtYs00wuCWMl0dKoxGFquD3Afl5ZVAO65fXLF9zsJx585evM\n00LJhYvt7vQwUFJCWSd+FW0pyPqtKMQw0QwNqkS0kqxS0YdXBKNW9fm2KOSwZjUVFzeyLAsOB7rQ\nNZq+tzTWsG2UMH01lFQorUNljSoKrz1LnEilsBs2lJK4vXktU6icCGFhc7Hjsu+ZyihSvTWB1fR9\nj3aewzSCMuzHiaurKxSCKdNa01qHrjrdy+2OGBZynEHJ31dKsayFkhWHapY+HBKdc1zvLpjigipw\nOBy4vLxEac16eCP0pq4hTCOqZB5uGwqJ169vABg6MVQPwyCywcbLWH1d6IcWUzF5JwP7sLk8r1fz\nPAuG0bfVKCi+nmWRbvT19TXjOJ4BADlnUkgo7fCuRduCsrliH6XJcto7PvvsM6y17HZbfPUDlFKI\naWVeItM0MQwbOWRT4BQCVgrTNHEzTex2O6wx5JqJsFS2ukKhqiR1s9kQY/4hz4+Qou4OuqUUbm5u\nGK6v6YaB6XBkOh5RpdC0jsZ1sj8fR4x32Ipc00YoT8OmpW0buk3HPCVevbxBKc1hXNj1HYfDLfub\nGy62WzbbnpwKF9seG0ZCkj1wZwyNaemcZ1kTy3LEsvJLH9zj/mbh9veOjGtk1Q1rkWaDs/7cSf/y\n6xRIVl+7Bq0LYYl0fVMPHZXuVfXqTdOc0Xs/6fUnokiWE61wJIXUICdIdSIgFENhIZc6PgxfnsCi\nlKldk4iF88ZekFOJ9y3WCFZFKXG9S9F7V6CKM9jUU6A5d5JPfNEs6g9KiRTkDczVeKsyksamJQbU\noDjFbOtKkbD2jsHq2kZScUquY29hJRqniTkKT9Qgju08yTcgCaLHGE4l+tmJXy9jjHRc6inLOge2\nwTiPdQ1ZeYoxsoifXquRNKi79/KHi5USouhZrWChiimsYaXtG6zZYl3H9mpkunfJy2dfME0ja1rR\nOXHzejzj7d5mQqtuQA8D11cDw8Ul97/yFVAZqzm/9ymuKGUpRTYNUxeME6LKOCfaSG8ZHlyzfXnJ\nNE188cUXfKPv0bYQU6ogf3t+bfO6Yn1DSTLW8W1D19yNpoqijmdP0a9S0J641uu6sqyR4+GG4/FI\niYXb13umgyza0yKdBOtajDHS8bYWmyNXjx7I6Vwpttstx3FPLpF72w6jPC9efU7GQdPTOsf4/BZS\nYSlFWMapQPFIRDMI0vDdxr2TwzuXTMkFnZNomOt7UZRizQkHFKtrsSj4sUIhIm58YzTFaHFSqz/+\nPX70vjECIT9310rJCEwin0fRp6K8lDtpxruunBNYweCZUvGQJRPiSN81HG8Cv/P93+HbH35F7jFr\nUc6ii3CvA4JgJCtiWAmrRNRGJ6xc6Q4utMMWYy3rKtIW6yotYJ0hS8iA00YMjyGR4kpKAaclTQ4F\nSsv6kKr73jrHGsWTINx0mRZlLROBkCI6B9CKkGR0mWMUk2JGUtGWQFALJg+03uOUPnesSil31IYC\nSWmCqh6LELFWk2MhRgn+UQkKmViTGrWzGCsyrVbfSS1OG69KRfjvBciSpBgrAhBqAiRCC1nHV3LI\nur6maRoxP9fGRVdxTS5GcekvK3kBt/U8+soTVmUpzmC3G2zfknOk67zwogsU6ylZgZPn31f5ivOe\nvu9pdgPWJdZwxFtNa0QzS3XiC4PVozTyDGcxi4u/IxGIYPW5i2uMod22qOOBkAMLEKrR3Htz3sCV\nulvXT1zcWAkSyZ1Y0RX4k7MEMWlNThHrpctmrBPGLQWHFhlECTglFCLjHTGuGFXkWQXyOmGbBptX\ndIJGFawyJJXRaJYsh19V7jqashd2DMNKzpG29ef0P5szDciEUykxzeWCcZ7GOtaU0RmaxhOTsIWt\ntaiS6aoWN6UkJkZfiUFRiB65WLTybPpBJBphlcleybROZIPOWA5HIbGYAsSMt9Ls0BR2fUsG4u1C\nSKkeZArLdMRWFFsxSpo5ppBVpKCwWJxpcI25w4KG5dxtpOJeQ8ws65E1JIbN5jxBBOozKQfHMa9i\n1PUSvKStPssom9pIOtUOIreQqYKjGu4TTNMke5wxWCN8+hPpZl5rOrDSnEYZpw7zqZt8MkqfcH8p\nZXKN+lbmrm45TUKXZaHz0lDTTUNJmZTuilDxRsjaT5VwOHeqewIhjiht2O56Dnv53FOReqnOyMWX\ngSYVhXUdawykZDEmYXQ9kBAY5xkVM1ovbBpNo4UwdJpYn667uuyPX8ZYjNFQUXDq/HVz3lNU3cdP\nNQf1cznJSn+S609EkVyUkB4cAiInB1SKaJMoLKJ7C4EUZ3LOrMuXnyxyukviy1qwawXRyWjnGYYr\njN+QXY9pNEovmDLirCWYQOkgkGRsYSAmRdECSZ+XgLOO3W5D4yAnhycTYiZj0TnTu8QwGJoU2PQ9\ncxlRxmNVxilLYzSudtWUUtweJkwNL8MZUox4ZemCiNCL0UQNydw9JDHKg9gOfXWPG5Y54FtN17Sk\nnDF+w+AKtrFgHJeXjzmsgYtHH5BwxCLFpnfXqGLRMYixEI1Wd4Xk2/prZ+y54LfGE0Oms7amO2q2\n24HNZsPy4B6PP/gmyzxz+/I15MwcJcGuaZoz0zCEgG12586tcw7btDgFOd4tAsfjEaUy0/FIjJF5\nmuTmDyuZgncSaNI2DVeXl3zt8VfIOfMb//jX+f73f4sPP/wQVzfU6c1M2zX0Q0PXNZKu5hyvbgQj\nN+x6ljlQci2E88JxXVjWhaHpGMeRpn5+X7z4nDgtzMeRT59+JlGrqufNvOJbh2ocaV54fPmIi8tL\nks7809/+Pjf7W74eMhcXW4xJrEax3B5J/ZY/fPEp26v73HvwdX7xW5e48ru8shO//sn/TVgiyu8I\nUdOoAcIptt1B1khYzJc/X0HbKg/KqFgISRNUxjrRx4UkKYkFQ0SRa+fFawlYKCqKI5tITAuN3lJy\nheRXYfLp8PB2kexUQ3RVpmCtjObiisoZo7UscMBKxHVOksDSu7oGcugsRLTK5JJR4ZQcp7i+vqbz\nDX/zb/7X/NW/8iv80l/8gP3tLVPOHF5NuBjxgDWFHGd0EBf0EhVlnEhAv/OYxmBdIsQbUprkGFoM\n5Mx0XGjbFtO20s1UFmc8czgQSfy/zL1JkyXZdef3O3dy9zdERE41AgQKAKc2kuDQkyCRarVJbS2t\npYUWMtNCWvVGn0C71jegWZv0MXolM0oLqdVsNAkOIETMQAEFoIaszIzpPR/upMW5/iKykEVh02bw\nsrIyq4rKeM/9+r3n/M9/iLXgracLnn6MLNOCoZKOE2FjMGZHB1hTyHmhmAVqotCsHJeJpUX+mhZC\nsMwT43HGVqO0ngivHwtdgmgu2VlNJJ3rwr4XfPKMor7uvqo6fVkmpAaWCjlq2lkpiS4EljJBzQy2\nFf1zxEwzN3EioYeJdw5JPbY4Sk0cxiMASZS3aDPM00iON9hSuHz6IcRCny0Fj+y2um+J2tLFdAu5\nsN8+IB49zAm2N3zmnTd483O/xtX1U974wm/y9OlPebx/wO31gWG70Rj5okBGzCO4DaYP2GHAmYkv\nvf05/GDZ7jdcvniOTYndboeYTJXKGJMWi8160xbLzfU11gqdN0icyUmo3uC2jqkuFBOZyg3h3LC1\ngRfPjixJ2PYeUwpXz5/idmfkUujsoEV4d2fdVWtlTgnrDEO/Jy4TOWyIeSSOo/KL5wmDcCOGzmtE\nek6LIuclQtQpxuAcuJ6ai4oCqxbUpmS8GOZxVBCharHS9z2+KE2p1IWck/p8L0eWOWON0Hl1NBLv\nmaI2nhIMMhWGYLicG79+TMwxcnZ2xrZv53Q1HCqM80xHIkvhycNzteWrFS+KWMfxqAWcH8g202Ew\nfYfd9hgjTGWmF4ezXsXlxeBtR8yT8qTjgY0XRYPF0fc9j3cKVGigigGrdK5l1inzZjeQykyNGl19\nWCLb7UJwZ8RSmeKC8W2KcmzNZhOoBdexLAs319enaZdd+eV9R4qV4/GIsZ7pNrPd7kml4PBcXak4\nbf9woFZHLQGqZZnUHs3uHL7Tc7zkFg1je5w1lBJJSQWHZ7s1QbBx151js2mhH04L7ikuHI7abIXG\ngdc9Xa3hSs7cHPSM63YdNgRuDtdQK0G0OdQp0qJ/phqLUscFZwM2GCTBxg/gB66un+LdwKNHBhsq\n0/GMabxhsxuockGtwlIMyMxl+1z7sy0xzuwZ8MFyKJFiC87uWKbMbrPwxbfO+Y2Pn/Pjq8x36paz\nMlCNZcwR4wyxvNrdQn2S7ak2EhFizvjeIkYdyA63B0TkxEEuKWMjmOXTwZhPXr8URbI6sxXlOTbO\nmbHqG6G+u5qOt8ZAfhqf5CUkrWjiW107LaNHLLWok0YxILnx2fQFyTFh831h3vpP/XNd8HrgS+Pc\nlHwKWhDU3N0ZTdJZaR5rsfnJz7lyhYyp5MbXVTRIfVjV2LNijUbW1mYVFwYNszDiqGXB+LvEvpNd\nVV3wzZfXivpCW69evM71eOlwYpFBU/fE3++8fr7Suv8d1s5YrXVWdEKaJ6zgSsV4RQHq+RmUSl/C\nqZuz1tK1qMtaXNuA7MlbF2dOyUbrsxYpJ57XesWoqFvf9yczd+ccQ/P4/NKXvsR3vvMdvvvd7/LF\ndz6vPqeHI9PhyFtvv0kSFUje9+b1LUZ3tf/y3pNuFE0KfcfhcGCMTS3sPOKaUKbriWkmLwuPL/ZM\nOZJTZffwMW+/9VmmZeYb3/gbfvTe+6SSuTi7Ypom9rtAHBdqhuNxoowv2F+PfPad3+L8wWPe/+Ap\n3qj13DhFtt2mbfr3lnlR0mls2WGvutShQlRRXgtZ4ulZnnhz7a91+kBWnm0t4IzHsG6+5rRWtStv\nCHL77fc/Qcqp0SxAk9dewVu+t77+//XG0hxr2u9vVmW7YcPlzcg8R66fH/jOd97lH37lHTbdhuQP\nbLZ7luORDAS7UhIySyqUqlG+btB4Zid3Dh2UckqVWxvbdU2uFmca2eoQMcxjaRQOfyc4KZlqVgQv\nq01Tw12QwtDibJdloTae4ZpaKGja2BoNu+l7MJ6aI3HR5DGpKty0jU+Y5Y7Ld/9dXvecGNXf1DnT\nAopso2nooapIZTs4g4rZckoICymrA1EXLFVgOkTioki4E0gps8yTfh7vTr7UKanvq7RJXM6VeVxw\nZcaUpBS3lj74+J3PEn8yc315gy1osMHQKy3ANG95aYErovZmIQSm5gN9fn5OFzp2ux3L8aDPzbXn\nWdWHNSX1Nx9EGtqfSaLPdGkCMO87OgfLfEmPoZge6TxXQTCSOC6RagTjnFqCNWvO+6Kp1YYydK4J\n3SBFUfGq17XsrcOa2uh6alVopSVPGqhlQYxoBPz6MI20yZ9ATSwx42yh6ztKbUieFKaUCE4DM7wz\nuKDURecFHVwmEGlTTKHkhsg5pTHFlOmG/iU6YHCOVBbKkjFUtn3HUiPj1ZGaE1PvCV7Uy701wbYf\nsDkTjac2OqQGTqn+p+82WJqPeYFpVECs75Rja+rC7eFWm7nHAdPEovMy0/W+pfFZxECw6oQQU8JY\naf7GBimKWM9TxFja5M1QSsbaNe02v7QvrZPPu2mBWlcaY5sdqANRByfnzElgp377BZFMC8ujaaEp\neQUTKtY1cbBtbgxVMOJwLjAvi1L1zB1CvSxKd3LofQkhkMblpXd95UDH3Dym2147zzN+5fiWouFO\nxhCaXaFYczqf43R3ztpmxaYpqlp3bLYDmcrxNmGNhxNPWteSta7xu9HvY4XDOOu69QZvDL5PVBFy\nTLDMPNzteH6McDNR/T1xowg1v7qgdU5tcS13NZvhbq9eP7/cO6ec08Cmv0vo/nO/5xf+yf+QlwAl\nNdlJUTu2VoDEZaLWQlwW5rl9sU/xuLuPfnpa7KTxeN9hfCCYSmVEcoRFNIo6VIJbIxy1ILAESskY\nk7WAVhdkJBjcoJZVtWaWoohbSeovsek7NWIPao/kxeK9x7YYylO4SRsHqMhHOZ/r2NR7z7J6x1Lp\n0Icag1ErtVRI1dIPG7ZhQ12O2gRYR5yU4P/wzT3We55djli7JZZA2D3EDY+x3YDrt5SUuSWRjSBW\n8KgPZ64/vyDXAve+kXrOGpKSebnY6drzNN7S7ZS+4HJ/OjjW+5BzhjZyvLvKSRR1n+NaSuJ4PJ6M\n30G5UtY79vtzjVX1Hmf9qcj7rd/5bYwx/OAHP+Bv//ZbDMPAO5/7LDHOvPe9d6nOsG/dpbW6wV0+\nf0bXDcSlkqzFBx2bGmPIBuaaOc4TVWCwPbEulFJ58OABS5rZe+HFVLidEs9vRp5f3fDee3+hxuji\n2e4+z/Xtge9//32ur17w+XfeZJyOjMcJEcuzZ4po7x+9zZe//GX+4A9+g6c/+oBnv/d7fP/DD3jv\n3Wt2ZcssmoqWcyWvDYq5d4h+4rLFIE3BVakUWSPMpU0GOqRkBKse18ZRciKN7T47B0UdGMQKQoaq\noh/TCkAyqozljnZhstJBVgFdrXpf7z/bnDPVqsjEYXCfMloDGhIpym9qJbmUCov60Q67Lc8+vuZP\nvvpt/t6vv8Ef/tFXMI+FtNtxRSIeJ8bDDVINm7NzTEy8uJ24nRJvPnqCWIupmaRWqhixjSutRcy+\ncdhZmpAvZwrQbbZYV6lBPdO7buByeao6gc7hS8b1PeQINWNNBdQJw2WPw9IF/bNXr/MkS2tqA9td\nT1wSuz6oz3BVsWWOlRojNkScFDIqgLKmf2ncW6umLFrvuDncYhAeP7jQAk56qJFluSHOR24un3Ej\nHpHKxW7HfhN4+vSKd999l9dff40QAkOvwrKrj29Z5gMFi9TM5dOPuL15QTAL+82Zpp11G0zt8Da1\n+PpKyZAOM+N4ZDdMxHJFnDZU2fMP/tt/zve//tdc//lXeX2vNo/ZCbVM2BipVjmFDh1De+cZhoG0\nzJyfn1NS5rgcGIYBLygKh8VZS+gcVuBwiGrkKRr5m7OK4GzocPHIze2Bcda4Xm8fkMbIcntgnDNS\nhaHbEM0R13e4rePYXJlcKwrFVEqzKRVTMZLVwUQEG4S+c2RRMa1BUwiNqNtJzoDVBFapkIMmuRnv\niTlrAIg1HCalF/TecXM8YDdb5jFSCux2ZxD0lbxKM3me6J3j0X7P0A24rpBTalOzchJDG1VcYd0Z\no4NUKkkiSMWqBg6bFrYby1wLtRQ84As8eO11clKRWY0QNj3zFEEKNc/kKmSnIVWhcyCOFy9uqLWy\n3W7ZbH2jDajgUKPX10jqjptlJmf42fsfst/vefDwHOM847RgTGKz6VWo6N2Jg9qFDlN0mqxaEo0K\nF5Emun25rV/flb7vlVvcwJMVnFvdeIypDBvVAsXGB/Ze7dz6ljcwpyPWBqwPGOfUylEgFaUwUMGH\nFuPdwl4yjpKFOWlhPs8z1lqGYTgh2vM8c3V9jTGGswcX7M/bTuuqAAAgAElEQVQ1DS/H1Bp7/T6b\n5uYwjiOUShfUIs43WsbUJpFdr3821kCzdOz3W2KMXB2P+CWz3e7oQuB4fIaRyO58w8PXHlG54qOf\n3XB7fcM0FroAS7wmpYHdTh1UnIUQBhiq+jd7dfQ5D4ZaLS8+uGS5nvl7j9/iydnA829+xMdpIYtH\nsqFkYS+v5hAvRd+rtYkrLVp+zQUA6FoTsJ5JQ99rPfV3hFZ98vqlKJKFillR5JbbXkpq0YkNPW6O\nFtJGBK+6Pokw30dsxWjsIU6TsqRWqDpiVH6g5tqrElhOJnPSOHslQ4wzm82ACKeksHJC0gAKxioi\nEEvGO9Gkvob01pJOav7185Vy172uL4L6BrcrqyG898rJKgLGORKWampDNB3G+1PoQ/A92WhykKsg\nTgtjE3rE90pFMeqde1Lf1rt7tl73Ufv7LgXW2vZyr64VtSEYNHutl/mp3muBvdmoEvmk/l2UHrM2\nD7VoMMX6u05FVH3ZXUEN+z0ueKVwBI80XpZtnKtC5XPvfJ79+Rl/8dWv8fTZR6RlYrvd8tYbrzPO\nk7oOFA3GiCmyRN2USrEtlEXYbHvGceRwuCGmmWWZmOPCtt8TnOPBxSP1uPSej5/+lA+vIs+vJr73\no5/ivefJa59BZEFwxHFEbE9NE9Ncub6ZGaeJy8tLSoabKSNk/vTP/ow5zfyz//yfIsfMo48vOFB4\n8XSmmy1jmjENTtbP6en49JjNuk5qctHD27w84VgRz7uEQj3sjThWW0IAG7QZy/nnf9dLBdm6hoyc\nnFdOa4d7SC33+NKlwD0l+Ksu1RBofLL6ySo15HA4QOiJKbHZBObjwne+/SO+/Lt/nzD0zDJiu57j\ntJBTwUrRgJmY+fjmSAgDYr06wKRKTVWRpqqNRy31NBW6v15XLYC1msTpnGPtGazX8WcBjPPNeUKR\nS4wq/YFTYtbaLNrG07bWU4oWzaY124JyWp1T1Gea9eAVrMZZm0q59y7fRwDXxlwdcdSyLDc7SqGS\nk6ZcrQKh8/1OqWGLuh147wjBscwHpuPMEAJ5ucW4ADhiSio+spZpnAh+IOaETwkpmiC6sDAuClJ4\nBPxCLolqKqkUal3YPHnI4zff4sYq/7WarDaXqZ72Stu+3zAMuGat9eDBg8Yz1L2l94ExRVgWUsqn\nSVPNijCG4InLqPqHaqCdFV3XMU+ZFy9UA1Ix0HvyYqkFWOlIqYIRBhuISzzFfa/IVWoOEcMwUKZb\ndTwqKpTsQ0sHrJUY1a0IMagTWNaZTNEz0QSr6FdbY6W29zQmEursUlqiqzVqJbau05IrtY2gS4oE\nwNQt3WYLRZoeJxKTFgyd1wCKlFX3ovfDq5NJ1u9rqz4H7wwlFpYYMei+ZRHmpFQotg7nFDFe5kqm\n4HqHcR21LPd4tMornqZJxeqiNCZnjHLnASsad2+cMMZFE/EK1FbcibWkqimac2yuUp0Wf7kKJRcV\n3juDSUWR/LIWTi/TxO6fc/f3tfvglr5TVRuaajDmbs87oZj1jm4TswpnFdKAbBSsyAnVQsg6wRNq\nTczzRN85pXQ0l4t1bd23dVuWBb8N0BIc188rrQhchbegNAOMUFtIje+CNq0p4bqANXcTwhACzjXd\nRoyntZxTQYI6RBlnefTaE463aqvItOBEm/F5VvccPV/ac+0CpRY23mOcAAoMXVyccVgmpuOBB11P\nXw44cYjxLAVKTnTbV/skr99t1bLo+XLHYT5N81CrQ60D66lW+0WvX4oiWQ9xHf2klDAlnzrxEptX\nb9ViC9Rq5FVg+f3DuCL4oce6gPEBEUvE4ayhUoi5YqUS0wgltHQ+wRuPoIedan+U9pFSYVMrgw+n\nm1YrzBkl5dfSvFGzqlGrxpyqvYoekOtLtqpdVQVfT4fY+jKGJkpbxT0rEuTFsb3Yg+tYcrMzc0Ep\nJT5gu14J+gSqGC7eusD5Lf3jR3Sbc7r9loRVD9jecVFD20jUXzU2JBs4vTArwr1206ulSm2jS3J5\n6f7XoEhuZwwdusGmFnhhqm5OTqreW6ujvKndKwC67jReXovpUgp9v1GELSVCCGy9xzrHZthqPrt3\nLY0oNe52wjjL48eP+Uf/5A853NzwrW98g4+fP+f7P/whn33zDX72k58i1vDg8SP6vucwHri9vCEE\nRQOOt4niDcfpqP6WMXLmPMVYPnz3u0zTwscvjrz/8XOOxRAPB25Gi7gtb779W1AiT68PpJTZbDS8\n5uLRGfXQU7LnxdVz+k3P9ZTIubZ0RPg3X/tz/t1f/jm1c/wn/+iP+MrmD/jcB+/zwG342l9+UwvN\nqoLOORWSVEL36RyrOSuPF1uQclfI6nsnCB5pyFZpTaM4r1SD9vydc2RTyGXBtrjXUmhTFsAE1oAT\nQUUoODSmtlZCuaMDwcv2YiY4xnHEGnt6R151GaOOLnHWEWPvA2Cw2x1FKjUtPHn9gjpF/uxv3mWS\nf80//69+n00HiCf4DfW8UFNUwoMpdMPAa29+VgsAF1Qo6oS0JKQoqlWb0HAqurZMH1RUZ/XwhqiI\npBRyWjiOB/rdnjgvOsp2ljkq77eaJtAxnkpCmvhuaYVKFZo40qgn6T16Snjo8SFQmkMN/pxjNWyy\ngSy44jFY5rw06sDSRsHaPMaUcF6RyZgTVWCRqLzpCguGGraUciAuC9fPn7F0DimRt9/+DNN4yw++\n912Olx9z8eCMXb/TNDAfKEuGkpBaefzoTVzXM5YF0pGz0uN8z2wTSzyoBiImkvkAwxNC95BlKWz3\nFcaOzz75Ah+/8Rmun77H5x71BJu5mSwZobMO7wLSqFK5qedrbtOmOOq7fDhQSiIMPctxPDUcJalg\na/WwrkbpXmmJNAYR203PpluYYmRaDszFYB1sBstC4TBN3B4OlGooVgtynGGMM6XcUbhyHiilp3fN\nFm6ZKVJZ5oJ3HaELajNZlAplZcH6QE2Zssy4EAhWWytbC95aYqNXhe2eUiveeIbtQJkjuzOLMR0i\n+bR2ttuBjVMhuSlF1/AhtoASpfKVrJaBVZxSs1zBV4eplVr0/a1SqaZyO8+Y6vFVqQdDv2/2k0qt\nuW02gTfXB+g9IuBcQKgsS6LGEW+14Dw/f9DOdkOUsRWYEFxGKPSdw5jKbtcxbB4xp5mzsz3LsnA7\nTspR9goQIdrQ3tFxmmDRapRyNcI4L/RhOJ1jtzdHfLBshgs9W+ZFBeZzoxV1fWvo7xDm6ThSatTw\nrWJ0GpcX5lkIQZ01vFc/92K0QKamlohqEdRFw/uOUmFeKmU64r0+R9sZnUjkO7vMFVGOsfF893sA\njfUu5bQHqxWoftY5R0yFzUbtWHNMjDmRUJBuu93qvjMfsdUj1pzS6bLo2EB8JcXI8+sbjDmwH3ZY\nN1DLyPEwc372GcwXHWlJHG9HjocjwTuGoQeUQrIsEzHOzC8KSKJ78yG+7+nosMYjDwJd8tzMI+IT\nv/P5Rxx++IzbtGDtFsRw31DgpfMgNIpT1slq5zRyGlk94wtuXQ+hnWE5nsTUv+j1S1EkN1Ky/qMU\ncomUvCKRq62SPfkEflo5sB7CIsrZMs5iG4pZjSEXMI0badFDXhr/2TZPS9cS7VK958HXkC61QdPR\nrjOWWJVvaZpvcilFi0aRE8IM98YBctfl30fdjDHqYdteXisN2TYtCe/0/2dMLFhb6TcDYnSMQ2oc\nWqf81WIDtnMMuz1FHDh/Qg8LyqF21lKPqdlycALnP4nkfRIdvG81w4n/2OJujSVZPWk0N13vRwit\nAWict/W+alFW7t17h7g7L8dVnZubN/H6GUIIbIZB41bV4+eOv2yFmBJD31OnmVwzZxfnnF2c0wfP\n0w8/4m/++i+5urzk2fPnAFxeXrLZ79ifb1mWxNDrhpjLxHRscd9z5Pnz5+ryYQw/+e43OB4Wnl5F\nDrHSPXhEmivd5gG3h8g0FXZDx7CxjONIioVYMsFUnO3p+4HLY2HnAohFTItrdpaYCi8Oka/99dfZ\nnT3iK3//98nLwsdPnvPgyZ7xw4maDUUKKeszXOqnZ9Hn5poiYtRXuEZgdW65e3fK+u6s1IiiCZNi\n9NCpQlOum/YcacI9jbRemRDS1tPqsVm54+TVtkHdL5Jt48J9Gqd6vXTK40lGXQqc02Yz10iRStcF\nbAK84XKEv/3md/iP//BX2Q3nWoR0A8lUappIx5GKhlGEvmMcJ5yrLDnhmke03NMkGKNR9avn6H0O\nYFzmhmKWk4CylqrR9kanQNlEpDY/Umle7rUSG8f9vv1TSkkdaKq+UzHmxtPXyVSpgLFs9mc6LUpF\nx/bJUvLL9m33rSVL0fd+9RYWEeW9IhRjEeMwrieEzDKP1LhgZMA0Tq9UsDQKmRikZmpJ5GjIKTKO\nR8bjLdvtlpSyjnqNpesnalqYlsw0HSHOxDhxfl4bEueJi6JzMkPvt5w/esx8eEowC2UasSYojbs2\nNB3LVAqmVna7HdPxgDdo6mcIGmYQFzwe79Q2LOd0WmG5RE26E/XCTuZO76IuGErDM64iY4JUMcWw\nt46u27BsVRw3Fkff9eAspnX66yg3N1cdVxQkKTliREjN99baHU7MibOeUsLiTpS2YF3TzmgQlhUD\n1lFogTG14G2gC4E5V46HF3Sh15ROCiF0lKz769AFnNV0vWVpQTfrX2IRqaSsMelqB2moJRFjo2I0\nfnmhYsRT59gsDl0T9+q7vG2+xTFGUjzivQrtUi3McyKmjGF1sdFzIafEUhfV85hCaXSqw+Eaay27\n3YDvPcRCrHfoqLVCboXiph/IuTYBW2VZIiH4BlIZqBoG1fn+tL+tnOPSvVw0rUBbajaV9wGinDPI\ny/7HpzVjjJ6FrQ456TvEIg6MUy74Sqs0xrVC9RbIDDTOrPeMl+MJMV4Dru7vEQBhUD1OLYVYCjUr\nuqyFbmFZNPDI9apjSosW0eskb81uyLVgcsZ6f0Ks1T96auu0ZT1kg1iaV3glFzi/2LPZbDTTYJow\nYsm1Ns7v3aTaCsSUubk5kGOkC1tCCNSuYKsnD4WxRna9hhqVHLFeeeaxpTG/6jxYz/7TQcZdDXOq\nrURTfq21So1dNTW/4PVLUSRXQGpESgYSsSTmdUyQoVbTTNMbAvYppGupiiY6URP13g/kCgaLVBUj\naHS7JQwdpiREtqTo8fsLig8sOWGgieU8R7nBGOjCBSnDfv86WIgpInhcjUr+Eggehs7QiVrgBPHE\naWk0AxCn7g2rmnQqC94GzTHJWfm4JhO80RQeEaoRfOjAqMCwLBljDds3P4Pf7tp4Q5Ph1AC+4rqN\ndu82EFxgGPYQAjmrQEdyViufhhrreEjHEPc9CV8Sz2X1QpWqgo1MJYkqSCs6mhepONO8QZuYUVxz\nzBAwjQt7x00ulJS0gGncq7IW5LmQjVWf4q5Z1Ng1Yc5QgiL40ikvNTiDNSBGX+i8ZETUDN16T+c9\n55//Am+++TZf+MKXVPgxz6Rl4r0f/ZiPnn7Az37yPiklrp99k5IqnXXYrsc4i/eWuIzcfPQeeT7y\nKy6y2TvedY/4MG95PxZC5xlTpdsPjPmG44sZK16VxCkTcqHeHsnG0G16+sOe5y9GrOlIaWYhYWrB\nSaUz8H9/9d/x77/5df7n8D/xO7/6G/z2r/0mkwSG736P73/vx6Q5oruvINmeDttPXsG61lFXpGaK\nCadmZR3bgaNzO3IyjMtBN9OqHXoSdZzpGmEgY9V+Kms4j4jgROk/q3F4KYV8zHgTtA9L2gQXo+i1\nenu2YJic8VWotbySE79e8zxjRAVQIoVYb3UkXjT+mAip6iiz6wdubkf+/V98my//9hf5vd94h+l4\nYLl2kLbcLs9wJOZ0IC4TxuiBe3a24/r2QHAeGlJcWgHQzZZxnNpEY2G33yJSsaYjLhlXDSkZHfU7\n5VqmadRRt7WUrGh+LomUqoYSeWlUME/Jqjr3VRCTmp/qTDbQ7XqcLbg5sbt4wmQMWRzWDBRGvGsB\nACHhomvj63SaUJFh22853F6S4sz52ZZaCylZLTashWHT3FsWluNzxsMlz55mhouHmP6MYITt+SO+\n+tWvkZbIV/7x73O273j//R9y+eI53mhBUVKk63eEw4z1T3n+7H22nWcInVqpzRMxJnJ2DMNnEb8w\nT5dcpQsevvOA6Zh4/PrnGT94D/ETyfWUadZi3ZhmSZgJWESUiuCtoauJtNtwczxiEPpu2wq4jLGq\nEVF/5UScM5iK9Yp82qwhLqbzGKsFxJITr1085JrIi+cfE2plqlabcO8o3jDdLixTxHjh0OhcNSuf\n9vXHTwA4Hq4ZXE/XnRHrTLc17DtFRKOB24O6hfQIjx7vyWRuXmQ6Y9QKDqFvKaydsfhNwJTMkjKl\n3DDQ8/DMkcvFqRmqDbUU6wjekMuR5agIpoiKGbteJ3nH4wGRgG/vXswtIIeKMYoGe6MFXACmeEu3\n3eB9x3iM5CrEFqI09B1piRr9PWz0DAmG3ncYm5nGhXlKHFNktz/HB8Pl8pzaAq6sNdgm7iJr0XY8\n3DSbOaWrOCsss3LyweBdxzIWMEKKCeOaw4h1WOuJRCiRwfWkMjeqXuChf6S6pzw1fUdu3FxDyhmz\nFr8IuXFeh+2GGCOHw0LKR87Pzxn8QzQ+trZAD4sPWwqVUhdsCErTtIbpoNaCx+OxFcqGfqvF8DTN\nFIEQPN3GkMtCFwLODCzLwsfPn3F2dsaw6ThOE3M8gtEGe3A9hMJmMygvP86wgTRHLq9v9H64uybd\nhV7Db4qj5sycCoI2+EX0vXry+mtM00SM6s8dUVBu0/X4ztGFW1IeuHj7jMXN/Oh7R8QEqsmkZSbF\nmU3wdM6z9cKSBLJqy6p4CoLMkCQhO0tXCr8WKz960JEvIy9qwvaem/Z+fPLyVW1DM2r6MJVFAcYl\nYjuv7zraJIYaMAViSYhVbvovev3SFMlLSkhOpBKJJZOiaV2NbygkIA2V+pSDtNsMdD7Q9z1lmVWZ\nXaFqnhihGrI1VKsirJlE2u3hwRlmt4XQqdo5RULXgzF3CJhALgtnZxd4J+o5iqbJVLQoCEGR3E0V\nJeM7sMHreNYo/zCbihMtWDYucFwN+q1VXnFDRhE9PBFpvMgC4qiuJ9uBWrdQtuSzM+xGCx7fOk/T\nUpRs8+g1ocdppBrSRuf3Oan3r5VKsTpMnHxSGzK1oiRFoOZ66jy1SNZRsrFGudzr/7sm4Rjz0u9Q\ng/dwj9tp1S/WCTZYbBayFU1ME6HrNSwl50gxcrrf5HJKiFvV6nDnK2naCF9E3TA6H5TfdjhSauLt\nt9+mlMR4c0OmcjiOyh4zQt/tmto8YuItP/z6n/L0w58h3/lr6nTD7LdgB54fZkpw7DcdiDCNhdwq\nxhgj5NJUzy1m1Arb7ZYQHBT1pjgst8TSxHjG48s5r8Ut/8sf/6/8gz/6j/gf/+v/hv+sfJHPDj3/\ne5x4/+qW9392jU0LvV+dU1/xXgTLxjtcFUiZ2dxRZvS/Bx33F+54mWXSsa+87Fph7yn4nbV3yNy9\nKcGKugR7h7hSiyLS8rKLSs755B4BOpH4tCv0ESqkqJ65JWu0ibHljgePTjb2DwJyk/mT/+N7/OD7\nz/iH//L3sL0lxQNpimwf7kkR5heOy8PIMAw6Xm7IZDVVQzmsFinWeObDNSG402cNvlPeoBhyLsRS\nddznLRKrChxLxWKhqbVDCNTaRqdWuM8hXZtS0FE2RZGgYTMQQo/dDUi1bC4umIeB4cFDFquonUUI\nyZFSJmcdr0pV9NFYw9AFKJXbnJBayNNCqYlYdNrmnMMaw/XtkYzF9mdcvrgmLQvjs0v8a8J+GDh7\n/IB//JWv8OzDj/j2t77FdrtlmUZSo3Ycbw9sH53j7CXeeHa7DTUveFd4/Y3X2gSvsNn2PH9xhTPv\ncdyd0w2FKUfG4xsY1zOXhWLV+YdS6DpdS3NcyCWz6TtyqkiOOp4OgZI9Ihrlm4HgPFaEaVTHjewr\nnfOqPQFcMZpKKJbcWbItVATXd5w9uMDejhyPE123Yb/fNSuujjTOuMuJYmC/GThKIBvPWR/u6Gop\nczuNbR/SNESK0OHYFk9uccU1Z+o6ofCOp+9/gHPC+dkZUoU4KQASa4WsKLnzTvdYFInMJXHMid7Z\nJkZTf+6UMpBJsZze3VIKw6a5r5z2B41Lrs3dId+b9Mz5gGuRxXGZMDYw7LYcDgfmecRYXV+brbq0\nSKkN4fXESae0Y5o4suBExZN956miNLCUtbCTYpQvXlTg6AywVEQK1WdibnSCKog4tvvh1ODXmkhF\nucsYQxGNKD7ME84lar3T/pgUmJZZ122CUgTnBZGC7xwemKVCrCyHdEKTQbg9jnTbnlwqqXiWWLk5\nQHQZ5wExdH1QfY76Our9HJcT/S+4DqsMTErWibktEEKHdYalLByPI13nMAYN/SmaB/CZz3yGeZ45\njplu2FNxLHGkUk784tKeYejVB7nvLSUE0rJAE9qXktViF8EGT61O6TbNNeVwUOrLZrPDuUBKhRB6\nbSRagd9lj7fqHvbk4QOMMTz94AXpEOmMQyQqhcOo5WK1htANWK+CyDkn9bzvOg07cxM2Jx56wxeP\nW55fPeMaw22shM3ulefBMsUT26AUtRrNpiK+RY0be5rQY5SfPy86CS710wXin7x+KYpkaLnoJVFK\napTqdUyoC1l5T4VaX13cATijVkwGtQdaLy3ONHkI0HhikopsXMD6DtMoCYpMtfHSiphRMbWSUmzW\nYB3LPLVaVjlL1LtQD2PWEU0LzjDq9ecoKhJxeph7kdO4TXmKQh86cIqqnozOgSqWYjx2u6XfPaTf\nnWFdT/Rtc7IW32+1+C3pRDepxpGpTYyoaPFq+FXqWsS+LNa7jyCv1zqCujMzR4sqRF82c1cIKzVC\n7Q/WQ/++eOhk19I24rVoB06RwyJCtQa3FteyFveVWs2JPnJ/5KLir7v1cV9oKOgIfFXB9qHTrHqp\nlJiY55HJWYIx4By5wjTPWiRZCzmSa6UfArtdT9x7ikzkm5E5W+WRVjVdr21cCnc2aFLXNa4UhpIU\npe1DR+y1+5+ypWQdgxYx1ExDmRPf+Oa3+e4P3+Urb3+eN5ZHfPb1N8jyEZcfH1hK1OS/TwFhKxEp\nGspzX6B5/z7pWLI1Z+uzFo34lIZQW3TicOLA3XsPjZE2UWh8i1rVylFEN2bRT2JPa+LucxTKiUv3\nd03BSinYl0SH+l7X9v7pwd8+vxO6zUC9WXjvJy9490dP+dKX3qLfHblNCSeeUhf8ZkMa1Soqo6Lb\n4DVeWaTea/xQhMv7pjwvYCxiHSUuusaNahlSoQlhtE80ojqKymr1eF8Me/fe3b2LoqES9U7MuzaQ\na3qiWIeen0o5oLQwlqL0IO8cyzwSY+Thw4fKuUXdEUTAeUNNGr60Rl1IhXmJHG6P7Lxhf/GY93/y\nLjnPPHi843iboMsnYazzHc+eX3J9eYUR5bZasVzdTAgRhyXFgpGFvjO8eH5N7x3OW5wr3E4R4Yo5\nVh6bjlosaZnpfMc4HzXyuiSsCGJbwEmjs+WYlDRXwTpPXSdTMTEMAzkmpiZ8CtZp01MX5hSpWZP6\nalUxHdbgQkBsZRkXctF9vus6DodRxT7tQa10G+eS3v+iaYmI0alJKTgRkhVSbqmv1BbfW3G10sdC\nrhlnLdYGupwxxjFPE84K235gcIHjcVSKBWCqrs1pHKkp0gfbUlZ1f5hzpKtgrSKZKiyLlFrJsTR7\nzF6nka1BrkZdbxzqXlGdpouq/3CzIkzlVIyWUuiCP61VDckwrTG4Z2MqmhhYml5lLeR9p81YjpHg\nA5kERRi6Xicr2VKSCsV81ze+v37vWgpiDSllnLtr1PWM1Gmk7iEFh2n2g+t+otoeHzxYo7S5RuVb\nP1+tK3UCUiot/EQbhhXEUoBOC251sVAnIDEGjEY059oChJoFm+8ctHj01a5zvZS3ntn4Lc4VnFi8\n6041hDHauGTaejXqYmS9nuLLsqykBn0GYpnSqCLaRadIperZY5zFdUEpkKL0OV2ZgmmJaMZa/Vs0\nxOz66pZaq054M1infvw5J5alMo0GH4TaAKrdbsfNcoOp+dScQwtTsetZr5OO1Q7fGEPfe8Y8Axln\nhc+99ZAPryLvf1AYcyJ+mlFDTGA1/EonRvpzzt2BYiJycsVSnUa6q9l+weuXokiuVTPjqRmNghZa\nbYkxGSmK4paTX96ru4Ct0+7SJijtpqSs7gXGGKJUUjtIbdWDbOu27Ps93vgWt+rIkkk5t0Kwqb9t\nxUthGAbOzx9wc/0+1kpTZUsTFvoT2rHyDJ0YSBkphWIrIk7V7jGRY8aZu7hlEWG/3XGbtDhDpCmq\nYawDJmy4eONt+v0jzG6DmpVHTOMEr9ZcilZVdLaikcJTRFOQjCGYO5HgWnB8kot8H/XVZ6RFbQhB\n72tLKRRpRXITa3njNDrWqvNHruXUnNxHjNWL2J7+DGhODVYwYulN3560nBBpY2BZFBU6uVlYPSjX\nDrrcS0d61WWt1VG64VSwVFHEf9gqn64vgHVtk2kFynhNXSy78ycscyI/eYjbOD6IGTveQhQORehm\nteNBPMW4ljRXFe02BoMi4JWCLBVn4OHZOduux9z6htAcqHni5mbi+YMNX3r9V3jx/AX/6o//N+K/\n+O/5zfO3+U9/63f53Ls/pp8LH95cc3Ot/ravunKcOJaihyfoeN8YpB3yCDhxGBtIMTK3TUVakWuq\nFshr0VYaGpxS0skHQE4nK63VLUYbAkV5U8mnQ+2+60KtSsmBSi13Ku1XXcvkcU4oRVodrk0JaDVa\na9V32LQR9XZgex65vT3yx//qX/Nf/rP/gn/6h1/AuzNefPhjzGaLrx3VdUpDKeWU3mRKwVTl0qVa\nIFe6jY6nqSpsWZKOtb3rSHlpDa1hXCLnw04PwUaPcsaCKY1PWNo7WhTVEWmccWl8ZwtpUtU7wjLN\npCXz8HyLc54uDLjNORTLnGb8GnubMhYQqcSoTixrwxKl0ZsAACAASURBVHq4vAQgTROhc0hOlJIJ\nrkNCYE4qrs0Iu/1Ddn2g5pk//X/+LbYmjuMljx+/xs8uf8h3v/eDxgO0jMeJeVGng2dPn5FzZvfg\nXCN6nefyNjJs1KP+46tCGo9s+g6HcG0yjx54hu6Sd955wJuvv0W4vabb7jjEkQWNS7bWU9F3W2rG\nVNGY4rDBmUroOrKzHA+JwSp6bp1jnBeWZeZ8O+A7xxFhHieYFyiVMRcstaUnCuRKApZ5Jk2JkjM+\nCFeXR+ZlBKkcx1tSNVw8ecg0L6RjIs3aGJ5vdyo29p6UM4dp1PfAwJIqRwreKDhi44y3vSbCNnHs\nxcUZnWTIifHmGrFOk+hyVp58Z6lJwSJnhd5ZvHeMFOg6TI6I0aAeYy3WdSSpFGca91sBiWIytqr9\nnSmVVCxeLEvj2mraYJtuSkcumZtDpO8DuVg67zg7c6fJ4uFwRS1KHfC2Ms0jx/GGhw8f6j5cHLEJ\n8Ocp8uThHpHKYCzjnPjo4xcYv1Eb2BibnmeiG3zjsyrin5dMGPYYAymriG3TEOU4pZbUK7igPOhx\nqXeTz1IRH5CSMTawxMLGqzBvmpNGf7tAmhO09E03FLxPmqha2zwtViSD7wrWg7WZwozzVpvgrIL0\n1KCEWqQ5sLTzSO5SZ7sW/DEfE+NYuby+Aqvi7dB3amlIVKDIwnHUJM7NTs/gpSZyskwpEyl45+i2\nO9wSdQpmDCnqpKULHbfHA0bUhk/WdLvq6LugU25R4XXf9yfR/OFwwFrLZrNpIkFLXio1Z8bbA2VT\ncIMQguPs4oLpsOBjJC+zup3Mk9ZCuVCdwaGe1aYIdUnIOFEBR491hSqFB+cLv/Orj/l/f/JTqul4\n8Sn1rDMOI4al6pjBBo/znj1K50Mq1itnfsmNX17Qs+bvOmg++Xt+4Z/8D3op2gh3ortaImt9pF1Y\nEwq1n3kVK7kPbUMx9jTqvisGa1N/3pXYBiEYS5A1UceekNK1yBRQG7dTUaYCOf0g9VTErp8LYEqR\n3od7fEBVWVp7VyhmUKcBc9eVv4zE3qGgxhiK39Pv9uwuHmD7DXir/MyU2ucySngvpd3PJjiq6Kih\n3gmN1u9I/vm7eJ/0fnIKqXfivdO/a2EtP1dcr0heAxQ1JldORfZ994L7Yrz7AiNFM81pg6xZD4b1\nea7o8QnZNpUqn65WXUVMCm4bHWsti9rHWdf8FespKSsnbXzSEpmjGsVTCs4qhaWK184by2CFQRI+\nZw2XQfnYq6fm2snWVXy40hXMKirQQ95bx7HZsOXa7Okq/OTDn/Hm2WN+5dGbfOun3+LffOOveOt3\nH/D2+SPSo4n3nnxIdJnxmD6ldUTRo1Uci77095+l3k+wxmri2KfAuSfu+h38efr3qyH//TVMlBOy\n/2kuzvq8uTeh+PTNq5Y2txBd27qRR6xVr9Fam2iwgjjRWGmTePzaA77/44/4t3/6N/zBl9/mYjsg\n1jJ0fYsEB+qdXV1KkV70c0lFbRgB33mmSWPH77+jIQRYIOfxnnjxbl3T/HO75tzCvUZpnsfTz9Wy\n7gMFLy1evqwjZsFbi6mWOM9IzhhjMdS7PSJpspxzjtvb2xN15uRLLk3Ml3RNpiVi+4CtqzD4bqJT\njeH5x1c8fPwIXzMffPAug+85XF9xdXXV1kxPSoVnzy6Z50iOagfZLc0iLHhSXnDVYnCIDcR8JMW2\nt+23WD+wxMT7Hz7DuZ6Lz/3KS+voTpBrTujfKmwu99YfQMyV3rRwB2M0QGgcmacJsT3i700XRcBZ\nUp6RJAxeOaOlaGORmpVcXiLGhJMAKE4jSxGWOjIvkVQN3nrEOo5XNxpxP2iSXO+VfjGmiSrKc86i\nU6IhhIbImxZZ7E977dB1mM2GwzSfqEtWlPcvQZE+SOqNmzJLjsjQRKwrYtnWaJWKxZGiFozOe9XI\nVPBGKGmhzJFSU2s+17Wp999Zr4h0nqlVNIwD9YEWEYZhIMaFtGh8s+8920c94VbIeQZWDYEwThMl\nG3oPw8aTpRKTuq9kUUqhsQYftPiJtajjjbfQRHNxHAlBo+HFcOeEtGQwGo6h7jwWXy0aGmKJS6IW\nmON0EsCt62Y8zoRQwdtmg9iCU4LXYJV5Oj0H10R8cZ5IOTIMHcd0A27AGMvGCC50hM6rhe0SSamc\nBHvG351TKx3QbjqcM9SjirvH8UhB2O3apmZaE12E6+truK3s9ztCZ7BBG7M4zRiDovLisEmL/fs1\nxOqWZaxt51RD6GWtDZq4P0OMmWEYqPXI8TgxDFtKE9CdRIwl0W0GXcdZyEXPy8FpwMgqdDRGz/BU\nM7nFXEtRSmaNiaXMSB+w3rPUyGbrec3s6d3PiMZCvEtSvn+dvldp8dutRvO5Kqe8/dzqnGWNb+AE\nJwrSL3L9chTJpSJeTfxNFWwCY6yqNVOi6jQVU1shi39lkTy5oD7FRRCvJ6b3jtXL0FlL31AbcBoJ\naiulxV2aChlDcQZn1CLIuV3juibiDez6DW+8+YinLz7i+npSz9PF4Kog0eDEg82YQKNXVIxoJ2uL\nI2BIS1Jek7OaVFUh7M4IvuM2grjQnDcqfrOn2J7Hn/kSod/jzj5HER3/CwljHBonbJiLwRh3smUz\nbYTtxCHS1NGlsjTBlP4SueNe10p9yQQf2oSdOzcE3Wy90Q1IUTtzV3y3IBVjjMbZikCJp8LLNLcE\nqXroO2NVOGaMdsO5Yt3LhTQ1KO9SNIY6x8RSmgAslmbn1F6+1nS402hIfbf1YFXqTCqKpJCKIjlr\n8djUxql5TBqjKVXOOfKykOJMkYQNcJhvMZK56DNvnDn+9jqyz4YJj9gBl0XFllYLxuLLqbivMSPV\nNCvAgvFqV/ha73mw3/P0Q8PNeOSwTMhh4a++/Q2evP4Gb735Fj/8v/6Kf/m1v+Bf/Hf/A7/26+/w\n60ycPT3D+p9w/eLyla+XqYa0RDAJYxOm9NRqqcU0SlCglkjOgqMnLpkUR3LbfKoIqRZSUjTPOR1x\nIaK+vFYFIc4FSlq0ITDC1MbIpRact5RaSQUEw8ozMFXIMVNLpfPd31kkh87iXHOlEcGYjloDptnW\nraiRiICDOM9sh40Ksgz81Te/zp/8n3v+8A//gN3jJ4yHI67vSWK5fvFcBXY1Y3JGLIgPlMbNF2so\nx5l9GJjrQrTqt25QPnzwltpim33wHKYjyzgxbLdqwZQzS0mql0iqHK+u+Uaf3DOaj7pX3j1WCyMT\nNA1wmWZ2O4/4Hd32NXC+ia0KKY9IFyhiuLm9ZhxHSm3JmAjOVxUdksF1TLESUyUcFhBd48s0KqdV\nPLfHWw7jLW+8/QaPH54Tx0u+/lff5kc//ZDrOfPotUd89P6PifNyQjq3w45h02GHDYXKcjxgg8WE\nDRvbU4eBUi0HEXa+Z5kOXLsj3bDhRz/9iB/88Cc8tI/5lbe/iD/v1Z5sgbopdMaAbYFM7Z73wVLE\ns5SOEHp6NzNF9MDOlU1nGYYtabolxoyVQE/lZjmSloWLPuCdFncxHRVsYKY6FcdN04JUx27ruT4U\nlqUQujMkZ8bpiMnwePeQsNkyLSPHaaKKYTIzuRg9T0qkLoVQoHcG70A4QkEnBClpJPySuZ1voEaW\nbY/FUquh3zpC7/BRA2xC77EhUHKi1sSclZpA9digTjHLrPuXc8JgPTORrrds3IDkwosPr+iCYb+x\nVDLJZXKBOWWCWAbjOMwTxWnYVXCGnNVys+93mGyQciSmI946Hp2f8fzqiC1wOBy4zbrX7XdbnAGJ\nkY23bM42jEtEKMQlg3V0pifaSq7C9Txp0+wDJWd6b6lJE1d3LRzjeppZUsTTYapjmhIinqFzYAXT\nWUzwHOKIqTrRrDVjvPn/qHuTWNu+/L7rs9rdnOY2r/k3riYulyuOLZvESiSQgAgpEjMkJCARZOQh\nkRFigCcMmTBhxghGSAywYJKICKSAZRGHOCQxjhWXy2WXXc2/6v//v+7ee5rdrI7Bb+1zzn11/8TM\nKlt6eu/d5pyz9157rd/6/r6N2NjZlnmaMMYyDmIpiulJWRFiIWXFYZAuzhA1zljaRlBUixLqWu1S\na+OZ5kLrbsiT+Mi/ffUWY+55/vIDrPWkLMJ64yJKiTrK2YYUCru7AzFGrrc9ZEu79tiU2N6uiHPk\ncNidUNyiNSoHtlcrYkhMU4Sgad2ELRHtQZmZQxhkozSJpadXjmnOQORqeytOGcqQlWzOVIrkJa+g\nroNt09O1nrZxPH92xfG4ZzjecRyP5JTEt7lYxjny7tU9N0Xj247tyvKm1bx72KGc2L82XoLL3g4B\nJ6bkhDCxurpmzjNTsugScVPEJonwXrkZygN/6We3fPcus3vztE9yqHou6x1eC6fdJuEbFwVTCGgN\n1jmmOTKrxDCNlFRONNY/y/GTUSSzIMiPrcZK1ePIvTtHL1vrecoURBuHVgqTNRAwWgR7p0VVndv9\n0mYW7818QpuRGOxcRB2szqEHKE02wtW5vtmw7n3lryVQqbb4CjULpKKXwkFOWkz/Qy4SXlJE9FZK\nwfaevLTijcJaTSIzTEEsqrYdyrX0m2t022Nb8Vech+mESJoaDnCJAC4t7TPf90KEd0FxeP94zI28\nQPg483uWlrn8nBEU28gG5n2EUl0gPgsac7mLv0Sopb0Hlxxla4UbLkEpiUlrsjmj0zmLGbtiQeHO\nfNuTpVMIKOtOIS6LZcxCNVhQq4XT+4iqoTKozGIjuHCwtdeUeWlvRtaNJqYFycwopTFa0CpBwH4c\ndZf9iYhtlFJ4ZbDOs+o3pKIYQiSrQBhmXn32OV/+6CUxFV5/fuD/+Ef/AFrDzUc3UhjdHfh0fNoq\np2kaQoyV138ZECPBIaUkShY0GV1kg5kjugjiUqim7OXx+FAXlJyFO1mUQlV6xSW/fbnmJYrnsNfV\nmD8m4SSfKDVfLKhYxvFlt0PGyPlZvUSytZICyHsYpyPHY+Sf/O4/p121/LV/6y9yddUQp8+weKwX\nTUKrCuF4FJpI7e44Y4UPmJUIeHnM2Rc0KxCiiKycM+SpBl9YUe2HEDD2jMQv52nMY7uo5XlIaUHo\nNc6L/3kuoiswTYP1zeleOOekfVlEkTbPgRiThEmQ0UbRNF3d1M6yKSoSIx/SjA4WMMzzRNs1tF3P\nm892xBLpWkfTOLJqePXugR9++hqz2jLOhYf9Eapg2ThP23eCXKdESpJG2mRJRUtWse5blHUSsmIt\nQ4wwjWRnSVozxsQ//ce/w1/4134ZVxTjOFOshEucnuV8pnmVIsEVvnK2fWM5Jsv+sKPvvGz0AdO1\ntftYPd/TTAgTOdsqqjtbYeVY5JqYjnbVczyObJqGtu2J6ciYIqlA26wZR3FK6LxD1RTMOYhzkDJG\nXEzmGaU01qrT+NVaAnpSpShZI24C05wpRPQcaRuNVrnaEYoXLCXV9c+A8cQYaKz4PE9zJOtC0efn\nIuVIVJY5ZhrjsL5hGsY6bi0pVWReG+YwSxKigVxtxJwSp4fFhWGZo61NlNSSM4Q54X0+CRbJmVjC\nRfexUjxiwjpDbw25TJLUWrsstvGomE/ak4UGp4wTfUdOhEpilcAemcdTHWdaS3S8Kssabyt1s5CV\nFtF/0YA6Ie1ai26pKJgOE946XF/9rEtAo1HBkKo4Ha2IIA4ptUO8pOIVoGndmetdhHLic0Zr2YBq\nIwFp0xjIDrzXJ/73NDmKVpggdAejG5p1L17fRRBbpW3VZDiaxmKybCy898SoiSWK+1MUlF9rLZtj\nu8SAFw7DkbbpKVqACW2EBrXUQ+MoiHnTCq8apWi6ljlOdT7XxBCZp1QpMIpp3uOPYkvovaZpDQ93\niZvtFeMwoOZE0zTM8SzsvqwthjmgVMZYRUoydy7OR5ve0o8FXZ5OxztlOeR4mis1kE0hV4GexaIp\nTEWE2DEsYWVf7Mf//vETUSRL58tjnfQqVS6kyjEtWdolzjtWnYQ8tG3P7onXKVomG7GJ6k4PbopC\n4C+qYIzEGacYiShi0UQMqdIDVBFz7lLOKn3xzbSUxoJOfP1nvsqbzz9j9/oP6AxEq/BKY1wkqRGj\nnbTPWEJBROhirJeFtgj0b40hhJmQMk4pShGfSKstyXTodsX1l3+BZrXF3D4DbZjqxKq0oKC2FgSl\nXBbAF84OJ6FU4VGBox670j4l1LssWJY0wsWz+Hg8Vhu3mZyt8K+NFku4ssQeX7ggXGx+LgUeSyGf\ntcJYjUrxVCQv3C1bDGOBkGZiEr64cWdfYGMkXGPxs7x0WBBl69lr2hiDs052MkDOy44yn4IvVOXU\nliJxxHGeiOOREo/EaQQyXb+mhFm6DSpz5S25OHFPKQofQRlI+lz8nYq7LJyylEO9prIoeGXxjef5\n8w/w/ZagG968+USS5eaJb33rW/z8z/88L+KX+Ee/9Y/57p/8KX/rb/4KP/tTX8HdHXi53vLrTzwX\njXVYC6UuLiHKBm7ZTYc4o6IlmxGlIzTCz9RZsyQPLi2z0/jR6uRbnus1y0JiJuVCiIGiVA3GQeJD\nS6ldnHK6/kYVXNdXGzoejcH3jzxNRFMeFcOlFOakHvlpGwPzDL4RLisq8/yloCjf/u4dr97+JrfX\nG/7qv/mXeffuFfMcaFY9KkQYDiilmKeI9xrlLKpEQee0odTJXikl9lzGkNNMIdUwGOEUdl0jCXUp\nEnNCdw50Eh6eEveKogu66ix0EfrV8kfGiwhIQeNcI12JdkWzuUI1HXOcsK1EyOos6vlpmgkhkerr\niufuTEyFgkJpQ0FhrEMby8PrT4lhYHN9hS1RXHF8EX6fabm7v8NoxWEyNOtr+psj+DVTcby7nzFG\n0RexCzNz4BAjOVdpp1boEFkXg7ET7TRze3WNay2vDke0aphKJgwjOWSs2/Abv/V/8fKnv8xPf/2K\nfrWhaVsar5mHB0RIWZbmF0pJAE9MMyEWuq4hJsOzl54URuY0oErA6R6jDU3jsFbTdI7CTIgR13mM\n0YQ4k1Pi5bOXvL174AeffMqzlx9h+55DdT8JGXa7iYLGuxUKTwozd+/EzqsxwifPuRAxYMSmMjmN\n0Y4wj9XZJqOUZYl0X+ww55xpN1dEo9iPRxoFQ+2ONK3DN56iqYW3UJxaZ9Fo1k3D/e6OVBLGgDay\n+UUXWmtQObE7PBBCYLVaoVVhmgahHhhLyRpjPDFJ+I3NChH/JeacaRpB83YPD6xXBZU7YigMhz1d\nq2i6TaXwKHzfVo/ogMoWY4RWMYURZWviWikSCBNjTSss3G7XMieVUnm9cqOVUkwVVAqpVHGi6Bh0\n3SAXo1BWLP4UNSk0CShQJ11ZA5IIT1GKOSVc49lGR4yzhBTpjG8qCDQhomgva+mcIn0FDJqmOdUX\nIU7EKPdxu11jjOFwfGDUEqntG8d21YOKlCh0oOk4se5b9LpnnCPzYaDvxY3q3Zt39OuW6+trAO7v\n7xmGzPX2lhAzMWasa1AoxiACPVcUKQZM0TjXMkXpcrtGn8SIKMOcMmGKIgyPiVUj3bsS5WvGGuYk\nqYXHutbNdYOyWV9RetFVpBSlxrGGN59/RtN3vPjwp3A2sX1+yziMOO959+ZzNquVbBzGiXV3th3V\n1uCMRelM611dHxNNu4Ep0OuJtL/HxC/IALCig5LAG7DFkmOgtArv6qboOBFyJlqx2MSIx3P+ly1x\nDxYUyJJiRqdS2+AAglBa47GmqTvfpz+2VeLFaxRgdC2alSB7aEzdiau6gyxGVZf2xW5OYezi1iA7\nxlwfMmOMoG1ac3V1xe3tLUWDTuKmcUI3SHjvcbaRAq/anBUlO95SanypUjitKMViWBZ3VX8m4fsb\nzOoaf/0C328ovq/ODBOoWsjV875U+i5F8iLSWL5/icA9dbzvOPA+2qwvvnZ+7aXgrW1uLZSO8889\nfo3LIJITogxnTlpNlzqfR/39WqCrimQmCl6fKR5LwS/owFkQdjqXC8Rz+d4Sw7kU6qUIH0wpJaLJ\nha8VRMCTkoS2hCAxvcY4ghale2Nh5Ry7IHZCJWucDF2KSnVBu7gWNcmNaE4bBlBVSGFQxmFbaNdr\nNumG3d0blCoMhyNv7/a8WN+wbW/ZfX7k9//5H3L9y2s++NKHxM8+/4J7K4WuAlAOpURJvNz3lGby\nlIg2oPQs7aqsxRHkPdT/VOw/MZZOqLGi2jU97krIYlZDc5Yi+YLvfwq4+KJDZZZhejleF1RU9AsR\nkMI0poZSJL2zaWRhDcDDIfIH3/4ev/SLv8B6e8XbwyuMFbsgEfaekwY1shk1ugam1PF5khBrfdqc\nyXit+oqKjogmQGGtI4TpcYemCg7rWZyCMmScq9M9W+JokwLjPNq3Mm8h/ON5f4Q4i+1T3X8s9yIl\ncbu4e5Div+97tLPEGsqgcsJg0SWjS2a3v8fEGWM9TbtieHBo4/G+rZaLlmOM2ARTKjitCUVjc425\nToUQEtoasraYLAipBtJ+xLDHjYHDPLLWhnXT1S5ag1NwKJnv/uATvvGN55impekcVmWmcp6Hz0iy\nRDfnIjxSqxUpBdrGoUrEarHbm48zrvGYoshFYrCdM4RqG7WgikZrhihaBY3h009+SLe5Yp5GAppp\nDCJANeL7HnJgGgahAiTpACollle60qmKVezDkXEcWXUt3jnmsJcCFpnvjZWAK8ZYx52mweCNpdGC\ngMecUTnSGotVgkKGOAk/XSlWnaJvV5VuJhHIOWecpnZUIeSEbixqCVYpEj9dKto7DhFtHF3vyOMg\nhWyWsdRYKWICNRSFhDZCY4oxU6r7Rahdu8suZtN4nNWUYSKjSDUa2WjEFz0X0ELJWsAJYy3WnFMV\nG9+IpWvdGB/2R1JefJ8VMUdUqMAHCxqdqpjeYK2sR652x3IphJwoSfFsvWIYClMYZI4sukbRy5xl\nF0RdWWKYULmII4gxWO2xTjNN42l96/oGrDhCDMNErqh2IbHpN3ivGceRuW52uvWG4/HIMEyyudae\n+/sdq9WmxqlXod4UMc4C8vuqeqOfRPaJ2sWsnVhVsFZoO5lM63qGYWAcJrS1WGXIVuaeBW3PEp54\nATTFR3O4cxL2laog3bue/fEgWo0sfPrdITIcjrjVmmwkStx7zxDPhgbLIXa4oMl455lGoZy13vHi\ndsXm8yPuC5aEZf3PyL0WuXYmBZlvVdaEFEkJobKVgszosQr4/mzHT0SRvKCtXLTJnZV2orIOaxz9\nesWqvanF7NMt2dZp8bRFs6ueiQA4B2haDNUKQgj5RjOlLLuznKRwAdTFLuNR8TiLEO3Z8w/5yk9/\nDfsP/wnhGCEJfK9VtY0rS0FYF44spfasRpyxOC/G6GmeKHUCSzkwh0IeIhTH5vkGt31OabYk2xOt\ngxTRJVJyJEcZTN5IS1k7e7GAnD/7UtSKTY4+PVCPi4xS0fNysnK7LJKXB/GyKA0hiE+mRtB5cy6E\n3v/9xWtymUR8Fa3EGFGVRgOFrBVWu0fiPmmZGJIVL0eMFv54bQPa6thAOfNnF8T7hDaqanlX3z/H\nRNdcePjWY7l+ywQRY8Q4h7GaVbshzpp7U0WJpkMpEfqVPJLHWTY7SpMt6FxTh0pGiN2g1NkSz1qD\n1nIdF4uvmIUbqHxL2/bc9CvaVoqE4fjAOAX++Dt/yifX3+fLz/88L26+yn/7P/46/+c3/yH/5d/6\nNb5+ewXf//THngujCzFnYtakUMhmseCTjWis9nYhRrDQ2DXKKcJ8OF2X5bk8eWxfjKGlYElZ1MlF\niX1fnEeSM6eEypIzSS9CvWXcF+6q88LZleXpwxvLWNJpXF+OkaUlfOoeuEIiYJ2lJMMwyUb0+Qe3\n6Kz42//bP8WUzK/8yr9De39g2u8wWoRIx2Fgtb0SvUCaiXHGO0OwBo04VZSYxXJtOfcUcM7gfYNv\nLNN+rlx7GU+hPn8JEchYpYkLCv/eBlTrOsZtg0oFrSzONrS9w2+3YB1zSJRp4jC94cXVBq9bGT8x\nnUAFbc3Ji/nZsxePKAuh8u7Xmx5y4bNPfkBSYNcdrt/QdmvK2nC4u8Noz3a9IccZcuRwvyeELAJW\n7clFM+fCHOumORayUlw/u5biazfgFehS+N53vkdWYPoWd/WMw/FzuX/G4pXjXiU+efeW62fP+YM/\n/B1edFvicSTWBVbmHwA5B4qi225QKpFioOkdb15/xqpviVMkp0jjhGo1p4lxPKK0cHuHg7SXF6Gl\nIPGFHALPr2+wruMPv/Mdxt3E5tkLVqsVRx2Y5sg47rm+uWIeDPOksMbz7nBHKBCcw7qu0vmEiuB9\ny2H3wGzgw49v6CgcDpKuaK3sjJ7frvj87gjK8uGLD9l6S9h9xma1wmwkanu/39P5hqbpcBiutx3z\nHLl/e4dtNhRCRZKXAi9g4vKcFmYK682KcYj0vqcUeHs/VEpPQyiZu4cHtl4EfosF3CLC8t7jXI/W\nA93KkuYt4z4Ty4z3DlJTFbSaw26PLpltf8N6vSYWGMNMHoV+qI3HF0XbdIwcmA5HrLI0/YoxZ/Eg\nr6htVII+901DToXRTChlSLEwzxOrm1460Ll2ZzIY24v4ThVimojpQNteczgeZcO36hjHkbs3P5Ji\nHAkNCaHGWTuFpYohF52KqjSqKhqLMeIM9L1Qou7u3xLTmp/++leZ58jduz1aW7yXjsdhJ++dY93E\nOMf+ONJ6z6tXn/HwoHjx/Jbrq2fsHo6nbio16VI85ZNQTmMWvnIp5zTHUJinQSgvKoFqQBt0EUet\nl7cvmWNgtz+QYuJ+90DbymdfbzeVOjaL45MWD/Uc5N4fjvf0bcdq1aBo2O12jLuJHMQ15u7djtub\n51iV+Nbrt+x2O1Zb6TC01jPWLp21jjnXTq/S5BhIDwPPnt2Sy8z9/Q5vMjfrLTfrhqfJtbJJ9daL\nmwvSKSIJ0q0ad6LeFJUpaZDumtVQLN7/S0a3EBeJSKyRgbkASrhX1jdY4/Cup6nRmOinTzAWLRGL\nzjLRkKm8HAR1taZnZCSSwDVY3VBKwxwysUQaTzGd0wAAIABJREFUbWQnpR1WC7cnK0CLT3MpM3k0\nrFbXfPzhR9zcNMz7iZQhpIS11xjdgTmIr6pKOOMwTp+iOwsK17SUmBimQFBBLKaioimFVnmiX6Nd\nj+9XwvUt4p4AVINymFOiJEiWWqAWKBGlxDv2vJjIIhxjPBXIl3QEXSefVAtZpR2qFnVlcYOoKUiX\nBfbm+gp3MLx580boJFkQS3tRUJ0oG1mKcIUUS1bXwqlSQjIFq2UfCGJHtiCsWmnmPOIaS0hCHVic\nKlQSNb0yBl1fk8q7lY9aZIeZz4VIKRK3PM/C1T0VeHUBWJALudTm1EKfhz0KTdetGHYPjPMelQfS\nNOBtyyHMqJLBVa67MeJtvVh8GYuyVpASRMQWQiBXhCYCKCtBKsZgAYfBr7bE7UtKcYzTa9I0Enaa\neP1AxPHBBx9w92bk737z/+Evfe3PP/lcTGPEdh2UTFQTKluKksIt5oxSDVML8SFipplIYAoRQ7WS\nqnzXohRTjHjXoLJ4PXtt8MbIvUdcSSgizisGSgKtG7IyJBVQVcRZlshSbSUOep5PG4YvOmT379Ba\nxGogQs0EzEW4zcoKymZTDVbIAavAakeKheerlhQyycG3vvlt9g+K6+tbgrfs3r2mFEW0HuMdx+GA\nVgqXNC5r5pUjTDNNNiitCTGirCaiBE01Lcc5E1GkpMG06JzrBkkWare0g+dRQiSaRhYLq7GuYZoG\npjxLWplWGMB3mVB2aPeMZr3hbh9wJvN8syYdI85Ux5YhSXxyozA49vcDfeNxriFm8W2nCC1qQdpy\nsWgd6VYNxzkQ50R8dw8vW8yVJb/2jEnjXjzn9qe+yp98+gblCsq2FJ9IakK5Fdo5tGlpWschBFKM\njLsBsmyswhDIxrAPEha1tpa7474KQjPP1tdEIk2RdnfpV2i3omk67u/vQDuIkuBVlAbtOaaMylrC\nFpSlmEyfHZ/vR1Iu9I1YhxbryEqKd0NDVCvGeabzHapYDJZiDGFONKYnNpHDEEhq5qMvP+dH37tj\nmo9437AOijxJ2uq4O+C3K5KZSXGQ+bqAASKZISdiyZQIXWO5vbkmziMdK7CREI+UJAE7DkPImbVX\nxDgy7j6l6VasWgM24VTmRePIjSNbzfHtG6FK9C2ts/QfvWRMgfv7A8Z01UVFMysRLmkjc/nKKMbx\njtaLSKpkWK23somLhbaA10Id0UnRdtKensZAKcJVtfZATg6dHVYlmk5S9ObDyLptaJxn26/4rIwc\nDgOffvaaq6sr2s4IYt6LEDtk6eyGaSJbR1CZbAzeOVqlMHOS7APOXvrH8QBF06+uiDGz27/DeDAL\n799YlJXXJwaJVXfilKFdwz7OhFwIQ6AbIzZGxmxQSpLnSiqSHpoKcdHhKBhDoF+vIAVMyadNOTkS\nc2EKWdBs3RKT4c3n79AaGl8oZcLqFa3b8ur4jqwDq+2G3f4e5WE4HvG25+XtDbvdAaXE/tQ4WQdD\nyWhruN8PrFYrVldbxmkix8PJrWOKk/jSW9GyNJ2AQGEuaJ3xRjGTOMQDIQT8SpxX5sOe3X5PdA1X\nNze4xjPHgimG1li8NRzGiYeHA/56Q9ILdU7RrzZMGcZ4AOOI8cja9BzCOzCQQkbVtfQQIimrGsjT\nCccZxTQEulXPPMO7t0eeX3XcTRFsQ7vSrNcRX4anFwRtGMeZRhlxqnI9xYElyFxrJHaelCFqrNaE\nuYj9If4L15n3j5+IIjnnwnEQyzetNMY6nO0wxtA0HdoKx1cKHPHofeq4tNjaansWG1RR0bFElDdn\n6kQVQxhjaGxzIrGLcXilRegzmiroJxQ0Tdfx7MVL7t8OxBwkhILMOB7Z2oyqPdpUJA3HGUvTWlx9\nmNMcZFfuNXNOxJhQKGJOj8RiWguNIaZ05hAX4X4azqbZy9+XXOKTYKpem+VrS3v4UpDxvmDq/ddc\nCtrlNQD6vooLqnWVqerz5eckvjeeUm9URXTDBap9iUZe0iyW49IIvJRyQhtTUief5DOSKUX1+yKw\n99+jlFL9aqXVuSCRS8rg8lmF7yr3YQoBQiBVgU/M0HtPchEdFFYrgrIoI+09gvgJWyOTuzX21PZU\nugYjZCX2dY+QUX2yU6MU2qbj5uYZTdOQS+R4PDAeBr7znT/l5f2eL3/lI+Zp4G//D/8T3/ra1+Hf\n+Gs/9lzoXhFSIheF0S1FpxouUu0QjaXVmth5dATCiCYTEIN7rMEgFmEiJMlS7FtNKIVYErYKR1U5\nhwmY/Dg8Rs6xjivOHPVLDvr/F90iESvNYXEKkXuaSyDGcOKcQ0LbFm0UKS9uLWJbOOWI9Z7uZsv3\nPnvgv/qv/zv+k//sP+RwFEcCpx3X11umOFD6Fm3EoimHRCwR6x3H44DKiqbrT52Lvu9PHQznHKbE\nitjW90e8uEOKeA22WZPnEYK4z3hrhYrlO4xRzFlTMjVWWPQVNy8/xq1vKFe3PPvyVwhZbAunaWIc\nBUH23rN2oqjfHR5QaoN3rtpjNRjVMk2TaCScxWnFcBj4/IefiDgNhdso0hRpfcfNzUfEMKOOR569\nfMEv/dIv8b/8r79Bu7rBuoacM4d5JipFGUfKMPD2Trivtm6Qt9stq8airOE4TaScQR2ZpolhnNBa\n8/3v/ZBN13P3+Y/o13+MKrLwffb6U27Xnv1+JxxeLe5GGY/WMxR1ms+KyjQu069btCnEEsk6SzBL\nFc7FGE/PeSRjcoJUxcW5ULQkEd7f7xiOEy+ef8jLL7e8enPPbjdAtlyvNlgaphh4/e4e6jz0vN8y\nx8D9MOGUxjoBAN4d79kNB5rrK6w2fP75pzQrmW8053mvhMTKOtq+4eVVj1NgsyFbTRiOxEl8+p3x\n6PUapSTpcp6TWO21Hdvtlv3ueGpr55Q51sjo9bpBK0tjxT4wT5GQCrl6aftO7ufD23uKVfSN48Pb\nK6Zp4rPhFV3fVZGXgFljjmjEX/vZzS3H45H93T138z3hKmAby8qs2D3MvHl7oO3EL1jmB3dq2Rvj\ncK0/PT9SSFl5vkrhMI60VsZDBqEaOo9WEeMaoFIymuYEcmitKdbwMBxQo6qJk5k2KRFvxchUee00\nAlokBZFCCrLmzNXjV+gbkRAmWuekEK+OPsY5GutrB0vYU8Mwca/EWctYQWFFAKvoOumQhxBoGvn3\ni5fPUGmmhEy78iSVCAFunz0D4NXbN8RpptGeXU2FbdqWzWZzQvibpoEaYrLdbglprp2jhcNv8N4w\nTdOp46aUYnP7jP7qmikmjsNIKpnrjdA/fvSjH9F6cbjouzWf7e+YxwNdI1RSAb80/XZDTDMFi3Ut\nq8016+3EPETKnMklYosiOSuuXqnQNAIO9e26Up56jBYf5nWnGA4PNK3m2brhqn26SPbeor3DFUXJ\nicNhB1rhrD/RQ8fFQab1GO2YdwNxuV5/xuMnokiG2rJVYiPljARuGGuxvhEyfvWdRasvDByQ4ANB\nRl2RgkxxwZ80hayrolPcHjkL3gyqZOE0W0Whtv3NuZAz2pDSucC8ubmh7T9n9xBAFwzq5LQAIhlY\nlPBilSYFgymCd8oCL2l4qgaPOO3AOeErDXuaEPDGCkd2OdGliNOPC+HLfy+F5fL1ywJk+XsRJV5y\neN/n/L1Pn7h8fWm9OUI481vLBed4KWzfD7lY2r6Xr7f8zmWRdG7xL8I7EJT8LHhZwhJSyixeyk9t\nEi7P+5JGwoWvs1W6Cr3Or5EqZ9FaDyphmxbXdNzPMyam6kGrcNpwiPL5FAbrHUaXk8etNubEjV4+\n16WbASCdhlJgud6mYPH0nZzrfv8gMay6EMeJN68/56MPntH6juN45JPvf//Hngm5IKUqnR2H/Sz8\nvCL8XKUUGItWGW8suQQxiy+yMKSSayrbkstWx9NyfaoFjVWPed+k2rHIF/dBXdznet1zfsyn/xcd\n2lTOL4sYsoiqv4hAb+Hqq4XcuzD3lXilT0kUlf32isMw8v1P3/J7/+xbfOOnPyLPEyHOrLuWOGem\n44EQI0VrrK7jDUVRCu00xhuGuuiIyjxejGFBai/HZNEa66XjkQoUY7EXItZSlrEoVJyiwLoG3/bS\n9WjWRG0lfh5JH2t8c6JKlfIUh9wKQp3n+jwuvFfRKQzzxDhNp3vX+lasy+Yg7equR2srGyNr2Fxf\ncTweWY0jIVfUDSVteWSjMoXINM3kKAl/2jjIQrEKUdIVxzkwTCPHaSSlQhlFbGhdy+E4Y43n7u6e\nq6tE0hISoJKENSijMcZhyeR09rUXMamI3EQvIVaLl8/8iXKVMxhDKmJHqWoIjVDIHH23ZjhEHh6O\nlAas86zXjtev7jBKse6l4OyzY9YiGp3GI43veHbTchgm9jHhtGHVeOZ5FrBGZeaUyNmcnoPlmfFW\nAkC8VbKRouCblqmkupmqtnIp4Zw9nXcuUvhj3cnr3Vp7KkDneaZkub6lSOJgjpk0y/yfiwNtCEU6\nOZurLSAc5mEeQcFquzqNa2ccIRRyFFGoc5aSAkYJXWksVSimEyiLbzqCykxTIsRE20qhWZBCzTcN\nh2k8zefn1D9B+0KKqBjAaIwzIiCeZzmfulGeYiKrs2OM1iJCK0lRkrhQqFJQGExFh5cwn0JN9atz\njFBB9Ik7my/W8IwktMZZBICbVc88x1PRuThF3N/t6FctV9c9sNhTKoqVLpjM9ZppnjDzhCZL8ekb\nxiggTimSdrfZbBimkZXvT8/ushk4BXMZe6pznHOM81ATGf1FJ9meQIlprtfYeVarFQ7p7A7jyO7+\nAecsfd8yj1ONZheKT0m5OsNkrjZrHqYZrSxGgW96nG/ZXjd88oPXxDxJqqSypHE8zXsJhUcoe0op\nSs6stxtSmNkfD9zedBSraZxm0zs88cm1YAGfvJKQnUIWepsSn2yZEzidf9ZZUi+Vwvz/qHx/Iopk\nMS73KKNp2h5nLKv2Gl1vckFSr5I5W4U9dbjqbiHcR3GMUIpqvg69kbZoLkoKYg2qBOH4BlBOoyqZ\nP5sWrUw1EKkogzFoqziOM8Y6vvb1b7Dfjbx69XtSzBnZsbZGxH/FakwRJLvRVtKEYsZWTi5KvIKL\ngrygCUWjTwpuJe3kFITGUNG6pGUhBSCfC80Y40kE9X6BuBwLKrwMnOVrC+/0KVR6KeYu/wBYZ1mv\n12LVVM60hfe5qroKCFUVPEnojUR9i/r+LHy65JUuqLdSjhhFnCPvA+bUKRDFuzYiJCn53Jqbq5XR\nI2eJ+pnatj3FXKcaVmJqHbfwzQARZRrxXFam0K7WtNOW1fYavUvMHLAK2sbS+i1N60hYVEKEE+Zs\nWbXUgGkGEDQFzgV5ZEnAkwhUhcIifMa27U+L3v3da/YhMs0T//zb3+LZ7Qu++sEHjOlp0/Wf+9rX\n+OYf/inDNLC5vmWYjmgUjbFCFbAWS0B7Sy6aQ4yQE229bv6EOAsymDMoqkCvxnsWiXoT27GcKSnR\ntJZc0yhPz/ppbHC6JvM81nvxxfZvAMqex6m8Rk0SU0lQZTJaVe9XZnS2pLwsnIViEjo7DsedxL57\nx2438Ou//nf5L37tV4lpx7OrGz775Ad0vmHdr6WwDIEUM6aGwjRGeJKH4cjhsOd6e/OoUzOOIyXM\njxB0WQxmGu8pc2QOEW0dtpWY61ASRWtClO5W16/BNhQ0EwbtOoJdU9yGmxcfMytN33TM44x1y3VR\nhDkyJkFelvhocQlQwjkvUnw7LxZpD+92DGMk25bXD3tcW7i2jnw8korBa8fqes3n7zrMvcM3DR9/\n/DF3D3vyMWL7nlQKwzhhqxf4GDLTXMWuGdgdTuigskY6MXEgzhNFSUH10e1LnLF8/vo139j8HCUW\ndvdHnn3tS0zDHevVLWkeSWHGGTBeYbMFY8Vurdo/JgpttyKlgLHispDiOQRh+TvnDK0/AS7GGHSB\ncZ4Ic6RfrxmnzP3dnu16xSc/eM2qv6ZpVxyHPcc0CJ3KFK62VzjXMO2PjOPA7hAwTYOvXcNbb8ne\nCOVDwfX1NVMaTtZoc0V6sYbGKHRIjPuZrmsxnVgPGrxY8WU4jJHehNpNkTlMa8SOrHbbtNbi2tI6\nUG2dS+cK0ohGRlIgDVEj/NsinRfbOEpN43z1+h0pJW6fXTMMA7e3N+SYYZSiMFZ702kYq4WYIcRC\nLjUSOwbmCbS20hXWmlQiCuFr55TJ84Bz/sSTXywcS/Xvvup7tDUCOBkR+8UoAWPFSMx5U4W/4l8s\n8+rxMLLdbJhLYQ7VPYd84uqnMFNSIeQgVI2ChEaVQqM1+xJqEe/p+hbrHPv9HqMV3rUnrYFsaqsV\nprGUIv7wqRa7WsNq5fFeMZbIPEdiyXTdit436CIOSm8PO9puhfItXhl+9KPP6LoO24gRwLIuFf04\nalnQZNE5LGtmSuJHPE0B5xxtu3SQZD7se3EU2t/vmIcJpeD25oq1c+zYydzlPU3TME4T9w97rl68\nYHu94vBwz3HY8/DwINSP8YjRLX1/Q9EdH37pBZ988o5h/JycothYTgXb+boZUWKZF2ZMB3FIzGk+\nxW3vj0e8FarHqvHkMD65HszziPKe1rUoUx2CjCJnAdJS9ev2jSMUqTOa2tFS6unC+6njJ6JIBmSX\naB3Wt2JZ5cQORdqkgsbkymt1+osXU+F8KqJWRKPO6G4BnYSQn5QohilZVMAxnXiBpRSxEbHCC3pU\n8CnQ1siDYSzr7Yar2yucA1OUIEc5ntopczBgRKAWlcb7cwG3kP+NUmStTrnjZ1RpKRQFdVhSglLl\nGC94mr6wV3sKhX0fAb483ucOP277c3rNy69d/nvZlVordm8Ll/fSpUApcW2Q/y+LlNxPWy4StdQZ\nUVkmgOW1LlOkLmkhJcukJ6ihoIeLLdnys0+jx4//L6+dJQL7x0cUVDSqVCcUdeF5bIzCO4fVCqs8\nRluKFsQxkVB68W7mdP7G2EfX9fIznT4vhsXFRCHhKKt+zbSdyGFmmo/MJTNOE692D3xwe0PXr574\n/PDx85d8+qPXvMkPjNMDpYiTg1GWjGy4FBFnNSmairoq3OJUkktFZ0EVsXhTSharU1T1QrNwy2L1\n4x2CcjE2lL4YRyVCRfGfGqenO2F1vYTVs/gyO/N07bSgQ1oQolQpH8bJ5ffKoVVCq0zbWaZJs9tF\nfNfRTj1zOMhzERNN60kkskoo4yl5IsVC69wZbVWP/Y211gzDQGNMvd+VIJJFwZ3jJNxYYzDGo73Q\nLFIqGKcoRQoFZRy2awlFUZxDNy2pOBHUabEZm4dZggqiWI01jdAynJKFUVtDiJGQMiEHnBPbqljT\nHBVgTYN1HdlH/FqTtSPVYjaXSJonrG1lHGpNyImPPvqIV2/+EFMMxBrjnBLONlAghCA2jVroZrLp\ntSRYolMIKVNiJBUp8KdpIqSZUODn/sI3ePXqNZt+g/U9OQVKDmjlKDqidCGrx6r7IjC6pGUqS9Fg\nvYUoQufTGDqBEeKgpJLE8Zo6rqzzzDkzhyDPuVEQEpvVmnnOTClj24ZkZTzmlAnVDstai/EJYhCx\n0rqn7Tt4uKeUwpCTCLsrVU5rCXUwpXpdO4XJAh6gDalophxlCrJaUPiiK5Iu8773npTquKspYgvw\n0DQthYj34kRUEEqAilbcBKwT3+8cq/g5Q8rEIA4e8kw5QhB/35IV8ywFZazdJmMtlExjPHEOxJra\nN42BpuvBgqrjIU8jWnPylb7s+Gm98OTLo6+jhTZSrCbFeFo7Fgu4lM+pvMCJSuO9xxRdhWgZZS0p\nRbKpc8dJ4lNDjops6FSRcSlzfKlztwAxpbpQaSN8YY1iCiONcjUBMlKyEgG/FdpkjLMEKWWxZsyV\nQqm1ZRzE9WHbrUloDocDqBldHF1rpUsH0lXWmra6WZRynlMv107MWVRvtCOnQAwZRSa7ajUYEsMw\nsNk2QjnI4goRp4nhYY+tAV9LV8x7XwEAzTCMlJTpW4+xG6ZBaD3GL2CPxbUd2liev3zJPEVef/Yp\nShVaKz70D9OEChI+lI0GJWDK/viAd4bWV4cNLfSazUbCVJ46jFWn+yL+MImchD4VOdOqdJ2H5JlP\nqAy++bOXvj8hRbKia7d419J2HWiDcxpVRBxUKCSnyGiJ5jRP++ZlBVEpUmNx2ZNTbRDrugPLCdNo\nspbFoURLCUecMZg8U+aMDq20OVsprhdk03nPMQU635CjkNK/+tHPsrZXfOv3vsnh/g1tD0nNZLPC\naoPGoEtBGYgqCSfQGkpOEppBZi6Qg3jkLvZpOUcKAaUTpSgMlinPpxYSLPQCKFpjKnKsF46ufhxU\nkEqWBUx+47RbXxb1xSoOOE1Wy/dPIjbriRetYUFBBNmV35e43VR3s0srBc4FYqoitgVZtDyOng0h\nEJYdch3UUVPDKTLGOVyN2S0n9wp3miS8ay921hnnuhOiLJ/jfM4lxToJ5RMNIFXLuuIMeSm6SkBp\njdOFHKHxt1zdtLyxHbPX+M6hYyY7i0oPjDwjasOqDTQ1o6kUaRmmGpFqT8LTS9swINdAgll2zs42\naG3qRqmgrOH69lZ6GwrevXvDMD4Q7+74doTt9vrJ5+Lnf/ln+eHb77K59fz+7/8RdvWCYhxjyJjG\nMucjbeXjlwI6ZmyMYhJXENsmLXoBdEZKnYJKIuDQRnzGjdDyAWh9h8uGpGSMpCQ2gVZJGEAKizjS\nCeKXqajwF1MuSin0syIioQmzjqScpdiv3sLTJO+jq5+yVhaDqu1qwxhHrHfMCYxuuN1aHu7e8O7u\njp/92s/wR3/wOzTra0rMTNlQ0GhvCcxi69V5Ss4cDntymmiMJhx2xJhZrbd4Y3GrDePwcFa/18LU\nFAXOYbxlGBPdekM4PtB6z2E8kFKmaTzDMNBtVoxottfXuPWW1fYK/fLP4doVOckzNE5HXF1AstYU\n4zDeYI6RaUyEOTGXEdeM6JhQdqZde0qKzIPMJ3p9xfbqGpNmdvf3hPnITXdNVjAedrzd3ZNS4sUH\nX+Ldpz/E2Zlf/ld/kf75hr//934LYyK/+Au/wA8+/Yzv/uAHxAIlNeQccVKjikDIzwyHA62x0v2K\nmSkUuq7lOA54DgzDxK/+5/8xv/af/ip/77//b9i9/SGH+5X4GM8RrQpd08n9VZZhEhF2u/aooiXA\nhQFtJWHLug5jZsJ0JKbCcRwZx4NwlztHRpOiFH3WSGSvMYamNRzHSNKRYuE4jbRXKzrVsv/+D2mV\nYduVGpzznHfjyDQf2aSV+KobxRAG4i7Ttx3XTctcElYXxhiIaqJLtbivHQhrNO2qQe+ONM7hO0Mk\ncZxlvnr39i2db05uPmqlcU5BGTEaDsNIY1fo2mEccyTME8qLLWYphev1Cq8ND4c9pWjmkoVGlSSd\n0tkOjOgkjLJYZUnJ4J0mJwfF8ubVAW89xYgt2jwmNJbiE8V3xHGk267x80zGogFHQqezM04IkRQL\nvrEou4AakaurDeM4s+rXWOt58/CWnGbmIIWP0hpvVpQcJXZYa7RpyaUwjjVsRDtiKPRdi+tbDocd\nUxjp1x0xZXzSFFeYCWhbKAlUsZCj6E6ArDQxiwe3bEYSJSdIGqc1JoOZC8pqlPbgJXchV26s1hrj\nKjg2ahocPjlCynTekWXfRciJ6Xgkxepo02yxztO1DckVVv2Wtm0ZBnFByb4TK7hxwlpotOZwkK5Y\nDIGUAutuzTiOdL2mye40/0Bms7qimcV1Z9g90DQNrW1ou47YeN7ev5X1vdpzLr8nxbJGWShlYj+M\neO9p1xumoGiajayvOmH0ERs8H/+5j3CbnrdvXmHmjF5voWjK9IZxSuwSONewbVs0mTf7B/Z3M195\n9iEPzARnccOezm7YbJ9GfW8qmh9TJlIwWkT9UxGv9pglfKWg2E8SU70ympwS+UlA7OnjJ6JIXmxe\nvGvwvjnnitfvL7wjWxPzvihw4BKJywu3dJEIlXIq+mLlQVIkXWmKMyFFrBYXAi5QrkevuXBsa8s3\n58xms+Hm5gbiIAIwXdEKXXlDFSWCaoWWU02nkx1fVo4cE1HgQrxxTHOioaaGVZcGU7nVcl3OiO8p\nhAD5G/Ij948f5/yeKQfL1y6///55n/7UkAO5jgjiEOMJ5V2ujdWGvAgr61s/KnzK4/e5fN9SKRgV\n1Pmxz3FZ2C9UkUvUGM6IwvK9pxDbp8/7TC15hJ7XxTNrffLURmucbwhVOCkBNy27wwX/Npcanwul\nKOHCGSNI0smkcUHyL4WF6uQZnEuELEhjrvw9ay39esM4T8whEFIg58jxsCPFpyeTlW34ix9/jW9/\n8kM++8rHvP50jzEaby1zjHSNJ02DYE31fsacK3os9JachY8oiHAR1F0rSY7USrzJUeQcoFJYTFEV\nHT4nGya0BCIsNA2VTojwv3DaKtJ1Wa6VVQo0ld/8+FnVWoMykCVJUWvx0MwlkDKUei2NFWTnn/3u\n79J5xYcffsgfffOb6AKd7lAl11RNRdetoSRC5UeKB6skRSlTo91zJqNOG8+lqyRiYYdGk1ESkVsy\nbd+fuIXKSHplq8A1AhY0bY9frUFpjscjDQ7T5RMP2jp3mg9jtYWUv1N9JsW73Va0Z0m+ijkJpcat\naJ3l8HAkxsjV1Q297bjbPaCM5t27d8zzzHp9S9/3TENHyJYPP/yQn/ryl3j9+jUxSqGsjOGP/+hP\neP7yljC3HA935JzpuoZkDckhnrjIxtlhOQwDjREk61nf8Tf++r/H9dWaw2HHpz/8Ee4v/yLOG2I8\nyoat2mkuAIZRF5oRY5jmOu9Xh5wUo2xQLqg8C53LeidjpMi1CqqgjBO3HeuwxgMTWive3e1wPglN\nC4WzljkW0JF+1TAHLd7+laaQQgTt8dowzBNTmIlaxl3Xt6KIOW3oRdtwPCa2VrD2EIJsALpWKGAI\n4t74RiyvdBA6oJXuSdNkQQ3Lucu13H9dKQA5Z0HwF9rce7oQpRStb9C6MB4iMUW5X6qcBNmPOnll\n8SMWFFHrszCcSnGw1koU+2lcivgQqM4dVMmzAAAgAElEQVQ0SVxiZoVz5dwxTYn1aksqEmahtUFb\nc6LQiS+6cFDP83siBAmFWtb3pnGkYqVw7ETcBrJukis1rJSTCHyhfMgcUtHnSmN0zoptYxakX+bo\n83md5/Aa5MJ73UFlOFbutXIe74Vzneq6klAEXVA5opPGe6FQaS2UmmEY6mvlU1fuknoaY6xBHwlj\nzclfXWvxVaYchL5hLcfjUWg+WtOvVnS+Y53Wcv4XVLFHrljqsSBfaqHuAvmX8WytpW861rM4V5R8\nkLVDC8KfSeiazjvPM9lauq7j4TjL2FG5Itlt9SV/mjmwfMaUEqkotJXxbmqQjqT11noxF8iJEmXN\neLpr/PTxE1Mkd+0Ka10tgpGdKlKTKMTiZWn/8vQ1k8AGijxYReGNtER0LpAzuvXCay5Z2n7aoHpL\nMokxR1JQuADeVwuayu9djkWc0zUSczqPI8Zpfu7nvsH3bMLHI43VpDifhDpFKxHpVF9AXdSpSJbJ\nXqOtQi3p10aji2flGlbO4U1E6ZmIE9sttYgI6iKhz8l6l61EePyALsX90kK6RIuXn708LovSS77y\ngtrO84xT+sd+JpTzZmIpWC9dLC6P5TMsn295wOovScS4MTglBd1lITyWxzG1i/vGU5ns1trTYnRp\ngycnyum6eOvqODKnzxdtRemKBgyqcZjiWF3dMh0+kxaTMegs19drg6rWgzJRexFs5iRcvRxwS6FX\nRQXL8ypBODJ5yAakiqGKGKFLm9XS9FfcGk/TX6F9x263owyvGI77J5+L//nv/B3+g7/6b/Py5ccc\n/+/fZno3cHf3htublzTKsTsccNqS04GcZ9DyXrkGVsmtk4h1SiIpjUVS4yISiKCS2LTXjARilNhT\niQJOoA2qGKIWvn5YBG0lo7Q4lnxBrtJ5vBTHoGvaGGCLQmfFrNMJcRc7RA2E07gqRayrFLlGxBri\nMnYyPH9+y2//g9/m8O4tf/M/+ncZYqStgqc0Bxrv6FbSOp+mifkwiHdxtxYqSkp436KMJeaEQTxR\nlVL4RkQ7680VcSxkErkEmjaS9YjzW8Iw0Kw3ZApzirSbKyYMzz76Ct1my+//8Z8SM3ztX/kG1ren\nlDG3qPLf2xjO04hWhq71gKbzjQRn5FzTqQpjqsmBhz2q9TXMYOBqu+X+cODzV6+5ef6MzXpLCIHt\nVc/f/81vcntzxWEaGPY7fvlf/yvMw8hv/O+/yff+5HukUPiZj77E+nbLu7tI4zpCsOzv70i6xTuP\nJuKsYX3VcdwnrkomhpG/8ou/wN/46/8+X/nohreff4+PXtzy/zL3Jj+XpXl+1+eZznTvfYeIyIjI\nrMihqrtd1S4aut1tmmqXsY2N6QUYCeSN9yyQ+B9gg1hZCNgCYmEQEgIJyXhhAcbQxkNb7barurvK\n7srKqsysyozhne5whmdi8XvOufeNjGw1uzrSq4jIvO+dzjnP8/t9f9/hkz9IvHp+y9vvPBWP2BAX\nbmnVKZpGUtmmKB73dVXj6rcYQmR1VhHjHpIiBsgqI1R7LaIyEq424kCRjITIKIVRDVpJEaJMpqqB\nkEUgqRQxjqQQeekN3aZhdQG7Vwe0brkNexJwturo6oqwH+lv71g/PKPyhgoZnY9Xd0sQxJFelxl3\nPc1bb+GUIsURpy1TL766q66hqhxVZSXm9xCJY6Yfp+LGJOuy4QgGDMNA30eaTtwI+jyiYgJbqE/l\n5lZGC5JuZKoVDgfClFBIuq1Smf1hy2q1oula/OhBa1JOhXKV+eTTa7qu4fzsAkgM/R2+D/gwLRqZ\nZf1NQqPAlgJPZfphosqKtmsLkOUJuSZnTV1JUT0OI84VWkbZe1Cy7+hW7DxVFoeHlBLOVAxDjzKw\n2WxEV4Iq+zAEH4ghoMs+NJ+LpaAPviTJVuX7HIlBoWJi1bT4FO8582TmiPpAziXSvrKkjEy8jMRc\noyQpN+vy3gs33FjRX41TIIxC6TwMo4gxrWOYDuX9CIXRp4iyjjFEmqqhUoppGCXCGiXuOEoSYF3d\nEmIuMdAKWwud9dBPfH71EqOEJ+y0IZT979QJZvFuf43qajQ4q5fHxzBxdfuCi3fe4+HDC5599Ss8\n/+RTfH+FVo52VZNSZugTZgS90aTsWTUrzEPL7X7k4aNzgpLgLlzFGN6stVGIMNz7SMwZV75PqzWh\n+EZDJoXAypRJBAqMwv3/KH1/ZorkqqqXi94UTz9dFJAgXEid0xcK1/uHYFJWKfE31YLM5ZQL/B5R\n1grylCHHRO1qlHLkJB6O83jiTUVj2zaMh16se+aCLXk++OA99tefE16M6JgIU49yDtOucdZJ0IcW\n3+I5RCOlRF0UmNYYPHLRx2lEV5p+ew3GcogG02xYXT6CpUsthUuSRBxVRE8LWnKCvs7vfS6+pME+\ncoZPOcHzY+f/fk+od8Ixnd+AL9y7+fdyzmXcJ6i3Pk2ZOyneF/TXfDHYZO7wpBnSWGOpjb0XFjEv\ntimlkv5zRLNfR4JPG4bXm4KZXzYLAHUqj1tGGBSkAYgSHiEcd82IgqqMqrwo0RNIsZwzKAm+VKX7\nEZpNZu7w5olAnl+ImVYy4Sf5Tq11gPhhK8VSENVVTV1taJtGkIDmlp/+ZA/pzYvJ3/5H/xB/0fLt\nb/4q/+6/8htcrL/LT69e8J3vf0jMllV3IcVVP9CPd4xhIGewSjaHXDh5KRfKSiqhM0XokmMR7JFk\ns9VOBLDKQvZkHcViK1OEI1l43iWFUof5rH+xwTk9YkwCDicJ8SALLUeQWMoEo1z7euaSG5mCKHES\nCEFcBqzTInSZAmTD22+9zSc//Ji/+Tf/Fn/u3/wLXL98xdUnn+KLsntKiRdXt8IlrmrCNJIA5zS2\nqtDG4YuQKGcljiFFc5C14W5/oFKSvBlCwjaORGIi4bqG9vycYZoIw4BZdWQaLp++xx/84If8/oef\n8fNf/wba1FhXqClEKucwhT94el9YBdM00jY1fgq8evmctpO4XOs0Pnis0lhn8f2O7C3nmzMq67i5\nveP65g6jNVOInJ9fst/v6Q97/tQv/yof/eBDPv/0M9qzM9bripsw8PStRwwHz0c/+ITrVzekjz9i\nipmug+0BzjYd24Ns+r6XwJpdGEhjpk8Hnj16zF//L/86IXkapaibDt8f+Ln33+PjT37MxeU5q86i\ndYUfp2VCp7Ut6YRpmczZRhwIQsqk6PBBfi/HQPCZlBTWCMiRfMJ1tdyROmOcRNdmRQnfiaAVMYgI\nyDjDo0cPONzu2Y6e1A84ZbG2IvhAsxJxVBwHjNbYugjIDgf8OC3IYdM0aFcdrevKGuyU5na7xSi4\n3HTCufSRxhqsqggp4v3IatWSqhbvI7vdhDGRuhHbv5glQrppmsXZAC1FzjQOErRRAJbZAYB8TC5T\nMVIhLWbKUTQwZEbvSfs9HS1h8tSuk+u/riBljPUkFDd3ByCJ8KqV5ndGkg+HA8ZaVC0o58yV9t7j\nbccUIQ7y+TRAFEGm96M83mhpYAoqjrY460g5kxb9BCjrOBwOpJRomoasEvv9nrOztQRu9CNVZfAJ\nBkCVycJYXF7atl32FB/lR6aFNZUzEITXG8l4lbG2WpDr+Xz6lHDOgLaElBhDwClxGMKIeQAlwTdt\nB9k7tKDE1lpSVzOMZR9wMjWqyx6Yo5frHk1MwjMPKeOciFinaSIWnnvbNgtotd6sOBwOBC81jjaa\n9cU5/f7Aks4Xju4ZwCIClcmLNATjOC56JGKAWRytJIHTqIQaD9RNx/vvvY1h5Mc/vCYrhascfgyM\n+500ZDHz1pPHbPuRx0/f4R//49/hq9/8eerWsv38Ix6en/Fo/WatzSJ8V8UjXwkVtXKO6GOpBwym\n0oThIKmqVvaG/fRmMeCbjp+JIhmKkGIOJ1OF74NCp+PYXDC6N4vQ4IicOgU5aaFaZEhakZDY0TRf\nAFpGdjlosqwIMvZOHpXdMgoD7hVl82Y7v17KhVulFM4oamXwWV47xojXmkZXaGPQ+jgWlAtPxgBJ\npeUzWWvBJEHl0gRRxvU5piKeQYrgLEiALp28pnyenBfv1tcLZbhfkJ4WrXPRf9ocnBbQ+jWKi8p5\nKcbv/V4+kVOdiArn51uK7pPv4fR9GiWbnCqNzqx0PhU8nr7f+c9Tu7vTm/z0MUvHn4+c6dkuSBbe\neJ9qAagMRlzO0JmlSEYLLcd7TwgKjYzrjJYYUK1lIcx4lDIoBXoWjdzrNwodAYg5LONxsjhqyIhB\nLKrkutOSvFiKoU23QqXM7uIxd3c3b8wmqpXln/z27+AOkQ/+0l/h8YtzQvScna+5uj2QtKHKmSFJ\n2lJKAaXs8VrJs1hE/rTle1GIGFZYy8XCjLkREQRjdg9QSjQFpPmzCE1CK3C58K6VIusvL5STErSP\n+VpGqFjk17n05f/lY7SsUjIuzMUabC74daGGnJ+f0+jEd77zHf6tf+/fpt7vhePGsFxPCbGYzMqI\nRSViMaS1pDrmNItEuOdBbq1lt9uRlQSgZC1WZykHqraBrFHGUreGYC0Yx9nmAc9f3fDDH/8EXbXs\nDyPDMOLaqTTxGW0Vi6j0tKlNiXHsqesWhRbOb0Euw+RJUQAIjawrCfGmnSaxiarahk23omtbPv34\nE+7u7mjr0hwZW8bliavnz9nt9pyfnzMcXhKSNEPr9YphOGCtZVUnyFpCPbJD66qIijIpjfziV3+e\nb3/7z7B+eMZ22BGmyM3L53RNS/fkCT/99EPutles12vhngezWHJlBPzIWezfvPd4JrKqyQm0dmgl\nDhc5SoOqlSURyHleM4SzG3NC5YTKkZAzVeOIOXFzfUeebOG1BZp2jU4d2/CKpCwxSFqo0hNOV2Ad\nPnvREDi3AC674mLBfM1ORyeHYzDFPKkr1pwBxr5Ha0Wz6miU4vrmhuvba2rTkZP432h9dH2a6T2u\nWJc2TUNGGHjDNDLrIGRdtUuRrJQiJk9KEaPmUXssUxJxMZr3AgnrKZHeSkS4600HJPrel3vb4UqI\n1vx6kjxY4ZUnk7EpCdVl8phqhfcjccycnZVAqhRAJ2IaIZXnMOLy0I+eaRjFNtBalDOIviSJBViZ\nxhnjlvVfuOeGFONiWTnfO/MkdtnvtAYle3RMIvIPKbJuWrTN9Lu9pHmqfI/2MBek8hy2nJPSsGdN\nNnP1wDIR8lb8rnWhrGmnUcqQktQD3kdy9qhKIqmNkihsawzjeCvfaRQEu7LiCDJN4nAz5wqcfrbT\n6yToIqzPmRghFcrejCJXxeHi9DjdgzVKaCvzPpalJgmHAyRF11Ssuw60KXtImTwWq91xHLC2Yui3\nQrE5P2PvJ+pVCfoKkYtu/aV7wnwoNV8zr72/Qs1MKqGzEFe1kQn8H/f4mSiSlVJUVVP+UbyJQyRr\nRW41RilcTFSzsO1L+CTaNCgClc1kC0lFwoxGW9A6o2LEGU2KY+neKkLSTFNE2UjoDT73GOfIzuK1\nhEJgFXoUpHMuRsmSAqZjZFUpkoVKa3Ft0AptG1BINKpV2MrinFhAVXUlyuU0EqPHlW7UqkSOmna1\nwa0usZuVjEbqEpqxXASz/64uDhN2Kewr29wrCFNKROOXxWNZNOK0+DqqMjKJ4ygLpj8upinIJjPf\nYLP7xFjEWKcFeUgnlmtyNyzf19xsLAEg5b+rk2I5KYUGNBlroautiC3TBErhGo2OEA6v0SbK+5iS\niNyiOhbOdQnOyCkWVKUkLBHFdaDwvmtjRVRROMKV0qyUI+SEqhtyrglTxqJRzQZVP0CNH2HGjGqe\n0ASFy+JBXKkKtEVpQYByaR5ijvg5wY65CZMNcwqlSAdCGOmaVhZYclEhyyHe2y3oClcbVhae6HdY\nnW340Rvui8vzB/R3t/yf//vfIeTAv//nfpPLzy/wuwOvLrb88OpzsrUoC029Znt7h3EKnycaW2Fj\noSZUhkDGpZLYqBPYTM6BrERQZBSk4ElGodSh+FFqUhQKikdoGdY62QSniDfbew3Qlx3rWAo1ZyVQ\nSAu3V5Vr0xV+rtaKOAktK+aBKUZcFi/r2jp86kGfga5xTaDfXmH0xDe+/j4//GHiP/9P/yv+9W/9\ny5w/fIQ34B2Y2LNiRUgZX4GyFqc1zuqyKU1YVzh8ShF9KpMAOZquZex3NO0ZKtQ0XUdVa9JkME2F\nPlvTx4gOnso6YtXyj7/7PYKuePHqOeeXD+j3B9FudA7jHPtxKhu+kU18GlAp44cXDLue2HfUzRq8\nwlaOMHlBvUgMhy1EizatxIn3GR0NKms601FVDT4npjAQ/YHgM9/7wz9ks17z4Oyc733veyQ0rq5w\nLrO5qNk8OuN2u8NVNdpZdrs9zjYEH1itOkmEVYJQ+0nzwbvv8x//Z/8JD99aM6me9kHH5sEl+/01\npso8fviI3/vdf8CmW/Hs2a+wag3DYUuOiWE8sKkfYXJkGLYEk2jqiu3nn1KdX6C1Bw0h7NnuXlK7\nCusyU/B4X7id2TD1wttUWFJUdF1DDeSwQ3UarQde9YbLy3OheY2BbKB1FcMw4dNEvVlxGAL9di/c\n9FxCoXSCnNBR0dUNZNEQeJtpQhY/HSVNcUjgTSTqgckPmH1AJYOr14xKY6J4OOcsU9WYAylrunWH\nNp6QbqmqFV3Xcn3zCmfh889+wub8gstLEcRpbVC1ISUpHLpW1v79MFDXrUyO8kjfH1DJUDlbPHFD\n8cSXa33VWe7u7jBOU687Ju8Fadeapl0LH3nohXuqNbvtvkxiW3LKtGtJ5Qve431Ety1j8DSthGNs\nt3usVVRGUGK0WYrOyYBKiqaqcO2awzSy2+2W6XLbrQnA4CGOPa6qMCjaumMaJiAz9Z6ggeBxOTMh\nReEcFx0nT+0cQxLLTRWFZjAOAaMDVimyVoTk6dYtMUeGYRCHk9YRQqKe6X9ZkG+rDFknsmvwMWMx\nrOpO1qtLEbH7fiD6QMiaw35gtVqBshIYNwZcyhwOB7quo21bvA909owwBbqm43DYgdF07ZpkEoe+\nZ9pFiRcfBtauYswJZy1N3UjTVpJTVU6EIPWNzgmnFVevXqGAR48eiT3iSqwEq9m+1tVkFZhyEWVr\nxTj2JGNJYSAPN0xJU5nMo8unEuAVE9WqIUThHz979wOGw8CqbvgX3/sX/Eu//qt8+E9+l/pr76Eb\nx6RGvvpg88b9IIRAIDOU4DUp9AMhjEIpSkKNQUGuFIGS4mwtF+szfvClO83942eiSAbpLESFo5Yf\nNfNdVbFNUcCJrdLrh/ghq4LMlUL2DUjm8TVPEdfjf0/5hLAuxNHSIUaUNgtKBTI6m26LWCEJR+sU\n7Tp93VPUdd7UWXi1R06UchVTCIRhpAkJ2x6FQIm5Wy2fuRSdp383ryG4Mko7osZz8WiVdIJ5FuXJ\nm3sjCp1VEQySFyu+0+c7Uhnud+hHLvTR3/oeUvvaay3/7Qvn5w2POUHHXz+vp8f97r68j/zF35Xz\nXVwaAOIs0hTV8uk5nEVsCuEzG+PQ0WFUhVIWqzwJTdYJzYktXRbcdRZ9vH4sn1VrKc7z/fc4I+wp\nBVKhh1hr2WzO0V/SHV9fX7Oqxcrn93//e/z5X/0WD995ysNPf0p0hvr2Fd5HfD/gp+H4Xgt1YUaD\nhXaT0KWZYG6isiQdqnzkd8tlIMjpfE3M53VGHVS5TzIzuv5HiymU1iJmXJDEY2E9v+dTq8CkTr7P\nk+fWxhSUKBcOYVnkjebnPniXj/7vf8Tf+39+i//gP/yP2B32vHr5GZvVGftXW7S1aGvIekYqrBRe\nUcQ92paGz7nF1hFgEpsHTOVIOqGLy0XlLLv+gB5rXF0xp+Z98vyKh0/eoV2dsTt4fvDRx7z77tc5\nuzij3+1p1ytCDlRuQyQS57jvJOKt2tXifFM5bFOXCZO4rDRtw5mWUJC5OZmKsn8Oq/DeU3c1XdfR\nFO7oOI7klHj54oWMzl0L2mO1YrNZ8Y2v/xw+JFzObLd7PvrRJ0xjKDaYE9/4k19nOPT0h4lf+sVf\n4Zd+6U/w3vvvSBKpqchZk1Ngf1doLUbukxcvXoitnq3Rr4m3Z3eemWtqS3CHco6ua/GvrWNLomrO\ny+9iNCop0EcnHGOsuBG0LWdnUlAopUpS53Hql7WBlHHWctvfyFh+vcbVtYzqvUcnKTwqV+6XOBGj\n2MspZI9xBhrXolREuYxzNUThFbtkWLcdTdOgOCKd0xgYhgHrIlWjyJIXt7gynJ+fM06ZcfTknMT/\nOLM4E83rYs65JMNmUgw0Xcu09/TDQHsmVKxhGNBay3tY1mODNZVoF8wslit6lJypmnrZe5QSe8ic\nM9MwEqbIer3Gupqbmxtigq41J8mukeBDAX7SMlq3Rvy9dUgYo7AOrJU1RezwhHZUVRVJy2vOqKVT\niGjaaBEjJvGPs7VlHEfCNMmaN3P+nS6Iq3ghO2eWmiLnLKI9n4RWmGSCpcqk8YjWF1//AgTJBLCQ\ny2Zr0ySaK9M0BC2ASVXZBXGVe5RCURE/aQlCy2gnwrnVakVKgXEUOqhra1IlHHg/iWZiPt9z0iMI\nndAYieH2XqhoOR0FlikldrsdMUa6SsC4uWYQ2ts9n4NlDZ7KeRiT+HWnFJjFe3PIklWIj7RSPH78\nmE8/f44zlrZdsdsdcN1ITvYL+/myH5T95HQaLU0niIXqXAtqKZj1MSxtvs//OMfPRJGs1FEsJf+Q\nrlFrLbHBqOKrmosZ9ZcUybqIdLLBqPuPkQ1eobIWa7aygTaVoWsszlmslhsoZKjLpltZSftDa7QR\nm7aF1iD5O+x2O4ZhYOMcTil8Py4b+MKRPEF2Kagq2hCyFB3ey4KalQWnWa/P6S4fU188Alst308+\n2RzmRX9GU+ex3SJKUidjuBBI8VjoiU3NbDl3jPI8LX5Pv7tYxmZzYSGo8XE0flS7OuFIa30vYW5u\nCk4Rw5kW8rrgUMZqeomMPi1O581NBFrHgnjx2DTSxFhtCOl+AbV4XJqyuOfjKBAkjjRrQcDnzyki\nr7i4Asyv09U1oaqYUmZK0O881I+oqg1JWZzqScCUDEYL37nKMvbfURwkinhKZ0gqMTCSlCLmTDKK\nkAIu3BdiCrJfBETI6B2lcHbNxfmb/SQ//MFHfOMXvspbb73F7f6O/+Jv/Hd8+0//a3zrm79E8/En\npOi5ubni+sc/IvieqrIYZ1BefL+VotCfsrh16OOEQtvi9T0FoQCVa+9Y/s/nG5QKKG3EhzyLQ4DS\nVgopvtjcvH54AomEzaXBSPdpNvN1nHNejPVBigKTkLFgGU1HIZyjc6ZdtWinefXqBc8eXvAXf+2b\nfHp9xf/yP/3P/KW//JsY3XA4JNrLS169esW6bjhbb4jjgA8B7RzJR5ytjveMS+ST5rCrarEe69a4\nJHG2qq4IfmK9esxu8qRJcxgiH199xrXacPn2W+yy4fH736A+u+H73/8+nz3/jF/8k1/n+uo5Dx9e\nMlRHL3HnatCK/taTjSY1lq6tqbPihz/+IW3b0q02DMWdw2vhzyeFeAULd4SURSVfV5brqyvW647V\naiXoVbGQevr0KavNJX2/xzWKh48fcnt34LPPPufTjz5hGr3IJ73n4sFTLh91rFeOv/bX/grExKNH\nj6m0Zdvf4KoL/CHTNB3DzSv++Xd+l7q/Y7CBR0/e4sMPPxTQgAbbVGSPFFpO9Bjr9ZoYMtMwYLuG\n26s7UJrz9bqsSUWzYQymNmQvo2mlxU40xcQ4CwIrByhGn0EZrFvRNA7vZf3qhz0pJbquJebM3XZi\n7A/kHPng2TOU0by8uub29oaqW3G+2bA/TJCh6RpyTuhJY8hlBJ3JSmNdjR0HlE6YusXHjDYVq6rB\nh5Grqyvq2tEUnnOMnqo2MqlMmXFIrJpIzAPKaPph4vziMcEbQtzjfULriLXiuBGVXtb8rGCYJBDK\nqcw4TijrqLVQGITOIWEku91E266o6hUJxaEXuy0logBCTEUMaDn04/0JohV+a2MqslbsDwNRg92s\nsT6KMC5GmlqjdCQUXqmfIsYkrK0wJqJSRMeJnEayDhgX8KPm0PecXT6gMg3XN7fUbQ3OkHKxkVWw\nj6OkrpGxzpD9TA3W0ggBzlgimRS9xGNXcp6mqUdr4WLP5E/vM1XSi5xCa2mwhhiK+FOyHkKKJB+p\nrMO6GqXM8r10KOIo07e6loCbVIrieb+TvS2zOetKeFdmvV4z9iPESO8nstG4riDEquLsfCMUJO/x\nIeODcLtDCBIv3bYYd9yjQwiyZyJao4uHDzAottstWimmmx3UFampwAiNyzkjIKaBwgUVbncK4jlt\nKggyvTdNXZyGNJU1SzM+hcjgBzabFR//4AecP7jks88+41lzxu5WKFhvOqZpWtyOtC4uVEpBcZIK\nQVJ6cxYHJZkaS9F9+BKR+5uOn4kiWQpJ4QjmEqBgKOI7ThBDPBzl9l84tBEe+fJ8p6+gysgdFmGW\nylA5RV0Jd0irVJAthKM8I7sF8ZIi8ljkiVr8uDEbYwrkLxL/uUieO8gYo3SZFCVoClKMaImrRWvq\ntmHUYuLddC1VXZPy0fIspblQPNImZIM8WuekE+R6ef+la1YgFkJKHb/HUrjP/34dnYM5flhoEPPf\nQwj3uMKSsFWaGlNEXkrdc5WYzwWwFNGnDhfzvxduac6CdKv7xeLryPKCWssD5Hnkannt8V8c6ct3\nxReeS3EfhYZSaFvx3FVKmpyURRAWqyLE0bKMitczZMGLiFl440Y7lAqlE0+L+jspuZZ84UbPCYuv\nv9dUbIyUnvmLkHLA2jfbvqSQePH8lYiD2prPn7/gO7/3XX7jl3+Fr7z7jM9/+inVwwtqp4leC/qW\nJCiEnMlWRG6KI7KdUiIo+WR6af5Or53y2rEUAsXCboYd5u9Vrqn75/LLjqQkIru4Wwsvni8iCXPj\nN1tHzteMLvy7qLKEXShBEnc3ExlN1Tn6fs/lZsOoM7/zne/xd//u3+Vb3/oWVsPL61fsDiIA8qNn\nt99xcXGBqxpJ8dRHz2+swxQ+II1CIWEAACAASURBVFpT1eLnqo0j60RUBqLm/MEjtnc7Rq8wzvHi\ndsfV3cTP/do32Xnoqg5tGp68/Ywf/t53eHV9x/XVLa4y7OwddiU+1UszbjV1syKQUd0KZRxaT9R1\nvUx8ILHf9yitWW8kgng49Gjknh7HEWdECNb30jTtdrul4F+tVjgnrihNW1HVitura25vt6gY0SrT\nNRWbsxUhJVbrlq5tePr0Ceebju32FqMjdVvRtjV17Tjs9+iUeXXzgsP2mrWFGCYuLs9EPFeQqShw\n0LLZm4KWU9bfUOLQVQY/STS2rO363jqutUa7eTqXF2BhtVpJc3uQFLhxiux6j3OGwU9iHVhX1HVF\nCAmjPMoWd4q+x1YVDy4uqOua2/2e7e0tY4BMpO0clTFs2o4pSaEzhkRIx8jgVduhrSIMUtCs12vG\nyTAe+mVqBCznRRyXIMQoWSBkhn5imkRXUDmJNNdGPl8MmYmJHAPVXCBpiYc3WgrKkCK7w8ihH3n8\nlbep64qmrRmGgevraxG3VxtCgt6PaKdxBVFXgM5ZHH9KIa7KNNanXKwiCx+60NwwRrIA4rTwxOUc\nnQjhfCTGkaoE+eQY8SR0Lc3PWIq8cRwXxPrU9WV2rJjXArI0zFhxnzFWaDfee3GoUUhTHVPRX4ib\nz3EqrAvQZgovXi0/WmehjGpVxNvycxqWpLUWmlPOEigEch2giTERs2hYlFbUTVXe/ygOXmVirtCc\nnZ0xTdNRBEou1K9WAoqMpDTKZFw4yh7oR2l2u6qV4Jws+1Ao7hez0HIBqQoY571YfFat0GNDSGWv\nN2XDlfXWakkPTjkSg2ccREhpkGZTkwkx0Lat+FNrxcNHl1y9uuHy7BKFIwThxa+6I/hwejjnJIit\nnNMcy9pb9txcQBuFQWsJe5mvxz/OfjMfPxNFslKSniLYvQYto0+Noi6Lf8iJqOZL480f0Kgo1nGp\nQmsRBxz9MTMGoTcoVW7oDJWGuhKEUWVBmEIUkUUqhFVtjVhilYjpBUnOmbquF5Q3xoik8x5dOeZN\nU37KSJA5cc8uiUB13eCcxmhH5TS77TUTlgsreehJ1+U1Xi8k1TJCmIt1ZQynRWQIAZ3yUoyIK0Em\nFePzWZCmchlFcyx25ueMiwNDWpBkdYIqL7ZvKS6c5dl7clFTl/+2eMieIMlwf1SuyqhkVu7PN+vc\njPAlhmG5RFHO7iU5Z5SdbfIUqnRIgiQdyf3z+P+USmOMYSwq5plyEEIgeY/TG6xpwWp0BcYmkroj\n5BUpVhityNngvYxpp3INOG2odQWYElltMUYWnC7KKG03CRo0TBPGfLFQltVIEF5KNDPKg/qS21lb\nrq9v6fuep+++zdPukh99/wf81//tf8Of/fZv8K/+xq/z4R/8U6yTRlQNE7V1YM2y+EaV0WUcGU0l\n4qmy2cQsanY1j3KTvK0UYeFU54zSiZBGFIqUBAnQ2VLZE0HllzTAAMopclBkI9IgNTcfJ+fsOF1B\niiclyEYOonSOiOVVjJmcIiFDW13y4mqPVg1ubWlIvP/sES9vHnN9+4L//n/4G/zar/wKf/Yv/xtc\nX13xg3/2XSqruXh4zvMXr3jy7CmqEjeLlBJtVQnXsoiVjDFst1sqZdntDigjXN6xjxz8lmkK7AbD\nq7tr2q+8z6/8+p/g/OwBfVBcXj5gd3PDNPRs1pdcXT/nw49+wGbVMo2BerPn/PIttLb0g4hgzOqC\npnJs00jsB/QUeHJ2Tt/3jLstrqrww8BhHKTYcmIvWddiFfbo6ROYJgietx9L3PnNzc1yHi4uLri9\nvaVrHEPf8/GPfkqKMPSZYRhZdyu0cWwPIxeXKx6/fcaDyw3vPH1EUzuiX3F7fcuji3OUUtzu9nQk\n1LDlH/3W/0FF4GLd4Icd7733jJ8+/5wXz1+BTnSdTCCsgxgScZpwJT2rqipS9nRdR6UsYZRCtHIN\n1hgJUQmelEQZr5X4hGclmpgpePZ9j1IGYyz7wyTXRBXY7e/KupUYQ8SZjFYVSmVMUuJXHWF3c80Q\nIu2qY9O29OMAlYSrjNPEFCNVzqRoqOoaqxT7w54cQFmhEMRJBNvjOOC9p2krsbsLE7e3t0fefQzL\n/pNzxfXNxGZTg040XUvMiim8wjUdbWXZ7yaGYSrATBanEDK4CuOM2OXFTNt1tGct292B6+tr6rrm\n6dOnEuWuNVc310z9iA+KMUaqBJOSda7WFqNFJI8uU18tAvfd0IumxVlcEiQUYBonbg9bum4tCGsK\nooHRFo1Gmcw0FD/4cENtHeEw4McJ11UYZ6m6ls2qE4rLMNBt1mgfsKVwRckkuI6QdML7UWiUWhF8\nlD3eGsxMHUT0SjFEpkkEbE29YkpemrMk43y0IVdF4KeEq2cqw7puynoWpaTR4uYzI7bGOPEuBpSr\nqZsKHaX8mRMxp0kev9mssLaSUBUiq1ULaIZ+5DAKPa5pa5SG1WqNKkEm+2FcGoP1ek1lWtk/tcMY\nxzQF4nZL9JIRUemS4GehamrC5EvwS2DXH1hdXmK1JAPGPpcpZkGStVAtc05o63BKE71oJggj3vd0\njdjOkSK2MlSupo+JqnHkUsA7MmPwnJ0/4sM//D7n5zW/8HPvig3Ja8fhcJA9ahZd+sQ0jlhdkQj4\nKaJSARGUwmoJnYoxY92X+Ai/4fiZKJJhRnwK/Dsjgbn8k5kXW0Rd+ss+YF7Qgnu81vIcgqGURxaU\nVw7Bpo6FYVFClgJXZS2dZ5Y3tBR2Jx3J6bhfa/FInl9nLpJh9huW33NVRY7FL9gYtLYiSoqBGDzR\nT2IJpyyqdG4zujofM6/uHhcz53tFR0oJo+/zlGUkfqRknD7f6edZvtk3/N0UbuDpaxNOAj1SJpuE\nruzxNdPRdH1+xlOU+N7ZzML7Suq+c8V8Tl8/3sRRvo+KH5FGXUZEUrTLzZ2miaROrxFFXddLkZxz\nJo+eFDO1rY/WP0RSnkgqyqgKQ85GGLlJPmfMBczXUJcrUTrcY1NQISiJT0L7mJju8S9PC8FMWlAx\nsi5+lm8eS2mtCdPIbhe4vr7m2deeUGnF1dUV3/3ud3n//WflPRy/W5tVGV0J0jY3RBqFNtXyPebX\nJjZfPB+qFPHFISZ6TAlhkYwOcSj44xTJId9PSpoRodfPOZTz6hwZlgCC+XfmxohYJjqHSF0/wlhH\nVpl+HEi3O772wXskVfG3/vZv8fHHH/MPf/u3+YWvfY133nmHF5//VBCcFBnH4qda+Mh13eLDgZgy\nXSWFZ7rbLo2BcRZXrOx+8vFz0IarbeT88Tv88p/+FriKsN+z2Txgv9/z6NEjop/47OUVxlle/OQn\nXN3c8ewrbzNOW8SZRRO9R3D5hqZpyf0IWkRj/WHHquuIh55pHMUeKovtmdKacRqk0TZGPv8woEoR\nNhzG5fsNIaAz7Pd7ttu7pXlNIbNq16zbCxK93NtZbLBWq5YHF+c8eusBh/3A3d0dfhTe6DQJ6hnx\nTLc33N5c8fThOTF5pmng4ZPHPH786B4AoGFZ42a3opRKwEhpzPWCRh75oTHenwyFJMmc2sq6q6Ji\nt9uhTU3dbBj6A9o5WhyHfsf5+TmHw4677YHRKionDgrD5KlqS9usaJqG4W7L3c2t2IAaw2E8oMo4\n2kaxL0xJBFSCqpZiqZamLiHiqKlYnznnjuFNCxpa9hAnPONhmAheoZWj60xxJEhYq4r47mgdqrWE\nXflJLOOwTizrrCMneP7ZT7l49Dar1Ypdv2Mce7bb4qLgR87Pz9luMzEqWTvJhbOQF8cLAUzEsWoW\nsCplSoAP2Motjdfjh4+IWYRz0QcRKWu9REcf9S731xXvA3l0OBSxpNLVbYcyhv1+z0XdnoBBQsFL\nXsR7pIx25fqYjpPQfBLmFCfRUwjKD+PoUQ5B7GMEVZJHW0s2FpTQ5HKJDT/ud8e6RO4fRdMcqYRR\ng88SguJqy7rt2BeaxDRNi+Xa6HtiyJyfNQUYC8tqOI4jIUw0TVUmKpNQIMp1E+KEwRTtVF6oj/2w\nKwi5eNunlNjf3dE0Dc4UfngSe7xApqkccZeIfioI/OvrdglSKW/MOUddi/HnDJBNw7gAZqq2ZKO5\nvd0K776qmaaJtjmT9MWVEWrSm/aDIECRKS4y2mmctQxpWlB7NetqluvoWH/8cY+fkSJZoVUt4+Nc\neE1ROrNgy4fJWbpCJAD5jUescNpgrGHKorbNIaCBWhsqm4m6kQBll1FKWreYhatiFMs4YFQBQyb7\nHhUUq/UaQrlJSsGljcb7EW0UVeU4HG4xdSNxnNowTntyznTVOVlZktdEo0hOMsTp93hkETSuJSu4\nOYysL87E69JpCJ6gBsJBFpiMRSmLtoXXlBO7IsBZEGUtojynBCXWCXKxLwYWpFfbSrx3s1inmIKw\nSQE0F9UisrL5OGpyWHKS4ANZDPJCvchE8sJnEe/aKh+z4GfUGaCu3cK7ymRcLTGafhzoy80M4lKy\n2CQh3FZjT7nJEvbhvUdpt2yCsy/pjAKL4HBucNTCbddIAxZrhwaq4uYwW0elJNxzpTJaQ3KZQRty\ncJgqo/xErc9Q/gxlHDkbhlET8KQk7ihjRgQaU+IVCassFUm8mXPCqkwwYmVUZxlTrp3Baw9xDoKQ\n720Mo1gYxQTqAECjGqk633A8uniLlzcvSH7k1dUdv58+5OLyEuscv//9D7m7/h+xZ46bmx1hN5Gm\nyK7yxZdYUczOoJZRtKABCpsNKk6gEpgalOIQpCDVUaEXnDehVSZHcLpMADIkElmDJ5GTWDmdkJm/\ncOioiXHCx+KRqzQqapwr/EpVfENDZkqOmDPGRpw1RAw+K7Hn8o6cI1YrHA7djTjtaZVl5TRd7aja\nM7a3O5yJ/Dt/5pd58eIFv/13/gG//X/9v/zVv/pXaTaX2KpiVdcY3eB94mZ7gwZ213fc3G6x1jKe\nT5C10BVczfNDZlQB0pZK1Tz++V/D1hU///QrnF8+oN2cMYye6uwcaxVt06FsxrmKx08ekvyGGH6R\n55/9hN/9gz/Ajwe+8X7gfHPOZA1uvebiYUWe9qzKRuhVwqwaslKc1RfkrOj7kdpUxJxp6ob+dsv2\nMLDZbBinHmIg+JHt9StyCqy0o8rSWO/7gaZp+Pzlc5qm4fzsUhox7xnHnj/z7W+jleXmf/3fSErx\n9fe/xuXDC95+9oRx3FM1Nb/09W9y+eBtYjqw6TIvPvrnbF+95Nmjh2gTiSaSG81+d8uTB+f8+Cef\no55Hnjz5JlN/AJNJyJg2eU9WFSHV6Byomo0UlCoTo8e6EuqQEqZu2O5H2qopuhBphlXWdM2Kuq4Z\nB892e4ttDMF7hpixTc32sOd8fUbXrLi7u8M6x9nlGdvtFpRaEtHazQaXkvj+kmm1JpKZQmI/Rg4Z\nVrVliBCiIHfKQF1ZdOrptPj7pqbhZvuScCeBItpptBOw5GbvIQZu4y1OKxoNm7fOOEwTrXOCXA83\nVK3BmIZxCmQr3sS7vVybKctIvIqBHAPZWoKHurpge7tD650EcjnNbtsT404S+5QjJkNMiqpbkXWm\nsoKc6iROCT5mLjoJ3NiXJgtl8CHTTxNm8DTGYZTm1YsbtIrs9r3sDcVFgzQBiaoykAzDkEnJMfSR\nmEGtGpKFYCIb1xXaiNAGuvMzXrx8yZMnT6hMzX67Q+dMsoIUW1eTvDT9q07s7VJI4qoVM9ZYvB7R\nymBcxegDzlnRl5T1JqWE0h6CxuQkmp8UicXdSqFxysnkLMziSVvsCkdW64YUxZM7+kStG2LQ9D7g\n6paHD97i1YvnXL+4o2kqurM16Ij3sidXtSMq4f1XzpFzzd31Tmzfgi4c5wajPcPNyKAGQCgP1iis\nMVhtqBuN1RCCUF1X+lIEgJOXJkhl2s2KPErgUKcdPkX6YeKsE2DHqEw/HVivO1xq2PuB7BrauqLS\nmvOzFTGKiDcqoKnJ1mInmKZAbStSVHhn8YcRVgdW5xfUdYMf3yyy0+szmcxrh0qacRCfaxs0qVB3\ne0acjrTGURlLbSvi5EnbL60iv3D8TBTJc7UPJ2P+glDlxXXhBD3Nb+4CkhG/zKQTNswq08J7Lbwo\npYR7BQmjhdNltXDcLOBcIe2no1DtiAaHRX17iiZra6BYo803qXGWmEskqjF0XUcYhoJklCAMH0iu\nwpiq8KnFHmsMI3nag3ZUG49RtdiwKUlFQolsIGbpkueiF4rDhT7xbIzygz6ixAuG/hpaDEf+7unf\nZz51yseEvFl5fPodyWjniE6fouhzp+69X77De56UJ8fMP2RGGPUXfY/nz3xKdXn9vc9/zs4ewuU6\nunFoNaPbMzJbUMkiyMxitkrOJciiICXz5SevLUJPbVgU9pIyV4pwI6MohyarKPZgShEVYlNXut2E\nwurC0bVGXiNFLA6FFr4yBU3DELKkRi2NTxq/dMJysTljv7vlEDzExNX1NYHMs2fvkE3mhx//mLNV\nRZi8fFPOoJyF1zr4Y5OiCp88L3+fefBZCNJEWMJi5LzMEwM5j0YfvZJnDwxBW974EeS8KJbR6dxY\nKYWgi+U6SvKCcv8mLT7ixR0HQCWLMgV5j2m5jyrrhC6iNDlGUhjJYUTrivOLDZUz7KLl1atX/P2/\n91uEkHj73Wc8efKEs7eeEoaBw3BApczdMHK+fijNdp+ZpsAwwDZP3KWEbdc8/sp7XJxd8t4vfEOu\nxaoWfmmKwi837gtONVURFD18KM/96tUrrg4jz19cQdS41mF1ZuospumoXFdoJRrjWmlUMVR1Bdox\njiPD0B/5vieBJFpZhn7P9d2tbKqrFbZymBxR1uBUjVgpajabMxkdJ3j49iPquubjjz9mf9hydvGA\nTGS9kQI0xpH1xYazsw2QRcAz9iQ/EscD3Ua4vmRJzooxCp86wm53IARQyoGp5PpD0FGVhUaTdSUF\ndEqEIGK0nGaallroL1rrxbFhpqrVdU3VuNJr9ss6UVdSLE/TJEgz6t53NSd6eoQPabWjspYwePw4\noeuGmFIJmRN704AWCpbKBErYzpBYb2qcFSqFAs7PL8k5H+OGy3tv25asMsZmrJIEt8kPpJihymhT\n45whxsQ4Dkw+Yl2LUhkJoZACHRImyXcwToEURbxptXi7+0kKXF9EXSmlAlIpYtZMIWCsorLSdETv\nRUOSo6Sv5gJApERM8pvduhOP391B5mmqpHD6RD8O2NriXMTpLAV3iuSoiMNEQBDnu90OSKw2Kw67\nLY2yxVM4EKxh1YqI2Y8jthWaQZjXDF3GhUZLI83Rjz/LFiEgSU7imV03xMljtJpFTyeUwCMtEcTl\nZN6fck5Laixq1r4c6YypTDaapiKSOEwjTlX0RKYh061q6u6S/fYOH2c0/iie11oCYebnmycn874q\njVCxZLWWRGToJ3KOrNoag8EZU8JUxIYuJsljCEoT0lTIrUqs8aKXYKvqJJ7ciItQnCe8SqFNBl9q\nOQwZJ+uZtsQQyTGKyHvyGCsR7VPfYxwYZ7nb7lAYbCUixNkq9fXDF19mlQR0jOV566jptXDgp8pA\nbbmIBoemMY5oYfR/dHDV6fEzUSTD0QFBLFJKIg3iK1sesIS+uS/ZSZM2ApuaXIrkwnEuh48FHbMO\nnRN15dh0HeuuoqkcxjpcLYhD8PdpALncPfN7nG+MqnKgDa5pyXWDMRZTIhWzSovA5HA4yJBZi1AC\nxNC67mR0Mux3gGbdtKiuwRcvUDvs6WyFcZ3whlHHwj8D5hgZPW+q8oblD3HmKF7PcxxyaUpmARgc\nP+vrzcryWfP9QtXHiD0RTB1vziPdYuYkn4ayAGU0bRe+6+xvK0rckWHeEMrndPXREH3+fNacjMjK\nc8jCZRYUWBDgY1Esv1s+w5zCliFOIkYIWd5PKg0AKaOMcMEl+nUOXRiIRFFrGxmRKqOYxiSBHMpQ\nKUdEEUtMsVFqudWzNigNEVOu64RBM409Wldo7VDKY3QgeUtSAZVEBJhSwthafCZjXMzfY/RfWiQ/\nffiEFCau7264ubkiJM/VyxfEHDjbtJyvOoZX1+Qg51m1LVEfBa4hybWv52YjKeFAkwUtTkI5mRXG\n6GO8qynqblUCCswcJ4smEoW721gWrvlrrjSnhzGG4Cn8N0gpYLRhmATBMxgJ7FCKqilBB2q+dso1\nqTXKmMJXFyU71lFbhyJDTNxtt6xqy+X5GdMYuLt5RVU1/ML7T/jlP/k1fuvv/zZJGz775FN+7zvf\nobt8wuVbT5mUxUfFWXfO4e4GrTVN3dE0LecPHvH4wdv8qZ//Obrzc8biJ26LV2pGBJtVSnSVLde/\nxahc2gi4221JKXBxcUm76viLT36T/W7ie//0d/j85eecdxblV8SwI6422LfewdiaqAx9H4vAyDP5\nwrs0Yh2llGKzES/S1WrFbrfjJ5/8mNWqZbU+I4TANoyoxhGj0EWq2vLBBx9gjOFstaZtW54+foJz\njt/7g9/jkx/9iL/w57+ND4GvfvVdTKX54R/+gK9+9X0uzy7Zbl9iVaRaKVkvt1dc1OCYIHtUSLSu\nFc/auuZifc7d4cDzz1/x7leesZ8i2hShcowYDDoHvAJFJKVAmEaUMoSgCDqhlIzdFYacNHVdL+mn\nKUlSnVhSWZqmYdcPDH2PohGurYV+HJYp1SyYqmux2EtaRtvTNAo/uqxDh36HUYrKOupKBJ1DCGTl\naJoWbRUxeuGATgHjIIaREAaUqXHOlXhotbzWoR/pQ+EYK41ydVn2J4ZxJOee5APtaiOTOAuHw8Q4\nbsnTiDGazWaDMYqxcLdDkoj427uepqloVh0Jh/cRlEUZ8OGAMla0Ohim4DFZcbeVe10nLemCzvD5\n7U6+73ncbbTcfxRBlTVoYxinidbWnG026N2W/X4P5BK/LKLSdbeiqSyjjziVeXRxJgmDPrGqO6KP\naDS2ciil6XcHHp5dctgdiCGRCuc+JoUPUigqhBbg5jRaIOmAL99H07ZMUdL16pLC54t12LyHuSLO\nPVKtpJidC+Csp8KttigcZC2fefSk4sJAktAXrMV1DT4E0mHks5cfSkO0qbEqEcnYylG3Ld57Dn3P\nyq1LZLvYARqK7kcL0HHoe2II+L4XNJiREAOjjygMZ11H398SJi/gRizrtNY4K9fwOA0yKVYanbUg\nsUaxOjsTDZg1JFJpdqTkSmTGGPEHT5gy2FqADABdUddO9BCrmlo1jCXd0RnF9NlI7wMfvPseh9sX\nbG+v4OyL+0EuMjYVT+geVjM4g9EV9ZipI9gJWDmyteyCZwiD1Il/zONno0hWR9RSkCmFioUbmylc\nS03SMhpPvLkYUGn2EYCEIK/5JM1OOIzLS5ZNNC8WJkpllC6WQcxCtPuF5Gn3OG/sVd3i2oYhK1KU\nCOJhGhfngll121aV8NHKm3BNvaTo6STRvsF7VO+pVi2uXVN3K7BuKTizOgrvFILizWjT61Zqb0JT\n731fb+Dl3G8Kjo87dtr3nTNe/260Por4jj/pXgE3I8zM56psUjOvaiyBJguyW0SUsxBqRqJPuWpH\nh4z7U4nX/5zfsqYIvBaFJcsCkWKxLMuKfhoL90/G+1kVI/mSOGWMiCCSmgtCKRrF6DyiKiktZx/q\nRRgYpXFRWQFWEKWo0ElSAFEyMiIJpzenLNzejKDTKYkVm5LxZioJd286bFXTdGtWIXC33ZJSsQAb\nJwadqXPApkRWGp8DVgl/cBaaZETkGpkdQ2afY+GCCtdbTpcpTZXOEE6uueN1c//fUBa6eZr0R1HF\nQkmM0k5oPVkStsj3n28WaWpNCd8p4h2SNCopFpP/vEQPj8GzaSpM5dCTUKCCl6LH1g3aWqb+gBkz\nX/vqM4ytuN72GDa83O1IDwMPn33AFDPnD56ytnLvX1w8YL1ec3n5gM50nF9egJWmWGmNc5aURFib\ncoAAxunSSB8bc0DOS11jK8cwjVRVjbnsePeDD/jI97z4yY8IfsQOFX4z0LQbXJvAdYLwF6TfR79s\n6ql8X4vFZM7iF6s1bbsiJi/cYyPXoZksrgFnxb7unXfe4elbj9nv97x4+Vz+fP6c3W5HXVdcXl6w\nP9xR5UZGu1XFMAxctmvG/sCDsw193+OHHWfrFsKAJRMmid2OyZOyeCZrxDcYraTRwaJzXmhZACFM\nwEjKgZQnyFLQWivUuDnGPIR0T7wzo8mJJL9b1n1rLdkXtFqJv2qMkYuzc6qqou97drvdPQ/4lMWL\nNqZMNhprNZU2uPz/UfcmT5JdV5rf745v8CGGzARAAByKVaTI6i6qTSxJbVpqWGjTpo3+Qi21ql2b\n9UKtNlNLMiu1tZrVxSoVARaRBEAkMjIifHrDnbQ497lHgkAVl2g3gxGWREa4P3/v3nPP+b7fp7A1\nrly7zDxPlCwNFen8tUzTEZVqyqhxnKaLfrPUnyvSNoenPOkUOubTXBnJ8j1OUwA9iZTQWIwpWFsY\np1E4t0pwZRSRcjVdyzAGtPVMMTE+7umaFdZZHg97Ypzp1ytyUbx5eMD5jn57xRwnSg3rMYgcssRE\nMoKXs1rYu/I+L4xaa61MzLSEQLkcMc6SSyQERWvbCkxQaGvw2jLFvcSXtz0hJ6ZxwrdNlQ1mXMW1\nTiHQ1I7uPE4oI9x5o7WkTGYhNcm8T39lv6r7s5U1f5om0TPnfGYxL8/KMjn+amfZGI8xBaWlwyz/\nf/WX5LeRsDEIh67Uruizq2uO5TVf7mq3vL2Re6lqp8+Gx7ovPp3aLlPkZR1s25b9bsfxeKRoXYk/\nRnwAKbFum5qMKyQPXVnh0uRIFOqExTvm44hRSkgwy++sn61Qznt/LFYMjakgJEXDvt7HN1dbtHdU\nsZ1IXGsSo9QBcmC42504hYmslmf691+N8+J1SUJ3CYtPxxeIqfawC7qac4tShJqcuEw5/5DXt6JI\nfroI5HqDtkuMaqxKUl0YdXW0q28okmN1+mnNTAAMUspcbvyUFTYpchKNlnQNYyVPRJRTaKfOOJgw\nC2RfFqaLiWopHI/7E5urSNGjrgAAIABJREFULeura+5SxLgG76VIXrBCy42cFTitq5Eos9muGFIg\njIEcsxTr1lCGBE5DY8B4dNPTta2MQaKMNqAWW+VthFrOGVeL2fikwE+a86h7KXKbJ+ETXy0sn3aN\ngTPhY55nUs4YZ0U3/gSzs4zVF9PC8hA3TXfuci8dY/k9F2nGPItzuyBxo845nJGuftP1bxX8S4G8\nxFUv46dSClNla2Y4TxFM/bvyXi/XwKgils0kXdFSu9ClJspppbH2bSnJcu1SSiKPsRIQATUxrxFZ\nT8ygrZURY4ESszRcjSbHULXRMg4r55GvJxXDPJdacSa8EjOZHNGr6bMUbNPiciFVk4lZwPRf85pS\npFuvcJ0j6Mzjl19yGgeGGOj1mmylY7O+uaIcTsxzFNSQuUgqUNQRrJKYSWQBVVjpjpyh99KNjTmz\ncGCliJXnQCPGJYXgEr2xDFliUa2I2L/2MwA4pYl6MStZQj5RChgn05pU0vn3pZzIWTo3ORVQGa3B\ndQ0hJUqReNKFpf2w33G1viUZkcIo7bFNyxASaEuz3jITUaZwe9szDjM/fP+K6VnLZ48Ks235Jz//\nGceiCLrldnvDdrvFu6p9RdL5hnnAJ8vGyiHEuBmTC05pyqwpU8JpahpnIVfjllKK1brHWssYI33f\nM4VEt/J8+EffY71p+JcvP+bh5W9ZNZ7nL17guyuyeYNarXh3/T4xBlKYpLunJVDnfncSc9npdJ7M\n5JB48eJdjuORDLT9ijGfREefO95595p1v6bRkU8//ZT//d/8K9m4S+H+/p5xEA36L//jL9DW8pOf\n/pi29Xjvudre8HB/T8qB8Xjg/3n51/zu5Uf8/IfvYNOEUQnnHW/ePGI1pBiJOtC1mr9/+SW70wFU\n4ic//hHDSZONAZ0ouUrQwkRKR0qJxDRSEjRmfZZfADRNQ+NXjPPjJaSprldFZUpWLDzVaZqwSbwV\nzhjW/YpUMo+Pj+f7fb2Wbt7ufod3nrkohjgzUNDOQElYbYnDREmRZtXRqEjOMx5LjpoYMskpbq5v\n2fSOcTwxz5n1aov3ntPpJFMzr4gxSeywk06sdJZH3rl9j8PhFcfjI942ONsL6g+JPZ/mjHOO1epK\nzHi7E84Zur4VSYuCft1xmnYMp0FCVgikU5KJrjbSrWxabp7dkjHsdju0VSgnnOLGWZquZZ5GhjDj\ntMFZwag1jVBAxlG68cZajscj3nuULdw93LHuW7rOkULgMARJpVSaKUVKUUQV0KXwcHwkRJEGDGGW\niYHOhHkWtnHbcvfqjtV2g1Ia17ZMc2BlNBaRs+WUCXGisHR/EzlEMQ8C5THgfcPKNoyHI1prZlWn\nn9qI1yQGpnmo5A+DwmKMeGSMgYLEOxdtcK6tyLOZxjumkDgej9w+24iJOCSm08A2K1zydGZFDBOt\nWZNC4M1+X42P0jALMV4C1yqascQk04w4EYcjV9fXWGv54IMPeBxO3KxviCnwu88+Jcwz+8d7bp9t\naJzFGEmpSykAGd9YrPUcjwd2ux0tlilFTNehlOJ4PLJunNQsWfw6JWW69RbVZcpp4DicxBi7WrPb\nPaLGAWc1z66v2Nxc8/D6Hj1NTKej7NFz5Pp6yykrppDw2nI87OD29/cDmwolFkpIxByJXqFayyZH\n7k/CM08rOTS8E6DMgdY5oldv1S3/2OtbUSQXCsZI+ZKSxAanhWgA1f1e8LXQW7AmX31lo6SrViTl\nLIzhfAMVimipinSWIplDHFhvnrPqPFbLKcYbi7UOkzNWicEnUySFJjVQDCkuGiBF0DMqZlREikad\npQtiHXkSTdrsLaSAK46YVdWsacYpknKS8dSiSSxQjMHUzqAuMpoOBUqWA0PRhbFGSrtsBN2Gwmbp\n1EUjhwWJSJUiwBaFTlKY6gqM1nnReRaUNdKZNNUEZZ8EamiNS0pkG1p+X8zprMUrgmBGATEEjLE4\nq9Gqaou1I6OYK7NUQEoAGWssj4/3HPcHYgqkZGiajhQTCoVzLRSNd22dBMiGYtZvp2ZdtNMS4lGU\nuJ8XbmLOEnyhlK4c4sIQ4pOOuZJRPaLfVkq4vKbY2iWtEdaAwbBuLHd5z2mKhMlhJksuj5BXwgnl\nRCm+dioKqRaiFIStqhQocedKd1n0e6Lvqt3R3EENHDFK4SoyUE7BGe20jCTniWi/+cEPSTYObeD2\n6ob5ODCnSKms1hIUipHebFFdw91pJoWCrlHoptbett7zmUxRRli/Ocu1McL9NPVe9NaTtOjYdLlM\nO3K+TGeWjq8NYl4ZTPxGyQjICHNlHcP+KDr/dlVB+YMEsqR0lg/IAS6DDihryEV8A5SKKdK2Mjak\nQ3McC1l1HI8z29UaW99H6xuUsYQpUuZCrAe7xnqG8Z4YA89vX3A67omP91w/f48vXj1QbM/IgdSM\nWK0l8nySFLKiC9nIVMIkzmEX2YBqBIhfQiIrOaikIoc+XdRFi1hH7+N4lHH8as2f//l/xa9++R94\n+PQLjnrHafOIX/ckNA/tjjDNqBJosORRPkecRrT3TKcdzjkOux29Nby5e2S17nh99yWbXkw7cym0\n1hGPe/bjiZcvdxxPe1a0mDHz+ae/wxjHO7c3hPWWf/1//N+8eO87HN7L3F71vP/eFR/9zS/EUDtd\nsV45Pv3or0nDPTpvUDoRtHQxJ1Uo44m+aznMI9vVNd99731+9fFHvPz7j/juh+/h/ZrdCUzbcNof\n2DQdTYrsxpPgPGfFPEdcG3CtQ2MkSdBppvGBnAq+b1AGdsORYpKETTlDGALDYY9GcbNt+PzuDq0b\n5pDO1AWDYg6BEiLaWFZYxikwxgjG4IohzQm/6tHWsR9mXFE83j1ys24wTUMicUoD2mp6B1PKpGFm\n23cYXzgcZh6Pj0zTgO88xnuU16RgiBmyVrS+xSvN3f4OVRS2WQMi/2i6Nfv9nnkKEmJBIc9HCgXv\nHZnI/rjDe4t1PVOa8OuO4gxhigzjTkxQ1jNHWcsbW7jZbjkOI/1W5BxRFGvSFS8FSiEG8SqYzso6\nFWQd7dotpSRpSlWWciyZYZbkSq8VWhl8o0WmWOA4jOdOdUoTp1E65N1mLd3watImZVIOrK9viM3M\nNIlcxyqIKgtKtohPKM6QkqGzkkOgYsaipXieZtRqxZSyeJ28pmgwyUu/IitBuKpCVCKdU8ozTSJL\nWPXSUAvRUophSEeO0yMfbr6HblrmDE3fEpXF+RtZu02CEJkeI9tNQ44F7TxDSUQD1q8wxjCPEZix\ngC2a4+HIat3jnSdR6PuWORaG8USaRtZdCyWx0Z79/q6abdeMxxO5TAyniWM68Pz2GuctwwnGeUKV\nhHYFn+CmXREYmefIMEZs29H7LSEOxGmL04aUdtA/Z04ZvME4iw0iR2mLZbaepltRUiAH8V0chgNX\n6yvm0xGlLa5vWLstpShCrZ9OafW1+8EUFFp7souEJMW9Os6cjEOrBmcLOgasUYRGGOFx2GOMk+79\nH/j6VhTJi+lGFSiqjpT1MjZfmL8FX81Q4zcIuaGObot0W8XkhIwZz13qi452MUEsHdilWxljrOlZ\nwlQuWcZzT5m159G/W5En0SilJJ3vkCPaiilwqqPLphG0SQiBZzdbKeZzpssa7wQnhqrC+ZhQRqOc\nxTYeWztJ2miMc9VWVruP4n5ClULRUiTncCFJQDVAnlkOl39T+kJ4yLUA13Use5561wVPijp1GS1l\nDbGGXjzp1GtnSTkTkxSgyugnHc4L3SKlxGmeOB4O3N/fCfPSW5xbvVUoLd1ieSvlbLw5g+urtcAZ\ne+50hhCYpuliyqnF8NJtDlkSeJafeZYCGMGgCZpJrszS9dbV9Le8t7ZtuL29ZvIt+zhxHAJReYIS\n9m7JCQjEWoOn5d4G1BOO9HI9RNpTZS3VCEeWTjdV6iAGU4iI+VPVQwK+oXwlsOXpS0a08kaapuH2\n2XtY1/Lw5gtKAqsMDIbHu51MTEqU4oxcJT1npyLoQjyGsxbNLuO9LOMzyeVDkuhajV4oKVq09EYt\nCZEXbbIuYtA5S1G+4RV0EYaq1SRVu61GkaOM6GSMLoc3Zx1ypcSUqbWM7IdhkOfRGDQFkzLWd4Tj\nyOH+kZt3GkLIYhqrBi1VMkYXmtYTg5gyjdFY6gbQt4TTnpcf/x0/6FesapxrjBHfunNsMhpJ2BSF\nx2VE/+Q+nCdxaBtrSDmTl8XcyLVcnp1lmmGt53A4kIviJ3/6U95/5xn/8n/9X/j05S/55Dd/y3vv\nf8jt977L9OkrmqYh1MCG9dUaYwxHYAqWVALD4chpPJGMZ3u95fFhTzqNvLp7Q2Md0zDy6nDgk1//\nPeMwUKYB6xRGRdarFevVFeve0l8bdqeZ23dXOB/5f//jX/Jm/5ruI8/z2xussvzZn32Hd243fLZd\n884HzwmnmZQDgSpT6deix84JVRT9pudPNj/k1598zK8/+jueP7/lv/hn/4xnVy2ladAmcBqOKBUp\nWpGSYoxyoUMIOC+TrGmamEbBzsnkO+MaWTtyKZx2or9c9VtWqw37/ZHd7gFrNdpqUBFUZtOvzhOt\n3X7P4XhE4wgpMqQka6nWFKOJp5G5CKIMr7h97x3ycS8YvnZF1644ncY60aLqmEXCcBhOogFvO2IK\nHE6joNhyoaBI08zLz3+HRnHdt8R5YtN52saJ7C/N2KYlh8CcIsoaVquNPGdqwcpNTLPQe5xr+PL+\nkRAKjfP01zfEmLnfnQg5k/JEzpbHx9/ifEvSoKxi1Xb4ViZqKQRyjHjforWkGOYUGKYRnySWXSnF\naTgxz6MceDdrWedLYRwmrFE0655m3fPlZ7+TxMpSqhcC1jc3hBDYnU70fc9VK6LVN2/ecH19jdaa\nzWbFaRrrGijmtGme6moiNYTWmmOchRbkqqTias1KG3a7A0ppnj9/LpIaZyRsI4tsSRvR3jbWYY1C\n64SxGVUCvumIKYvZEfCmAzJTCFjfMM0zTkkNMeVBUKcl0awa3tx/yf5k0VaisG1RNMYTDKQQGYYj\n3liKUdhU0Nqye9zXyHTxp6y3N3S+oaTI/nHH1XbNzfWGtrE8PjzgrcJte/b7RMkSu/3qi9cYDdv1\nFdpKRsDxYYc1hq5riWNEFUXjO2zTCZrQigSqGMVq5Ym50GiNNR7vIWaFyorRRLxrGY8DMQV6a4nh\nyLrrsUUJVzll4nGExp+TAq33zPPpa/cDlQsxR6Y0k0rGuBoyNMfalBEviqprQAy5Skj02Xz/h7y+\nFUWyWCk0SS2aRX2WXcAiBZCo2YToir7uddY4IhrH5WfLH8vPeKqj/Sp/8auM4Kc66QUDtlAQlFZo\no5hTQqboF51sjvlMjlh0ZDK2u4SPxDmSS6Y30m1cTGal6vaMd5WQkSkh4KyvSpKqCq3fciyXEXup\nncqlcHn62Z7qcnP9O6l2yeWzm7cKtq9eV6WfsgbVeVz19Dv66t87aymrJEJVjfFiqnvz+hXHw4EQ\nJlSBrm+FxPHEVLdoh7/6M5fXYrxZivev+/3LZ4rV5BdzYnlKnso4BFNX6qFNRqxlGVrU7vL5fqhj\nrZgLuRgxZ1QTH0qfg2kWZ/1C6c4KdEyiXa4mM8560WrRUsLlzjnX1L4ikwWKmAQXzrISPbvVudIm\nvj5gRTifIhexxtF1mpwj4bQTRY9SFGWYh1F0e1bXp0WK5EVIXOr3boyq8o/aWVeKubLFUbpObQr2\nifrj/Ey9pRsWo2VadHVf++6f/AytICeUkYnS0hFa7tunkiFBGNbDnuL8GZYDn1EaQ0Zl6Y6Pp4HD\n4QgvhBqwaNudc1XHh3Rq0kCcJbVJK4tz4LTBKsWXd18yDkdMIyQJgiYlT7EXWc35QF4W6s7b93Oq\nh2erpLteajEteMy31yWQDcr7hpQSwzTQdC0//vEP+OtfHPgP//4X7O+/5KdWE/yafr1mSoExzBzH\nNWjFKXOexixeAN8Y9rsdh8cHWuDu9Wt+9fJT7u/vOe53rPueZ89u+eAHzzFGYXTi+vqaHArzFAll\nxPvM97/7HhnFp598zm5/z6p/jzhFbGO4vdpyfLyjM4bWGtIYabyHNGOKwil5/nKMUBKPj/d0XccP\nf/B9/uZvBj7627/h+XbN6uqK/tm1hCF0iilE0V+WjDaaFCKxUoaWZsnyvaZ0oe/IJCJXv4cihkCM\nohcWUyfyzOlK9kGIKm3biv6YQiqVWIOEKi29nKbIZ1FKvAyxiLb1NM4MJ2Eh66r1vL/f4ZyBzRVh\nlkOeKgrrNDorCpqYquzMGBrfksLM6XRCKYNznqINKYPOhVjHV1lpFjVTUW6Zb8kzbOWzTVMgThHj\nPMNw5HQcWZstJcs+h5JD8jQHxqkwzBnfNuiiiTaCLmejnrUW0zWUlJljIM4TjTGYih11zrB2W8LU\ncDzuKTHRtz3aKKa6XlAbWVc316Q6FRwnMXX7FOtnAW1FwtF1HX0vVIvj8UhbTdfiaXJYK/QYXbXc\nqSBSLBRzKRJ8gRBuIpl+va5GzPm8riz3QKnvTytIOZLO9UZGacUURulc1r3MO4dSQkaK1V+jtRjh\nlJOa4Xg4oJ0CA1OY2bQbVCmEeaZoQ7FWfFdZTOammDNzOKfEeIpYK4X+aX/Aecu6F7LHbrdjnE+8\neP4crRT3r+8oKeOtIyVBwB2nkRjLGWXojAHridPIWGQ/oOJJSwiUYcJZT3VwkSj4qgNXFNAG71uR\ncORH0IYUImGOTEE00Y1apvLSwe/aVvYSRIJpbcecvkE5UAkkIUXRvi8NGGeZhxmjwK2ssJ1ZuP7V\nC/SVdfcfen0rimQFMuZCxso5Q8qhbqLy35RSyOqywH3tz1nMJwrO2gqlUFUbqRXoeiFzuXCFZZNY\n4h6XC1h1r9oJLidEopsppUEpEQzkHIkp02grEoEkZolN20ixk0UDFmPkcDjw7vWtdB52O5zRXF1v\nqnREMUYR7ze9F4i3s+AFtL2QIxZx/4KTof6OJQ1vkaiotGignxTJStUCJbPMCxfTkjEyGlFao7KE\nqeRaHeryJDSEt9P8zmZBLjXIXIJ0ZOGtglv0jvIzHx8fZGMJQ/0ImfE04Lyl9warhWBgtYznUg5Y\nJSN7bWTR806+02maiCFTsntLoxxC4HQ6neHpoleWLpqkKpazTvrMYC4XCkNKWQwtSy5KymfZhRw0\nZk7Dgf3xxHGY2Y8aVp6cNbEUyIlQEk6L/q2kXFFnhbkkSAmV3374ValGvVTJHYiBMFdJg1s66oCu\nzm/y2ziir3tlFCEVSsULemNZb3psvkangJ0H8JYpTrVYrVGfZYlhl58T68TCazEv5iJ8Y4nbrgWc\nkfsizIE0p7cMYYAQl9VF83nWmBd9NvB+08sUpLO4HGyKcKat8UzjfKGmhAQxiGzICcWhECiIscc6\nQ86JUCJGZwnQCIHXD4+U8ozGteSaZGWMImZJ+xIagiFW3bt1js535JzYeMtnj3c8fvEZtx8+e2tK\nBTJpminnKHajNA5NDpFSJwUpJaYg77tTNSijTmm0UoJPKnLQXjBT3nqGcUYpTdOviEPmj3/8T7h9\n9oKf/+wnfPLxR3z8y3+Pf/EeAL5fYbuOw7QjpcRtf804jswsOKqWIe747We/4dPPfsvx4UtMyry4\n2fLf/OzHbDYrrq/W0tGaHtiueklLzYXxEGmu1vy7X/2KZrPlxdWKlAxXP/ohmIbeadamcNVZPv7F\n/8mbT/6aP/3j7+HTQNOJyVqVQkyVta3BGYV1HusdIY780fc/5Kc/+mP+4i/+gn/3f/1bvvfh9+m3\nV6yuNkRrcF5X5OHAZ7/9BO8sN9/5DtYaqNG7c5mgdnpTCKQipAWVCo2SdWQYZmIuGNcQZ2lGxBAx\n2pK1rFdaa/r1Cu0sMUpRPefMHGWtdEZjlSWWgNKyv6EVh8cdHs1mLQVdHAfmEFmtVrz7zg1Fwf3D\nHqUdrquTsTGC0UynkWmepbM6jPSt5eZqQ79qEE+jF0oPipwVUdVpGLIPjjGR44xSpU4iNG23opTC\nOM0UYwkZTlkKvDdf3F+SUylsNhvCtGN9Jd3vbt1hvSEMo3xOJeg+csK1DRDFmG4NIWXyNOEaJ4Y7\nrfC2oV4GxtMEZJlCaY2dA2Odvt7d3eG95/pmw+l04nQcMMbR2RaHNEjGcTzvCdfX1+RZfl5KieE0\ngdHENNO3jXgW0GijUVkaHinL+xxmMUfeXm2xrmGcJpy1vLm7o+s6oW+lamj2FrKuMtGFLKFlqmsV\nzsgUOFVzpNYSt26tPMMlK4aDaJrDGNmnA+PpROca0hQIJVKyZdKKkmRKlpHwkILGayGpGCucctF+\nN+z3ex4fH3jx/Ja2FY3442Pg5ccvWa1WrPqe43FEVyMrRuOMl5yCUXCHToG3jlIU8zCjdEJSazU5\nQYhiiuxWN2Qic0i0vRzSlFqM/mKYtP2KeAJfNKpoYYgncK0momjalpgGnPfEk9RNDw8PtH1bvTG/\n/zonL3vpujfOoVEM8yB+JFWY5pmcI6vVRrwz1sjh2Hztj/za17eiSIbFKCbj2KLy2e1uzrpamGON\nerVf/7ZjjOc2ujN+sSRXp7hCFzFjfbWgkOJKkFJiKDBvOVZTSozjiPVvswqXxcf5hrZtz5viPM+4\nRjTGaRxBLQ5rK/pBb9isevaHR0znRUIQpBA19XMnEaU+6fS+3fVeig8TLxSBvCDeJKf3IrAoRRiM\nRhLenNXnonAZceecUVl4vrlAjpdxlFlGITlhamGTagddKRnHL13chCzK58ASrc8yB1WL+2EYGMeR\nZzcbrDH85jd3/O6zzzkNR67XV2w2m7McRimFaT1XV1f1GlcTZS3u5XuY2e0eZGN17ZM/l5HN8jAt\nXeeia2x1uXSSSql60SJIKTHwCYbrIscB8oKcM1xfX/O6X7O7L9KdKYJ5U8qIRAYlJ/8nxU7JmVjT\n1mJY0rPkmrY1kTGfvzlI2pEr0zFXlHMkCtdXF5lkKEXjzDcXmFqdMXrDcCSUAUhYk9AlkOKBmUzW\nGe08IWpUkVATitxf0tWW9ymDQ9HLmvorU5Yx6FIECje5RsDDuVtsn3R7F+mAPh9W1T9YJDNFvBHS\nx3Jv5ZCJRtO2/XmcrrVmGGSs2tgGinT2Ssk4NCkFhhTIJWO94ep6i+VAciKLUTHTdh1jmGVBVQHX\nNKQcMEax3W5JqRCixECnVLCNpbeG4bCn8w3xyb0fQqD3DTlfsIm2rkNGX4gx2hpx6ufa0XwyiUrI\nNGG5dqmiqUw1JdniyfEI1vH8O3/El68fJHXsas2/+B//O/7tX/4VL19+yu5wou9XfPHqNdM0cXv7\n/C0GbymF7zx7wZAH/uR77/PDf/5P+d4HH2DMRE6Bw2GHVgfGfGKzakEFnHXoonmYDhitedb1fPzr\nTxhtz2r7jNX6BnIm7g+8/OTv+Tjs+eEPnvO9TYcJj8wx4a57TtPMpr9hHE9iZrWaVAI5F5wqtK1n\nd3/P3TTzP/z3/y3/5l/9b7z86Nd8/Hcf8977H/CD/+xPGMxAOEHXrXj/2XtYp1hY6OtVL4bs4Z55\njpWrO4mRjLr+J8ccIyGILXWcA2ly2L5jf9hzOO3xzvFstWYcRw6HA1NJzCnSmjVFJSyJELOw7TU0\nNyuZPI0jV53ICo7jyDRHvDO8++IdSpVS/H+/+jsxYW2foZUhxokUCyFndHEY29JoT1FyAFcp8eb1\nHS/efxe9avjs5Uu6xtAZx3440TpP03bEkjmexDBXqqdmGI/iK6lr4bAgLtEErYnGMM6ZEmZa55nn\ngdvnz6R7XTSuWXOY9pis6UxH6xuMUaQQMMrhu4bTKTENgyD6UBCL6Fx9w3g8knVku9kwngZSkvcn\nkcwT816kJVYpvvPuewzDwBeffUHbtlhvySmgtIRDtBtBtJ1OJ1arFa9fv+Zq1eFcbYIYR9GGYQwY\n5yllqvtVgWoUX23ksDDnWK+PvB+npDPdNA1aSUMlzrIzqwhKN6Q4i/9EQyGhCShtaXwvh5swkpMY\n/ZQyzKcTTSOR6eN+Jk9gssMmhy0NNmSG/YGiMpvrdylaMT7cC9fZeRrfMYVIjJnj8Z7tdot1hnEU\nvrdzjs1qLVzvApAJQ4biePW7N3zvg55tf8PheI9WmRgT2/WWFCbCFFFF47RCq0Kew3naUqRrI5Ps\nWkNN04T20jWe5ojWmcY3YC1dvyGFzJylLiJKkJayln61ZhjfkOeJ7XZNspYpR8ZRjJjDJFKcm9uv\nce0hdI+oCtkqrKm68izNrxzlUNl1Hb6xnI4nFAbTejng/Kcmt8ilkLVU+EYrNK5u4OCKdLJC0cxa\ncC3jN8TvZmXFwIYS6L0SaYJUHJpkFVMqKBLWQttqGhSkSCpGdJMYDJa5BEqUjatpGkJI5DAT5rHq\nrLxEWKqGmANRgIvSudKRlCeU9jjXMM+ReYCDGWh8oPdbYpjYdB0hJXKaq95TkZ1h1gUzT5hhRE2R\nyCxmvuLAGDRG0F9KijJTxwh6kQKk+HaxUUcn52ACZWpnIZ/14Bol6ECZZWNrET7PMyVLNHTOGare\nhwhTnjDaI7OAVOUIgCoYq8+M6LGOVlRJnA5H1uuVPNSNIkaJ9rXe8vr1a/I8ME8nvJdkqVJgtVlB\nSISuQ1d+9T5ItGnbtuQMwyTMUm0lIKDve3wlTYyhSh6sqd1R6UYLZzGTEX4qSUQ6ktciBr+iZow1\nxCIFqkqZNAfmWIhBOtVDgdNKs9cOrxQJy4xDE/FZoVTC6JlhSpTscLogmfeC3alfEYlqKElSzBvv\nMFkS7XLOpKzOeuyYA2QtU40Cecpn7fZXX4WWohKZSDKenoIlkjAk7RhMzzBnbCtGzDnOWAWjkohR\noyNKF3QylJLZWeHSEhMr7bDGkMqlK75Me2YrKVamQJudHE5qEEiY83k0O5RRIuj/kZWr+CKHmDoR\nGqvGshQjI2itSSxTIjlozmRIYIMc+GakW940jlA1v020mKbjnWc9j4dHts0zyjSDkkLfeStkjaCZ\n50Dfi/HB+V4YwsMkC4YJAAAgAElEQVQBSsDpgC+ToLf6nlYnVloTY2YyG7Z+lMLWWhKRlCO6XIJ1\nQghn1GFMkga6oBEbo89d+mGWjlkqibaxlDxhjWFIhVgMRwp+a8jht7gCg3mHf/qTd/nRD25ReF69\nesX4wyv6VUsIjqZ3hBQqfukKqy3Pr6+5vblimPekuOOm73n9+p54OmGtZeW3lRsbKdoSS6FfOXwT\nefbsGevtNX/1y7/lN3/1l7z4zoe0qw2Pr19x1Vr+9Ed/zI+/+33G4xsKwiROMUvCaTxScmZC/syV\nCWcTIUjnzXqhBBin+Of/9c8Zx5GH/R374Y5f/tUjxr7AN/D8xczmu1cYK3uGd6044aMcYlOaqkRC\nkatMSduOqAZCSEyzHLbKNHMokWdtz3O/ZTrtyPPEwTpOQczTXbtChZkhDoTa7ffeo3QiMVEGwdn1\n646SZk7Hg0zcnIfieDhI02N9vWV9uGaYJlKJKAO97ggEVIooqwg5EErgur/FGIV1CadWfH73BfMY\nWHUrxsNMCZowaQ565vqqk/0rCR/8MQjRpFtvmOeZ17WTaVxDbwvp+HCWnK2sw6177h7u8a3nk99+\nxvObjq6TIbtJidY5rppWRt/zJFMm4fWRghh3FReJwv44YNSJ7WolWnCtSGlCqUKIE+PxwHq9Rq1b\nhmFgtVphjMZlS7da473nWPW3ft0T8oQdRHtslCacRq6urvB9z+effUHf9/R9i1EGrwzxeJJGlhYa\n0yEqrLM0zlRetTRNGm9rUZhovPD+tQrElLFOUgsIEdvO5MaQU0L7Dl0nWhK8UrBK4WyHIpKS7EnK\nKELUxBTwjYABnO+xzrFqHCkeyTGSkuLVFw9neobgMhepRyKqRDGJ47jDRktJso4s0/a+WzPHqntz\n4JTiatXx5u6VSGV8Yr1as3vc88Wre/p+DTGBtYxJUodXKzHbzfMoMiVvyLpgXUdkrBCpTFAK7QxG\nQUgFUsLZhPPgZsd2fcXD6XcyzcyFdbvGpSvG+cjKeqZ4IoZCCTOnnLFtwxgjHzx7wddp8dy6QY0H\nyhworFHK0JAIwBRHjHI0/pacMs+vtxzHQQyJWp8lp3/I61tRJEMlmOoisgCdq1LiohfVSmOVIhf1\nFtrs6csYabc7bUBVtl658GMNDdrU7mhReN1dfn+WRLWz5rQsXWVNKRbnjMgrYsTUiGWMJmFo1408\nlE1HyjNzkI73kjCklBRfKSViUtKRzQVrPZIWXcj6gtuKccZRgy5SEBerlajriwnuklb3tPNI/W/P\nXdRqMHRdW3XPQIW4e/ck/YcqU9FQUOfTdDGIWWscmecZraUb7qzHV1NeyaLTomSKvuTWL7zFxjrG\nmIih0hxS4jQNPHy+43DYcdo9YHRms3LM88z9fE8pqr53QdC07U7iRjtx57pGOvNN353lIlprxuHy\nvVtrscajQjVrlHI+/S7TgHLuKJez3OApUu80z1jnME7L+SFALJHd7oHx4UGwWNoxHU9E36O8R8hp\nipKCsIUVcqgxBXSorm45pRdzQay5IvKiXIQAkaeJXAulUup3XJIE7ZQlTVI+z5gihPHrnwsUaMHL\nGTIhHilFzDUxBaYQhPucAjkjnQKTUHMWPm0lzxQLWoueMWt5ZoYUMCWhy1NUnnTtUVaCRwrna12V\n98JCN1VCk9OZV/1Vje7TV6ZQvCPGRCmJuf49Ywxxmqtxr+qU6yRFV6RctWswR3m+vTVoRNO9P+25\n7SVi2qoJ5wwxSwytkNKS8DVDoCiFqyEUWguhoqNjHDIxTkxjoG09YTmUGoe3Hm8NShtclUq8te6o\ny1RkiW9vu44MqKjP6W5wSaNcdJ/TNH1FNmZhODIdD6ik2fTXzPsJ50Rn3fctbf8ubfshzgvSq6gM\nBkpWhATTsZDSnseHI8oKzeTTzx8JIYDW9Ou1dNwOh0uQRs41US/w7HpLSIk//89/yn/585/x8ref\nYYvlZz/4M/7oBx9CDoTpxGOT6Toj5uP7PTpGThtJclM6EskYa0A7DocDh8OB1lucMdzf39PYQn/V\n8j/9z/+CaZoq29owDKJJNkZspErLYb3pOzlQxVlkNDFJbz7lqjuV9DCtxWg2T5mcDNoV7t68Yt31\nvPfeC4Zh4P7hSFxY28cjxl8CkVJSNE2Ha7ywmau2uRSFVpau72mtZhgGCZUIsqbvjwPFWHyrhbKY\nwZGlI+Y7MUVT6FIiHieGOLFaewn+sA0FeLh7QBnHlJJoi48D1lrapjnvWTrpmrY4ns3Dh+MR30oi\nnzJd1Q9HpsMBDieuupUYUa3n/mGHNo7VakW/MrLOG4tKCecatJOfPwwDOSYaKzjPzsnU9HGayHFm\nnmfmlBkHcEoOFr5xGFkwKNnQd1umceb999+V959fEmPkqhr9uq49y4/EmyLPQNGFw3CiWTUY55hi\nwDlqAJint1UymRLOSY7B69cHnDNCTMj1cG01TeNlv0jSuVUq1wVRzNgpg62TvUyhxItXYpFuCmYW\njK9YTC5hXlY35JIZxj0hKtablmgd02HCOEfjG6ZpIsyzIPs68bM0jSePIreQWOpCTLnqe7VMGueZ\nppV7vmTZe4FzET2PimAyXbfisP8dw5s966YFIwmOpEyxDqu1xHGriDaOzaZh0pE8TAzzke56TZ4j\nm5VnjKHSrhQxSjNk5Q14w7xeYVPDfjrxu9ePfNivKdZxHAe0s4QwEDSkmFmv17TOc311BQ+/vx+U\nacIDzjVk63Elo0NiqpP7ZbI4zzNTnDnu92itaX0jeus/8PWtKJILdeyhALUYcIwwbM8ZwApnBAOl\n/xDGna4INYDKQShFNEjGGHSKb+kirbXnIln++qIFvrynRZqwjCYFs1bNbktUblFvFVpk0UIXCtrZ\nCw96MYzU4pj6v0tR27Qe2zoa52gax1IaywNWzl1jVf370sGVqxnjhV98KfoTl/qjpreVt2H6KMGg\nFSWmqIueODOHkXEaMdqRsywCMsIGiqox1RHtG6wWHVKsmetaS1pdrtqvEOTGfbh7w37/SEkjjTUU\nIt605+9L1QOPYPmkCA4pomMQNmMpMInm1GZ3KRKWT/kENbYUI0txvKS2LUXmMu5frvFCJ8iLJjgD\npchdqRUhDhwOO9nkEtItUYpSEpQgSXQ6yyIjx38xA2aR0SxG0kw9EKIoZelcyD8azt3D5X0tspXL\ndygbdX6qU/+GlyqXz5+KmMLO3JH6s8RsVOrVl84MqlRah5jhKLp+DiVRrynXMEdVP4cYUHXRFZe0\n3JucJUlPnwHMU9LINxfJSimK1mSiGJG0ImslHcjKktbnEI7L4XSRsERVhIENlCy/09YDnchioKn6\ntmlMOO2qQbfeKfGSQLmYRa21hBr+IKlookW2SkINtHUo588c6aUoWV4LPWUxlD39nnmyjiitIRXx\nela/p6p0hqXAXrpX+4PEum42V5jiMNqJbE0H4rwnzhNjtpwOcm+GnGiqXGyaZkKMYsDMilW7QitQ\nylX6i8J6d5ZQye9cZFeFGGdSHAnzzKrvuL6+pmss4/2B7c0WUwIhycbaOIVKmWI0xjdko6Vw1Qqd\nixBbjMwGS5E1gyyc1qZpSHFkGAeKET3q89VzTJlROhLmmTwiB+raNFiuayFhrGIYpHBd9OMxZgk/\nUAalHahETBFlpdiYtMGvXEXv3ckk0Vox402ZVd9DVgynI4MasLarpjxxuZqaimd8y1wRnhiDdyKF\nEu9LIOSE9U7CfXJabn65HtagrSVUM+LpWFn8qbBdbcV8pyQJr9lsuPJWpDtKvmddoPFS9E3TdJF3\n5YJKogOP1RgG0PctaJHXaa3w3jJHz3GcUDXkKuSLNME6fX5OTJKUPm9l2uScxTvL9PBAKQlX1+WU\nM8fhwDvPX6AVTHWdmEPCe0uMmVevXnNzc8NqteJ0Op0pR0viqq840lRqgiuF1ao/F0s5CV5Wmbrn\na2HYJ5Uwzgklp06plJLFWmmpP0LK57TR832vDMpYkUdqCZWi5IptVFjX1OaG6JGVtlhXztMhUGf5\nIVkL3ScHYijkZJhLrpHZF2PtOAZ01sLjV4V+bVkZe04CDFWTbK1FK5EOxnwEDDEFwMn3mCXAQ1tD\nnBPH40DTOJrGM9d0vfM6lLMcDLRCuYTRgh31zlK0Yh4KeWmo1Z+97DFKUb8fUCnKvuJsDXGxsn/l\natKLkaIk/tx4h1OaTU0kjN9Q7+WYaK3s/9EobBZes3FaQnu0xlkDxRKRNFm/sP6fQAf+sde3okiW\ndLGaeV7SGTtVcjlrcnPV6FEU5htqgbbpZGPPEExB1Q1RZ+nKZrUY10Ar6SzZimrrukbG2kluDmfN\nuaubUpAHoZJVzwEhWkyGu/2R4TRy/7ijJdFvVsKkTFF0mSWR5sA4iSZ4CoFS6oaeRR9TTB2lmEDJ\nI+qwF4h9e6IYi+6s6IldU4u9pdOez8a5paAyunmrw5NSwpR8MahRi/04nwtHXRfzUHEr0zicF6CU\nEvPxKKNibUVTNQ0yEsTV8aVA1b9480jf92c9ppAEDEpBnGcOhx2NtxJKEgeMEuKC04o4S0fLe4/R\nrhYVDmelUMYI9WSoIz1jDMVqSMIrzQr6RhZGMV7KguhrMZGWAq4Wk7JYLQi4RcJ+CSsBUJOq5ooj\nqqZC5RiY7u94+PIV91/ccb8zlOYa06yYi/wubwxeNcx5kgU6SuFYiPgip/hQOxIhRVmcxTyOtgLa\nX0yamEthVkqRiUuRgjwvDn31tmb96SvnEzkpcWCXxDCNlDTiSxQXfxa5SUkS9GG1xWrZKIqpSW8i\n2pRDZ9EEMlnLdEdQdfXAV+9Naww6mSp7Qt6zVswxnK//cp2t8ah6n12oNL//ErRXIdegFuGfQy4R\nowxt48UIF5N0vIvEZkcyU5Hv3yzTj2pudUajGw1lwtTo1YfdPQYJajFa8GBaa5K2NE0joTpDoF8L\nk/n+8QGrG57dbrkPljmMNG6Lcx7tW7kfcyDrctEcL4fXhezx5ICzFMwhRjHXAnGKNKbFaIezzUXP\nbTkX3ssEp+2eYftbUnuNKZoUUtVMZmIcKUUQl8MwEWdHSoqYNc4bOtvQOEWZI9ZokZjlBGmhkYjk\nyCJkB2NEB1mq3EJQl0E6gk5z9+YLUko8f7aiW7c8PLzBtw6tEh+oNXGY0a3HPN+SckI/7LA6o4NI\nSlRxxDnRtq1Iq+J8puN4Z1iv12dJyqtXr2itO69z4ziKVKWrevV5IISAMXI/axTjNNM0rdAl5sCs\nxFiljUMlOUwSQGEZTjPDUcxb77//PofhRAhBvCcUhqPwmbebjRwzS0EbTZglPCjkmWGeyGWPNorG\neTnAj2IonjMMw4hxlsZ3lYhSzv6TGCMsEwwLujjudw+UfW24pEdc29CtevI4yBqdEqu2Q1tD23Wk\nmv7aeM+SNJmRkKLDcUI7J5rgOGOs4sP334Fc+Pzlb+W7DiPaNqw31zRNIyE0WGKBtmkwVjOdBpw1\nNF1T/SPgrSKniSnPNM6jVEEbMSWGaWROkU8++YTGO959dkucZqJOHI/7ek+NfPHF53z4neeUJE0a\n78Vopkqmb1tKKRwqT3oKgZACU5gpc6Hv1kzjSGsbaQApCJU/nmJAazGPaQPWCJN/mEaRgoYk9YLW\noJY0T4fK8oxaY7GupeQga7d3uLYhRTERUhRN01ZD6LE+39IUW+5dbRRG14PPoeqOq7H3mEQP3a02\nKFWYgiTTpnLkxbMr0VP3PdM0cdjt5ZqbKNpoJSWeTH6FnxznwHgaUEbTrrc8vnnDOJ7YrDu6rmF/\n/0b2YjRGKcHnqcIpDqxWPapJHPcHmu0NjXNkDFNJLIEJxkouglKGFAtZsEcU50l6FE9NLVZ3xxFv\npKGmDZgc2B3mipDzZAWfPryGr238ZlzbUHNUMUYRSpKYdhma0DWOxhmGlEUC17bnyc4f+vpWFMkK\nBcVQe0K1WFlQZ/UfpShZYO/mGzZSY1ytpiWmV6vq0FdUw14iscgaQJsLOqxUo9HCUS4kKOZcmMiG\nnM8GuktXUmGNbA5yU8TzhmW0opgooR05nn9WphBLIaSIKarKHJazqgj/QwhkM+MWDFF36QrDZUyr\nRJdyLu6UUtLFrP/N8tmMXhrNT7rlCJ0gl0KO8vMXKUCcJ3H8Jvn9p/1OimTjyKkQwkRRcurVWgr4\nEAeJOuYy+jXWyKKCUGutNhilxdRSRpwuqAQ5FowSsodRwgrWWgv7uMgGoTCi41NC5igKVJWUKGvO\nXcylgFg2S7XoiQs13lkOJlIk1xOlUudO6/LKOUMooueNctJP04k8B+I4SDhL0YSSwVpC1hRTD3hK\n1dhXU01s9WQu/eHz92SMJipV41zjuYeuSx0N1jHaMsmQG2TpPteCCgnpeFt0c3mlPJPyojaRrlBJ\nsvBl5FqYkqs0pKCp3SnjpMit2EVTqdQpS4fDGNGyK1VImnNHUy6nQiPfvSoynShamJnn5/5p57R+\nZ/9A4J6ElSD4tOWgHIscViVh7wmS0BpURQkZ5BlLpWBNZV6rVE+WiZwVfefpmhZrpEtjCkKDSflM\nEJmfdDSW5+KMctIGbRsI+aLx17oePi05prfkXMuz6ur3+3TqsVxDuR417pzqOaDOWZTcY+dbolzW\nynZ1TdNtiW0vKXRphzMN1ibmIN+tdE8tm40jJ5FsGVdoO8ObXSSOIzjhlSal6XSDN3WkuxR1YUYr\nw2Yj3N1pPgjbtF2d37+1lmfPbpmOA5mMazxGK64217VLnEEX0kmMT8YIVosyoop0s2PmHGyz0HRy\nzoQRilWSAoeDpHFtTwyBcZqIMWCMJ4Z8YX0DSovZ1ygx1qY5SIfVOYZ5oAAxQoihattVNVULQWAf\njjx7IV3iRUeeSpbIbDTGdvVghRgWgzCIdSfdO2edTF9M5QKPVU/b9OQyEseAcWIUb705TyymaWI8\nShPjVNnvxskBaRxHQpECaLU1bFdrUkq8vn+DLuItaWrHeqEAZVWpHSmSYiQjRljXtKRpYA6Bx+MB\nW+kDOUsGgG8aXn35pfCJr66YpqkG3gizV6QGXORDGpxx5Bwk7CPIGMRpWZ+brkUTGXNNSZuDeDVU\nFnKKyrSd/z3Z0bIHLiZylDrvWalkVGXHL1Hi1lqJbVb1YFkpQmGQZlapPqan02JZx2TPyFkY3EZJ\nEl0pBZUSU4i0CsHbVe5liqVea/fWAXj5DMtUMaUEJZGeFG05abwq5FibIEbWVuOlUlxqlpQEAam1\npu+6MxgApHFQinT/YwBtnASkKFkPwxyk6aAOGKuEaJULTePp+5bTNEtxDFCk+ZGyBKD5HjIRl7NM\ny7RhzonWXGLuc9J1gl0Eqag0sRiS0ljvcCmSU2JIEb00C4DOeV6ddphWE7ISHX6IX1skK6PRVhFi\nIeRA45rzJOAp8SnnTAkXylWmoNwfXvp+K4pkKFgjm+3SQSwlU7RsZk4ZtCoE7eVifcNGWsaRnDN9\n02LomObpgqBaxukmUZjQJoM1pDITUvz/qXtzGMu2Nc/rt8a99xkiIiPz3vuGqq4qmsaALgkJCRsh\nIeEhYSA8DKQ2cbuxcLsdPJz2cBBISIh2EBIIhJAYJBwkcGipu6q63rtDDjGcc/bea/owvrVPROa9\n99XDK7aUysjIEyfO2WcN3/p//4G1aMGi3CK6ybqKWUDRzWJ7qIMxSGuE5kj5xHB3IPgjt199xemH\n31ClkeZFB03RyETTEVgRQ3CRVhIuBqxouycOk3ooFvDjQBg8Ljg8Bl89w2i1PVGV/2V7wddCwLeG\nqVqIVwPiegHt9HW2VqDWa1tk2yQRT5PCmhLnyzNzWihnvWfn8/naWmqtMeUC1pBE42s/Pj5Qhwnv\noiasOW1rvTkeMNFjncPZbimXS1eeVnZTYD6dkZKxSVv1YfM4riryKnm9HkDWpEiP1jMF2/leanfn\nMSkAlRDAB3Uu0MVOF7XNixdDb3spNcSZHl7ThaFNOh8CqN0qLmcVX7ZSsdVixfPp4SPz5YlP3/6G\n548/UIqQkyWbkXy8weeEbQ0fDSYYpA44Gs7VniIYEa/oDWIw4ghWUeNJXug/rTWqUW9Sa/siYvWe\ntjR3lI3e7tO0xZ+jW0g5s1zUML6hC1VuI6md8WQGY1ib1QAYA9Z3m7t1JfpAMB5nHDvnabWSEA0A\naUpxEIdGnBvDedFWaJWGt9KRXlERrrGk4PTn0INwzRmzca43V4efuWwTsOoyoguxILliDbRUSHlh\nH0dyqcSdHlhqrVAbu87x9yawkrQlZx15vZDOZ25/8Ufs946bncVGg1m1PeiMRZqjFkPcFSoL0/CW\nmzc7LvMndvsBO3rsEFmlMZjA3RRZjHqHihVSnQlYnNG54n3oXH7Bd7TN0cd3UsnnOS20pgeg4AKx\n+z5DF51Kj4G1gVK1K2U6bSRMATcdydMNts3Ys2HNF+XtOo81rjueeMhKpTLOQvU8n+F2GPl0mdmN\nUa3OpDDev2NZLirKDboI79yA9X33chY/TkqdCBPTpG4Dp9OJgmU47nl6eua422sEcq2cmdlPIwb4\n4bd/SbCGN3c3lJJp1uC9wzhwVlgvc6cuCDjL3e0t5+dPlDxzGEeqGB7Oz5jLqafHBRpVnRv6Bjyv\nS+/GGUVs3Y5pN5DWQkoKDkxuoFrHw3nmvMwMh4l2SqScuLu7o3rL4/MTv/2zf6KcWD9ge1v45vgV\nMUY+PX5kWZ94c7zFuQjeKYBjPUe357KsrOvMZX5mGCam46FTACJZ1IbvPF84LzM1O26PljFqUfuQ\nM5d55cPTR5oE3n39SwZn2JEZp7csy8LTwzOlJH71y19y/Jt/wrfffst5viAoJS6YQEorxitiXeZE\nzosGYJTC+fzMbjpwni+cHjT0ZC3qBhLHPa4Kfoi8f34E79nFgTQX3v/wl/hp4HDY4RF8X7cenh4Z\nYuAPfvULjMDT8gACo/WkkrBimS8L7+6/IeeVH57f40whDpbT+cwf/uGf8Pxw5s3NG1wYGCar+6fz\nahlYCnM+d01C4nyGm5sbbOdhDx09D4PyhmWt5Kxe+bbaKyVjSSvr2jQUxYJIxofA5aIdA+1Kak0i\nstCcMLzbk9PKQ5v1sxwmyEJZMiFGxFZKaxSzUsUQdiNehOW8kJMW/GnInTowYLIeYFKDT/NMjCMh\nRmaE2EG6GAec0ZAsOs3w+fGESGXsnOeC0kTmdWUgwilRykmpL8Fju41lcEEPSkWYl0Qu6KF4d2Bc\nM+tlxk6OMI0YDLUmis0YCdjVcjy847Q8EiXQ8oCNjuqh5UbOFRdGsofgBIzn9Om3eLfy9buvyMlQ\nzQ/UecHgqQbOvmBSIruBWQL54YIfPIw/3g/2hx03u4H3739gGke8gxY1wGer+1rJWgOdLwxj5Pnp\nRG6Vw+3dz280X1x/TYrkL0R61l75Klcy40889svrM7/Y/pDXXL/tMU3Mlfv4GhV+jdS+/B65Po8e\nk16jyJr0BR3ZlBfekneGMA4YH1jXmdrjtHPWFhMd+bJdQS9G7bKs11O/HXXzt05DKkrtr0XA1o56\nm0armlimb+4FS3ztUWyt+lZu7+OKuImmkW3/dhhS/3pDY690i1YV+ezIBdaQrG4Oivzq1+31/fqJ\nz+rKRZWX1/P6sSIadlKvCXKm27hpu8vYl5O+9iB66p/Yq1hw8z7ePqfrhNmQ/E6zuL7vV///5Wva\nfJZt//7GGU8pUXNTdbx1OO+xHfXzzuFcF6j9aGxt41ujOqxRYnCzRo0jX41T/Yjsj+6j7UU1dITc\nGLw3nyGpry/vBg0owLIkjQ0XD6Z5bHP9UGG6uwvX1Lzi1RFm2RpaxtBs+ywkRItb5dq9fp/Wmo7Y\nb36/KIe9x1JrUdDvvUinDP0uskVH5WujVgGymvV3DmsT9ZXOaPTs0jmSr9FqRZ4y9C5C7ZMlhKDi\nIutoLWFEC3Db6VRGTO+W2Gsr1lrD4bBnGB1rLTgXWFfItTLPK6YnqV7f0xfF/7UjdD3YvNxUEdHE\nvc/CQ+RHP/+zhyLRBEOq0mP0vm2I90vcfEqJ0Nznz9eEQsHywpcGyMuFVgu2Z+AIlSpdINm7VNva\n/Zp3vR34StNY+LXTbUIIxDAq3xEtDGtSZ5DXHOEtICPNSpUYh0AM/jPNxWt7Qr3sZ/dz66g5YxFe\nhJIpVZwPROuZVwULal/mPZoCtjw9sY/qzXs6ncBr1/DSbbGcc0Q/9PGWr3NQQxQsay6YHiefiq5N\nubZ+j1wPNanXPev0+KTuBIO2m2tOVwvSTQgdY2Qa9yyr8Px0ZnFwGHs4jbWcz2esER4eHpjkyLv7\nt6yXmecn5QKLj5SSuZxnsB4fB1wYGHcDrQWWjzPrOlPSitkN1zlinAryhqBz4LjfYxBSWpl8xIbA\nsq7U1hiD5zgpnWQYBpw1PD09UVK+BklQG2MXwXqvFDwR9Xp21mJsIEahVblqTV6v2br3vIheN+sz\nF8K1nb5RkLa1YKMAbvOqGYhdmGdLpr6aU1cRbacVnU4nxqCBUa1pWE1JGjaz2a+21kdf36MU2ex+\nwaVqd7n2eW3179tpryLGRXn3IWin4c3tLQ3LUgphiNfOqLM677YMBtVbSQd2eud7GAlWQ1ZKyuyH\ngZRPXBa11RsmNS3IPbEvBHMFxeLQ14oQMKNgvOu/N4JVDrlBmNeF2ahA0LjODS4FE4Zei9luv6sJ\nwWL0QFLL0g8dA+NuUqpk1sLWUSlVKOeZPOl+FX7GicIYc0Wuq0hPRXZw9fXm2g1oTrvu1hmi0W71\n73v9tSqSt8VrQ/8UbdHWM1Kvdlkb4vfltcU3l5w7DaGy3YteUuBsxDSHtPn6e1//fX0tV/7iK4Su\nNhVhbJuv1Tbuab5Q0kW/3zcJadL5WNuCX1lTpmbNpo/OMQ2NuLNYY2lS2RLVrB3xLvaWmJApva3i\nGX0EI0SnDhVLfVWMGkWUpNTP2lGbuGQr/rYNpnTUdUPwvLGMIepb9eHa8g/WYWO/974T+pzFi9X2\nKGDMtplzbRSMV68AACAASURBVOfXUpQaI7UL95RykovatUlVT+JNhGg7rUJbgNLb1QFpCV91gWit\n4oKHpvQa8ZvfbMT0WDwR5YJtJvjGfB6A4r1aBW6tL12c5YVKY18WShFNYqp5xpSVsp7Iy4mdH7Fu\n4kFWnm2g4tj1tqjWh1UDWUwvTIz+aWIIVhdnaZ2W0Q9str6I2nQzfPHQfb3QO/vi8HL1BTf+Z4um\ncXxHGBVdN6cnEIeMDikLZSmcHxYkmm7DBrZnnQ9Bx2ZphWrUuUSMsCu6oQuNVrs9nbFo4pYayRtj\nabXohoKwSKWJcBjUNk19uDUu2jjlrNlmf/Y9AAQ/USRTq/pw515wVTxF1Ju2WJBWmaq7ric6V62a\n+Q+B0Rst+5vau3kqb+4OHAaLrYUmKk7bAnbWnDHOsR/faHGyJFrLjJNhWU/c3b7jsswcxjvsIdKc\nqhe2opH24tbz5UFc2muKRaO1RCqFYbzBiHrDl1bQSG/7CkR4GScbmLCF/pzOH8jrwsFa9iEye4jJ\nq8gv6EE2zRfS5QJRaQNScqcFCbnzPdOyMgYtIEx6xosKREtF5388YpwWsRgDPuCrp1XTnXCsCueC\niuB2u4m8pt4V6etEK0gr3N+/o+XEp4/vGQb1nd9a55s3/rIs15bpfDozjBGpjZrqNRwo55fuV626\nf6w2IaLjQJpSaUYfSLbx8PSJGEeGKSKmkc4zNWcGEzgeb/nuu9+ytDM+aNv+OB6IccT35Lj3nz7R\n8iP3d28oLZPKymXOeB+QC5RiKPLYf4eenFJeKU1T9wy6poFh8F6pJCljfSB6x+odS05QjdJDrIau\n3N9/TcqNnLTVvLbKw2+/4+uvv+arr76hlcynT5/4/ocfOBwOxOC5mY6Umlg71UOMChzn3Ck9QXnS\nw6hFhHcjzyeldxxvbwjWq0Xb8qQc8eghJdZSKB6wkX3niKek7hbTNHE8HrEGSlr0MzQgtbAmUfeY\n1oj7kacPDwQ8y1LJtfHu3ZEYd5xPhcNhT5FCvqjYcAgeET14Waf8b6mNIY661tYGprKuurar37Il\nC4Rx0IOIHyhNkJoQwMuAqfV6oD0cbpQ+5JVekXOl1gxV3XUGP7CeL9zd3UMMsHmeW0fc9WS65hmH\nHUvSFDzLglCJoeGChpbIyTN5z7Q7cDYXaq0MITKvC7kKd/dvMR2AqrVSc9HDgbEqfkSBh1KLim6B\ncp4Zx5Hb/ZG0LDQD97/4Qz58+EAzhubUwSOtCyKV3T6S8kzNC0M80GrVOOxhoDndr47HI5iddiP8\nyHKB77/9lvt3txzMnhAG1lKIctQuFRYbIqkJLgZqbRxv7rhcCinP3OwnpsMdTwKLqKXld7/9QDzc\ncDnP3L95y6eHR2JPTvzycv1gFceJbCNPT08cR4/Ul8CpDRgr3lOqepdH57HL+pPP+VPXX5siebuu\nfFm7hRJs6JIB+bHI5fXVzAsbw9B64fbyWNNVxvq1e/VcL3/DC/r8+tdsr2ezqBJ9Ep0UBlwM6t9o\ndYMIWMw4dPV36SiO8kJrEVXTts5d8r1IsCpOwzi6nQLOVDyZ+fyswQVTxLiARN8L6s5yNeYqjmol\nv/AWRah9s/lSHFRruxbJNLkWqa9FYtu9eM03bUbvohaVwuYhorL7jhQjm0kDzkJrVdXEVRea1l5i\nYqEjBFfertA2ZN445S2K8jNbVvqIHXpIham9eC605qEXk1dx5auiYvv39v5fj7ftNWxF6BVxdoGa\nV2RNWHJX7l90oSrqMyzjxC7csgavIsxtfLxiz+vnrxSL7TNrTpHyrUg2XUUuTfF4MRZnX4/HHqJR\nLdZ2d4oN8W71Oka/vIJXzp8LlmPnkbdqqHlHqjMXc4KWtbMgKowDCOrK2Dm5hkl6cmXY5pOGYojV\nyO+tYt+oE69pZBtSZtHF3XZBUmfVX9//z1GpAErRomf73Vdx5Ss0rtbN/1qR61xfkKPWGtN+h/WO\nVipVKh5F/cfosVZ5kkkKxjqscZ2mpUJBGwcV5dRK84K1Qqv6upaUmfYDo9kjzv9ojL24qXzRFeiI\nuqZcVoSGiJri6+M2p5qK9fFHPw/buH3tVCN4C3lZukZCkGqwr+wwbUfvLYpsbUE7OndKj6vNNKf6\ngNgpNKVViohyO3tbcwuQodNgpJnP5pvpxV0zaKhNbawpMUbPOA2s80XRT2kglpIbxTWc047NulyY\ndsMLYtgLYZGXrlIpjSIVb9x13VbhbuePW+mR03pAM6KceuWVe6ZpoLXIOidNrGsFvGMInseUtKgs\nep+wOn58DNzc3vLhh488PD1y3B+uyGlpelBvYq7Fja5JGg6DER1fokigcwHrDXd3N0p9sRY/eASr\niXwdYdw+/5wzJVW8H5DmSaWyu7nl8fTM4NX14fbNHSklcl4pPRzK4Jh697PZwLwmzmuiNg15KkU5\nourH3q6cYxF1HhARrHPspx1Ss7pf0Pj4nMEavN8RgkOiJ58eKKWwSsNZQ/TqiZ1rufJnNxqDtz2Y\nSLYxY8m1sumSFAQy16hv/fxr59K2a+dQ96/KdNjjfOB5eVZ+MYZmXuxJN26qGBUTO6tdUpylrKnb\nr76ATdd5Z3XW+A4qEUIfCwOd/6RrvrPaIUYLTGpTHYgVkIrFkHNSQK9zk4/TLbDrWiX3Qqfra53r\nSK21hqGjzdK5vRvIo042jh+ePrCuK8dpR9zp4ey8LLpO2K4rsdrJWBaNod7tdpSSdE3qybvWWqTz\nd2sD5y0hjsQ4USvIs73+n5iuv2iqLQCD9U4plM0yZ7X829bj2jLR7xXEE2jNINWS+2F8HEe8O+HM\nT5ep6qZTlOcfJmp5Jq0NO7yy8n3dPW4a1qbWsP9/Q5J/olB5vanoQ4w6HKBF2k9dG3rhrcXaqmbr\n/gV1sRZy0uefpvFqmdTvH8a8+v19h98232EYVEC20RaswXhH8APBBZwTDrc3nD4G1ssJ11sOm1Bi\ncJYiFek0C5FOuyAQomEaRrARax3V+J5Kd+H06T3GBca33yBxR2vCagcyN7jg8d5d29ca+sEL/eLV\nPf2MZtHf12VNUNUNwFTlLecucjCmC4U2kUdZtcJHhZPOWWonyW+WZtIFItJbL6Z/tKVUai19EU6U\nkni9+G2vLa2pt3n8tSUsWKy8UELo6X5OLM4OsCXNtYAPjmrX62a6FffOhc8K5WVZlGe8iQN7K297\n38Zw3XzXlJFSWecn0vmR0/vfUvIFlxoxWx6T4ROGIIYgotY9RosRZyy1vNjgea8Fseufj7OOhsF1\ngyFtwX3eSnb25RBxLfirpvjVXo43A66+qOC/vI53B3LVexvGAScan72cF2ZJnNwztibEGooYFumC\njP46XBW8QMxaWJ1k1Q4DXVhpDKCLo7EOab2N2UWLpgljj7jeBCrRdwunXFibtpGD+zlJrl45F6WX\noKKorXsRjcNt4TO+W7uFlwVys4qqtXLJM5JRkZgoGn5/vyd6x+X5gZu3R6Ra2lpZS+12jDtCHEiS\nqLOKj6ILeEam4Hm6JMbhwHS8ISdPwhJejWuL+qhun+lrulZpDYt6sBsL4+hpthKtwQ8RY4W5VI1S\n73x8eBmfXcWGMR5j9OA8DBHGwOPDI6WdibcDgw1kqaylkEtmshCtCnCprRfMkNdE9WCHEVplXbTj\ndmnCuN/rIdxFrB+YdjuGYaKhPsQV5dpvfGT1HlahkR80mctYLbo9jtPpAruJEAbKolHK49ijmkvn\nNDrHMASWy7kf0By1r8fbPPbdGSnNM3E89uVI95CtuMg5QXcpoFQolSCefQxqXVUaXgSpWfcBY3hO\nF8whMp4q53llWQvz2ojDxBAdl3Rm8Yav/+BXuCo8fnzUlniwmFwoLeO8Y/BapKxrUv/gJRGjv9oQ\nbu/50+VRvYWBOA5ULzjju9uFrgthiEzHkd9+/xtyKpTlRGpCDZ5LR4SbVUHhb757z85Hbm+PGBGe\nLhdtTSedgwWHGId36kaw5sS6zHhj8X1/al7pZQ6jHZW+VnrT3YOqfsZTNORaSfMDjonDNLIU3WMv\n5xOtFm4OO2iCR1hS0oawg+fziSIj4xTIpxO7aWQ43lFaZV0XDocjuVXGKRKMoocGUbu5qu4QNXdv\nYmfJuTDVRoxdvNsEj6Guibgfuayr8nxzomKwXgsz25N5jdHU3XVRbUzoSbvFVpb1rBophOfnZ27v\n3+DEqihOIiFq8XpeNIzGUih1xrjKuGvQDDXB+fmJJnpAWE1jPp85ryrg3O121JJxxjLtJx4/PRCG\nyLDfaeHXO6XOWNaOBO+nAQUSC8Zafv3rX5PnhU8/vMd5z3DY4Y0QvVoJLpdT3xsNpS48PWX2+z3W\nDJjWFMhwXNcd4yyfnp4JUTgeD6TLgrSRuzf3DGOgGQNGDxoVf6VeOGuxBFrT52s9Gno/WLw33Iw7\nwp3h42/ek5ZEyJ7ZWoa91gBpLQT/0wVtrpVglA5XBVwcOoXvxZBhoyhJUfG5OPCDZxjC79hpPr/+\nyiLZGDMC/xMw9Mf/lyLyHxlj7oH/Avhj4J8C/46IfOo/8x8C/z567vsPROS//X1ezGsu6Pbvz15s\nT5z5ObhpK6Sat1jTw0E0S/Hq6qD8NH+1Mnv9e14jp/r1y/+9uCS84sd05A8jvSBXj9XdbkdAzeI1\nSz30sAvpSS/99FUaQ3RYGwCvVmFNKLYQTcM6wzQ4QrDYlvFkbMsUq8W9aY1aX4piMa2j6faK5m3U\nEbH2en9e2pEaoC61qmKwaizp64jaa7HJi0+09NZW2wCkDp/q6bB7mxrTJwjUTQHeC5XX93vjMn6J\n9L4eA6UmnFrMd9TaQtVkj2p78mDtvpbdnWRDKLSw9Fdxxpco+evDw4tzycvrq7V2G5zM8/Mjj58+\nYiQT5pm4JHIVqjdMXtOOtM/eUJ/Zz/1vt89i640IitRK5wLXV2Nte/wGnby8l81/W2M5qlGnlthp\nSj91xcEjSceGkZfI99dj3osiLU3J12rXhuBN55waUSeJtgm3wIq5Iji1vPBSr6/d9B6DMer2YCz1\ni05RKUXRlX59+fm/vny/x6YjlsVsC2BVkZftvDhpap7fC2RVgnckr9OTvNPOjsWy6xZg+errbWmu\nrxv9UiuteOUAqsg3aqGQT+yOB8bdgQUI44vKRBH0XhR/8X5e1pmX//HBIqKHOus9oQWSoKhUkc/G\nrcjL2vYabUvdPi14C9lQamJnXrv4dNCAF2FjDN0ZqDbom5LGu/ZI9A0oiBHxA35Um8kYI3XzW5d2\nnTsvrh/KQwyDua4bzijaStHHxhgxNZGWmSHEKz91WRbGcWS32/HQeYw/tT947zFicMldD5iYzdmn\nMgwqblQeaR+fokLnkjIlN6IfsKh3sHEDYRwoqeFChNPci3XHnBQJTusFgiPjeHh4YOidyZwz66yJ\nebv9QJg8ZOX0hnHEGk8u7YpgbxoHaYZx2PF0OlFaxQT1Om4lcZxG3ry50xjsswaAjOPAEAKXOZHX\nomi1SOeKrsQ4aKGdtZsSnFLP1nXl4UGDmcbDrR7KRXn+ubejBcGHqE5GaPhG6IEcpRRNm62NcQws\n89KF6QPBGY0rlUorSSkO3tOScnY3SiCiByasYX9z5PmkriiTgWW9EA2M9kBwlpw1HbN2Pjeb+Fxe\n72WF0l4oajbr/R1HuXKXY4yvQJCeCNqS0m+673dFrp3IbX1KKV2551fahnUEEeqi9n9WHLk0sBUJ\nncPfKoO1HWxr+AAYx3xKtKZR9cOoISurAc+AFRXjGSAOA889CtsNI6WDa0rlbKScGePw2VzbACBj\nDPtJueun/p7VbauRk3Zlg9M1zAXLNA2d/29wLlDSCeM83nlqzzZovctosrpy5JSwBOI44b277p2b\n9errOSoIy5qprYNodWFyDoPSUQ+7I8F5LvnEw8dH4v0dOMM8z8yXCz6+hL69vlrr9LH+eTvridEi\nZr1+zts6q25DhpR0DK+/f5bI74Ukr8C/LiInY0wA/mdjzH8D/NvAfy8if98Y8/eAvwf8XWPMvwj8\nu8C/BPwK+O+MMf+CyM9kSevbwPcerhVFXkzO2q61qlJ3LhLXDALlZ57lcNgrV6fBGiyhKlrUmram\nixH8AM4WgoXBgmsWZw2mgPP6d5OKjTqbvXeAY0kXvFURXUVb0q2CqTNiPFmEOBywbs/T/MDBGkKr\niIfBRkwWQlW+7ZJW1lqY9jtGpzYn0laM6OAbpwPeqsfgaU5Ye0FmmA5wPDS8swQvSEvgRiSEbnWl\n15LS1fKstUrJK2osXq9FY2sQSqbVokk/65maMzkrpN5EeeGhOaiFbF4EexstpUnFiKEZofbvOhcI\nNuBMR+9ao1SPwRJDwdbK2lIPoFChYS4AlmaDWu+1Rm2dL82KEcg5fda+lrTHmEIY1NqnMOKKtsit\n9bQivTvgMOZMzpndbqctLGO50KBUrBWGYKEJrlvWSS0Y0xjjgK+W09MnXDozysL7x48YKUiufHRC\nMQNTi1ykYKunelXpuiqEZhhG21u0Vr13a73yh7MoYiVWMN3FAvoG3g85YrYiy+IHFT4Fu6fIDBai\nMVpsNLm6Rnx55dLAebzVVpkTMC3g2h7TDkhwmBpZ1kRBsF7Re8zAFhlYjHCBHiiho63RWLqFXLRb\nIf9S0JcqCAkjFePU4rH0UrGWVVuJXhS9QA84v4tuISUhTb3Ua81djAjQWFdFpuKopv5TmF4OQEX/\n2Aa2eeWv2oG0XDjcjhxHz6eHb3n39kBKiVIDYlZCiJSWCWbQIoeKFC08W11ZUyPaHYc3XxGO7xju\n/xjacy+oJm0jY7SIddozuCLA9EOZbSrCcRoVjwvYaCjN4WPUePj6jBMDktH+gb9uaqVmbvaHa4cm\nWMcNle8efiDGQBPPmp74cG4MzmPSQmmZS224rOJRsZZ5KdQ1MxrPeZ4x26bsJoKfOH7zNbtf/gIb\nB0wYdDOdV85lptUFZxtWLNFP1CXRaqXlBSw0X5nXcj04i0Crlbdfv+Xx4weWdMbazLB3xKi+y+M+\n4mLk6eE9uUy44JXChcHYQG2G0XpKSyzrCWcD+zhx6Xz3EBzL5YxzjuMUIdfuoGR4OD3rgZyBOEbC\nIFzSqduERfI6I6ZCyVxy5e3bt3z7/Xtqa+ynkVwbz5eVcqlkb66c27WL+YKLUAzlZJnMwO4YtdgJ\nA0OweCtcpCCp4kT4/vtvEQN39+/4xVe/0MOcaQxjxC0FqcL33/6gB58QkLQQgaXoer4bJ3xVys9u\n2LHmwnwp7A9fY1tmzWeaSTydLxx2e371B3/A09OFJgM1g487vDXM779DXOT+179G1gs5aSrf03zm\ngCjCbA1SCkss1CJc8ovOpJXM/Zs7nFG7VHEJxGPKAjljo8OFgKvw5le/4i/+4i/47v0Hbm9vSacz\n9XhgON4rradW7sKOGGAwjuIsJc1clpXjfkeQjJHGvBSKRAaB1jTOPew8WRJPjyeWRYvr1Walv8wL\n4+5ASUL0kxa7a8bbQBgi1WXW9YmWGliDG+PVknNshtR1AUkqdow8Pj/h4o5jd0toteGcJ0igtY49\ntaqHzaaUCwWDHDU38rpQq0fEkEQIYSQlgw0LuaycTk8c7t5Ctez3e+1uthUTVdDnraYBxzhoN6p3\naM/zhadcuLk74q3l+cMP+DCp2M864mGnYW1pZTfutWj3DpGm/SDTKF6QBmN3rvrh4Zm0wu0hshsC\np6dnPn76jvuv33Ib32BCxMQdLgyIN9hoMcPUU0MX5vnMx4f3mHLh7e09h5vAWoFkuH1zw5IvLC4w\n+sj5/IR3MyE48vL4k/vB3qAe8DFAXQm+ULHEMNFaoeakNUYp2GAQhPFwABfIvyO06svrryySRSuT\nU/9n6H8E+LeAf61//z8F/kfg7/bv/+cisgL/xBjzj4F/Ffhffu53vCBQraNsG6msc2GtctrYstR/\nZicdnL8+jyK/Wjioo0U/JdqXTXxrtWtU9fX96uZat8dt6MuPkW0RNZt3cG0xaztkKxm7Z2JHa4x3\nBHpoiDHQNj5d6ehYLwB7upkzmiimCW+F0HokrTR8U3rD64/6NTroupq7tkzJShPZknSMqPCpdOV0\nTZm8FkrJbEpZwWBMxXXVeJOmBZLtPqWv0GBendg+953uXpEGLfZavbpfGGeptXQHiE2EJlfPREV7\nO6/41W2/ottV/SGbUf5yQaO93TBhbcO3QK0Ba4WU9BS8qe4butlUrc6vv6O2guvJeaZBlYqlYiik\n+QQlMzin6EkvcmsziLGYZmjeX90oMOpY4UQJKtY4KtLbzVDF4LrfcROdwC8ocb9H1nKdoh0l9Eaw\nPbJbzCZuhN/pnfbFGLFGvb1rjKxbahyNulFgOj3fY7TdJso1l/6aBhT9lkYn2UsPKpErUu7s1qUx\nYFx3H9An1oNKn4Maa6Vo81/xFuorjqJ06g+03hEqyOZa04RcVJixCfc8ep8UvX9BlkV03oYQwNGF\nL+a6dmiQn3o+B1RFnUsCl/FxQLyj+cD+5p45aSjMRt8xHeW3Vrl6r9GNKxXDaQKViAFXsd19gd7l\nstYSrOtK+ddzQ65C1u05W2tgHTkttKK8/yZF3VOMxsPWqt2eYCxSa08GNKRaaEUtALdDo4hh3O+I\nw5747ivc7RvcOOKHkbRmfNMUt5qflUJht3GugMPQ7Tythboo5zyOSkPLOWN2hjh4aJWaFOUdeyhE\n3XyAuxfxbtejh4uKgEutuO4OoD65DucdrjaqJKR2kKBULvOJ2pSaErxS1JbzqtqOquvadv+GIfRD\nsu49D9//wOEXB8bdBGvhsmpC4P54YF5XatbDngsesySgXVFs1ztq1wSzVMhV7TBxfcyJ4LOndf9y\n34XIrSmN4N3+9ooQbj7iUlt32CkKgKBCQUR/pvV+m96bLSVS/XyHWtk5z+HmyHfvn7vmQ9HaMUTW\nop0zu5u0g7QqKreJm6dhpDbpSH8hdLrbJrRelgs0j0UdNKb9SAgDeclgPTGOPH76gbgbubm/Y/7u\nO9aaaVb3qGE38eHhE6yOYxgRFdzo2iloUBCG2CkgrR8U8F4deaRd1y0rRcEWKqVamhRac9e/sS/J\nmW3rPLRu3Sg9xQ/V6khRRF6so6pxDNY64jCR27Y26ZpsnGpPzLZftaZzS9R9ImftRiGWWlYwL12l\nbc0Ko4rLxHT/eQyn+XLtegbrCHGglnwdL9t7McZQUqF2OPE1Mr7kpKBS1yDEV122F978izPW9vPq\n4hKu43JzjBFRn++pu0rp4zuQ0vnbgu1ORKUnlELKFecHjNUI8VqrrkEdtd+e3xvbQ+B+fLUOGWzr\ntOlrZ0U1HJvrTjEqKN/ey/a5/L7X78VJNroL/R/APw/8JyLyvxljvhGR3/aHfAt807/+NfC/vvrx\nf9a/9+Vz/h3g7wAM4w4RXZhCDJ1jombXYrsdVS3gXiKdf+qqtV4dGcQpKiVbiAK9XSsqVEnLyhoM\n9VZ5fBq9CM5uRt26AZWm1IMxDGzFtw5q1yeVWvqE4Hhz95bv48hsdFMyDoy3GK+FyTb5rortZnl8\nVB7bfr/De8s0TazzGWNEFc7DgA+B6fiWMB7A9N+H+cwOTTBXdXqpiZoFgwrzSsk0UUunVio1Lz11\n59I3pARSt4yKHrTROVrW6oT3VoU6vQhupiPu1nZbPPeFPdv1c8aQob+WWjNLUr/Oobfq7dXmDKT2\nFjwqXAQ9gLzY+2nx7KtgWlO/61ZoDQ2cMXrwSct8Nc0XLEUghYTxryfyS7vbO6+v7aIuKt42lvTA\n/PE78nzh07d/jlkv3MeRagzf5RNJDMXuaHaHIVJ7kcB221D+l0EN1zUYwmKDw4rBtUaRLuSEPrk/\nP+xE1z11e5FsnAqwcJ5qGlY03U48PzsvjFGbNWk6nzaBlbqulB5cA4KGdGwF7hZNvTatOIrRsnQ0\nHrFQzQu/ur6iDegcMYo2i9HUsR7isvlq+74YF9fpQiJ/daHfQ2k27/IrfcjSfcgNUnUAu/giNH1N\nLxqnSDQBsGSEtGbOl4T/6ka9UueZdVnYD17nRB/bIoblvGAk4L2lGEf46lfc3f+a+Snjjt+wPi0c\nD7cMzmIi2E5x2Dau11SS6ybTdLA410U/veit/TBaclaluXOsrxIy6V2JOMRroEUYR1ptzB8/MD9/\npOQzLV2QWsntzCpVW/j6CqhlIdqRahypCaUJow3EIeKmI6nC8NWvGQ8HhsOvydUhdeAQ7/C28PHT\nb8hViFiiMSxrRgMjVoyRvhlrYmmZG9M0cX6+qAXVMDA/P2BQVyJkYFlXHv/8L7m/v+d8mpnnmfs3\nb65jNISAylKUS22t6kxaK6y1ki8JJWRpYTL26PrT06OGcdRCdJ7oNcZWpFHzynxS7YNzQbmg1pBW\njcfdTzf8n//3/8UvfvVrrIuc00xtws3uwCEEhqqt8Forb94qJeJy0dS8480NwzBgnQr+Hp+fdT+x\ngfN5ZX+8xVhLmHZ4o6r8+XwmDAO3t0dyTvz5b36LtZbdbtdt69QZyJlGiJFdH0vOR/bjnn/65/8M\n6zQZMudMWlasqxwOOw7HN6TS+O7DR+7u7rHdKjPVRJPCu+MNuTY+fvcBJuWOlqIBUK3vCaV0AApL\na3A83iIiPPyFfm7z5Ylc4P6rP2T9/szD+sgffPUNu2HH83wh7iynZeb7f/z/8Kd/+qfYGPizP/sz\nbm+OpFYpizpiWGvZHQeWRSgkpklzBAKOYB3r5ZnogyYl1gaT2nBqGmkXdxqV8Ys0SslI1860VoCA\nMSr+i8NOwzd69WtC3+OaxsBLURemuSSOh1swDS9Ri96iOohUdL8y1qjws7aeE9BotSAtd1qaFmxp\nyeDUfs33cbrmQiorBIPLhmm/I1fRMRMj59Mz4zhiBC6nMzThcNxjjFz1Pgbdk4+7IzLt+PD+B1pr\nvHlzqzHns3rslw0g8pblctGiuYbuRe70cNX0UF1qwRrPzWGHiJBKYV4Tpeicvr29vaLc1lpKU82B\nFsoBKXDh/gAAIABJREFUsSDVsiyFOHhC2PN8qQyPmbs7C1SWmqhSGPcHhmnkTm7Js3K0T10X8eW1\nVt2lQhV8RwydgSzrNYxMSgcttyARKVSpL3Hvv8f1exXJnSrxLxtj7oD/yhjzt7/4fzFfGnn+1c/5\nD4F/CHC8vReNUOyxyWp+d0UsN17LVnxtaOSPLmvUL699ztXbft6jnEiHksaD296+oHZWXSXaHKaf\nmjYek3L+tlPZC8FfM9orxnrG3UQYBs6t0IzvKKcQWkME3BB6ZriqfX14QTA0FagrLzerF4EM2CYM\nY8MP6r9JnLQoaQ1ErYR4dVo0xSCihWhrjdqK2gWtSQdMqUh3mFBEOb+ceqnd2s50ZE5RZbHmatXW\nepHV+qTQFrxaw1150K/QZbV/a93VovWEQYeYnia3ib9EsPJyzzecvFxt7joyiSIjoLHhOIs3ShMJ\nJaoFUFGrHCOokGA7qcuWMJiutAbT0dNwFXhJT3jMLKcH0jqT5zNczgyi6m9bDa1aKo5mPNlqepiz\nTsNmakNdWV94w7X21Lm6dUPUHcTaF+eKPjmu924rqzZq/bULst2fvjh8xlf+8Vy7/l1rD98wvUjt\nivViwdTrD+jr6a/fXl/XCxWk9flGlatX85d/XjjZVr2vEVwvCk3rhavRcJ5tHvyuJpjeD3k1vjpv\nsItcQOebCN1hQdcP0wv2rUuBLVgTMcaxrgvr0sjFUKrB4Gkt0UwXpqJuMwJcVk2I9HHCOIc9vsXc\nfMMoZ/AjjcJxdyBaQ/Wvos1fHcZeU4au76nbBGorakNuXnynHeoi8tq06Mvn2b6urVFLRkpG6PSM\nAn5UK7oQghY76wlTGwVDswF/GBFXEAmYXWR//0t8c4Tbbxhujvziqz/habmQa2GZuwDJOEqzyLzQ\nfMO6CR8iy/rUX4+iR5hGKpXQPLlodO/oBmyzlLxSxHbdgPD8KfHp0yfe3NzinOP5+VmFTN0Vx7ue\npGrVuchYXY9rVb5jdIEQvSLRRtdaI3pvW67MST2gtVjptpilajfI2o7AbzaWnv3+yN3dLafTCR9G\nbm5uSGsmldxFtE3dg7qANuesB1oHj6dH2lPj63f3V+5xy4rCTtOeYRo7F1ijvwdnWRcV1aXFs+YV\n2wuoSy8U5u4XPfWENe1C2euYuLs58Ph8JqXEfr8HH3jqwVA3t2/wfuD0+IB1J9zmwkJlXbN2FUSd\nK2Yy86yx1jRh3O2uh00Rw93dkWVZOJ9nhmFgv9+zrjPjNGEdLGlhmgbSopSBbR9IaWEaR3JKnE8n\nSs7XVFEtaiq3t7dX9Nv0btkmBHdDvHY+Nt981xHHMURyp+lZzJWLDFxF+mUrUzbNUkf6Ndq+J2V6\n5TGn84wVlZlsGoGKWsSF3tWe5zNx2OOc0oEEND00VZoVPJVW1IFCo7gVZcU0RLo7lFQ9LAX1Ro9R\n9UshjrQlqbhXhHGakNYouehYKp8Xerq+K9i4igarTdOOWgsPD0/sDvuruDGYgDcW5w3S72WtuheM\nYQs70nVzAwWFjLWudw8quSRSStdEyG0PE9DuIQbbtn1ADxtLSgQv+LgHP6gsodHR/Ubww7XOy2vq\nbjM/Df6Upi410TiMNCy9BnAO3ztxS15Jy4r0GsAEvf+/WyL++fX/yd1CRB6MMf8D8G8C3xljfiki\nvzXG/BL4vj/sL4E/fPVjf9C/97OXMZq7/VowhXkpkLEG673yhFCxzU9d1eg+k42oBRv002Lrv8Ni\ne9tXXXYsKS2E4LFeC5drBGhCkR1UrZ9SIWxo3avcdW176+JYu8OAj/2E2xJSheI8NIMXr++lb5ot\nF+I0vhK1qam+tzpxMJ5h3DHs9sSbI2HaafGGMHQ/47a1Z3nZODd7tVKUQrEuC9jQvYkLZTlT8sra\nW3h5VYTFWovxQReeMPQN2mmBZhSpTrXgcJ2bzFWsCEppse6FxH9F/a1yENd1JZeVYafcKFviKxpE\nL9K76Ki2DJuwiFf2Z73wkNooKNfUNHst3E3RlmcVLUh1Y2rK7xwjFtHip5mrobhyhoWSM4glzZlW\nE2t5ZJ4/MT8/Ecg4b5FT1reeKy0LqzhWHBkYJROvHQYtQrsbNyKwtoaRzdC+G7TjoKIUhR47inkR\nZRlRekLHaDGdSrMJMVtr2pKy5kp7+fHENZieflWyuih48yJiGYaB3NTT2jVwAtUIrSgv75r21tta\njaZCHF4K+bYVr1vnp1UsUfsB1lGoiKgwKlq1BUKgeRWabnPgd7XB9GC2IvLiqW6wLGnBj9rudx0t\ncHbsVKYt7rVTMYwWKbGjRakI8yycnhO+BW72h57GmTE26IExelrw2OAoWYjDjvHuhvHdP4fsf8nI\nAxlP6pvXzRS5OG2/bqi32i22z1BldSLwV9GvCh30ni5SVZXfqRupq+W3NmlrXMMEDscdtfb43ZRx\nUvBWwFtKNoBnrhfmizD5t0QP66kwOoOMO8b7O95982vaWqiPK/5XX/FHf/K3eTwnTkWIxyMfK3z9\nR38T6x0PDx85PT+zv3nLECytfYB1prSMDb63S/Ve2E4raPu+tqeKHy1P6wOyJHa7HaAuH84H/uiP\n/oRlWZjPF0KI2NFxOc9YBykVYtD0uVI0VrqmQqkaJ1xb5rKs3IUbWs2IbB0o7RzEsFP3jpaZ4o7T\nfNIEwn1gSVwFy1Q9dOU1kYG3t29YUmZNlXSeCSEyHEbm84V1njnc3UJwXC5nRGDaDUzjnpTU0Wdd\nV2LUcBDrHfud57xm7QY1RfZDL6LHNlBa5fHxEWsNc1LNxe3NDdZa5pTJa2KZC1Yy5/MZgDd3N/im\nBcs333xDa40PHz8R/cBXX33Fsq78xW9+w7S74c2bI8/zwu3xjnmeCQ7mdabtd/hmeXp8og0a4Vyx\npFQY7nTNni8XnDO8f/8RaNzcHjHG8O7dPTln5uVZBZ7OktOFaYg8PX9kmibu3r7h8fkJKZXdMHJ6\nVAHhbhgZYmQaRxUYomtzboX9zYG7uzsuy4J1jtPzs97H4MlGsENkiAO+6Z5YzyutVY7jxFrqlcK4\ndTIFw9VeFC3Onp5mvDPcHm8w3rJkjZ/2Q8RkdX7KrVEtPD1/AuDt2zeE4LnMmWnaMe4mpGnK3ZIS\nZjv893RMqeo64byucyEY3WNSwoU9Pipyn3O72oimqoE/48FQWsUPUe3pjNGaxXlOp5OuH5brfmCM\nYV21exvHQIiRMES+f/+eX37zjdIqciF3WtfhoBHmjw8ftWuDCisHuli37+XKphCMi2A8vsB5eb7W\nARuNw08D1iq9MNXGkgqXpwuggl1vhSaW0zlhXUZK4ePDAx8ePvD87LGhkS5ndnHg/Q+fOL796qf3\nAx81Tt6pJ7OTzamkMIRBY+RJ2NKwUw8X875TY3+68P6p6/dxt/gKyL1AnoB/A/gHwD8C/j3g7/e/\n/+v+I/8I+M+MMf8xKtz7W8D//jt/iYAPO6qxzK3ohyHKGfKmo4alINZ+Vij96GoqynJ+oIpc8+RD\nUO7iXBKml8+7oCfbWoUYgwoCa0Na1jZeQE9QVjlmWrAPpDxjUSFaqYJ3KiYqNbOmAnGirYaLgemw\n4zBEXAyImB7Bqb/HNGG/O1KNMB32eGuoOXE6nVhiYHSe0UWW5UJumd20J9MYdncY51jTpd+LUdGP\nXJDeqk1Uaknk+azm8fOFnJV/WEsinU7aAu1InwsW47wiZi70Ykv/lL7IlKYIs+/BGVUsY9iD8Vdf\nVGMscXekpBXnLbUs1JqQ6Pj46QOX85lW4WY6grFX/0ZdwLT11ZqB4LAFtfWxHuMtMeoBo0jpPM3t\nYKB+07kKtYJZViR0X8y0kkpmN3lEKvOa8VW9WnPOLLlQa6GVBE2u/DZDoqWZ5eM/4/T9t+R1xqcF\nWiOVE6YWUhqZJcL+Fmt3jBhkUGFakJdgkuhGGuoxWlzoJ3n1wKwiNBoYbX3Z1oVNnUbgrac5bXtd\nudgGfM1qHWc9zRsqBlfolnA/fUVa53w7Spm51PeMRoh+wEhhVxq5FnVLsQ5vPcZDQahWeZtx47kX\nQ3VNHVsQfIMSejurdE9RAVpijAYpicHSeREDBqE41R/EHv0eQsB4e7VY/KnLVkOTqHQRp/zDSiVE\n2x1IEnivfGvREI5Ge0lUrI3UuwveNKpR0eHZecb7e3I98Xx64Ku3X7NWz9rUuzPkjAue0e6oQ2Dd\nvWN6+8fUBufTdzSBD999q3OvrJyrw/UQDmt1XBtrMOLQDpTt48MydDFZ64i+MRrgMnm1VSutqSWi\nNURvCDXw/PCsSVlD6K3R1qkCgnMBMx5UUHrzNaf2Hj849vmXDMegHMiScYc9T08n/vhv/Svc/+pv\nYO6+5umy0ssI5t09w03AF/AuQitc8kpNif1uxz4eOMtvSK3wLJFmM5ITab1wux+BQEkV5fHoeHLO\nECJYGpUVsSP5XAjRYb3Sui4lgdWWeFkro9GvqwjOGHwVLp9OTGOkSNL3jIcmTM1ysZXn52esEQ2a\nMB5KJteXcKXWGud1oa6Gp/MJ7wLjeFB6Qqs0Z6i5expXoVEJfsBZ4eOnZ0rK1IvGr+8OBz590CLw\n7dt3PDw8YKthPSt/1HWnlVozSxGwHkzB2kh+OqvrTlQP6pQz++OhW+fpPFjaDEb4/vvvcc4xxQmH\n4zTrYWmMu97Gt8TJ4UqhSiLVzOF2RLJgamKwlTdvbqjGEYwn7Cxp/UTwFkMk+B3nOSO20gaD5Bma\n4bCfsDRamal1ZXSCGQK7nhJ3fnxQmsXzE6UU/sYvf8EQAw8/fCDnRqUxuAFTDW2t7MLEsy/U0thP\nO2Lw7EMkp4XROZzrlnPOsZYzrSiH24KOPbGk00yzg3LIBUITLiVRZvX+raWxrJXmtBPmrIYC1VQo\nTa3iWkpqHxg8NSiA9jw/K81jGHS99Y5K4bJkahMmPyo6asFbSytFaQ0UzvNZPdVFxbpm8Ho4Ch6D\nsOSVwQXS6XRFzddFsHZglkJoBtOcit6b55wz0//L3JvsSpJda3rfbq1xP100eUnWJUoQBGggFSQ9\ng95BL6ChnqMeRNM71jsUNBFUBagFSmJd8SaZ0Z5z3N2a3Wqwtpl7JCN5KUCDNCAIZoSf427mZnuv\n9a+/GRz96CgpMTjH88dPDMNAWGZobk3ebKBRRFGEgqI1tgWvlRAwztEZy7t37/bJwDYVDqtM8I22\n9N2RuM6kVHCup8RKVLIv1KK5/+HAEiQgKyXRMZ1OFwE4aiFWyUQgnHDuAWc7PrxOLGukdqXtXT1j\nr7mEmSVeuIs9Kq/MrzP5rHg4jHz6/JVQKr8dBzyavv9+QauN0A0/nr+K00cSUEF3GpQllkJUCn9/\nJywFZ3mdRMRv/P+PFnDAb4H/vvGSNfAPtdb/QSn1b4B/UEr9t8B/AP4bgFrr/6KU+gfgf0WMKP67\nv+5ssW2otaG+Qrq3bQwJovyHb0eU3/09t7y/KlBVVbKZ3o6cU62UothM+GsVW5+UI1jhH1Z9tQTb\nfq40JHATD5YqBvYJIdb3/UDfjxKjqTU1C+KpcxMsOYcqbbxXhceGFsGSM1bsuaqM7Sqw5kgtmU5D\nV6u4PoSIJmJUbKJGdnWxeEUqYo3CXVtXQVRSJs5nUpH0uJrEZxK1jXSNFMnt2INcyjUCVPyJLVvg\nicI003vVaBYN/b/hxW6c4PPlzOV0BmXoBoexYg1ktJMGZBOqNV71dlTFbmxvXYfSFU0bse3hJhLL\nXcI1nWu3crulB7Qu+/aPJHolKEVCFcjEmAjTGeKECjN1WSjrwjxPWCq0Ec+aKrloabqUFU4puqXW\niYANJYKxWkVU6KrYpm0TP6MF0c60+19v4/jma11rm5rIta3tHtfcCE1Ru/r6lwhP0k8IH2+7l3PJ\nKFfxjYutaTZwMvui1oRTTfiIEisXpAMv6MZmEiFmUBXN1U6xCs9hT4hTWuFRVAyhuafcfDq0lnue\nyi83wDQfa0lXkbFmbUE67TvfbQv5SyvJ7d8hN3qPoVYZrT8vC+pwhAThnAjVYJ1nXQPUitEKowtJ\nG9YKD3cPKONRbmC9LOQiLgDWXj2uNwHKLdVC/8K6BewimA0EkIbxKtADGWDdJoBuaPR2f4vdnSGM\nT/iHCbUqTJ/QVFSVeOQaItOSmaNHmXu6v/sN6umJ0I0UZXF9h59k/lFqxVqHN5awZEIQ4/5QKoYM\ntlJMIalMrQWdCwazrwFCn5GRd63ix+qcxVhQq8Y6QyVQShVevq67VZnxQk3LVqPoRCDX9A/asFtv\nynNS9uecojBumxAVjPlLissmptoEhDEkuv64f1fb62KRJD9UEd4uoKxBVS1jf5Dgh+a8saQoTbJt\nwiPrUNVSG+0tJZmq0eJ5Y0pY5zjeCZL3+uUr82WbNsh0xTd3mS/Pr2Iv+sPY1jdpvoxp66PVXC7n\ndp/oNtnMjH6kM44YoUwXiipc6gnvnXj61io2aEYRqyLmBErcEmouGAs5ZKpVKCcI73jomM8XjCpc\nTs+M3nA4HLhEmWRoI3x+jeyxfUMjr8Ehhqwk3a8fOqJaCWFpSKuVP8bQW7H/KynhreWyrjeCxC2J\n0clztwrXtmt+5CklcknNppEWDGRRbIJjQZTJspbGuO7PodYy/cjtHtksJK8TICshYJSdxlG17F0K\njTVX7ZMdOkoVqqMzdrdB9V4S7/q+lxh4rQmrCOe9vzb2Gzq7rvL5tqnE5plujJNaIsmkTNlteiLP\nyLws2JyEntMoovkGTrHWENZlP0+03kXVMWcs1zU5hkyMmX4Y8F6RfMR6h7K2NQ+yp9f2veeciGsg\nrIFSF2pNWCM0OaMMKVfCOuFqpsSCKgJwbEYNIuSTPeJ7h22pgcLr9qAlibCUTdiuW3CbiP8cLQBG\nG/TfDiT/Te4W/w74r77z95+B//oXfuZfA//6b/0QioImUqqFRvgW1TZiz6YU1tg9PWvb7P/i99wu\ngiSqqsItRLh6VmlyFiVfSIoQFcsiwRYSmHOlCYRmuF7bGN/3XTPVltz0jW6BkUhIMXo/MIx3aDuC\nFg7usoSdp5vbRiaWZTCnwKAdyzyTU8RbsXkJqu6bo/WSNDW/PIOZSXXAHyq97zHFMMWFFEK7jUQ9\ne7k8SyEYFnIKrNMFato9H90mTmw/Za2jNMu2xkJFNW6wVSJ8M7aTh2DLZd9CDZRGG7FukjHPynEc\nUEqS/rz3XP4olIWn97+hHw70/QFnOyiKWkWDW0pLxOl7itIoV9E5t0JLkICKuAFYa3H9QEqJ0Azf\nDQqTZWHKtaJy077qq1hSFNrsXtbZiQI7LNPeYHjjqUtiuryQv/7Em8Gyas8pTo3j6kk585pgqR6F\nx2DRVhBYlcpVaNc2M60UfTHo1DjRDfG3SjWnEwFZndk4rC1Gu0jBt9VWaiv+dcUp6UCNNsKv0r+c\nRKlNQRcFSlONxrkOVQ3z6wemy4maI9SMsyLGUVX8Mk2RxkXa1rL7YdPuD0olAVWJ3d1tkaeNcJ+K\nEs53Lc0xpflaK62bi4ZizRGTr969v3SsOdFhdwHsfrZG/4VYdCs6b725RdgnxcWKuFgMd3d8Pr0w\n/PD3HDrL+eOPzGgeDkfuDXirUPkZrTMvuaM73PHw/vfM2aCUNG6qhmth1bx8t2f+ttHeKF+bd/Pt\nZ91oFdvfpS3uO1+LZRr32zknXPBWJItXNZISVytpfE//w0C93DH07/EmYS8vkCPn8EeC7fjNf/qf\ncP/4Dvf4L1D+AeN6Op0Yxx7jRlGEW4PVDpUK9+OByyoiwHl6psaVfPrIfHkh1YDRGaudhJbkeV9H\ndXMoOi1n4jpzZ0ZUknsj1ZXD4IVGkBbWuNDbO0pKLFmEeneP90IJ+/Kp8TGj8DhLRpuW3BXFmWO6\nLNh+ZJ5kTegHL4LdIpOl3S87bvzHQeK317i7R8QUUVXCJeYYmFJp1o1VAgtsLw1W+177vmcYBqZp\n4vV8EoFhJ0WMa9zOfgvg6D25KiqK07rgvWctASYpbofOs4SVQuUySyjSwfdc5onjMFIULMtCNwyY\nsJKyuHxUDKVGOj9cp34xsa4Luix0h1FQeBRW2+YqJOtAjJElzCik4CraMC0rh8MR2xnm5Qs5Rr58\nPTOOIz+8fyvrrzc83D2h0iJBU7nj0Pe8fP7MYg2dt4z393JdFfR9Bw2c8MDheKRzFqcVbujIasA6\nizaSNGit5enpLZfLBVUry+nC10+feP9G3n9wrTEuUnx3nXgG13KlQapcKDWJsM9ypQU2T+VSIzkm\naV6Arusk2dAaDuM9ny5fodbd73mZJOpZuOBtDfc9S44YK+N8cqM5Brnn5vMFqxWD7zi/vIrPvLHE\nmOm6QSiazlEyONdRSuX5+QV/6Pf1fFkW1nXlMMq043i443w+S3GCaue0y7CoFXIVqlyhMq8LuRb6\nhqiHIDoerTVHq0gIKBJzBW24nEUH0I/yfGyNYEqVksV/OZTKy8sLuWoeHt/RHw6korB+wKge4zwx\nF4yi2QYupDSjbWUOgYM98PXlhXP4yLEfqMWxrGeWtPD8cmK8fxCrTt+J6Ps7x9fnE8Mw4Hwv55oK\n1luclyTJNWbmELDOUZsTTm8dVRkRaf6Nx68kca+itXBdc0PSYEOlbl72V5CY7fXQUAbTdDCqSuyv\n0mQyRUmRW2uRce2+iUr3JZuXlpjFKgXwzq1FBCKqmF3oFnKgFEcIkQb2stY2qqtgEdsvay1rE1Ck\nG2Py3GJAbUPd4hp2Vw8R/7UYzfUCJkE3o+woPForqTMxBKyWTTK1CGpqoRZJ08spAZncCPSpoY/f\nXrd2oduDpQBlhCe1CfP2DX/jBVfaglOxW9JZQwFyju26FkqIeGOxrhNucxbOq9UakJFoVbG9R5H3\nrAgvTBu06qjNrkcsdoQeYlEUK+JFqzVoT1Jp74rtTTFyiyZvx1acbD7T03TBOM9h6KGzfLlcWFy6\n+tq2di7nRNYOjccoeeiUuqK/StHQzIJWWVBlzY4Uqy26tFa0khFh5WqpY7QIC6qqu+PFdv9vAscN\nUa4IOk372e8dko6FbIw4sGLt97wsLIsoiFMtQmFp1m5VFYLa4rHL/v4asUcrqN0qDq4TBN3uY610\n80SuUIu4vKFp5h8oxY62337qv8pJtuLhpoq0ctxye1NuhvlCV7i1MdqnQEooKpvtIiis1zgsrjvy\n8MMbvO9xteKdeIZWIvPziYKiP7zj/uENVXXUUlmmmRQCrhM3gauwSSY2t+ejlKKqLNMQs2klKnwT\n3v3963Bd926akHoVMMrfiQ1XyVls6HxHWkf8wWJt4Tg8kNYZN09Unxje/5bxzXuKGQARMRulcQpC\nRdAWZVuAA6QoftclJHSYqWlhfX2hhInRW0iVeJqpZOwon3dbL9Z1ZY3SoK/rKmtTKq25qTt/2VhL\nCImaM2kN8gwkET7Zpk+oNaOARKLHiYOAhqjUrgOoqVA3GkttE0NjdiS/5kIthTWvrWG5pgOKbgNq\nbUKvkjFat+SxTMorWlvmJnbquo77+3vuvePz15dmFyiIq1ESTbxPt5wnpMIaotiB9h3kLVhEqEax\nFogwxyR3RklcVimM0YoYM+pmMrI1f8IDl3ukIl23VpbX6YxpqHOuMqEdehHXpUZlWJYFqsaP4gYR\n18CkJpniWQ3aistBSoSYeb1MooOgcnf3wDpPfH2RkXfvZdJYSpEpqr4mnRolIIbXmr7rJP2wCoq9\nCRG3NXlDUjcNw7qupCATUqU1uhXJtTWTyrawrJ2WprC6E5F2gZIjaE0MEXTFeyvid3VFZ2/FtbEl\ngwpibb5pugXwEiRXnJiaqYAW68QUMjWUhg4v4qRS5Ht2TaS+8d9zzmgnCa1b7PhmP+uc2++x29pH\noqm18IaNlXXMWratrRQRjWstblkbgp8VUAuxZLxSGK3IJWKNIbdmY7On3e4v6x01Si1VYhHhrZYg\nJZBANFrcfdndPFta7drSdcvVqrE40bxYBWmZmcqMxqCsIVJQKmO8eFaX9r6nxrv/zo4ggBJSw60h\nUJXYA86rhIaYm3tJtWtDrbst499y/DqKZCpWR9AGU2VzsrrxgrKolFNFstX55WJ526DEh7LRNLSM\neQsZna8bSkGMsreFZlkWjBUj6l0UpBS6UTVijBglqv5Uy85zilU+9xrDrjQ+h4BTFVMDUYOyCq8L\nY7O2MdbKmK/5I5eYyC6L9ZaWiGNrBC1aTxds57nrByoSzd13Djt4QVUjhHVmWWILDalYhPecQ6Tk\niFGwJii68ZXc2BxChAeZaYk9sI/gtod0e0BdN4ql1eaDrIxw1bYxb1NhW6W4XC6Ukug7w7IETi+v\neO8Yx5FQYDzci61Q3niYFV00WkeWWlDaNvqDKIq96/G9WPAtQVS7aZVQldF1pCgWRhoFG9eoNr/J\nZoK9jedvi48Yo9jjJUk5urs7EJeV5fKKqZljZ7hcvsgmR7PcC4lwWSn6gap6anakUlBmBjPKwi9v\nL41AyddR/4YoNq891URatjUhKcl43zqN1oJ6isjz2sgoBblRJ5QS+zSjkHv6FzpurZHgDiwYT66r\ncNLXRZwQciKpLETfLDQUXSzJqpbg2CgHSBFVshb+u5F7Jqtrkp1SWzEPqwJVxfvXWOG96ZoaXUTv\ngsRN1JRi3FPvvneEEOj9IA1HraimQsdoztPcIoEdIYZrZPrPV5qU0UJAELQlFnos0+uK/u0R9+QY\nnGaKC+PYY6mcw4Q3lXdP/xEPb97y9bTS9SOX11eomdyM6UMInE4iXNKd+4b6syHFfUvj24WK+rrW\n3N6fKcf23emrctyI76+gy9cidHNkKVkKqENvUJ1hyQ+SVkbCd/fkcMKPnkrG3P8d2Q6gPJeU6axC\na0NUEed7UgsIIkZ0rqzTs1grXk64eKKsZ/plJi8XAhO0omF0I0nL6NtaSZJ7eXmhu+/oxoH1ciZM\nM2M/cJkCpm/fUdYits1SMHfOU9bI9OVFxKk5ithTaarOzHMAlaWxRu6lnDNpDnQNnU654g3NoeXY\nKBMdAAAgAElEQVRm0likSN746sYYnl9OLVVuJYWM7g47hUIZCSjKNcqIXbHblJ3PZ0H1AJXbd2Hk\nuze6EtfIyzLRdR3Hx2HfS9yxxzhNprTxNnw9XUgN1LnkxOvLM70T8VTJEVIlrYnz64nUKIhbseXd\nQGkezDEnlpacOj49yP2WRBAWasaROZ8vKJ3bfVmEOjgFilaMbqBTlnVeyKagnebxzXvGcSTWwvPn\nF94/PTB/fub9/ZGnw4G1Vr5++cRvfvg7dGd4/vqFwzpw//gAXhoObxwqV+6cbeJ26KzBdh6t0m5z\ntxXvIcXW+Boe7x8YfNuLvGepaaeUyHMkxe794Yj2gpAbBqpO5JqYypmSM+MoYStKwzolYip4T+Nm\nFzovVJHPnz7QdXcoxGWhVvkedEv2NFrhO9cmgoU5BJQxdNZTa+M9Z4m1D0sTeI4HLqcLtdDiumeO\nxyNaCxB2Pp/RysoaYeU1Yu0m1+Pp8R3GOE6nC+M48vr6hYfxiDaSRqxUJYQmFW++z+M4olqNk6sE\naIUQUEZ0UWuc23TYYJwg0k9v3yD6isjRSXjaNE347kCMC7XIetYPnre//5d044ixjm58oBTox4EU\nhZqxT9aSIsXC/cOAU4qOyvz6hU9lovcTa06cw8Tjw0A+Id/RKh74Id36+lwP3x+pSjGvM71zgvg7\nvdNTShXazBITfZXG2jkn/s2/EHX9veNXUSSrWulrJFVNwRK44ZyaKx85t/X0l0IHtGnoDYmwGvpe\nPJeJGVU0XeeIIWCosk0aC1WRSsE6TzGKSMEoQwwy5jwcBpSG0+mE6g+EUPDaknLC9wlVDXGJmOoY\njz3TOgu/rUR0AaMKNUCKBd1XbAWlMpba6CMdqplnx5ylqKuOuC6gLa4hCOt6wWgYho7D0OO1kxF2\nLs19QkYmtRTWEiklNwXnJqTSEkXpOmKzSlHVInEVSjha2tBhpHCviloU2YlPs9O90E6apc4mpugO\nYn+TckML/IZqBWJe5QHvLX48oI3nYRwwnSblAFk6WRQoCzVChyFoEVxSFdpZctejvPhDHqwo1GNO\nUCoZ4cAZ34SYbXQ5jkfxqAS0ks3QeUF8YwyM3tP1IzZnptMzKQWWrkPzgg8fyFneo0uaFaE+DCZz\nTokvS+Wz7VjsgCorB2dY84FY6k6V0BVUrmQCGIM1G5eyoGlK24173DypjcpgPEZZdMqYHCjGNGsh\nORcaTSMpMUlHCR88r99G9t4eRusWO53J9YJ1GaUyVYk3pdI9Nr9ikvAoqxHDfIvdoGoUiqk9eNZF\neuUwWbGqQFIZ0867IrZcKLC1oqp08ilnakmYWlkpFN2Q3Qo+CmKetSaZX84L9dbvNCSlVPNuLZQU\nZHNDEUMLbvBtsqF0E/82lFeb5jPb6KFVHBNePn0g//53jA9Hvk5nXH/AWEuJK7lYiu9Yjk9cZrHf\nylahekfOGl1WNKKut8ayTCuuyvmJT64RJxsr1IVCuhbHJTfXEHEJSc1Qf/OHr+07V0rWsdKS9rxV\nHKyVdmlDzzAYZyRmXivU3T2hvU9KiWJHePyPSVGESJ3ylNqmWCFgrMXajuLyjobmZcaozHDwrGum\ntDF9jic+MZF0YLosgqBmxXk9YU1sTXbblGwh5RXbQm+Mt0zLxP3dPbVN8+I80/cepRXrdGH0jqVk\nSnMnqUbvfrWbe8yaFaoWxkNPCZmQZorrWaeVrnPcjweh18WMVgbjpXHVXmGUZb4gky1t0IeRl/OZ\nTncsNZAKVOOxOZCrRVrwThCzKiPkGMUlZZqErqWd43B/QPUyEV1VIrgKCV4uZ+hHhvGOYz9QykKN\nKxYpAjcev9KacTyAz9i+5/J8JiXNlC44b/B9TyYT58C6rjL+d56UIynOIr4u4P3A68uZ+LKQDiN3\n92+xQ2Q9T+gWLz+MD2hrmdZXigFvHUbSSljKmewKP338yrt37zg42wR1mvnrK73eACvhbus1EedI\nypXLsqKcuE547zk8PfDy8oJSFusVkURKK8PdgLvrSSVx8INQLayIOMtSSMvM/XHkTz995OHpkTVL\n44Gr6ATWap5fpWC0WaZ4KQWMdyJ4ltkxa4h03YHz+ZWXMuHdwDoXOutI6wmnB8hItLW2gGuhUBrf\neVKWJFylZDJstcL7USicxjJYKdRzkQao2kYDSYkUVmnqjRZaixPQxvaeJQUu68yb/oGYhW7oBk1M\nCw/9HR0GtGMxolnSWXFZZoaHA1jD0HmyWeX8nERcO9s1UKehtyHJ+u8NMa4kKn3vyTFKEGmO2CTW\nkL3rmKaJ8emRUiTYJuSAEu0txogA/dOXz9zfDVQWrI74AaKQJsVhokRiWVnLTFZRHJmMRRfFwfbk\ntPKyTsy6YKZCWkXcN/Q963mh6x6IoVCWL+R14cL4/f2A2BJhRRCt3YElRLwTMNE5xxxWRm/J64SC\nNlX//3b8KorkWkXBb7VCp4TBMYfL7nO4jTp0KwJ+ibeoEEuQipKCuc2DN6FDRuGaGngOJ9JlZVQF\n5w33nZfxXhHxktGOWoNwohqyHGMUMUezkFlCgKzp/Igqmfk8s04Lw+GevM6UdcUYJ+PfhroqI121\nKkWiSqvGmG+dGtBV0Isbu6hYC7kUpmUlXyY6LdYrc1bkGOHGSzDHRA6RnLJEFWvDcOjb73TiFFKr\nKLpKRcUsYykFaxunDH5AOY91Hu08ne12dFkp4SXFdWn0joJ1nfDCVCGua0PRHNP5TN/dMx7u8e5A\nrlosz5THuIr1Dmjcy1pJYUtsU+06iM1VUAGtNYdhxHUd5/MFTW3q+UwJMjLsG28rpELXaXQbWRnj\nMNoJ2mhFrLYJvsZxpBbLOSzEaeblxz8J9zEEht6ji2aeBa2PzYnBmiOdP6J8j7OVogpG+e2Glg29\nFlERVxnxUltqFFkoEkq8iHVVggi34JOt2NVG3DIkCa9R0KqIl8RHV0Zbzaj4GyrJ9w4tuiYMirAu\nvHx95nJ6RWvIyTAtAecKSnsp2pHIbK0NVbWIbMTxISl5ViQMw+xIqeGapGezOFgYxC9TqDqqSSRp\n/4bQGopwyf/aOXjvyCGiWijK3hM0/oZiS3m6EZCqKrz0hh5u1KeNLyrPtubHH3/kt7//e946mT54\nJ+Nrjefpze8Zu46+jS43FHibEIQ1khvVqpKoYaUoaVpSXCXS2jlUyQRvxApSSRw03AhN23e48Y/r\nNj5u57LRCW61E9p8Ky7e7h1jDE4JWi7n64XW0HnZLLMUxaRvrRWlWY/inKIFeQ8lkIomhcQwdnz9\n0x/JyxlnFNpbauohF4xzWGX3KPZ1nVG68vDwwKevH7FV1g2sQ/cDKYnupOu61sDKqD/GiNfyWtfo\nGiCvW+a0j49LEY3Ky8uLhHk8veXDS8QYQXTPzUmAlHakHWTqkUtkTQrVmianDYd+IMfK/eMTH76+\noqzBW0sqsIWj5AopZ5ZlEbRSa+7u7nDGYruh6T4QWzLvOGi9o/5TWDlfXrCdxzfbKqWU0DhSIQC9\n98yXiZwSx35kvL8npSj2hlZRouLYDfTD3Y4w1lpZ1xWnRAuhjcZow5u3j6xRAJPz6wmVJWlR10rf\n97JP5NzQTMmUtFoTlpllWkgl8+apI8UTqwqcTeHp/ok3dw8sLyeGoWN6OYsjKorj8Y5aK/Nl4W4c\nMEPH+HjPFFamsKJ7TVWFw3jAWM1vf/c7lFW8vr7iDo6hE8eK5TzRa4s7dqS6Cayz0HHWqYVWtfXU\nyHoZUmyuKEViiLUm5UIIQvXBabF0K4rcisJpPrMsC/0gfOZUEkoXxtHv1IcYBc0ehmFv0DdKpjxv\nipfTLLZtFXIR2mAq1zWoNKqFCC39DjYNg0ytN7R1owXUWrlME9Y5jLN4xHr1dP5KNwy8PH/GOcfT\n45HLRfjD59dXsve8eXximkQ7oJRqNNCKL04mp+2/jXZtnbAt2VVd9/UYRY/Q1nTXddxbuV8fHx/5\n8c9/4vTq8dqyxMA0r5iupx8PqOIJa2JZEyHJni6g2QVjBbSrtbKcIyTFNC1CGc2ZYRiYp5UcxP7V\nFIP3B/7wp+/HUitddsHlMN5Jw5ZWlBr3NW1zStHlSofbrvHfevwqimQFGCsjDKeNFJXtHG75hNvG\n8MtHKzCqEZ/hUlshUqC28SoWQyXlSiqJJZdGtteoasQUX1uUkkI5JvE3FTWmoL2bJVdBQhEUYYf4\nX19fUTiMzmhlxXrIikuBc6LEjWm9IiIUqGIpB0osgpBNTjdXCZQilorZEgBzxlcl9gi52ac1t4aS\npTC+KuqNGO4ri7EOGj9VRIRSqNUWbmF0U8gaEegZZ/cks1vnkP2hz8J3rhV0zPsIRgQwKxpBxYbD\nAe9GcjWCdmkpjClJCkmtUUrU37VISo/EBLf0vBQRFxJazoyWz1ZpYjKFKo7S3BbkO5E4TJS831WZ\nbFpx3BRzqrmqALpkaorkECCLub+qEaMNGhHEhBBJ1ZATJCOpeaVmQlNSc1Oo/Lx42QuYRptQzbVi\n46ju93vOVCpGSYFcaqNcNJoASs7Z7OUmjbvxC1Y5wGZ9oRSokilJjP1jjFhlqNZRgwTYWKSYtqoJ\n6VRtjV77jA1ladgl6tY9pr1npbbnr9FLmlBCebPV9BLKsolNcqWobzUI3z0XCabbr107+fa/myPO\nty4rt9f/1r1g+46893z8+JGffvqJ+3dvxM6oZMx4wPsOfXhCq2sxu3kV7/fTzqvfgofEE/rWbcU5\n2aS2KPQtnXL7KNvvu37O62ff74tydZvZBMBo8805CQ/3JnDlm+u5hddYSghi93bzbG9F+uYgQCvI\nRSuCJHOiGXrLHA26GjSV2Bx2yDIhkM9ZME1p33VX2taW4mitOHGsq9CntlCQ7dw2DuPWlG///c1z\n1BwwEhJsdAoXjBlwzuy++5uGYgts2kSTtRpxC1Hs34OzlmWZMQ1tyiGhMTgnGhWlEGeUoglp86u9\nFlP9cE9C0hFBs84B66U5Dylxf38PWgRP2vaEKM4esU3h1hgYjwd0FGpJCSu6GmoMqA5qVsQlYYyD\n7kYH0ASJqELX9VzOK0uMeNfROUNChLS6SKz3sizyuRtVR6FJJDprSOUab2yUxXnLukaGrkNVifg2\nIXEYe0n6LDJFkhUL/NATchIXGmcwnSetF5QB78UOTVuDdRZljexnzhPJ6CioowaU0sxBJpHWS7Fo\nqlDTnHMs00xRIjBWxtJMLeRz3D5L6kpL2tww5iSUrnVdxbu/ccJFmNme841XzZXmGUJoov1vD9W0\nELvQthamaWnT1laDKBHZ3Ypxu05Q3xxukzSbo0VMnKfLN41zXFcBCkokLYHHscMqi2l7qgylFH0/\nEtbtZ6M05I3ylUJszh9Vor2dlxAVrcFoOt3Jd6ElZbeqijZSE6RUubu7o//yTC0FN4qbV611dxAr\npeD8QFoWamnAQSuUrRfOO6WKWUEsLM0g4TgeWtLvTE0QcoZVpnZT+P6mUFJsDixKLB81ONvyAFoE\n9rYmfru2/u0FMvxKiuQKzFEM47XpqcA49vvJCr/N4qwVU/4bJfvtoUVxhKqymeYcEdsyI3SEFIFK\naTee0pWIqH5Jwn82o8W7jjUsaOuEYJ4Da0ykrAgxgW6emzFJIAYTMa6sa2SeVi5TpDOWiowVdYVD\nc2PQDdnWG9+3bTBrDJgiVmydawEdWkN7uO/fv8H6AX84gDac50kI8+47fn8t9EErjzYGY8TL2Lqe\nXBWdg1wVuYjQJlN3kd60SNFrfYfvO9x4EDcI6/cCIQRB2EuKzcFBY6xsYss6cRgGrNNcTl+JMfL0\n9i1gqVXTdZ5uHMg5QrIsywSAMq5FXTehi7O4TgruvhtlcaFStPgMWy/qX5U1lYwzBii4AgetWyGu\nd/FDoaKNIGopV/FvZnMW0BSlOR56/NKTDp4SIjYkyhQkDtU5alqZQ2ItmmQqsTRfYFXIulDD0u7D\nVgC2YpYqvHOhWEgHrFCYVrjq9h3HNZJLplTVwm4KScniXZs4QilDVGByW8B1K0iM2/SUf3E4IxMD\nuc4K1kCeZ/HLVZVaMtb2WNcSqOpGd5ExljiMXItNrYUCoivYiqDY7c239D15r+ZgYTQqS2S8LjTR\nqwjwdIGCodStnvtlrlhMs/z+Iq4gm12gNQ3BV1cRoGkUC9maWo9QrxzOjctoreX59Mrpzz8SU+I/\n+1f/Oako8TkuEldvnZOx/I2It94UosZ2oqcohU2Qt09ZYqB2ndA7KiRnqVU2farG2GtB+M0jHPP+\n+YXlooRaocRRYXO1mBvNZvsskqh4bQI2tFs1cSlVo9EUtVBKxemrUAdkLeq8I+ZMiFF8d8PKn//p\nj/zwcCBMC+fnH1E1kcIMtWBaaqSuCiicL+fdliylwOVSGHzHMs+YLKE9BoUfx10wp42s+ZTKMAwi\n6pkmzDBIsWxFdLULg41ijY13O47UQfGPf/h/cKNH0CqNa41I58SZKMUAVTN0Hd4r1ioe3TnPzWva\nMK+R+TxjfM8yrxIB7zOpNNsx63Fdz+jls28ooEYRlgveWg53IzEnLpeVnDJByz03TZM8Qxk+/Okn\nur7Herl3Sqm8f/sDYZ2FguTE4el0fqbWTGm0JeU7tHU8v76yLAvjODIMg/CF50883Amvd1kC4+FA\nSZU5rJSauDuMGBTT1mBZxxoDX76+EEvmwoXBdzw+3BOnyLwuONNzdxh4/+aJZZ55fv7C0/jA4W7k\n9fWVkjLv37/nMi0ok7h7eKAbBkbnef/miaenR5YwczyMmLjS+x6lK9ZoTi9fSaUIT3haWJut2eA6\nQEb7pRR++y9+RyqSWhsW8WyPTTg5Ho9471kazY4Q94Ytbw2QlSS7lDPeXOmCG5oLTZvUnDGEs6sJ\nrdA69P0+kRAbd1lMtvU4xIq1qgE2WgSuN036rZWj9z0pJaZp+ua9bxHOcRwJPjGdL61plWbMKMN0\nntBeJpt//vEnnh7fSu1gpAl/Pp25u7tjGIWiYJzYAU7LjEqVvutxzrNcJsIaUdXSOwlEC0loG0qB\n9QbTWWk6GviijQBmnes4TxdMf+R490BBi7g3V4Z+IETDNGXW2LQTqmI7jy2asM7UkPj68SfW85kP\nH7+yriu///vfsa4rj3dHPp/PhDVRosYMPas/fnc/WJbQkGLFsix4L2mAyyxcaGvtTsnbANQ2nPsG\nKPnnjl9HkayEuqCcwegW40sRBMvINicAkdhJ/QLboo3oBa2oGyraXBTEszNjjAWtSeKWREgRa5qi\nXytybp0eYtUlJlyaXApg2gi1OR5kjUuK0oRQOUTSmtDWY3RFO4cpHUYLx3BdV2ou9IPfF3tt1D5O\nkk3xOnrdN30jaXa1JQ8q12GMIDMBNrXUfh1KvQYTSASmbJ6pyGhbt4I3N42U3ETSddumMlc3Y1yQ\nh3crkEMIrQC4Fskos9NjSinkm1CI1OJkTaMlpBSIOYirgO+R2OqM8z3KtIhXEqUo5rBAEvSnKBEF\noBRKW4SDLSENonm0pJzRWoJRlJJxqm9m/WixOcqlIQeIMEA1EVyl7I4otNhba0TZrqreUUGNbrHd\nDdmSZWy/P7diT++F37cuBxtqqDWNlyxiVfLuKyLfyzdUC0Wucq5VV3SRUZalSmA9v0xT0FqjGs9V\noqSFEpOzeD4rDSFKQlzVuqUdshdf7QxuunDhR4tvR5GUu338uL0aslIyCGkoU1GNP4uMBDZqhLzH\nt+j7945ac0Nsr3+Xa8XdIPb7OW9NCkIBKRsrRV9fu9Na2vjz+fmZsIgFl+17lG2vNaWNJOXzbd6o\n28/muvm5K1CNOlLEp1Vprv9faVnTEJqNUt+iHLeTsluLou10b9FioCU3/mUa5eZzqpSE02xiYZrg\nT1H386k/i3zdhGXKGHG70IasNTUuvH46YXUmTa9kFem1oVSxabTekgOUdEV8jXGUkgnh6k1uvZVJ\nSROIboh4TILqUW5/3uznabTe75MNoQehPmQr/sr9MFC2RkHDvjG2pUi3qdjtuW5NT0rS9AkntQEz\nxhCn5jbUHFPiuoJWdP34s2uusU7hnCbEWe4PlUkhokeNMZZ1DeRY8L7DuYRYdxWq0lSt6LRhDQlF\nkdAkoO89UJnCRCqG8XgAJRSxWw9dYwz39/fizdumhesyMbgDnffMqxRmvWm+10limFUV5LK3PXMI\n7X640gTWpWBAHBuqCCDRijmKk0DM4nhRCjjbscwioj48vsEZQ05J+M+9Zb6c0daKsFJpck301qFq\nxmpx/5Bnunzz/TvnWC4XsU7LpXHb5ZqyOTZtP9OaQl3Bmo5pzQ2ZVru4HK5WjRsKv3mM15smfXuG\nNqBOfr/5iwmhOB5tQVTtnmxocakyaZT3/XZtu/09t+uAPCcSfmGa7oha8f7AGhdSKKiiWGMktiZz\nae4xGM3Xl2fue7HR63qpE15fXyDK2rQJ8sOySsKflWso7hP2m4mb2ZpnWrORMnd3D4QU5VoZLX7g\nVgR+Whms6ZDAJEutMgUuNVKzZD/oEgnrhZJWnN+SUVviqxEThFwVnfWsVKby/T1BV4vGUBNklcht\n/bW2359NmVBpyg1Sf/vc/i3Hr6NIrmUvSnQSA/rSujnTCVJac4E87Tf8947OFwnk8Aoz630jKCVS\ntSLowjj25FyhdMS48nWdKdrwtquUlNDTgnCAJeVJW0FhfPVik9V52eRTJc8rVCneilFcUuAUF7S5\nJ+sZazJaJ0rIrFPicBiFk5yhWLE/8qp1oGhxqzDCn5XRqMa6TiK5VY/p71H9kawd3nppBkrz9yyR\nOE/NwqjF1voObSxVGegtw91BEMs2XhrskdfXV2Iu0g1n6LzH2w7XdaAUYRKkN95QLeZZUJ6NgqCU\n4nR6pRt6fnj3A8PxwMvLMyGB6Q5c1gveKTrVk+eZNUw410ZtRh68Wis5Zsxo0HOAtfD101f+8Md/\nJDl4+/SGu7s7Ho53GGVAg9M9IUmBP/SS3tcrEfuFFBuXuWKa36utihoCXamQpJGKKYJvi1yQ77N7\neOT8+c+4XhMuF7QeqCmhcuLxeI/Nng/JU51DDRanEl3NvHLAqYhtm0zKBa2jjHarEzpPTSghZYtN\n1V6sQLUaXQQxrrWQqFLgUdpYU6F1IueANQMU3Syqrt7L3z8K1Iwu0mTNJJa4ShoTClc1uausZFKN\nYtCPReUohI7Nq9cK31EriZV2VhNzJZaCjonUFlLbaBS6aqwRDrIyrXlr43ZftPhiK0uyVZ5b/dcX\nL2cPlBoaBbmFqpRCFVi5CamcNCdaCsGUEjFKQSAR6lcLuG1TCspiu5GX84U//elP/O43P/Dlywfe\nvH/H2I24zrKEQEe3j0jned6fo1pFzJOCuDPYCrOylFrIqVAtdNaJnZ+BOawM2mC0IraGTUIOro2C\nbDB1LxaMMaQgxEelZW0rtdJ1fi9Aa60456lVmphtilJrxXaGvCZyyaQqwkHhQ0JNhZxDc2SQgAxX\nNHmVNEoLvH0a+ekP/x6bV8ah4GuCLG42aa2c1wlVKlYb+sM9NSS+fPiM6yzHN4+4CrEs9IPH+crX\nL59xU6J/vKM/jswvK8t84o19wqiOT8vC8e4e0hldV9Yg97c3HaA5nS8SzVtWLi/P3N2/wQwjtcDl\nIgK8Y+PtnpdXEWE715qAla7rmJYLKim8tbxcXuB4TwDWWmX0j8JazdcaGNyAcR212ZBJUqvn+PDQ\nXHISKVaoBdWmal1nOBw6LlExXU7NNqvw8vKF3g1McebleeJ490DnPa/TRynurGU6B+Z1xYya4/Ge\nwTzw08dP/PjTB+4fHzkenihmpYQF5x1fPn2GN49YLQ29LR3z5QKj+AJ77TC6siwTTjtSmwbWWnFa\n0RmL6T2n04nns8X1d+igiGviy+UVpzVWw113h3OOoeux2pBCIKwzDJrHuyf+/KdPvH/7DucsyllO\n5wmrLDlW7u7fQKk4XUlplcmel+AcZ5xEyGtFzNKY+H7Aa82aE8pbqlL092ITeHx4hFyYzycRzdqO\n0+nE8PhICAHtnPhxRwnuqKXQGSeIq47UEsgxo3HiK50yw9DT+YHT6UTfj2ANpYjta0oZa7udArSG\nlVISJVURYurmkGUMKiuWad61D6oaqmtTL6uwVlDfdZ1a090JaAa7dZoKqonpFdUa1pxYmYlEeuvo\nTc9SCj9+/CzuJ40a5XtDWjOv64Kxir/z7+i9YXQ9lzxzvlw4HA701uH7DtOgmJxhXRZKrIxPB2qz\nntVoahYa58MPA2mODS0esFVjDw1d7gZiqiw6U1u4kG1uSV5bcllZY+Q8PWNqgXXGV7G/ffP4RJgn\nsI7XUHiZvkAuDMf3fPhy4lP4vgVcNaCsoesMOQqFsnc9KRry0DH0npgDxlSCiF/oBmnE5nn+xX3m\n58evoki+dlgiSlJVoiSLuqIlpsW1/rWjFCmSgV2UtfF1S6mN6yaI2YYK1lyoWVBI26x+ahHsa0eb\nqkzNlW5OuUqhDGjnqAVBGauo6J1q5mWVlgQl44qir6T823GtfB4Z2dcqCMgm7FOq7GJF4bvqvfuF\nLXWKfXQMGz/tKu4pSFHj/fWcN8upDbXexr2bGn/7d2PMPlLcYnO3IlspRW7itO31KSVeXl6+EffN\n88w6zVRfsQw7gkouEvKSyl4kxJwIc8RnzR/+/T/y04eP/NOHD9y/e8Pj/VuWJTU0XmNHv/OoyMIh\nrbUyeBFh7Bwk2Bsro40gVSXJddNtcNa+5xwWSkO3vLHEJdD5nqicRGs2ND1lAW+dUVgFSgtaZU0T\n4bXfqbS8u3wWQBWUOAz/BaL1F0/Ez9CK29daa9uzIMV0bU4Kv/h0bSjFznMQ3mosGVNkkqNQqCwI\nZ1ViF2SqiDm3ovQGFm9IsFzb2lDbjZss6IkCtRn3y3tsrOV9SlLFCSPHSFaCyPBXnvFSCtZdUdzt\n3DZP1v3Zaj7K5ua6bVxA0+7N7VoKd62TdK0KH//8E+/ePIKWZ5hcSCHi1LchILXWnSrg+z10sjAA\nACAASURBVF7Y2aZQtFCdyBvHVwryy+WCGz2xhc1sfuQ/F5Ns39WtZdn+d/r6/H9Po3Hl3dtvkOla\nKyUmaCijasmBW+qc3BJ1v07OO9bLDEnSPmNYCfPCu3fvcCWwnD+REKcJaiXlRC6FOC/yecJM7zy9\nE7eKuAa2dM/Pnz9LUeQc6ymilgW04jdv3/Py/IXL88R4OHC8vyPFhZwSb96+5fOXD6iC+BXsSJ2M\nA621e4ESs4zRlZIR7D79qcIpddpgjW78ZIn8lbXQ8PLywrzAGmMbn7ewHnMVeSol1oyxrBijmj+8\nUL1KluTTrSF7eLjHewuvq/gKFyALQvxyfsUMPX0vtlUbmpnWsAvFrDFELeI8qt6R1XmeUfgW4V53\nCs7r+URJmfvjEd3sGeflAlXuW22kmbbKop2l95Y1RUE+FdRSd7vBreiKDXl0zkGRiefD4x0pRIxV\nHI8jqlYeHx/puoHD4YD1jiUGuiSe8qUkctYoqzGuNYHOsiXpSiOoYbO2tGr3uN9SV733Us4FjfcG\nWx2phOatrHBdR2iCPWh2gIus5V3XkXJkXWecu05epB5Q5Jr2RnObFGkt6XO3z9rP98qteS1Znudt\njdz2vm192fUDwLqGdj27dg9fm+DbiZBS4rola63MKnNMEDOxiHnBRgfZ6GPbZyulMC0Tzhsp+AfP\nu3fvKJ8/sU5iKVebgUCuGaWlMVOd39dTuRaCRm8TtZSgFE3JpaURtlCWXGWia5teI7VpKTTNSaXz\nA8syU1Jlmc5Y6zG2Y1gjzmguMTL2R86XAMpRdeJ8uvD6uort53eOfT82skmllJnKitejaB9u1vnN\n4Sc0/vdteNM/d/wqimSlDa47os2AUZaKcCGluJXizzgnRQ7fLypACkTKNa1OghGuMc/eOhGaFYVW\ngsAdq3BlpjXQW4VTcOWvFMLSfP6yLBYxSWiIbIqG7CqmGFzRHJ3DmIjtFSpDWeTnihGUPKaVnBRJ\nA8pjFbhOrNXS5bzf6K6pWjfXAG1M28haulZnSWsihFn4WTG2zyXXxfcDRluqFlGR7weJUdVCEzBO\nHshpOjOvgdN5QmsRAMR1paTEuS0Cm4p7Eyttf7z34nXcFlNrrZjhR0mvSinuC79qlnrn8oJt2fKl\nFC7zvP8u5xyXy4Xnrz/y8vXC//hv/h1aOX77m3+JKW9YTpo0B167E/f3CnPfU7TGDzKuCTlSqYzq\nZ8mGpZAXsWGLbRGgsySE4ybG1ys6Rc4vn0nziTtnMccjc0pU35OyItcZreFudOhouC8IOtrCMbIG\nWyNaRRE7aI2ripzaplMTlYA2FaPHdr/KfbtRU+T7R3htehvLl939ZF+wNbuZybYQ6KoaRvuXh9WO\nbLWICnPl+fQq6WApUbRm6EfyeZZkIwVxCw9plj5Oyf2y2dp5ra48X4RCkbU0Y7mFWSSl0FniT6sW\nZLcoGdsZ1RDRKnHJvW7+y/Azodm3RykFZ3piiTcbiqEovfPLZSwv5GdtjETButwU01Iobffrtunp\nkigxcRxG/u3/9G/5L/7Vf8np9RNLd8ZkEYI656iO3R1j6MVIX4plWei1sZg2rnVli50HbY2MTrVr\nTY5sklLASgO1PV97826ulAIFe2F4S9vZ+OG3oh9o/Pm2gWwF+Lqu3xTYW4S2alOZzQ8+p0gJiofj\nHWGaeP30RwwBk1fW12eygsPhSMiRKb1CKwicsRgtNm0pJeKykmNAF4UzhlQrOUeOxwPawMvXZ9Ia\nGcLA1+efYA50znCqM8vpmb//+9/jrOH/+N/+ZwqV3oo+YYs7d0azLIs8J7W2DVi8yAcvBch0Osto\n2Vv84Hl9+UrRcDweCeuKM5YPL19lGuUdKSdU3+G95bIK0mjRXC4r1sg1OwwjZpRApFoqH3/6QNfJ\nhKE/DJRSMM0u7fQ6E9PK2B05uE5EZgeD/7vf8scvHzktE53pKFRijS0RNjHPCWsMpWTmy8xUJ+zQ\n44xj7DsZRzdKVIgrOa3UNWKGTgKR1iZ4ZmV0HtCEVFiWFeuEVmitpViNjkrcTlTmYGUdXlIi0+4X\nY0C1HFat+d3vfsPTm3v+w//9B7qu43e/+y3n04l/+viV9+8t79+/pescy7IyaEkxHQaFptCPEuKh\nMXitiak0px6huBgPaIXVGm0VNWsJNPFuF9LlbOj7IyWKmNIqmlORoT8c2IJjKvLc5Sp+y6iK7zty\nFv2NMUaszYrCN299aT5Mm7BEpnkS1HwY9v1k8y0ex2Ffv5dpxukr17nWKv7kObepQ8G5RhnkSvnY\ni3GuIsztWfbW7ftrSglXFdo63OGOeV3JtfDww1vied2Bq01QD+LHrij8+Mc/olTl/bs3jMPA0vdo\npYhtz+n7nrjOhBDoOrcX3FsRWUrh0B8kCTg5SolcpgtVwd3TI0Y3W8RU6TvN+esLoQA1oo1B2Qol\n8vzlxBoWvPJ8+Tqj6Pnzhw+8/xe/5/X5hRA1Lnj+9//z/yJ3kaeHB/TqmaYZdP/d/cCNsp9Xo7HN\n8IBSqVbjs8VWReIKNPR9v9cyP9eA/LXj11EkKw3KQRWeKPXaTVlz/Yi3N9I/d7RwZeFhNg9SVQQ9\nUUjBYFTl/nCkcwathSebyXgNhhZCESupZpIWVwLhCkqhopR4z0pCGlTjqN5gE23hY+fCqFY4CdLd\nNjpNQ5LF81EhQRK3yvWrov4a8JGVRGpu1+TnSON10xQhoNG20Ua+VbKPhwMxJeqnL4RF+NKuxT6X\nlL/prqu6ouAbn8uNh717td5jlNg0ee/JWdAzYwzONMeLsAhnzjpqQ1yVUqzzTFhE6BTXyJ9/+kgu\nin68w3dH8goffvrC48PA5XTCmf+Xujf7keTK0vx+d7XF3SMik2SSrKruGqg1rQGEGc089aP+fwF6\nKEhCA2pouqvZLG6Zsbm7LXfVw7lmEUkma/qlgWoDCGaSEb6Y3eXc73yLof/sthVNLcGI8oIqKkQY\nZ4zw5lr6TkaQEmkztULTVuqaUTlBWEnLlaxX4Ta5jhJmEZXVijWKg7EUY+iWwkxkRfjruVaIiWyk\nG7A9t228qiY0e13kKPUKCa2vHBhKae4NZefVvkYNSy6SylQ1rX4WysGvVJhbIUQrdDdFNzJCZKwp\njdaNuKuFF13TL9XAG4KyKe1yFd60/tkYrE5T1yJdmFd0vG0sqfa7NSWOuhNrOlUlQvbPzeuGiNI2\ncHk94e6L00Sj5ze0WiznpONQa/MjVh/TOjbfdacND4+PezG6zgvDMOze4K87QNZaCUBpC27ZXRPE\ncSQ1h4zSXBD08YBxFtsOhMbaRvvKH93f7UolYzY0+RO84dfPY7+v9WNk+ufjr0JzoxG+rWIbk+2Z\n0Tj2qZJdIqeFmiZSDbwdHI+TpqRAjEIBii3uOWexnFS1op2FzZqMRr/wHSkFzk9XaskM48jN7Rse\nw710Cqvm/PjE4SjBCOfnicvDE59/8VkTJ4ofsaAdYgXmjKJ4L6LqFNHaCZrc9aQQ294hgQ4hVLzv\n0dqy2cZpYwhhkTNyyaSk6ceBpw9LQ1VFKKmtw9sq631VpDVAESusEAKXS8CoitXs6ZW+FaGC6LOv\no9O0oJQgm0aDSkmoLlpTqZjOcDyNLdZYrOxujjcUxAKstJjl2r7D5sxTinjkezMyNsFWRegeIS4o\nXHNzylANxShSTqxrbPNONw2OJCKqVizXWlmnlUJhDQHFS8fudUfUGENNkdE5UBmLEYePkrGu43SQ\nICjnKtYpVDFUJdxjqyzGOXlmO7AR9y6payL2tXVtbPNKl2JYiraSZa/FOsoaxJGpAr6Sqqz7VEn2\nS1ECfJQ1GDS1KJyXufP8fCGY0CKuPxbqbsDOi55EaCIbcvsayaXxkI3VeCUWpjEJ6l3yhlZv3Qk+\nOWdL26v29SCJ9aFxcrhdY2Ds/UdzfF/PtKbk7YAyUHPaRaOuvWZsqLuuYDcdEU2Aq17EhCklxjri\nnCMV2dOWsFIUjMcDCvsR1z+vC6E0kMhI4mXMiet0prOSjLrx4M/XmVMILEug73uqNhxPb5izCP5j\nVCjXc3P79tObgWmplgpCKnhjMXvYy8dGwVtK4Xav/t1xkqmKWqwQr3VFqYKmyMLUvkstZd9ofw1J\nrrW+3BjTxFWN57dZ/Zim0Awx0fWG33524vb2BuzKjz99z3W6R+mEG0/4hhqFmqlkSJWuOHRRoIy0\n6/OEVxKp6Y4j1mSefvyBEhcoCac0zjaEt9mWUcVipdbKGoS7ZK3GtpbTbsHjPMo64WcNJ7zviVFM\ny7UG6xQxidAm5bgr3qsSb09rLdZ5sXl51ZbZ7FFCliTDlAreesZ+YF3XvSXxukCy3u3tPrF16vZW\n0lY0OKN5enpqCPTM+/fvZUAWQSO0LlQTKXEENF6qGdISmeaZ7777ju9/+IkfHp54++XvMMqxLIHn\n8MxN8nifuV4tvXcQG72kc1g0pQkHa8oo/SKksC1wodbaiquMUonu1XeZl4WwnDl4RXhepUWZVkoL\nMymloq1lcIiNXtDcFkWgshZBjnGGbtUSlEGR+w9sllsKw7I0v14EJfRO/HKVNrKIa0UtRooniqiK\nVQvnaBLS3IrInCoag7HyPUrKvzrxlRJuGwW8tdhYsKnSKYPOFZMrc28IsYgft5J2Xm0t2x0d8Q6v\njaSQVaSLYSBV8MpIdHAMTVinWiFWpF3Y2pCxyue0Wl57o4DUNsdr+fUTvlCJNlW52NCVIlaOJTX0\noz3n0gkqF2N8Eb5VOZBvKOs2vm1nKKkwhZX7y5n/95+/4a//6gs+PD9SjOL45rbxC7uGQBusNTgn\nSXdKiShMOlfSDu1HSWR7elh2CoC2Dt+PLdlN5mFOL/ZQVNm0SwX0S+t336hfbYgb9WITsb2mZGze\nrvuGW0oTNypis6CzDXFfa5BuXdlM9kXw+8N3fyLNF/TywDo9cP/+n/ns7g3UyrKC8lJUUip1rlRX\nMc0PWGuNaWFGKSz89OE9rgogYbxjmlZOJ8+b33xNXVfG8cDDwz0/vv+A95pOKf7h//5/+PF44vaL\nI5iesky7PZhVmqAQD3ilwGrimuicB2uZpgvee05H8UyP00xcA9Z4slJcl1W0FZP42PvO8nS9Qoz0\npmsFF+JuEjNdd+B6fsZazc2bOxF654TVije3N7sNXOekQJ3mhetlYhhv6PuBtSwUCnMKKGV4nC7c\njJ7fvf2M+Trx4XxmCYH5OHIaD/LcSm0+xu1g7xv1wGnW6Hi+nEErDocBo6EazXyZGTqPJkFJdN5i\nx44YFXOITNfAugTZG1Nmvk5Ya/ni888xxojLRYwcDycOp1OjrASGzvHVV19wvTxLcMrtWw7jnQAc\nS0Hh+W//6T9ye3viw4dv6TpNXisHf8Q7z9h3KFWorOKbq6RIE9HXRm3waF3RxrF1kTvvd/u1WkW4\nPnYj6xKw3YCymTWvHN684fx8petkf9TaSjojBddJUTlNV67TJLHzTgTmCqFbUCNd13E+X9nsGkuB\n0+m00yBAulBbB2aaxNHKWkvfdYRXCZkpbtHNdvcd3mKQc6p47zgebiSDAdWoJnw0j6cc8W3hkph5\nx1QTaZkFLDOWusR9fdgE83sRX6GmzNgPQCGuM/PlKp+/CamrVoQQGI6yVllvCDnhjdoPBbDxpFe0\n1fRGkz4klLO4oacUS+cPeOtRuZLWwPl65XQ6UZQihcB0vfLj939k6D3zw3umyxXrB24/vxPBbe+J\nBe4vj2SzUp+vKH8Dg2d46/l2+jQnOZdCURWjxOt5SYUaM5+9vaWuQULkNnCgofobteXfX5GsVPPj\nNW3SGGp64cgBv+CYfOoSpLZNKPVyahDvxPoLjpExGl0Knde4w8jDk+Z8mZvVzvDCX6zCu9wR4I17\nVCq6FJTe/EtV8/VdqWkVSygNDil+69bfr6WdPiPavloEitjPOHvYJ5l2Yo3m+15oCzFSsTJJSku3\nSyLIcU5Q4KqsWFc14YC1lliKuHqUwhICbitM2kabW/55zUVcE5rl3nZKnuf5o9aQMYZlmukGUZLS\n6C0hBJ6enliWeW9tq5zRRuG9TLwUV0CztglfciYvgcefPhCWjEIK+hgS3okoyjlDmGfCIvwkxcbd\nbmMhFzYnD6U1sVmuad0WYkBn4d7WlDFVikBdpcCsOTN0joszECV+XL77i0+3MRrnDF3ROCNFwY4I\nGy1R5SpRNr5wUUB+QfbrL+OxtwJp+5nNJWKbF9uE3hDnT41/ORyaP3t4rFUirpWSDUgjSXy6VuFL\nNo5saqhL3dYQLT+/0y2UImRRImsjqXe5fY+N7lGKGGQqZWSegNwrLQW1LqBbzPW2im2v/ecWL601\nWnzQXuZlqbtr3Gu0ffvzVlxqrYW3tlnxvSo4Uym7mw1GM68LGM26rlzniWVdGVvRuXNctW4dk7y3\nOimKVMS6qqqXDoPOm3/3C681pUhO9SNk6vWzwhg5SG9uHCiJFv45Yl8/5izL5vYzO7lSKOqlO/f6\nv+/vJzwftlaztZZYWyBHmOlMZZ6eUUox3J5IFLQW9wpjBDnUub7QPNrGDgVd4KgND0+PjDe35NiQ\nrbhy1w9ysFXSDi0l4J0hzguT0ow3PTGvHDsr2g8SWgtfWNLNhF4yX67cdCNJlBY7LQNjwUnIFKYF\ncpSyi4bX9Sq0NKXEIeBayRpU83VGwWm8kdAdRNhcSsI1bvzGH1ZKcVki1opz0brGXRykBkGKixIk\n/+l8pqwad6dIy4rVmtG7NufERtCYlh5ZXJt7EkgVY2SdF5TylE3PgibniDMOo+SgVnJmTZFgFbV2\nlCrx77nqfV1ug4hOWzrjeGogy2t+7FZ4GWOw3omLSa7NclIcDDQKSyUtE51RHHvP03zGGfHgLRlx\nqVJbx6LuVA5xhylCm6Kw2aoZY/YCuWwdirYGoKWjWnIVb99h4PHhubk0yOtKh8xJ8mubn9ucVU1f\nkFMlpUznzU6ZUWwIakFt1pKv1pPNDSPGuBfN1tr98LnNRWsb7YvSeOt6n2ubheq6rjuQ9Yt5rSTJ\nsJQih0/bEPV1pdcO64RPb73bv9vrbuTG2deq4q2AWjkmcQ6h4rwHo3HG7gLk7tjjs0Y3Nw6lX8LD\nYoxCx9CSXJuR7uPWSdjAB5BAj1x6clakFAhhIcQFpytGI1z4HLl7e8dyFeOBNWdxvYozlkrNkcty\nJtSOaj9NtzBO3E2sczjjiEvc7fv2fRZpY25rn3QJ4Hr9dOH9qesvokjOVbFEjVMKrySAV1tJ4+qr\nFIOlKFJrmf4K9ZKCkUhfrbAa1iTCEqcdVRm0CoTmAuD7kcPgGY4WSoRl5bb3PCrH83nm/votfujp\n+kE2e6XoiyIrw6oySWW0NfT+MzqjSTFwc7ghDx3LhzdM0yPr+p6iNZ27oXM91BlFIsYFpw1OD5Sy\n7ifB7ZQTSm4nNU+sBe0sqTxgOUA0kHuUH1C5QhIbnRgFWfS2oxSP7SQNS1lNtZU4r4T5mVIS1hnW\n6cI//+OfuF6vMqF0JqTA4XRsXOyVZbru1IreCCdzUdKCefr+e1JcxG4IQ+dE9e97z8OH94KuaAlg\ncTZTlWJaN5GMoGhlWZjywvd//BPPD2ceU8WUjn58R6k9WmVCSqS8sPwg6E8uPdZeeff7BWc0qURK\nypAMKRWGTk60vbLt0CQcZFsVRYs9UbSZWiI6X9ABTLiSl3uu159Q4YI3mhgKvYGZSi0B5xW6cxgd\nGVxkZOCgep7NO0IqDPPKxdfGH48sKUgrSnlR/ZuCGwyidUrtsFBa6MGmJBYKkLGF2rxepcvScOQK\nJbfn7BJKZ4QwYSg2NcrFL685J2jUnjmceZyeuC5noS5oUY931bKWgMbhrIO8Ae8VbXuMNSK0jNAb\nOajELHZVrlaoidrGryqKskQmF+mVEa6llpauNy+FpTEGdCaUF5uh+mcO+IpErJvodFs7RNhSqEQK\nuhRImSXKIVYU7NJS1o0mtW0qSinIhSkFvO1Y18ipH/nDH/7A8dBzc3fL+XliWRKn24GsC9aC7eUQ\nV22mWk/OkZQi6yLjuzOGUCQJSmtLSJnLtHJ3OhCDdLtSlnWsGAmFyesmAuyEhhIl5CG34APdkPdc\nCs5IclnKCW/FUxZoxXFl6LoXGpQWF5GMCOy2jlotYhdlinSq8iaGrImiHWr9F1g+QJ4Y1IKJUQ6V\nvhMkvVYcmjVFapHEt2VZpNBFUWJC68rgLGteOS8L490NMQYKmRhXSohcS2zFqNBo1JLIuvCb332B\n7xwpXTkMHmix86pynq5UZbCdeBUvl4DVninOWDfgOg81s0xPUnx3PTGuOC9F2XWGki01wCUmlNP0\n3RGWhTQUUsrkuNIpBdagyszb2xGlKst6ppRETIemUcmUKmKs8Sic2MF4hvGFFkCLIc9UEokvbo8M\nXc9UM8EpogbtO0iBWiI1V+H+G0uxHalkrpdnqoYvv37H+Xzm+bKidU9NkPLK8e4Aq+H+/l6EVlq6\npmWKrPlKreKT33c3WN86ihHG45HvHu75/e9/j5+Fs2urYpovTPPMzW2HtR1zrFQ8RTnOZcL3HSVE\nFIlKIRmPyhIyUkvgzWcnbu90C7aRsaNMD6WAsiil2zwQS1Ft3D52UtIoDGtLh8uihkMpxbUm8J5L\nkLnWD0fWNTMeb2VOdwHjxAoU70jXRWhkKXIaTth+ZEmRZCUwzALGiqD2cLxpiK/G6srz81nQ4KEn\n5Uzf94RVAk7Eq1wAvbVm0BKqpK2BYonni3RwWpFZ2kHw9u4oh6WaqZ0i1MRYPMoYseBTECl0zjOO\nIzlE5utECoHOWnxLvt06YSmFdlhh1ydsRWcpQkEoWok42yh0RkTOixToy6DonMEYSOsFpSpRG5wR\nm7iKdJqNkSAbbTQOSzeOZKtQg0WNDtuNLBfxYxqtpUeh1kC8XNAx0pWe3vY8pTMFzWA9aokcjiOx\nLFx//ABFM6qC//IdoSoW1RHdCb18IgsCsKt4qutmhWi8prOep8sDt0NPCgtH3xFyIioJdKPIwW/w\nh1/faH7+Pv/qn/w3v14R2JU88J9Xw8La/fN0C/YWY/t71S+v05RBSsnJ12uFcVb8hxuFYBxHQsxc\nQhAHhpjQumB91zx0JXLXKREUmCKIz2tBjPMavTSuUS0NzFDkWBpy5qjGgHWomsRZoKV2WeNYXiFO\nWumGgihqjtS4opwmZ0cp4hFbjMEWj0XTVcNVvyCT230xxhBSoaTEdJm4Xmce7x+EsD/05CrtZrO0\ndnRO5JpZgggV6A7kVFmKFHAqFTH7NwbTFrTOeda4ykIBsPG4GkdwWSSyVTVfyawSz9OZ+8cHcqrY\nfsTnjqIF0YjQvJgl8atQmZaZJcdmkycWW4qtQyAoA9t92zoHDUlWSvhwlCSfqS5i9r9eqSVRctzd\nUaSQ2oohOagZY3Z+qCaiCaAyVVXhrCuFqhqqQQx9XpIj5TNWlDKklPf32Di+UtB/zDUV1JePEALh\nWtqtLNq5yFJ8f5rPW6ogJpqCbRZfqYiifSvUhgShapKq6LTRQpRw16zDKP1K0NGcWNgz+Pa2/0dc\nX7YY7ordTvPte+acoJZd/CLPD1T59HfYfqbkF979dp9kireWpQCiLRZciueCLAMCDEpx6nf0VQ6+\nyoiXsLGW+x9/4P7+ni+//mov/nLODK0rs21EwN5SNcYwNyHq4XDAabF50nX7XHpH6F4jPyWL2zSv\nkLttjdudfdrvbaPjNYL8+no931/bwoHcj+3+CE+2jadS0EU6bTlLbG0qmfh4T1kuTI/PGJswRouj\nj1ZUEqCIaSaXSKkZpTPWgbGVtAgqdE0J15C2QBDT/zWRcqKzGpVaV6qhv0VVnG+0sEYpSymxLoph\n+JjfWxu1Y5s7pU00rcVqkCbyMi3N0lgFSlA0+bfMg1pFnS9c680vVyg9aNUSS7N0wWrGOUOMEhav\njWM8jO2ZSUt62wNAuP8pJQ5N0DcOHTlLW9way3xdSDkxzxN9HeidbxQL4eqqCiGKoGoYBtZ1Ia5J\nwlhqEYqVFd2FUQZlPH3fs6aVkqHrhAJESMQoXFldMp0fuL+/Z+x67m6O/On5iXWesMaj1Eo/Dqgs\nqHXvpBVfUkTVJF1X6+ktGNcRl4mqEp3rsYidnNLim13aQS0W+ZyDle5sWhtirMUfWnuhtRQUaIPr\ndOu6tQ6MDG6KAp1bx6okrPFSpM4Txnm0lhRGYx3GFYzvqGGm1CzhVGOPclYoYK/XlPLiDiTrl94F\n5RtwlT+h/dnH36b5KGrn9S+N0uiGYdctOCcHUunobV77YjOrtcYrL9qfFPcD/IZ+hxDIr3jgIOjy\n687Z6/VXWQnvqjW3ca3QRYKRlBLHGWOMBAGV2pIkJQkz6bp3FABizJzPC+8+e4u3nVCNEPvHzJas\nWSk1EMIMuuJ7RwmRME+SLmwkH2Er6reuwVE3qqDtKQlCzeS0sBbNkixRZfSvCPdeU8qUUpiNS52k\n81G9o1qh/GnciyVnVb9YO//c9RdSJAuappvgAfOKYN0G4LYpVfkfn3yVnGQB3P9eWsIKUFQFZeUV\nMkCmrIFpqWjd4awUxAffcxwsfRJz7oIk0XlnIWRpOTkR9TnnCHNgnieMFig/hUqokVzlRKprQeUF\ncsV5yGg04gSx5pVhyxbXprWZHIO14jFYKqVmUoxC9M8LNRVcjTLNlSP1DtcdsVXAGIzBt+ALtlZt\nyqzzwjpPzJcr//QP/8j58UyxnpAixwy5tXe++/ZbWdyp9H3PZZ64Xq8sl0xoQi5vFZ1OkspjDKWA\nVoa+77m5uRESfi68f/9eJnlTzU7XmRA2HrNBpQmt4W/u3nEab/l2WbhGxfOc2wZWW+ukE06zKjxP\nC5c5IoLlijYV7wxD3+8CrY/ayq0QMhqM6cgxEKYVk1bCeg85YUtiXc9YlYg1EdJEKYmM3RdQu2fA\nt8mpA65GrCok2+gm60qKilpbwQ5o40XIlRJUacntBdKrVvvrwouNj6/UzkF+XUzXsg+WlgAAIABJ\nREFUqvdx3SYEOZlfnV1LQzq9VpS8kOPLvUVrsoJgK8lBpFIbP7X3w86P39ALKeYjSsuG2CuD0pVA\noeQoqnItKYaUFiJSKhiFqZqQ5bVNKyRSidRUhF6wCQJ/5cpZCpNSmjDGGNlwmmBut+lqohqgWU3J\nwamCICq1CWqLjPloFaGImK2zlufzmb//+7/nv/zX/43b21tqrdzf3+NaEcPQ0kBbGk/XCzp+c3ti\nXVcu1zO97yTW13jWtGLUS0QqsAeSpHUhl4prXYCUBN0Xcau4V1i7ecW3ePDSnFl4QZS2Q5VsFi/R\nrPvm/yoat1ZIWyuyit0kOVNTpi4rl+lCfP89R1vJ1yvP8crht3cyTmqmritWG1QRv1sA7w0xLpzP\nj1jt8U6RIuIqkjMHLGpJnLQH71EoktWQQcz/moiJJDz7/GKxBVJwWiPWnNKBED6pbnvDxu1OJQot\nynvhppfSYu1f7LGuUySlypQixdqWPir3ek1RLDM7AUVSLKxh4nAYWJa4F0/THHh+XghB1jytNefp\nurtpbPfcaIjzJKii9fTNni4VKVS8dxy7gZorh6FrYMLCHMS14M3dbYu8PjFdLYPveXv7hn/4//6R\ny2ViOFic65mnTM6JakZqNeQSqWYkpAlVodOG27u34AxPl5mu64jLynRd+OKLLzifr1xX+X6284Tz\nGZMibw7iNW11wTs4doqcJpw70PeGqBTdeOR46lE5QkxsqZNLkjnaj6PQFKhCrfI9NWeWnCipkErh\n7dt3SGLrKjZ/JTJ2o4Ab1ojrhVLkUNu6OOFMIYTE48MTd7/5rQBsxoPvGA89YV5ww4jyjqw0/niD\nNxYdVnG8iFkixxsdao1BACXtdhT49V6y0d22ArI0eqTSlmHo0FaJhqhWFj+TlWJt1KmktVjtLWW3\npRsbl73UF5/77VCZQiQsK8ZZhsOI7zuhPlXhc1sribSbm9fW8d32lWHoW1xzpqaIdUbE+Vrhu25P\n+0s147RBVbg8TfSD5/D5HcMw7q5T87QwXWeWKTF6hW3x8XlN5AaeJJUoJdL1hvk5EcPKcp14fnho\n4vrCEhYSFmccl7kBbPPM0+OVKVmmVLhkDcvM/TXxYwR96PE3ny6St8PodvCnSrR8sRrlHKUkaqPU\neOPJZEIR44SUf32f+cX7/Kt/8t/w2goAVdWOAG/IlHMb4bBQ8y+VnB9fuuWFF8QtTjdkQQQ7qxJ7\nGFQlE4k1EhLMATonm/vt6YRxHT5M0k7Wot4sQDYycQqC8kzrSlgTg7McxmGPx9S2R9sRbQ5Yk3HG\n4o3BW09Fs5BY84LzAa0OexGybWx9P6CBFCK+91SnhOdcK74oypxYgyw2nRrx4wm0IRjhTpuq9tfL\nUbwB13ni/v2DID/WErThp/sz67riHieWSTjEp9MbYoxclhn0BT8OGHPCdeKX6JTBqgjpGa7PzDmR\nC2AM0/nC9TozzzNdy4SP60qsklO/rmIPZ42c0N99PjKOA+/efk5qHPSM8M9SQyAH78lJfK5t58lp\nJs2JDx8e6axhPIpilgxGC1qgG5qxFw6b14kBHStpmjBxJocHag6YpLEqkOOMprWkleTRb3wrZw3W\nKEqQQixpD7Wn01YcFWzBWsUyVWoRv20oxBqhVnLJu7OCMS8L7jbWa60YtXVJhDuttRZhgnlxJqm1\nkquEuIgHaUUhqVW/Ni9ylIALrzXnpzNpWiAkfBOxqlqZ1oVUEomGzBvDnCPVaq5x3RHbEBYOtqKz\ngqpQYl4rSGBDSDVygImpyILVOhtGKub2XZogFL3z8vMrROfX1glrNTFuzivN8SXL/QmlUtqGWrMg\nlE5bKRqT2IdV09YbralaIoJV0qAr3dBzvjxhB8v333/Phw8fOB6PKKWY55nnh0dBBA9D20DlwBSa\n1eHpdBD7sOuZ8+VB9AFUYpo5Xx4wztINgzzD9EoVnzLWtA0z59b30riWfhVj8zDvx73FWuWkv/fa\nXjtvaF66bfvG2Qr6baxt/393salSKGDA+APKWZ7uv8PowHh0nK+V09sbOm8gRemwodDasa4rOUPX\njVKwXZ7lvVOS6PBiqM0P/bWg1hhPXBcUGW8rzmpQBe8105R3PUDOldvbkc5bYlooKRNWCSyQNjNU\ngwigsgLE+u/y9CwFXtfhvZNWdixcp8w8rcylUpWo4b3WuNGRLk9QJZpdIWiy1x0pVvruwDBIm/b2\njd/tLudlous6fvvVly0kQgrc4yBxxnGeyRUuj8/NFlOsvHIU3/dxHFFWE84B7bQgb7VCzjzev9/p\ngcYY1jnw3Xc/0N9+znjnydUyjiPTckX7W56uz7y9FcHijz9+z+3dOw7VEK8zynX448jnp3ecz08Y\nH1hLZl0TKQl6e3N7S6FATrw9HTn0hut55s3pluPoWdYnVEmMg8dpy+3bzxgOPVNeqNXgbCcdGSdh\nFyiLqoYcMkuQItQqofpUY1DO0TnH02UVClHK3NxJDPHSgr1E0C7dlqI6Kgk7dlJ8lkR3d8scM13X\nMdy+wSokDEMtaN/hlAPvON59Rr1M4AxlnsitLtgL1I2HTssCyG3tlXaE7NP8ErXNpZJzJZVI7xze\n9Xz17msZG/NMrZXOSZS8aLQrOZYd63O9+3g/CBKKVJVwgtcs9LxDJ3anU5agDotCOaEabCL6DUi5\nTmecM9ycBozp0EZqrOlyJSnF3elG1qcO5nmVgJLjiVor66xIYeXNW0/XaW5uB4bD59zfXznPF7rD\niBtPmP4tIS7EACUnpmllXSPT/RNH2zGfLzw+P3F7e8twOBBD5nwunK+JNSrevL3hu59+4rwUHq8S\n6uas4X7uuWYYP/stdrgh/gpzwHiHQdE1bUhJmU5bLjVwWVbivDBqg4mF6iLKGbRq9LJ5+dV95ufX\nX0aRDGxZs3Kqe1n0twLhl2KnT7yOemlbqlYg1wpaNeEEoqxVWrieKMW8LtQS6ZoYRDfOpPcenTSl\nCp+npoIxrRXYYpmXZYGqd7P1EIJ4d7aY5lLYGztKCR9GHLZUazFKsMlmqg61oSNNuV6KiN6qxnjT\n4ikzKa5c0hParfhqUSahLNQMFU3ZUMiY9tjSHBPfffcda1yYlsxlWbksUQR+xtDZAV0t8zXhnOfm\nNJJKlg2m7wjXZqukNKaKy4BrSl8xUn9pH429tOiuT88SuKDFqkVM0Y2clo1DdQ7VO2LNXEOgWE1Z\nE2Le34RqWkuyE1kM1KMIYqbrIimCh2Hnq7226XkteJMY3hchaM1ZuJ45U1KkRIXRG1KqcNoQoXl4\nmlYM6b2wk9a3ZfPm1UixpDEUWyhZ0GtQhBSotLYaWwjIx3HCL2P95RC4o4I/E+7lnJugREIgchY+\nsP1zXN4ior3NkD7HKEWkYu829FpTijyjnDKUQjB1H4vb/c21oKoSEkOu5AqoQtFS5L2Ix0QAmqu8\nt0SWKuxGq2qouQip2tzWLzGon7pe+4q+vkx9mWe1eTi3GrJRYEQ4uHmmK9XYGUqhjEZnsYu01rKs\nK0NnmCbpoHRt47TO7Wb0uaSGJr201jfhj7WtaDkHwjK38ZZwrTu2USfWFgv+el3bxpZiQ+Pt7nW8\nIfn7M21jI7XCYxvbSiniK2rONm74WdjA9hqh+ZmXnKDRcEC+w/IgY9L3ButGAQtaQaCBZZaWrXe9\n3Jt1CxxChF3Nl35dAto7rPekZSGXgu893vSsi1ibbVSjEJddXLc5DSj1sSj149Y4DW2WvaLUtK+h\nuiHyQtU2rc4Syk7OQn3LtRBTxhQpjqy15CRhCbKuGCRSesW5Due6XZxsreVwOHA+n3cx1jiKe8nl\nciGlxDAMlPbMc4nEKM8hzbPoNmKjGqEw1sr+ZB2+9+QSUUls0Z4vE77r+eKLz2SP6UeM6TifV7Tp\nON32RN3xP//mN6SaeH//HuV6fvrwyPj2c5Q2xFRQueLGjvFwy314T4ziyfz4/MjozR7gpbWWdVx6\nQXRWOKrzJPuXcw6r3U5JKBlQkJVGoylVeMVUQ06KUjRuo260cZrNC+XCOr/TKzIIHYuNKtleC5pQ\nTWFbKEhBYV1Haj7K1EKxlrHrCOuMkqSwhrx2pDVgSyt4sySabvoI4Rhr0iaQ3eYRL4fObf6+cIIT\nw3gDFGIsrCWQc2Loe5LSlPhCtTBGUjhF5Ft3+uYWziX0B5m/W/DRpquptYrAvBSxUNNyEJxj2NcI\n54TXnXPmvE77XKm1NK/mTqz3WlHtnMMPHefLRPEwHkZCSFAtqbnvpGSEWqRFL1GU8K4rCqqVZ9Rc\neWLILHMghQWjKqUk1rhQ1QndtGKbwLzrBo43t3z7T9+wrJFpCihnwSmul0Ck4+ZwixmO5Jb6+/NL\n7F4V3fYfcqGaSqmiz4g5o6tFVcUUxckG56RWeUW5+R9dfxlFckO5lK57mxqzmVmnTZdCldKF1/nq\nry+r0/7lcytQc6rYocNhUCFAtdSC+OlZy92pI4SFdcn4zqI7g3WOYRDxS0jynvO68HyNhBRIKYsJ\nueuprT0Vc+LD0weeH59IIRIbt1BZg+vkFOlNxhjoUcTcsRZFSBegpxtGKgo/Dljr9iAOisg9uErE\nb60JlQuHvEAx+GNP9e+oFInGLQatV2rzokRXrssD3/zzn/jmu5+YrgGUbcpXicFeSsWPR/EOXsR3\nVnsnfGcFy1qxWnwptRKUDm/JuIaAqpbKV2BVVKXp+55kxYou5YV1uTKMR0w3Mtzecjweueszve/4\naZ25xsx1LiQDGsfBH9C9QpsIKZOdkw3GGO6fz3z303d89eUXDNFih8Yds21RbfG4W6s210Kumrg8\nEy4fIH9PqFdMFNRXnQyuVrp40wqGiB4983xl1mKf1VlQKJ6eE/MSuM6W1Y1EE1C6YqsnMXI6Cl9X\nFlfPiByW1iURY+P6qs154YVfWisYW9DO7O10kyoVJUprJRSVWjRLkkhwhcOQKLW01/j0iXtZDUPX\nseaVNc/iwqEVZVSEUvDKEtQqjgBFYVQnqvEmtgum8T8rdEY3PmYVB4cMtRpxT3BOuINF5rE2hX5T\nqJdMpuzCMVr7v5bC2vi1lUrN6ZPfQb5H5uQH4nyVlKhB4sd1Fnsor2icOOH9Uws0segmXDFdwWpL\njhGNEXcPK2NlmWduDjcYDN3J83/9H/8nf/d3f0e6e0N/c8dXX/8GgOvlmev1wnIVd5bUWuNb8TlN\nE75Te1F9uVy4fXPLZ75jCesu4gLolfjWrrFRN7oOoxQhLK/42uKjHlnbOtcChNYVZxTLvEigglIY\nKwb6tda9XSyJnFuwyFY4SyESgxx0rPVo5wiXZ+bnK2q94oaOMGXWOaLtSo6C7BrXsVZYa8a0w1aK\ngRJFwFaTYllmhuMB5T0pBJxWLDmSjYyTaOBy/oBW0GlJCU1rbpS1LIEfWUTGh8OB+bowzas82ir8\nYusctbwo1tdVXDlCXMhU3KEJetaM05HqDdd5ZY4Ruo6aPSXOWKWY0sLjdM9pkHCn3nu8F44vIdG9\nuRPXDzLKwLJATBnyyqk7SUGuHRpxdOlczzRNPD08o4sAHsPphl6rphEAi6XoStbiT5tzlMOdVqjG\nnSzZUJ1lPJ0o2uDffMl/+av/RD0OjDe3qH5EO4/Xjuf1ilKKaZr5j/1/5YcffuD6fObpxx8ZvGde\nztze3TG6A2/ffsYf/vAHSsk8Pv2IvzngvaEbHPHygdvDga4zrGXBdop+rPQ93N0ecQr6wbCuM0kJ\nZeY0jHuxlkvBWYmVTik1oElTlMJ6j0qZ0grgqjRa9yQdUQq880KbxFE7QXBDFYqkUgpvJSQoJfl7\nP/pWZMo6E+KC1opzCDhnyEasF1WOpLCQTMF2js/Gz/j+2x8ItXI8nSilEENby3UmEeS9SyAHTWc7\nwjTR94cXsE4rcsnM83Xv8KixJ9bM4+WDHIJNRhuN9o5iFF5biipksjhOVXh7c4tWko6HAX/UmNqz\nxsB6vaCsJEla9ZKwWmshxoDtNIebE/YqeQi5SkDZeHNo9BUBAH0GkxMnZ1GDJavIFOFtOXB3FJpY\nd+PoB8vT4xXvPZenibhE3n15i1KV2+NASgsxLvi+0rmALT2X+3sJnawi8ptxfP/+nvl85u3tOzp7\nQ3cY+XD/QB0Gns4/8tndHc8fzszVcHx7w798+CP/6//yN3zzp294ui70b++YlkScH+jdrwj3nBwI\ntPd0znJ5fuQaZ0LK5Chg25VK0YolS6CNrlEoUV33ydf85Pv8q3/y3/KSDpn4xaqyn96AvdB5jbj8\nWeEeDUWl7MW1rjtQLT9XWkxwVfTjQK2ZZTnLRtu4vNZK7LHrelBKFLBlap6HLxHQ1kmUaCmFh/sz\nl8uELVCk/0cuiVQaDcIYOZkXxKLOWZmEokLBNZsd1RCqqislR1qSByk32oA2O+9LIaiNqnLCLNpQ\namy+gJFaMz/99J7vvvuB6boQgpi0F1X2YqyW0hYa05B4Ob1qpV9S1Rqnc/ve4/FICmW3mNuisUul\nbbqWYRjoBkVOwsM7HG/Ev/T2jfCXvbT875fnhtBZtPMYZfa4UWMVShfWEtliMGOMPDw8cRwPfPH5\nCdCkzQXAfhzXW6tgvlXBmhPkgKmFUAq2yP1zWorkYqVo2qjBqrbDFOJVaVCEmpniSkoQlGx+6Eol\nYOsg3QElLgrGKNa6IbEOpaNQR5RGle0zVuDjIAhxH5DjXmycZCmUbNuMJezlXzMntjkktldFCnOr\niLVQs7hJhCLRtKlI4p9CvH+VERSaZhtUtUhnExVVizhxGLfHxe8dHwSh+bkf5YtSnX1cbfNSuMQv\nNnefujYesLW2BfFsgsYN3RfUqdaCU6CR8B5VqnhZbymLbCikFCybAGdD6q21WK/48P5HHh/v+du7\n/0ypTYCkNcNB0MJpugq3sn2W0hAh13m0Lk00lrDW8f79B9599ju6w0jY9BW1EhrdYV2lAC6ASdLN\nee17ut2j7fdqls8aJknEXIPQpfre45st1EbjehH/gQiXTUuMU1gra5Ise6JDePzwyLqu0vbXFpWF\n16utYQmJU3/AKU0KElWc0ksS52vfZknx6gSJC4k2iqhat382NLBScqKWgq8vncOcM1VtVoyREsX7\nN8aVGMLuHCAdHukcKqOJ6YX3vdGTQpQHv0ZYowiLcpG2fMoZpS1dZwTRbjHA67xAqXg0WScBB7RY\ny20+8SklSteBgnkJlNoilKsipkJMBa8NVYs4LRbRW2xWcVu3ZRP/gcKZDmUcMYmPvTcDw82Jai13\nX/+Om7vPwXtM15OdUBysdpRO7LxcN9J1Ha4fWM4zf6yasfcc0h3d2HHjj/TjiO9HCbsBrLZ466FI\ngW+VGJda51Cv6IDGGDoDioI1Cm80aChVbFyNlnppO5xt47Y0GoPE1RsqrcuEjIPSDsqqNpcGDLal\nLIawIhkAllyrdJsaBUJ+UWOsjBlThLKTsvCN6wpZZYwT7YUqhbHrhELnjWgMKHvdUavGuQ7QhGWF\nUoQe6DxosVTd9rqtu4Yxe7ZA14nL03oRD2SQ7riKKxQve0Z+8bTf5o1u9nu5JJzWOO0IqVlLNkcK\n1UI7tnXVOUdScsAdBlBo1hac1TlH1Lr1O6U7qJXYpjlvWFu0tABKjlBlTg3D0OQ1K6ITk5Ac31nx\n6Tags0YrKwftRj2VrmLBdopxHIlBKEbHN2/ojzcEQB9PnNzAw/nCV199zZ+++YboWsJf19EfbljW\nTKka73toIUT1V9qkm8VejEH2q7bWbVkQAsI0vrZRexdNawHx/rXXX0SRXKlUJUlplUypBfLL4r6L\nXRoKVX5FxV/rixcs24bdNmSNFG8ly++XJjgbxgNd1/H0PhCCOCkM4xFtOnbPUa3pBsex6raZSSyp\nMQaVFj58eM/luvAv33yPUpq3d58BmlgtNQeyUZihg7SgWgFktZa0oXFEWVEvWy+tAAzCl6xileSM\nKJiVUqS8koFheINxHbUmahY/xJxEwq+NRmuxHiol8/6nR7759keMGdB6IFdxEhgGT61i/5QRX1+j\nxNZLdhAJfQAIOZOrxIN3Xcd/+N1vqHViHCXlaSteK2LWr5RiGI90w4GhE4GVMWpHt0opqPXC+enC\n5Zo5qB4VLaHahgpkKehSxSuH1hnrMn3thG/3wwO9H3j7diTHQHLSIja1UorBe7dzvkpcxKGhFiwt\nHa6Is0ApibDIBHcIpXErvoxSnJSDUqjXBW0MR8Zma9ShtG8ts0KuFVdlMasklMooCs57SlZUHek7\nBZ2hNkFgjHnn0JciIr2tEyL+zelVU6jxV4ui6rq33neLoZJ+tVC21krxXsCaXqJCtYiHlDGEFIm5\nbdHaYIzQKbZ0LVUKJVdxZNkQL1Wx1dBZcWXJRUI7VJV0Mus0OUoxp9pmVkuh6Lq3wrf7LEIQjbK/\n7vUMsgjO84RxrS2ai3gNm81nuKCrEJmMEomsZjvkLBQSWnU7jUtrSypV6Fe8Lror5/OF3g/88Z/+\nib+r/zulZOal2bQ5g9EWax2xgrae29vbj6Klp/MDl8sFYxy3tz0//fQTf/zv/8h/fvvfsEozB0m4\njLlRohoaXdti37fUqq3QCyGQStp9cnPzNo1z8xqvglqfnx4xXu0b8ZbeeRhvhDZiJURhS8lyThwQ\nYlyFN68Un39+x/M5kVLAKov3PXbsmecZ3w+sLeJ7GAbishLzgkIK75ATqlYJoaiay3TGGkNdA847\nSWiMkWVeOX3xlrSuqJxIOYuDShIRUy6LFNHA9TILl0wVvLfU6nDWYLRrBZEkzlUiCsfhMJBzJawJ\na3rswfL4/MT1KZDwMv6VRVnodLdzOWuteF64/X0vaLDTL37zIPP10A8oa9DW7G1zQiXkwnIVoZ52\nXqzezhdqrfSHA6MxTMvMPF9b8qKAEdZa1nTFqx5ne4o54v3A7979ju505PD1b9F9jxsEyQxrYSmQ\ng8LkTF5nutEQE3RdT4gF63rcaPjrv/1bTqcjMUys68ybYeThwz33D+/prUXFyNE4So7M55n/8PVb\nbrqxFS9S8PbNYYOuh7yIl37z8KcUQn0pGnUjiptNpJgFOdFKkdDUktHO0VnXRJOKagcKq3SY0Chl\nUVmRs0Jpj/PynK7LFe8N3jopQq3CYBtIohjHA8aIF/SSFqE8ZhgHEZP7vieWgrOOd7/5LTEnnh+f\nZJ0cWhpvUZQY6e2CL4UlrLLva8VgGrWugR8ZibyX1FJZG63VFD8I1VAZ+l6LyDXO2E4oJ1syLcDl\nLBZ9fd9TcuXp8cJwaJ1gI79LFn1Hdzhg2gGNFuBVhWVCP3Qi9E8JZwSkWueJnCOHw0htoE6iuY6E\nzMPDI4fDyNu3n/Ptt98wDAO//euvOD9fmeeVnCMhLvTDsaUAOkwTyYaQWHJG5YzVlTVGqu/Q/R1L\nUgxv33D39ddgPB8enrm6xBdvv+Th6ZmkLEE77k5fcH288Psv/orv//sPPP8U6W7eUZzY9fV0jP2n\nkeRSI/3gGbpu77xrrQlF9ndrLTkIYGgxlFoZWvLpa+ra/+j6iyiSFTTLKWkj0E508MKzU0riEuv2\nC5+4tNZ7KlXVol5Xr8RQW5rVa84zaIZ+JB5OPD8/svnwe9eBVoRV0ISq2Af1ugZy2gzxn4Uje5nI\nCWzXk4tuiK9DKYf1Dt05allBGazRYnejNLbzWOsbr7dFoRrTTkkKZzYHiSasqZsADJxRwErKK0ob\ntECfpAbPLevE9TLz/HxpFBVLUZJ5L4DnhlIJR1trTW3twkqL2K0tvedVAVErjatZd4Xvxsk83N5y\nXCOlVvrDkb4bMTo2FF2UsbVN5HmSFJwYIzmpJqRovtjNnaDmLAWUFleC7RnM84XLWazsnIG+xW6n\nLHSSXOIeNyxkROkGZOupVZLntLUS0ABQq5i355eYYFXl/TU0nq5CayvRqHobhCK0yRVUycALwqtz\n4dA7yeDLebceyqWgKIJyKjkkagW1KcA3agO1tLQ9uQRprs0+p/7sEPmxRdHrq5JAyWfLJTYrOENV\nlVK2VEr90WkcRPmskedfaiXrsluaqSooc81izZeVcL81BYN8j50P+3quF/muWsv7CkM7y58Lu6nc\nJ79HrSgKEi3sGgzdfKYbmqgaMl8IoAQhA8hK1hbVDtlav/BaKy+Ctg2J6NwovLxYeX640N8e2z0S\n3UBWgoBWJUVJ38umGELYv/PhcGCapGC6u7tjadHr1ns0cpCPfNwF2D6H1fYFDWlFcq6ZuAZyS/Cy\n2rTADiRO3jtCWMg5SYdJW4yWgl54uoLQ7eupfnHKyG18SoTui5VZjYoQIiXImuW9Zw1i2k9LJtNa\nRL2l2TKu00oMmc5CDhn3s01uQ4kvT89iq6cNVUkxUFRbc7TEFet2cFqXFdNoJrlIESDPbuPNl52y\nYEwvnMSY0boSQxQ+rvb0vmOKIiKzfY+uEqiwHUZ0m6MbZxNoNms01xTptIUY8Uph+0587bWm1rRT\nbGqte5gTRhPWFR0kfvfmeKLrHPM8k8KK2bpxukOnxru3Bj/0/NXf/E/YcUS9+RyMJ4REbI4omFfi\nZKVwtmMl753Ozndkl1Gup9a8xwSH6czl8YG8zlg7MjhD1/Vc5mfGoQMy1sDxcCIWAUJijEIbur0l\nYYWSZy2xudGYQboX13luItYTsYk1QxPaKuukQG5jUhmHqhILjrF7Gumm2KlVOq7GO7TzzZ1GxmEq\ngljbhmTH2IANIxZxKVZSEJpaSVCjjBVlPUtMpFpwvpNx1suBMwWZ22mNpNyoWyW3zkJGO0NIobmr\nABW01cQlNTci3QR0CutFtKkqdN5KJyWn/dC6jTEA5zpSLLvNaJoT13nCmeauVAQUyb5gm46kUFt0\nvTgdbeLb/WBdXhJFN70HWpFyQedN8yCrZS5VahRrWGN4pekR0DLGlXU1uH5gCw0RHZUmpIArharE\ndcmYAdMbbIb+MIA9ULTjcDQoLCHMeNfz4/0DscIwntBZo7JjyRcel0g6WOLdza0EAAAgAElEQVSa\ncPOK1uDUp1NY13CVA0lNhCD0s1rUjvJv+6NzjhyiuDm1+x7+/RXJ4lv8Apsp0qvWIrTW2avox09d\nm8doCAE1DoLKvfIDFW9eQQ2zlvjf6zJj7WEXiDxfzqy5YN8NDMMgaua22a9qwnvfNs+L+ASXhcPh\nwLoU+tHgbE8sFl0q2nZQIo+XMzHNfHnssdZgtSanQii01rEg1EXBYTxRdWGNGVULnROpU1UVckYb\nKVC1qizLTEmB7H76/5l7sx/Lriy977f2dIY7REYOLFax3P1gCTLUkgwPgGH5yX6yBT/4yfZ/agOy\n25oAAVa3AbWgVtvdVV1ksYssMjMjI+Lee4Y9+WHtcyPYlSTqkQdIsMgiYzj3nL3XXuv7fh+dD4Th\nluANFEcl8u233/D551/wy1/+EsyOZJNunK6HhlwS2ugr66jdOK9WjVI1lW6LyW33PYTAMDjevHmD\ncWpWqbWq1rJW1lLoh92TDKMWSut6pZxY4kJcZ9K6kOalfcbCmgvW96RVO4Ibb5Jq2oJmadluiPSc\nLo9M08z5NNEHg786hTs2Q8X2nHR+AGPpeofJKx/e/oax9xibqLWQLViB06LOeR226YjwXbzQdx0p\nqK72IpWTSSzLxFpmlrUDW5FiWOqMtVWNbNlCMqzvH673zVxlIDri0zAQo7zh0jCIbfwvQDWKq7m+\nFSLXQnb7++fGuu+7rMt4W6ipkNKMA3zomFpXoeRVQ1OaqW2794W2oYkgRVrkdiUYi1CxqGyEKqhz\nUDt6qUTqGhntTrFUrWAQY8gx4wQcsiGnqf5pxP6cFf3x30VxXfo1PakohcQ0JqieViqJFSteF2kp\npFLJNRNyVuap0Q106ypv49IQAvM8U7LKqP79v/sL/rt/svLms5fkWgjW44PX1EJxWCPMy4X3H+60\nO9Rrl8fNik96eVuZponz+czl/p4vfvU5b376KYfDQZ0VqwZ1SGOLr+tKyol6ubQkr3jdVNc2oRnH\nUWkdQDZtXSsJnKUPO0Uc2nId/SpJojnf5QmXpwdHfW5CCErejJ7pnJsMoyNT6b1lbc/b/cMj/bgn\n9I44X7CtcM7rU/Tuq1evGnJy0qj7adbudorE52l8a6LkqtObJaoxd4vwNlpwbBr1482e3gfev3tL\nzpk+bFIL06ZGFesqy1ooOV07S6VU1jwjvpJmPSgdXhyY5wseg3dOI6uzSsfWZix6+fIlAG/evKHf\n7zidTtcDvbGGvqEIL5cL2ar+PK1P604FTpfzVXoS+o7LrM/BbrfDevXbWKufwRqVxJDmyDh03P70\np7ibA4uDLMJ8mcll4cW4Y7c/8K6cWjrkRI4rkgtv7xaC77BGC+1pjVxO94Te8zhPvA4D07s7/uz/\n+zNOpxM+LwTpwMPL2x3jXggGXh06OhGcjZwvM5337IYRFzzn85kwajfOOMd5mhBrcCk23Nug77pV\nMxftAFutZRhGCjqdrCLErFOAVHWClVvjyIjFGK9JoJtsyFgtQH2HcY6cVB4WS0WK+j9iXFlNJa6F\n83nFodrykhKXtOKMZbELvhso4qgmYILVia51hKKpv8F0lALf/PpXnO++4Sb0GJuIdVYjW61q2m81\nR9c39rVT9GXOWeVkxmFtac/GTE0aorTf76m1apOtVm5ubnBWEyR3+x3OC3NqJK2UkVT0UOiDUp/E\nEAaNQJ8vK4ejv6Z/1prpuoCNOpkMfYfg24FRn0vrHX0YoBqmIpzjAo+P/J2///c5n8+cTh/o+4Gb\nmxvWOCPSGODOYkx4MtV2A2VVU501PUPoOX7yB9y4HQ/vvyV4j7EdPnS4buXnb37Gn/zLP+aTTz7h\n/d0jx9dvuPnZZ3z95a8p2fLLP/8r1k8/IdYenKcsaq43y8eLZO8diEaEp5SIs97fbLjK0TbjZCeW\n0PWs88J5nRn3+x/cZ55fP4oiuVKv3RfT/mSvpzARHZhqtOU2ov3419maVtYFJE36layhlkIulamF\nTdQKczrj0kI9H8k2Ih66nePDh4lgR3KaWJeKCaEZ4By9cSxxVaRPWgjeMLg3KDtzobpM9g4naPcw\nrXSmsut6OlMo60w1PdkaihWcBVJiGDtM12sXsWS8CRSTEbFYUXNKXiupgBGPlQHXBY27FUeZf8uw\n+4z393ccb4RYM7mofOT93UKpL4lZ5SXWFIJt4Pak3UPnPCa0As7ohlNNJVXV/OgYeKAzQU2DXYe3\nCePg5kZTps6rIeasWqo4gWQObsSYTPDKlV3miZwSOSvT0fmBUjzWj/RiuF8MhMaPTYUqhVIj1hZi\nNVhrKDlSasJIoqwzR2BYCzlUpphwYUBwlCxQW5Kd1xS5mk9kMTAeWS7fIiRKXeizJ6WMK0kPI0V1\n287vOIhAdlqImUi9rNQHy8XAJXhAMLGSfYG1p7IgpmCw1GqoqFYv50rnfCtAFZ8FoKEcqgcqGU21\ny2qIy2KwshX7GlcrVg9Lqt+3eiKW9BTP/ZEri3Yad5124U0RYkkEqZginK3HVA2SqKXg0DF9Mk4P\nrhWVLYhyj2PRTXhdI30AMQW3nd5N2xxbT8h3DYOVlfLhrKP10anGUnOF6sltUfuhYt9bD8aTV+0S\nazywYakZQYkcnbVN89/rxusSlEovO5Bd+/kjrhtYGy86FB3xUiGuSYv3rPreOWVO5wuXaaEzUaVQ\npZFQvEOcxa6zjnKdb/dK6McdlKqpVSVxORf2L3vevf0NxhZev/wH2pHqtNi6v7/Xhb51iQNCbUzx\nK+O4mZNK1gNNSVk53CIoS34h5UKoQSVsayHb0gJP9L1ywZKprDHjnMGaqM/nmvHGUWJhSYYX+1uI\nPV+++5IXu4Fx1/F4f48VNcYVI5jWzXp4f8FWsFULzlN9vBIPqui4fOMfm6b9RhohwKBaVqfIyGWd\nmR7O/HR3xIlwOp30QFEs06UxjIeAtYbT6RFrPH0/siwR4YBlRVIkVMslL2QKuRX71imdw1J5cTww\nnSaVahgLPhB2O+bpRCmVOS7K1DWGebqwzEqjCH7QcJmyIk4bLTY5TDZc1kcQIaUFsZawV1NZnYSK\ncHM4Kje6FswCXTfgW4PCieGUAlNeMXnl0Hmc35P9gewdefDkarhfM5fYkimrToREhERi58Z2sFAO\ntHMWmzt8Alsry+U9l/mOhw93TOdHXnSOXZ04DIZQJgYRvNcgJHHCmlcON3vGcQfSCqyup7N7jDUk\ninYXnRA6R4oquyjGM2WhLBnnRBsXAiuK7DvHqAm4YSCRqDjcMPD4/twIMT05VYJ3inKtEFeVpPkw\ntgO8yhtU0w85z/h+1CAuBLq9NkCc0hnm5UJNE953xMZ4D90NRRwuaWe5G3aI9RA6hm7Ap5nHyyOV\nmZANvlpSm7yIdYozFcdgPffnB8J+R/WBSMQYjai3voOSKTZr0Syo1ApDcIqjXUqTOjnhcX5kGAZG\npwEm272LMVJTvgaLgMqd5rhyWRf6Iej616mmOKZIh6GvQq6Jk1G+OVgeHlfGT1/q4eai2uF4nijT\nI4dOOCWVO4oR9vsdpSVq9p0j5YhJiU6EfucJ8cjdu7d0Wbh59RrbH5lyYnf7ipQy4zDifcewqhRt\nfP1zhuORePmc8eaG2+NPedhnSjR8+9t/y+x3TOdKP3g+nB/pvceZj+8JBm261Eb/MN7Qh55licQ1\n0fU951m11uw6qivgPd5CaJzo3+f6URTJReDstRCwRoctXfWUmsjrTKZgnCFv0oDvubYN1lrbtJ0q\nI2jpvthaQLzC+22Hr+1haIYVEeHh4Vum6czDB0voZ8K4wzjFrKV55uHhgYeTaswOhwO7LvDwcMI5\nwzAMpGKYi7JOx2GglsL9+UTv4XjoVXdam8HHe0Ibl67rqsZBD2lZG+lDFEDeNkXjG4IuZ3JLd0ol\ngiTefflLPpwsnB6Rw5E1LfzFn/8H/uaLbzByg7OdBqMYoyg7Y3Ci4vXnwHQ1HNnr+HHrDontcGLZ\nBy0mag2E8Qkxdf33sQ2DJ1d242mZ9APKetK7v79Xdq/tuVxmLuuFYkawVnFvNat0hDYSqs+4wvlJ\ngmOdXJMC+2yxRbtxG55vGIbrCbsIDCEwjHvK4YbH81s657B0LMvUOkTaoc9xIq6Zuq5IWqBmbCkg\nkJOQYiEZx1L0lO8o1LkSy4pFi16hUmoirfk6ysa3hDnZEh61cJnnWUdcLdq0lvqMa/a7C4QWTk6d\ndVa/Xm460Y9dKRqmmLDBQw0kY4iibuxYKslYPTQ5Q82QKkqiaNaT3N6mKko7CUG7JdUpBSi3Q4VI\n0ZF+07ksWdFaVaC4hoZbUvua6g6nKtbPWks/DN87Jdo+V2m8zxgjwzBoN31WFnUpRdmZVijBU5PK\neawY1cVWjX/fujib4dTZSmryHucdvbdYG+i6Ae4f+Vf/8o/Z3+752R9+dpVUKMZIHdLbXzcZgWoC\nDTGuV23wfr8nx5nzNPP2m28R/l/2xwOf/eEf4HvlfT4+PkIu9F6TxKrRNW9qqZfWeLxzdMGrZpy1\n4S1p/k+dRoRBuz1rXtu0QdeXnDPOdwzjQD/oZ2hKxnU9S5moVdfCw37k8a/vKPHCq9sX1GVifnjg\nGAZlra5R0/LQdTOIZb1MQKHbjdzfvaOUwn7ccWnjdycbBz42o5KuHcMwYFAUp+88gx24vb3lfD5f\ncXp3d3ccXuxx3hBbQEg47FR61SYIIhVjCodd4P7dqszdajkvGfEeb4Ru0HCcaZ2IMeP7jt3uAOjh\nyHrP3h/18835ajINGPbdcJXiHPpRqUfWEJfIdDpDEXb9DQDzukCFPBdKrLihgstI7whjaEjOjuqs\nTtqkUI1l7x0v37xmONwwvPwpdjgQnaYP5kkLozKpGdAN7vq8PeeoO6+NjxgT0zSTjDCdHzndfct6\n9y3z6UyOE7Uopuzm5qDc9po47nUCOC8XvHd0xmC9dlersVjv2Q07ahWWtJCbx4AMp3NGnMXZjpR1\nDxluj9f9oaZMWjMihYr6UsZuR/JwmRbevb9nP75QzFybTvZNspC3PbDCOm8x7S2Ay2iimht2WOeu\niZ01Z2JVIyc1U5Oh5si0LjixVGd5++GBLuzY725xtVJ8j/UOtzvguo6/+w//MZ/+7D/m3//rP+b+\n4T1v9gdMnSkpEYwhjGqQHI9Hwu2BGCPBOna1Z6VwOZ1YL2d6Z7He6JRy6ME6ShVsTThrcOPAsizs\ndqo3f3h4wGAZRmVjx6zSnyVllnW9GrhLrbx69Yq0RnKJeOdU992C0GIqamV2wn7cYahMy4ILgUs7\n9GHqdWL17dv37Hc9xtmrrGKTXoTgdV8nqWQzZ06nE69vjnoo9T2vXr1CTCUMI6Yqmu2wO1JzYY5n\n5nXi7/y9/4R1nflPX9/y2Wef8ZvfPvAP/4v/jH/2v/0Lxn7HLhVlSBfwxpPXRLUfN9mpMdRcm0pb\nkl4xuk/llLAFBqex9matpBqpOVO/pzv9setHUSTX2roixlxLAvWeq+Nd5ZmRUsw1jvRjlzFcNwXF\nYMqzrnPBidG5eoXQ3MWVrOa8ahX5MwSMGFJekUUQ6yBGMIacV2JeWwyjZewDXeeJ7xbWdaZWRaIp\nsibTY540wMax8QSrbNzZzXFZ1SdXW2GJqFnKFB3RG0NM2qX1rRuYS+smFO0ipmWmLoF1OjMc9izz\nzOnhrCaTfcdctONkWuQzxuFFN/fn3TuxK84ZutBdtUelFE0+LCq/iDnriRolGzjRoA7VbuvYSe99\nZkuMU+nEU1yufrPmWi8FTMOMiUZkGuuujuMrYYMWzVsNoXP0fY8N2vXYgli2wvw7PM6qUcsSAtYF\nvFf3uFCxrnC5pGaKq4gzOBcoaSGiZlEaraGxRCgI2Ro166n6/Bqjux04anmiLuSsRb++1ALypLPf\nRleARmOjuuONEkF9ih7dFq1NK/n8+qEOLM2UmXML4LCGKqobTrTkvWawq0Z/F6m1qXvZ7jrCkz69\nlKaXbpMZ0bGP/r1a5TRVT57FLOeM3zTY25tuNFL3eQrh912lbBGohhoz0tBZrh0cTTtMCaLa9vZ9\nZaMotHu3PYNXzSltTTHKTS65IOSmfa78za8/51d/9Ze8+clrTFWzZK16mFWz8HdjstUEtlFJlOTg\nvWc6P7DrB1ynJricM+Nxx26nf7SYnYkxkTBY0ZhYK4FihDSnK0e61tqQZE/87OuLAldigkiF4ttz\n7a4egtSCOvQumSdzc0xIzdotni/gC8vjPftDp2Y6cVgxWCNMjaVaG9s+NAmEcYGSV+1UtYMgPH0G\n2zRQjIZ2ONFUU2stKUbUpKp/PRx2jGOvhVtR5rE4JUVol35rjKgmOcZFyT2CYsaMpxjb/B+qzRan\nMeQprcSsk5pu6Akh0Dk1IM+XSd+BWnW/aCY05SV7wm4g5sSHy5klRTrn8batN6VScpv6VAiNiOEQ\nrBgua8Rb3zwTeuOthWG/5/jJz+gPLzEv3iBh5NL2K1PBimlF5tO93NaPzY+hz595Io1Yx7xMTKcP\nXO7eU+OqwUhDoA8OZ4TQO2p1FDJVbItn923t1GdJR+dekwCdmn0LGWt8M9zrwdI4R1qSEkXK5itB\ng3Cr0+fAOHI1zGuhVJiXhDiPsQMVR66zTq2u+QYFjGWbT+ldU99PrkpUMtaBU5O1MQZKIRZ04lFB\nsqEsMxVhWlfEFawINsNaBBo1CDypWkhCv9uzfynsb3+CFUsuKzaAmEy8KG6vHwf8/oj0I+uHD8Ql\n4argR+WCx+lCTAt5uiCl0g8jGxc7L1mbKS13YVsvuq5jmVZNmXTu+m6M43g9dG/NlUOn0iS2NbRs\ne6bSQ1It2CzYUqGtAWLN1YjojCBWqGvlfJ4wFsZx1Odym3I4Jce0MZr6B0SbG8SFoevpej2kVBGM\ndY1S4rDOkRr6E+M47Ac+fCjsb/a40BHGgduXrzidLwQ/IOt0XeO8scQU1Sf1kctarbdyqtdaQ0Rg\ne/6bbNBbR8xVD6NZJVdrk3r+PtePokg2Bbq5ArkhZITZa9HRd2o6iOtMigoYf4rv/e6lJCqNci5Z\nN2p4Mp1RWwSEFI67wK6zdL0WXDmn1tnQ8IAaoxa7s26cqcApnpAK437AGYupGse6rBPzMmFtQKwn\nT4Wc0E5BErDCvtuj0gVlE2+d4m1T7YcerNE8egpLmklropiMdQHTeWwIdKOOCbYYyvt3b8lz5bJE\n7ItPuSz3fPmLhb/+1Re8vZ+oZiC2rvXGazbWI87Tt4fseWGyJfOoi1xwRgvplBI1NWOF03F9XoTQ\nKVfUNoPNWi4M45ZcpJiZmvXnzevCuixq9POBQwjksGJzoRBVwG8hWAvGUXkKz1ADgzJKwTCONxyP\ne+ygbvWUVpZlogujxmK3IqKUBj8vilMztSDScDMPb+mq/m45RS6zagQ7V9UxLEVHNBnirJi0h9Vy\nHyvG9wQbsM2sFo3HBxUL6T3VRLBrIWyljdLrVX8LT0WVd54cC5SCZYt35rpAXAtTeZJjIEpKqTw3\nKf7uZa12W7ZCLqfKuiZyUZJMXCIW34pLQ2mcZ5Lyh6UZhGxuB5aaFJlXdXGqVfFCguhYCFRqYpQZ\njRFcS49LTWpD28xNVaJByoma4g8W+6WUayGs+2CiFOXVbiSb6gzFPDGLty7b30aUbVQQY0zrIhmS\n1QNVXVeSZB4fz3gbiMuFf/VP/5h/9F/+51d98NbJ9t6T03r9HtfJS/vsvfeYJi3w1rEuCykVfKfh\nLt9+8zV3zvHZZ58xjiO3L446bbmsVx3x9icW3UxPp4hppBiqBgu46+FEJRCqZ9fpByWTsxZ6pRSm\n0+mKkyoJcpzxTiVi1hQ+vH+PKQlroKYFL5WaIx8+fKBW4fb1G4z19E45pdO6qM7UGmJKHF68UI10\njYzdkfv7u+sGdg0AabHl8zw3g5sWA7eHF/qsV50CnM6PTc+v/51pSZ6XKXP7YqA04keRRGpdZrGO\nsqoOtbiA6wYqmSy6fzjjcdbjh0CMupcsJbHMCfHqGfFDd8UsTmedhBljSCWxLAlWjVgPXaefMUKq\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mE3G+EKxhmmckR2rvQJ6CfFJMlJLU14FluizkmOlC0/faQMQwxYV1mxilRHWeFYdUaRIP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\nO57I3vPm9T1Pnt6wvXrE7e0t6WRuM0NXuA6RJ1dbA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bVaKiVZ4+CkUk4mWqiaqc5xNyZq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dm0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ja/0WZuZRhE3U9ue33fmsICV09p6ZZGG2hDn0h1a6yLENWuaE7rNlKHwwH7YvaoejOHvIMH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\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lqm+rNXO221BqxfafLwb8vOtLUSSbVf1vnW5OIeK6levygFLEoqy9Lx7IPhw6m77nuMy6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FvXlRA8a55oOMQljWiFlCbNCyVlIUSkLW1KobeUpE2neY3WfP7UWq8nQEDGR1XGgVarvslrSr/Z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mkPBdy5wLNzc3fLpv8a6ARHzTMRVPxj0+x73uRXG4x4j++5vNTu+bJAxzYJoD1hp2HTRWBe5P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\n7mqa5Kc+82meC9x8/X1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ZJ6TqXmNEFn1ZMEYlDeE48Hkb0/NICEoZm42NKCN2stJpM7sDwvfqmyPHW2GA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QTSzbedJJ7LV6c45Ph1T8fDr4uV58v+XykmX6OpGMgcLn5+cLLeP5JidPlkUSdFwb7+lrolrI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\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sdky4Uc6Z19hYrMMnCzG6K5DjpXZAfJXvHYvj5/N52+RoQnFgLoqBNCcCDTv6dfN5rhc3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H/PRPvc2t4xXPHl/y0bf/mOdPrymaFYMacc+vqEXF9sVTHrx9ys999UvMMs/h4SHf+Nlv8Nbq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Qg2ZV1Fxz4EitEvleOpFEyTa+ODqIu3DqWEoa6ppLiy1sppPHJ1fYbTnpgqxsnmMkfxErTc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\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vI8PdHc5WQjwznxO20WyudoQM2ktRbq1nmgJ9t8b1LfM8ksIF16zZ9BvuXn2CqcI6tgrS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Y6VJI+REDOTvSlP33mLo2ffQ3CJg5NniGPP5fUFmwfXVMawXl1w83jB5vqS+5/5GBbJOAoO\nbh7h3DX95cBkscfVo8/gsTTRcHn6Ker6Bp/68CeYzfeJcUtlEqCZHh7TbbY8/yV/lM3ydfI6EPWC\nqrGMruby3meJ44A1M+oDyaP793hmNuHJRz9SrDl7R8hqyumjJ5y8+iWEvWOWTx9ycfmI+cGUJ/d/\nDcbAjee+lOvhbbSd0XQ1i1t32bv1AqfvvIW1Ci0tMSTGpAmrLWdXK/ZnRyWXWkuuHj1ldGuy64rG\nFUHXbTi6dZO+u2azecpkcYs4rBmXp6R4iU2S1ei4M2148xf+Z9yYkGqParpPVYM9eJari2t0hKOT\nu7z75m+ghAEp0bMabVpK5F7RmBqRFVIK5ovbCFkuIzELstTEEZp5RdO0xCRx48j5vY9ip3P89gpp\nGq5P7yFEAl0zXj8ljZntuKTSFaaakJTCjWeEOFK3Daaac71ZY+SGejoFNMkHnA+sQymkahMxKdEP\nPSFFNOCGAV1bUhLYqiELQRoTRmnw5QJJGtCyrDE1IJMjbHu0qRlTxDYTRAzUU4sfB4QKiJgwVpVM\nasxI0yBJ6DajhcZ3A42ZIIzGBU9IIGtBcA5VZbwsJqUoLAf7z6OqjFU1IsAYCr825zIx2qtuM3ae\nZtIy9gNaD4wioqPBuwHEUDKEfoQxkLRFGU3Xe7wbqOqWFMKOWZtIwSGVwG+WCClxaaRuW7wbEUlS\nV1NSE1ACxpDLgcEViY5pK6LblOuECMwODkgqosQhOTnqukKM4GQkJU3IAT92+LHkmOumYujWBJkx\ncleg0hVjLOVGVIWqDK3VaJMZRwc+c3GxpDZD4RwPA4vFAre5JERPzD3KtKy3IxWRYbvE2IxLjlCV\nA8CNvbtcb9Y0pqZtE926YzafYOsFyQs2IlC15bnpN9uSX86JSEbGRDNZMD+8yThEVt0WVle0qqGd\nJX79H/9D7r/7Mb7xW76H1eoR3r/CdDIhhICyMPQrbFXTLA5JfdFDS11uLNZPn0Ba4/stzfyQxcFL\njD7hw4p7b/wSr3zRV3H9+BJZz7lx5xZSeebhLmHkc4c5kSJXZ6eM+Qwp1vT9uwT/8zT1+1huT5nX\nz7J++AmikMTRYfSLJJExN/aY3bpDDgatLcokIh7vu/L4KWFlmWwmqcrrJ5UprrWWsV/TWEs/DCg0\nOQaiGmjqliF6tEigAuM4MJkdMzu+SZYCo6rdgbMopKWSNG27m5pKfMhYWZE1QNH7qrYUbnPKkASe\nYbdR1vT9iG3aEoHMhTfbb7flJjv2hU4TQylUSkGOCq0V0Zf8bAqRSmtCdFS2IamRnA1SW6aNKu9v\nqjBvExnjCmFAEphWGi92PeWkqZTCpcLTlq6n2y5LRp6Mk1tsNUXmCiFBCIVE0m8vMdrSb/0uZiQQ\nYiTFTEaj6pLhVYUoyqbvsGqXGU6lt6B1wUrm5NHC4IYBoRQqFya/tRZlTRFSxAGkot+MRHcNPtKv\n7mNaxbAOSK3ZDCOzScvebB9kySVPXjrCigppK0glvqqVAmN+5+Hn9/j4fR1yhRAT4BuBP/d5n/4B\n4MeEEN8LvAN8CCDn/AkhxI8BnwQCZfr7e5IVdt+g0AJCCY97Gah0U6JscoKUiRyLUz4ER8ZhrSUn\nDchi6FKSsjGNyDEwDj3WapRIpFTgw8pWJL+TM2hBcAPSC6SuII4ELRFSl+9rTVnt5bIusFrho0Cr\nCYSEj56mNjgSZIH+XVzKSilyksiwRVuFCFfIDlCP+EPPPcO7/a/RNvsoVZNjy2WzQA2Ol0TLp1++\nw3h5n6m5zePrxPtuTBDSIYIgIhDBl0zRv/xUYtFVZhzH0j7Opa2fdw1/T7GaCZO59cz7MHXLeHXJ\n4ANtVePiwN7NG4yjJ3rH7OQ1iAOxapF2gRUjP/p373G9ibw9vMOdbaZdJvbqPfq+Z3M44X/5ix/m\n6974bk6XH2c6V4wjqF4TYkPcvENrBZPDL+KVP/wKWSRMNQOdGMNIWJ+xfvd1JocvcPT8d7Ndr6ia\nKU/e+ihu85Sh60EPnD9+jFWJ/fkcc3yEbp7ji46PuDh/F1W1jNfn9I/eol40TI4nnD4eGS/WNItD\nzu6/ztXj++VirDS2mhBjxOcNIl+xPHuIVntcXTyhO3vKsFmiVEU1y2zXHedvN0xmc9K4ZYyOlB1P\n3vg0UjQoO+Hp9WOMaTDCkJKhmtzizitfjx+e4sMpy9OA60aCi2yvzxCi4ejZF7l+skZl8Jsn6MkE\nYRr04piprcgYRpWwwPU7D3n2xffz8J038H5kenCbarbH5tETvNvSLo45ePa9GFruvO9rWF6uSalj\n+/rIwszYO3mNev+IbvUUe3GKiMUgY6xCKoVWGS0nJXvZNrRzQ7xc89zLH+D0/J2ynsqZW4cvlouI\n2HAuPovzW64v3mR+4wRjZjx+81d49uarrJ9+lsnsGZRdFVOWP+Xgzpfz5P49yFtytcfbr38Smcpk\nbb64SR8cxliCAGNrgt9iTMHf5ZwIPqMqixUev7xiSGuMFIxb6MeO1mpstcf26QVhc1U4kpVBioaJ\n7hhT2fDszRQiRNxwiTQGFTxaGSpdWKiT+ZT1+il5uYT1mtEo0ArlPU4YvLSMElweSXFK24LOmrwO\n9G7F1oGLAxM7RaFIukbZBmE0JiV8CJh2yrbbklJRW2IUw/oSYWVZW8aIMQ3RDai6QgSJkuWCPTpH\ntWMbp1SUOYQdiF4VCreSBqOhj5mq0ggdkKIihUA3DBj925eBhBYGMzksa0HlECLRTip87jGyZvSe\namoJyaKFojJlwjYOvx0fS9RKMY491takIKiaGdSZ7XDJ/sFNBh+KClhqZJOL1ENbVKrxoUdbS8yZ\nurF01x1KS2SSIDSmNnTDlqqZEFIkxETYriF4shVkYWjqGTJbbGVwoye5HiUN49AhrMDoijF4ktBo\nJZBtTYojymhctyJ2A6sQmNiKoBwiLnH0XK475lXDXt2y6kauH9/Hhw3z2U2ErhBWY40sGwdR4f3A\nYtIwDh0p9cijBikkmydrxERhZEMaPVVtmRw2ZKnoOseinrDp1kRRwP/FxueI9hBvNcNqTb/5f/nK\nP/Jt1M2M97/3G9CqIrqAd6V9bswO8j8OiKrgp8K4IY4d56e/gI2O6dELHJx8Gf26RwqBVIHnnv8q\n1qsNN+5+gPPTB0iTdmZPCeGSsD3j6vwBlZ2DmrC319I++wKPP9ki5wNn3T2+/Ku+i7c/e87h4hYp\neJ48+EXC6tNkuU/bfhmnD95E6orp0Uuk3LB3eEKQLe28ILB8H1Em4NOWGLfEoUxDO+dRsuC5yLZs\nS0QmhYytK5abFdN2RggBLRqQihATKQqCd2SpaKwo8byUcOOWnCXBS7IoZw8lgQja1rihw5oaJRRR\nJXTK+BDpXSmv+mF38yAleI/SFVkWxFjymaraow8dccfOTd6XElos2xSzM5L2zpESZOUhRMbssbps\nc3ISOD+UqIsvscNx2OL9iItbrLWl0EnGZEtynmpaY7UlK12kFUphclGub7bXpOipbEM0AwKNUAoy\nDH0PCGIIxO0uWuc3CNVh1B6YCVEHiAFBEdxIW2PMFAu4ccAozTB4tJblpqPRxLEnbSPL8zOGbkm1\nl6jMHovDBSEpssoFT3drypgzwi5om0NidsW2KQRJRKSyVHXLOHjU73Le+j2Pl///kEE8m3/qr/9H\nwK75rxUqRyKFMapERPiIbmtcDGhbrEPRZbSy+NyTCUhlGYYeLTVWlDJN8glpa5Lv8BIUpaEZcRTs\nMmRfJp0hlulEigolY9GZh3IASETIDiFkaRqqsfBogUrV5Jj4X7/zC+MK3/Nj/zlKyrIKwCAt+KBB\n17R78A3/04dZnSWOzAmXteE9YcFb+5GpUazMb/Dgb/08r9y6w8HhC4wTw4/993+eGEomMIdI8JEf\n/iNfGFf4k//8Z3blkhKUN0LjckCKTIzFGidzIgqN1hWL/WPGJGk0LPaPEFaXX1TXsT59QkIT1ucs\njm5x/fgtRjfwdX/8xzl+cc5DVhyPFVGtaFODDHMO2gNuCMOf+yvfyqsvPGX5+AmpOyPjyNN9NBNc\nf4mdHrFdrRl7UMpAPaM9eoZK6d36VbGRHa2tMZMJN557lWlrcW7k4dsfQ3Yrnj74NERFXU2Rco/q\n1gn1pGXYDqg0MN07QLc1tql555O/igwBUTWkFLh+ckFd1/TrDcGtCamsjtu950l+hQwtL37Vv4Vs\nFbVMPL7/WR69/QYHhzcYNhsuz+8VNJepcbFH5wR6go9h16AF12V03eLGhGlrju++xnMvv4+UAlJ7\n4nrJ0yefZXt1xrsP7zM1LS5JjPZkIQiRgrZyA5WaQmNLnKauUTIhYyaLhAg1SWhwA8n0SGFIlUYS\nefbFL8Xt9NhKKZ58+pM0BwdIo/4/6t7s17Z0vc96vnaMMcdsVrv7qr131a7Tn6pzjnOI49hS4mMD\nioKIQEpyEUBIICFu4AokQEhcGnGBEAqXEXCBklgKCiSS7cQJsePePo6rTlftrqrdrLX3amY7mq/l\n4punjO0IOReRnPkHVO0111xjft/7/n7Pg/KZ9fWHaG0ZVit2w5aJXSCArA3JJKIwZcrpHUpY+u6C\npmnBb9htnjN2FY+++uc5vvMKV9cf8/S9b3Pj7iO6zZY3vvlT9FdPSalj1QV0gm57zdHxK3zvOz/H\nsHlJNWno+x6jbtBWNUrWRCy2kgglOblxq/Alc8/y8n1ElmTvcEnsJyiCfv0C70ZqPaOymiF42smc\nrvccHM8Yuy0ujAx+y8niLs+e/oDD6RHddodWmShyiR2FxOTgiCgUQ9eXzKBOKGMYhh5VNdjGYHMu\n9AchiEmSU+EeR7fDVgKZWrSwxHBWDp6yJkdVWLrCELJjlB4jJLOmphsSZINKoJtZweSZMuky9aTE\nqeqWMDpCGFCikC9CThgp2FyeU58c4LoeJSQha0xVF6VtyojsCeOIshZhappqWvJ5TqJNWUEKrQr1\nwUWiSEgHQkLIjhgFuq5QPpP2cgUhLWH0ZXXrA7oyexWpIYxbmnaGH0eUnlJwhR6lYyFw6KYg4lL5\nQjUaovOkKNFGkpIjkhk3l8WYKFuapkbbWUGl7bGPIXmMkntmalXsdapQJ1JwxUznQQVP1BFTlTZ+\nCCWGISlcNOccXb9CqogggBeE0ZFVhawEtbWEtGX0jmH5gqELHB3do7ELIKCExc4rhgSVbnDjADIj\n80AYSoZXCMV6e864WXPYzujdFsectpkQdcBaTRAVdTVndKUMHdxIlolIopEF1L/ttoTtklytuXX7\nm1RHr/Lw0ZfZXl5w494DqqYmG4WWVVmTG0Mc+yKvE5E4BJSMnP3e38OrQFPdI7kNm/UHBD9y/9E3\nGPo1H7/3y9x65Ssc33qTly8/Ivue4ew93n3vFzk5vc9k/hqzG7fYpR2NPeXm/T+F23nkZMHFJ48x\naiT0PUO/I/sVpIHEjiFXhNEhRUt/tqTr1yy3T5H2hJNXvo5d3KCuD5BaEeIO3VSEJNC5XKaUyEVN\nbxpk3HdMckYr9kiv8t1BApkD3R6pR4ggSqkqq4gICZEDOUt8hkYZYhwZfSArXTYsWhc9L5SsaCxT\nX2trfOxK/BFRONEpE+KI1RI/OrrNmsliQdxf0rPQSFvRmJqUM0aWuKVSirF3JfNPJrkeF4uEpSD6\nFMpajCwF+JAiwgoSniADaedAKiolMXKKRhFEQoSMUA0pFwYtSTDGUA6mSmBsg8CjRV0iSyJh9xfq\nvncMccAoiVYB53qQDSrJ8p5I0GZvOJUlVqAS+14UkGAMPaEf6XYrNk+fkVlhq4qYN6imwk7u0NRT\nRG1RFGyYJhJNixAZnwV+GEuUwWiUNIVDTtojYCM/+lN/PBnEvyhd4V/OSwARhIiFS+cdsi6mnnpS\nLD8u75DaUluDEZpkFakuhzc1BrLU6KpmOp8Q1juiL8UYKKUTkqCqFT6M+zxLRJgCN1ZG4vKwt4El\njDX0uw6lBZkCeBaiqDdD8Ggri4EkZxCF36v0H71ZZFEoQcaoAowPYLUlZUEcJcc+8nST2S08D+sZ\nHy1fcJIOkdZQL27xcTCMIfPp8YwbzxIuJFQM+ASIfWvxn/dKGWUM47ZDaiBHPLGYbwKwz9ENwxIZ\nPNpYFp/7CqaZFryQqtEq0x7dQBoYFwfopuWgmvDu418nf7Hhg35DIzQ2rehGxffDJbX2/LOm58+K\ne/yt/+7X+S//x9cwzQQ9eZ1d6EEY6umC6e1XuHz/Hep2hjGBIUpyusQvV+yiZn54g92Qmd24iUBx\n/vQHvHjnV0n1IZ//Mz/B7dsP+Pjt76Am96kYWC4vCcmR1k+ZNHNi8hwf3+Xj5+csbj1gdlAxncx4\n8r230fWCJBXjZkncKaazBUN9yN27X+Lsw4/YLq+pGkvvr3nvd38D084Y15f41NEcz3h5cYZwWybC\nIQJ0ccDaBqEFrndIIxn7gSF6clJMRM3h4V1md+7iug3v/OavcOPGLc7PPqaZKO6+9irL1TX3Hzxi\neXbJVEkmx/d4+uH72LZBCrDTA0YvCP1YvuT9FqsXtHNFCJnNJvL6F25xudyiHBzefh1laogD5+fn\nRLdDZ0EfOnLc8OLjlwghmM2PWZ29jUsbKnWANgeEsMVSk7Nn020xak7qA1I6+hiYTY9w/Y6TO1/m\nwVv/Otf9kvXZC56/85KbD1/la3/urxK9x203dNdbvLPslkuef/w9/LDDWMvq/AVa1Ny8+UVGF1jM\nJyBr8JFH3/gRfBgxIrK9fsm4vMDnyHa9xNgJyniqyU2G3ZLmYE5MI364wkpFHzpUbCGNDLsrrD5i\nc73ZR5KmJGXphsjh0SNgoJnpst7VFdl7VGVLeWjSYttDlJiAtYi0Q+srxjFQN4dsri7QelG4200N\nQdLKgGbB2K9xwRFDJIiWetKgmhakQlFhpCkoPy1IMhIYsDPNRLUoafCxYLBCCBhbcXX2MVkKVGX2\nkgRBrRrGXQ+iRKVq3RC6ASEi9aRlDAqhDHU9wcjyzGsaj65bchL0bsSvu/2zSKKUxKKR2tLHLRpF\nUiWrhzSoLKirmlEFcoz4mJBhjQ4OHzOJzPpyh6qLOjqkyDRLhGmKHALQUkCWSFFypyGAsROkUQiR\nqewUpSQ+jKVkF0eiVFBNmbYLok9YI0gZrNJkLYvBMWfGQSB1RoiAFAUhaeu6XIZEIKqGRkmiiiQ/\nUtm2mMusYLW9JvvMfD4vuWdRhoTTNtP7Eu1KjPgYaOoZ9VFLc++ElCn/pkozjgN9GJERXC4GS5FF\nGSrImhwcIgsOZjdI1YLo4ODoGJc1Go2oDC4UlrsPPYvpIa53KFOXTWIYENLw4uwj5vMDzHzC8mXk\n9GufY7lc8/f/xv/OT/y7P4WoSvxuu7pmOgd0MehJO4E0slteMpmdkMaO+b23GNwVbrnCr56gTGZ2\nfJdNv2T79Iybt3+M5uCYi8tnSO8IbmSz/pDbpzepZ6d4lZjf/jr3736JYdcRNlfkMKDSIYvjY2qr\nOHv2PovpMb2z1N0Fz188xgtDXd9BKIs7esiRDNysv4E2Dd7tuHr2HstgWMyPkcct2oCRE3IJLOIj\nSFUMp0IYUApD4fNqIZHBI0ifyURjv2LwGpH2hfEoC+pK6n30QhBDoouxrOONQHhV2NDBEUJPhBKd\nEYIsMlGU8rAQQEhorbG6wbslcezQBKzOuHFDGsvGYHQ9Uil8syAJmBweEtyeLTsGhC8FuRwzKpWL\nt5R7HF/wyLqUPCutC9EpCjo3kuYBISUqaVIaCFkUtrEvZxKRLOCREaqqLSVRIi6U7K5SghyKRtz7\nveRJNkztEaoS+G5gdiDwWWCVJsZIohxqUyr5fL/uGHKPSAU5qKUipJ7gtlgjObl/E+x9pDVYWTbp\nQdeIqDF7bbuWFTF6jCiXZa0k2jQYJJGC1/SU5884btDyjz/J/RNxyNXa0C6OUNoQgwfh8LstMkhc\n6pHaMJnNic6jMCSR6IddOeVLha1rpBZ459i9/AHd9YbgFe3xQ6rJIdH3BO+IfdgHqDNGW0bnULLA\niXPOSFFG4zmxZzru1xcpIZMipa7w3UaPNJqUIyoLpJmS6P/IzyXy3jxGafLiPUKByIEwCL554zZp\nUvFL5+9y0CdGqTAImghWzPC15BkLHtoZf/VrryGcQ6gfmk5+/7/9/33JLDFSEV0qkPcQEFqjfCLG\nvds6Joyuefi1n2B6egR+ZHW9pL94Topwuf4IO2k4OL3N5dMP2L14wnx2Qud3/KN//As07hLbBarq\ngG7oGaUh2BJ1eOt9yXj3Jc/uVJy/eMjp/WPs9Igbd25yef6Y648eoxYts/tv4MYIQ+CV2YSYI8sx\nsTg45MbDe3g/ErpEjJlHiy+x7dZsliPf/ZVfxmiHCAMpeq5XL7DtIdOpYOhGQtgRRsfTT77N4aTh\n8jvv8iR0kD3t5CZ2omjaI+r7D7h48gPcuGHcbnh89ZSqOcA0NUIXcHXMW7TrCeEKoReEVc9sccB0\n/pCDk1O2XWR3/Zz1xUWhbQgJ0iCVYtrMQJbp2Kq7YPV4SVs3kDLPn/UoFfC94oMPn3Pz/ltIP2Db\nG/TbK/rNNa985cvsLstqSWvNYr4gIRj7DjmZUVWzAl4PK5q6Zv28QsjIpx/+Fo/f/o2i6p2Uy0rV\nTCCbAgnXAiNgTB3b5Y66fZWj6W2aeUO1OKLfrbj49AOqpuHm9AZaz1FCU1ct89MF69V58ZrHligE\nqe9orOXqyWOeLM/4lF+lqUoefUQhQ0BWGpSgnk+IPqNUhbGGSKKdtri+6E2VKS1rpSSSzMHhCU/O\nPqBbfoSUhujbkj/unnPrlYd4qRF+x/2vfIvd6gkvP31Ce3SMyolu8wKbG5Ko2O4GlB6ZTo/pdmuU\ntpj6lGgzhJE+9gibqU2FbQ9ACnarK5SA2HXM5lNirrBNy/VVx2JyAyktlSwP6GQC46gQssZMJEp5\nRito7IycFG4sCEI/jORcNhfCVKAyMjZM6wbXFyNSjAJQVM2U0TkOX/0SOTmqqoKYkMIyuIiaBKb1\nAWjP4Ppysc2JKBPTakLntrh+y4ilkpqoNWPfo2U51NYHC0TIoA0ix1J4IYAEFyOTekLaDwNiht3Q\nFzPTODJrWnKy5ctIWjCKNgGUA28lR/rtDmkNGf/ZClgqRXAZIQUqSUI3FBNfpUnREbIn5YiUkugd\nWtYlCtEVPJIfRzIdKUaMrkBXYA0aTQ6Aygy7LbqqGTYDRkuMqYkx4lIgeI/Kmd12TQwjo3doo8rW\nJPl9QRmCTCg9pW6KPjonQ+cO8TkQQ0fCEGNZMY9DkZcINKa1BbOWKrzbovSUnB3SGGTKhORoFse4\nfksMCa2LVU86RTU9LAW9MCJSxiiNwOKzxqoaUuDo9EE5ZMSOV1+/zzvv/Aav3vs8d+6e8v3vfo9b\nd97g5dULJu2UmAUy58KeD57kRtrpIUIbZK5pjl9laj7Hlfs1jLxFMIHt9YrpYsrk1l3q6U18liib\nmSxu8+Kj3+bwjR/l4vwxd978i6j2Dk17i0xLZUFNeuy0JSTJ4cEbJJl47fQhq0++gxYD7333HW7e\nvM0oKy4/ecLYD3gGpJkwrW+DmhCrA6andzhoFyhTgdvhtlsSCVNZogCpNaRiyUOKsqXKhUSgK0FM\nHi0qUiomz/lsRvCFE++jIGdPpecFDyoleY/Uy4zldycTOQp8TGhjESZhpaSZ/H7hPaWAIiDJhKzw\nzpFiiffZaoIfB6qpRTeGseuxUtGmKTHs6SrKMFxeYOoZnoCWkhADkYBCoVSFdyNZFOSf1TXdboU1\nE1LOkMuZpDYTEoLBDWQpqcwMIRTD0BWJDAJlKzKiIA+9Q0rNMAayKHryEH2xIyaN0nXZPKuMqRqS\nG0phEYGInr5zIAJKCrpxhOBZba+wokOLSXkmeE/Gk3OkOZhRNy26mtNAZvoXAAAgAElEQVT1G4Lv\nyBiEhSwyra3JQoLIpBT3Rf+0nxaXbbVRBpUCftwglWIYHFVlcO7/PwH7B86X/2LH0X85r5QSwfek\n7YoQezIOKWdIYjn45lQKVUqhVMZFTzNt92P2iJCKMXfs1s+o9ITmsCZ2HX64JMWCHpNS7xF3FW5Y\nI4wpjU3VEsOIT4LG6GJwShGpShMyOI/WpRAn0MggQFDaoSik0Xi3LX90f+glhCGGASlMaTzqyT5G\nEBBMqVRgdSl5ky/w4sVFebjfnvLb7pIv7hIouEmNvD7j9E9/C6oWF9YQqzI5/ucccv0PjW+hBOaL\n/IGi8lUJlxJKKsbRc/b+d9Gf1tx97VHxVg+KYBN10kitWF++4OSV11jcuMduecWrR3d4+39+TrAN\nafspTZT45oBx0aPO1ziZWBvNnVFydv2SX/9bl/zF/+oBm6ffpzt/QrKKm699nuXZBdeba/r1S+bT\nGe+//BC/fQa1pn15n911x3B1TXU0x3vB61//IrWIjFcfc3xK0cQ+uST7nqY5RCpN10c+9+ZP8+Ls\nA2qjkaZm8+xd9CTRmAPikMnKcHH1Eq4uaeoptRiLpGHWMkVTHxzjnGN1+R7BbSE1jKahOTxCCoGL\nMGx7fAfnn5wXTAwDoi72F2knKCuIPpFi4PT+K4hUoa3h2eMnuO1ur5M+L5QN03Dj7gNSN/LdX/oF\ndC343Dd/DDs9ZHl5jW1PsTpxffEp3XrJEDaoZJgdzVmfbdgOA1rVpLhjLd4u0Rkp0YcFpq114USP\nmyKliCkxEmknMzonSeWMxvLyCbvhkOHJEzIjjTJsrp6hQqbfPC5rOZ+Q1R1u3X2N1fn7WFshn3lc\nOCMMhvm0Zth1TOqW6/45tr7JpG6JzjG4HbppqMyU3BQeZEw7/OjYrbYcHc/YxMj9Vx9RLyYM6y1D\n6Ll88j2M7pnefoDbjUi1YNxtaZspq4tnZBXR9SGNkLRHd3j50cesrp6jyYROsonXtE3FtK1xwXO1\nucAiMVVDP16TgqFWMLEGnzJjTDSTQ7zrMYevMG8W9OOScRjIsmXWWlxYk7NitdygjUQoQ1WXwucg\nIo2Y0R5OqRIorQslYd4QKcXOFCVkRUwBIaGtZgW+X5fVq6I07+P+uaNTGUm5vkNZQ44RYwwaWyai\nLqD2YpeEIoQBn0opb0w9jdYIk8sF15hSrt0XzKLIyOCQKZWNmFSFKJJK81sk0FWRpYRQCh/ztkUI\nyRhHslG4cSyCCAVCVKjkCTJTLSzDbkDKgvKqmjnBbTB1RRjKF7ioBcJngvdIKUBClgaRDc1iAaRC\nQMiBIMT+gGEQZHIQJN9T+TKZ080EJySmmRKCo50tig1rjNS1IeSRlC05BczcIkaDrVR5T11EqCIk\nkKrBjwNogUqBYXT4YUeUHnRCtwaio20VQlSIoAnCE3NgHMumxTYaRLEiZlsXykYeCS4wug6vArqy\nxRQvy+UkhDVSVQXGHwvDPTqPERByQBmNzK4cOrzFHi04FhXBr+htz2t3XucX/u7f4cd+8qeIMSNk\nJDiPUgKVA4PvaerFXnQkUc0ppJHNsKWpZ0ymJxzcKJfw6CPb3YqTWw+4uLhA1XNe+eJPcvbiXb7x\n5/8KAY3StkyIhxV+WOG6HcqaUmDdrpCLAwiBrutYnb3N6fSQ85dP8S+es40danqf+cErpFCh9BRl\npmBmGFMxDqU87VxCaoGUkRh6lLG4wVHbhpw9zimkNIjo0ZUmiAxWsxvKd2AMA10/Ys0MhSjbFFEX\nKsq+bKVQRcVtJsjsyCKRg0GkPXZTpBKNJCFRZYuRJVZrQsxgBZXO5CSxzYQgHM10QhgzUsLicEHy\nAZ+K4KWdHhOHWKZUMiIppkeA4DxZ7nmzYqAxhol0xcxWVQihC+1AaYLrGOMWv+uJ48B62zOGnsPT\nGyW3GwJSKozR1O2MIQS0bvDeUU1aUi468aAUlaiJQ9F6O+eJjISdJ2SHkRV+9J/hVRMJ77YYWWFN\nzY2jU3zIJOFBmaK9l7LEpITYX8p7qsmMMGi0KcW3kCVER0z731VKe5NtwcTG0WHrKSEHYuoheUIo\nm/7RJ5Sa/LHPl38iMrlvPrqX/+///j/FtmYfARBkWd4g4QdyhCT1Z2+AnU5AaXLISCP3+dSBGC27\ni+ccHN0ihiXdckkQGatnSFXYuwq9x5EVLpzIELPf/2IKageZUTHQj2sqW5OkIqcEwWFlQxSOpCUy\nZXwOGK0RXvC//rWf+QM/13/4f/y3RKXRQpQH3Q/PpNZQK8M/+Cjz/tmOtx69wpgbjKn43uUleMm3\nX7zDz/4Pf5OHb/0od2l5VN3lv/iZb5L1FCkiwnlqbfkbfyiT+1f+n/+LFHJZfXn/2ZhfioTzESVl\naT9GSYwRaWs+95VvIOqSV/N9j1CKJDwmwWp5iUyZYb2kYs1f+k/+OvHw81z/2s/zVCTaozfwz97j\nWq5ReYGcNHw5vsb5PTj6QPOL3/6Pefp7v8TgLski0zRTGnuClAavKm48eotkZvSrkZAtXoxIHHn1\nhPXLJ0R1iLQT8nDJ9PSUWw++xIuP3mH75MNiqxESoxp6H8iipbUe4Qv+rTqZsR1W9Ncv0PqQFEsz\nPSfP0a0b9KtrumHE7UYm0wWD69GNLQepelaUuTh6P5aiYxA0TcPYB4wpxhZlTTG6JLh18xGPP36P\nxcIyrD3d7mPc7hxj7tBMJELPiNJycHLM1dkZUlqUNPSbC9qqrGeGJPFpB8JCUliZGUfPF976Js3B\nCdcv3mN9ccWkqnj58lOU0hyf3OLpB4+RNqN0BHRp7GLKF1AYceMF0+mraNWgK1umRjGSjSGjCnc5\nBYZxhYgeYyqurpdUkwZtGurK4DblkDKfHRFjZoyO24++wrMPvotMcV88kkwOZ1y8vKZpZszaGxzf\nu82n7/4zou+pjGV5cU1VaVCyMBMbyZf/1E9z/eKSaVvz/vf/Eacnn6duNN3lU7z3bK+fEf2OEHva\nw89zcPiQdz/4RebiFKa3OH31JjdOb/POr/0dxtVzFkcP2a57JvMFOMdqe4W0hjtf/SatbXn2+DEP\nP/8mwzBw+eS7hKFD6kMckqaek4TH9wMqJ3Rd4bs1Qhbmo5blgJPCgFKSkDwJwc3Xv8z10+d0m5do\nWZVCLQE3liydkiBNKdhKrUnZUdmWse9xfgNRUtk5prIEv0cmSoPUxR3f7E1IUteM3UjdTsGn8rxU\nkEJXsGO9J2WPqjXJlZhA3Vh8luXZR8GfCSEQWSDxkBxOVSQvSD6jZSks7bZXTOczRABTNRBGlLH4\nmFFG0vc9QqjyPlPwQcKW3B5hP5WNkSg1UkCIaW+eLJ9zpRQ5JrSZEIVDZQMu4GMpXBktkaLESlxY\nkV1C2gqpJiglECJjsiKLSEiKEBymsrjdhiQFtdXkIYLJ5RApijr7h5QekSUi7ad0whF9pp7UuOUO\nWUvGfXwkDju0VvRhwCSNVBmZDNJDlAGQoML+ewVkzGg1LZGSOBLSgJY1ox+xVpeCnWkYuzVh6GkP\njxBSk2Vhquqw3xKkhAuZfnRU7QRCpHMrdF7g/DmvfuXPcHpwykcf/oBHP/KvUZm6hKkBFCymxwz9\nmsniGFFsIAQS2tSQS2YbEikrsi+H6cKlj2RbI8YOGRy79TXCaGQ1w0wschzorq+wqmIYz9HNjHp2\ngqznoCxheQUyQxz43i//L5z/3s9hppbeVZyefh0vMhfrLdPpPWbtTZKQJF2TkyATMdUMISMuKupZ\nKQZLYYqC15fDkKzMZxa0MZTPcDIKTcPge6zSv4/vErKs5pEopckEgosoMjF5pKSU/Iwi71FmZSJq\nCt+ZSAwZJQxZRGqpwQj6wSFkxKDxKZPZF//0dI8nHBA5IWShEgx+RElbOiqCcoBORTOudSF/RF/E\nL3G3xgSIMtN7R/SXhCSxpmF+eIRpNXVzhECDFOVznUFSBjKV1YwulGmphJwSVpWBX9/vEMox+gRB\no1NCq4yDPRoxEPoiShFZUpuS8c5SoCtLyAkREkZNSFqSKJPYnCMyQ1U1uNRhREOMiTE4lPB471BS\nkmLG6oasMtZahDIkH0gxFu5xDOw2lwhhUEGQtSQER2OrUlAXmb/w1/7zf4UyuTmhtSGNA13XgWow\nxoBOVGo/JdCqtJ/3GAmVBDmXgoCSFclrvBuRtqJzI0gwh6c0QjMOOwKRGEs7OyaHRpByIAeFMoac\n/H5aDLW1uNExn87oeldc7zmidUXIsUxGcwRTgOBxcKU89YdePoxoIgnQpiaMPZgGGTRX48ByW/OF\n+2+wURaVFC+eP2XwHVdPnuL8UxAdH+2e8vjggD/barRMBLFD5pqsJGHvX/8Db2VMmKrCuaEYjoLD\naM3oM0ZPAIfc/+xKCnIY+PgHv8vRrXskn5jcPMaPJVuqakNtMt3O0R5NCSny/u5THqk73PvqT6LH\npzx/+5+y/NqfhquPSJ8+wbhTVv4J9/XX+bX8LmGAW298mdgt8bH8AdWLBZtNj6wPefbJB9RWMGln\n0F2wu96SqgmTtmL38gVdfkZTTZjOThgur/n+h/8n9cySVMSHjBaZ0XVlTRR7uq5kq4JSZJfoR8nD\nN7/F7volY9QolRlXV1w9+bjA4OsZeqJxYUDaipAcs8MjSGD1jKPXXsNtlrh+xbjd0nVXbDbXVHrG\ndDbHj64gaqTk7JP3McnRLQdeee0Ndu6IbtXTLR+Tk0GFgsLZLC9pqgk5K2LqmE1ht1lTKwvRcfzq\nfU5v3GH95Jz17orDG3f5+P3vE/IWnSMXZ+9h20MWkzlazelTzd03vkLWcHL7Pu/+9q+CTIR+QJsa\nM5niN4bJ4T38sCJlQRp2ZasQRQFv0xMHz+LkkH635OjO53j4jXu8OHuf5eVZ4ZDaBXbSMoyOOA6M\nwvL93/l1mqqA7U1VE5Njc7FmMZ9j57dQwtDvrmhnEz59/7sIIZg0B4zRkxw07ZRazFheLamsYrte\n8uqjr3Hx7F3q5iHJR7rdFXW1QJ++QkJC1rhxxdd//C/x4W/9ImI45+I7F5zFt6mP7yPVAX1/jp0c\nEUcw0yMmaHLOvPj+M7QVGO/5wT/+uwwpYXXJu4awpZ0fcHnxIcOYaNsWdGQ88yxObiFjx/zOq0wW\nJ+xWA2GzZH35FCkNLnk++t3f5PDmTayuCCF9JhNQxiCGiJIVVTVlm4otKfSBpDrqakJVVVjTlCmG\nD1ir9pNLCMW+za7bUlctXddRkxjXT7Bqigulva0qix+GEuGaz8gx0jQVIoCQmm61op1MGLynqSp8\nLrxOISiTERlRFMW1RhAVTKdTYpbktCrPEtvQux6javrtEq0MWWTCZouupwx9xgrHOHjICiMFg3dF\nniEAlfeTG4EQ5YsTtZ8ASA0xEZWmkoKYS+s6RIfUhlbOyRZ8Sggo0hqlSymwNSghMVXBM03bBS4M\npJiw7QRBxGdR7E3Jk/e1YR8iKY1YXRNDRgpN6iO50sQQkUmhhSZGhUQzMwcMbtwbNsF7h8UQVCxT\nZ2MRKSK0JKQeDXspQBki/NDeqaRB5MRkMiNZy3a3o66mdN2Wqmq4ujqnqmuCu2TW3IIk6NdXeO95\n68d/mm//ws/S3r3NpJ3y/Nk5IXYsl0sWs0Nm8wNizLw8f0rbTKjqhhw9YEjBoZsFObqCydQVuB5h\nJELVZBLCTCCD7K4IIeAHhzYWnxNGJuTQs3vxGCMz5x/+NtXRPRbzQ3K0pHHE9RdliuczOUuWHz/n\nxus/QhwyOm7YhQsElpO5YhifcXb2hGp2gqwOqNSchCHENQCjd6xWZzST6X7iaRFCIXLZAgz7TKxS\nCkzJx0tdDmU+BZSQRF02qTkXBr93I8hMzomsFFJqFMXkp7UlhBEhI5PJbF/2dEWhHSNJlo7NOI6E\n3he7iNIF5yVVKZgLjUDis6CyJYoXyGgDjW9IQqLVnpWc+3IRJJOiZth1KCGZLabYkwUx77fZMZD8\nfXLI5WJoFGhDcuUMkBHkGLGqEFCklKRcFNyRhJW6CCzIpFzQfjJrJpUkG4MUcV/+UlilMEKR5jcR\nRu0jcyWeFJIvWD8hsBQZR6LERhCxIADHEe8GpDJkVf5267qGqBEYpCqa+BAzMQ6s12ty2G9k1J7n\nnQWz+S208oQk92ziErmcySN86P7Yx8s/EYdcoQxUM5Q6oq0D2XsQAbQipkTwDi0yIY1YZYghlPhB\nDkTvGd0SQY0LA03TopQh7KlliURWkkoqxuT3sgmFkhICeDwpZRABURVhhEsZhGaMBcheSUvIIDBg\nyhePEAVLUh7SmiT+KCfXGEUmklAF8dFMEDmSnOdY71Dnv8NZ/y0mRzOmiym6PeSBvsPJwV2+uBDs\n/uEFB3cOmd5/hLkuRQYrRbGy/TCC8IdeShXyhEQhJcWGkgNClfWNlLLcgmUkhAhS07sd55++VyZr\n159glMXtPMdHN5jcqDFR0XnPk0vJZPo673z6Owxpi3Wa09tv8YV8h1/95O8D3+QnPv8mu9kR29WS\nV08l/cvb3PuRB+xevkTmjpAtIXlu3zxC2orF7AgXel48/Zjb9z/H1T/5eYbNBX4Fk6MFOg3Mm1P6\nYcnq/ANuvnKX52fPSQlsc0TvPVV7hNQVbrOhntYsjm8wDp7F0V1Y97w8X6N0Yn7ccnXxnFRPaCY1\n3foSW2dWqw3ReerpghyLMSlETx82LM+fcePmLdYpY5oT7p+8yfe/81sM50/QyTIOI1Vbg7DoKqCi\n4ujkAcvLK8Zxg9sNTOZ3MNMav75GeHBJce/B53jx7AnGSvxOcOf1LyCCJYyBq+fP8C9HFic3aFvB\nbvWENOxIasLswSNkfYgbA16uSOES1UlWSZOyYrPpMbVm7HqkzeQ9SmY2v1MU1tMpQmiYzYov3YMW\nBYbum4gXGjU9Ybm64HrYIHLk5PQVoojouiIOgctPn6BJ5NRTWYNtNNJakqgQoyL4DUO3we12aJN5\ncvESYwzzxf0S1/AOS0TUushddM14veL2l95gGCEML0hXn7Ac1ixfnnHv4ddw2y3Xn7yNnB9xfX3J\nzNzg4uxjtG6oF1PCbsTqlt6/wJoGO/0q8+NTkjGszs6RbJGjJrsBO6nwg6I9/DKN65EiE2JEpfIs\nuHnzDtveU7UtddVyxQtU03Dz7td5+exTnn7wOyi/4vD4NnnXIeuGseuYnNT0q3OG5VhKqkMgG4Wd\nGRaHc9abJTfvfJ6DVDGdTkEadrueyewGL58/Y/3yXaTbMoaxkNmHjsGDbixDv6GuZqTKYmuD947a\nLsh4mskE3Szo3Q7tFSl5NpeXhG5L3Zhyqb3qiVZxrVpstvTrItRItSWlyHrsEFiqypCnB6wuz5Bj\nIKBp5jWprrBmRiRip1NImVl1G7Uvo4Ak+kBblZKNZ0BLjd9PrjUaYqR3HU3T7Fm6DZlE8iXClVIk\nJEcWipgl2jZ4H7BVVXKPORRUmdHYWlLZCQrBNjm0kvsDp0Jqg8ueKBTSKHwuvQ+RKDp0ZYsJUkmE\nyghrEUmBj4WGIEUhM1A6FMFpEpCNIuKQIgKGJDSiUoX2MHow4jMZQVPVIAU+BuqmZhx6pCo6dZkl\nRAM+4XFoM2WxWDBudkgh2O3OcXS0kwmyuc3oJWqRSRcb3vzxfwtkw92vfYuDowVKSQ5vHZMuHOdP\nHmMfVHzy+ANiGHjt818ixYwQhhwz3cvnICJs1tRtixYCkUrsjhjIwpRtRd+Tk0MoixSWrCQEz2x6\nQHQeWbe0NwXj6pyDB1+nrueECD6uGf0SlQX9+iXvf/sfsFhU3P3KV9l8+DZSJQ5sgqZGWkN3uSaL\nkVSPhGGD25UiYxe31OYUke9h9JR+d8laBJpJRT0/wtS3iL5HpAaJIDQtppqgdIOWHt/3GK2xSRKS\nI4qErVpSUoVsYBQu+BJR0ZBdorh1FTkktKpwwTH6ASMlImtyLgKZmBwieZTVezxnQsRMDIkoYlGX\n7/n05EQfQUsNORGDKVHHEJHZ42LAyEKNKNYUh1aRbrelioow6HJmEGXCmdHFKGYVUZVLoKlKjyEl\nqJVFKoEyLWEMJCReZGprkSlhZC4Hz5xJlv0mROylUTXeJ6xVheCjZPm5YkRJi1SalDNKlNiGEJI+\nRcAjXCYJsLYc3FstcTEQfMEnCl+iDlVVWLr5hyvtlDBaIFwgikwzqXDjSBBl4yyVJmRVsvNkggtY\nbRiGoVwu/rjnyz8JcYW33rif/97/9F+TUsCIki1DFfhxUUoKhC1rFCi3IKGLtteNPW7YYM2Cl+cv\nODg5RmqF1kWLqKXB+VBQNCIzjB2aQnKIGWLOIBKV1OVwC5BSuakLjYiB7bpnOqs/s66UNaUpq8GY\n0apo8v6w1vc/+pv/TWFZjiNjCjS6RspUGLxD5rKL/Px3T+j9ktMvfBkVBY7A/NZt/slv/2+c/8pT\nbh29yvTBTaqrxL/3H3y1OKK9R8kK70f+9r/xl//A//Pf+YWfRcliW8qRgpiSidGD1WL/cxmIZcpr\nmwkpgiIQJIQcsaZFqkQlbQmcX18yO7nDz/z1v83TYcLz90fW1QJR1dyIHWK3QrYHHB0ueFYl4sWa\nrdlxc3bAf/bn3uQv/OU3ieOObohkAu3BPVRVE4ctn3zyLncffalMcGLg42//HFZFVttLmuY2n77/\nm9x89R7Liw2vP/oaXV7z9KN3aeVNNmHg4PAGy+uXGFt/hhBSIuKGS07ufIODW4c8e/8DZsd3mS0O\n2Swv0aoiK8l0cUTTVDz/4PcI45YxR2IXGWPm8PQ1mnZOP16y3TkODw/xux2531JNLdXEcHX5HBFk\nmZ63LXo64+rpY0RUBc2EY0wJ5TXTyjKmDNmwuP0q1y8+pW1b0vqK5AZyNaUbA5ODOcPgMEKSkFSV\nIYmAkOXmHn1CUVZGw7glJtAIkJYbN++QbM3l8jl5G+lWH2HqFqMnRCIqm7JxSJlRZiQFim+SKi30\nnMoGQEJEcHjzLnlwrM6fIXJNN1xjJzOsLmIDqSJDv6Nfb5EkKio2XWRxMEM3ZZKRc8BISxx7BgSg\nqeqaqmpopg1Xl09JLpCD5Ks/8W9S1RY3XvP0u/+U68vHtNqRQma3E+hpW6xX3hNdyYSd3H6FT99/\nhxuvfJHNsIbU44JAyxlt03B5dcZsesDiTsvyyRJRLbj52j3CesN6tWVEYmxm6LZMpnOm0wkXTz9C\n0pCVReSIyYI+RJRMVLUmubEk9CpDv9kxPZjjdpnN1adIUaPrCblqypTGJ+K4JskSj0JW3HnlKzx5\n7x3mJycM2x05XjFtG4ZxLNV+O0UkMFmgakWMvliIZIHTB1/MTD66chhJApc9J8c3CF6gZ1NkChgh\niYHye9qbwBQaZRXaR6Q0DCFiJw0ijGy7NRcXT7BScHLrITIKpK33zFtLspMiocAVpm/0jDGhRbno\nq6So9IRID0YhFMW25gNKTEjZYRvD2PdIXe0v27Jk/5JCKYuLPURRnvPWkqXH+72edd+x9z5QaYOP\nAkgoIZDsRRY5EsW+TW8UPg5IAVo1xDginCDLQtERQqCUxEWHwGKqGu/7fTawfEekIBAx7PsfATdu\nMZVFRkk3OGxdAQlrFDkOCGH2WU9TPid76s7ohpJTNBpiRqUGlRMu9lxdP8eHgbZtyUIymR0gUiRR\nUZu2YOhUD6qmmt/icHqIaixCSKaLOc8ev0vwHXUzYzI/IkVoJzXVbIY1VcmRInDbLUIJDu49ZHv9\nnFrrYmjrR4QMVJNjRC5MemFFKRaaGudH7OwYgi//Ll+mcyjDbvmUYXnN4a2HJO8hdjA9LJ8JHJtP\nfpdn3/vZkndVGm1ndC8+Ylh1TA8O6fpLRg9C3UVYjQsKn0N5mohj5GQBdlqsBEBVl4iMDwIpBUbX\nkO0eRWdB7PX1GqITCDOgYindSiicZDMjK/XZ775QEwQKi0yRLMrnT2mIIZGFRMcSZYw6EmMoNkSj\nGPxAJWtiSmipEUmUVb4sz2mfRmIs6LLkylaiahqid0iVyEMRWRmdGLqeelLRjUMpme07NUKIfXG8\nJ3qHDwPK2rINyglbzQgJDAZHmXBmL1B7UQUmQzIY0xK8wMybYg4j40OPCwFtJggqBEWkMo49VSUQ\nusHIPXNZlL/DnDNKN0XEgy+xUVkGbOW9KYfnIRTeb/IlrhVHDyJS69KnGkNPTkOZxGPw0Zet2NDj\ns6QygigKSjJ5gdIZU9JX5Jz58X/73/9XJ66QcyYnhxQJtEAgP7vVK2GRlSCkQFVXSFERQyZmj5ax\nCBHkHDcM2HlF73fUojxApLa4GMgil1apiFia8qCSqtyOBMioypeWH4hKIbLEiIx3HqRgNm+KFliK\nUjizDQlP9ANKCaJTCFP/kZ8rEpEpo5SgMhVSlLxRiAmEp54YmumMw3u32Kw7hFTM799it1pzfPwA\nexQZ2ZLlEfXhISoVdJmQ5cCv9R/99X22uvl/qXuzWEvT/T7recdvWN9ae941dHd1VVed7j5DnyF2\nPCc2UhJwjJESfAFCcAFIcBEJwS3cIiFFIHzFBTMIgyFBkAgT2ZGJcGJsn8HHZ+jTc3fNu2rPa/iG\nd+TiXeegxEZK7py63qo9rb2+9/3/f7/nAVzvkRWIoBHKg1JEH4uqOJcM0Bg8FbJMm2NAqUxw5Q9F\nqggi0xwfk/Sc59M+bdhjtvuCt/MR7y4E186Q24pFNPg+cVM09G3iZ9Qx17Ob/I/f2vC3f+8f8BM/\nd8S/+Eufx2+e8+SD38V0DSJO7DS7nL7/Tbr9fTbX5+zd/iLdwW2ay08YLi949c2fpZo1tIuR5fol\nSMkbb/8UtrvF4St3GPuBJBzT8oKqqfj6//lforubNPMddnZnDE8+RvSPuLo+R79xl+QmNsuBECM2\nv8X65Bo3XDE/vMN+d8jNV1/j+uwZ77/7GyxfjlTNHKUVVy8/JfWG2eIGq+slw0ZjZh0pjAQP6+sT\n9OWSJAP7h0cYVaZte42hqve5uvg+4fSC4AU8G1AycfXiBVU1I4TNRUoAACAASURBVPRrAhkz22P0\nfSFoyIzfrBGyRqSROAYmaXj13tssbuzz2Qd/CN4zn+/i+hElBU8/fZecNEJJZF2zd3yH82cvGNwp\ni4MFKUXc+gqFwMkydfCp3PSTsEhV0TYVicDkA+uLE0RS7M4P8WlAz3bw0SEiBBHIPhJ1xc7NA/YO\nX0eISHP2mOB7xmFEyZosQNkFzf4t2vESlOLy4hzfL1lfAiJgbELN9mlay3B9zScffYd29za77S7C\nRdzyU3RaIaXk4uQ9RJbc/fwvsPP6m3z4e3+P3f1bTMM1VlquNieQEvuHO1xdPaedz2ibhtMnZ8xm\nM/z6jIffOydqQXZrqtTgIui9OeN6w3R1jREWFzy1qZjWa6YUkLZlfvMNxvULdo5fx852aBYt8/YW\nw3DJ+dlD9l69yfXpU7IsK32pKoRyVM0ReJhkybFenD1kZ2+XkBOyDqSxIsaKxdFNrq7PEGEgh0zK\nCZdLJlfkmn51SbvoMKJCGE1VHRKkIMsaKTwuOBptULIh+xEpBKYR+GFAKUFtDdXePuPyipgmJDVZ\nDdv1paaaddzt3gahEakmVZJMgKngx/rBYyuNjJYsMqnV6BixskyH4jCwWa3AQtsuykPTFdRTUIGU\nPG4ayEaTJkdKAZEi1ggmXyacUhV8GiIx9GDbWbGlJcOARwRNDp4rt0FNE873GC0IorBhox+QQhOJ\niKSxO3ukqiHnCakV0tQF3dTU+OTIeCpZkSlmOWMVSkiGyRNTIm9tmUqU8nNlDNFFglIYY6isLqUm\nVYQCSoDrB8bkChZr8tjaIHPRtnrvqU2DtpKUQKeK26/fJ5LIpnzvefIo2bK+usRdPUY0HW+881Ue\nPv2Yw/kO7XxOd3yDHD2r80t2D2+AlNR1zcmzJ+TJs75UHL95H5Mtfr3Cdi31zgxtLGlcE6eJBGzG\nS7Sw+OtLXHS45YrdG68hosTYctHTpqKExCOJAEYRcyT2PVa2zO4cEYcNQktySgi3YnIBwsj8+Iu8\nXs/pV2vqvY7VR9/myn9MNZNMbk1d7RG4YuNeIDcNG1mQcVU1I4yOEDfUqisbKQKJBXU7p1JFcpRC\nLjlUUwyJ3m1wmxEzaZS1oMukUipJnjzT5hoX1lSzliFGbNUwuUxQjkYXFXK5nUGWAoxAi5qoICYH\nrvQw/DgQc2RMkOJIrSs2k9vKUSIierzMRF3YvhlTuhBkpCyfN4SA3GZSc/ZI1WBrg+5Kfrdc6DJW\naZzcIKiplKIzhslvUELRqjLsqitdIj+uHDi9cmQkbVWQfhbIuqJWFTEm0lYyIqlZVJJp8mThsLXF\nmHkh8sQe1bQEn4lZUamSD5ZS4aeJKDOVECVz6yYqXRGCI4miCc45s9qcIVTJvaugyBpGPyJkZvIT\nxmTctEFlhRCq9KR0RodALA2qcl6KDh8lUUyQNVX1T1nx7Ctv3sn/x6/+e+W15QNKW7KWZYwedXlD\nzgU2nIXZTkRyCchHR92Uxq93Ee9cyfNSqA0FIiwLG0EXZ7mUpa2cciSOHolAakVKFFFELlDwjEZt\nAclCRsj6Rzer5Ibt5ynFiiQS//2//B/9Q9/Xv/k///vEkEgiEfPWKU059EoJWlv+g19d8+V//h6r\njWb36BjTWvx15HrnlN/5H/4mX67f4MVru/yYbPiLv/wFxA+h6+VVxP/0F3/lH/qc/9Lf+9vksEWi\nmSIECLmguHL6/zoJcptpRpXiHqn8fNL243POyJTRpsLYXb5z8g3+8//snEXd4iePd4KL/T3O+hUz\nK9n1EKeRZtYxlzWnYWLtHLcPbjCJnnf25kjX8ld+sebtr75CVg7hV0wXjxB2H9veoN0/ol+f8OKj\n30dODmGPCH5JPd9l57WvUHW7KFOQOm644vmTh5A97eEr7OzeZPKBnU7w0Q++CaLj1s3XESmyPj9l\nTGuGi1N2Dg65ev4J+/c+z/rZe9DsMq02jMsNTR2J1R1u3Psi7eGM4fKMj7/3d3HXL3CbQL3/ClMw\n3Hr1Hoe3XuPkyXOOb99HZElSERd7EBWVMeQYGK/PiSFzcf2QbnGTWbdPPdsth61xxbD2iAC5DSxP\nH5fV0TBwdX6CVhXznT3qxT5jCljTcPvOPS7Pznn82cfMZ5nVxXMWu7c5fPVtnnz4Daqu5sa9N3nx\n9Bn9yadsXjzn+HM/wXr5AhUD6+srpv6cdnfBepyodEOkYtYuyMbggkTkCYQnCQhjpJvtMpvNcb1j\nDCNZReLoqOcNw+qaFCJNZxk3HqsqtK0xtWKYRpq6Y+o3ZAeEkb6/oJofEIVAqYLKmcY1pk7cuf8W\nIivW19/h8vEZehAkk6j3X0NrSXO44PLxB6yef0jVHJOGGcEa6pkibEZMs2AQgm4+J3gPquL4xm1O\nT56WLKkxjP1A27ZMMZWcpxVlqh/K1wIBv/3YnAvGSWZIKkMsa3mpMm7KeDS1NUxj4ubduzSLjuX5\nJafPPyIL2IwburYcDipjkaoulq7JkWQiDuWiRRIkmdjfOyZVNdJaBIp501E1io/+6BvUs5pp6EGq\ngh3KCaEk0Xmaeo73U5ksyYxqZswOjlk0c1YXV3Q7uzSNIU0Dzx8/Kjrm1ZowXBGHidn+IUlulaF5\nG98K0FT7aG2ZvNtOnQrBwbkRLcr7qA8jm80GrSqsLGtUYzQ5lIiY8BWyLhOnmAKTvyAOE6qpqOpd\n0hiQuEJu0IoQIjknlFD46Amqx+NQuSb3CRMy2USEyD+64Occ8SSEbqhNjXeUgpYwpQ1PKd4YVWgF\nMQ0IW4Q/WipkKNsupQtqLIvy/u7TBLKs6VN2pHGiampsVSMxOKkhFQ1xjh5TV/hpWTaPOSFEU+Jl\ntqjSxzihrWbyAZtLfC3nsqnUVhGcYxxH4hippC0kDKG4uHhCt7/gfHXKW1/8s7Tzu3TzA84vzjg6\nOsKNa6xWuFDy326cmM0XTJs1Q/IcHh+hRCEItIs9RAz06xU6Ry7PT8qEXEcuXjzl/ld+lpwEylhy\nkghjkbaGECA5pn5N1W5L30jiMGx1sBnX99SzGr86xezeKDZRIsGVgqNpdvHDwLB+RhU9D7/91wmu\niETGcWB+9CVMc0TWNY8++Ca1LT8/xy6quwFolCwSFCMLv7rqagZfNq45ZFKcyCSUtFjT4r1n01+S\ncqRqLEqU3Gf0ecuIVQz9pghWVIU2RSalTE1lDMpUZSCkDC54TFVBlPjBwVaznWMZNoXoEbbGu+3P\nBIvUWywZBm1alNAoWdCjOZfDrZIan7bmNFUy6tPksE1Bf/1QI5xjKsRYIYi+fM4sgK3QJOSIxGFU\njd8+vxWCaZq2g7EMhMIYFhIfQnkP3kYSpJSkJBAqkgQYY7Y5WNDGFL8A6kcfK0Tp9UyDo9IWPy1x\nUyoaZoqprQzbplLAzWVrIwE/udILyQ4htkItW7ParDDGbN8DKwQJlUDVmml7TpXREf2IzJqf+av/\nVE1yE86vsKJGbbMWybvyh6YK0quu5yULK02xm+myjxdCMI4j1lSE2FPVLUJqwrDE2BZti03D0uDH\nDVmVDJUAVFJbkgBlRaE8KQ0YVIlMxMLXyyIVu5EvNrYsI2Kr+VVbb479E/KxIVJ0nKJwXQtSTBS2\npMg4J7n9uffR8svMqpHNuCJfBfo4sHNwwGs3XmHtPZ1zpIOd8gKP0zaPHPmTZMl+6ssELUtkFIWr\npyRClOas1uXNvvwsBUIrBJkkJCEFrLKI5MgyI9BYrUCt+Jv/ze9Qxwe8+/gD7u2/ySfuinS6pKlq\nKixCNogsebw542a9x1Ut0FlwdX1O01R878mKL80t/+2vnfOvze/ws/9M0QWnV75cbrQJskzoTcXN\n+z9N706ZtbuEfoNWDddXl4QsaLo9Tk7e49ad+7z24Eu4CEnCODp2j2+QwsDRq1+ivzrl7/+t/5TF\nrKZOnuaNu1RynxAT89ufQ4gF9uZbXD17Qj3bZ+eVL9Ad7HF9uWR0l1x++pL1yTPi1TXBbVjcuM/i\n1peYz3dYXp3x3nd+m/39fR6+9ykqNxy98QbLq4fED95lQlAf32OKPZIJWzecvv9NVszJKKasyfGS\nev4q47ChqirG/gShKsTmGtXdQ4rAxWZgTxqkSnz2rT/g+Xvfpao7pnBBWoHRM05PnnD+/BkprVmf\nR67On5Q1aT9gdjsuTt+jkjNyzDTdjNl+S0ySrgIj6nIBEoVviozUVZlOCyKhzUgRGTdLmr1d9vcP\nuLo6R+U9Yk5sTl8Q4sTz0ytM07J3+ArZO1b9hK5bhvWKYRqQQtHOdot/vNIFoCIFRIM2DXfeekC/\nWTJdPGZcP0M3c9qdI8b4hKur99lcr+hObtPUmp2DtxiEZHH3Da6ePS+FmIVi/63Pc/beQ5yf8JNH\ni8hH7/0RO3s7hOwhWhSRYX2OXeyRY8YNHjtfIKwqaB4hqIwkhYmm3WVYvkSZhjxCVpm6alDzXW4d\n3yYnTRgviC6hrWaz2XB1/ZJm1qJMS7vYQcRSfKmaFtHO2D16hYP9PTYXS5bnLyAt8RkImn51zstP\nn1DPG6yyLKUgeEnVNSi9YKH3cDFQyPcQRaDpLGF0kALjtKZb7FCpGcPLS2Iz0c32ynry4CZyT3FL\ntkyrMz45eVTEOrOSnzV1RUiRNASCHtiMa2TSeKmJkiKPQSJloFKF0CCVoLGGyuyQURQ+Y8a5iRg9\nbbKk5PF9KZJ5WZr8trGkmHHr9XZKasgy4WJGKFU46RQDW9ccMcXysLbzmpSg0kWcAGEbN6gIaSrb\nwMljW7nVGw+M3gM1GIEf1kgEQmdi79n0E/WsptIVQhbIvqr1ljIiMdmUVbaS5Gy3pabEet2Tk8LU\nBatWqCsRH0eEssV8qEAaQ/IQVUGkpcnTr0aCgiANttJYa4lCEsaBum2RWRCMxkhFs7vHrFsQGBn9\nkqOj1/noO9/j7tsVwfd89v3v8Gy2z527d7kaRrq9Y1aXG85ePAMNRijquWTvzS8yTD1Vs8APK8LY\ns7q6oOs6nB+p2j369TU3Xv8CPlCUtKkYErXuIARynMhmRrUwZKG3BjeBbDXJjUhjqZTGD9fo/VdA\nt0ihEN6hq1x44SJgdvZQXYsQgvt7/wk5rAm+iC5U1RZ5kZTMj3b47I9+k9xr5PiCi6vfo9aH2PYB\n9d5NwnqFNJbz5z3YlmpnF2larG1JGqIHHyMxJtrZHlJt44cxk7VAb4kJwU1IW0qn2U+I6Fm0M6bg\nEUSur07KQzVlrNhmbcNE1S4Yk8SQEDkS3QB+QoUe56ayQZ5ekEKg0g1JCybxkhxV2caaGu962q5B\nWYUUqhBTxhVN1WJCYHN+Rds25JQZEFjTkmUkpcKbdn5E6AqZEkIVUohSTcGaRRAIZAWmLpIFpEbm\nBkm5zGIKU9jliNEKqTJaV+Ts0RJSyuRQVLo/pB2U8bZCK1VKYlqgumY7TNxh0Qp88ijVkGJRi+do\nS2GMiEiSMUxEIchk6mZetgMhE4Sh3T8ihxLpiTGWvzMjy/tISFilSVkg1Qz0H+9A/f/9+1NxyBVS\nsVjcJPgehWRKoQSLUyKRkaZGyEwKQHJk4WErHNOyZKCKUq8pwOEcMHWDnzY/yrHlMKGURQlB8j1S\nF+ixpbzBu35Zbig5l+JDdGX1u71NKSXIJhLzWDA8MaNELHnWLAnqTyAd5IgfV+UP3DTEXJzxowsY\nUyHwxGgYh0s+2SxZ7N/m9qyj1jPS9Yq5jGgLwtVEs4/SkSglAkVWsvis/9GfpalIOYJPuChBJJLP\nCEnJB8kJKFagrCQiblXFafuilBmrKsiZRCIKiPKad/9OxVt/TnKvOebxo/e49ZUfI//gB4yqobIt\nzyrJg1Zx1c4JY+AV0fB8uEZ1NdpnVqLms8uX3Npt+K/+469zq9vl7k92xOWEMAmExo+nbM4/o9p7\nncXibWL0NLM5Z0/eJ+fI9fVTBlPTj5mPrtccHh5y9uhdYk7c/fJfYnOxwqjE43ffY+fGTX7uV/4a\n49WanZufQylJTAPTuOHqySMur0/oL1bgRoZgsNLx8MnHhGHJbL+lX050nWVx44v0Q2AjFGeffcq4\nM2NMkftf/PO4cWJ9/nW8mnHx6IxXv/gOp7nm+PCY/nqF2jwmTYF+WqPqDhE8gxvYe/VrzPa+xnxn\nh96vyD5i53+ZaXlGyjDf32E4OSUlmL/6OeaLPe5+5Wf4+m/8d5xefUKnd6CqySLQdjNymkhTZlwH\nFt0e56ePkCbTxw1du2BanzOOjtlRhxsDm+Waur1JXVtScqymNY0aUSmzWp0hhMLHiDQVIk9FFcuM\nq/WS8XpNbQW4Ne2sAmlZ7B/SjxuCm8AarOkYVtdIKambxRZILmnbGSrDMKyJznPjtQfcvP0qT589\nZGfvgKuwoTu8R79cIoeB7C11m5nNFiyfXSHULpvkkU3LrDqgutshTM308jFPPv0Qg4GQqJQAIZjN\n9vFpxPmRiGOxv0DlI+I0gu5QsqdfXyKiotvdLw33tuPG6/fx08hs75iXp89IecBKCHFi8+Ix/dkL\nosv4FEv2k1K6mR0dkaeRcVijlKcf17Rac/pipF3sc/LpB+wfvM7R8S3Onn/CzTv3Gc4uMI0hpcDe\nwT4xekY/MKs7TCNRUdNvzpC2IubEsPJYDTF5zM4xSkeknGO0xI0bhmEgK02eGlZXj6htw5P3vo2t\nd9hcP6edzajaBiVbskjsNZZ+6hFCwUxRiz26mq1BLBduuDKoXLBextZkDXiBVILEWIgXyWGEAh1R\nMhIpl36V0raYJkBrRGWxskSpRK6QlUXbOdkFkhUIb9BSFSnFmKmEIaaJYVxR65Zh7ZASXN4ehkWJ\nIcQYi343ClKKWKtpmh2gTOKV2MePE95PzA9qmvmAkZoeBVNBZqUhk0wmK0nyHqk13o3YxhLHjLGG\ntqlh25Zva4PQCucCtamZxjWqaUghl9x48kyXG+Z7e0RRJDEmx5LPHh0hCrLS2KoqTX+dkaFgmi5O\nH/H84YBwA1XX4qJj/+CYi5cnjOOIaQTT6iW/+3feZ2dnXgx604bbr99ncXyHq/UpXduxWfXoyuJD\nKJEtInUzAylYLi9o64bZ4pCuO9iWqQNTGmnnB0giKUVkvUAUiDEildgbwRVpwniNljtEkRjDgFln\ntHaQAzoXPbnUM5KZkbMHPYc0oOoF3mlMLZE5FS13N0e2LebgF9m78TXC5lNePPkI84GEPuHcJZc/\n+C5TXnPj9pcQoyIJzXjSkWRCN4eY2SGmacmAahqyj7joCobOlUGPMgaFBSEwSqCsQKc5MmaSdIyr\niRw8O/s7pCjJoYeY0FIgnGDjl1R1xzg4ZqZCSA1dXZjYCrIMaNOBKazsgmv0IAu6ExGwjcYnz7Qp\nvRi3vkAL8KslUniiyqzXNUY0JCUYU48MGlNZpo0reflmwAuYfGbWtkx+iRKamEGriugDKIvSdRnk\nBY9QFbUpG4UYDbouFxbnQuF6B+jHgdpYohZoAW7KaIpyO6SMH3q0MSQiKZSzhDSSKXi0rbBIehFJ\nIWC0QkiDTImUJU3XMk1TmdhKAb5s3CGVjHAFCIlICVuX/kVrWma6DDRDLqQFH90/9vnyT8UhN+eM\nCwPRB7LWKCUhJWxdk5CkcWQMhVdodE1OkP1EFqWBJ4RAIsvUkqLki9OIVTUiFz5jxhVGoC8PpSwj\nIgtyGJhiQP3QTBZjufxnVVa43heuW4rllpsTQigynrQ15GRdId0fj31kyloRDGMc0EKTpUdmQ3Sl\nbbh6rjj+0g4/GeecvvcBP9Gs2X8tY3YTv/L/fI+vvvYWb7x+wL17VeHfVpboi/mM/Menx8mNaKmK\n1c1WOD9CzgjUNpgfCb746VVIZKlAZWKO+MFjK4PfrmCULCihy5OX2DuZ1PdcSk+3N+e3n73HF+/s\nM3u6ZBl7vnIueRoUr+k9fEg03rNTK+Lk+L87w88HwdoEXsY1vyWuefgLf4PfPPu3iOISMRlMo7Fm\nwdGbP8XqyaeY+SHG1vhpSd11RJdpa4vLkddeu8V0fsrZw++xd+OQ6+fv8vAPfx2hF8wWd1i0A/7s\nQ56/gBAiq9FjlaZd7PPR+7+F3EwcHd8jxGekZo9bX3hAp+aM3/wtPvfz/yxJ1CgXOXv5mLqdcatp\nMPWc4eolKIusaz589zsYIlV3SFO3JAUPv/0tmnbOchPpFgekuMTLDrM8J6XI61/7JaKsmfwFVjVc\nL9f41RlUc84f/T77h6+w89p9xrMTrl4+wmcYh4nr+Zxp+ZLd2T5aG7JX7B6/Sj9cE6c1tx+8zcXl\ncx6883mu+x5VHRHHa2azW1SzQ+YHByVaIxXSACTi5oRv/fb/zv7NOxzMXiP6DadPP6ESAqlBZ02d\nJ6JtqHaPCP2a11/7Ao/yGsKINBV+HBg3A23bEH1p/i4vXyKUptYdta3p1z1aB8Tk8HmDGwe6m8f8\n+M//ZUQzJwa4e/+Ahw9/n9uvfo6n73+TzeYlymtUc4t2Zxfvlrx6+GfIBGwUZEaeffZdsihF0Ga+\nS79xWKmpjheEfs3+/IDTs3OknrHoOvppxTgldvd3aLoF/XKkaW8wbE5Zu5GsQchA8BtWFyuGsxM2\nw5rZ3g5+KpPpkBI6OcK4ARWQqkXalje+9A6ffPBNum7G9fICt1xj53NaaRg2E4tuFzeOdBWI4QmP\n3v0MJRQf/+HXaSpNCD1SaZTuCKLk+Md+g0i+eOxzRnjJNHq6xR7HN+9gG8tm7bH1LfqrZ6yuX2J0\njdENk0usWJOniSmdU7cL/DTQdhXjuIbgyaqY9E5Pn7N3fAgYcg7ENKFtxTQN5T1AWFJMxKS3mCEY\nh6JLF9mQVIPIqSCZUomWGdsUS5Lu0FmgUyakDJVCaU3wHrsryT7jwoTfuEJcSB5jW2LI2JgZ/YpU\ngTCWRmqy0GV9LhJWL4jeFcwaCl0pnA8IDVVjCd4V8olWyKTLoEEXUsA4Fk7s4BzaVKjKlqGJKMXc\nHAJCgRaZqBQKRbZVAfjnQBSJGEpxTROxQpFS+b/C4MFs17zKoruKyScaW4MBKcp7rekM0+SRIpNi\nAqHIqfCFo98wbxqy98hmF2sqhFBcrlbs7Bk2yxfgA3Nt0K90SFlMULmDjz79NvkH3+bo1l1uHt/n\n//ob/yuvfOFz1M2M/Zu7XF+vuPfgi8QwcO/BOwitWOwe0a9W1HWDrg1V1eGnRH+1pK7LqpymAyi/\n6+U1KSxJeoHVNdO4QSJpuz38FEh+JKYRvXOEyIYcJoTvy2EwrElxBEA3c7KfSH5Eq4oQSwdHZFDd\nDUR9i9vzH+fu2xt+57/+d9DVHCWX6PSYiwvNYvYqdfclpDxEaEPd7DBMDhUUSRsIEe9HpBJIFEmW\nmKB3mSDcjyb1cQwIa0FnQrbMb8yJo8NgSGRyWuBdX2KUcWKHgi7NlSIEgVQSNwxoVaFn87LB0Wmb\n0S75wIJEbUhRlKiRGAsXOQuyC+zs7UBMJASzpsYHSZSSEMYSFXI9xha0mzaKpEZC8EwxA5KhX5LR\nkEquPPiEMhLhN0gMQkpcLuKFzWokZ4g5EX8YWywt/BLT0jUhlAlrFB4lFEJKRudobE2228GiL+U/\nlyI5FumKH0ZGU1EpRRIF02akKu+dW/SaQZZpcsxIrQmhqIAhI2NEK0XWZQstsiCkRPATtmqotpEO\n8U9wdP1TccgV2zaeqSuS9whhkUaQU0QQqZq2tBxxRHw5kApBJfX2D0NDDCX3qi0pxAJWzkWbqK0g\nCc31+RlaGGbtDiEFshAIKbBZMA4jpqnKzTODzwl8wc1kwo9uHyJRNL+5bA4344htavSfYB+LsXwv\niYDYFipETggiKRvcKHn7a4Ff+/W/xa8+GLn5k69QV8eImLisTti1kfr8CYcHDV99818g5W22xRik\nLuuyf/RfDh6UYxiWpNgxa3eYktvazyCmjDZl8lHsNg5SxjQWYy15i09DBMSW7dh0kvcuV9y68wZd\nsvhZRPzhN3nwk2/zu4sL3riAF2LNpdmlWp4zZoMwNZWtueyX/NnLnnTbcGBnXDTwFz54zPqNT3j+\ngyccfWkXRMm8+RRRUrP3hZ8mTyvitGFcT1R2gd0/JuTAxaffIDxdYbqGZrGH6V7l6K3bTOMl1ewQ\nrVqC37DuT+HqMVbusjvbZb25RNuaH/+5fxUhA5vNBa/oP0fIgtWL5+S5pH3z81ydnHPzza9w/uhD\nNhcXaK25HHu6XUuq9sGvuXr6Gbfv3MVdXbC+WKFEz7x9hfqGZew9i24BShPrNxCyp5I17YHg8uwx\n42qk3j1EH1SsLy8Im0uoMkev3+fRD75L/uD77BzegaDYnJ+RethrLFfnzxAxUR/eJWyuODt7ichr\n6rrms29/i2FzwuNvf53dvVfJUiBkYn36hMGPdPPbyGBwcUCqI+59+SfYv/2An/rlf52nH36H9dnH\nVHXHG1/5cZqDY6K7RmfN/Og+afI8fv8DVvElTx9+TN02rFY9u+2cB2//GN//+/8bLz9+l67rGL1B\nWlkYjtWCrCWzRWmH98OaupmxM99l//47hOaAsLzAKMMYHDdf+QInH34DOd/l6O6rdM1tlIu89w9+\nHTFpVnbJ/oO3UTqyenmC7fYhS1ToCZfPuXXzJlp29KsL5JR5dvaULCTu4oqJJUdHr5D7Ddebj8j+\nCt3eYbNZEdIKsmY5XVBVBkhsLp5jtaI1DYSEEXB9dYGOA6t+hRAZZMP8oOX84fc5e/Fu4QdfnNHZ\nmnq+ACnKgy0lwuiQQnJ9tWYxVxjVMqVAtygr4ywNqKZslLKnaSpAkkdoZxXjsEFpy2zvkGEMnD99\nyTiusbbFySfMZwuUWpSugrA4t8JWGmEq2tmc6BPGKjaTo5m1JGMQKRJC4Oj2bVKk4JNEKc2MzmGE\nJQaHzwUPZk1DjhBjojINeYsOEyKRnMc2M0TICLlLUhkpiqJdG0OMkUqXh1UKI7n4WUk5Mmt3EVVR\npZb38wkdMxGPbQq6SKqKnFzJ9klN2JaUQ55oqg5COSz/FpMDMAAAIABJREFUcPjgfESERK0boqQ8\nS7IsFihVBhVJlgx2zpGYMpFSNFJKEl2PVAY3TkXdu8WXuZhQppRCpUtkWxTIWgrcFKl1i9SCcXJo\nmZBZkISADMvNsihbZdG6r6dirpuCw9qGmBJKlIuiqWYkIai63R9tKmP0dPMWRxGKyKxAhhKtExJ0\nyZV3C10O0Mtr3v+D38BN11y+gMNbr6E44pW7D1ivl0iR0PWMpt3BuZGqniFsBbKQBmRaoWQELGhF\n7i9Lyz0EEhFh27J1Xff4MJJTj0BR7+ySUqBZHJKzIfZnIDK66srzWSikVhBSiQgIhaxakqqwOZFy\nee0IInF9gTYVy01Adhk73yMysacfMOaB9foSxj+EUKEqST/1IAzzVlNVd7GHr1NXHcgf5lvB+4mm\n7ZhCxOoGRELbquTGfcDahmEcMNIQfJGlJJVRtS0ILdVgpUEw0e40uBiQyWPiDC1LjEBbXXoaqVyG\nlNr+DrMnSYFSGSWaoq7NmVxbnA/UQpNSZnW5JLmRKfRg9TZa4XHqHJE1MWWmIYBORBJCaBpbIbWl\nEoJpGBHSFjGJkOi6xflNkS6EBhnB5UiIEe1rjFGEOOGmkhHPY7kMpC3TupKWXClyBpdTIVoYRRa5\nxBvDxBQdccpbVrcrA8Af0lNC2Gq53fYwWwabKEnY8qaRghzDVukdthnhwkAmZ+qmwXuPDwEry5b/\nH/ffn4pDLmRyVqRUhAspK4Qfsbawb0MssYGUJLiJlCkWoJwKp1ZkQgYpYmkSVwXATAK5hTsrJTg4\nOCiYwPWafsjEyyvMjqWuLXXdFsSMLFMMIUCaiujLzaK2Fd6HYtIVGqPAp5G6biFFMn/8hy7JJJEQ\n0kAKZAUixmK6cRKpKn75S1/hF995yvUfPEbKriDHdEb3a/6C+i4Pdma8/tpPk4UlRY8QhQgxuAGb\n/wS6QlbkHDFVi1Dg+2v8agPLAa9AHh+g64YYJpSw1LqUIJIfSy5Z5hKUF5oYHTJlsInV6imPPttl\nfWsfKRruvf1VLr/7Pl/9/Gs82nyC9COXNmCnXTrnsVmg1xFdz4hxIkyOKlbcnnecSscTfc76NHCY\nBDGCNYIcLLapcaur0nLWDUJZ6sMbXJ0+QVeWN37slzDzQ9LlEy6fPkVKiW4sIi84e/yYveN9wtUT\n0uqUZX+JqhT3btyiWrdcPv+Ei2eJ/vo5myefkIOgmyuk7eDBj3Fw8Bbadpw/ecFsfgtzr+Hko48w\nsxnL4YzV+TNmTSRXlovPnpNYlYlmqnj2+DuYLOgvnzCctFyvH1Pv3GAzwL0v/yV2bh+TJ0HwmcXB\nHsuXL3jznZ/FLBpsfUh0a6xRDMueh5/+Effvv8PoT3DhMeuTgdXZKbu3Xqe/foFIE5JUAPBnl3Td\nLjWHmGoskwvToNSMEHu6as5wfcpscYQWAucf8dHvPePozhfZu32Lo3s/ye23fh6lBImJ9fUF0UuS\nUHz6nQ9Y7Oxti08LdrtZyeiuX9L3I9//9h8UxufRaySt6NojRCyq6xQN0+aqZLjqGVXdkuKA2Dng\n+ekFuv2UCsNmvCbqzM3X3uL4zR9nNjsojWIDl+cvuH3/ATnvsnd4TBItm2mJiB2r558g1Iy6uY2r\nNS5ZnJfEZk5Tz6nmiaG/RgtFFxQxBKYY6doaLxZM4xJsi9I1br0u+KYUWRzeZHlxzuX1CUZqsndo\nLSEolK25eXufLEUBvPs1+zc7kswksYe2mX7M7N26g25a0uYa1leoYWC12iAlXJyeMt87QlWay+Ul\nVmtSckjblGl0I0tRqS8bmZyhbffL1EVI6nmDVgLbHSIVNEkUg5otD4EYAqqpkEKSY2DYjEDC+1w2\nXCKQPYQ4IiuFDwKcZvDXKC2Q1pApPQctoe7mRdIQItJq/Fgyt0IqjIz4EKmriuALYF5pSZomkAmF\nJPiJjMSPRTHrJl+KQikiZbFIJiJRGAQFe2QrSWTrrq9r0pSJVMQwMY4XaCPJ3gICPw1oVVNXbTk0\nxlVRtaYyGVUykKRgGIbtQWZbnPFxu6YtCCmlBClAnAI5RJLvUVUpOVszg+CRti45YqFQc8XQe2xV\nUUmNYsKFoWzGQqLqFrg0MoUJ1/fYbkajqvKw95lmPisP9ymjlcIKTU6KFCbY5sOVLJanmAubFC2o\nTSl9IQUwFRS/qTDVvKx9ZeTq+SOODl5hEDBPnnV/yluv/gK7B8dkKehajTBlC+LGHpDQ2sJmJuCH\nAT8O+DChtEElibAFj5WJCKFKaXtYMvRr/HDGbHGMqWdYWRMrcKtLVD0vcaL+kiwrTFNvp42qFAjT\ngBhdEW+sL6GqkSkS4xLhJrLYME0984NbHL35U+ze+zN869f/DQ7f+Vf47Ft/l8XiTcarZ2ir2Nu7\nyyxJjEvo6iYr9x7LR+9SNzdQdg56gapagmzx2SC1ondTybMSabRFWvDTWH6/skbWQAwEJKii1Abw\nPqCrmuCn8loTiqoxxSxoauQWcRVDKOSRFIgxo4RFkYjBFbxXyiRXit4KweAK6STnVJTTytK0C3wY\niKK4AbSqCtd8JpApIrQhZIM2lK9FKzqxj9Lbi3KOZCRJzogxYIQmOYdVVeH/R4duDHIwJDUVE2BV\nsrdCV8X2ljI2S6JI+BAZNj1BSkiBTV6zmLWlxIbcDvYESsgimZEVzm+KlwBBjPFHB9cUQjFI2qJm\nTjkXzbXSpJBAbbGAlEuo0gKRBEmA/ycAJvwpOeQKss9kIQkJUt6gs2Aa1vjkC+0gZowqzMjK1KQp\nIWwqzWKtMdIgoivFsuAQMYCqkMmhUvF/x1xeULqF/cqQ93fJrtwYfHQIoclpKq3AWGIJQgmcG4uE\nIRcRRKvLYSUlSYwOJTVZ/vEWWBQGHevC5JURKxqENUQE2szKYd7MyHyO+v7/gnKlUFcbQxINOymR\ndaC9dwsZEkobxqlHmZpGKjb+j+dSqqYFLTBTYsoDXnjMzgKzc0jSGh82RD9hTUXyCe9LDAQBkgAJ\ntFaoWDI4efSENNFR0cxqnEossmKaIh9OH/Oqf5tm0ozTiu6FJNTnRH3Ao6trvqpfYRMzm+CJG0mu\nR2bDnF5NbPyG/+Lf/QZ//ft/FZ8DOQSMgOgcUkmmzQbGFUpJNpcnzA9fI0mDu1px/eyEfvWS+WKX\n7AdWF8/YuXWXWw/uIrxnGA2tfRXbdpy93PDd3/y1svIZVviwxg0nNEf3cZtr7P7bXL84RT7/kJef\nvY/WLdH1XO4e4C5OCOuXDEoTgkNVHS4tOLhxGz1p6p23WQ4b+vMLjm7/BAf37mCrBacP36c5fUJ3\ndJvFjTtAuYBJFcrDVjbUe6+irGG8mAjNOZcvnkCukLLneHePSKbaO2JuWqYw8fYrX+PixSnGVFyd\nvsDWNVLU2FbTTz1SVSgxK2DxlEneYbTBjyNKRjZjDylQWcHi9deR6oyH739I+G4FHlSOTFiyEix2\nKq5PT7l185BHT98nq4icrlmeFCC3qSuiKQcdr2aI+Zw4rUguYWUkuISKazKKnZ0Zy37EkHDBcX72\niK/9+b/C2aPP+Pjkdzm89Q4377zN6mpFPet48cGnXJw+wdYd3R7s3Hibpx9+kycffAu98wbJnXOw\nt0PYPEaKiuXmYxx7BREYZnQHN3FDJAoQOaKTpzn8PMa2rJdP2PQrFot9GBxjNmRhsDO1hf73rF4+\nJoSJdtYhlSjkEamh6ohJMvmMIjN6RxaGrCpqA32vyKl4319++n1irqkkpOxQCExVkaNH1IZxuqYW\nLbUtb7/W1mglaGYdkYnNqkcpQT+tmclDIg4hy9eolCDFEtFKMYKUyAjNzpzVaonPUzmIZI1ImiBG\nciwTKj85kvdYm5A54vvEZpo43L1FVy1ABDISqS3YQAiBKQwYPUOhS3TLaHLUiOyQSqGVJ05T6UxI\nQ5ocIkxkEdCmFGeMhGQlISuauirFWRIiRvy4KeSLzRVRRry3JKML3shY/PUGkSWqtoiUmc1bsjDI\nKEmK8vtR4L1n7B1JjEijCwknBgQjwuhtb2NEh3KwrewMbSIhbErMYBPKozRIgiwHbUHeosMC1rZk\nLfGxiH/G3qGsKY3zIDBmG9EwumAinScpgZUG3c5IW8qOFJraCFabFZUuFCCRQcqMkBKhGkLyxFCG\nC2FrSVQiIZIhTQJtNElkXErYpiWM2+l4zlycPyUTueivmR0c4JeJWbvDySefcnryjLv377O7f1xW\n5mPAVC3JFVJAUonUb/AuUHcLWtuQvcMNq4Lnmlw5WKlCKXC+6HBXw4Yb928zbZb0m2uipBCPskCK\nhnZRk3OCAOiEIBViA4ZsAmkcEWkijo4UE4LA9YuHpJSojMG1HQ9++t8GWfHP/bXvFGa007gucP9z\nP8sUF9R6jpzfwAjB7/+t/xAzNQjr0ZXEuw2LxW1CmJAy4P01KjdoERGqRWZdxAUuolVh8YvsMNoS\nfCLJhHOBxlbE5LG2IceAMBaVIAtFTuUco03G9SOmnhFSYvp/qXuzn8vy9b7r85vXWnt4p6p6q7qq\n59Pn9Bk9HDuO4wyOLZIoUqLIQg5IGCFEEInEBRH8DdwgEMkFIIRQgpRg2TKJhJJYRjnYDp7wieMz\n9+n5VJ3qGt55773W+s1c/LY7kGOQuQF7X1W3urrevWrvtZ7f8zzfz6dmjJBopxjHEe00WklEqKAA\nqxG2yUfcco8wA3TNLIxtq1TO0i8gVd069lrTV4XKmSQivW7h+hTbAVmK3FjPohKyxHWWkiSDXeCp\nKNshIyAlzg344qlaYBYKkwwlQSnt55BK7WP6ovFpdaKaitUOUsaU3A7W0rQw8X6FIefcJjPTtk1H\n9lQGgNFvkUIzWMecdszTFdoctCl0ZxEltT8zZ7KSbaKQJSlFpNQEUf8lJuoP8PrDUeTWQg4bQtnv\nOWnFKGa61HG9hcE1YHCpTa831wxoTDHUmhmnCVn2bmuRKTWx7Jf4vA9ZSQG5wagpqiVEcyEnSRWV\nmCK9GxjnTTNQ+diCaX6HEBJjHOSA299MbzY7RDE451qUMyWE/t51BYRBqkxWjmpXZLcg6AOMXbZW\nvjbkWlFacLD8SR7/41/kwQsaY++0m9e6Q5tbmAenZFmhtNBASJECWPe9KuHG5pVIJ9GxQ5oFlMKc\nPFZrVDGN/ydbwM52pt18pUDkgpJd23GWkhQqWlV6YAvEGlDPPMIkDl96hev5Q/K7Z0wv3UZeSO5U\niFcTduXZGcV3GDmhQx/0uF0TD1xFz5v3P8e3n3+Jf3b8Hm//7sgrn9UQM7vdFbaPuPUxYdyxGA6o\nYUO3OmIOE4MtnF28haktEFXiBt0tWB1+khISq/Up19dPOHn9T0KNfPCNL7G0Dp/OiLtLapip/YLX\nv/hvkNLI9sm3GC8ecXjnHqZXxGmEKDm8excfoDt9QLp9RJjG9iW2S8TilIPVCctbr9Jpg3/4iPXq\nhGl3xTd/7Vc4Or2P6C23XnsdZxdcf/AO23Fm3s1srp9R5h1SW1a37rGdtnz2R/4s8fqKlDbcnJ1R\n0sjm/N2WUL9zn+M7rxDHwHe/8auU0TPl2FSrsemmM2BlR5Sa7ugur7zyOY5eeoFnT99nfvqIabxB\nKIE7vsX1R+8Rts05Pvktso6EfI1TCTsMlKvKnTc+zaN336VbOy5u3qZ6Rc6OWDLd0IN1aLek0sKf\nxqyar1wrFIbkt4jaGKXGCDbX51xfn+O0olsfI6Xld37lH2LqxINP/ijbmzO+8Vu/zeroBcaLtzh7\n/iUEkepfZHV0QozPQR4gx3cJz9+CTvH1R9+mhm9x4H4C2R3TnZxQvcU6mK8eMk0TSEW3WCOqomx3\njHJDoeBMs8oJIVvXkULVPVJZjDaNPysKskoUhlwWTNtLlnePGboTynbDxfNHpDCjNIx+BrOghh3V\ng9ATZSp0bm7BDjegpWGcm1DBLVakPCGUI5XaihbrmHNBOQd+wnaOkBOL4YCcAyFGFosVImdSihT2\naCcKNQmUKozjrpEQamVQPaIY7FIRZUeqCVU0VTuULJQikUYjQuBwoUm5kPyINY1LGePcVqNcxooO\nKQQhTo1jmSNCVkKOUH3bqxOGGBJCZMgjBo3sDLWEpl6vmZolenD46KFWkvcsFguUcFSZ6Y7WZBTK\nB6Sy9Gjm3HBrpBkRItkadBLEGrBFU2lh5ELTASujkMUh95IfbRSqGpKWKFVgm5l8o0mUOhNuRopM\nBApWOlRn2mEnG1KekErjRFOlhxKQ2ZF9QOqKsoZQZlSRSDlQEkzjhhoDQi9wy44aE9YpcIZcNTlk\nst8gpKIzFqM0QjdrFtohYqRKg1YOpRKJ2vaL8w4COANVaFJR+LRruMdNZvYTxiQ0mqOjI4RM3Ixz\ny6WsBvrFId/+1j/lCz/8FxBVs93dMLiBzfaS5bAihoQmY0SlFoWUbQdS1VbACNECeDnNTYecCpMf\nWR/f5+LpQ47vvMLm+VPynr3dm0Ok7pFy34lLiRQDpqNZ3uaJlPZTUO1QpmfeNF65NpCr4PLJNwib\n7yIyLMsXuXXvhzD2sBX91fKpn/ybbX+6Zsw0sz17j/n8Kapf8+kf+Rm8f0YYA9/97jcR02Ounn2F\nMO1QqjKWymJ9RH/wKpsPHhLEgLVrIgo3LOjWa5Q2+N1I0QJK4c1Pf5bLqwuunp+TSoZcqBJQihAi\nxspmX0sZPQz4KbTOq66U2mqSvu9JIpORDIvWULOiZ2Ru7OtaULJHGdXCjftrVNqDHbNHVdYiiDWj\nrUQkRZgzxlp0r5C5kRakUHu7aStWjar71Yq838PNhDiihSSFSM0BfxmYdjPz9grMjiJnROhBLDhc\nniK7dgCtgHJDuwYlIYSiN5mbKbJYDVAkRhdEZY/6qxTan6mNbKzqKZJSQlWL7Rwp1iZ0ErLdrxAY\n2aYXUJqiWUmq1IgcPy6Y/yCvPxRFbq3gbFPeQkZIgyg9Zxdfxtk7GHkfqzWpBmwvSH5G1ELwW0Lc\nYqQhS4FPBSsrlMR2c0lFtsVvkQmlYPolIrW9Kx8mrJXMU9ljyEaMsuQ4Y6ShpLaaIJWkpMa+qz4h\nRMVZjaY50rNSiFzIv89+rDUL4upVcK6pf7XCFgWigcZzAkkrtP3yZXbqV4nja1QbUMNMCR52Hepg\nSUgZISHEuCciKEr8fa4lLa1YS+sQ6CpaUl5KSp6RuiPkiRJLK+hLQrJf7ahtdFNKbR0d2UZjh+4u\nDx58mXLxMu6W5XJ+hvrKQ549e4tbR8c484AbpTjLgVcWK6Y4Y4VpyBNdCbuIz4kpnTFMtyj3Bty9\nFxmHh/zn/+4v87d+48/h41Nc30EtXL392xA3PK0HHJ4+QDlFunzM2w8/5MVX7uEO75FSIKVCyoqj\ng5e5evIOtRYWywN2F2co63j51S/wyH+Dyw/f58EP/AlUvMbLO/h0ANvn9LpH20A4+5AgeiqCVJ9y\nef02OYtGwegGouwo0nFrHbh69zHlpZcwB4dcj88YN5dcbDy9FcTrc862F5ANZwqWB2ugsFiu2W6f\nYM0G6STWCuL0Eb3M/PN//D+Q8ozShYPDu6AGTh58kavtJQeLY77yG7/WxtvdLbQGazXzdMXh7VeI\nJbRdcSHZbnZkP/Otb/0L3AffwgmF6tZUFlg5kGbH7dc+wflbv0maI9fbxNHdN7D9hior02bHwe0T\nNrvI3Vc/xUcffA1bJal6Ss1INzD6kRKaZVAhmMcbOiEZxzOEM2h7hF4fIKqm6IpCorojbh3cxZcZ\nqw1lDhjTUUfH42+/g1sJdh99k+vHCWuuifkKLQakeZdx+wFCSfz0EXdXrxHMljpIjsUrLNxfRPgz\nNtsE45ahO6ZWiVIQ1EXbh/ORKQVC3iJZQjzn8uYjbr/wA5w9O6M/uM1iucYerJifn1O1aUVvrVht\niQWc6VgdvIC/3BDLTZsSWRjckkzmSC6Yp4iWHr08RVpBf9wjtWlGHwE1CQ7Wt9tOYgGZO6SUrXsu\nKhGJXawpBczQUu4mRXyYUM5iyfg47x8YihJ2lDRTckCagZwLUkEWGWkUYbqizWUcudAKWgogqMoS\nYqFzhr6zrUNoBfMsUFqSc5tapzLSFUcRMM8jtbTxqDQaXROmFnbXW4Z+SciBcX6G0wMkTxUW5ec9\nAUE2rnUo5IunVBU+zktM49CwYSUiDFQ6hFAkmdlQMEKiisR0HTEkakxs/Q5R20SkU7XB56tEdIdI\n5ej7BXG8xk9bgmlWLlEaM3zoFighUa7DTztM39a2KpY5F9h6hKskFINekvxMVgYlcmuUSMmw7PHz\nFikNTtT2+NaJnCpO9/hxx3Jp0NqSraZIRQkeWSLOLBCmjcrjnk+aY0KoQvLbj/+5VkfyM1oUjBRI\nI0ge4jYypedoCVULRJBkk7FSInNiSlvkXKhF0Q8Du+uPuPWJ13hw77N84Y/9OIgl3bJr1yYllutj\ncowsDg73WuXE5EeGYdlWQ7bbPWu9NNsniuh37QC+PsKP16wPD8gSyjzjukOQglAKXamkFBFK7/Fz\ncp+xkShn98r5gkyRIh3u8BS/O2OzOWfd32P7/Mvk9By1us1pdxeRIY6XuG7N5uo5McyYrqemNuYW\nUqPlDeHRb3ERZrr1PZQ64lM//FcwSHTecXnxYQuKe8E3v/m/cXTrDioVPAUpO1S/ROBIu5mrZ48p\nx7fQU4dbGr7z/jst5FgzRjmKVijZ7GbOuXb4LBWJYPITzjmEKHjv980wiCkirIRUqRaoipBm9H5d\nppaWpUih7MPfljhHrG7M2uY0yEjZVmtKquTSCA5VKkSKzL4gU2oILpXR7Y5GDBnkhK4SVXoitCxR\nt0DavSBKaBaH4IntoBgT1jS2bcWS96H7lCeMlhQlqdUgrYIiWBqLROB9RHXtvdVaEVWincWHHaWY\nffHfVilySg3ZZzvE/nCqVFv+LFOElPdZgcroI84paqrM5Y8YXYFamHYbqpJNH1kiuVbWx59vLi5R\niGmkSEMNzaVcS0AgGucQQ907oytlP2Ks+70QhfeBfrUmp0yJhRybTMF7j1YdJZVW1KXfY9wVKhWl\nIXrflJE57aH/mhwjWfi9utCQa0Rq9z1vKy/uII2lakXNjTuHlc2g4iNZaag0YkNUnLz5U4zvXHCw\nPqZOd9A2U1cvUAawY1MQaqERUlFFQ7r8qy+poGSopY0eM4UiBUa7ZhuSjSkpFKTJo6xqt7Ba8CUz\nKNdUfTWTRFN2hsnxF3/qx/j2L1WuYsBGjzHw0uGKr9/8Oj94+Vc5SAf8Ks9IJfDiasnkE5FrUtZs\nw8zQLbg5v+Do9j1Obc+P/uCf5Uv/0wd84/Qtnr3949x6fYHtjkjzzOr+J8kxcdAvyTUw35wxrA/4\nzI/+BGqxpOoBUSbm82vs0DPdPEEIwflH71OZWUjL2Te+Tu56jl/7fg5e/CwAZX4CIbN76++h0w7d\nOfzVY5A9yp6iZdf2LeNECjuccNS0o9YOWwXX82M8kusPn5CTQBZJ7BZYkbg+e0Z1K0TVICZy1Dw7\nv0FmyeNH79N3Fh9nXAc3ZxeUarHrA7qVQdQF3XJJTIk4bYjTTKmK9373Icf3jxmWp9w5ucO3f+dL\nqJjIJXD15HnTYB+vyBjuvvQaz95/izhvEWpB7Xum83cpMiOLwtaOp+EMoQ3TxSVHL3+a3dWGo+M1\n9Gvuv3HCo698hXDxhGeP3mOKVxhdsEc9qAFlHJ2xyN4i9jtTJyd3CH7k3uGnyDkR5pES22hKqWbU\ni2V/Ys+SGrf4UNoNXTS29dXFU9ZHn2QaH1GqYd0t0bLgMyip6frbrFaOKh0ij6iSMN2Cafs+Mk6o\nfbfI50vibiSUC5zpqEmClCzdYm8P82h3h5OjjpIVR6crlLak8TG7zSOW3SnSKIqW+zWA1pXaTlus\n0m1EqSVSxJZGrqohDgVYpyEUipjQwpCzJ+tCpqIzCD8zX59TREv3S6upUmBEZRgOUMrgc2pGrtIA\n8loZXG6O+HG6buHZIsmpkHOkpEiKM0upWiFdCloXiJKKorcLcpFAbjiuELBuQc2CrusQOZDngLCG\nkHID95MRUqOUQKseqFATru+JcwJRUdqRkqdGjzGaFCYUhWW/QhQHUpFCxIfE4eltcihNPtA5+q5D\n5JkYm7o95AB+S/KbFh4eoQ4NZxhDptSZoT/kOkyUklgfrqhzwA6HCCnIqmCHdQPvm54YPTX5Zins\nFiD2n7XU9veSyAjnWoJ8uaTEgtRLqpJo6xA5IYpH1KYSddoRCiiZSSUiiiTERA4RGXeUPOG0Y9zd\n0FnHdPmMobPsNu8Rrw1Kd0jXo2SHEYaYN8xh29TaNeNsTxUJa5bgVvgwoXVASUs0rRPn5x1le0NJ\nTQutdMNDXU8bJArjK0JIkgChKp1bM5cbTu99gjHe5ZNv/AlCqjz86Jrt+bcYDte89slP07mOWivj\ntMFoBwqC9wjAzyM+ehyaHD1zjKyOjonXWz785m/yqR/+CezqFvPmCtP1SFHI9gDvG2ml5BZoVgjQ\nAmkspEpJGWkq1SdyjijZt886CWpC2IHe9hQJd9/8M6zufJqUHd1wQJwCuUykELm+/IBuOGC8umzB\nMlGoJSLT3A6100NEqKT5A+689jnC9ROuzx+jjcSu7iGWHZ/7whFHL3+Brz38b9k8fxdpO8ZsOD5+\nBZmXDXU1z9jlGiMEtw+PefToEaJrFlVZBSm0fEwmtj3SxvnAdY4cA0aXhrtLEZUrStmWOxKyFelC\nojPNAJjDx2rqXAXOasbxBmMcOQVyAhF3oCRTaMru7XZEKguikOIMspIFDK5DdR2xJmS3RCHpFgal\n9tzV4kC2glIKgTSG6luXVArBQrZgelaKHBsLuJSCjwFpWod2jmO7B2qJ9k2uYo0k5YhSkugDQlZk\nEeQKJWQkhpJjI2alTJEVYxRUyW57g9GOHMa2aiYERnVI0UACkcqiW7bmRU4o/qjt5AqBsq2L4ucJ\nbVUbwepI9bWJDCjEaUuVFkyipEgpAiUdJU+U1E4F4EgSAAAgAElEQVQGJXgMmqoSQSZUdbhuSfah\nWUuEwYebFrJQlhgSJe5QspJLpAaLMobR71BFoclIZT62gdQqyBJUmMnOkEhUXel+n/Z5+6nbyYq6\nN6/F9gWPFYyUbU/ItV0fef/7kN/6JRarlkhUC8HB4efQVRFExGjTilvaArYU39s9zkW23bJ9tVuQ\nQMGXgDYdMUbEvnjNqiCrRuaM0LqxcwkgQFUQmDZqMYk+dhhZWUvDPCaebK549Prr3BkfcnP+EbdP\nTvlJdZvraSSkiiGxzJaYPV317YupYXfxHh9yl9MXXyKUv8PDi0f87H/6af7mz/8l8uYCP88YaylK\noY1BZMny8BQ59JAl5ALEtpMtBJjauIfdAmkUSh6wO/8Otd5w9u43uX78Fnfe/CFWx3fx0xn+6Tfx\n0zOG259hvTrBTzO6P8TqNePujBLCnpFZub45R+tDMLt24pYdMhayNKTs4OAOxmRSAZZHSDSd7Zh9\nsxgpYZjnmYNuTchbjm6dEMYdKUw4taaGyjZf0i8OuDy/Ic8ji+EYZCHFkd55wkcV2e34+ltfZvYb\nlIhoaUC00ES5KfSLBc/efgdRM8assF3Bz1dAbklY2s6ttgeEOLI4XrG9+gilDBfpAS99/lX8pnLy\nyks8f/+Kw/sLXHQge1DdPu0d0cJRczOeYXrM+oQXTj/N48dPuH16m/e/+r9z94XXeP70Q5SBFBPU\nQo0blFLsdiOr1QFTTdx897cpdWB16yWmsKFIBQGUsNB3DFRkEKTdhqI2THNEGNkKBpVR9j6yr8jR\n4+OI9AlpMp1dEovCre+iq8LISDbNd8+UmOa3Me75vgPukOvbuM4iXCaHHWXySLdEaEtOkW6w1Nhk\nNSI75uj3xh/dAOy6o+6Tvsr0xFIoGERoOlhrJPQHqLSmlNSKOdr3u2bPzcVzFosVu3iNyCB8ABpb\n9vbte8x+x2q1Yp490jqk0iwWkhQ8c1TM0zmlBrrlbWSRgERLxzjviDXQGcvsZ5xRzLuA8AGFR6pM\nQpFjR5GKZARW/N46g8JYCUkiTNs3HPqu7QTmgu4k1XVk2RA/2Xt62+GnG6QyoCvdUcecI84NWG3x\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tG5NSaHzgfZdPJYF2gixoQP8QGog6JS6u/zl//T/5KkfrIy42l/Bsy0MCu3LOOq5Z93c5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ziDZtI9D3d6BqQhxZn54SokdW2zZTSjHHhXmZMAK0KMc2t4XaH+UHCo4M3lQzeRypR0XpIV5i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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "with detection_graph.as_default():\n", " with tf.Session(graph=detection_graph) as sess:\n", @@ -353,7 +338,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.5.3" }, "widgets": { "state": {}, diff --git a/object_detection/utils/visualization_utils.py b/object_detection/utils/visualization_utils.py index 41d80db..43ccad5 100644 --- a/object_detection/utils/visualization_utils.py +++ b/object_detection/utils/visualization_utils.py @@ -19,7 +19,9 @@ The functions do not return a value, instead they modify the image itself. """ +import os import collections + import numpy as np import PIL.Image as Image import PIL.ImageColor as ImageColor @@ -290,6 +292,9 @@ def draw_keypoints_on_image(image, outline=color, fill=color) +state = np.zeros(len(STANDARD_COLORS)) + + def draw_mask_on_image_array(image, mask, color='red', alpha=0.7): """Draws mask on an image. @@ -320,6 +325,35 @@ def draw_mask_on_image_array(image, mask, color='red', alpha=0.7): np.copyto(image, np.array(pil_image.convert('RGB'))) +def pluralize(s): + """ Convert word to its plural form. + + >>> pluralize('cat') + cats + >>> pluralize('doggy') + doggies + + Better: + + >> from pattern.en import pluralize, singularize + + Or, even better, just create pluralized versions of all the class names by hand! + """ + word = str.lower(s) + # case = str.lower(s[-1]) == s[-1] + if word.endswith('y'): + if word.endswith('ey'): + return word + 's' + else: + return word[:-1] + 'ies' + elif word[-1] in 'sx' or word[-2:] in ['sh', 'ch']: + return word + 'es' + elif word.endswith('an') and len(word) > 3: + return word[:-2] + 'en' + else: + return word + 's' + + def visualize_boxes_and_labels_on_image_array(image, boxes, classes, @@ -370,6 +404,7 @@ def visualize_boxes_and_labels_on_image_array(image, box_to_keypoints_map = collections.defaultdict(list) if not max_boxes_to_draw: max_boxes_to_draw = boxes.shape[0] + description = [] for i in range(min(max_boxes_to_draw, boxes.shape[0])): if scores is None or scores[i] > min_score_thresh: box = tuple(boxes[i].tolist()) @@ -381,13 +416,14 @@ def visualize_boxes_and_labels_on_image_array(image, box_to_color_map[box] = 'black' else: if not agnostic_mode: - if classes[i] in category_index.keys(): - class_name = category_index[classes[i]]['name'] - else: - class_name = 'N/A' - display_str = '{}: {}%'.format( - class_name, - int(100*scores[i])) + # if classes[i] in category_index.keys(): + # class_name = category_index[classes[i]]['name'] + # else: + # class_name = 'N/A' + class_name = category_index.get(classes[i], 'object')['name'] + display_str = '{}: {} {}%'.format(i, class_name, int(100*scores[i])) + description += [class_name] + # print(display_str) else: display_str = 'score: {}%'.format(int(100 * scores[i])) box_to_display_str_map[box].append(display_str) @@ -423,3 +459,9 @@ def visualize_boxes_and_labels_on_image_array(image, color=color, radius=line_thickness / 2, use_normalized_coordinates=use_normalized_coordinates) + description = collections.Counter(description).items() + description = ['{} {}'.format(i, pluralize(s) if i > 1 else s) for (s, i) in description] + try: + os.system('say --rate=450 "{}"'.format(' and '.join(description))) + except: + print(description) From 1a5e1f7467c0a5474bfcd650a4131fc92ced6f9f Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Sat, 18 Nov 2017 20:33:11 -0800 Subject: [PATCH 002/174] pluralized names for each entry in pbtxt --- object_detection/data/mscoco_label_map.pbtxt | 6 +- .../data/mscoco_label_map.pbtxt.py | 481 ++++++++++++++++++ object_detection/utils/nlp.py | 163 ++++++ object_detection/utils/visualization_utils.py | 69 +-- object_detection_app.py | 16 +- 5 files changed, 674 insertions(+), 61 deletions(-) create mode 100644 object_detection/data/mscoco_label_map.pbtxt.py create mode 100644 object_detection/utils/nlp.py diff --git a/object_detection/data/mscoco_label_map.pbtxt b/object_detection/data/mscoco_label_map.pbtxt index c8a4d57..9401030 100644 --- a/object_detection/data/mscoco_label_map.pbtxt +++ b/object_detection/data/mscoco_label_map.pbtxt @@ -21,7 +21,7 @@ item { item { name: "/m/05czz6l" id: 5 - display_name: "airplane" + display_name: "plane" } item { name: "/m/01bjv" @@ -161,7 +161,7 @@ item { item { name: "/m/018xm" id: 37 - display_name: "sports ball" + display_name: "ball" } item { name: "/m/02zt3" @@ -336,7 +336,7 @@ item { item { name: "/m/050k8" id: 77 - display_name: "cell phone" + display_name: "mobile phone" } item { name: "/m/0fx9l" diff --git a/object_detection/data/mscoco_label_map.pbtxt.py b/object_detection/data/mscoco_label_map.pbtxt.py new file mode 100644 index 0000000..4858236 --- /dev/null +++ b/object_detection/data/mscoco_label_map.pbtxt.py @@ -0,0 +1,481 @@ +items = [ +{ + 'name': "/m/01g317", + 'id': 1, + 'display_name': "person", + 'plural_name': "people", +}, +{ + 'name': "/m/0199g", + 'id': 2, + 'display_name': "bicycle", + 'plural_name': "bicycles", +}, +{ + 'name': "/m/0k4j", + 'id': 3, + 'display_name': "car", + 'plural_name': "cars", +}, +{ + 'name': "/m/04_sv", + 'id': 4, + 'display_name': "motorcycle", + 'plural_name': "motorcycles", +}, +{ + 'name': "/m/05czz6l", + 'id': 5, + 'display_name': "plane", + 'plural_name': "planes", +}, +{ + 'name': "/m/01bjv", + 'id': 6, + 'display_name': "bus", + 'plural_name': "busses", +}, +{ + 'name': "/m/07jdr", + 'id': 7, + 'display_name': "train", + 'plural_name': "trains", +}, +{ + 'name': "/m/07r04", + 'id': 8, + 'display_name': "truck", + 'plural_name': "trucks", +}, +{ + 'name': "/m/019jd", + 'id': 9, + 'display_name': "boat", + 'plural_name': "boats", +}, +{ + 'name': "/m/015qff", + 'id': 10, + 'display_name': "traffic light", + 'plural_name': "traffic lights", +}, +{ + 'name': "/m/01pns0", + 'id': 11, + 'display_name': "fire hydrant", + 'plural_name': "fire hydrants", +}, +{ + 'name': "/m/02pv19", + 'id': 13, + 'display_name': "stop sign", + 'plural_name': "stop signs", +}, +{ + 'name': "/m/015qbp", + 'id': 14, + 'display_name': "parking meter", + 'plural_name': "parking meters", +}, +{ + 'name': "/m/0cvnqh", + 'id': 15, + 'display_name': "bench", + 'plural_name': "benches", +}, +{ + 'name': "/m/015p6", + 'id': 16, + 'display_name': "bird", + 'plural_name': "birds", +}, +{ + 'name': "/m/01yrx", + 'id': 17, + 'display_name': "cat", + 'plural_name': "cats", +}, +{ + 'name': "/m/0bt9lr", + 'id': 18, + 'display_name': "dog", + 'plural_name': "dogs", +}, +{ + 'name': "/m/03k3r", + 'id': 19, + 'display_name': "horse", + 'plural_name': "horses", +}, +{ + 'name': "/m/07bgp", + 'id': 20, + 'display_name': "sheep", + 'plural_name': "sheep", +}, +{ + 'name': "/m/01xq0k1", + 'id': 21, + 'display_name': "cow", + 'plural_name': "cows", +}, +{ + 'name': "/m/0bwd_0j", + 'id': 22, + 'display_name': "elephant", + 'plural_name': "elephants", +}, +{ + 'name': "/m/01dws", + 'id': 23, + 'display_name': "bear", + 'plural_name': "bears", +}, +{ + 'name': "/m/0898b", + 'id': 24, + 'display_name': "zebra", + 'plural_name': "zebras", +}, +{ + 'name': "/m/03bk1", + 'id': 25, + 'display_name': "giraffe", + 'plural_name': "giraffes", +}, +{ + 'name': "/m/01940j", + 'id': 27, + 'display_name': "backpack", + 'plural_name': "backpacks", +}, +{ + 'name': "/m/0hnnb", + 'id': 28, + 'display_name': "umbrella", + 'plural_name': "umbrellas", +}, +{ + 'name': "/m/080hkjn", + 'id': 31, + 'display_name': "handbag", + 'plural_name': "handbags", +}, +{ + 'name': "/m/01rkbr", + 'id': 32, + 'display_name': "tie", + 'plural_name': "ties", +}, +{ + 'name': "/m/01s55n", + 'id': 33, + 'display_name': "suitcase", + 'plural_name': "suitcases", +}, +{ + 'name': "/m/02wmf", + 'id': 34, + 'display_name': "frisbee", + 'plural_name': "frisbees", +}, +{ + 'name': "/m/071p9", + 'id': 35, + 'display_name': "skis", + 'plural_name': "pairs of skis", +}, +{ + 'name': "/m/06__v", + 'id': 36, + 'display_name': "snowboard", + 'plural_name': "snowboards", +}, +{ + 'name': "/m/018xm", + 'id': 37, + 'display_name': "ball", + 'plural_name': "balls", +}, +{ + 'name': "/m/02zt3", + 'id': 38, + 'display_name': "kite", + 'plural_name': "kites", +}, +{ + 'name': "/m/03g8mr", + 'id': 39, + 'display_name': "baseball bat", + 'plural_name': "baseball bats", +}, +{ + 'name': "/m/03grzl", + 'id': 40, + 'display_name': "baseball glove", + 'plural_name': "baseball gloves", +}, +{ + 'name': "/m/06_fw", + 'id': 41, + 'display_name': "skateboard", + 'plural_name': "skateboards", +}, +{ + 'name': "/m/019w40", + 'id': 42, + 'display_name': "surfboard", + 'plural_name': "surfboards", +}, +{ + 'name': "/m/0dv9c", + 'id': 43, + 'display_name': "tennis racket", + 'plural_name': "tennis rackets", +}, +{ + 'name': "/m/04dr76w", + 'id': 44, + 'display_name': "bottle", + 'plural_name': "bottles", +}, +{ + 'name': "/m/09tvcd", + 'id': 46, + 'display_name': "wine glass", + 'plural_name': "wine glasss", +}, +{ + 'name': "/m/08gqpm", + 'id': 47, + 'display_name': "cup", + 'plural_name': "cups", +}, +{ + 'name': "/m/0dt3t", + 'id': 48, + 'display_name': "fork", + 'plural_name': "forks", +}, +{ + 'name': "/m/04ctx", + 'id': 49, + 'display_name': "knife", + 'plural_name': "knives", +}, +{ + 'name': "/m/0cmx8", + 'id': 50, + 'display_name': "spoon", + 'plural_name': "spoons", +}, +{ + 'name': "/m/04kkgm", + 'id': 51, + 'display_name': "bowl", + 'plural_name': "bowls", +}, +{ + 'name': "/m/09qck", + 'id': 52, + 'display_name': "banana", + 'plural_name': "bananas", +}, +{ + 'name': "/m/014j1m", + 'id': 53, + 'display_name': "apple", + 'plural_name': "apples", +}, +{ + 'name': "/m/0l515", + 'id': 54, + 'display_name': "sandwich", + 'plural_name': "sandwiches", +}, +{ + 'name': "/m/0cyhj_", + 'id': 55, + 'display_name': "orange", + 'plural_name': "oranges", +}, +{ + 'name': "/m/0hkxq", + 'id': 56, + 'display_name': "broccoli", + 'plural_name': "broccoli bunches", +}, +{ + 'name': "/m/0fj52s", + 'id': 57, + 'display_name': "carrot", + 'plural_name': "carrots", +}, +{ + 'name': "/m/01b9xk", + 'id': 58, + 'display_name': "hot dog", + 'plural_name': "hot dogs", +}, +{ + 'name': "/m/0663v", + 'id': 59, + 'display_name': "pizza", + 'plural_name': "pizzas", +}, +{ + 'name': "/m/0jy4k", + 'id': 60, + 'display_name': "donut", + 'plural_name': "donuts", +}, +{ + 'name': "/m/0fszt", + 'id': 61, + 'display_name': "cake", + 'plural_name': "cakes", +}, +{ + 'name': "/m/01mzpv", + 'id': 62, + 'display_name': "chair", + 'plural_name': "chairs", +}, +{ + 'name': "/m/02crq1", + 'id': 63, + 'display_name': "couch", + 'plural_name': "couches", +}, +{ + 'name': "/m/03fp41", + 'id': 64, + 'display_name': "potted plant", + 'plural_name': "potted plants", +}, +{ + 'name': "/m/03ssj5", + 'id': 65, + 'display_name': "bed", + 'plural_name': "beds", +}, +{ + 'name': "/m/04bcr3", + 'id': 67, + 'display_name': "dining table", + 'plural_name': "dining tables", +}, +{ + 'name': "/m/09g1w", + 'id': 70, + 'display_name': "toilet", + 'plural_name': "toilets", +}, +{ + 'name': "/m/07c52", + 'id': 72, + 'display_name': "monitor", + 'plural_name': "monitors", +}, +{ + 'name': "/m/01c648", + 'id': 73, + 'display_name': "laptop", + 'plural_name': "laptops", +}, +{ + 'name': "/m/020lf", + 'id': 74, + 'display_name': "mouse", + 'plural_name': "mice", +}, +{ + 'name': "/m/0qjjc", + 'id': 75, + 'display_name': "remote", + 'plural_name': "remotes", +}, +{ + 'name': "/m/01m2v", + 'id': 76, + 'display_name': "keyboard", + 'plural_name': "keyboards", +}, +{ + 'name': "/m/050k8", + 'id': 77, + 'display_name': "mobile phone", + 'plural_name': "mobile phones", +}, +{ + 'name': "/m/0fx9l", + 'id': 78, + 'display_name': "microwave", + 'plural_name': "microwave ovens", +}, +{ + 'name': "/m/029bxz", + 'id': 79, + 'display_name': "oven", + 'plural_name': "ovens", +}, +{ + 'name': "/m/01k6s3", + 'id': 80, + 'display_name': "toaster", + 'plural_name': "toasters", +}, +{ + 'name': "/m/0130jx", + 'id': 81, + 'display_name': "sink", + 'plural_name': "sinks", +}, +{ + 'name': "/m/040b_t", + 'id': 82, + 'display_name': "refrigerator", + 'plural_name': "refrigerators", +}, +{ + 'name': "/m/0bt_c3", + 'id': 84, + 'display_name': "book", + 'plural_name': "books", +}, +{ + 'name': "/m/01x3z", + 'id': 85, + 'display_name': "clock", + 'plural_name': "clocks", +}, +{ + 'name': "/m/02s195", + 'id': 86, + 'display_name': "vase", + 'plural_name': "vases", +}, +{ + 'name': "/m/01lsmm", + 'id': 87, + 'display_name': "scissors", + 'plural_name': "pairs of scissors", +}, +{ + 'name': "/m/0kmg4", + 'id': 88, + 'display_name': "teddy bear", + 'plural_name': "teddy bears", +}, +{ + 'name': "/m/03wvsk", + 'id': 89, + 'display_name': "hair drier", + 'plural_name': "hair driers", +}, +{ + 'name': "/m/012xff", + 'id': 90, + 'display_name': "toothbrush", + 'plural_name': "toothbrushes", +}, diff --git a/object_detection/utils/nlp.py b/object_detection/utils/nlp.py new file mode 100644 index 0000000..576fccc --- /dev/null +++ b/object_detection/utils/nlp.py @@ -0,0 +1,163 @@ +""" Natural Language Processing (Generation) utilities """ +import collections +import os + + +PLURALS = { + 'apple': 'apples', + 'backpack': 'backpacks', + 'ball': 'balls', + 'banana': 'bananas', + 'baseball bat': 'baseball bats', + 'baseball glove': 'baseball gloves', + 'bear': 'bears', + 'bed': 'beds', + 'bench': 'benches', + 'bicycle': 'bicycles', + 'bird': 'birds', + 'boat': 'boats', + 'book': 'books', + 'bottle': 'bottles', + 'bowl': 'bowls', + 'broccoli': 'broccoli bunches', + 'bus': 'busses', + 'cake': 'cakes', + 'car': 'cars', + 'carrot': 'carrots', + 'cat': 'cats', + 'chair': 'chairs', + 'clock': 'clocks', + 'couch': 'couches', + 'cow': 'cows', + 'cup': 'cups', + 'dining table': 'dining tables', + 'dog': 'dogs', + 'donut': 'donuts', + 'elephant': 'elephants', + 'fire hydrant': 'fire hydrants', + 'fork': 'forks', + 'frisbee': 'frisbees', + 'giraffe': 'giraffes', + 'hair drier': 'hair driers', + 'handbag': 'handbags', + 'horse': 'horses', + 'hot dog': 'hot dogs', + 'keyboard': 'keyboards', + 'kite': 'kites', + 'knife': 'knives', + 'laptop': 'laptops', + 'microwave': 'microwave ovens', + 'mobile phone': 'mobile phones', + 'monitor': 'monitors', + 'motorcycle': 'motorcycles', + 'mouse': 'mice', + 'orange': 'oranges', + 'oven': 'ovens', + 'parking meter': 'parking meters', + 'person': 'people', + 'pizza': 'pizzas', + 'plane': 'planes', + 'potted plant': 'potted plants', + 'refrigerator': 'refrigerators', + 'remote': 'remotes', + 'sandwich': 'sandwiches', + 'scissors': 'pairs of scissors', + 'sheep': 'sheep', + 'sink': 'sinks', + 'skateboard': 'skateboards', + 'skis': 'pairs of skis', + 'snowboard': 'snowboards', + 'spoon': 'spoons', + 'stop sign': 'stop signs', + 'suitcase': 'suitcases', + 'surfboard': 'surfboards', + 'teddy bear': 'teddy bears', + 'tennis racket': 'tennis rackets', + 'tie': 'ties', + 'toaster': 'toasters', + 'toilet': 'toilets', + 'toothbrush': 'toothbrushes', + 'traffic light': 'traffic lights', + 'train': 'trains', + 'truck': 'trucks', + 'umbrella': 'umbrellas', + 'vase': 'vases', + 'wine glass': 'wine glasses', + 'zebra': 'zebras'} + + +def pluralize(s): + """ Convert word to its plural form. + + >>> pluralize('cat') + cats + >>> pluralize('doggy') + doggies + + Better: + + >> from pattern.en import pluralize, singularize + + Or, even better, just create pluralized versions of all the class names by hand! + """ + word = str.lower(s) + + # `.get()` rather than `word in PLURALS` so that we only look up the word once + pluralized_word = PLURALS.get(word, None) + if pluralized_word is not None: + return pluralized_word + + # case = str.lower(s[-1]) == s[-1] + if word.endswith('y'): + if word.endswith('ey'): + return word + 's' + else: + return word[:-1] + 'ies' + elif word[-1] in 'sx' or word[-2:] in ['sh', 'ch']: + return word + 'es' + elif word.endswith('an') and len(word) > 3: + return word[:-2] + 'en' + else: + return word + 's' + + +def update_state(state, boxes, classes, scores, category_index, window=10, max_boxes_to_draw=None, min_score_thresh=.4): + """ Revise state based on latest frame of information (object boxes) """ + num_boxes = min([boxes.shape[0] if max_boxes_to_draw is None else max_boxes_to_draw, boxes.shape[0], len(classes)]) + state = [] # if state is None else state + for i in range(num_boxes): + if scores is None or scores[i] > min_score_thresh: + # box = tuple(boxes[i].tolist()) + class_name = category_index.get(classes[i], 'object')['name'] + display_str = '{}: {} {}%'.format(classes[i], class_name, int(100 * scores[i])) + print(display_str) + state += [class_name] + state = list(collections.Counter(state).items()) + return state + + +def describe_state(state): + """ Convert a state vector dictionary of objects and their counts into a natural language string + + >>> describe_state({'skis': 2}) + '2 pairs of skis' + """ + description = ['{} {}'.format(i, pluralize(s) if i > 1 else s) for (s, i) in state] + description = ' and '.join(description) + return description + + +def say(s, rate=230): + """ Convert text to speech (TTS) and play resulting audio to speakers + + If "say" command is not available in os.system then print the text to stdout. + + >>> say(hello) + 'hello' + """ + try: + os.system('say --rate={rate} "{s}"'.format(**dict(rate=rate, s=s))) + return s + except: + print(s) + return False diff --git a/object_detection/utils/visualization_utils.py b/object_detection/utils/visualization_utils.py index 43ccad5..878d29c 100644 --- a/object_detection/utils/visualization_utils.py +++ b/object_detection/utils/visualization_utils.py @@ -232,15 +232,15 @@ def draw_bounding_boxes_on_image(image, """ boxes_shape = boxes.shape if not boxes_shape: - return + return if len(boxes_shape) != 2 or boxes_shape[1] != 4: - raise ValueError('Input must be of size [N, 4]') + raise ValueError('Input must be of size [N, 4]') for i in range(boxes_shape[0]): - display_str_list = () - if display_str_list_list: - display_str_list = display_str_list_list[i] - draw_bounding_box_on_image(image, boxes[i, 0], boxes[i, 1], boxes[i, 2], - boxes[i, 3], color, thickness, display_str_list) + display_str_list = () + if display_str_list_list: + display_str_list = display_str_list_list[i] + draw_bounding_box_on_image(image, boxes[i, 0], boxes[i, 1], boxes[i, 2], + boxes[i, 3], color, thickness, display_str_list) def draw_keypoints_on_image_array(image, @@ -292,9 +292,6 @@ def draw_keypoints_on_image(image, outline=color, fill=color) -state = np.zeros(len(STANDARD_COLORS)) - - def draw_mask_on_image_array(image, mask, color='red', alpha=0.7): """Draws mask on an image. @@ -325,34 +322,6 @@ def draw_mask_on_image_array(image, mask, color='red', alpha=0.7): np.copyto(image, np.array(pil_image.convert('RGB'))) -def pluralize(s): - """ Convert word to its plural form. - - >>> pluralize('cat') - cats - >>> pluralize('doggy') - doggies - - Better: - - >> from pattern.en import pluralize, singularize - - Or, even better, just create pluralized versions of all the class names by hand! - """ - word = str.lower(s) - # case = str.lower(s[-1]) == s[-1] - if word.endswith('y'): - if word.endswith('ey'): - return word + 's' - else: - return word[:-1] + 'ies' - elif word[-1] in 'sx' or word[-2:] in ['sh', 'ch']: - return word + 'es' - elif word.endswith('an') and len(word) > 3: - return word[:-2] + 'en' - else: - return word + 's' - def visualize_boxes_and_labels_on_image_array(image, boxes, @@ -363,7 +332,7 @@ def visualize_boxes_and_labels_on_image_array(image, keypoints=None, use_normalized_coordinates=False, max_boxes_to_draw=20, - min_score_thresh=.5, + min_score_thresh=.4, agnostic_mode=False, line_thickness=4): """Overlay labeled boxes on an image with formatted scores and label names. @@ -404,7 +373,6 @@ def visualize_boxes_and_labels_on_image_array(image, box_to_keypoints_map = collections.defaultdict(list) if not max_boxes_to_draw: max_boxes_to_draw = boxes.shape[0] - description = [] for i in range(min(max_boxes_to_draw, boxes.shape[0])): if scores is None or scores[i] > min_score_thresh: box = tuple(boxes[i].tolist()) @@ -415,17 +383,11 @@ def visualize_boxes_and_labels_on_image_array(image, if scores is None: box_to_color_map[box] = 'black' else: - if not agnostic_mode: - # if classes[i] in category_index.keys(): - # class_name = category_index[classes[i]]['name'] - # else: - # class_name = 'N/A' - class_name = category_index.get(classes[i], 'object')['name'] - display_str = '{}: {} {}%'.format(i, class_name, int(100*scores[i])) - description += [class_name] - # print(display_str) - else: + if agnostic_mode: display_str = 'score: {}%'.format(int(100 * scores[i])) + else: + class_name = category_index.get(classes[i], 'object')['name'] + display_str = '{}: {} {}%'.format(i, class_name, int(100 * scores[i])) box_to_display_str_map[box].append(display_str) if agnostic_mode: box_to_color_map[box] = 'DarkOrange' @@ -459,9 +421,4 @@ def visualize_boxes_and_labels_on_image_array(image, color=color, radius=line_thickness / 2, use_normalized_coordinates=use_normalized_coordinates) - description = collections.Counter(description).items() - description = ['{} {}'.format(i, pluralize(s) if i > 1 else s) for (s, i) in description] - try: - os.system('say --rate=450 "{}"'.format(' and '.join(description))) - except: - print(description) + return diff --git a/object_detection_app.py b/object_detection_app.py index ab73001..bf22546 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -10,6 +10,7 @@ from multiprocessing import Queue, Pool from object_detection.utils import label_map_util from object_detection.utils import visualization_utils as vis_util +from object_detection.utils.nlp import update_state, describe_state, say CWD_PATH = os.getcwd() @@ -24,8 +25,9 @@ # Loading label map label_map = label_map_util.load_labelmap(PATH_TO_LABELS) -categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, - use_display_name=True) +print(label_map) + +categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True) category_index = label_map_util.create_category_index(categories) @@ -57,9 +59,19 @@ def detect_objects(image_np, sess, detection_graph): category_index, use_normalized_coordinates=True, line_thickness=8) + + # Describe the image + detect_objects.state = update_state(state=None, boxes=np.squeeze(boxes), + classes=np.squeeze(classes).astype(np.int32), + scores=np.squeeze(scores), category_index=category_index) + description = describe_state(detect_objects.state) + say(description) return image_np +detect_objects.state = [] # poor man's class/object + + def worker(input_q, output_q): # Load a (frozen) Tensorflow model into memory. detection_graph = tf.Graph() From e1f6be8cc01e70e8e54822039c3732577f9704c2 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Tue, 21 Nov 2017 10:49:04 -0800 Subject: [PATCH 003/174] pandas in environment.yml --- environment.yml | 1 + object_detection/utils/nlp.py | 38 ++++++++++++++++++++++++++++++----- object_detection_app.py | 13 ++++++------ 3 files changed, 41 insertions(+), 11 deletions(-) diff --git a/environment.yml b/environment.yml index 7cbd262..1491067 100644 --- a/environment.yml +++ b/environment.yml @@ -44,5 +44,6 @@ dependencies: - protobuf==3.3.0 - tensorflow==1.2.0 - werkzeug==0.12.2 + - pandas==0.21.0 prefix: /Users/datitran/anaconda/envs/object-detection diff --git a/object_detection/utils/nlp.py b/object_detection/utils/nlp.py index 576fccc..e56617a 100644 --- a/object_detection/utils/nlp.py +++ b/object_detection/utils/nlp.py @@ -2,6 +2,8 @@ import collections import os +import pandas as pd + PLURALS = { 'apple': 'apples', @@ -121,21 +123,47 @@ def pluralize(s): return word + 's' -def update_state(state, boxes, classes, scores, category_index, window=10, max_boxes_to_draw=None, min_score_thresh=.4): - """ Revise state based on latest frame of information (object boxes) """ +def update_state(boxes, classes, scores, category_index, window=10, max_boxes_to_draw=None, min_score_thresh=.4): + """ Revise state based on latest frame of information (object boxes) + + Args: + boxes (list): 2D numpy array of shape (N, 4): (ymin, xmin, ymax, xmax), in normalized format between [0, 1]. + classes, + Args (that should be class attributes): + category_index (dict of dicts): {1: {'id': 1, 'name': 'person'}, 2: {'id': 2, 'name': 'bicycle'},...} + + """ num_boxes = min([boxes.shape[0] if max_boxes_to_draw is None else max_boxes_to_draw, boxes.shape[0], len(classes)]) + + if update_state.states is None: + # Initialize a matrix of state vectors for the past 20 frames + update_state.i = 0 + update_state.window = 20 + update_state.columns = pd.DataFrame(list(category_index.values())).set_index('id', drop=True)['name'] + update_state.states = pd.DataFrame(pd.np.zeros((20, len(category_index)), dtype=int), columns=update_state.columns) + update_state.state0 = pd.Series(index=update_state.columns) + state = [] # if state is None else state for i in range(num_boxes): if scores is None or scores[i] > min_score_thresh: # box = tuple(boxes[i].tolist()) - class_name = category_index.get(classes[i], 'object')['name'] + class_name = category_index.get(classes[i], {'name': 'unknown object'})['name'] display_str = '{}: {} {}%'.format(classes[i], class_name, int(100 * scores[i])) print(display_str) state += [class_name] - state = list(collections.Counter(state).items()) + state = collections.Counter(state) + update_state.states.iloc[i % len(update_state.states), :] = pd.Series(state) + state = sorted(list(state.items())) + i = (i + 1) % len(update_state.states) # update_state.window return state +update_state.states = None + +# for i in range(update_state.window): +# update_state.states.append([]) + + def describe_state(state): """ Convert a state vector dictionary of objects and their counts into a natural language string @@ -143,7 +171,7 @@ def describe_state(state): '2 pairs of skis' """ description = ['{} {}'.format(i, pluralize(s) if i > 1 else s) for (s, i) in state] - description = ' and '.join(description) + description = ', '.join(description[:-2]) + ',' + ' and '.join(description[-2:]) return description diff --git a/object_detection_app.py b/object_detection_app.py index bf22546..f84f9a7 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -31,7 +31,7 @@ category_index = label_map_util.create_category_index(categories) -def detect_objects(image_np, sess, detection_graph): +def detect_objects(image_np, sess, detection_graph, utterance_frames=20): # Expand dimensions since the model expects images to have shape: [1, None, None, 3] image_np_expanded = np.expand_dims(image_np, axis=0) image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') @@ -61,11 +61,12 @@ def detect_objects(image_np, sess, detection_graph): line_thickness=8) # Describe the image - detect_objects.state = update_state(state=None, boxes=np.squeeze(boxes), - classes=np.squeeze(classes).astype(np.int32), - scores=np.squeeze(scores), category_index=category_index) - description = describe_state(detect_objects.state) - say(description) + state = update_state(boxes=np.squeeze(boxes), + classes=np.squeeze(classes).astype(np.int32), + scores=np.squeeze(scores), category_index=category_index) + if not update_state.i % utterance_frames: + description = describe_state(state) + say(description) return image_np From d525828e7d6804072185c465bbffc8c54e50676d Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Tue, 21 Nov 2017 10:55:39 -0800 Subject: [PATCH 004/174] NUM_CLASSES 90 despite 80 COCO limit? --- object_detection_app.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/object_detection_app.py b/object_detection_app.py index f84f9a7..464f2b9 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -21,12 +21,12 @@ # List of the strings that is used to add correct label for each box. PATH_TO_LABELS = os.path.join(CWD_PATH, 'object_detection', 'data', 'mscoco_label_map.pbtxt') -NUM_CLASSES = 90 - # Loading label map label_map = label_map_util.load_labelmap(PATH_TO_LABELS) print(label_map) +NUM_CLASSES = 80 + categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True) category_index = label_map_util.create_category_index(categories) From 6b06d70882a40b4fb8952953bd544da9c3ebbf78 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Tue, 21 Nov 2017 11:02:41 -0800 Subject: [PATCH 005/174] num classes --- object_detection_app.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/object_detection_app.py b/object_detection_app.py index 464f2b9..ef00573 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -25,9 +25,8 @@ label_map = label_map_util.load_labelmap(PATH_TO_LABELS) print(label_map) -NUM_CLASSES = 80 - -categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True) +# though mobilenet can handle +categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=90, use_display_name=True) category_index = label_map_util.create_category_index(categories) From 63dc5be6a317d5d37438e9baeb07371b40c590e8 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Tue, 28 Nov 2017 04:36:04 +0100 Subject: [PATCH 006/174] adjust threshold back up to .5 (two places) --- object_detection/utils/nlp.py | 2 +- object_detection/utils/visualization_utils.py | 23 +++++++++---------- 2 files changed, 12 insertions(+), 13 deletions(-) diff --git a/object_detection/utils/nlp.py b/object_detection/utils/nlp.py index e56617a..fdc1a6e 100644 --- a/object_detection/utils/nlp.py +++ b/object_detection/utils/nlp.py @@ -123,7 +123,7 @@ def pluralize(s): return word + 's' -def update_state(boxes, classes, scores, category_index, window=10, max_boxes_to_draw=None, min_score_thresh=.4): +def update_state(boxes, classes, scores, category_index, window=10, max_boxes_to_draw=None, min_score_thresh=.5): """ Revise state based on latest frame of information (object boxes) Args: diff --git a/object_detection/utils/visualization_utils.py b/object_detection/utils/visualization_utils.py index 878d29c..f426de8 100644 --- a/object_detection/utils/visualization_utils.py +++ b/object_detection/utils/visualization_utils.py @@ -232,15 +232,15 @@ def draw_bounding_boxes_on_image(image, """ boxes_shape = boxes.shape if not boxes_shape: - return + return if len(boxes_shape) != 2 or boxes_shape[1] != 4: - raise ValueError('Input must be of size [N, 4]') + raise ValueError('Input must be of size [N, 4]') for i in range(boxes_shape[0]): - display_str_list = () - if display_str_list_list: - display_str_list = display_str_list_list[i] - draw_bounding_box_on_image(image, boxes[i, 0], boxes[i, 1], boxes[i, 2], - boxes[i, 3], color, thickness, display_str_list) + display_str_list = () + if display_str_list_list: + display_str_list = display_str_list_list[i] + draw_bounding_box_on_image(image, boxes[i, 0], boxes[i, 1], boxes[i, 2], + boxes[i, 3], color, thickness, display_str_list) def draw_keypoints_on_image_array(image, @@ -317,12 +317,11 @@ def draw_mask_on_image_array(image, mask, color='red', alpha=0.7): solid_color = np.expand_dims( np.ones_like(mask), axis=2) * np.reshape(list(rgb), [1, 1, 3]) pil_solid_color = Image.fromarray(np.uint8(solid_color)).convert('RGBA') - pil_mask = Image.fromarray(np.uint8(255.0*alpha*mask)).convert('L') + pil_mask = Image.fromarray(np.uint8(255.0 * alpha * mask)).convert('L') pil_image = Image.composite(pil_solid_color, pil_image, pil_mask) np.copyto(image, np.array(pil_image.convert('RGB'))) - def visualize_boxes_and_labels_on_image_array(image, boxes, classes, @@ -331,8 +330,8 @@ def visualize_boxes_and_labels_on_image_array(image, instance_masks=None, keypoints=None, use_normalized_coordinates=False, - max_boxes_to_draw=20, - min_score_thresh=.4, + max_boxes_to_draw=50, + min_score_thresh=.5, agnostic_mode=False, line_thickness=4): """Overlay labeled boxes on an image with formatted scores and label names. @@ -421,4 +420,4 @@ def visualize_boxes_and_labels_on_image_array(image, color=color, radius=line_thickness / 2, use_normalized_coordinates=use_normalized_coordinates) - return + # return From 4a138ca8067bfad279a1c14d127827e49c1ea79a Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Tue, 28 Nov 2017 04:46:27 +0100 Subject: [PATCH 007/174] object_detection/utils/visualization_utils.py is read only now and return statement is nested --- object_detection/utils/visualization_utils.py | 23 ++++++++++--------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/object_detection/utils/visualization_utils.py b/object_detection/utils/visualization_utils.py index f426de8..878d29c 100644 --- a/object_detection/utils/visualization_utils.py +++ b/object_detection/utils/visualization_utils.py @@ -232,15 +232,15 @@ def draw_bounding_boxes_on_image(image, """ boxes_shape = boxes.shape if not boxes_shape: - return + return if len(boxes_shape) != 2 or boxes_shape[1] != 4: - raise ValueError('Input must be of size [N, 4]') + raise ValueError('Input must be of size [N, 4]') for i in range(boxes_shape[0]): - display_str_list = () - if display_str_list_list: - display_str_list = display_str_list_list[i] - draw_bounding_box_on_image(image, boxes[i, 0], boxes[i, 1], boxes[i, 2], - boxes[i, 3], color, thickness, display_str_list) + display_str_list = () + if display_str_list_list: + display_str_list = display_str_list_list[i] + draw_bounding_box_on_image(image, boxes[i, 0], boxes[i, 1], boxes[i, 2], + boxes[i, 3], color, thickness, display_str_list) def draw_keypoints_on_image_array(image, @@ -317,11 +317,12 @@ def draw_mask_on_image_array(image, mask, color='red', alpha=0.7): solid_color = np.expand_dims( np.ones_like(mask), axis=2) * np.reshape(list(rgb), [1, 1, 3]) pil_solid_color = Image.fromarray(np.uint8(solid_color)).convert('RGBA') - pil_mask = Image.fromarray(np.uint8(255.0 * alpha * mask)).convert('L') + pil_mask = Image.fromarray(np.uint8(255.0*alpha*mask)).convert('L') pil_image = Image.composite(pil_solid_color, pil_image, pil_mask) np.copyto(image, np.array(pil_image.convert('RGB'))) + def visualize_boxes_and_labels_on_image_array(image, boxes, classes, @@ -330,8 +331,8 @@ def visualize_boxes_and_labels_on_image_array(image, instance_masks=None, keypoints=None, use_normalized_coordinates=False, - max_boxes_to_draw=50, - min_score_thresh=.5, + max_boxes_to_draw=20, + min_score_thresh=.4, agnostic_mode=False, line_thickness=4): """Overlay labeled boxes on an image with formatted scores and label names. @@ -420,4 +421,4 @@ def visualize_boxes_and_labels_on_image_array(image, color=color, radius=line_thickness / 2, use_normalized_coordinates=use_normalized_coordinates) - # return + return From 6b0aaf47ba7dc07afd064ab957880570da143e1f Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Tue, 5 Dec 2017 09:51:51 -0800 Subject: [PATCH 008/174] new radar.py classes to hold radar data, WIP --- object_detection/utils/radar.py | 21 +++++++++++++++++++ object_detection/utils/visualization_utils.py | 21 +++++++++---------- 2 files changed, 31 insertions(+), 11 deletions(-) create mode 100644 object_detection/utils/radar.py diff --git a/object_detection/utils/radar.py b/object_detection/utils/radar.py new file mode 100644 index 0000000..dcea585 --- /dev/null +++ b/object_detection/utils/radar.py @@ -0,0 +1,21 @@ +""" State vector registration (consolidation/filtering over time in an intertial frame) and buffering """ + + +class SensorBuffer: + """ Container for list of dicts containing sensor samples for past W samples (W = window width) """ + + def __init__(self, samples=10): + if isinstance(samples, int): + samples = [] + for i in range(samples): + samples += [{}] + self.samples = list(samples) + self.now = 0 + + +class Radar: + """ Intertial 3D position of all objects detected over the course of a session """ + + def __init__(self, category_names=10, category_names=10): + if isinstance(states, int): + sensor_frames = pd.DataFrame(pd.np.zeros((20, len(category_index)), dtype=int), columns=update_state.columns) diff --git a/object_detection/utils/visualization_utils.py b/object_detection/utils/visualization_utils.py index 878d29c..f068c65 100644 --- a/object_detection/utils/visualization_utils.py +++ b/object_detection/utils/visualization_utils.py @@ -232,15 +232,15 @@ def draw_bounding_boxes_on_image(image, """ boxes_shape = boxes.shape if not boxes_shape: - return + return if len(boxes_shape) != 2 or boxes_shape[1] != 4: - raise ValueError('Input must be of size [N, 4]') + raise ValueError('Input must be of size [N, 4]') for i in range(boxes_shape[0]): - display_str_list = () - if display_str_list_list: - display_str_list = display_str_list_list[i] - draw_bounding_box_on_image(image, boxes[i, 0], boxes[i, 1], boxes[i, 2], - boxes[i, 3], color, thickness, display_str_list) + display_str_list = () + if display_str_list_list: + display_str_list = display_str_list_list[i] + draw_bounding_box_on_image(image, boxes[i, 0], boxes[i, 1], boxes[i, 2], + boxes[i, 3], color, thickness, display_str_list) def draw_keypoints_on_image_array(image, @@ -317,12 +317,11 @@ def draw_mask_on_image_array(image, mask, color='red', alpha=0.7): solid_color = np.expand_dims( np.ones_like(mask), axis=2) * np.reshape(list(rgb), [1, 1, 3]) pil_solid_color = Image.fromarray(np.uint8(solid_color)).convert('RGBA') - pil_mask = Image.fromarray(np.uint8(255.0*alpha*mask)).convert('L') + pil_mask = Image.fromarray(np.uint8(255.0 * alpha * mask)).convert('L') pil_image = Image.composite(pil_solid_color, pil_image, pil_mask) np.copyto(image, np.array(pil_image.convert('RGB'))) - def visualize_boxes_and_labels_on_image_array(image, boxes, classes, @@ -332,7 +331,7 @@ def visualize_boxes_and_labels_on_image_array(image, keypoints=None, use_normalized_coordinates=False, max_boxes_to_draw=20, - min_score_thresh=.4, + min_score_thresh=.5, agnostic_mode=False, line_thickness=4): """Overlay labeled boxes on an image with formatted scores and label names. @@ -421,4 +420,4 @@ def visualize_boxes_and_labels_on_image_array(image, color=color, radius=line_thickness / 2, use_normalized_coordinates=use_normalized_coordinates) - return + return From 16aeb536a2a739529e89d5ed656074746ab8a205 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 5 Dec 2017 12:35:59 -0800 Subject: [PATCH 009/174] Got rtsp streaming working --- environment.yml | 70 ++++++++++++++++++++++++++++------------- object_detection_app.py | 9 +++++- 2 files changed, 57 insertions(+), 22 deletions(-) diff --git a/environment.yml b/environment.yml index 1491067..6763334 100644 --- a/environment.yml +++ b/environment.yml @@ -1,24 +1,45 @@ -name: ai -channels: !!python/tuple -- menpo +name: object-detection +channels: +- jlaura +- conda-forge - defaults dependencies: +- backports=1.0=py35_1 +- backports.functools_lru_cache=1.4=py35_1 +- blas=1.1=openblas +- bzip2=1.0.6=1 +- ca-certificates=2017.11.5=0 +- cairo=1.14.6=5 +- certifi=2017.11.5=py35_0 - cycler=0.10.0=py35_0 -- freetype=2.5.5=2 -- icu=54.1=0 +- ffmpeg=3.2.4=3 +- fontconfig=2.12.1=6 +- freetype=2.7=2 +- gettext=0.19.8.1=0 +- giflib=5.1.4=0 +- glib=2.51.4=0 +- harfbuzz=1.3.4=2 +- hdf5=1.10.1=1 +- icu=58.2=0 +- jasper=1.900.1=4 - jbig=2.1=0 -- jlaura::opencv3=3.0.0=py35_0 -- jpeg=9b=0 -- libpng=1.6.27=0 -- libtiff=4.0.6=3 -- matplotlib=2.0.2=np113py35_0 -- menpo::tbb=4.3_20141023=0 -- mkl=2017.0.1=0 -- numpy=1.13.0=py35_0 +- jpeg=9b=2 +- libffi=3.2.1=3 +- libgfortran=3.0.0=0 +- libiconv=1.15=0 +- libpng=1.6.28=2 +- libwebp=0.5.2=7 +- libxml2=2.9.5=2 +- matplotlib=2.1.0=py35_1 +- numpy=1.13.3=py35_blas_openblas_201 - olefile=0.44=py35_0 +- openblas=0.2.20=6 +- opencv=3.3.0=py35_blas_openblas_202 - openssl=1.0.2l=0 -- pillow=4.1.1=py35_0 -- pip=9.0.1=py35_1 +- pandas=0.21.0=py35_0 +- pcre=8.39=0 +- pillow=4.3.0=py35_1 +- pixman=0.34.0=1 - py=1.4.34=py35_0 - pyparsing=2.2.0=py35_0 - pyqt=5.6.0=py35_2 @@ -26,17 +47,25 @@ dependencies: - python=3.5.3=1 - python-dateutil=2.6.1=py35_0 - pytz=2017.2=py35_0 -- qt=5.6.2=2 -- readline=6.2=2 +- qt=5.6.2=h9e3eb04_4 - setuptools=27.2.0=py35_0 - sip=4.18=py35_0 - six=1.10.0=py35_0 - sqlite=3.13.0=0 -- tk=8.5.18=0 +- tbb=2018_20170919=0 +- tornado=4.5.2=py35_0 - wheel=0.29.0=py35_0 +- x264=20131217=3 +- zlib=1.2.11=0 +- libtiff=4.0.6=3 +- mkl=2017.0.1=0 +- pip=9.0.1=py35_1 +- readline=6.2=2 +- tk=8.5.18=0 - xz=5.2.2=1 -- zlib=1.2.8=3 +- opencv3=3.0.0=py35_0 - pip: + - backports.functools-lru-cache==1.4 - backports.weakref==1.0rc1 - bleach==1.5.0 - html5lib==0.9999999 @@ -44,6 +73,5 @@ dependencies: - protobuf==3.3.0 - tensorflow==1.2.0 - werkzeug==0.12.2 - - pandas==0.21.0 -prefix: /Users/datitran/anaconda/envs/object-detection +prefix: /usr/local/anaconda3/envs/object-detection diff --git a/object_detection_app.py b/object_detection_app.py index ef00573..da76c9e 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -99,6 +99,8 @@ def worker(input_q, output_q): parser = argparse.ArgumentParser() parser.add_argument('-src', '--source', dest='video_source', type=int, default=0, help='Device index of the camera.') + parser.add_argument('-u', '--url', dest='video_stream_source', type=str, + help='Url for rtsp stream. Don\'t use with `-src` argument') parser.add_argument('-wd', '--width', dest='width', type=int, default=480, help='Width of the frames in the video stream.') parser.add_argument('-ht', '--height', dest='height', type=int, @@ -116,7 +118,12 @@ def worker(input_q, output_q): output_q = Queue(maxsize=args.queue_size) pool = Pool(args.num_workers, worker, (input_q, output_q)) - video_capture = WebcamVideoStream(src=args.video_source, + source = args.video_stream_source + + if source is None: + source = args.video_source + + video_capture = WebcamVideoStream(src=source, width=args.width, height=args.height).start() fps = FPS().start() From 6118cdcbb754c51035aa1e6bf9c3c763838601ba Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 5 Dec 2017 14:34:05 -0800 Subject: [PATCH 010/174] (Documentation) - Added environment update section + command. --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 8ea493c..c7c8892 100644 --- a/README.md +++ b/README.md @@ -12,6 +12,9 @@ A real-time object recognition application using [Google's TensorFlow Object Det * Number of workers `--num-workers=2` * Size of the queue `--queue-size=5` +## Updating the environment +`conda env update -f environment.yml` + ## Tests ``` pytest -vs utils/ From 662e0ae458b39273fb45e002206708bce6b1dc0a Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 5 Dec 2017 14:41:33 -0800 Subject: [PATCH 011/174] - Strike through outdated readme notes. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c7c8892..c30cb49 100644 --- a/README.md +++ b/README.md @@ -26,7 +26,7 @@ pytest -vs utils/ - [OpenCV 3.0](http://opencv.org/) ## Notes -- OpenCV 3.1 might crash on OSX after a while, so that's why I had to switch to version 3.0. See open issue and solution [here](https://github.com/opencv/opencv/issues/5874). +- ~~OpenCV 3.1 might crash on OSX after a while, so that's why I had to switch to version 3.0. See open issue and solution [here](https://github.com/opencv/opencv/issues/5874).~~ - Moving the `.read()` part of the video stream in a multiple child processes did not work. However, it was possible to move it to a separate thread. ## Copyright From aa6bfd710d34c52e61f924ceee7029bbcdbdbc63 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Tue, 5 Dec 2017 17:26:46 -0800 Subject: [PATCH 012/174] standup notes --- docs/standups.md | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) create mode 100644 docs/standups.md diff --git a/docs/standups.md b/docs/standups.md new file mode 100644 index 0000000..6fc632c --- /dev/null +++ b/docs/standups.md @@ -0,0 +1,24 @@ +# Standups + +## Standup + +### Hobson + +* Researched Watson for NLP and Grow for analytics. Researched time series forecasting and recruited a DS contractor. +* max count filter on objects seen, research knowledge graphs, data structure for storing "radar" +* no blockers + +### Alex + +* got streaming to openCV working by install ffmpeg +* working on docker container. considering installing anaconcda despite the double-containerism (reducndant namespacing) + +### Ashwin + +libstreaming library crashing on android emulator (android studio) faster pro emulator, and also fails on phone + +### Parking lot + +* knowledge graphs are NoSQL connections between facts. Any nosql database can store them. I'm using a dict of dicts for now. +* Alex decided to pursue the minimum viable docker container +* will work with Sujeeth and Bala to deploy the container to AWS \ No newline at end of file From cfb3b89450c6a21bfb81ddb5ea2c6fdf9592cb96 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Wed, 6 Dec 2017 10:06:06 -0800 Subject: [PATCH 013/174] updated standup --- docs/standups.md | 29 +++++++++++++++++++++++++++-- 1 file changed, 27 insertions(+), 2 deletions(-) diff --git a/docs/standups.md b/docs/standups.md index 6fc632c..a4cf34c 100644 --- a/docs/standups.md +++ b/docs/standups.md @@ -1,6 +1,6 @@ # Standups -## Standup +## Standup 1 ### Hobson @@ -21,4 +21,29 @@ libstreaming library crashing on android emulator (android studio) faster pro em * knowledge graphs are NoSQL connections between facts. Any nosql database can store them. I'm using a dict of dicts for now. * Alex decided to pursue the minimum viable docker container -* will work with Sujeeth and Bala to deploy the container to AWS \ No newline at end of file +* will work with Sujeeth and Bala to deploy the container to AWS + + +## Standup 2 + +### Alex + +* Wowza working, pulling from the right Anaconda package. Pushed to master a version that takes a url working +* Jenkins build + +### Ashwin + +* stream is working on android except camera permissions, has dialog box from another app make that work +* finish camera permissions dialog box + +## Sujeeth + +* help Ashwin integrate wowza streamer into the OCR app + +### parking lot + +* permissions problem only happens with first install of the app, not with each new release, so that's good +* RTSP server: link had `rtsp://prod*pemdosa*{servernum}:{port}/live/stream` example urls only have server num and port, +* alex forgot to send link to gennymotion -- rapid android emulation +* talk about CI, sh scripts that call python scripts +* CICD apps released through bitrise From b7240be9e4b66d86abd4a7d896d349b60eaaec8c Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 6 Dec 2017 12:04:30 -0800 Subject: [PATCH 014/174] (Jenkinsfile) Added dummy Jenkinsfile for CI (and later CD) --- Jenkinsfile | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 Jenkinsfile diff --git a/Jenkinsfile b/Jenkinsfile new file mode 100644 index 0000000..50933bc --- /dev/null +++ b/Jenkinsfile @@ -0,0 +1,14 @@ +#!/usr/bin/env groovy + +pipeline { + agent any + + stages { + stage('Hello') { + steps { + echo 'Hello World!' + } + } + + } +} \ No newline at end of file From 486fa93f756c221e9f77bf88529ffb10650f132e Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 6 Dec 2017 14:49:22 -0800 Subject: [PATCH 015/174] - Minimized the environment file (maybe it could be smaller) - Creading Jenkinsfile (unfinished) for Pipeline integration and deployment --- Jenkinsfile | 57 ++++++++++++++++++++++++++++++++++++++++++++++--- environment.yml | 52 ++------------------------------------------ 2 files changed, 56 insertions(+), 53 deletions(-) diff --git a/Jenkinsfile b/Jenkinsfile index 50933bc..13610a7 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -1,14 +1,65 @@ #!/usr/bin/env groovy pipeline { - agent any + agent { + docker { + image 'continuumio/anaconda' + } + } stages { - stage('Hello') { + stage('Build') { + steps { + checkout scm + echo 'Placeholder: call build script' + } + } + stage('Deploy - Staging'){ + when { + expression { + currentBuild.result == null || currentBuild.result == 'SUCCESS' + } + } steps { - echo 'Hello World!' + echo 'Placeholder: Deploying to staging/dev' } } + stage('Sanity check') { + steps { + input "Does the staging environment look ok?" + } + } + + stage('Deploy - Production') { + steps { + echo 'Placeholder: Deploying to prod' + } + } + } + + post { + always { + echo 'The job has finished.' + deleteDir() /* clean up our workspace */ + } + success { + slackSend channel: '#ai-nsf-jenkins-jobs', + color: 'good', + message: "The pipeline ${currentBuild.fillDisplayName} completed successfully." + } + unstable { + slackSend channel: '#ai-nsf-jenkins-jobs', + color: 'warning', + message: "The pipeline ${currentBuild.fillDisplayName} is unstable. Check it out here: ${env.BUILD_URL}" + } + failure { + slackSend channel: '#ai-nsf-jenkins-jobs', + color: 'danger', + message: "@all The pipeline ${currentBuild.fillDisplayName} has failed! Check it out here: ${env.BUILD_URL}" + } + changed { + + } } } \ No newline at end of file diff --git a/environment.yml b/environment.yml index 6763334..5ea42bf 100644 --- a/environment.yml +++ b/environment.yml @@ -1,69 +1,21 @@ -name: object-detection +name: object-detection-test2 channels: - jlaura - conda-forge - defaults dependencies: -- backports=1.0=py35_1 -- backports.functools_lru_cache=1.4=py35_1 -- blas=1.1=openblas -- bzip2=1.0.6=1 -- ca-certificates=2017.11.5=0 -- cairo=1.14.6=5 -- certifi=2017.11.5=py35_0 -- cycler=0.10.0=py35_0 - ffmpeg=3.2.4=3 -- fontconfig=2.12.1=6 -- freetype=2.7=2 -- gettext=0.19.8.1=0 -- giflib=5.1.4=0 -- glib=2.51.4=0 -- harfbuzz=1.3.4=2 -- hdf5=1.10.1=1 -- icu=58.2=0 +- opencv=3.3.0=py35_blas_openblas_202 - jasper=1.900.1=4 -- jbig=2.1=0 - jpeg=9b=2 -- libffi=3.2.1=3 -- libgfortran=3.0.0=0 -- libiconv=1.15=0 -- libpng=1.6.28=2 -- libwebp=0.5.2=7 -- libxml2=2.9.5=2 - matplotlib=2.1.0=py35_1 - numpy=1.13.3=py35_blas_openblas_201 -- olefile=0.44=py35_0 -- openblas=0.2.20=6 -- opencv=3.3.0=py35_blas_openblas_202 -- openssl=1.0.2l=0 - pandas=0.21.0=py35_0 -- pcre=8.39=0 - pillow=4.3.0=py35_1 -- pixman=0.34.0=1 -- py=1.4.34=py35_0 -- pyparsing=2.2.0=py35_0 - pyqt=5.6.0=py35_2 - pytest=3.2.1=py35_0 -- python=3.5.3=1 -- python-dateutil=2.6.1=py35_0 -- pytz=2017.2=py35_0 - qt=5.6.2=h9e3eb04_4 -- setuptools=27.2.0=py35_0 -- sip=4.18=py35_0 -- six=1.10.0=py35_0 -- sqlite=3.13.0=0 -- tbb=2018_20170919=0 -- tornado=4.5.2=py35_0 -- wheel=0.29.0=py35_0 - x264=20131217=3 -- zlib=1.2.11=0 -- libtiff=4.0.6=3 -- mkl=2017.0.1=0 -- pip=9.0.1=py35_1 -- readline=6.2=2 -- tk=8.5.18=0 -- xz=5.2.2=1 -- opencv3=3.0.0=py35_0 - pip: - backports.functools-lru-cache==1.4 - backports.weakref==1.0rc1 From 2ca3e747cbdf8bfcfef268c5703b244f62b83e82 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 6 Dec 2017 15:07:27 -0800 Subject: [PATCH 016/174] - Minimized the environment file (maybe it could be smaller) - Creading Jenkinsfile (unfinished) for Pipeline integration and deployment - Minimized environment file even further. --- environment.yml | 14 +------------- 1 file changed, 1 insertion(+), 13 deletions(-) diff --git a/environment.yml b/environment.yml index 5ea42bf..a84351c 100644 --- a/environment.yml +++ b/environment.yml @@ -1,29 +1,17 @@ -name: object-detection-test2 +name: object-detection channels: -- jlaura - conda-forge - defaults dependencies: -- ffmpeg=3.2.4=3 - opencv=3.3.0=py35_blas_openblas_202 - jasper=1.900.1=4 -- jpeg=9b=2 - matplotlib=2.1.0=py35_1 - numpy=1.13.3=py35_blas_openblas_201 - pandas=0.21.0=py35_0 - pillow=4.3.0=py35_1 - pyqt=5.6.0=py35_2 - pytest=3.2.1=py35_0 -- qt=5.6.2=h9e3eb04_4 -- x264=20131217=3 - pip: - - backports.functools-lru-cache==1.4 - - backports.weakref==1.0rc1 - - bleach==1.5.0 - - html5lib==0.9999999 - - markdown==2.2.0 - - protobuf==3.3.0 - tensorflow==1.2.0 - - werkzeug==0.12.2 prefix: /usr/local/anaconda3/envs/object-detection From 1dd74054a6c47cea45aa504ced748f79dec407dd Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 6 Dec 2017 17:30:31 -0800 Subject: [PATCH 017/174] - Minimized the environment file (maybe it could be smaller) - Creading Jenkinsfile (unfinished) for Pipeline integration and deployment - Minimized environment file even further. - Added Makefile to simplify Jenkins scripts (consider: using fabfile) - Updated Jenkinsfile with build and test stages. --- Jenkinsfile | 15 ++++++- Makefile | 112 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 125 insertions(+), 2 deletions(-) create mode 100644 Makefile diff --git a/Jenkinsfile b/Jenkinsfile index 13610a7..8f205f5 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -11,9 +11,19 @@ pipeline { stage('Build') { steps { checkout scm - echo 'Placeholder: call build script' + echo 'Creation python environment' + sh 'make create_env' + sh 'source activate object-detection' } } + + stage('Test') { + steps { + echo 'Calling make test script' + sh 'make test || true' + } + } + stage('Deploy - Staging'){ when { expression { @@ -41,7 +51,6 @@ pipeline { post { always { echo 'The job has finished.' - deleteDir() /* clean up our workspace */ } success { slackSend channel: '#ai-nsf-jenkins-jobs', @@ -58,8 +67,10 @@ pipeline { color: 'danger', message: "@all The pipeline ${currentBuild.fillDisplayName} has failed! Check it out here: ${env.BUILD_URL}" } + /* changed { } + */ } } \ No newline at end of file diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..666b88e --- /dev/null +++ b/Makefile @@ -0,0 +1,112 @@ +.PHONY: clean lint build + +################################################################################# +# GLOBALS # +################################################################################# + + +PROJECT_DIR := $(shell dirname $(realpath $(lastword $(MAKEFILE_LIST)))) +PROJECT_NAME = object-detection +PYTHON_INTERPRETER = python + +ifeq (,$(shell which conda)) +HAS_CONDA=False +else +HAS_CONDA=True +endif + + +################################################################################# +# COMMANDS # +################################################################################# + +## Delete all compiled Python files +clean: + find . -name "*.pyc" -exec rm {} \; + +## Lint using flake8 +lint: + $(PYTHON_INTERPRETER) -m flake8 --exclude=lib/,bin/,docs/conf.py --ignore F401,H301,E203,E241 . + +## Set up python interpreter environment +create_env: +ifeq (True,$(HAS_CONDA)) + @echo ">>> Detected conda, creating conda environment." + conda env create -f environment.yml + @echo ">>> New conda env created. Activate with:\nsource activate $(PROJECT_NAME)" +else + @echo "Error: Install conda" +endif + +delete_env: + conda env remove -y -n object-detection + + +## Build (Get interpreter running) +## TODO + +## Run project tests +test: + pytest -vs utils/ + $(PYTHON_INTERPRETER) -m unittest discover -s object_detection -p "*_test.py" + +################################################################################# +# Self Documenting Commands # +################################################################################# + +.DEFAULT_GOAL := show-help + +# Inspired by +# sed script explained: +# /^##/: +# * save line in hold space +# * purge line +# * Loop: +# * append newline + line to hold space +# * go to next line +# * if line starts with doc comment, strip comment character off and loop +# * remove target prerequisites +# * append hold space (+ newline) to line +# * replace newline plus comments by `---` +# * print line +# Separate expressions are necessary because labels cannot be delimited by +# semicolon; see +.PHONY: show-help +show-help: + @echo "$$(tput bold)Available rules:$$(tput sgr0)" + @echo + @sed -n -e "/^## / { \ + h; \ + s/.*//; \ + :doc" \ + -e "H; \ + n; \ + s/^## //; \ + t doc" \ + -e "s/:.*//; \ + G; \ + s/\\n## /---/; \ + s/\\n/ /g; \ + p; \ + }" ${MAKEFILE_LIST} \ + | LC_ALL='C' sort --ignore-case \ + | awk -F '---' \ + -v ncol=$$(tput cols) \ + -v indent=19 \ + -v col_on="$$(tput setaf 6)" \ + -v col_off="$$(tput sgr0)" \ + '{ \ + printf "%s%*s%s ", col_on, -indent, $$1, col_off; \ + n = split($$2, words, " "); \ + line_length = ncol - indent; \ + for (i = 1; i <= n; i++) { \ + line_length -= length(words[i]) + 1; \ + if (line_length <= 0) { \ + line_length = ncol - indent - length(words[i]) - 1; \ + printf "\n%*s ", -indent, " "; \ + } \ + printf "%s ", words[i]; \ + } \ + printf "\n"; \ + }' \ +| more $(shell test $(shell uname) = Darwin && echo '--no-init --raw-control-chars') \ No newline at end of file From 07eb83ff73a5a42883b034a44cfc1d2f17cdea1f Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 6 Dec 2017 17:32:16 -0800 Subject: [PATCH 018/174] Setting up Jenkins Pipeline in Blue Ocean. --- Jenkinsfile | 131 ++++++++++++++++++++++++---------------------------- 1 file changed, 61 insertions(+), 70 deletions(-) diff --git a/Jenkinsfile b/Jenkinsfile index 8f205f5..29f6992 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -1,76 +1,67 @@ -#!/usr/bin/env groovy - pipeline { - agent { - docker { - image 'continuumio/anaconda' - } + agent { + docker { + image 'continuumio/anaconda' } - - stages { - stage('Build') { - steps { - checkout scm - echo 'Creation python environment' - sh 'make create_env' - sh 'source activate object-detection' - } - } - - stage('Test') { - steps { - echo 'Calling make test script' - sh 'make test || true' - } - } - - stage('Deploy - Staging'){ - when { - expression { - currentBuild.result == null || currentBuild.result == 'SUCCESS' - } - } - steps { - echo 'Placeholder: Deploying to staging/dev' - } - } - - stage('Sanity check') { - steps { - input "Does the staging environment look ok?" - } - } - - stage('Deploy - Production') { - steps { - echo 'Placeholder: Deploying to prod' - } - } + + } + stages { + stage('Build') { + steps { + checkout scm + echo 'Creation python environment' + sh 'make create_env' + sh 'source activate object-detection' + } } - - post { - always { - echo 'The job has finished.' - } - success { - slackSend channel: '#ai-nsf-jenkins-jobs', - color: 'good', - message: "The pipeline ${currentBuild.fillDisplayName} completed successfully." - } - unstable { - slackSend channel: '#ai-nsf-jenkins-jobs', - color: 'warning', - message: "The pipeline ${currentBuild.fillDisplayName} is unstable. Check it out here: ${env.BUILD_URL}" - } - failure { - slackSend channel: '#ai-nsf-jenkins-jobs', - color: 'danger', - message: "@all The pipeline ${currentBuild.fillDisplayName} has failed! Check it out here: ${env.BUILD_URL}" - } - /* - changed { - + stage('Test') { + steps { + echo 'Calling make test script' + sh 'make test || true' + } + } + stage('Deploy - Staging') { + when { + expression { + currentBuild.result == null || currentBuild.result == 'SUCCESS' } - */ + + } + steps { + echo 'Placeholder: Deploying to staging/dev' + } + } + stage('Sanity check') { + steps { + input 'Does the staging environment look ok?' + } + } + stage('Deploy - Production') { + steps { + echo 'Placeholder: Deploying to prod' + } + } + } + post { + always { + echo 'The job has finished.' + + } + + success { + slackSend(channel: '#ai-nsf-jenkins-jobs', color: 'good', message: "The pipeline ${currentBuild.fillDisplayName} completed successfully.") + + } + + unstable { + slackSend(channel: '#ai-nsf-jenkins-jobs', color: 'warning', message: "The pipeline ${currentBuild.fillDisplayName} is unstable. Check it out here: ${env.BUILD_URL}") + + } + + failure { + slackSend(channel: '#ai-nsf-jenkins-jobs', color: 'danger', message: "@all The pipeline ${currentBuild.fillDisplayName} has failed! Check it out here: ${env.BUILD_URL}") + } + + } } \ No newline at end of file From 1a8d688e63db96164270845f7bea53a2c12c9f21 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Thu, 7 Dec 2017 11:26:09 -0800 Subject: [PATCH 019/174] Trying to make subsequent commands run inside docker container. --- Jenkinsfile | 1 + 1 file changed, 1 insertion(+) diff --git a/Jenkinsfile b/Jenkinsfile index 29f6992..33135c6 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -2,6 +2,7 @@ pipeline { agent { docker { image 'continuumio/anaconda' + args '-it' } } From ed013b4377fb099a55f37636c9188a276c2efe7e Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Thu, 7 Dec 2017 11:58:46 -0800 Subject: [PATCH 020/174] - Moved `build` and `test` commands to bash scripts - Modified `Jenkinsfile`. --- Jenkinsfile | 10 ++++++---- bin/build.sh | 4 ++++ bin/test.sh | 5 +++++ 3 files changed, 15 insertions(+), 4 deletions(-) create mode 100644 bin/build.sh create mode 100644 bin/test.sh diff --git a/Jenkinsfile b/Jenkinsfile index 8f205f5..706d915 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -12,15 +12,14 @@ pipeline { steps { checkout scm echo 'Creation python environment' - sh 'make create_env' - sh 'source activate object-detection' + sh 'bin/build.sh' } } stage('Test') { steps { echo 'Calling make test script' - sh 'make test || true' + sh 'bin/test.sh || true' } } @@ -37,7 +36,7 @@ pipeline { stage('Sanity check') { steps { - input "Does the staging environment look ok?" + input "Does the staging env look good? Good enough to deploy into production?" } } @@ -51,6 +50,9 @@ pipeline { post { always { echo 'The job has finished.' + slackSend channel: '#ai-nsf-jenkins-jobs', + color: 'good', + message: "The pipeline ${currentBuild.fillDisplayName} completed." } success { slackSend channel: '#ai-nsf-jenkins-jobs', diff --git a/bin/build.sh b/bin/build.sh new file mode 100644 index 0000000..3024611 --- /dev/null +++ b/bin/build.sh @@ -0,0 +1,4 @@ +#!/usr/bin/env bash + +conda env create -f environment.yml +source activate object-detection diff --git a/bin/test.sh b/bin/test.sh new file mode 100644 index 0000000..156e2e8 --- /dev/null +++ b/bin/test.sh @@ -0,0 +1,5 @@ +#!/usr/bin/env bash + +pytest -vs utils/ +python -m unittest discover -s object_detection -p "*_test.py" + From 1098090de829917ed57d7e61492f60fdd6acecb4 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Thu, 7 Dec 2017 18:00:16 -0800 Subject: [PATCH 021/174] - Dev tested + modified Jenkinsfile --- Jenkinsfile | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Jenkinsfile b/Jenkinsfile index 706d915..bf5c391 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -12,14 +12,14 @@ pipeline { steps { checkout scm echo 'Creation python environment' - sh 'bin/build.sh' + sh 'source bin/build.sh' } } stage('Test') { steps { echo 'Calling make test script' - sh 'bin/test.sh || true' + sh 'bash bin/test.sh || true' } } From 3411ea500f51f63a085c2ec4a635591f6e75b364 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Thu, 7 Dec 2017 18:14:54 -0800 Subject: [PATCH 022/174] - Changed `sh` --> `bash` in Jenkinsfile --- Jenkinsfile | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Jenkinsfile b/Jenkinsfile index bf5c391..b956710 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -12,14 +12,14 @@ pipeline { steps { checkout scm echo 'Creation python environment' - sh 'source bin/build.sh' + bash 'source bin/build.sh' } } stage('Test') { steps { echo 'Calling make test script' - sh 'bash bin/test.sh || true' + bash 'bin/test.sh || true' } } From 661430c3f41d6e47266eb07515735ee341685c3c Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 09:22:32 -0800 Subject: [PATCH 023/174] Trying to get jenkinsfile to run without error. - bash --> sh, added `sh 'bash'` command. - --- Jenkinsfile | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/Jenkinsfile b/Jenkinsfile index b956710..0f9c2e8 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -12,14 +12,15 @@ pipeline { steps { checkout scm echo 'Creation python environment' - bash 'source bin/build.sh' + sh 'bash' + sh 'source bin/build.sh' } } stage('Test') { steps { echo 'Calling make test script' - bash 'bin/test.sh || true' + sh 'bin/test.sh || true' } } From a4e2ccebead5637b27c84bf83c9efd12bbf98da8 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 10:50:43 -0800 Subject: [PATCH 024/174] - Testing out method to get the conda env working in Jenkins --- Jenkinsfile | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/Jenkinsfile b/Jenkinsfile index 0f9c2e8..a7cff78 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -8,19 +8,24 @@ pipeline { } stages { - stage('Build') { + stage('Checkout') { steps { checkout scm - echo 'Creation python environment' - sh 'bash' - sh 'source bin/build.sh' } } stage('Test') { + environment { + CONDA_ENV = "${env.WORKSPACE}/test/${env.STAGE_NAME}" + } steps { - echo 'Calling make test script' - sh 'bin/test.sh || true' + sh 'conda env create -q -f environment.yml -p $CONDA_ENV' + sh '''#!/bin/bash -ex + source $CONDA_ENV/bin/activate $CONDA_ENV + pytest -vs utils/ + python -m unittest discover -s object_detection -p "*_test.py" + ''' + } } From 5d5dffd18a2565441ee07de45e4e640fdedd0dcb Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 11:08:56 -0800 Subject: [PATCH 025/174] Modified Jenkinsfile to work with local conda installation in Jenkins instance - mapped volume from ec2 instance conda packages to docker container conda package location. --- Jenkinsfile | 1 + 1 file changed, 1 insertion(+) diff --git a/Jenkinsfile b/Jenkinsfile index a7cff78..af87766 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -4,6 +4,7 @@ pipeline { agent { docker { image 'continuumio/anaconda' + args: ' -v /home/ec2-user/conda3/pkgs:/root/.conda/pkgs:rw,z' } } From c8ab7df35cf2cb6526601ad9adac1b98d74feb39 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 11:12:48 -0800 Subject: [PATCH 026/174] - Fixed groovy syntax error --- Jenkinsfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Jenkinsfile b/Jenkinsfile index af87766..c3a49d1 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -4,7 +4,7 @@ pipeline { agent { docker { image 'continuumio/anaconda' - args: ' -v /home/ec2-user/conda3/pkgs:/root/.conda/pkgs:rw,z' + args '-v /home/ec2-user/conda3/pkgs:/root/.conda/pkgs:rw,z' } } From 04fc0e4978cf55a4ef9667ae94fff5c1952a4e07 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 11:22:20 -0800 Subject: [PATCH 027/174] Modified Jenkinsfile - Volume mapping to `/opt/conda/pkgs` instead of `/root/.conda/pkg` --- Jenkinsfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Jenkinsfile b/Jenkinsfile index c3a49d1..8aa48d6 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -4,7 +4,7 @@ pipeline { agent { docker { image 'continuumio/anaconda' - args '-v /home/ec2-user/conda3/pkgs:/root/.conda/pkgs:rw,z' + args '-v /home/ec2-user/conda3/pkgs:/opt/conda/pkgs:rw,z' } } From e2beb26c283889f05cc8231949419e07d5f4bf2c Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 11:38:41 -0800 Subject: [PATCH 028/174] - Changing volume mapping rw access to default --- Jenkinsfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Jenkinsfile b/Jenkinsfile index 8aa48d6..c14abe6 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -4,7 +4,7 @@ pipeline { agent { docker { image 'continuumio/anaconda' - args '-v /home/ec2-user/conda3/pkgs:/opt/conda/pkgs:rw,z' + args '-v /home/ec2-user/conda3/pkgs:/opt/conda/pkgs' } } From 7ed03994b7e90f13c5ce6447055e62f344133454 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 11:50:05 -0800 Subject: [PATCH 029/174] - Mapping two new volumes (`/etc/passwd` and `/etc/group`) to docker container from jenkins machine to attempt to solve conda permissions issue with rw with `pkgs`. --- Jenkinsfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Jenkinsfile b/Jenkinsfile index c14abe6..29ec34b 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -4,7 +4,7 @@ pipeline { agent { docker { image 'continuumio/anaconda' - args '-v /home/ec2-user/conda3/pkgs:/opt/conda/pkgs' + args '-v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/ec2-user/conda3/pkgs:/opt/conda/pkgs' } } From d9958368e636301950d890b81db0c5bb9edd9674 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 12:26:08 -0800 Subject: [PATCH 030/174] - Mapping two new volumes (`/etc/passwd` and `/etc/group`) to docker container from jenkins machine to attempt to solve conda permissions issue with rw with `pkgs`. - Added user flag to attempt to get permissions right. --- Jenkinsfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Jenkinsfile b/Jenkinsfile index 29ec34b..423137e 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -4,7 +4,7 @@ pipeline { agent { docker { image 'continuumio/anaconda' - args '-v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/ec2-user/conda3/pkgs:/opt/conda/pkgs' + args '-u $UID -v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/ec2-user/conda3/pkgs:/opt/conda/pkgs' } } From 8d52c543c9494d37c6f57ec35adaefd83eadc173 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 12:52:12 -0800 Subject: [PATCH 031/174] - Adding USERID env variable --- Jenkinsfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Jenkinsfile b/Jenkinsfile index 423137e..ee19337 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -4,7 +4,7 @@ pipeline { agent { docker { image 'continuumio/anaconda' - args '-u $UID -v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/ec2-user/conda3/pkgs:/opt/conda/pkgs' + args '-e USERID=$UID -v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/ec2-user/conda3/pkgs:/opt/conda/pkgs' } } From 9de20ddf89a282cb11aac40eb5e86675bed81bab Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 12:55:25 -0800 Subject: [PATCH 032/174] - added conda info, going to see what's happening on Jenkins/docker image --- Jenkinsfile | 1 + 1 file changed, 1 insertion(+) diff --git a/Jenkinsfile b/Jenkinsfile index ee19337..9d7e574 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -20,6 +20,7 @@ pipeline { CONDA_ENV = "${env.WORKSPACE}/test/${env.STAGE_NAME}" } steps { + sh 'conda info' sh 'conda env create -q -f environment.yml -p $CONDA_ENV' sh '''#!/bin/bash -ex source $CONDA_ENV/bin/activate $CONDA_ENV From 13234780a4ee84cf67d99cf334c7db0251ccbc31 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 13:20:09 -0800 Subject: [PATCH 033/174] - added conda info, going to see what's happening on Jenkins/docker image - Changed anaconda --> miniconda3 - added env name, will remove if already exists, added rw permissions to last volume. --- Jenkinsfile | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Jenkinsfile b/Jenkinsfile index 9d7e574..f0f0016 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -3,8 +3,8 @@ pipeline { agent { docker { - image 'continuumio/anaconda' - args '-e USERID=$UID -v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/ec2-user/conda3/pkgs:/opt/conda/pkgs' + image 'continuumio/miniconda3' + args '--rm --name ai-conda -e USERID=$UID -v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/ec2-user/conda3/pkgs:/opt/conda/pkgs:rw,z' } } From da6069bfc25d92af3e03ff14faa0eb21391de1b5 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 13:25:41 -0800 Subject: [PATCH 034/174] - added conda info, going to see what's happening on Jenkins/docker image - Changed anaconda --> miniconda3 - added env name, will remove if already exists, added rw permissions to last volume. - Added `conda clean` before installing env. Interested in potential new err msg. --- Jenkinsfile | 1 + 1 file changed, 1 insertion(+) diff --git a/Jenkinsfile b/Jenkinsfile index f0f0016..b7d3cd4 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -21,6 +21,7 @@ pipeline { } steps { sh 'conda info' + sh 'conda clean' sh 'conda env create -q -f environment.yml -p $CONDA_ENV' sh '''#!/bin/bash -ex source $CONDA_ENV/bin/activate $CONDA_ENV From 5c13e366bb313415cd053033829ac7922c56c391 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 13:27:19 -0800 Subject: [PATCH 035/174] - needed to pass a flag to `conda clean`. Passing all argument --- Jenkinsfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Jenkinsfile b/Jenkinsfile index b7d3cd4..0bb9d17 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -21,7 +21,7 @@ pipeline { } steps { sh 'conda info' - sh 'conda clean' + sh 'conda clean -a' sh 'conda env create -q -f environment.yml -p $CONDA_ENV' sh '''#!/bin/bash -ex source $CONDA_ENV/bin/activate $CONDA_ENV From 3637163feb6d31b577d2a4d9c9f98d1563f05f81 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 13:32:13 -0800 Subject: [PATCH 036/174] - Attempting to run pipeline without docker. --- Jenkinsfile | 3 +++ 1 file changed, 3 insertions(+) diff --git a/Jenkinsfile b/Jenkinsfile index 0bb9d17..bb50db7 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -1,12 +1,15 @@ #!/usr/bin/env groovy pipeline { + /* agent { docker { image 'continuumio/miniconda3' args '--rm --name ai-conda -e USERID=$UID -v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/ec2-user/conda3/pkgs:/opt/conda/pkgs:rw,z' } } + */ + agent any stages { stage('Checkout') { From da3c492d2c7d51be6913e4dfd82c3cb755cb7569 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 13:36:50 -0800 Subject: [PATCH 037/174] - Blindly attempting to elevate privledges with `sudo` --- Jenkinsfile | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/Jenkinsfile b/Jenkinsfile index bb50db7..71ed37e 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -1,15 +1,12 @@ #!/usr/bin/env groovy pipeline { - /* agent { docker { image 'continuumio/miniconda3' - args '--rm --name ai-conda -e USERID=$UID -v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/ec2-user/conda3/pkgs:/opt/conda/pkgs:rw,z' + args '--rm --name ai-conda -e USERID=$UID -v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/ec2-user/conda3/pkgs:/opt/conda/pkgs:rw' } } - */ - agent any stages { stage('Checkout') { @@ -24,8 +21,7 @@ pipeline { } steps { sh 'conda info' - sh 'conda clean -a' - sh 'conda env create -q -f environment.yml -p $CONDA_ENV' + sh 'sudo conda env create -q -f environment.yml -p $CONDA_ENV' sh '''#!/bin/bash -ex source $CONDA_ENV/bin/activate $CONDA_ENV pytest -vs utils/ From 0b30da45d9ed8e3d00a721d7a2e0cbc8e30dc86c Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 14:08:59 -0800 Subject: [PATCH 038/174] - Stepping back on specifing the env. Changed permissions for the `conda3` folder that I'm mapping to. --- Jenkinsfile | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/Jenkinsfile b/Jenkinsfile index 71ed37e..141c24c 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -4,7 +4,7 @@ pipeline { agent { docker { image 'continuumio/miniconda3' - args '--rm --name ai-conda -e USERID=$UID -v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/ec2-user/conda3/pkgs:/opt/conda/pkgs:rw' + args '--rm --name ai-conda -v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/ec2-user/conda3/pkgs:/opt/conda/pkgs:rw' } } @@ -21,7 +21,10 @@ pipeline { } steps { sh 'conda info' - sh 'sudo conda env create -q -f environment.yml -p $CONDA_ENV' + sh 'conda clean -a' + sh '''#!/bin/bash -ex + sudo conda env create -q -f environment.yml -p $CONDA_ENV' + ''' sh '''#!/bin/bash -ex source $CONDA_ENV/bin/activate $CONDA_ENV pytest -vs utils/ From f536babd6bab3c4c7366c95b74fc733fa726a2db Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 14:49:36 -0800 Subject: [PATCH 039/174] - Copying example jenkins file more closely. --- Jenkinsfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Jenkinsfile b/Jenkinsfile index 141c24c..dfa5ee0 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -4,7 +4,7 @@ pipeline { agent { docker { image 'continuumio/miniconda3' - args '--rm --name ai-conda -v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/ec2-user/conda3/pkgs:/opt/conda/pkgs:rw' + args '--rm --name ai-conda -v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/jenkins/.conda3/pkgs:/home/jenkins/.conda/pkgs:rw,z' } } From 9f4dda4a97289060e76303b9b71fdb669f07955c Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 14:58:06 -0800 Subject: [PATCH 040/174] Changed env variables (uid, gid) --- Jenkinsfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Jenkinsfile b/Jenkinsfile index dfa5ee0..2096586 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -4,7 +4,7 @@ pipeline { agent { docker { image 'continuumio/miniconda3' - args '--rm --name ai-conda -v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/jenkins/.conda3/pkgs:/home/jenkins/.conda/pkgs:rw,z' + args '-e GROUPID=docker -e USERID=jenkins --rm --name ai-conda -v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/jenkins/.conda3/pkgs:/home/jenkins/.conda/pkgs:rw,z' } } From a31d3b75909aedb3cec3af4f322852dd1f0940c2 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 15:02:31 -0800 Subject: [PATCH 041/174] Changed env variables (uid, gid) --- Jenkinsfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Jenkinsfile b/Jenkinsfile index 2096586..d5c0309 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -4,7 +4,7 @@ pipeline { agent { docker { image 'continuumio/miniconda3' - args '-e GROUPID=docker -e USERID=jenkins --rm --name ai-conda -v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/jenkins/.conda3/pkgs:/home/jenkins/.conda/pkgs:rw,z' + args '-e GROUPID=495 -e USERID=498 --rm --name ai-conda -v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/jenkins/.conda3/pkgs:/home/jenkins/.conda/pkgs:rw,z' } } From 0b9f169c2e35121e7e100d19d72ac2777b6dc458 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 16:22:20 -0800 Subject: [PATCH 042/174] getting info about pkgs dir --- Jenkinsfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Jenkinsfile b/Jenkinsfile index d5c0309..bd639fc 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -21,7 +21,7 @@ pipeline { } steps { sh 'conda info' - sh 'conda clean -a' + sh 'ls -la /opt/conda/pkgs' sh '''#!/bin/bash -ex sudo conda env create -q -f environment.yml -p $CONDA_ENV' ''' From 8da58ce4eababede53a7507f4958fe49aa331832 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 8 Dec 2017 16:27:38 -0800 Subject: [PATCH 043/174] Experimenting -Passed `-u 0` to container... - Got rid of `sudo` in conda env creation. --- Jenkinsfile | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/Jenkinsfile b/Jenkinsfile index bd639fc..4c583eb 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -4,7 +4,7 @@ pipeline { agent { docker { image 'continuumio/miniconda3' - args '-e GROUPID=495 -e USERID=498 --rm --name ai-conda -v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/jenkins/.conda3/pkgs:/home/jenkins/.conda/pkgs:rw,z' + args '-u 0 --rm --name ai-conda -v /etc/passwd:/etc/passwd -v /etc/group:/etc/group -v /home/jenkins/.conda3/pkgs:/home/jenkins/.conda/pkgs:rw,z' } } @@ -22,9 +22,7 @@ pipeline { steps { sh 'conda info' sh 'ls -la /opt/conda/pkgs' - sh '''#!/bin/bash -ex - sudo conda env create -q -f environment.yml -p $CONDA_ENV' - ''' + sh 'conda env create -q -f environment.yml -p $CONDA_ENV' sh '''#!/bin/bash -ex source $CONDA_ENV/bin/activate $CONDA_ENV pytest -vs utils/ From 220692013bf10735032c965db8f6722590357309 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Mon, 11 Dec 2017 14:00:26 -0800 Subject: [PATCH 044/174] (CICD) - Added comment to clarify docker env - Running pytest on all avail tests (currently failing) - Got rid of docker environment information (`conda info`, etc) --- Jenkinsfile | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/Jenkinsfile b/Jenkinsfile index 4c583eb..3207dd0 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -1,6 +1,16 @@ #!/usr/bin/env groovy pipeline { + + /* Create instance (Agent) to run Jenkins pipeline on + + Section creates a docker container with anaconda installed on it for jenkins pipeline to run on. Arguments work + as follows: + `-u 0` user ID, which has root value. Good reference about docker UID/GIDs (https://goo.gl/bYFxVh) + `--rm` If a docker container with the same name already exists, remove it + `--name ai-conda` Give this docker instance the name `ai-conda` + `-v _:_` map volumes. Specifically, map `passwd`, `group` and the conda packages volumes to the container + */ agent { docker { image 'continuumio/miniconda3' @@ -20,12 +30,10 @@ pipeline { CONDA_ENV = "${env.WORKSPACE}/test/${env.STAGE_NAME}" } steps { - sh 'conda info' - sh 'ls -la /opt/conda/pkgs' sh 'conda env create -q -f environment.yml -p $CONDA_ENV' sh '''#!/bin/bash -ex source $CONDA_ENV/bin/activate $CONDA_ENV - pytest -vs utils/ + python -m pytest python -m unittest discover -s object_detection -p "*_test.py" ''' From 06b66ed843dd02f323724ce440fc786cbbba67ca Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Mon, 11 Dec 2017 14:01:56 -0800 Subject: [PATCH 045/174] (Fix Unit Tests) - Unit Test Fix: importing `unittest.mock` instead of `mock` --- object_detection/builders/box_predictor_builder_test.py | 2 +- object_detection/core/preprocessor_test.py | 2 +- object_detection/exporter_test.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/object_detection/builders/box_predictor_builder_test.py b/object_detection/builders/box_predictor_builder_test.py index 3f6a574..22a0a10 100644 --- a/object_detection/builders/box_predictor_builder_test.py +++ b/object_detection/builders/box_predictor_builder_test.py @@ -14,7 +14,7 @@ # ============================================================================== """Tests for box_predictor_builder.""" -import mock +import unittest.mock as mock import tensorflow as tf from google.protobuf import text_format diff --git a/object_detection/core/preprocessor_test.py b/object_detection/core/preprocessor_test.py index 109df7a..1b466cd 100644 --- a/object_detection/core/preprocessor_test.py +++ b/object_detection/core/preprocessor_test.py @@ -15,7 +15,7 @@ """Tests for object_detection.core.preprocessor.""" -import mock +import unittest.mock as mock import numpy as np import tensorflow as tf diff --git a/object_detection/exporter_test.py b/object_detection/exporter_test.py index 5b16fc8..edf8d82 100644 --- a/object_detection/exporter_test.py +++ b/object_detection/exporter_test.py @@ -15,7 +15,7 @@ """Tests for object_detection.export_inference_graph.""" import os -import mock +import unittest.mock as mock import numpy as np import tensorflow as tf from object_detection import exporter From 363d7dd9428c95ed649f8abc9b11287027c5eeae Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Mon, 11 Dec 2017 14:03:03 -0800 Subject: [PATCH 046/174] (Unit Test, Removed unused Files) - Safely removed scripts to create pascal records of tf models --- object_detection/create_pascal_tf_record.py | 181 ------------------ .../create_pascal_tf_record_test.py | 118 ------------ 2 files changed, 299 deletions(-) delete mode 100644 object_detection/create_pascal_tf_record.py delete mode 100644 object_detection/create_pascal_tf_record_test.py diff --git a/object_detection/create_pascal_tf_record.py b/object_detection/create_pascal_tf_record.py deleted file mode 100644 index 443862f..0000000 --- a/object_detection/create_pascal_tf_record.py +++ /dev/null @@ -1,181 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -r"""Convert raw PASCAL dataset to TFRecord for object_detection. - -Example usage: - ./create_pascal_tf_record --data_dir=/home/user/VOCdevkit \ - --year=VOC2012 \ - --output_path=/home/user/pascal.record -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import hashlib -import io -import logging -import os - -from lxml import etree -import PIL.Image -import tensorflow as tf - -from object_detection.utils import dataset_util -from object_detection.utils import label_map_util - - -flags = tf.app.flags -flags.DEFINE_string('data_dir', '', 'Root directory to raw PASCAL VOC dataset.') -flags.DEFINE_string('set', 'train', 'Convert training set, validation set or ' - 'merged set.') -flags.DEFINE_string('annotations_dir', 'Annotations', - '(Relative) path to annotations directory.') -flags.DEFINE_string('year', 'VOC2007', 'Desired challenge year.') -flags.DEFINE_string('output_path', '', 'Path to output TFRecord') -flags.DEFINE_string('label_map_path', 'data/pascal_label_map.pbtxt', - 'Path to label map proto') -flags.DEFINE_boolean('ignore_difficult_instances', False, 'Whether to ignore ' - 'difficult instances') -FLAGS = flags.FLAGS - -SETS = ['train', 'val', 'trainval', 'test'] -YEARS = ['VOC2007', 'VOC2012', 'merged'] - - -def dict_to_tf_example(data, - dataset_directory, - label_map_dict, - ignore_difficult_instances=False, - image_subdirectory='JPEGImages'): - """Convert XML derived dict to tf.Example proto. - - Notice that this function normalizes the bounding box coordinates provided - by the raw data. - - Args: - data: dict holding PASCAL XML fields for a single image (obtained by - running dataset_util.recursive_parse_xml_to_dict) - dataset_directory: Path to root directory holding PASCAL dataset - label_map_dict: A map from string label names to integers ids. - ignore_difficult_instances: Whether to skip difficult instances in the - dataset (default: False). - image_subdirectory: String specifying subdirectory within the - PASCAL dataset directory holding the actual image data. - - Returns: - example: The converted tf.Example. - - Raises: - ValueError: if the image pointed to by data['filename'] is not a valid JPEG - """ - img_path = os.path.join(data['folder'], image_subdirectory, data['filename']) - full_path = os.path.join(dataset_directory, img_path) - with tf.gfile.GFile(full_path) as fid: - encoded_jpg = fid.read() - encoded_jpg_io = io.BytesIO(encoded_jpg) - image = PIL.Image.open(encoded_jpg_io) - if image.format != 'JPEG': - raise ValueError('Image format not JPEG') - key = hashlib.sha256(encoded_jpg).hexdigest() - - width = int(data['size']['width']) - height = int(data['size']['height']) - - xmin = [] - ymin = [] - xmax = [] - ymax = [] - classes = [] - classes_text = [] - truncated = [] - poses = [] - difficult_obj = [] - for obj in data['object']: - difficult = bool(int(obj['difficult'])) - if ignore_difficult_instances and difficult: - continue - - difficult_obj.append(int(difficult)) - - xmin.append(float(obj['bndbox']['xmin']) / width) - ymin.append(float(obj['bndbox']['ymin']) / height) - xmax.append(float(obj['bndbox']['xmax']) / width) - ymax.append(float(obj['bndbox']['ymax']) / height) - classes_text.append(obj['name']) - classes.append(label_map_dict[obj['name']]) - truncated.append(int(obj['truncated'])) - poses.append(obj['pose']) - - example = tf.train.Example(features=tf.train.Features(feature={ - 'image/height': dataset_util.int64_feature(height), - 'image/width': dataset_util.int64_feature(width), - 'image/filename': dataset_util.bytes_feature(data['filename']), - 'image/source_id': dataset_util.bytes_feature(data['filename']), - 'image/key/sha256': dataset_util.bytes_feature(key), - 'image/encoded': dataset_util.bytes_feature(encoded_jpg), - 'image/format': dataset_util.bytes_feature('jpeg'), - 'image/object/bbox/xmin': dataset_util.float_list_feature(xmin), - 'image/object/bbox/xmax': dataset_util.float_list_feature(xmax), - 'image/object/bbox/ymin': dataset_util.float_list_feature(ymin), - 'image/object/bbox/ymax': dataset_util.float_list_feature(ymax), - 'image/object/class/text': dataset_util.bytes_list_feature(classes_text), - 'image/object/class/label': dataset_util.int64_list_feature(classes), - 'image/object/difficult': dataset_util.int64_list_feature(difficult_obj), - 'image/object/truncated': dataset_util.int64_list_feature(truncated), - 'image/object/view': dataset_util.bytes_list_feature(poses), - })) - return example - - -def main(_): - if FLAGS.set not in SETS: - raise ValueError('set must be in : {}'.format(SETS)) - if FLAGS.year not in YEARS: - raise ValueError('year must be in : {}'.format(YEARS)) - - data_dir = FLAGS.data_dir - years = ['VOC2007', 'VOC2012'] - if FLAGS.year != 'merged': - years = [FLAGS.year] - - writer = tf.python_io.TFRecordWriter(FLAGS.output_path) - - label_map_dict = label_map_util.get_label_map_dict(FLAGS.label_map_path) - - for year in years: - logging.info('Reading from PASCAL %s dataset.', year) - examples_path = os.path.join(data_dir, year, 'ImageSets', 'Main', - 'aeroplane_' + FLAGS.set + '.txt') - annotations_dir = os.path.join(data_dir, year, FLAGS.annotations_dir) - examples_list = dataset_util.read_examples_list(examples_path) - for idx, example in enumerate(examples_list): - if idx % 100 == 0: - logging.info('On image %d of %d', idx, len(examples_list)) - path = os.path.join(annotations_dir, example + '.xml') - with tf.gfile.GFile(path, 'r') as fid: - xml_str = fid.read() - xml = etree.fromstring(xml_str) - data = dataset_util.recursive_parse_xml_to_dict(xml)['annotation'] - - tf_example = dict_to_tf_example(data, FLAGS.data_dir, label_map_dict, - FLAGS.ignore_difficult_instances) - writer.write(tf_example.SerializeToString()) - - writer.close() - - -if __name__ == '__main__': - tf.app.run() diff --git a/object_detection/create_pascal_tf_record_test.py b/object_detection/create_pascal_tf_record_test.py deleted file mode 100644 index dd29c6c..0000000 --- a/object_detection/create_pascal_tf_record_test.py +++ /dev/null @@ -1,118 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -"""Test for create_pascal_tf_record.py.""" - -import os - -import numpy as np -import PIL.Image -import tensorflow as tf - -from object_detection import create_pascal_tf_record - - -class DictToTFExampleTest(tf.test.TestCase): - - def _assertProtoEqual(self, proto_field, expectation): - """Helper function to assert if a proto field equals some value. - - Args: - proto_field: The protobuf field to compare. - expectation: The expected value of the protobuf field. - """ - proto_list = [p for p in proto_field] - self.assertListEqual(proto_list, expectation) - - def test_dict_to_tf_example(self): - image_file_name = 'tmp_image.jpg' - image_data = np.random.rand(256, 256, 3) - save_path = os.path.join(self.get_temp_dir(), image_file_name) - image = PIL.Image.fromarray(image_data, 'RGB') - image.save(save_path) - - data = { - 'folder': '', - 'filename': image_file_name, - 'size': { - 'height': 256, - 'width': 256, - }, - 'object': [ - { - 'difficult': 1, - 'bndbox': { - 'xmin': 64, - 'ymin': 64, - 'xmax': 192, - 'ymax': 192, - }, - 'name': 'person', - 'truncated': 0, - 'pose': '', - }, - ], - } - - label_map_dict = { - 'background': 0, - 'person': 1, - 'notperson': 2, - } - - example = create_pascal_tf_record.dict_to_tf_example( - data, self.get_temp_dir(), label_map_dict, image_subdirectory='') - self._assertProtoEqual( - example.features.feature['image/height'].int64_list.value, [256]) - self._assertProtoEqual( - example.features.feature['image/width'].int64_list.value, [256]) - self._assertProtoEqual( - example.features.feature['image/filename'].bytes_list.value, - [image_file_name]) - self._assertProtoEqual( - example.features.feature['image/source_id'].bytes_list.value, - [image_file_name]) - self._assertProtoEqual( - example.features.feature['image/format'].bytes_list.value, ['jpeg']) - self._assertProtoEqual( - example.features.feature['image/object/bbox/xmin'].float_list.value, - [0.25]) - self._assertProtoEqual( - example.features.feature['image/object/bbox/ymin'].float_list.value, - [0.25]) - self._assertProtoEqual( - example.features.feature['image/object/bbox/xmax'].float_list.value, - [0.75]) - self._assertProtoEqual( - example.features.feature['image/object/bbox/ymax'].float_list.value, - [0.75]) - self._assertProtoEqual( - example.features.feature['image/object/class/text'].bytes_list.value, - ['person']) - self._assertProtoEqual( - example.features.feature['image/object/class/label'].int64_list.value, - [1]) - self._assertProtoEqual( - example.features.feature['image/object/difficult'].int64_list.value, - [1]) - self._assertProtoEqual( - example.features.feature['image/object/truncated'].int64_list.value, - [0]) - self._assertProtoEqual( - example.features.feature['image/object/view'].bytes_list.value, ['']) - - -if __name__ == '__main__': - tf.test.main() From cf0d53602d655341b0a7245c53569ff95397f92c Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Mon, 11 Dec 2017 14:58:31 -0800 Subject: [PATCH 047/174] (CICD) - Force re-creating conda environment (fixes build) - Added comments explaining the Build + Test step --- Jenkinsfile | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/Jenkinsfile b/Jenkinsfile index 3207dd0..7572661 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -30,7 +30,23 @@ pipeline { CONDA_ENV = "${env.WORKSPACE}/test/${env.STAGE_NAME}" } steps { - sh 'conda env create -q -f environment.yml -p $CONDA_ENV' + /* Recreate a conda environment from the config file + + `-q` quite mode. Remove to see verbose output + `--force` remove previously existing environment of the same name + `-f FILE` environment definition file + `-p PATH` Full path to environment prefix (replacing default) + */ + sh 'conda env create -q --force -f environment.yml -p $CONDA_ENV' + + /* Activate the environment and run unit tests + + `source ...` activate the environment + `python -m pytest` run `pytest` as a module + `python -m unittest...` run `unittest` on the `object_detection` directory, pattern matching all of + the files that end in `_test.py` + + */ sh '''#!/bin/bash -ex source $CONDA_ENV/bin/activate $CONDA_ENV python -m pytest @@ -64,6 +80,7 @@ pipeline { } } + /* TODO(Alex): Fix Slack Notifications. */ post { always { echo 'The job has finished.' From f65c45808ab6fd3bf9101e38c136af00644bf88e Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Mon, 11 Dec 2017 17:42:22 -0800 Subject: [PATCH 048/174] - In Jenkinsfile, commented out deploy stubs. --- Jenkinsfile | 2 ++ 1 file changed, 2 insertions(+) diff --git a/Jenkinsfile b/Jenkinsfile index 7572661..5d23e03 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -56,6 +56,7 @@ pipeline { } } + /* stage('Deploy - Staging'){ when { expression { @@ -78,6 +79,7 @@ pipeline { echo 'Placeholder: Deploying to prod' } } + */ } /* TODO(Alex): Fix Slack Notifications. */ From ec88292400efb104a2c53543295d70db49a73a2d Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Mon, 11 Dec 2017 18:08:51 -0800 Subject: [PATCH 049/174] standup-notes --- docs/standups.md | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/docs/standups.md b/docs/standups.md index a4cf34c..676db91 100644 --- a/docs/standups.md +++ b/docs/standups.md @@ -47,3 +47,30 @@ libstreaming library crashing on android emulator (android studio) faster pro em * alex forgot to send link to gennymotion -- rapid android emulation * talk about CI, sh scripts that call python scripts * CICD apps released through bitrise + + +## Standup 3 + +### Hobson + +* POC for maps +* + +### Alex + +* code for wowza works, needs to be put on dev and prod +* jenkins build working, unittests, then deploy + +### Ashwin + +* stream debugging (permissions) +* NFB OCR branch for sujeeth, rabbitMQ, linked to user info + +### Parking Lot + +* What next? OCR chatbot +* Bug in Jira for portrait/landscape rotation +* display of video? +* cloud emulation of multiple Android devices +* 9854010344 + From 99d5c67ebe42d31ae41536711fffd04d72179ddc Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Mon, 11 Dec 2017 18:16:47 -0800 Subject: [PATCH 050/174] - Createds script to install tensorflow `nets` library (i.e. clone the tf/models repo). --- bin/tf_models.sh | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100755 bin/tf_models.sh diff --git a/bin/tf_models.sh b/bin/tf_models.sh new file mode 100755 index 0000000..2c79e0f --- /dev/null +++ b/bin/tf_models.sh @@ -0,0 +1,32 @@ +#!/usr/bin/env bash + + +CURR_DIR=$(pwd) +SITE_PACK=$(python -c "import site; print(site.getsitepackages()[0])") +MODELS_DEST=$SITE_PACK/tensorflow/models + + +cd $MODELS_DEST || mkdir $MODELS_DEST && cd $MODELS_DEST + + +# Spare Checkout of tensorflow models repo +inside_git_repo="$(git rev-parse --is-inside-work-tree 2>/dev/null)" +if ! $inside_git_repo; +then + git init \ + && git remote add origin https://github.com/tensorflow/models.git \ + && git config core.sparsecheckout true \ + && echo "research/slim/*" >> .git/info/sparse-checkout \ + && git pull --depth=1 origin master +fi + +# get slim directory, set it to python path +# TODO(Alex) Set PYTHONPATH env variable, don't just set sys.path +python -c "import sys; sys.exit(-1) if not list(filter(lambda x: 'research/slim' in x, sys.path)) else sys.exit(0)" \ + || cd research/slim \ + && SLIM_DIR=$(pwd) \ + && python -c "import sys; sys.path.append('$SLIM_DIR')" \ + && echo "Added slim to python path" + +# Return to original dir +cd $CURR_DIR From 23c2b67d55cb18e71827fac79adf6e7aa430478b Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Mon, 11 Dec 2017 18:20:29 -0800 Subject: [PATCH 051/174] - Removed `imshow` commands. Running headless by default. - Added cmd to turn on GUI --- object_detection_app.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/object_detection_app.py b/object_detection_app.py index da76c9e..64ecb30 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -109,6 +109,8 @@ def worker(input_q, output_q): default=2, help='Number of workers.') parser.add_argument('-q-size', '--queue-size', dest='queue_size', type=int, default=5, help='Size of the queue.') + parser.add_argument('-g', '--gui', type=bool, default=False, dest='gui', + help='Show a GUI/Graphics, or run headless.') args = parser.parse_args() logger = multiprocessing.log_to_stderr() @@ -118,6 +120,8 @@ def worker(input_q, output_q): output_q = Queue(maxsize=args.queue_size) pool = Pool(args.num_workers, worker, (input_q, output_q)) + disp_graphics = args.gui + source = args.video_stream_source if source is None: @@ -135,7 +139,8 @@ def worker(input_q, output_q): t = time.time() output_rgb = cv2.cvtColor(output_q.get(), cv2.COLOR_RGB2BGR) - cv2.imshow('Video', output_rgb) + if disp_graphics: + cv2.imshow('Video', output_rgb) fps.update() print('[INFO] elapsed time: {:.2f}'.format(time.time() - t)) @@ -149,4 +154,5 @@ def worker(input_q, output_q): pool.terminate() video_capture.stop() - cv2.destroyAllWindows() + if disp_graphics: + cv2.destroyAllWindows() From 9787879299605aa60785f3e75befc5ec4ad640e5 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 12 Dec 2017 08:28:14 -0800 Subject: [PATCH 052/174] - Changed argparser action, simpler interface (thanks Hobson) --- object_detection_app.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/object_detection_app.py b/object_detection_app.py index 64ecb30..0ff1224 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -109,7 +109,7 @@ def worker(input_q, output_q): default=2, help='Number of workers.') parser.add_argument('-q-size', '--queue-size', dest='queue_size', type=int, default=5, help='Size of the queue.') - parser.add_argument('-g', '--gui', type=bool, default=False, dest='gui', + parser.add_argument('-g', '--gui', action='store_true', default=False, dest='gui', help='Show a GUI/Graphics, or run headless.') args = parser.parse_args() From f864d01635d159b44a9868e022561f9f446ea676 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 12 Dec 2017 09:34:56 -0800 Subject: [PATCH 053/174] - Refactored NLP as python package - Added TODOs to `nlp.py` (logging, documenting) --- object_detection/nlp/__init__.py | 2 ++ object_detection/{utils => nlp}/nlp.py | 5 +++-- object_detection_app.py | 2 +- 3 files changed, 6 insertions(+), 3 deletions(-) create mode 100644 object_detection/nlp/__init__.py rename object_detection/{utils => nlp}/nlp.py (96%) diff --git a/object_detection/nlp/__init__.py b/object_detection/nlp/__init__.py new file mode 100644 index 0000000..aa1baea --- /dev/null +++ b/object_detection/nlp/__init__.py @@ -0,0 +1,2 @@ + +from object_detection.nlp.nlp import update_state, describe_state, say \ No newline at end of file diff --git a/object_detection/utils/nlp.py b/object_detection/nlp/nlp.py similarity index 96% rename from object_detection/utils/nlp.py rename to object_detection/nlp/nlp.py index fdc1a6e..f71051e 100644 --- a/object_detection/utils/nlp.py +++ b/object_detection/nlp/nlp.py @@ -126,6 +126,7 @@ def pluralize(s): def update_state(boxes, classes, scores, category_index, window=10, max_boxes_to_draw=None, min_score_thresh=.5): """ Revise state based on latest frame of information (object boxes) + TODO(Hobson) Finish docstring (Need to know all the args and the return val) Args: boxes (list): 2D numpy array of shape (N, 4): (ymin, xmin, ymax, xmax), in normalized format between [0, 1]. classes, @@ -149,12 +150,12 @@ def update_state(boxes, classes, scores, category_index, window=10, max_boxes_to # box = tuple(boxes[i].tolist()) class_name = category_index.get(classes[i], {'name': 'unknown object'})['name'] display_str = '{}: {} {}%'.format(classes[i], class_name, int(100 * scores[i])) - print(display_str) + print(display_str) # TODO(Alex) Convert to logging state += [class_name] state = collections.Counter(state) update_state.states.iloc[i % len(update_state.states), :] = pd.Series(state) state = sorted(list(state.items())) - i = (i + 1) % len(update_state.states) # update_state.window + i = (i + 1) % len(update_state.states) # update_state.window TODO(Hobs) Is `i` used after this? return state diff --git a/object_detection_app.py b/object_detection_app.py index 0ff1224..3cafaaf 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -10,7 +10,7 @@ from multiprocessing import Queue, Pool from object_detection.utils import label_map_util from object_detection.utils import visualization_utils as vis_util -from object_detection.utils.nlp import update_state, describe_state, say +from object_detection.nlp import update_state, describe_state, say CWD_PATH = os.getcwd() From 113a3ad59730359ea8d736e2f9bf602da49ed904 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 12 Dec 2017 09:38:15 -0800 Subject: [PATCH 054/174] - Moved NLP module to top level (outside of `object_detection` --- nlp/__init__.py | 2 ++ {object_detection/nlp => nlp}/nlp.py | 0 object_detection/nlp/__init__.py | 2 -- object_detection_app.py | 2 +- 4 files changed, 3 insertions(+), 3 deletions(-) create mode 100644 nlp/__init__.py rename {object_detection/nlp => nlp}/nlp.py (100%) delete mode 100644 object_detection/nlp/__init__.py diff --git a/nlp/__init__.py b/nlp/__init__.py new file mode 100644 index 0000000..b8aa57b --- /dev/null +++ b/nlp/__init__.py @@ -0,0 +1,2 @@ + +from nlp.nlp import update_state, describe_state, say \ No newline at end of file diff --git a/object_detection/nlp/nlp.py b/nlp/nlp.py similarity index 100% rename from object_detection/nlp/nlp.py rename to nlp/nlp.py diff --git a/object_detection/nlp/__init__.py b/object_detection/nlp/__init__.py deleted file mode 100644 index aa1baea..0000000 --- a/object_detection/nlp/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ - -from object_detection.nlp.nlp import update_state, describe_state, say \ No newline at end of file diff --git a/object_detection_app.py b/object_detection_app.py index 3cafaaf..d6b35f6 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -10,7 +10,7 @@ from multiprocessing import Queue, Pool from object_detection.utils import label_map_util from object_detection.utils import visualization_utils as vis_util -from object_detection.nlp import update_state, describe_state, say +from nlp import update_state, describe_state, say CWD_PATH = os.getcwd() From ee3af45a6d1f6cb68c9f8ce64ed157d832ddb724 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 12 Dec 2017 09:57:19 -0800 Subject: [PATCH 055/174] - Refactored Voice output to be defaulted to off. Can be set on with `-s` or `--say` command line argument. --- object_detection_app.py | 19 +++++++++++++------ 1 file changed, 13 insertions(+), 6 deletions(-) diff --git a/object_detection_app.py b/object_detection_app.py index d6b35f6..e88ff17 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -29,8 +29,11 @@ categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=90, use_display_name=True) category_index = label_map_util.create_category_index(categories) +# Voice Output +is_voice_on = False -def detect_objects(image_np, sess, detection_graph, utterance_frames=20): + +def detect_objects(image_np, sess, detection_graph, utterance_frames=20, voice_on=False): # Expand dimensions since the model expects images to have shape: [1, None, None, 3] image_np_expanded = np.expand_dims(image_np, axis=0) image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') @@ -65,14 +68,15 @@ def detect_objects(image_np, sess, detection_graph, utterance_frames=20): scores=np.squeeze(scores), category_index=category_index) if not update_state.i % utterance_frames: description = describe_state(state) - say(description) + if voice_on: + say(description) return image_np detect_objects.state = [] # poor man's class/object -def worker(input_q, output_q): +def worker(input_q, output_q, voice_on=False): # Load a (frozen) Tensorflow model into memory. detection_graph = tf.Graph() with detection_graph.as_default(): @@ -85,11 +89,13 @@ def worker(input_q, output_q): sess = tf.Session(graph=detection_graph) fps = FPS().start() + while True: fps.update() frame = input_q.get() frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) - output_q.put(detect_objects(frame_rgb, sess, detection_graph)) + + output_q.put(detect_objects(frame_rgb, sess, detection_graph, voice_on=voice_on)) fps.stop() sess.close() @@ -111,6 +117,8 @@ def worker(input_q, output_q): default=5, help='Size of the queue.') parser.add_argument('-g', '--gui', action='store_true', default=False, dest='gui', help='Show a GUI/Graphics, or run headless.') + parser.add_argument('-s', '--say', action='store_true', default=False, dest='voice_on', + help='Say commands on local computer (for debugging)') args = parser.parse_args() logger = multiprocessing.log_to_stderr() @@ -118,10 +126,9 @@ def worker(input_q, output_q): input_q = Queue(maxsize=args.queue_size) output_q = Queue(maxsize=args.queue_size) - pool = Pool(args.num_workers, worker, (input_q, output_q)) + pool = Pool(args.num_workers, worker, (input_q, output_q, args.voice_on)) disp_graphics = args.gui - source = args.video_stream_source if source is None: From 74cfdf34b3cf4c41c1aa20faf0a61d98ca17c713 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Tue, 12 Dec 2017 11:31:51 -0800 Subject: [PATCH 056/174] image datasets --- docs/image-datasets.md | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 docs/image-datasets.md diff --git a/docs/image-datasets.md b/docs/image-datasets.md new file mode 100644 index 0000000..14bb4fe --- /dev/null +++ b/docs/image-datasets.md @@ -0,0 +1,5 @@ +# Image Datasets + +- [Open Images](https://github.com/openimages/dataset) -- 9M images, thousands of classes +- Common Objects in Context (COCO) [competition](https://places-coco2017.github.io/#winners) and [data set](http://cocodataset.org/#overview) -- 200K images +- Pascal VOC 2010 [dataset](http://www.cs.stanford.edu/~roxozbeh/pascal-context/#statistics) -- 10k images From da31d5a014a74157837b9106e72891feafe03ef7 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Tue, 12 Dec 2017 11:41:44 -0800 Subject: [PATCH 057/174] ignore tf model files --- .gitignore | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 56757f2..a3ccb40 100644 --- a/.gitignore +++ b/.gitignore @@ -89,4 +89,8 @@ ENV/ .ropeproject # Custom -.idea/ \ No newline at end of file +.idea/ + +# tensorflow model weights +*.tar.gz + From b09a803884a50eeb967de122881453be5c2d861a Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 12 Dec 2017 12:53:00 -0800 Subject: [PATCH 058/174] (MQTT Layer) Created MQTT Layer. Currently working - added mqtt package to env - `mqtt.py` configures connection to explorer/agent dashboard - `displatch.py` outlines an extensible NLP command dispatch system. --- environment.yml | 1 + nlp/dispatch.py | 74 +++++++++++++++++++++++++++++++++++++++++++++++++ nlp/mqtt.py | 66 +++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 141 insertions(+) create mode 100644 nlp/dispatch.py create mode 100644 nlp/mqtt.py diff --git a/environment.yml b/environment.yml index a84351c..c2dd1f6 100644 --- a/environment.yml +++ b/environment.yml @@ -13,5 +13,6 @@ dependencies: - pytest=3.2.1=py35_0 - pip: - tensorflow==1.2.0 + - paho-mqtt prefix: /usr/local/anaconda3/envs/object-detection diff --git a/nlp/dispatch.py b/nlp/dispatch.py new file mode 100644 index 0000000..e11c5d6 --- /dev/null +++ b/nlp/dispatch.py @@ -0,0 +1,74 @@ +""" +Module interprets agent commands and dispatches an action +""" +import json +import typing + + +def on_message(client, obj, msg): + print('on msg: ' + str(msg)) + try: + json_string = str(msg.payload, 'utf-8') + payload = json.loads(json_string) + except json.decoder.JSONDecodeError: + return + + cmd = interp_command(payload.get('command', 'default'), list(dispatcher.keys())) + + dispatcher[cmd](payload) + + +def interp_command(cmd_str: str, actions: typing.List[str]) -> str: + """Output a discrete action to take from a user command in natural language. + + Args: + cmd_str: user command + actions: list of commands available from dispatcher + + Returns: + str: single command for dispatcher to execute + + Examples: + >>> interp_command('describe what is around me', ['describe','count']) + 'describe' + >>> interp_command('count the number of objects in view', ['describe', 'count']) + 'count' + """ + + # faster, but less readable + # return next((axn for axn in actions if axn in cmd_str), 'default') + + for axn in actions: + if axn in cmd_str: + return axn + return 'default' + + +class Dispatchable(): + from nlp.mqtt import mqttc, AI_TOPIC + + client = mqttc + topic = AI_TOPIC + + def send(self, payload): + self.client.publish(self.topic, payload) + + +class Echo(Dispatchable): + def __call__(self, msg): + self.send(json.dumps(msg)) + + +class NoOp(Dispatchable): + def __call__(self, msg): + pass + + +dispatcher = { + 'default': NoOp(), + 'debug': Echo(), +} + +if __name__ == '__main__': + import doctest + doctest.testmod() diff --git a/nlp/mqtt.py b/nlp/mqtt.py new file mode 100644 index 0000000..bd2a5f4 --- /dev/null +++ b/nlp/mqtt.py @@ -0,0 +1,66 @@ +""" +Module establishes communication with explorer and agent dashboard +""" + +import os +import paho.mqtt.client as mqtt + +from nlp.dispatch import on_message + +EXPLORER_TOPIC = 'nsf/explorer/command' +AI_TOPIC = 'nsf/ai/response' + + +# Define event callbacks +def on_connect(client, userdata, flags, rc): + print("rc: " + str(rc)) + + +# def on_message(client, obj, msg): +# print(msg.topic + " " + str(msg.qos) + " " + str(msg.payload)) +# +# # TODO(Alex) Remove dev test echo server +# mqttc.publish(AI_TOPIC, 'echo: ' + str(msg.payload)) + + +def on_publish(client, obj, mid): + print("mid: " + str(mid)) + + +def on_subscribe(client, obj, mid, granted_qos): + print("Subscribed: " + str(mid) + " " + str(granted_qos)) + + +def on_log(client, obj, level, string): + print(string) + + +mqttc = mqtt.Client() + + +# Assign event callbacks +mqttc.on_message = on_message +mqttc.on_connect = on_connect +mqttc.on_publish = on_publish +mqttc.on_subscribe = on_subscribe +# mqttc.on_log = on_log + + +url_str = os.environ.get('AIRAMQTT_URL', 'preprod-mqtt.aira.io') +port = int(os.environ.get('AIRAMQTT_PORT', '1883')) + + +mqttc.connect(url_str, port, 60) + +mqttc.subscribe(EXPLORER_TOPIC, 0) + + + + +if __name__ == '__main__': + rc = 0 + + while rc == 0: + rc = mqttc.loop() + print('rc: ' + str(rc)) + From c667eed7dac2c034dc1ae826b4f56478d0f76ccb Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 12 Dec 2017 12:54:23 -0800 Subject: [PATCH 059/174] (Bugfix: package/script conflict) - renamed `nlp.py` to `core.py` to prevent module/package conflict. --- nlp/__init__.py | 2 +- nlp/{nlp.py => core.py} | 85 +---------------------------------------- 2 files changed, 3 insertions(+), 84 deletions(-) rename nlp/{nlp.py => core.py} (62%) diff --git a/nlp/__init__.py b/nlp/__init__.py index b8aa57b..7af7152 100644 --- a/nlp/__init__.py +++ b/nlp/__init__.py @@ -1,2 +1,2 @@ +from nlp.core import update_state, describe_state, say -from nlp.nlp import update_state, describe_state, say \ No newline at end of file diff --git a/nlp/nlp.py b/nlp/core.py similarity index 62% rename from nlp/nlp.py rename to nlp/core.py index f71051e..5185ccf 100644 --- a/nlp/nlp.py +++ b/nlp/core.py @@ -4,88 +4,7 @@ import pandas as pd - -PLURALS = { - 'apple': 'apples', - 'backpack': 'backpacks', - 'ball': 'balls', - 'banana': 'bananas', - 'baseball bat': 'baseball bats', - 'baseball glove': 'baseball gloves', - 'bear': 'bears', - 'bed': 'beds', - 'bench': 'benches', - 'bicycle': 'bicycles', - 'bird': 'birds', - 'boat': 'boats', - 'book': 'books', - 'bottle': 'bottles', - 'bowl': 'bowls', - 'broccoli': 'broccoli bunches', - 'bus': 'busses', - 'cake': 'cakes', - 'car': 'cars', - 'carrot': 'carrots', - 'cat': 'cats', - 'chair': 'chairs', - 'clock': 'clocks', - 'couch': 'couches', - 'cow': 'cows', - 'cup': 'cups', - 'dining table': 'dining tables', - 'dog': 'dogs', - 'donut': 'donuts', - 'elephant': 'elephants', - 'fire hydrant': 'fire hydrants', - 'fork': 'forks', - 'frisbee': 'frisbees', - 'giraffe': 'giraffes', - 'hair drier': 'hair driers', - 'handbag': 'handbags', - 'horse': 'horses', - 'hot dog': 'hot dogs', - 'keyboard': 'keyboards', - 'kite': 'kites', - 'knife': 'knives', - 'laptop': 'laptops', - 'microwave': 'microwave ovens', - 'mobile phone': 'mobile phones', - 'monitor': 'monitors', - 'motorcycle': 'motorcycles', - 'mouse': 'mice', - 'orange': 'oranges', - 'oven': 'ovens', - 'parking meter': 'parking meters', - 'person': 'people', - 'pizza': 'pizzas', - 'plane': 'planes', - 'potted plant': 'potted plants', - 'refrigerator': 'refrigerators', - 'remote': 'remotes', - 'sandwich': 'sandwiches', - 'scissors': 'pairs of scissors', - 'sheep': 'sheep', - 'sink': 'sinks', - 'skateboard': 'skateboards', - 'skis': 'pairs of skis', - 'snowboard': 'snowboards', - 'spoon': 'spoons', - 'stop sign': 'stop signs', - 'suitcase': 'suitcases', - 'surfboard': 'surfboards', - 'teddy bear': 'teddy bears', - 'tennis racket': 'tennis rackets', - 'tie': 'ties', - 'toaster': 'toasters', - 'toilet': 'toilets', - 'toothbrush': 'toothbrushes', - 'traffic light': 'traffic lights', - 'train': 'trains', - 'truck': 'trucks', - 'umbrella': 'umbrellas', - 'vase': 'vases', - 'wine glass': 'wine glasses', - 'zebra': 'zebras'} +from nlp.plurals import PLURALS def pluralize(s): @@ -126,7 +45,7 @@ def pluralize(s): def update_state(boxes, classes, scores, category_index, window=10, max_boxes_to_draw=None, min_score_thresh=.5): """ Revise state based on latest frame of information (object boxes) - TODO(Hobson) Finish docstring (Need to know all the args and the return val) + TODO(Hobson | Alex) Finish docstring (Need to know all the args and the return val) Args: boxes (list): 2D numpy array of shape (N, 4): (ymin, xmin, ymax, xmax), in normalized format between [0, 1]. classes, From 904692f2aa3d2e7718e39f51c49745ae69d58ae3 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 12 Dec 2017 12:55:02 -0800 Subject: [PATCH 060/174] (NLP Reorg) - Broke out PLURALS mapping into file for readability. --- nlp/plurals.py | 81 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 81 insertions(+) create mode 100644 nlp/plurals.py diff --git a/nlp/plurals.py b/nlp/plurals.py new file mode 100644 index 0000000..ec30afe --- /dev/null +++ b/nlp/plurals.py @@ -0,0 +1,81 @@ +PLURALS = { + 'apple': 'apples', + 'backpack': 'backpacks', + 'ball': 'balls', + 'banana': 'bananas', + 'baseball bat': 'baseball bats', + 'baseball glove': 'baseball gloves', + 'bear': 'bears', + 'bed': 'beds', + 'bench': 'benches', + 'bicycle': 'bicycles', + 'bird': 'birds', + 'boat': 'boats', + 'book': 'books', + 'bottle': 'bottles', + 'bowl': 'bowls', + 'broccoli': 'broccoli bunches', + 'bus': 'busses', + 'cake': 'cakes', + 'car': 'cars', + 'carrot': 'carrots', + 'cat': 'cats', + 'chair': 'chairs', + 'clock': 'clocks', + 'couch': 'couches', + 'cow': 'cows', + 'cup': 'cups', + 'dining table': 'dining tables', + 'dog': 'dogs', + 'donut': 'donuts', + 'elephant': 'elephants', + 'fire hydrant': 'fire hydrants', + 'fork': 'forks', + 'frisbee': 'frisbees', + 'giraffe': 'giraffes', + 'hair drier': 'hair driers', + 'handbag': 'handbags', + 'horse': 'horses', + 'hot dog': 'hot dogs', + 'keyboard': 'keyboards', + 'kite': 'kites', + 'knife': 'knives', + 'laptop': 'laptops', + 'microwave': 'microwave ovens', + 'mobile phone': 'mobile phones', + 'monitor': 'monitors', + 'motorcycle': 'motorcycles', + 'mouse': 'mice', + 'orange': 'oranges', + 'oven': 'ovens', + 'parking meter': 'parking meters', + 'person': 'people', + 'pizza': 'pizzas', + 'plane': 'planes', + 'potted plant': 'potted plants', + 'refrigerator': 'refrigerators', + 'remote': 'remotes', + 'sandwich': 'sandwiches', + 'scissors': 'pairs of scissors', + 'sheep': 'sheep', + 'sink': 'sinks', + 'skateboard': 'skateboards', + 'skis': 'pairs of skis', + 'snowboard': 'snowboards', + 'spoon': 'spoons', + 'stop sign': 'stop signs', + 'suitcase': 'suitcases', + 'surfboard': 'surfboards', + 'teddy bear': 'teddy bears', + 'tennis racket': 'tennis rackets', + 'tie': 'ties', + 'toaster': 'toasters', + 'toilet': 'toilets', + 'toothbrush': 'toothbrushes', + 'traffic light': 'traffic lights', + 'train': 'trains', + 'truck': 'trucks', + 'umbrella': 'umbrellas', + 'vase': 'vases', + 'wine glass': 'wine glasses', + 'zebra': 'zebras'} \ No newline at end of file From 399f15e7cb134b4abee9d63e627d38b26756c983 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 12 Dec 2017 15:37:11 -0800 Subject: [PATCH 061/174] (Dispatch Org) reorganizing mqtt and dispatch functionalities. - merged `mqtt.py` and `dispatch.py` into single `dispatch.py` module. - Deleted `mqtt.py` standalone module - incorporated dispatcher + messaging into main method. --- nlp/dispatch.py | 90 +++++++++++++++++++++++++++++++++++------ nlp/mqtt.py | 66 ------------------------------ object_detection_app.py | 10 +++-- 3 files changed, 84 insertions(+), 82 deletions(-) delete mode 100644 nlp/mqtt.py diff --git a/nlp/dispatch.py b/nlp/dispatch.py index e11c5d6..b7a9eac 100644 --- a/nlp/dispatch.py +++ b/nlp/dispatch.py @@ -1,29 +1,82 @@ """ Module interprets agent commands and dispatches an action + + +How to extend NLP commands: + +1. Import: ` from nlp.dispatch import Dispatchable, dispatcher ` +2. Subclass `Dispatchable`, implementing the action in `__call__`. +3. Expand the `displatcher` with a command: `dispatcher[''] = ` + """ + import json +import os import typing +import paho.mqtt.client as mqtt + +EXPLORER_TOPIC = 'nsf/explorer/command' +AI_TOPIC = 'nsf/ai/response' + + +# Define event callbacks +def on_connect(client, userdata, flags, rc): + print("rc: " + str(rc)) + + +def on_publish(client, obj, mid): + print("mid: " + str(mid)) + + +def on_subscribe(client, obj, mid, granted_qos): + print("Subscribed: " + str(mid) + " " + str(granted_qos)) + + +def on_log(client, obj, level, string): + print(string) + def on_message(client, obj, msg): - print('on msg: ' + str(msg)) + print('on msg: ' + str(msg)) # TODO(Alex) Replace with logging system try: json_string = str(msg.payload, 'utf-8') payload = json.loads(json_string) except json.decoder.JSONDecodeError: return - cmd = interp_command(payload.get('command', 'default'), list(dispatcher.keys())) + cmd = payload.get('command', 'default').lower(), + + action = interp_command(cmd, list(dispatcher.keys())) - dispatcher[cmd](payload) + dispatcher[action](payload) + + +mqttc = mqtt.Client() + + +# Assign event callbacks +mqttc.on_message = on_message +mqttc.on_connect = on_connect +mqttc.on_publish = on_publish +mqttc.on_subscribe = on_subscribe +# mqttc.on_log = on_log + + +url_str = os.environ.get('AIRAMQTT_URL', 'preprod-mqtt.aira.io') +port = int(os.environ.get('AIRAMQTT_PORT', '1883')) + + +mqttc.connect(url_str, port, 60) +mqttc.subscribe(EXPLORER_TOPIC, 0) def interp_command(cmd_str: str, actions: typing.List[str]) -> str: """Output a discrete action to take from a user command in natural language. - + Args: cmd_str: user command - actions: list of commands available from dispatcher + actions: list of commands available from dispatcher Returns: str: single command for dispatcher to execute @@ -44,14 +97,16 @@ def interp_command(cmd_str: str, actions: typing.List[str]) -> str: return 'default' -class Dispatchable(): - from nlp.mqtt import mqttc, AI_TOPIC - +class Dispatchable: client = mqttc - topic = AI_TOPIC + root_topic = AI_TOPIC - def send(self, payload): - self.client.publish(self.topic, payload) + def send(self, payload: typing.Any, *, subtopic: typing.List[str] = list()): + if not subtopic: + self.client.publish(self.root_topic, payload=payload) + else: + self.client.publish(self.root_topic + '/' + '/'.join(subtopic), + payload=payload) class Echo(Dispatchable): @@ -69,6 +124,15 @@ def __call__(self, msg): 'debug': Echo(), } + +def _test_mqtt_loop(): + rc = 0 + + while rc == 0: + rc = mqttc.loop() + print('rc: ' + str(rc)) + + if __name__ == '__main__': - import doctest - doctest.testmod() + + _test_mqtt_loop() diff --git a/nlp/mqtt.py b/nlp/mqtt.py deleted file mode 100644 index bd2a5f4..0000000 --- a/nlp/mqtt.py +++ /dev/null @@ -1,66 +0,0 @@ -""" -Module establishes communication with explorer and agent dashboard -""" - -import os -import paho.mqtt.client as mqtt - -from nlp.dispatch import on_message - -EXPLORER_TOPIC = 'nsf/explorer/command' -AI_TOPIC = 'nsf/ai/response' - - -# Define event callbacks -def on_connect(client, userdata, flags, rc): - print("rc: " + str(rc)) - - -# def on_message(client, obj, msg): -# print(msg.topic + " " + str(msg.qos) + " " + str(msg.payload)) -# -# # TODO(Alex) Remove dev test echo server -# mqttc.publish(AI_TOPIC, 'echo: ' + str(msg.payload)) - - -def on_publish(client, obj, mid): - print("mid: " + str(mid)) - - -def on_subscribe(client, obj, mid, granted_qos): - print("Subscribed: " + str(mid) + " " + str(granted_qos)) - - -def on_log(client, obj, level, string): - print(string) - - -mqttc = mqtt.Client() - - -# Assign event callbacks -mqttc.on_message = on_message -mqttc.on_connect = on_connect -mqttc.on_publish = on_publish -mqttc.on_subscribe = on_subscribe -# mqttc.on_log = on_log - - -url_str = os.environ.get('AIRAMQTT_URL', 'preprod-mqtt.aira.io') -port = int(os.environ.get('AIRAMQTT_PORT', '1883')) - - -mqttc.connect(url_str, port, 60) - -mqttc.subscribe(EXPLORER_TOPIC, 0) - - - - -if __name__ == '__main__': - rc = 0 - - while rc == 0: - rc = mqttc.loop() - print('rc: ' + str(rc)) - diff --git a/object_detection_app.py b/object_detection_app.py index e88ff17..19a5406 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -11,6 +11,7 @@ from object_detection.utils import label_map_util from object_detection.utils import visualization_utils as vis_util from nlp import update_state, describe_state, say +from nlp.dispatch import mqttc, Dispatchable, dispatcher CWD_PATH = os.getcwd() @@ -29,9 +30,6 @@ categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=90, use_display_name=True) category_index = label_map_util.create_category_index(categories) -# Voice Output -is_voice_on = False - def detect_objects(image_np, sess, detection_graph, utterance_frames=20, voice_on=False): # Expand dimensions since the model expects images to have shape: [1, None, None, 3] @@ -139,6 +137,7 @@ def worker(input_q, output_q, voice_on=False): height=args.height).start() fps = FPS().start() + rc = 0 # mqtt client status. Error if not zero while True: # fps._numFrames < 120 frame = video_capture.read() input_q.put(frame) @@ -155,6 +154,11 @@ def worker(input_q, output_q, voice_on=False): if cv2.waitKey(1) & 0xFF == ord('q'): break + if rc is 0: + rc = mqttc.loop() + else: + print('MQTT Connection error!') + fps.stop() print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed())) print('[INFO] approx. FPS: {:.2f}'.format(fps.fps())) From 07a5879a44da99c639aa98be08c182a332321aa5 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 12 Dec 2017 15:37:39 -0800 Subject: [PATCH 062/174] (Moved back `radar.py`) --- object_detection/utils/radar.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/object_detection/utils/radar.py b/object_detection/utils/radar.py index dcea585..721d76c 100644 --- a/object_detection/utils/radar.py +++ b/object_detection/utils/radar.py @@ -1,5 +1,5 @@ """ State vector registration (consolidation/filtering over time in an intertial frame) and buffering """ - +import pandas as pd class SensorBuffer: """ Container for list of dicts containing sensor samples for past W samples (W = window width) """ From dfe04797c9264f5a3f3f3179af58f5751986ae81 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Tue, 12 Dec 2017 16:10:51 -0800 Subject: [PATCH 063/174] __file__ instead of cwd() --- object_detection_app.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/object_detection_app.py b/object_detection_app.py index 0ff1224..4999d70 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -1,9 +1,10 @@ import os -import cv2 import time import argparse import multiprocessing + import numpy as np +import cv2 import tensorflow as tf from utils.app_utils import FPS, WebcamVideoStream @@ -12,7 +13,8 @@ from object_detection.utils import visualization_utils as vis_util from object_detection.utils.nlp import update_state, describe_state, say -CWD_PATH = os.getcwd() +BASE_DIR = os.path.dirname(__file__) +CWD_PATH = BASE_DIR # Path to frozen detection graph. This is the actual model that is used for the object detection. MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017' From 10d2a02aa16f1aa214eb270f7eb9bc91a5ec9caa Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 12 Dec 2017 16:34:58 -0800 Subject: [PATCH 064/174] (Working Demo) State of the feature before the video demo was posted to the AI slack room - added more verbose print statements to mqtt events (e.g. `on_publish`, `on_connect`, etc.) - `Dispatchable.send()` now takes a `dict` type for the payload and converts to a string. - main pyscript now stores object detection state in a `state_q`, the size of which can be controlled via an argument. --- nlp/dispatch.py | 14 ++++++++------ object_detection_app.py | 39 ++++++++++++++++++++++++++++++++++----- 2 files changed, 42 insertions(+), 11 deletions(-) diff --git a/nlp/dispatch.py b/nlp/dispatch.py index b7a9eac..adbc45f 100644 --- a/nlp/dispatch.py +++ b/nlp/dispatch.py @@ -22,11 +22,11 @@ # Define event callbacks def on_connect(client, userdata, flags, rc): - print("rc: " + str(rc)) + print("Connection to client! rc: " + str(rc)) def on_publish(client, obj, mid): - print("mid: " + str(mid)) + print("Publishing AI Response mid: " + str(mid)) def on_subscribe(client, obj, mid, granted_qos): @@ -38,9 +38,9 @@ def on_log(client, obj, level, string): def on_message(client, obj, msg): - print('on msg: ' + str(msg)) # TODO(Alex) Replace with logging system try: json_string = str(msg.payload, 'utf-8') + print('On msg: ' + json_string) payload = json.loads(json_string) except json.decoder.JSONDecodeError: return @@ -49,6 +49,7 @@ def on_message(client, obj, msg): action = interp_command(cmd, list(dispatcher.keys())) + print('calling ' + action) dispatcher[action](payload) @@ -101,12 +102,13 @@ class Dispatchable: client = mqttc root_topic = AI_TOPIC - def send(self, payload: typing.Any, *, subtopic: typing.List[str] = list()): + def send(self, payload: typing.Dict, *, subtopic: typing.List[str] = list()): + payload_json = json.dumps(payload) if not subtopic: - self.client.publish(self.root_topic, payload=payload) + self.client.publish(self.root_topic, payload=payload_json) else: self.client.publish(self.root_topic + '/' + '/'.join(subtopic), - payload=payload) + payload=payload_json) class Echo(Dispatchable): diff --git a/object_detection_app.py b/object_detection_app.py index 19a5406..c6c6613 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -7,7 +7,7 @@ import tensorflow as tf from utils.app_utils import FPS, WebcamVideoStream -from multiprocessing import Queue, Pool +from multiprocessing import Queue, Pool, cpu_count from object_detection.utils import label_map_util from object_detection.utils import visualization_utils as vis_util from nlp import update_state, describe_state, say @@ -31,7 +31,7 @@ category_index = label_map_util.create_category_index(categories) -def detect_objects(image_np, sess, detection_graph, utterance_frames=20, voice_on=False): +def detect_objects(image_np, sess, detection_graph, state_q, utterance_frames=20, voice_on=False): # Expand dimensions since the model expects images to have shape: [1, None, None, 3] image_np_expanded = np.expand_dims(image_np, axis=0) image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') @@ -64,6 +64,10 @@ def detect_objects(image_np, sess, detection_graph, utterance_frames=20, voice_o state = update_state(boxes=np.squeeze(boxes), classes=np.squeeze(classes).astype(np.int32), scores=np.squeeze(scores), category_index=category_index) + + # Persists image state in a queue + state_q.put(state) + if not update_state.i % utterance_frames: description = describe_state(state) if voice_on: @@ -74,7 +78,7 @@ def detect_objects(image_np, sess, detection_graph, utterance_frames=20, voice_o detect_objects.state = [] # poor man's class/object -def worker(input_q, output_q, voice_on=False): +def worker(input_q, output_q, state_q, voice_on=False): # Load a (frozen) Tensorflow model into memory. detection_graph = tf.Graph() with detection_graph.as_default(): @@ -93,12 +97,30 @@ def worker(input_q, output_q, voice_on=False): frame = input_q.get() frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) - output_q.put(detect_objects(frame_rgb, sess, detection_graph, voice_on=voice_on)) + if state_q.full(): + state_q.get() + state_q.get() + + output_q.put(detect_objects(frame_rgb, sess, detection_graph, state_q, voice_on=voice_on)) fps.stop() sess.close() +class Describe(Dispatchable): + + def __init__(self, state_q): + self.state_q = state_q + + def __call__(self, payload): + state = state_q.get() + + if state: + description = describe_state(state) + + self.send({'response': description}, subtopic=['say']) + + if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-src', '--source', dest='video_source', type=int, @@ -117,6 +139,8 @@ def worker(input_q, output_q, voice_on=False): help='Show a GUI/Graphics, or run headless.') parser.add_argument('-s', '--say', action='store_true', default=False, dest='voice_on', help='Say commands on local computer (for debugging)') + parser.add_argument('-sq-size', '--state-queue-size', dest='state_queue_size', type=int, + default=5, help='Size of the object detection state queue size.') args = parser.parse_args() logger = multiprocessing.log_to_stderr() @@ -124,7 +148,11 @@ def worker(input_q, output_q, voice_on=False): input_q = Queue(maxsize=args.queue_size) output_q = Queue(maxsize=args.queue_size) - pool = Pool(args.num_workers, worker, (input_q, output_q, args.voice_on)) + state_q = Queue(maxsize=args.state_queue_size) + + dispatcher['describe'] = Describe(state_q) + + pool = Pool(args.num_workers, worker, (input_q, output_q, state_q, args.voice_on)) disp_graphics = args.gui source = args.video_stream_source @@ -167,3 +195,4 @@ def worker(input_q, output_q, voice_on=False): video_capture.stop() if disp_graphics: cv2.destroyAllWindows() + From 779b3ed2b1595090421da772e49a9089f4bfc0cd Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 12 Dec 2017 17:04:47 -0800 Subject: [PATCH 065/174] (Refactor NLP Organization) --- nlp/command/describe.py | 16 ++++++++++++++++ nlp/dispatch.py | 7 ++----- object_detection_app.py | 19 +++---------------- 3 files changed, 21 insertions(+), 21 deletions(-) create mode 100644 nlp/command/describe.py diff --git a/nlp/command/describe.py b/nlp/command/describe.py new file mode 100644 index 0000000..76ca726 --- /dev/null +++ b/nlp/command/describe.py @@ -0,0 +1,16 @@ +from nlp import describe_state +from nlp.dispatch import Dispatchable + + +class Describe(Dispatchable): + + def __init__(self, state_q): + self.state_q = state_q + + def __call__(self, payload): + state = self.state_q.get() + + if state: + description = describe_state(state) + + self.send({'response': description}, subtopic=['say']) \ No newline at end of file diff --git a/nlp/dispatch.py b/nlp/dispatch.py index adbc45f..f4afee9 100644 --- a/nlp/dispatch.py +++ b/nlp/dispatch.py @@ -4,10 +4,9 @@ How to extend NLP commands: -1. Import: ` from nlp.dispatch import Dispatchable, dispatcher ` -2. Subclass `Dispatchable`, implementing the action in `__call__`. +1. Import: `from nlp.dispatch import Dispatchable, dispatcher` +2. Subclass `Dispatchable`, implementing the action in `__call__`. Call takes in a python dictionary (the `payload`) 3. Expand the `displatcher` with a command: `dispatcher[''] = ` - """ import json @@ -129,12 +128,10 @@ def __call__(self, msg): def _test_mqtt_loop(): rc = 0 - while rc == 0: rc = mqttc.loop() print('rc: ' + str(rc)) if __name__ == '__main__': - _test_mqtt_loop() diff --git a/object_detection_app.py b/object_detection_app.py index c6c6613..e62c35d 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -7,11 +7,12 @@ import tensorflow as tf from utils.app_utils import FPS, WebcamVideoStream -from multiprocessing import Queue, Pool, cpu_count +from multiprocessing import Queue, Pool from object_detection.utils import label_map_util from object_detection.utils import visualization_utils as vis_util from nlp import update_state, describe_state, say -from nlp.dispatch import mqttc, Dispatchable, dispatcher +from nlp.dispatch import mqttc, dispatcher +from nlp.command.describe import Describe CWD_PATH = os.getcwd() @@ -107,20 +108,6 @@ def worker(input_q, output_q, state_q, voice_on=False): sess.close() -class Describe(Dispatchable): - - def __init__(self, state_q): - self.state_q = state_q - - def __call__(self, payload): - state = state_q.get() - - if state: - description = describe_state(state) - - self.send({'response': description}, subtopic=['say']) - - if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-src', '--source', dest='video_source', type=int, From 7ad16cb17a0b1f1d73a0cfd95b96110a379e44dc Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 13 Dec 2017 10:38:21 -0800 Subject: [PATCH 066/174] (Unit Test Fixes) - py2 vs 3; `dict.iteritems()` --> `dict.items()` --- object_detection/core/batcher.py | 6 +++--- object_detection/core/post_processing.py | 2 +- object_detection/evaluator.py | 2 +- .../meta_architectures/faster_rcnn_meta_arch_test_lib.py | 2 +- object_detection/models/feature_map_generators_test.py | 4 ++-- object_detection/utils/variables_helper.py | 2 +- 6 files changed, 9 insertions(+), 9 deletions(-) diff --git a/object_detection/core/batcher.py b/object_detection/core/batcher.py index fdd698c..984734f 100644 --- a/object_detection/core/batcher.py +++ b/object_detection/core/batcher.py @@ -78,11 +78,11 @@ def __init__(self, tensor_dict, batch_size, batch_queue_capacity, """ # Remember static shapes to set shapes of batched tensors. static_shapes = collections.OrderedDict( - {key: tensor.get_shape() for key, tensor in tensor_dict.iteritems()}) + {key: tensor.get_shape() for key, tensor in tensor_dict.items()}) # Remember runtime shapes to unpad tensors after batching. runtime_shapes = collections.OrderedDict( {(key, 'runtime_shapes'): tf.shape(tensor) - for key, tensor in tensor_dict.iteritems()}) + for key, tensor in tensor_dict.items()}) all_tensors = tensor_dict all_tensors.update(runtime_shapes) batched_tensors = tf.train.batch( @@ -109,7 +109,7 @@ def dequeue(self): # Separate input tensors from tensors containing their runtime shapes. tensors = {} shapes = {} - for key, batched_tensor in batched_tensors.iteritems(): + for key, batched_tensor in batched_tensors.items(): unbatched_tensor_list = tf.unstack(batched_tensor) for i, unbatched_tensor in enumerate(unbatched_tensor_list): if isinstance(key, tuple) and key[1] == 'runtime_shapes': diff --git a/object_detection/core/post_processing.py b/object_detection/core/post_processing.py index cda26f2..5983ca1 100644 --- a/object_detection/core/post_processing.py +++ b/object_detection/core/post_processing.py @@ -131,7 +131,7 @@ def multiclass_non_max_suppression(boxes, boxlist_and_class_scores.add_field(fields.BoxListFields.masks, per_class_masks) if additional_fields is not None: - for key, tensor in additional_fields.iteritems(): + for key, tensor in additional_fields.items(): boxlist_and_class_scores.add_field(key, tensor) boxlist_filtered = box_list_ops.filter_greater_than( boxlist_and_class_scores, score_thresh) diff --git a/object_detection/evaluator.py b/object_detection/evaluator.py index 28ac118..45f03dc 100644 --- a/object_detection/evaluator.py +++ b/object_detection/evaluator.py @@ -154,7 +154,7 @@ def _process_batch(tensor_dict, sess, batch_index, counters, update_op): """ if batch_index >= eval_config.num_visualizations: if 'original_image' in tensor_dict: - tensor_dict = {k: v for (k, v) in tensor_dict.iteritems() + tensor_dict = {k: v for (k, v) in tensor_dict.items() if k != 'original_image'} try: (result_dict, _) = sess.run([tensor_dict, update_op]) diff --git a/object_detection/meta_architectures/faster_rcnn_meta_arch_test_lib.py b/object_detection/meta_architectures/faster_rcnn_meta_arch_test_lib.py index 17e1f62..1f7744a 100644 --- a/object_detection/meta_architectures/faster_rcnn_meta_arch_test_lib.py +++ b/object_detection/meta_architectures/faster_rcnn_meta_arch_test_lib.py @@ -271,7 +271,7 @@ def test_predict_gives_correct_shapes_in_inference_mode_first_stage_only( self.assertEqual(set(prediction_out.keys()), expected_output_keys) self.assertAllEqual(prediction_out['image_shape'], input_image_shape) - for output_key, expected_shape in expected_output_shapes.iteritems(): + for output_key, expected_shape in expected_output_shapes.items(): self.assertAllEqual(prediction_out[output_key].shape, expected_shape) # Check that anchors are clipped to window. diff --git a/object_detection/models/feature_map_generators_test.py b/object_detection/models/feature_map_generators_test.py index 690723d..f445e05 100644 --- a/object_detection/models/feature_map_generators_test.py +++ b/object_detection/models/feature_map_generators_test.py @@ -63,7 +63,7 @@ def test_get_expected_feature_map_shapes_with_inception_v2(self): sess.run(init_op) out_feature_maps = sess.run(feature_maps) out_feature_map_shapes = dict( - (key, value.shape) for key, value in out_feature_maps.iteritems()) + (key, value.shape) for key, value in out_feature_maps.items()) self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) def test_get_expected_feature_map_shapes_with_inception_v3(self): @@ -93,7 +93,7 @@ def test_get_expected_feature_map_shapes_with_inception_v3(self): sess.run(init_op) out_feature_maps = sess.run(feature_maps) out_feature_map_shapes = dict( - (key, value.shape) for key, value in out_feature_maps.iteritems()) + (key, value.shape) for key, value in out_feature_maps.items()) self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) diff --git a/object_detection/utils/variables_helper.py b/object_detection/utils/variables_helper.py index 1e091a1..b27f814 100644 --- a/object_detection/utils/variables_helper.py +++ b/object_detection/utils/variables_helper.py @@ -122,7 +122,7 @@ def get_variables_available_in_checkpoint(variables, checkpoint_path): ckpt_reader = tf.train.NewCheckpointReader(checkpoint_path) ckpt_vars = ckpt_reader.get_variable_to_shape_map().keys() vars_in_ckpt = {} - for variable_name, variable in sorted(variable_names_map.iteritems()): + for variable_name, variable in sorted(variable_names_map.items()): if variable_name in ckpt_vars: vars_in_ckpt[variable_name] = variable else: From 7b6440bf9065fa130dd4ea3ee834a0f8100d11e7 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 13 Dec 2017 10:42:36 -0800 Subject: [PATCH 067/174] Revert "(Unit Test Fixes)" This reverts commit 7ad16cb --- object_detection/core/batcher.py | 6 +++--- object_detection/core/post_processing.py | 2 +- object_detection/evaluator.py | 2 +- .../meta_architectures/faster_rcnn_meta_arch_test_lib.py | 2 +- object_detection/models/feature_map_generators_test.py | 4 ++-- object_detection/utils/variables_helper.py | 2 +- 6 files changed, 9 insertions(+), 9 deletions(-) diff --git a/object_detection/core/batcher.py b/object_detection/core/batcher.py index 984734f..fdd698c 100644 --- a/object_detection/core/batcher.py +++ b/object_detection/core/batcher.py @@ -78,11 +78,11 @@ def __init__(self, tensor_dict, batch_size, batch_queue_capacity, """ # Remember static shapes to set shapes of batched tensors. static_shapes = collections.OrderedDict( - {key: tensor.get_shape() for key, tensor in tensor_dict.items()}) + {key: tensor.get_shape() for key, tensor in tensor_dict.iteritems()}) # Remember runtime shapes to unpad tensors after batching. runtime_shapes = collections.OrderedDict( {(key, 'runtime_shapes'): tf.shape(tensor) - for key, tensor in tensor_dict.items()}) + for key, tensor in tensor_dict.iteritems()}) all_tensors = tensor_dict all_tensors.update(runtime_shapes) batched_tensors = tf.train.batch( @@ -109,7 +109,7 @@ def dequeue(self): # Separate input tensors from tensors containing their runtime shapes. tensors = {} shapes = {} - for key, batched_tensor in batched_tensors.items(): + for key, batched_tensor in batched_tensors.iteritems(): unbatched_tensor_list = tf.unstack(batched_tensor) for i, unbatched_tensor in enumerate(unbatched_tensor_list): if isinstance(key, tuple) and key[1] == 'runtime_shapes': diff --git a/object_detection/core/post_processing.py b/object_detection/core/post_processing.py index 5983ca1..cda26f2 100644 --- a/object_detection/core/post_processing.py +++ b/object_detection/core/post_processing.py @@ -131,7 +131,7 @@ def multiclass_non_max_suppression(boxes, boxlist_and_class_scores.add_field(fields.BoxListFields.masks, per_class_masks) if additional_fields is not None: - for key, tensor in additional_fields.items(): + for key, tensor in additional_fields.iteritems(): boxlist_and_class_scores.add_field(key, tensor) boxlist_filtered = box_list_ops.filter_greater_than( boxlist_and_class_scores, score_thresh) diff --git a/object_detection/evaluator.py b/object_detection/evaluator.py index 45f03dc..28ac118 100644 --- a/object_detection/evaluator.py +++ b/object_detection/evaluator.py @@ -154,7 +154,7 @@ def _process_batch(tensor_dict, sess, batch_index, counters, update_op): """ if batch_index >= eval_config.num_visualizations: if 'original_image' in tensor_dict: - tensor_dict = {k: v for (k, v) in tensor_dict.items() + tensor_dict = {k: v for (k, v) in tensor_dict.iteritems() if k != 'original_image'} try: (result_dict, _) = sess.run([tensor_dict, update_op]) diff --git a/object_detection/meta_architectures/faster_rcnn_meta_arch_test_lib.py b/object_detection/meta_architectures/faster_rcnn_meta_arch_test_lib.py index 1f7744a..17e1f62 100644 --- a/object_detection/meta_architectures/faster_rcnn_meta_arch_test_lib.py +++ b/object_detection/meta_architectures/faster_rcnn_meta_arch_test_lib.py @@ -271,7 +271,7 @@ def test_predict_gives_correct_shapes_in_inference_mode_first_stage_only( self.assertEqual(set(prediction_out.keys()), expected_output_keys) self.assertAllEqual(prediction_out['image_shape'], input_image_shape) - for output_key, expected_shape in expected_output_shapes.items(): + for output_key, expected_shape in expected_output_shapes.iteritems(): self.assertAllEqual(prediction_out[output_key].shape, expected_shape) # Check that anchors are clipped to window. diff --git a/object_detection/models/feature_map_generators_test.py b/object_detection/models/feature_map_generators_test.py index f445e05..690723d 100644 --- a/object_detection/models/feature_map_generators_test.py +++ b/object_detection/models/feature_map_generators_test.py @@ -63,7 +63,7 @@ def test_get_expected_feature_map_shapes_with_inception_v2(self): sess.run(init_op) out_feature_maps = sess.run(feature_maps) out_feature_map_shapes = dict( - (key, value.shape) for key, value in out_feature_maps.items()) + (key, value.shape) for key, value in out_feature_maps.iteritems()) self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) def test_get_expected_feature_map_shapes_with_inception_v3(self): @@ -93,7 +93,7 @@ def test_get_expected_feature_map_shapes_with_inception_v3(self): sess.run(init_op) out_feature_maps = sess.run(feature_maps) out_feature_map_shapes = dict( - (key, value.shape) for key, value in out_feature_maps.items()) + (key, value.shape) for key, value in out_feature_maps.iteritems()) self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes) diff --git a/object_detection/utils/variables_helper.py b/object_detection/utils/variables_helper.py index b27f814..1e091a1 100644 --- a/object_detection/utils/variables_helper.py +++ b/object_detection/utils/variables_helper.py @@ -122,7 +122,7 @@ def get_variables_available_in_checkpoint(variables, checkpoint_path): ckpt_reader = tf.train.NewCheckpointReader(checkpoint_path) ckpt_vars = ckpt_reader.get_variable_to_shape_map().keys() vars_in_ckpt = {} - for variable_name, variable in sorted(variable_names_map.items()): + for variable_name, variable in sorted(variable_names_map.iteritems()): if variable_name in ckpt_vars: vars_in_ckpt[variable_name] = variable else: From 00170a036d890e40728f396fdaf4b718a543dd74 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Wed, 13 Dec 2017 11:00:41 -0800 Subject: [PATCH 068/174] dict to list mapping pairs --- object_detection/utils/nlp.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/object_detection/utils/nlp.py b/object_detection/utils/nlp.py index fdc1a6e..c77c48a 100644 --- a/object_detection/utils/nlp.py +++ b/object_detection/utils/nlp.py @@ -178,7 +178,7 @@ def describe_state(state): def say(s, rate=230): """ Convert text to speech (TTS) and play resulting audio to speakers - If "say" command is not available in os.system then print the text to stdout. + If "say" command is not available in os.system then print the text to stdout and return False. >>> say(hello) 'hello' From 8f28b70e8f58b5ba10c96542a4521910649ff82e Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 13 Dec 2017 11:42:13 -0800 Subject: [PATCH 069/174] (README update) - Updated README with API spec - Updated README with program args - Removed `Dat Tran` copyright from README - Added `.DS_Store` to .gitignore --- .gitignore | 5 ++-- README.md | 82 ++++++++++++++++++++++++++++++++++++++++++++++++++---- 2 files changed, 80 insertions(+), 7 deletions(-) diff --git a/.gitignore b/.gitignore index 56757f2..6275117 100644 --- a/.gitignore +++ b/.gitignore @@ -16,7 +16,7 @@ downloads/ eggs/ .eggs/ lib/ -lib64/ +lib64 parts/ sdist/ var/ @@ -89,4 +89,5 @@ ENV/ .ropeproject # Custom -.idea/ \ No newline at end of file +.idea/ +.DS_Store diff --git a/README.md b/README.md index c30cb49..52ecfea 100644 --- a/README.md +++ b/README.md @@ -6,30 +6,102 @@ A real-time object recognition application using [Google's TensorFlow Object Det 1. `conda env create -f environment.yml` 2. `python object_detection_app.py` Optional arguments (default value): + * Show all commands `--help` * Device index of the camera `--source=0` * Width of the frames in the video stream `--width=480` * Height of the frames in the video stream `--height=360` * Number of workers `--num-workers=2` * Size of the queue `--queue-size=5` + * URL for video stream `--url=` + * Turn on GUI (defaulted to run headless) `--gui` + * Turn on vocal commands on MacOS (defaulted to silent) `--say` + * State Buffer Size, how many "states" to capture `--state-queue-size=5` -## Updating the environment +## Development +### Updating the environment `conda env update -f environment.yml` -## Tests +### Tests ``` pytest -vs utils/ +python -m pytest +python -m unittest discover -s object_detection -p "*_test.py" ``` -## Requirements +### Requirements - [Anaconda / Python 3.5](https://www.continuum.io/downloads) - [TensorFlow 1.2](https://www.tensorflow.org/) - [OpenCV 3.0](http://opencv.org/) +### API +Our API is accessible via the MQTT protocol. + +#### `nsf/explorer/command` +We subscribe to a topic coming from an Android client. Incoming messages should be encoded as JSON objects that match +the following format: + +```json +{ + "id": 123, + "datetime": 1234123412341234, + "command": "describe what is going on around me" + //... +} +``` + +#### `nsf/ai/#` +We publish to the root topic and to one or more subtopics. Subtopics will be related to the +commands that ought be executed on the client. For instance, if we expect the client to read the +text response aloud, we will publish it in the `say` subtopic (i.e. `nsf/ai/say`). + +Messages should be encoded as JSON objects in the following format: + +```json +{ + "id": 124, // ID for the current payload + "command_id": 123, // ID of command payload (payload this is in response to) + "datetime": 1234123412341234, + + // Was the service successful? + "status": { + "code": 200, + "message": "success" + }, + + // Prefer kwargs to args + "args": ["arg1", "arg2", "arg3"], + "kwargs": { + "key1": 1, + "key2": "kwarg2" + } + + //... +} +``` + +Here is an example of a response for "say": +Topic: `nsf/ai/say` +Payload: +```json +{ + "id": 124, + "command_id": 123, + "datetime": 1234123412341234, + "status": { + "code": 200, + "message": "success" + }, + "args": [], + "kwargs": { + "text": "there is 1 person and a chair around you" + } +} +``` + + ## Notes - ~~OpenCV 3.1 might crash on OSX after a while, so that's why I had to switch to version 3.0. See open issue and solution [here](https://github.com/opencv/opencv/issues/5874).~~ - Moving the `.read()` part of the video stream in a multiple child processes did not work. However, it was possible to move it to a separate thread. ## Copyright - See [LICENSE](LICENSE) for details. -Copyright (c) 2017 [Dat Tran](http://www.dat-tran.com/). From b58fcca50552249028feeaec75d49d0825b0bbe0 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 13 Dec 2017 15:03:59 -0800 Subject: [PATCH 070/174] Notes from API Review --- README.md | 40 +++++++++++++++++++++++++--------------- 1 file changed, 25 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 52ecfea..4610f04 100644 --- a/README.md +++ b/README.md @@ -36,49 +36,53 @@ python -m unittest discover -s object_detection -p "*_test.py" ### API Our API is accessible via the MQTT protocol. -#### `nsf/explorer/command` +#### `dev/chloe/explorer/statement` We subscribe to a topic coming from an Android client. Incoming messages should be encoded as JSON objects that match the following format: ```json { - "id": 123, - "datetime": 1234123412341234, - "command": "describe what is going on around me" + "messageId": 123, + "serviceId": 53453, + "userId": 4823942, + "timestamp": 1234123412341234, + "statement": "describe what is going on around me" //... } ``` -#### `nsf/ai/#` +#### `dev/chloe/response//` We publish to the root topic and to one or more subtopics. Subtopics will be related to the commands that ought be executed on the client. For instance, if we expect the client to read the -text response aloud, we will publish it in the `say` subtopic (i.e. `nsf/ai/say`). +text response aloud, we will publish it in the `say` subtopic (i.e. `dev/chloe/response/1324234/say`). Messages should be encoded as JSON objects in the following format: ```json { - "id": 124, // ID for the current payload - "command_id": 123, // ID of command payload (payload this is in response to) - "datetime": 1234123412341234, - + "messageId": 124, // ID for the current payload + "statementId": 123, // ID of command payload (payload this is in response to) + "timestamp": 1234123412341234, + // Was the service successful? "status": { - "code": 200, + "code": "ch-vis-000", "message": "success" }, - // Prefer kwargs to args - "args": ["arg1", "arg2", "arg3"], + "action": "say", + "args": ["arg1", "arg2", "arg3"], // **Prefer kwargs to args** "kwargs": { + "confidence": 0.87, // argument that should always be present "key1": 1, "key2": "kwarg2" - } + }, //... } ``` +TODO(Alex) Revise Here is an example of a response for "say": Topic: `nsf/ai/say` Payload: @@ -93,11 +97,17 @@ Payload: }, "args": [], "kwargs": { - "text": "there is 1 person and a chair around you" + "text": "there is 1 person and a chair around you", + "wordsPerMin": 200, + "voiceGender": "Female" } } ``` +### Agent-Chloe Configuration +- Should be configured on dashboard. +- Response to explorer should have a delay, whether they come from Chloe or the AI. The explorer should not be able to distinguish between human and machine. +- Want to design intentional fallback from the AI to the Human agent. Thus, we need two buttons: the random send (either AI or Human), and a **SEND!** that forcibly sends the human response over the AI. ## Notes - ~~OpenCV 3.1 might crash on OSX after a while, so that's why I had to switch to version 3.0. See open issue and solution [here](https://github.com/opencv/opencv/issues/5874).~~ From 3d982249a95e30e579cba3fb0ecfdb5a46f08057 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 13 Dec 2017 15:19:22 -0800 Subject: [PATCH 071/174] Cleaned up notes from API Discussion - Made API examples consistent - Cleaned up Agent-Chloe experiment notes. --- README.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 4610f04..330424a 100644 --- a/README.md +++ b/README.md @@ -52,21 +52,19 @@ the following format: ``` #### `dev/chloe/response//` -We publish to the root topic and to one or more subtopics. Subtopics will be related to the -commands that ought be executed on the client. For instance, if we expect the client to read the -text response aloud, we will publish it in the `say` subtopic (i.e. `dev/chloe/response/1324234/say`). +We publish to the root topic `dev/chloe/response` via subtopics scoped by the end user's id and the desired action. For instance, if we expect the client with id `1324234` to read the text response aloud (i.e. the `say` action), we will publish to the following topic path: `dev/chloe/response/1324234/say`. Messages should be encoded as JSON objects in the following format: ```json { "messageId": 124, // ID for the current payload - "statementId": 123, // ID of command payload (payload this is in response to) + "statementId": 123, // ID of statement payload (payload this is in response to, see above) "timestamp": 1234123412341234, // Was the service successful? "status": { - "code": "ch-vis-000", + "code": "ch-vis-000", // -- "message": "success" }, @@ -88,15 +86,17 @@ Topic: `nsf/ai/say` Payload: ```json { - "id": 124, - "command_id": 123, - "datetime": 1234123412341234, + "messageId": 124, + "statementId": 123, + "timestamp": 1234123412341234, "status": { - "code": 200, - "message": "success" + "code": "ch-vis-000", + "message": "success" }, + "action": "say", "args": [], "kwargs": { + "confidence": 0.87, "text": "there is 1 person and a chair around you", "wordsPerMin": 200, "voiceGender": "Female" @@ -104,7 +104,7 @@ Payload: } ``` -### Agent-Chloe Configuration +### Agent-Chloe Experiment Configuration Discussion - Should be configured on dashboard. - Response to explorer should have a delay, whether they come from Chloe or the AI. The explorer should not be able to distinguish between human and machine. - Want to design intentional fallback from the AI to the Human agent. Thus, we need two buttons: the random send (either AI or Human), and a **SEND!** that forcibly sends the human response over the AI. From 98d49e8168e66080e9c97c6212d5e17e71a95f95 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Wed, 13 Dec 2017 15:39:10 -0800 Subject: [PATCH 072/174] ignore tmp files, minimal klugie radar (just pasthrough to update_state) --- .gitignore | 7 +++++++ object_detection/constants.py | 20 ++++++++++++++++++++ object_detection_app.py | 24 +++--------------------- 3 files changed, 30 insertions(+), 21 deletions(-) create mode 100644 object_detection/constants.py diff --git a/.gitignore b/.gitignore index a3ccb40..f733d32 100644 --- a/.gitignore +++ b/.gitignore @@ -94,3 +94,10 @@ ENV/ # tensorflow model weights *.tar.gz +*.dev.* +*.master.* +*.bak.* +*.broke.* +*.fixed.* +*.works.* + diff --git a/object_detection/constants.py b/object_detection/constants.py new file mode 100644 index 0000000..8077d14 --- /dev/null +++ b/object_detection/constants.py @@ -0,0 +1,20 @@ +""" Constants that depend on the object detection model (tensorflow network) being used. """ +import os + +from object_detection.utils import label_map_util + + +BASE_DIR = os.path.dirname(__file__) + +# Path to frozen detection graph. This is the actual model that is used for the object detection. +MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017' +PATH_TO_CKPT = os.path.join(BASE_DIR, 'object_detection', MODEL_NAME, 'frozen_inference_graph.pb') + +# List of the strings that is used to add correct label for each box. +PATH_TO_LABELS = os.path.join(BASE_DIR, 'object_detection', 'data', 'mscoco_label_map.pbtxt') + +# Loading label map +LABEL_MAP = label_map_util.load_labelmap(PATH_TO_LABELS) +# though mobilenet can handle +CATEGORIES = label_map_util.convert_label_map_to_categories(LABEL_MAP, max_num_classes=90, use_display_name=True) +CATEGORY_INDEX = label_map_util.create_category_index(CATEGORIES) diff --git a/object_detection_app.py b/object_detection_app.py index 340abd4..9e972c4 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -9,29 +9,12 @@ from utils.app_utils import FPS, WebcamVideoStream from multiprocessing import Queue, Pool -from object_detection.utils import label_map_util from object_detection.utils import visualization_utils as vis_util from nlp import update_state, describe_state, say from nlp.dispatch import mqttc, dispatcher from nlp.command.describe import Describe -BASE_DIR = os.path.dirname(__file__) -CWD_PATH = BASE_DIR - -# Path to frozen detection graph. This is the actual model that is used for the object detection. -MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017' -PATH_TO_CKPT = os.path.join(CWD_PATH, 'object_detection', MODEL_NAME, 'frozen_inference_graph.pb') - -# List of the strings that is used to add correct label for each box. -PATH_TO_LABELS = os.path.join(CWD_PATH, 'object_detection', 'data', 'mscoco_label_map.pbtxt') - -# Loading label map -label_map = label_map_util.load_labelmap(PATH_TO_LABELS) -print(label_map) - -# though mobilenet can handle -categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=90, use_display_name=True) -category_index = label_map_util.create_category_index(categories) +from object_detection.constants import CATEGORY_INDEX def detect_objects(image_np, sess, detection_graph, state_q, utterance_frames=20, voice_on=False): @@ -59,14 +42,14 @@ def detect_objects(image_np, sess, detection_graph, state_q, utterance_frames=20 np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores), - category_index, + CATEGORY_INDEX, use_normalized_coordinates=True, line_thickness=8) # Describe the image state = update_state(boxes=np.squeeze(boxes), classes=np.squeeze(classes).astype(np.int32), - scores=np.squeeze(scores), category_index=category_index) + scores=np.squeeze(scores), category_index=CATEGORY_INDEX) # Persists image state in a queue state_q.put(state) @@ -184,4 +167,3 @@ def worker(input_q, output_q, state_q, voice_on=False): video_capture.stop() if disp_graphics: cv2.destroyAllWindows() - From 25a880d3e1927d5341848990b7f42bcd5f5ac906 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Wed, 13 Dec 2017 16:54:34 -0800 Subject: [PATCH 073/174] just try to import --- object_detection/constants.py | 20 ++++++++++++++++++++ object_detection_app.py | 3 +++ 2 files changed, 23 insertions(+) create mode 100644 object_detection/constants.py diff --git a/object_detection/constants.py b/object_detection/constants.py new file mode 100644 index 0000000..8077d14 --- /dev/null +++ b/object_detection/constants.py @@ -0,0 +1,20 @@ +""" Constants that depend on the object detection model (tensorflow network) being used. """ +import os + +from object_detection.utils import label_map_util + + +BASE_DIR = os.path.dirname(__file__) + +# Path to frozen detection graph. This is the actual model that is used for the object detection. +MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017' +PATH_TO_CKPT = os.path.join(BASE_DIR, 'object_detection', MODEL_NAME, 'frozen_inference_graph.pb') + +# List of the strings that is used to add correct label for each box. +PATH_TO_LABELS = os.path.join(BASE_DIR, 'object_detection', 'data', 'mscoco_label_map.pbtxt') + +# Loading label map +LABEL_MAP = label_map_util.load_labelmap(PATH_TO_LABELS) +# though mobilenet can handle +CATEGORIES = label_map_util.convert_label_map_to_categories(LABEL_MAP, max_num_classes=90, use_display_name=True) +CATEGORY_INDEX = label_map_util.create_category_index(CATEGORIES) diff --git a/object_detection_app.py b/object_detection_app.py index e62c35d..461382d 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -3,6 +3,7 @@ import time import argparse import multiprocessing + import numpy as np import tensorflow as tf @@ -31,6 +32,8 @@ categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=90, use_display_name=True) category_index = label_map_util.create_category_index(categories) +from object_detection.constants import CATEGORY_INDEX + def detect_objects(image_np, sess, detection_graph, state_q, utterance_frames=20, voice_on=False): # Expand dimensions since the model expects images to have shape: [1, None, None, 3] From 79b8da877f4024844511620f19d940d7d29fd398 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Wed, 13 Dec 2017 16:56:22 -0800 Subject: [PATCH 074/174] one level up --- object_detection/constants.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/object_detection/constants.py b/object_detection/constants.py index 8077d14..5a0464f 100644 --- a/object_detection/constants.py +++ b/object_detection/constants.py @@ -4,7 +4,7 @@ from object_detection.utils import label_map_util -BASE_DIR = os.path.dirname(__file__) +BASE_DIR = os.path.dirname(os.path.dirname(__file__)) # Path to frozen detection graph. This is the actual model that is used for the object detection. MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017' From 6f7c8a44a4159628ebbde07c1623eb6017df1d6d Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Wed, 13 Dec 2017 17:13:56 -0800 Subject: [PATCH 075/174] incorporate new constants file into object_detection_app --- object_detection/constants.py | 5 +++-- object_detection_app.py | 26 +++----------------------- 2 files changed, 6 insertions(+), 25 deletions(-) diff --git a/object_detection/constants.py b/object_detection/constants.py index 8077d14..082547c 100644 --- a/object_detection/constants.py +++ b/object_detection/constants.py @@ -4,14 +4,15 @@ from object_detection.utils import label_map_util -BASE_DIR = os.path.dirname(__file__) +BASE_DIR = os.path.dirname(os.path.dirname(__file__)) # Path to frozen detection graph. This is the actual model that is used for the object detection. MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017' +LABEL_MAP_FILE = 'mscoco_label_map.pbtxt' PATH_TO_CKPT = os.path.join(BASE_DIR, 'object_detection', MODEL_NAME, 'frozen_inference_graph.pb') # List of the strings that is used to add correct label for each box. -PATH_TO_LABELS = os.path.join(BASE_DIR, 'object_detection', 'data', 'mscoco_label_map.pbtxt') +PATH_TO_LABELS = os.path.join(BASE_DIR, 'object_detection', 'data', LABEL_MAP_FILE) # Loading label map LABEL_MAP = label_map_util.load_labelmap(PATH_TO_LABELS) diff --git a/object_detection_app.py b/object_detection_app.py index 461382d..21fccd7 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -1,4 +1,3 @@ -import os import cv2 import time import argparse @@ -9,30 +8,12 @@ from utils.app_utils import FPS, WebcamVideoStream from multiprocessing import Queue, Pool -from object_detection.utils import label_map_util from object_detection.utils import visualization_utils as vis_util from nlp import update_state, describe_state, say from nlp.dispatch import mqttc, dispatcher from nlp.command.describe import Describe -CWD_PATH = os.getcwd() - -# Path to frozen detection graph. This is the actual model that is used for the object detection. -MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017' -PATH_TO_CKPT = os.path.join(CWD_PATH, 'object_detection', MODEL_NAME, 'frozen_inference_graph.pb') - -# List of the strings that is used to add correct label for each box. -PATH_TO_LABELS = os.path.join(CWD_PATH, 'object_detection', 'data', 'mscoco_label_map.pbtxt') - -# Loading label map -label_map = label_map_util.load_labelmap(PATH_TO_LABELS) -print(label_map) - -# though mobilenet can handle -categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=90, use_display_name=True) -category_index = label_map_util.create_category_index(categories) - -from object_detection.constants import CATEGORY_INDEX +from object_detection.constants import CATEGORY_INDEX, PATH_TO_CKPT def detect_objects(image_np, sess, detection_graph, state_q, utterance_frames=20, voice_on=False): @@ -60,14 +41,14 @@ def detect_objects(image_np, sess, detection_graph, state_q, utterance_frames=20 np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores), - category_index, + CATEGORY_INDEX, use_normalized_coordinates=True, line_thickness=8) # Describe the image state = update_state(boxes=np.squeeze(boxes), classes=np.squeeze(classes).astype(np.int32), - scores=np.squeeze(scores), category_index=category_index) + scores=np.squeeze(scores), category_index=CATEGORY_INDEX) # Persists image state in a queue state_q.put(state) @@ -185,4 +166,3 @@ def worker(input_q, output_q, state_q, voice_on=False): video_capture.stop() if disp_graphics: cv2.destroyAllWindows() - From 578c2506c637375649a29d50772d256a0738a7ca Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Wed, 13 Dec 2017 17:22:47 -0800 Subject: [PATCH 076/174] object_detection_app.py --- object_detection_app.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/object_detection_app.py b/object_detection_app.py index 9e972c4..c494298 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -14,7 +14,7 @@ from nlp.dispatch import mqttc, dispatcher from nlp.command.describe import Describe -from object_detection.constants import CATEGORY_INDEX +from object_detection.constants import CATEGORY_INDEX, PATH_TO_CKPT def detect_objects(image_np, sess, detection_graph, state_q, utterance_frames=20, voice_on=False): From c24d5a08400a6f7245be974be50d9e7f1cc47dd8 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 13 Dec 2017 18:10:21 -0800 Subject: [PATCH 077/174] (Color Labeling Package) - Created color labeler package in `object_detection` - created ColorLabeler class with unfinished functions - Added sklearn and scipy to environment - Renamed variable in `object_detection_app.py` to not conflict with outer scope --- environment.yml | 2 + object_detection/color_labeler/__init__.py | 0 object_detection/color_labeler/core.py | 86 ++++++++++++++++++++++ object_detection_app.py | 8 +- 4 files changed, 94 insertions(+), 2 deletions(-) create mode 100644 object_detection/color_labeler/__init__.py create mode 100644 object_detection/color_labeler/core.py diff --git a/environment.yml b/environment.yml index c2dd1f6..d967868 100644 --- a/environment.yml +++ b/environment.yml @@ -14,5 +14,7 @@ dependencies: - pip: - tensorflow==1.2.0 - paho-mqtt + - scipy==1.0.0 + - sklearn==0.19.1 prefix: /usr/local/anaconda3/envs/object-detection diff --git a/object_detection/color_labeler/__init__.py b/object_detection/color_labeler/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/object_detection/color_labeler/core.py b/object_detection/color_labeler/core.py new file mode 100644 index 0000000..e4c0874 --- /dev/null +++ b/object_detection/color_labeler/core.py @@ -0,0 +1,86 @@ +""" +Assigns a human understandable label to a color value + + +""" + +from scipy.spatial import distance as dist +from scipy.spatial import KDTree # TODO(Alex) use cKDTree, need to upgrade scipy +from sklearn.cluster import KMeans +from collections import OrderedDict + +import numpy as np +import cv2 + + +def determine_color_distribution(image_np, box): + ymin, xmin, ymax, xmax = box + wd = xmax - xmin + ht = ymax - ymin + + # Get center area of the object inside the bounding box + obj_center = image_np[(ymin + ht // 2):(ymax - ht // 2), (xmin + wd // 2):(xmax - wd // 2), :] + + +class ColorLabeler: + + colors = OrderedDict({ + 'red': (255, 0, 0), + 'green': (0, 255, 0), + 'blue': (0, 0, 255) + }) + + def __init__(self): + """Init L*a*b* definitions of colors""" + self.lab = np.zeros((len(self.colors), 1, 3), dtype='uint8') + self.colorNames = [] + + for i, (name, rgb) in enumerate(self.colors.items()): + self.lab[i] = rgb + self.colorNames.append(name) + + self.lab = cv2.cvtColor(self.lab, cv2.COLOR_RGB2LAB) + self.labtree = KDTree(self.lab) + + def _create_mask(self, img): + contour = None + + # construct mask for the contour + mask = np.zeros(img.shape[:2], dtype='uint8') + cv2.drawContours(mask, [contour], -1, 255, -1) + mask = cv2.erode(mask, None, iterations=2) + + return mask + + def _build_custer_model(self, img, k: int = 5, mask=None) -> KMeans: + + # reshape image to vector of pixels + flattened_img = img.reshape((img.shape[0] * img.shape[1], 3)) + + model = KMeans(n_clusters=k) + model.fit(flattened_img) + + return model + + def _centroid_histogram(self, model): + + num_labels = np.arange(0, len(np.unique(model.labels_)) + 1) + hist, _ = np.histogram(model.labels_, bins=num_labels) + + hist = hist.astype('float') + hist /= hist.sum() + + return hist + + def label(self, img): + + mask = self._create_mask(img) + + model = self._build_custer_model(img, mask) + + + _, idxs = self.labtree.query(mean, k=1) + + return self.colorNames[idxs[0]] + + diff --git a/object_detection_app.py b/object_detection_app.py index e62c35d..015c44b 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -28,11 +28,15 @@ print(label_map) # though mobilenet can handle +# TODO(All) Expand number of classes categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=90, use_display_name=True) category_index = label_map_util.create_category_index(categories) -def detect_objects(image_np, sess, detection_graph, state_q, utterance_frames=20, voice_on=False): + + + +def detect_objects(image_np, sess, detection_graph, _state_q, utterance_frames=20, voice_on=False): # Expand dimensions since the model expects images to have shape: [1, None, None, 3] image_np_expanded = np.expand_dims(image_np, axis=0) image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') @@ -67,7 +71,7 @@ def detect_objects(image_np, sess, detection_graph, state_q, utterance_frames=20 scores=np.squeeze(scores), category_index=category_index) # Persists image state in a queue - state_q.put(state) + _state_q.put(state) if not update_state.i % utterance_frames: description = describe_state(state) From b793b5855e2c896f6ee50b63dad8e3c017a91606 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 13 Dec 2017 18:51:00 -0800 Subject: [PATCH 078/174] (Histogram approach) first iter on simple hist approach to color detection --- environment.yml | 3 +- object_detection/color_labeler/core.py | 48 ++++++++++++++------------ 2 files changed, 27 insertions(+), 24 deletions(-) diff --git a/environment.yml b/environment.yml index d967868..7e51ae7 100644 --- a/environment.yml +++ b/environment.yml @@ -15,6 +15,7 @@ dependencies: - tensorflow==1.2.0 - paho-mqtt - scipy==1.0.0 - - sklearn==0.19.1 + - sklearn + - scikit-image prefix: /usr/local/anaconda3/envs/object-detection diff --git a/object_detection/color_labeler/core.py b/object_detection/color_labeler/core.py index e4c0874..18c139f 100644 --- a/object_detection/color_labeler/core.py +++ b/object_detection/color_labeler/core.py @@ -32,25 +32,26 @@ class ColorLabeler: def __init__(self): """Init L*a*b* definitions of colors""" - self.lab = np.zeros((len(self.colors), 1, 3), dtype='uint8') - self.colorNames = [] - - for i, (name, rgb) in enumerate(self.colors.items()): - self.lab[i] = rgb - self.colorNames.append(name) - - self.lab = cv2.cvtColor(self.lab, cv2.COLOR_RGB2LAB) - self.labtree = KDTree(self.lab) + pass + # self.lab = np.zeros((len(self.colors), 1, 3), dtype='uint8') + # self.colorNames = [] + # + # for i, (name, rgb) in enumerate(self.colors.items()): + # self.lab[i] = rgb + # self.colorNames.append(name) + # + # self.lab = cv2.cvtColor(self.lab, cv2.COLOR_RGB2LAB) + # self.labtree = KDTree(self.lab) def _create_mask(self, img): - contour = None - - # construct mask for the contour - mask = np.zeros(img.shape[:2], dtype='uint8') - cv2.drawContours(mask, [contour], -1, 255, -1) - mask = cv2.erode(mask, None, iterations=2) - - return mask + pass + # contour = None + # + # # construct mask for the contour + # mask = np.zeros(img.shape[:2], dtype='uint8') + # cv2.drawContours(mask, [contour], -1, 255, -1) + # mask = cv2.erode(mask, None, iterations=2) + # return mask def _build_custer_model(self, img, k: int = 5, mask=None) -> KMeans: @@ -73,14 +74,15 @@ def _centroid_histogram(self, model): return hist def label(self, img): + hsv_img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV) + mask = self._create_mask(hsv_img) + masked_img = cv2.bitwise_and(hsv_img, hsv_img, mask=mask ) + vhist = cv2.calcHist([hsv_img[2]], [2], mask, [32], [0, 256]) - mask = self._create_mask(img) - - model = self._build_custer_model(img, mask) - + black_hist = vhist[:2].sum() + white_hist = vhist[:-2].sum() - _, idxs = self.labtree.query(mean, k=1) + cv2.bitwise_and() - return self.colorNames[idxs[0]] From 6b982bc966b635b152449a6647a017bb3d4321ea Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Thu, 14 Dec 2017 10:29:11 -0800 Subject: [PATCH 079/174] found it! --- object_detection/utils/radar.py | 85 ++++++++++++++++++++++++++++++--- 1 file changed, 78 insertions(+), 7 deletions(-) diff --git a/object_detection/utils/radar.py b/object_detection/utils/radar.py index 721d76c..e92b07b 100644 --- a/object_detection/utils/radar.py +++ b/object_detection/utils/radar.py @@ -1,21 +1,92 @@ """ State vector registration (consolidation/filtering over time in an intertial frame) and buffering """ +import collections + import pandas as pd +from object_detection.constants import CATEGORY_INDEX + + class SensorBuffer: - """ Container for list of dicts containing sensor samples for past W samples (W = window width) """ + """ Container for list lists of pairs containing information about images for past W frames (W = window width) - def __init__(self, samples=10): + Each object (vector) represents an instance ("the first person I saw today" not "person") in that video frame. + zero or more vectors may be present for each video frame (image) + [ + ['category', 'person'], + ['instance', 1234567], # this will be the same value across consecutive frames once object tracking is implemented + ['x', .5], ['y', .25], ['z', 0.0], + ['width', .12], ['height', .34], ['depth', 0], + ['black', 7], ['white', 2], + ['red', 5], ['blue', 10], ['yellow', 2], + ['purple', 6], ['orange', 1], ['green', 1], + ] + -1 < x < 1 where 0 is the center of the frame, + 1 is far right + -1 < y < 1 where 0 is center and +1 is the top of the frame + z is TBD + width and height are in the same scale as x, y, e.g. width = 2.0 / pixel_width + depth units is TBD + colors are pixel counts within a portion of the bounding box near the center + """ + + def __init__(self, samples=10, category_index=CATEGORY_INDEX): + self.category_index = category_index + + # TODO: samples is actually 10 DataFrames each with up to N rows, where N is the maximum number of rows to be tracked if isinstance(samples, int): - samples = [] for i in range(samples): - samples += [{}] - self.samples = list(samples) + self.samples.append([]) + else: + for i, row in enumerate(samples): + # list of lists of dicts, lists, Series where each element is the "state" of a detected object + self.samples += [pd.DataFrame()] + for j, obj in enumerate(row): + self.samples.append(pd.Series(list(zip(*row))[1], index=list(zip(*row))[0], name=i)) self.now = 0 + def update_state(self, boxes, classes, scores, category_index=None, window=10, max_boxes_to_draw=None, min_score_thresh=.4): + """ Revise state based on latest frame of information (object boxes) + + Args: + boxes (list): 2D numpy array of shape (N, 4): (ymin, xmin, ymax, xmax), in normalized format between [0, 1]. + classes, + Args (thats hould be class attributes): + category_index (dict of dicts): {1: {'id': 1, 'name': 'person'}, 2: {'id': 2, 'name': 'bicycle'},...} + + """ + category_index = self.category_index if category_index is None else category_index + num_boxes = min([boxes.shape[0] if max_boxes_to_draw is None else max_boxes_to_draw, boxes.shape[0], len(classes)]) + + # FIXME: unify self.update_state.states with self.samples + if self.update_states is None: + # Initialize a matrix of state vectors for the past 20 frames + self.update_state.row = 0 + self.update_state.window = 20 + self.update_state.columns = pd.DataFrame(list(self.category_index.values())).set_index('id', drop=True)['name'] + self.update_state.states = pd.DataFrame(pd.np.zeros((20, len(self.category_index)), + dtype=int), columns=self.update_state.columns) + self.update_state.state0 = pd.Series(index=self.update_state.columns) + + state = [] # if state is None else state + for i in range(num_boxes): + if scores is None or scores[i] > min_score_thresh: + # box = tuple(boxes[i].tolist()) + class_name = self.category_index.get(classes[i], {'name': 'unknown object'})['name'] + display_str = '{}: {} {}%'.format(classes[i], class_name, int(100 * scores[i])) + print(display_str) + state += [class_name] + state = collections.Counter(state) + # state = sorted(list(state.items())) + + # FIXME: not used + self.update_state.states.iloc[self.update_state.row, :] = pd.Series(state) + self.update_state.row = (self.update_state.row + 1) % len(self.update_state.states) # update_state.window + return state + class Radar: - """ Intertial 3D position of all objects detected over the course of a session """ + """ Inertial 3D position of all objects detected over the course of a session """ + + def __init__(self, category_index=CATEGORY_INDEX, category_names=10): - def __init__(self, category_names=10, category_names=10): if isinstance(states, int): sensor_frames = pd.DataFrame(pd.np.zeros((20, len(category_index)), dtype=int), columns=update_state.columns) From e22274a53bdbdae8060616fa2230963b2a80d393 Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Thu, 14 Dec 2017 11:09:33 -0800 Subject: [PATCH 080/174] updated readme file --- README.md | 103 +++++++----------------------------------------------- 1 file changed, 12 insertions(+), 91 deletions(-) diff --git a/README.md b/README.md index 330424a..849a949 100644 --- a/README.md +++ b/README.md @@ -4,114 +4,35 @@ A real-time object recognition application using [Google's TensorFlow Object Det ## Getting Started 1. `conda env create -f environment.yml` -2. `python object_detection_app.py` +2. `conda install pip`. If you already have pip, you will need to `source deactivate` first and then run `conda install pip` to make sure there are no errors which will occur in the OS +2. To see where the source of your pyhton files that you are running are, use `which python` +3. If it is not where you have installed the conda environment, you need to change the source for python + a. `vim environment.yml` and look at the first line of the file which should say something like `name: object-detection`. We are interested in the object-detection part, so remember that. + b. `source activate name` where name will be replaced with what was stated in your environment.yml file + c. `which python` and this time the place where you installed conda will show up +3. `python object_detection_app.py` Optional arguments (default value): - * Show all commands `--help` * Device index of the camera `--source=0` * Width of the frames in the video stream `--width=480` * Height of the frames in the video stream `--height=360` * Number of workers `--num-workers=2` * Size of the queue `--queue-size=5` - * URL for video stream `--url=` - * Turn on GUI (defaulted to run headless) `--gui` - * Turn on vocal commands on MacOS (defaulted to silent) `--say` - * State Buffer Size, how many "states" to capture `--state-queue-size=5` -## Development -### Updating the environment -`conda env update -f environment.yml` - -### Tests +## Tests ``` pytest -vs utils/ -python -m pytest -python -m unittest discover -s object_detection -p "*_test.py" ``` -### Requirements +## Requirements - [Anaconda / Python 3.5](https://www.continuum.io/downloads) - [TensorFlow 1.2](https://www.tensorflow.org/) - [OpenCV 3.0](http://opencv.org/) -### API -Our API is accessible via the MQTT protocol. - -#### `dev/chloe/explorer/statement` -We subscribe to a topic coming from an Android client. Incoming messages should be encoded as JSON objects that match -the following format: - -```json -{ - "messageId": 123, - "serviceId": 53453, - "userId": 4823942, - "timestamp": 1234123412341234, - "statement": "describe what is going on around me" - //... -} -``` - -#### `dev/chloe/response//` -We publish to the root topic `dev/chloe/response` via subtopics scoped by the end user's id and the desired action. For instance, if we expect the client with id `1324234` to read the text response aloud (i.e. the `say` action), we will publish to the following topic path: `dev/chloe/response/1324234/say`. - -Messages should be encoded as JSON objects in the following format: - -```json -{ - "messageId": 124, // ID for the current payload - "statementId": 123, // ID of statement payload (payload this is in response to, see above) - "timestamp": 1234123412341234, - - // Was the service successful? - "status": { - "code": "ch-vis-000", // -- - "message": "success" - }, - - "action": "say", - "args": ["arg1", "arg2", "arg3"], // **Prefer kwargs to args** - "kwargs": { - "confidence": 0.87, // argument that should always be present - "key1": 1, - "key2": "kwarg2" - }, - - //... -} -``` - -TODO(Alex) Revise -Here is an example of a response for "say": -Topic: `nsf/ai/say` -Payload: -```json -{ - "messageId": 124, - "statementId": 123, - "timestamp": 1234123412341234, - "status": { - "code": "ch-vis-000", - "message": "success" - }, - "action": "say", - "args": [], - "kwargs": { - "confidence": 0.87, - "text": "there is 1 person and a chair around you", - "wordsPerMin": 200, - "voiceGender": "Female" - } -} -``` - -### Agent-Chloe Experiment Configuration Discussion -- Should be configured on dashboard. -- Response to explorer should have a delay, whether they come from Chloe or the AI. The explorer should not be able to distinguish between human and machine. -- Want to design intentional fallback from the AI to the Human agent. Thus, we need two buttons: the random send (either AI or Human), and a **SEND!** that forcibly sends the human response over the AI. - ## Notes -- ~~OpenCV 3.1 might crash on OSX after a while, so that's why I had to switch to version 3.0. See open issue and solution [here](https://github.com/opencv/opencv/issues/5874).~~ +- OpenCV 3.1 might crash on OSX after a while, so that's why I had to switch to version 3.0. See open issue and solution [here](https://github.com/opencv/opencv/issues/5874). - Moving the `.read()` part of the video stream in a multiple child processes did not work. However, it was possible to move it to a separate thread. ## Copyright + See [LICENSE](LICENSE) for details. +Copyright (c) 2017 [Dat Tran](http://www.dat-tran.com/). \ No newline at end of file From b8b9d71e00a2d35a37a53f43e64d057d509e052f Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Thu, 14 Dec 2017 11:18:43 -0800 Subject: [PATCH 081/174] Readme Numbers updated --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 849a949..a66f114 100644 --- a/README.md +++ b/README.md @@ -5,12 +5,12 @@ A real-time object recognition application using [Google's TensorFlow Object Det ## Getting Started 1. `conda env create -f environment.yml` 2. `conda install pip`. If you already have pip, you will need to `source deactivate` first and then run `conda install pip` to make sure there are no errors which will occur in the OS -2. To see where the source of your pyhton files that you are running are, use `which python` -3. If it is not where you have installed the conda environment, you need to change the source for python +3. To see where the source of your pyhton files that you are running are, use `which python` +4. If it is not where you have installed the conda environment, you need to change the source for python a. `vim environment.yml` and look at the first line of the file which should say something like `name: object-detection`. We are interested in the object-detection part, so remember that. b. `source activate name` where name will be replaced with what was stated in your environment.yml file c. `which python` and this time the place where you installed conda will show up -3. `python object_detection_app.py` +5. `python object_detection_app.py` Optional arguments (default value): * Device index of the camera `--source=0` * Width of the frames in the video stream `--width=480` From dcd178059f71efff7d300ab9e77b288ecde1ec8d Mon Sep 17 00:00:00 2001 From: ashwinkannan94 Date: Thu, 14 Dec 2017 11:19:57 -0800 Subject: [PATCH 082/174] Update README.md --- README.md | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 330424a..12e1a53 100644 --- a/README.md +++ b/README.md @@ -4,18 +4,19 @@ A real-time object recognition application using [Google's TensorFlow Object Det ## Getting Started 1. `conda env create -f environment.yml` -2. `python object_detection_app.py` +2. `conda install pip`. If you already have pip, you will need to `source deactivate` first and then run `conda install pip` to make sure there are no errors which will occur in the OS +3. To see where the source of your pyhton files that you are running are, use `which python` +4. If it is not where you have installed the conda environment, you need to change the source for python + a. `vim environment.yml` and look at the first line of the file which should say something like `name: object-detection`. We are interested in the object-detection part, so remember that. + b. `source activate name` where name will be replaced with what was stated in your environment.yml file + c. `which python` and this time the place where you installed conda will show up +5. `python object_detection_app.py` Optional arguments (default value): - * Show all commands `--help` * Device index of the camera `--source=0` * Width of the frames in the video stream `--width=480` * Height of the frames in the video stream `--height=360` * Number of workers `--num-workers=2` * Size of the queue `--queue-size=5` - * URL for video stream `--url=` - * Turn on GUI (defaulted to run headless) `--gui` - * Turn on vocal commands on MacOS (defaulted to silent) `--say` - * State Buffer Size, how many "states" to capture `--state-queue-size=5` ## Development ### Updating the environment From 30ba21e849f1aca21c141058a2d400d89c5e20cc Mon Sep 17 00:00:00 2001 From: ashwinkannan94 Date: Thu, 14 Dec 2017 11:22:07 -0800 Subject: [PATCH 083/174] Update README.md --- README.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 12e1a53..0ccb2de 100644 --- a/README.md +++ b/README.md @@ -4,12 +4,13 @@ A real-time object recognition application using [Google's TensorFlow Object Det ## Getting Started 1. `conda env create -f environment.yml` -2. `conda install pip`. If you already have pip, you will need to `source deactivate` first and then run `conda install pip` to make sure there are no errors which will occur in the OS +2. `conda install pip`. + *If you already have pip, you will need to `source deactivate` first and then run `conda install pip` to make sure there are no errors which will occur in the OS 3. To see where the source of your pyhton files that you are running are, use `which python` 4. If it is not where you have installed the conda environment, you need to change the source for python - a. `vim environment.yml` and look at the first line of the file which should say something like `name: object-detection`. We are interested in the object-detection part, so remember that. - b. `source activate name` where name will be replaced with what was stated in your environment.yml file - c. `which python` and this time the place where you installed conda will show up + * `vim environment.yml` and look at the first line of the file which should say something like `name: object-detection`. We are interested in the object-detection part, so remember that. + * `source activate name` where name will be replaced with what was stated in your environment.yml file + * `which python` and this time the place where you installed conda will show up 5. `python object_detection_app.py` Optional arguments (default value): * Device index of the camera `--source=0` From ce21d7796e7443715900c49a1ef7d11b85cc512a Mon Sep 17 00:00:00 2001 From: ashwinkannan94 Date: Thu, 14 Dec 2017 11:23:10 -0800 Subject: [PATCH 084/174] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 0ccb2de..08e3805 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ A real-time object recognition application using [Google's TensorFlow Object Det *If you already have pip, you will need to `source deactivate` first and then run `conda install pip` to make sure there are no errors which will occur in the OS 3. To see where the source of your pyhton files that you are running are, use `which python` 4. If it is not where you have installed the conda environment, you need to change the source for python - * `vim environment.yml` and look at the first line of the file which should say something like `name: object-detection`. We are interested in the object-detection part, so remember that. + * `vim environment.yml` and look at the first line of the file which should say something like `name: object-detection`. We are interested in the name of the file. * `source activate name` where name will be replaced with what was stated in your environment.yml file * `which python` and this time the place where you installed conda will show up 5. `python object_detection_app.py` From 2c5f58087c5cad63ce819ea5d0553f18fb6e7143 Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Thu, 14 Dec 2017 11:28:16 -0800 Subject: [PATCH 085/174] Up to date with master --- README.md | 99 ++++++++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 90 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index a66f114..08e3805 100644 --- a/README.md +++ b/README.md @@ -4,12 +4,13 @@ A real-time object recognition application using [Google's TensorFlow Object Det ## Getting Started 1. `conda env create -f environment.yml` -2. `conda install pip`. If you already have pip, you will need to `source deactivate` first and then run `conda install pip` to make sure there are no errors which will occur in the OS +2. `conda install pip`. + *If you already have pip, you will need to `source deactivate` first and then run `conda install pip` to make sure there are no errors which will occur in the OS 3. To see where the source of your pyhton files that you are running are, use `which python` 4. If it is not where you have installed the conda environment, you need to change the source for python - a. `vim environment.yml` and look at the first line of the file which should say something like `name: object-detection`. We are interested in the object-detection part, so remember that. - b. `source activate name` where name will be replaced with what was stated in your environment.yml file - c. `which python` and this time the place where you installed conda will show up + * `vim environment.yml` and look at the first line of the file which should say something like `name: object-detection`. We are interested in the name of the file. + * `source activate name` where name will be replaced with what was stated in your environment.yml file + * `which python` and this time the place where you installed conda will show up 5. `python object_detection_app.py` Optional arguments (default value): * Device index of the camera `--source=0` @@ -18,21 +19,101 @@ A real-time object recognition application using [Google's TensorFlow Object Det * Number of workers `--num-workers=2` * Size of the queue `--queue-size=5` -## Tests +## Development +### Updating the environment +`conda env update -f environment.yml` + +### Tests ``` pytest -vs utils/ +python -m pytest +python -m unittest discover -s object_detection -p "*_test.py" ``` -## Requirements +### Requirements - [Anaconda / Python 3.5](https://www.continuum.io/downloads) - [TensorFlow 1.2](https://www.tensorflow.org/) - [OpenCV 3.0](http://opencv.org/) +### API +Our API is accessible via the MQTT protocol. + +#### `dev/chloe/explorer/statement` +We subscribe to a topic coming from an Android client. Incoming messages should be encoded as JSON objects that match +the following format: + +```json +{ + "messageId": 123, + "serviceId": 53453, + "userId": 4823942, + "timestamp": 1234123412341234, + "statement": "describe what is going on around me" + //... +} +``` + +#### `dev/chloe/response//` +We publish to the root topic `dev/chloe/response` via subtopics scoped by the end user's id and the desired action. For instance, if we expect the client with id `1324234` to read the text response aloud (i.e. the `say` action), we will publish to the following topic path: `dev/chloe/response/1324234/say`. + +Messages should be encoded as JSON objects in the following format: + +```json +{ + "messageId": 124, // ID for the current payload + "statementId": 123, // ID of statement payload (payload this is in response to, see above) + "timestamp": 1234123412341234, + + // Was the service successful? + "status": { + "code": "ch-vis-000", // -- + "message": "success" + }, + + "action": "say", + "args": ["arg1", "arg2", "arg3"], // **Prefer kwargs to args** + "kwargs": { + "confidence": 0.87, // argument that should always be present + "key1": 1, + "key2": "kwarg2" + }, + + //... +} +``` + +TODO(Alex) Revise +Here is an example of a response for "say": +Topic: `nsf/ai/say` +Payload: +```json +{ + "messageId": 124, + "statementId": 123, + "timestamp": 1234123412341234, + "status": { + "code": "ch-vis-000", + "message": "success" + }, + "action": "say", + "args": [], + "kwargs": { + "confidence": 0.87, + "text": "there is 1 person and a chair around you", + "wordsPerMin": 200, + "voiceGender": "Female" + } +} +``` + +### Agent-Chloe Experiment Configuration Discussion +- Should be configured on dashboard. +- Response to explorer should have a delay, whether they come from Chloe or the AI. The explorer should not be able to distinguish between human and machine. +- Want to design intentional fallback from the AI to the Human agent. Thus, we need two buttons: the random send (either AI or Human), and a **SEND!** that forcibly sends the human response over the AI. + ## Notes -- OpenCV 3.1 might crash on OSX after a while, so that's why I had to switch to version 3.0. See open issue and solution [here](https://github.com/opencv/opencv/issues/5874). +- ~~OpenCV 3.1 might crash on OSX after a while, so that's why I had to switch to version 3.0. See open issue and solution [here](https://github.com/opencv/opencv/issues/5874).~~ - Moving the `.read()` part of the video stream in a multiple child processes did not work. However, it was possible to move it to a separate thread. ## Copyright - See [LICENSE](LICENSE) for details. -Copyright (c) 2017 [Dat Tran](http://www.dat-tran.com/). \ No newline at end of file From ccf61f128f0dc9c7681138e388ccc8f2cec17880 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Thu, 14 Dec 2017 14:56:44 -0800 Subject: [PATCH 086/174] style guide and bugfix by Ashwin --- CONTRIBUTING.md | 59 +++++++++++++++++++++++++++++++++ object_detection/utils/radar.py | 11 +++--- 2 files changed, 66 insertions(+), 4 deletions(-) create mode 100644 CONTRIBUTING.md diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..e49229a --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,59 @@ +## Style Guide + +The primary goal is to produce readable code. +This means being consistent with patterns used by many people, except when the "crowd" favors less readable layout or syntax. + + +### [PEP8](https://www.python.org/dev/peps/pep-0008/) with these exceptions + +Follow the [Hettinger interpretation of PEP8](https://www.youtube.com/watch?v=wf-BqAjZb8M) for beautiful, readable code. + +* max line length is "about" 120 chars +* max complexity: mccabe_threshold": 12, # threshold limit for McCabe complexity checker. +* type hints are encouraged but not required + +Here's my Sublime Anaconda plugin configuration. + + +```json +{ + // Maximum McCabe complexity (number of conditional branches within a function). + "mccabe_threshold": 7, + + // Maximum line length + "pep8_max_line_length": 120 +} +``` + + +### Documentation + +Markdown README.md files in any folder containing significant code. + +Use [Google/Numpy/Napolean/Markdown](http://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) docstring syntax, **not** the original ReST dosctring format. + + +#### Example Dosstring + + +```python +def add(value, num=0) + """ Add a float to an integer + + Args: + value (float): first number in the sum + num (int): the integer to be added + + Returns: + float: the sum of `value + num` + + >>> add(1., 2) + 3.0 + """ +``` + + +### Workflow + +* branch off master whenever you begin a new feature/task +* commit often, mentioning the Jira ticket number in the comment, where possible (e.g. "#") \ No newline at end of file diff --git a/object_detection/utils/radar.py b/object_detection/utils/radar.py index e92b07b..cc01926 100644 --- a/object_detection/utils/radar.py +++ b/object_detection/utils/radar.py @@ -54,14 +54,16 @@ def update_state(self, boxes, classes, scores, category_index=None, window=10, m """ category_index = self.category_index if category_index is None else category_index - num_boxes = min([boxes.shape[0] if max_boxes_to_draw is None else max_boxes_to_draw, boxes.shape[0], len(classes)]) + num_boxes = min([boxes.shape[0] if max_boxes_to_draw is None else max_boxes_to_draw, + boxes.shape[0], len(classes)]) # FIXME: unify self.update_state.states with self.samples - if self.update_states is None: + if self.update_state.states is None: # Initialize a matrix of state vectors for the past 20 frames self.update_state.row = 0 self.update_state.window = 20 - self.update_state.columns = pd.DataFrame(list(self.category_index.values())).set_index('id', drop=True)['name'] + self.update_state.columns = pd.DataFrame( + list(self.category_index.values())).set_index('id', drop=True)['name'] self.update_state.states = pd.DataFrame(pd.np.zeros((20, len(self.category_index)), dtype=int), columns=self.update_state.columns) self.update_state.state0 = pd.Series(index=self.update_state.columns) @@ -89,4 +91,5 @@ class Radar: def __init__(self, category_index=CATEGORY_INDEX, category_names=10): if isinstance(states, int): - sensor_frames = pd.DataFrame(pd.np.zeros((20, len(category_index)), dtype=int), columns=update_state.columns) + sensor_frames = pd.DataFrame(pd.np.zeros((20, len(category_index)), dtype=int), + columns=update_state.columns) From 76882f5562408f249280afa93ef27037874eef92 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Thu, 14 Dec 2017 14:59:01 -0800 Subject: [PATCH 087/174] style guide and bugfix by Ashwin --- object_detection/utils/radar.py | 1 + 1 file changed, 1 insertion(+) diff --git a/object_detection/utils/radar.py b/object_detection/utils/radar.py index cc01926..3daf612 100644 --- a/object_detection/utils/radar.py +++ b/object_detection/utils/radar.py @@ -83,6 +83,7 @@ def update_state(self, boxes, classes, scores, category_index=None, window=10, m self.update_state.states.iloc[self.update_state.row, :] = pd.Series(state) self.update_state.row = (self.update_state.row + 1) % len(self.update_state.states) # update_state.window return state + update_state.states = None class Radar: From a6998da896bb6eb7dd6cbfd14054d1b866d477cd Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Thu, 14 Dec 2017 16:22:25 -0800 Subject: [PATCH 088/174] (Color Hist v1) - Created first iteration of color labeling via histograms. Going to test it out now + clean up the code. --- object_detection/color_labeler/core.py | 89 +++++++++++++++++++++++--- 1 file changed, 81 insertions(+), 8 deletions(-) diff --git a/object_detection/color_labeler/core.py b/object_detection/color_labeler/core.py index 18c139f..0bbc03a 100644 --- a/object_detection/color_labeler/core.py +++ b/object_detection/color_labeler/core.py @@ -53,6 +53,18 @@ def _create_mask(self, img): # mask = cv2.erode(mask, None, iterations=2) # return mask + def _get_bbox_center_img(self, img, box): + + # get space measures + ymin, xmin, ymax, xmax = box + wd = xmax - xmin + ht = ymax - ymin + + # Get center area of the object inside the bounding box + obj_center = img[(ymin + ht // 2):(ymax - ht // 2), (xmin + wd // 2):(xmax - wd // 2), :] + + return obj_center + def _build_custer_model(self, img, k: int = 5, mask=None) -> KMeans: # reshape image to vector of pixels @@ -73,16 +85,77 @@ def _centroid_histogram(self, model): return hist - def label(self, img): - hsv_img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV) - mask = self._create_mask(hsv_img) - masked_img = cv2.bitwise_and(hsv_img, hsv_img, mask=mask ) - vhist = cv2.calcHist([hsv_img[2]], [2], mask, [32], [0, 256]) + def label(self, img, box=None): + + # Get center of image (via bounding box) + if not box: + box = (0, img.shape[0], 0, img.shape[1]) + + cropped_img = self._get_bbox_center_img(img, box) + + # Convert to Hue, Saturation, Value color space + hsv_img = cv2.cvtColor(cropped_img, cv2.COLOR_RGB2HSV) + + # Flatten pixels in image + flat_img = hsv_img.reshape((hsv_img.shape[0] * hsv_img.shape[1], 3)) + + # Calculate indexed histograms of the Value dimension + n_bins_val = 32 + bw_bin_thresh = 3 + + bins_val = np.linspace(0, 255, n_bins_val, endpoint=True, dtype='uint8') + hist_val_idx = np.digitize(flat_img[:, 2], bins_val) + # hist_val, _ = np.histogram(hist_val_idx) + + is_black_pxl = hist_val_idx <= bw_bin_thresh + is_white_pxl = hist_val_idx >= (n_bins_val - bw_bin_thresh) + + black_idx = hist_val_idx[is_black_pxl] + white_idx = hist_val_idx[is_white_pxl] + + n_black = len(black_idx) + n_white = len(white_idx) + + # Calculate histogram of Hue dimension + color_flat_img = flat_img[~np.logical_or(black_idx, white_idx), :] # exclude black or white pixels + + # Mathematical approach doesn't provide human understandable color ranges. Going to hardcode... + # color_centers = np.arange(0, 360, 30) # produces 12 color centers + # color_ranges = (color_centers - 15) % 360 # maps to 360 deg bin ranges + # bins_hue = color_ranges // 2 # ranges need to fit in uint8 space [0, 179] + + # [red, orange, yellow, green, cyan, blue, purple, pink] + bins_hue = [-8, 7, 22, 82, 97, 127, 142, 171] + hist_hue, _ = np.histogram(color_flat_img[:, 0], bins=bins_hue) + + total = n_black + n_white + sum(hist_hue) + + output = { + 'black': n_black / total, + 'white': n_white / total, + 'red': hist_hue[0] / total, + 'orange': hist_hue[1] / total, + 'yellow': hist_hue[2] / total, + 'green': hist_hue[3] / total, + 'cyan': hist_hue[4] / total, + 'blue': hist_hue[5] / total, + 'purple': hist_hue[6] / total, + 'pink': hist_hue[7] / total + } + + return output + + + + + + + - black_hist = vhist[:2].sum() - white_hist = vhist[:-2].sum() + # vhist = cv2.calcHist([hsv_img], [2], mask, [32], [0, 256]) + # black_hist = vhist[:2] + # white_hist = vhist[:-2] - cv2.bitwise_and() From 0a8d8797bb9d687d42ac01f38029a7897468115e Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Thu, 14 Dec 2017 16:22:57 -0800 Subject: [PATCH 089/174] constants for color and bb --- object_detection/constants.py | 4 ++++ object_detection/utils/radar.py | 12 +++++++----- 2 files changed, 11 insertions(+), 5 deletions(-) diff --git a/object_detection/constants.py b/object_detection/constants.py index 43ba391..6108d2a 100644 --- a/object_detection/constants.py +++ b/object_detection/constants.py @@ -18,3 +18,7 @@ # though mobilenet can handle CATEGORIES = label_map_util.convert_label_map_to_categories(LABEL_MAP, max_num_classes=90, use_display_name=True) CATEGORY_INDEX = label_map_util.create_category_index(CATEGORIES) + + +COLOR_KEYS = 'red orange yellow green indigo violet black white pink'.split() +OBJECT_VECTOR_KEYS = diff --git a/object_detection/utils/radar.py b/object_detection/utils/radar.py index 3daf612..03f8dcd 100644 --- a/object_detection/utils/radar.py +++ b/object_detection/utils/radar.py @@ -34,13 +34,14 @@ def __init__(self, samples=10, category_index=CATEGORY_INDEX): # TODO: samples is actually 10 DataFrames each with up to N rows, where N is the maximum number of rows to be tracked if isinstance(samples, int): for i in range(samples): - self.samples.append([]) + self.samples.append(pd.DataFrame()) else: for i, row in enumerate(samples): # list of lists of dicts, lists, Series where each element is the "state" of a detected object self.samples += [pd.DataFrame()] for j, obj in enumerate(row): - self.samples.append(pd.Series(list(zip(*row))[1], index=list(zip(*row))[0], name=i)) + self.samples[-1][j] = pd.Series(list(zip(*row))[1], index=list(zip(*row))[0], name=i) + self.samples[-1].transpose(inplace=True) self.now = 0 def update_state(self, boxes, classes, scores, category_index=None, window=10, max_boxes_to_draw=None, min_score_thresh=.4): @@ -90,7 +91,8 @@ class Radar: """ Inertial 3D position of all objects detected over the course of a session """ def __init__(self, category_index=CATEGORY_INDEX, category_names=10): + pass - if isinstance(states, int): - sensor_frames = pd.DataFrame(pd.np.zeros((20, len(category_index)), dtype=int), - columns=update_state.columns) + def update(self, sensor_frame): + """ Add or update all the objects listed in a sensor_frame vector to the radar map (inertial tracking of all objects). """ + pass From 172082df7d26721177a95903586e83f0b4cbe42f Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Thu, 14 Dec 2017 17:34:31 -0800 Subject: [PATCH 090/174] docstring --- normalize.py | 44 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 44 insertions(+) create mode 100644 normalize.py diff --git a/normalize.py b/normalize.py new file mode 100644 index 0000000..ba36914 --- /dev/null +++ b/normalize.py @@ -0,0 +1,44 @@ +def normalize_x_and_y(image,xmin,xmax, ymin,ymax): + """ + Takes in an image which will be provided and then computes the normalized bouding box information. + Args: + xmin - this is the left most point of the bounding box + xmax - this is the right most point of the bounding box + ymin - this is the lowest point of the bounding box + ymax - this is the highest point of the bouding box + From image, try to get image width and height such that we can scale it appropriately + + (xmin,ymax) + ---------------(xmax,ymax) + | | + | | + | | + | | + | | + ________________ + (xmin,ymin) (xmax,ymin) + + + + The output will be the following 6 parameters + x - the left most point and is scaled between -1 and 1. to scale, can do (xmin-image_width/2)/(image_width/2) + y - the bottom most point and is scaled between -1 and 1. to scale, can do (ymin-image_height/2)/(image_height/2) + width - this is defined as xmax-xmin. to scale, compute (xmax-xmin)/(image_width/2) + height - this is defined as ymax-ymin. to scale, compute (ymax-ymin)/(image_height/2) + z - set to 0 + depth - set to 0 + + (x,y+height) + ---------------(x+width, y+height) + | | + | | + | | + | | + | | + ________________ + (x,y) (x+width,y) + + + + """ + From 7bbd8789bec71408fa281b1c0aa03e610bcc3f1d Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Thu, 14 Dec 2017 17:40:35 -0800 Subject: [PATCH 091/174] code added --- normalize.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/normalize.py b/normalize.py index ba36914..be30804 100644 --- a/normalize.py +++ b/normalize.py @@ -1,4 +1,4 @@ -def normalize_x_and_y(image,xmin,xmax, ymin,ymax): +def normalize_x_and_y(image,xmin,xmax,ymin,ymax): """ Takes in an image which will be provided and then computes the normalized bouding box information. Args: @@ -39,6 +39,13 @@ def normalize_x_and_y(image,xmin,xmax, ymin,ymax): (x,y) (x+width,y) - """ + im_width, im_height = image.size + x = (xmin-(im_width/2))/(im_width/2) + y = (ymin-(im_height/2))/(image_height/2) + width = ((xmax-xmin)/(im_width/2)) + height = ((ymax-ymin))/(im_height/2) + z = 0 + depth = 0 + return(x,y,width,height,z,depth) \ No newline at end of file From 2a2563e0aa82db8796ebcff2d393c23bb0a93a1a Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Thu, 14 Dec 2017 19:39:07 -0800 Subject: [PATCH 092/174] updated test cases and code --- normalize.py | 69 ++++++++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 59 insertions(+), 10 deletions(-) diff --git a/normalize.py b/normalize.py index be30804..b071c47 100644 --- a/normalize.py +++ b/normalize.py @@ -1,3 +1,5 @@ +from PIL import Image + def normalize_x_and_y(image,xmin,xmax,ymin,ymax): """ Takes in an image which will be provided and then computes the normalized bouding box information. @@ -8,8 +10,8 @@ def normalize_x_and_y(image,xmin,xmax,ymin,ymax): ymax - this is the highest point of the bouding box From image, try to get image width and height such that we can scale it appropriately - (xmin,ymax) - ---------------(xmax,ymax) + (xmin,ymax). (xmax,ymax) + --------------- | | | | | | @@ -28,8 +30,8 @@ def normalize_x_and_y(image,xmin,xmax,ymin,ymax): z - set to 0 depth - set to 0 - (x,y+height) - ---------------(x+width, y+height) + (x,y+height). (x+width, y+height) + --------------- | | | | | | @@ -38,14 +40,61 @@ def normalize_x_and_y(image,xmin,xmax,ymin,ymax): ________________ (x,y) (x+width,y) - + these test cases are for a 100*100 image but generic code is built to run any height and width + >>> normalize_x_and_y(image,100,100,50,50) + 1.0 0.0 0.0 0.0 0.0 0.0 + >>> normalize_x_and_y(image,10,90,10,90) + -0.8 -0.8 1.6 1.6 0.0 0.0 + >>> normalize_x_and_y(image,90,10,10,90) + xmin is greater than xmax + >>> normalize_x_and_y(image,110,190,10,40) + xmin is greater than image width + >>> normalize_x_and_y(image,-10,10,10,90) + xmin < 0 + >>> normalize_x_and_y(image,10,90,90,10) + ymin is greater than ymax + >>> normalize_x_and_y(image,10,90,10,190) + ymax is greater than image height """ - im_width, im_height = image.size + #TODO: Check if I really need this next line or not + im = Image.open(image) + im_width, im_height = im.size + if (xmin > xmax): + print ("xmin is greater than xmax") + return + + if (ymin>ymax): + print("ymin is greater than ymax") + return + + if (xmin > im_width): + print("xmin is greater than image width") + return + + if (xmax > im_width): + print("xmax is greater than image width") + return + + if (ymin > im_height): + print ("ymin is greater than image height") + return + if (ymax > im_height): + print ("ymax is greater than image height") + return + + if (xmin < 0): + print ("xmin < 0") + return + + if (ymin < 0): + print ("ymin < 0") + return + x = (xmin-(im_width/2))/(im_width/2) - y = (ymin-(im_height/2))/(image_height/2) + y = (ymin-(im_height/2))/(im_height/2) width = ((xmax-xmin)/(im_width/2)) height = ((ymax-ymin))/(im_height/2) - z = 0 - depth = 0 - return(x,y,width,height,z,depth) \ No newline at end of file + z = 0.0 + depth = 0.0 + print(x,y,width,height,z,depth) \ No newline at end of file From 3c4460b97042b0a610cbc7560fc3338e8400c0c4 Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Thu, 14 Dec 2017 19:39:43 -0800 Subject: [PATCH 093/174] changed print to return. --- normalize.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/normalize.py b/normalize.py index b071c47..dbd1fcf 100644 --- a/normalize.py +++ b/normalize.py @@ -97,4 +97,4 @@ def normalize_x_and_y(image,xmin,xmax,ymin,ymax): height = ((ymax-ymin))/(im_height/2) z = 0.0 depth = 0.0 - print(x,y,width,height,z,depth) \ No newline at end of file + return(x,y,width,height,z,depth) \ No newline at end of file From d4772512169ab1412dfb8882b4c7007b57e8662b Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Fri, 15 Dec 2017 07:00:54 -0800 Subject: [PATCH 094/174] updated testdoc with complete suite of test cases --- normalize.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/normalize.py b/normalize.py index dbd1fcf..05c6f4c 100644 --- a/normalize.py +++ b/normalize.py @@ -53,8 +53,21 @@ def normalize_x_and_y(image,xmin,xmax,ymin,ymax): xmin < 0 >>> normalize_x_and_y(image,10,90,90,10) ymin is greater than ymax + >>> normalize_x_and_y(image,101,150,50,100) + xmin is greater than image width >>> normalize_x_and_y(image,10,90,10,190) ymax is greater than image height + >>> normalize_x_and_y(iamge,0,200,100,100) + xmax is greater than image width + >>> normalize_x_and_y(image,0,100,-10,100) + ymin < 0 + >>> normalize_x_and_y(image,50,50,50,50) + 0.0 0.0 0.0 0.0 0.0 0.0 + >>> normalize_x_and_y(image,50,100,50,100) + 0.0 0.0 1.0 1.0 0.0 0.0 + >>> normalize_x_and_y(image,0,100,0,100) + -1.0 -1.0 2.0 2.0 0.0 0.0 + """ #TODO: Check if I really need this next line or not From 2b68790295becef1f7c05b80add18516054dbad9 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Fri, 15 Dec 2017 10:24:06 -0800 Subject: [PATCH 095/174] NSF-4 #start-progress add style guide and constants for vector --- CONTRIBUTING.md | 3 ++- object_detection/constants.py | 5 +++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index e49229a..ae8192b 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -56,4 +56,5 @@ def add(value, num=0) ### Workflow * branch off master whenever you begin a new feature/task -* commit often, mentioning the Jira ticket number in the comment, where possible (e.g. "#") \ No newline at end of file +* commit often, mentioning the Jira ticket number in the comment, where possible (e.g. NSF-4) +* brief active voice comments, with optional Jira transition commands: `git commit -am 'NSF-4 #start-review integrate location and color vectors'`) diff --git a/object_detection/constants.py b/object_detection/constants.py index 6108d2a..1578b2a 100644 --- a/object_detection/constants.py +++ b/object_detection/constants.py @@ -19,6 +19,7 @@ CATEGORIES = label_map_util.convert_label_map_to_categories(LABEL_MAP, max_num_classes=90, use_display_name=True) CATEGORY_INDEX = label_map_util.create_category_index(CATEGORIES) - +LABEL_KEYS = 'category instance'.split() COLOR_KEYS = 'red orange yellow green indigo violet black white pink'.split() -OBJECT_VECTOR_KEYS = +BB_KEYS = 'x y z width height depth'.split() +OBJECT_VECTOR_KEYS = LABEL_KEYS + BB_KEYS + COLOR_KEYS From a67b6dd35ba3b4aca5691cf88d78ddefdfcf8486 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 15 Dec 2017 11:51:49 -0800 Subject: [PATCH 096/174] (Update State Vector) Updated `update_state` function to return a dictionary of information instead of aggregations. - added `update_state_dict` function to nlp core that returns a proper state dictionary - fixed doctests in `nlp/core.py`, including `pluralize` and `say` - updated `describe_state` to handle new state format - in the main script, changed call to `update_state` to new function. --- nlp/__init__.py | 2 +- nlp/core.py | 100 ++++++++++++++++++++++++++++++++++++---- object_detection_app.py | 8 ++-- 3 files changed, 95 insertions(+), 15 deletions(-) diff --git a/nlp/__init__.py b/nlp/__init__.py index 7af7152..79fbff9 100644 --- a/nlp/__init__.py +++ b/nlp/__init__.py @@ -1,2 +1,2 @@ -from nlp.core import update_state, describe_state, say +from nlp.core import update_state, update_state_dict, describe_state, say diff --git a/nlp/core.py b/nlp/core.py index 5185ccf..4a4646b 100644 --- a/nlp/core.py +++ b/nlp/core.py @@ -3,17 +3,19 @@ import os import pandas as pd +import object_detection.color_labeler as color_labeler from nlp.plurals import PLURALS +from collections import defaultdict def pluralize(s): """ Convert word to its plural form. >>> pluralize('cat') - cats + 'cats' >>> pluralize('doggy') - doggies + 'doggies' Better: @@ -42,7 +44,7 @@ def pluralize(s): return word + 's' -def update_state(boxes, classes, scores, category_index, window=10, max_boxes_to_draw=None, min_score_thresh=.5): +def update_state(boxes, classes, scores, category_index, src_img=None, window=10, max_boxes_to_draw=None, min_score_thresh=.5): """ Revise state based on latest frame of information (object boxes) TODO(Hobson | Alex) Finish docstring (Need to know all the args and the return val) @@ -51,7 +53,24 @@ def update_state(boxes, classes, scores, category_index, window=10, max_boxes_to classes, Args (that should be class attributes): category_index (dict of dicts): {1: {'id': 1, 'name': 'person'}, 2: {'id': 2, 'name': 'bicycle'},...} - + Returns: + + Example Output: + { + "cup": + [ + { color: [] }, + { color: [] ), + ], + "person": + [ + { color: [] }, + { color: [] }, + { color: [] } + ], + ... + + } """ num_boxes = min([boxes.shape[0] if max_boxes_to_draw is None else max_boxes_to_draw, boxes.shape[0], len(classes)]) @@ -78,6 +97,53 @@ def update_state(boxes, classes, scores, category_index, window=10, max_boxes_to return state +def update_state_dict(image, boxes, classes, scores, category_index, src_img=None, window=10, max_boxes_to_draw=None, min_score_thresh=.5): + """ Revise state based on latest frame of information (object boxes) + + TODO(Hobson | Alex) Finish docstring (Need to know all the args and the return val) + Args: + boxes (list): 2D numpy array of shape (N, 4): (ymin, xmin, ymax, xmax), in normalized format between [0, 1]. + classes, + Args (that should be class attributes): + category_index (dict of dicts): {1: {'id': 1, 'name': 'person'}, 2: {'id': 2, 'name': 'bicycle'},...} + Returns: + + Example Output: + { + "cup": + [ + { color: [], score: 0.95, ... }, + { color: [], score: 0.88, ... ), + ], + "person": + [ + { color: [], score: 0.99, ... }, + { color: [], score: 0.75, ... }, + { color: [], score: 0.85, ... } + ], + ... + + } + """ + num_boxes = min([boxes.shape[0] if max_boxes_to_draw is None else max_boxes_to_draw, boxes.shape[0], len(classes)]) + + state_obj = defaultdict(list) + + for i in range(num_boxes): + if scores is None or scores[i] > min_score_thresh: + class_name = category_index.get(classes[i], {'name': 'unknown object'})['name'] + display_str = '{}: {} {}%'.format(classes[i], class_name, int(100 * scores[i])) + print(display_str) # TODO(Alex) Convert to logging + + obj_data = { + 'score': scores[i], + 'color': color_labeler.estimate(image, box=boxes[i]) + } + + state_obj[class_name] += obj_data + return state_obj + + update_state.states = None # for i in range(update_state.window): @@ -87,12 +153,28 @@ def update_state(boxes, classes, scores, category_index, window=10, max_boxes_to def describe_state(state): """ Convert a state vector dictionary of objects and their counts into a natural language string - >>> describe_state({'skis': 2}) + >>> describe_state({'skis': [{'score': 0.99}, {'score': 0.88}]}) '2 pairs of skis' + >>> statement = describe_state({'skis': [{'score': 0.88}], 'cup': [{'score': 0.87 }, {'score': 0.66}]} ) + >>> '2 cups' in statement and 'and' in statement and '1 skis' in statement + True """ - description = ['{} {}'.format(i, pluralize(s) if i > 1 else s) for (s, i) in state] - description = ', '.join(description[:-2]) + ',' + ' and '.join(description[-2:]) - return description + def count_objects(state_dict): + new_dict = {k: len(v) for k, v in state_dict.items()} + return new_dict + + state_counts = count_objects(state) + + plural_description_list = ['{} {}'.format(i, pluralize(s) if i > 1 else s) for (s, i) in state_counts.items()] + + comma_list = ', '.join(plural_description_list[:-2]) + conjunction = ' and '.join(plural_description_list[-2:]) + if len(comma_list) > 0: + delim_description = comma_list + ',' + conjunction + else: + delim_description = conjunction + + return delim_description def say(s, rate=230): @@ -100,7 +182,7 @@ def say(s, rate=230): If "say" command is not available in os.system then print the text to stdout. - >>> say(hello) + >>> say('hello') 'hello' """ try: diff --git a/object_detection_app.py b/object_detection_app.py index 015c44b..94b657f 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -10,7 +10,8 @@ from multiprocessing import Queue, Pool from object_detection.utils import label_map_util from object_detection.utils import visualization_utils as vis_util -from nlp import update_state, describe_state, say +from object_detection.color_labeler import estimate_color, update_state_with_color +from nlp import update_state_dict, describe_state, say from nlp.dispatch import mqttc, dispatcher from nlp.command.describe import Describe @@ -33,9 +34,6 @@ category_index = label_map_util.create_category_index(categories) - - - def detect_objects(image_np, sess, detection_graph, _state_q, utterance_frames=20, voice_on=False): # Expand dimensions since the model expects images to have shape: [1, None, None, 3] image_np_expanded = np.expand_dims(image_np, axis=0) @@ -66,7 +64,7 @@ def detect_objects(image_np, sess, detection_graph, _state_q, utterance_frames=2 line_thickness=8) # Describe the image - state = update_state(boxes=np.squeeze(boxes), + state = update_state_dict(image=image_np, boxes=np.squeeze(boxes), classes=np.squeeze(classes).astype(np.int32), scores=np.squeeze(scores), category_index=category_index) From 45ceec269456779443e8ff3dd37b00b1fbad6a6b Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Fri, 15 Dec 2017 11:55:12 -0800 Subject: [PATCH 097/174] add constants --- object_detection/constants.py | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 object_detection/constants.py diff --git a/object_detection/constants.py b/object_detection/constants.py new file mode 100644 index 0000000..1578b2a --- /dev/null +++ b/object_detection/constants.py @@ -0,0 +1,25 @@ +""" Constants that depend on the object detection model (tensorflow network) being used. """ +import os + +from object_detection.utils import label_map_util + + +BASE_DIR = os.path.dirname(os.path.dirname(__file__)) + +# Path to frozen detection graph. This is the actual model that is used for the object detection. +MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017' +LABEL_MAP_FILE = 'mscoco_label_map.pbtxt' +PATH_TO_CKPT = os.path.join(BASE_DIR, 'object_detection', MODEL_NAME, 'frozen_inference_graph.pb') +# List of the strings that is used to add correct label for each box. +PATH_TO_LABELS = os.path.join(BASE_DIR, 'object_detection', 'data', LABEL_MAP_FILE) + +# Loading label map +LABEL_MAP = label_map_util.load_labelmap(PATH_TO_LABELS) +# though mobilenet can handle +CATEGORIES = label_map_util.convert_label_map_to_categories(LABEL_MAP, max_num_classes=90, use_display_name=True) +CATEGORY_INDEX = label_map_util.create_category_index(CATEGORIES) + +LABEL_KEYS = 'category instance'.split() +COLOR_KEYS = 'red orange yellow green indigo violet black white pink'.split() +BB_KEYS = 'x y z width height depth'.split() +OBJECT_VECTOR_KEYS = LABEL_KEYS + BB_KEYS + COLOR_KEYS From 9ee4e50d547d2787b61ee1a760b69d9055bbd6d3 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 15 Dec 2017 11:55:20 -0800 Subject: [PATCH 098/174] (Color Labeler Module) - made key functions accessible from module scope (in `__init__.py`) - Tested and finished `estimate_color` function - Updated `_get_bbox_center_img` function taking into account that the bounding boxes are values between 0 and 1. Needs to be tested. TODO: - test new `_get_bbox_center_img` - Complete NLP command for color labeling - test end2end. --- object_detection/color_labeler/__init__.py | 4 + object_detection/color_labeler/core.py | 225 ++++++++++----------- 2 files changed, 114 insertions(+), 115 deletions(-) diff --git a/object_detection/color_labeler/__init__.py b/object_detection/color_labeler/__init__.py index e69de29..328f4a8 100644 --- a/object_detection/color_labeler/__init__.py +++ b/object_detection/color_labeler/__init__.py @@ -0,0 +1,4 @@ +from object_detection.color_labeler.core import estimate_color as estimate +from object_detection.color_labeler.core import estimate_color +from object_detection.color_labeler.core import update_state_with_color + diff --git a/object_detection/color_labeler/core.py b/object_detection/color_labeler/core.py index 0bbc03a..b6ce16c 100644 --- a/object_detection/color_labeler/core.py +++ b/object_detection/color_labeler/core.py @@ -3,158 +3,153 @@ """ - -from scipy.spatial import distance as dist -from scipy.spatial import KDTree # TODO(Alex) use cKDTree, need to upgrade scipy -from sklearn.cluster import KMeans from collections import OrderedDict - +import pandas as pd import numpy as np import cv2 -def determine_color_distribution(image_np, box): - ymin, xmin, ymax, xmax = box - wd = xmax - xmin - ht = ymax - ymin - - # Get center area of the object inside the bounding box - obj_center = image_np[(ymin + ht // 2):(ymax - ht // 2), (xmin + wd // 2):(xmax - wd // 2), :] - - -class ColorLabeler: - - colors = OrderedDict({ - 'red': (255, 0, 0), - 'green': (0, 255, 0), - 'blue': (0, 0, 255) - }) - - def __init__(self): - """Init L*a*b* definitions of colors""" - pass - # self.lab = np.zeros((len(self.colors), 1, 3), dtype='uint8') - # self.colorNames = [] - # - # for i, (name, rgb) in enumerate(self.colors.items()): - # self.lab[i] = rgb - # self.colorNames.append(name) - # - # self.lab = cv2.cvtColor(self.lab, cv2.COLOR_RGB2LAB) - # self.labtree = KDTree(self.lab) - - def _create_mask(self, img): - pass - # contour = None - # - # # construct mask for the contour - # mask = np.zeros(img.shape[:2], dtype='uint8') - # cv2.drawContours(mask, [contour], -1, 255, -1) - # mask = cv2.erode(mask, None, iterations=2) - # return mask - - def _get_bbox_center_img(self, img, box): - - # get space measures - ymin, xmin, ymax, xmax = box - wd = xmax - xmin - ht = ymax - ymin +def estimate_color(img, box=None): + """ - # Get center area of the object inside the bounding box - obj_center = img[(ymin + ht // 2):(ymax - ht // 2), (xmin + wd // 2):(xmax - wd // 2), :] + Args: + img: source RGB image + box: bounding box (ymin, xmin, ymax, xmax) - return obj_center + Examples: + >>> from skimage.data import coffee + >>> img = coffee() + >>> result = estimate_color(img) + >>> result.get('black', None) is not None + True + >>> result.get('white', None) is not None + True - def _build_custer_model(self, img, k: int = 5, mask=None) -> KMeans: + # Colors are within sensible range + >>> 0 <= result['black'] <= 0.4 + True + >>> 0.1 <= result['white'] <= 0.3 + True + >>> 0 <= result['blue'] <= 0.1 + True + >>> 0.4 <= result['red'] <= 1 + True - # reshape image to vector of pixels - flattened_img = img.reshape((img.shape[0] * img.shape[1], 3)) + # Order of output is enforced + >>> abs(result['black'] - result[0]) < 0.0001 + True + >>> abs(result['white'] - result[1]) < 0.0001 + True + >>> abs(result['purple'] - result[-2] < 0.0001) + True + >>> abs(result['pink'] - result[-1] < 0.0001) + True - model = KMeans(n_clusters=k) - model.fit(flattened_img) + Returns: + Dictionary of color-frequency pairs. - return model + """ - def _centroid_histogram(self, model): + # Get center of image (via bounding box) + cropped_img = _get_bbox_center_img(img, box) - num_labels = np.arange(0, len(np.unique(model.labels_)) + 1) - hist, _ = np.histogram(model.labels_, bins=num_labels) + # Convert to Hue, Saturation, Value color space + hsv_img = cv2.cvtColor(cropped_img, cv2.COLOR_RGB2HSV) - hist = hist.astype('float') - hist /= hist.sum() + # Flatten pixels in image + flat_img = hsv_img.reshape((hsv_img.shape[0] * hsv_img.shape[1], 3)) - return hist + # Calculate indexed histograms of the Value dimension + n_bins_val = 32 + bw_bin_thresh = 5 - def label(self, img, box=None): + bins_val = np.linspace(0, 255, n_bins_val, endpoint=True, dtype='uint8') + hist_val_idx = np.digitize(flat_img[:, 2], bins_val) + # hist_val, _ = np.histogram(hist_val_idx) - # Get center of image (via bounding box) - if not box: - box = (0, img.shape[0], 0, img.shape[1]) + is_black_pxl = hist_val_idx <= bw_bin_thresh + is_white_pxl = hist_val_idx >= (n_bins_val - bw_bin_thresh) - cropped_img = self._get_bbox_center_img(img, box) + black_idx = hist_val_idx[is_black_pxl] + white_idx = hist_val_idx[is_white_pxl] - # Convert to Hue, Saturation, Value color space - hsv_img = cv2.cvtColor(cropped_img, cv2.COLOR_RGB2HSV) + n_black = len(black_idx) + n_white = len(white_idx) - # Flatten pixels in image - flat_img = hsv_img.reshape((hsv_img.shape[0] * hsv_img.shape[1], 3)) + # Calculate histogram of Hue dimension + color_flat_img = flat_img[~np.logical_or(is_black_pxl, is_white_pxl), :] # exclude black or white pixels - # Calculate indexed histograms of the Value dimension - n_bins_val = 32 - bw_bin_thresh = 3 + # Mathematical approach doesn't provide human understandable color ranges. Going to hardcode... + # color_centers = np.arange(0, 360, 30) # produces 12 color centers + # color_ranges = (color_centers - 15) % 360 # maps to 360 deg bin ranges + # bins_hue = color_ranges // 2 # ranges need to fit in uint8 space [0, 179] - bins_val = np.linspace(0, 255, n_bins_val, endpoint=True, dtype='uint8') - hist_val_idx = np.digitize(flat_img[:, 2], bins_val) - # hist_val, _ = np.histogram(hist_val_idx) + # [red, orange, yellow, green, cyan, blue, purple, pink] + bins_hue = [-8, 7, 22, 37, 82, 97, 127, 142, 171] + hist_hue, _ = np.histogram(color_flat_img[:, 0], bins=bins_hue) - is_black_pxl = hist_val_idx <= bw_bin_thresh - is_white_pxl = hist_val_idx >= (n_bins_val - bw_bin_thresh) + total = n_black + n_white + sum(hist_hue) - black_idx = hist_val_idx[is_black_pxl] - white_idx = hist_val_idx[is_white_pxl] + od = OrderedDict() + od['black'] = n_black / total + od['white'] = n_white / total + od['red'] = hist_hue[0] / total + od['orange'] = hist_hue[1] / total + od['yellow'] = hist_hue[2] / total + od['green'] = hist_hue[3] / total + od['cyan'] = hist_hue[4] / total + od['blue'] = hist_hue[5] / total + od['purple'] = hist_hue[6] / total + od['pink'] = hist_hue[7] + output = pd.Series(od) - n_black = len(black_idx) - n_white = len(white_idx) + return output - # Calculate histogram of Hue dimension - color_flat_img = flat_img[~np.logical_or(black_idx, white_idx), :] # exclude black or white pixels - # Mathematical approach doesn't provide human understandable color ranges. Going to hardcode... - # color_centers = np.arange(0, 360, 30) # produces 12 color centers - # color_ranges = (color_centers - 15) % 360 # maps to 360 deg bin ranges - # bins_hue = color_ranges // 2 # ranges need to fit in uint8 space [0, 179] - # [red, orange, yellow, green, cyan, blue, purple, pink] - bins_hue = [-8, 7, 22, 82, 97, 127, 142, 171] - hist_hue, _ = np.histogram(color_flat_img[:, 0], bins=bins_hue) - total = n_black + n_white + sum(hist_hue) - - output = { - 'black': n_black / total, - 'white': n_white / total, - 'red': hist_hue[0] / total, - 'orange': hist_hue[1] / total, - 'yellow': hist_hue[2] / total, - 'green': hist_hue[3] / total, - 'cyan': hist_hue[4] / total, - 'blue': hist_hue[5] / total, - 'purple': hist_hue[6] / total, - 'pink': hist_hue[7] / total - } - - return output +def _get_bbox_center_img(img, box=None): + """ + + Args: + img: + box: + Examples: + >>> from skimage.data import coffee + >>> img = coffee() + >>> cropped = _get_bbox_center_img(img) + + Returns: + A cropped image around the center about half the original size + + """ + # get space measures + if not box: + ymin, xmin, ymax, xmax = (0, 0, img.shape[0], img.shape[1]) + else: + ymin, xmin, ymax, xmax = box + if sum(box) <= 4: + ymin = int(ymin * img.shape[0]) + ymax = int(ymax * img.shape[0]) + xmin = int(xmin * img.shape[1]) + xmax = int(xmax * img.shape[1]) + wd = xmax - xmin + ht = ymax - ymin + # Get center area of the object inside the bounding box + ystart = ymin + (ht // 4) + yend = ymax - (ht // 4) + xstart = xmin + (wd // 4) + xend = xmax - (wd // 4) + obj_center = img[ystart:yend, xstart:xend, :] + return obj_center - # vhist = cv2.calcHist([hsv_img], [2], mask, [32], [0, 256]) - # black_hist = vhist[:2] - # white_hist = vhist[:-2] From a8b7cfa57b53debb0ac8ce613e191ad5ae2afc8a Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Fri, 15 Dec 2017 12:00:58 -0800 Subject: [PATCH 099/174] new transformation file which contains normalization and labeling whether in center, left or right --- nlp/transform.py | 163 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 163 insertions(+) create mode 100644 nlp/transform.py diff --git a/nlp/transform.py b/nlp/transform.py new file mode 100644 index 0000000..faf4e85 --- /dev/null +++ b/nlp/transform.py @@ -0,0 +1,163 @@ +from PIL import Image + +def normalize_x_and_y(image,xmin,xmax,ymin,ymax): + """ + Takes in an image which will be provided and then computes the normalized bouding box information. + Args: + xmin - this is the left most point of the bounding box + xmax - this is the right most point of the bounding box + ymin - this is the lowest point of the bounding box + ymax - this is the highest point of the bouding box + From image, try to get image width and height such that we can scale it appropriately + + (xmin,ymax). (xmax,ymax) + --------------- + | | + | | + | | + | | + | | + ________________ + (xmin,ymin) (xmax,ymin) + + + + The output will be the following 6 parameters + x - the left most point and is scaled between -1 and 1. to scale, can do (xmin-image_width/2)/(image_width/2) + y - the bottom most point and is scaled between -1 and 1. to scale, can do (ymin-image_height/2)/(image_height/2) + width - this is defined as xmax-xmin. to scale, compute (xmax-xmin)/(image_width/2) + height - this is defined as ymax-ymin. to scale, compute (ymax-ymin)/(image_height/2) + z - set to 0 + depth - set to 0 + + (x,y+height). (x+width, y+height) + --------------- + | | + | | + | | + | | + | | + ________________ + (x,y) (x+width,y) + + these test cases are for a 100*100 image but generic code is built to run any height and width + >>> normalize_x_and_y(image,100,100,50,50) + 1.0 0.0 0.0 0.0 0.0 0.0 + >>> normalize_x_and_y(image,10,90,10,90) + -0.8 -0.8 1.6 1.6 0.0 0.0 + >>> normalize_x_and_y(image,90,10,10,90) + xmin is greater than xmax + >>> normalize_x_and_y(image,110,190,10,40) + xmin is greater than image width + >>> normalize_x_and_y(image,-10,10,10,90) + xmin < 0 + >>> normalize_x_and_y(image,10,90,90,10) + ymin is greater than ymax + >>> normalize_x_and_y(image,101,150,50,100) + xmin is greater than image width + >>> normalize_x_and_y(image,10,90,10,190) + ymax is greater than image height + >>> normalize_x_and_y(iamge,0,200,100,100) + xmax is greater than image width + >>> normalize_x_and_y(image,0,100,-10,100) + ymin < 0 + >>> normalize_x_and_y(image,50,50,50,50) + 0.0 0.0 0.0 0.0 0.0 0.0 + >>> normalize_x_and_y(image,50,100,50,100) + 0.0 0.0 1.0 1.0 0.0 0.0 + >>> normalize_x_and_y(image,0,100,0,100) + -1.0 -1.0 2.0 2.0 0.0 0.0 + + """ + + #TODO: Check if I really need this next line or not + im = Image.open(image) + im_width, im_height = im.size + if (xmin > xmax): + print ("xmin is greater than xmax") + return ("error") + + if (ymin>ymax): + print("ymin is greater than ymax") + return ("error") + + if (xmin > im_width): + print("xmin is greater than image width") + return ("error") + + if (xmax > im_width): + print("xmax is greater than image width") + return ("error") + + if (ymin > im_height): + print ("ymin is greater than image height") + return ("error") + if (ymax > im_height): + print ("ymax is greater than image height") + return ("error") + + if (xmin < 0): + print ("xmin < 0") + return ("error") + + if (ymin < 0): + print ("ymin < 0") + return ("error") + + x = (xmin-(im_width/2))/(im_width/2) + y = (ymin-(im_height/2))/(im_height/2) + width = ((xmax-xmin)/(im_width/2)) + height = ((ymax-ymin))/(im_height/2) + z = 0.0 + depth = 0.0 + return(x,y,width,height,z,depth) + +def position(a): + """ + this function takes in the image as its argument. + the function then calls the normalizing function to get the values of x,y, width and height from + normalize function. + Once the values of x,y,width and height are received, the function returns what it believes to be the + position of the object. + The position of the object has been defined in the follwing manner: + if the x position is less than half of the image widht, it is in the left side but to check if it is + centered or not, I check the value of x+width of box. If the value of x+width is greater than 0 and the + value of x is less than 0, i say that the object is centered. In the case that x < 0 and x+width < 0, + I say the object is to the left. In any case, since the width cannot be negative, if x >= 0, I say that it + is to the right as the position. + + Also have accounted for error messages. + + >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",0,60,0,100)) + >>> position(a) + center + >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",0,10,0,100)) + left + >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",50,60,0,100)) + right + >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",40,60,0,100)) + center + >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",110,60,0,100)) + xmin is greater than xmax + see above error message + + """ + + if (a != "error"): + #print("test passed") + x,y,width,height,z,depth = a + position = "" + if (x <= (0)): + if (x+width <= 0): + position = "left" + else: + position = "center" + if (x >= (0)): + position = "right" + + print (position) + else: + print("see above error message") + +a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",110,60,0,100)) +position = position(a) \ No newline at end of file From 00066ba9945e62630d154bd843348ee30e9cc0c4 Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Fri, 15 Dec 2017 12:02:20 -0800 Subject: [PATCH 100/174] modified file. No longer need to use. All under NLP branch and filename is transform.py --- normalize.py | 68 +++++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 59 insertions(+), 9 deletions(-) diff --git a/normalize.py b/normalize.py index 05c6f4c..faf4e85 100644 --- a/normalize.py +++ b/normalize.py @@ -75,34 +75,34 @@ def normalize_x_and_y(image,xmin,xmax,ymin,ymax): im_width, im_height = im.size if (xmin > xmax): print ("xmin is greater than xmax") - return + return ("error") if (ymin>ymax): print("ymin is greater than ymax") - return + return ("error") if (xmin > im_width): print("xmin is greater than image width") - return + return ("error") if (xmax > im_width): print("xmax is greater than image width") - return + return ("error") if (ymin > im_height): print ("ymin is greater than image height") - return + return ("error") if (ymax > im_height): print ("ymax is greater than image height") - return + return ("error") if (xmin < 0): print ("xmin < 0") - return + return ("error") if (ymin < 0): print ("ymin < 0") - return + return ("error") x = (xmin-(im_width/2))/(im_width/2) y = (ymin-(im_height/2))/(im_height/2) @@ -110,4 +110,54 @@ def normalize_x_and_y(image,xmin,xmax,ymin,ymax): height = ((ymax-ymin))/(im_height/2) z = 0.0 depth = 0.0 - return(x,y,width,height,z,depth) \ No newline at end of file + return(x,y,width,height,z,depth) + +def position(a): + """ + this function takes in the image as its argument. + the function then calls the normalizing function to get the values of x,y, width and height from + normalize function. + Once the values of x,y,width and height are received, the function returns what it believes to be the + position of the object. + The position of the object has been defined in the follwing manner: + if the x position is less than half of the image widht, it is in the left side but to check if it is + centered or not, I check the value of x+width of box. If the value of x+width is greater than 0 and the + value of x is less than 0, i say that the object is centered. In the case that x < 0 and x+width < 0, + I say the object is to the left. In any case, since the width cannot be negative, if x >= 0, I say that it + is to the right as the position. + + Also have accounted for error messages. + + >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",0,60,0,100)) + >>> position(a) + center + >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",0,10,0,100)) + left + >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",50,60,0,100)) + right + >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",40,60,0,100)) + center + >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",110,60,0,100)) + xmin is greater than xmax + see above error message + + """ + + if (a != "error"): + #print("test passed") + x,y,width,height,z,depth = a + position = "" + if (x <= (0)): + if (x+width <= 0): + position = "left" + else: + position = "center" + if (x >= (0)): + position = "right" + + print (position) + else: + print("see above error message") + +a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",110,60,0,100)) +position = position(a) \ No newline at end of file From e454eb8ecd4b492f514f262045845a7eb78a2122 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Fri, 15 Dec 2017 12:06:51 -0800 Subject: [PATCH 101/174] style guide --- CONTRIBUTING.md | 60 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 60 insertions(+) create mode 100644 CONTRIBUTING.md diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..ae8192b --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,60 @@ +## Style Guide + +The primary goal is to produce readable code. +This means being consistent with patterns used by many people, except when the "crowd" favors less readable layout or syntax. + + +### [PEP8](https://www.python.org/dev/peps/pep-0008/) with these exceptions + +Follow the [Hettinger interpretation of PEP8](https://www.youtube.com/watch?v=wf-BqAjZb8M) for beautiful, readable code. + +* max line length is "about" 120 chars +* max complexity: mccabe_threshold": 12, # threshold limit for McCabe complexity checker. +* type hints are encouraged but not required + +Here's my Sublime Anaconda plugin configuration. + + +```json +{ + // Maximum McCabe complexity (number of conditional branches within a function). + "mccabe_threshold": 7, + + // Maximum line length + "pep8_max_line_length": 120 +} +``` + + +### Documentation + +Markdown README.md files in any folder containing significant code. + +Use [Google/Numpy/Napolean/Markdown](http://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) docstring syntax, **not** the original ReST dosctring format. + + +#### Example Dosstring + + +```python +def add(value, num=0) + """ Add a float to an integer + + Args: + value (float): first number in the sum + num (int): the integer to be added + + Returns: + float: the sum of `value + num` + + >>> add(1., 2) + 3.0 + """ +``` + + +### Workflow + +* branch off master whenever you begin a new feature/task +* commit often, mentioning the Jira ticket number in the comment, where possible (e.g. NSF-4) +* brief active voice comments, with optional Jira transition commands: `git commit -am 'NSF-4 #start-review integrate location and color vectors'`) From 6e6e2d8ecabd371b18f9b3ae793abdafe4a41839 Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Fri, 15 Dec 2017 12:12:56 -0800 Subject: [PATCH 102/174] removed the normalize.py file from repo --- normalize.py | 163 --------------------------------------------------- 1 file changed, 163 deletions(-) delete mode 100644 normalize.py diff --git a/normalize.py b/normalize.py deleted file mode 100644 index faf4e85..0000000 --- a/normalize.py +++ /dev/null @@ -1,163 +0,0 @@ -from PIL import Image - -def normalize_x_and_y(image,xmin,xmax,ymin,ymax): - """ - Takes in an image which will be provided and then computes the normalized bouding box information. - Args: - xmin - this is the left most point of the bounding box - xmax - this is the right most point of the bounding box - ymin - this is the lowest point of the bounding box - ymax - this is the highest point of the bouding box - From image, try to get image width and height such that we can scale it appropriately - - (xmin,ymax). (xmax,ymax) - --------------- - | | - | | - | | - | | - | | - ________________ - (xmin,ymin) (xmax,ymin) - - - - The output will be the following 6 parameters - x - the left most point and is scaled between -1 and 1. to scale, can do (xmin-image_width/2)/(image_width/2) - y - the bottom most point and is scaled between -1 and 1. to scale, can do (ymin-image_height/2)/(image_height/2) - width - this is defined as xmax-xmin. to scale, compute (xmax-xmin)/(image_width/2) - height - this is defined as ymax-ymin. to scale, compute (ymax-ymin)/(image_height/2) - z - set to 0 - depth - set to 0 - - (x,y+height). (x+width, y+height) - --------------- - | | - | | - | | - | | - | | - ________________ - (x,y) (x+width,y) - - these test cases are for a 100*100 image but generic code is built to run any height and width - >>> normalize_x_and_y(image,100,100,50,50) - 1.0 0.0 0.0 0.0 0.0 0.0 - >>> normalize_x_and_y(image,10,90,10,90) - -0.8 -0.8 1.6 1.6 0.0 0.0 - >>> normalize_x_and_y(image,90,10,10,90) - xmin is greater than xmax - >>> normalize_x_and_y(image,110,190,10,40) - xmin is greater than image width - >>> normalize_x_and_y(image,-10,10,10,90) - xmin < 0 - >>> normalize_x_and_y(image,10,90,90,10) - ymin is greater than ymax - >>> normalize_x_and_y(image,101,150,50,100) - xmin is greater than image width - >>> normalize_x_and_y(image,10,90,10,190) - ymax is greater than image height - >>> normalize_x_and_y(iamge,0,200,100,100) - xmax is greater than image width - >>> normalize_x_and_y(image,0,100,-10,100) - ymin < 0 - >>> normalize_x_and_y(image,50,50,50,50) - 0.0 0.0 0.0 0.0 0.0 0.0 - >>> normalize_x_and_y(image,50,100,50,100) - 0.0 0.0 1.0 1.0 0.0 0.0 - >>> normalize_x_and_y(image,0,100,0,100) - -1.0 -1.0 2.0 2.0 0.0 0.0 - - """ - - #TODO: Check if I really need this next line or not - im = Image.open(image) - im_width, im_height = im.size - if (xmin > xmax): - print ("xmin is greater than xmax") - return ("error") - - if (ymin>ymax): - print("ymin is greater than ymax") - return ("error") - - if (xmin > im_width): - print("xmin is greater than image width") - return ("error") - - if (xmax > im_width): - print("xmax is greater than image width") - return ("error") - - if (ymin > im_height): - print ("ymin is greater than image height") - return ("error") - if (ymax > im_height): - print ("ymax is greater than image height") - return ("error") - - if (xmin < 0): - print ("xmin < 0") - return ("error") - - if (ymin < 0): - print ("ymin < 0") - return ("error") - - x = (xmin-(im_width/2))/(im_width/2) - y = (ymin-(im_height/2))/(im_height/2) - width = ((xmax-xmin)/(im_width/2)) - height = ((ymax-ymin))/(im_height/2) - z = 0.0 - depth = 0.0 - return(x,y,width,height,z,depth) - -def position(a): - """ - this function takes in the image as its argument. - the function then calls the normalizing function to get the values of x,y, width and height from - normalize function. - Once the values of x,y,width and height are received, the function returns what it believes to be the - position of the object. - The position of the object has been defined in the follwing manner: - if the x position is less than half of the image widht, it is in the left side but to check if it is - centered or not, I check the value of x+width of box. If the value of x+width is greater than 0 and the - value of x is less than 0, i say that the object is centered. In the case that x < 0 and x+width < 0, - I say the object is to the left. In any case, since the width cannot be negative, if x >= 0, I say that it - is to the right as the position. - - Also have accounted for error messages. - - >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",0,60,0,100)) - >>> position(a) - center - >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",0,10,0,100)) - left - >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",50,60,0,100)) - right - >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",40,60,0,100)) - center - >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",110,60,0,100)) - xmin is greater than xmax - see above error message - - """ - - if (a != "error"): - #print("test passed") - x,y,width,height,z,depth = a - position = "" - if (x <= (0)): - if (x+width <= 0): - position = "left" - else: - position = "center" - if (x >= (0)): - position = "right" - - print (position) - else: - print("see above error message") - -a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",110,60,0,100)) -position = position(a) \ No newline at end of file From 4abda2b7814cc05cdca97ad5174aca63fdfdc77d Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 15 Dec 2017 15:36:57 -0800 Subject: [PATCH 103/174] Code that ran during the demo --- nlp/command/__init__.py | 2 ++ nlp/command/color.py | 27 ++++++++++++++++++++++ nlp/core.py | 11 +++++++-- object_detection/color_labeler/__init__.py | 1 - object_detection/color_labeler/core.py | 13 +++++++---- object_detection_app.py | 7 +++--- 6 files changed, 50 insertions(+), 11 deletions(-) create mode 100644 nlp/command/__init__.py create mode 100644 nlp/command/color.py diff --git a/nlp/command/__init__.py b/nlp/command/__init__.py new file mode 100644 index 0000000..08b8fc9 --- /dev/null +++ b/nlp/command/__init__.py @@ -0,0 +1,2 @@ +from nlp.command.color import DescribeColor +from nlp.command.describe import Describe \ No newline at end of file diff --git a/nlp/command/color.py b/nlp/command/color.py new file mode 100644 index 0000000..a46cd6c --- /dev/null +++ b/nlp/command/color.py @@ -0,0 +1,27 @@ +from nlp import describe_state +from nlp.dispatch import Dispatchable + + +class DescribeColor(Dispatchable): + + color_tmpl = "The {} is primarily {}" + + def __init__(self, state_q): + self.state_q = state_q + + def __call__(self, payload): + state = self.state_q.get() + + if state: + + obj_name, object_data = state.popitem() + + if object_data: + color_freq = object_data[0]['color'] + + max_color = color_freq.idxmax() + + print(state) + self.send({'response': self.color_tmpl.format(obj_name, max_color)}, subtopic=['say']) + + diff --git a/nlp/core.py b/nlp/core.py index 4a4646b..435714c 100644 --- a/nlp/core.py +++ b/nlp/core.py @@ -97,6 +97,8 @@ def update_state(boxes, classes, scores, category_index, src_img=None, window=10 return state +update_state.states = None + def update_state_dict(image, boxes, classes, scores, category_index, src_img=None, window=10, max_boxes_to_draw=None, min_score_thresh=.5): """ Revise state based on latest frame of information (object boxes) @@ -128,6 +130,8 @@ def update_state_dict(image, boxes, classes, scores, category_index, src_img=Non num_boxes = min([boxes.shape[0] if max_boxes_to_draw is None else max_boxes_to_draw, boxes.shape[0], len(classes)]) state_obj = defaultdict(list) + if update_state_dict.i is None: + update_state_dict.i = 0 for i in range(num_boxes): if scores is None or scores[i] > min_score_thresh: @@ -140,11 +144,14 @@ def update_state_dict(image, boxes, classes, scores, category_index, src_img=Non 'color': color_labeler.estimate(image, box=boxes[i]) } - state_obj[class_name] += obj_data + state_obj[class_name] += [obj_data] + + update_state_dict.i += 1 + return state_obj -update_state.states = None +update_state_dict.i = None # for i in range(update_state.window): # update_state.states.append([]) diff --git a/object_detection/color_labeler/__init__.py b/object_detection/color_labeler/__init__.py index 328f4a8..f3e380e 100644 --- a/object_detection/color_labeler/__init__.py +++ b/object_detection/color_labeler/__init__.py @@ -1,4 +1,3 @@ from object_detection.color_labeler.core import estimate_color as estimate from object_detection.color_labeler.core import estimate_color -from object_detection.color_labeler.core import update_state_with_color diff --git a/object_detection/color_labeler/core.py b/object_detection/color_labeler/core.py index b6ce16c..bbc4783 100644 --- a/object_detection/color_labeler/core.py +++ b/object_detection/color_labeler/core.py @@ -44,6 +44,8 @@ def estimate_color(img, box=None): True >>> abs(result['pink'] - result[-1] < 0.0001) True + >>> result.idxmax() + 'red' Returns: Dictionary of color-frequency pairs. @@ -61,14 +63,15 @@ def estimate_color(img, box=None): # Calculate indexed histograms of the Value dimension n_bins_val = 32 - bw_bin_thresh = 5 + black_bin_thresh = 5 + white_bin_thresh = 7 bins_val = np.linspace(0, 255, n_bins_val, endpoint=True, dtype='uint8') hist_val_idx = np.digitize(flat_img[:, 2], bins_val) # hist_val, _ = np.histogram(hist_val_idx) - is_black_pxl = hist_val_idx <= bw_bin_thresh - is_white_pxl = hist_val_idx >= (n_bins_val - bw_bin_thresh) + is_black_pxl = hist_val_idx <= black_bin_thresh + is_white_pxl = hist_val_idx >= (n_bins_val - white_bin_thresh) black_idx = hist_val_idx[is_black_pxl] white_idx = hist_val_idx[is_white_pxl] @@ -100,7 +103,7 @@ def estimate_color(img, box=None): od['cyan'] = hist_hue[4] / total od['blue'] = hist_hue[5] / total od['purple'] = hist_hue[6] / total - od['pink'] = hist_hue[7] + od['pink'] = hist_hue[7] / total output = pd.Series(od) return output @@ -125,7 +128,7 @@ def _get_bbox_center_img(img, box=None): """ # get space measures - if not box: + if box is None: ymin, xmin, ymax, xmax = (0, 0, img.shape[0], img.shape[1]) else: ymin, xmin, ymax, xmax = box diff --git a/object_detection_app.py b/object_detection_app.py index 94b657f..725b7fe 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -10,10 +10,10 @@ from multiprocessing import Queue, Pool from object_detection.utils import label_map_util from object_detection.utils import visualization_utils as vis_util -from object_detection.color_labeler import estimate_color, update_state_with_color from nlp import update_state_dict, describe_state, say from nlp.dispatch import mqttc, dispatcher -from nlp.command.describe import Describe +from nlp.command import Describe, DescribeColor + CWD_PATH = os.getcwd() @@ -71,7 +71,7 @@ def detect_objects(image_np, sess, detection_graph, _state_q, utterance_frames=2 # Persists image state in a queue _state_q.put(state) - if not update_state.i % utterance_frames: + if not update_state_dict.i % utterance_frames: description = describe_state(state) if voice_on: say(description) @@ -139,6 +139,7 @@ def worker(input_q, output_q, state_q, voice_on=False): output_q = Queue(maxsize=args.queue_size) state_q = Queue(maxsize=args.state_queue_size) + dispatcher['color'] = DescribeColor(state_q) dispatcher['describe'] = Describe(state_q) pool = Pool(args.num_workers, worker, (input_q, output_q, state_q, args.voice_on)) From c8580923f80763431aca071c937068618e0b8662 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 15 Dec 2017 16:05:54 -0800 Subject: [PATCH 104/174] API Conversation 2 Conversation about API on Friday Dec 15 --- README.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 08e3805..9ec435e 100644 --- a/README.md +++ b/README.md @@ -38,7 +38,7 @@ python -m unittest discover -s object_detection -p "*_test.py" ### API Our API is accessible via the MQTT protocol. -#### `dev/chloe/explorer/statement` +#### Android --> Chloe `dev/chloe/explorer//statement` We subscribe to a topic coming from an Android client. Incoming messages should be encoded as JSON objects that match the following format: @@ -53,7 +53,8 @@ the following format: } ``` -#### `dev/chloe/response//` + +#### Any --> Explorer `dev/chloe/explorer//response` We publish to the root topic `dev/chloe/response` via subtopics scoped by the end user's id and the desired action. For instance, if we expect the client with id `1324234` to read the text response aloud (i.e. the `say` action), we will publish to the following topic path: `dev/chloe/response/1324234/say`. Messages should be encoded as JSON objects in the following format: @@ -74,6 +75,7 @@ Messages should be encoded as JSON objects in the following format: "args": ["arg1", "arg2", "arg3"], // **Prefer kwargs to args** "kwargs": { "confidence": 0.87, // argument that should always be present + "source": "chloe" // or "human" "key1": 1, "key2": "kwarg2" }, @@ -99,12 +101,15 @@ Payload: "args": [], "kwargs": { "confidence": 0.87, + "source": "chloe" "text": "there is 1 person and a chair around you", "wordsPerMin": 200, "voiceGender": "Female" } } ``` +#### Chloe --> Test Harness/Agent `dev/chloe/agent//response` +The test harness gets the same message as the above section. ### Agent-Chloe Experiment Configuration Discussion - Should be configured on dashboard. From c3549e843f19ee8e2bbb73aac073232091051de8 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 15 Dec 2017 16:08:58 -0800 Subject: [PATCH 105/174] Fine Tuning Readme --- README.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 9ec435e..99137df 100644 --- a/README.md +++ b/README.md @@ -55,7 +55,6 @@ the following format: #### Any --> Explorer `dev/chloe/explorer//response` -We publish to the root topic `dev/chloe/response` via subtopics scoped by the end user's id and the desired action. For instance, if we expect the client with id `1324234` to read the text response aloud (i.e. the `say` action), we will publish to the following topic path: `dev/chloe/response/1324234/say`. Messages should be encoded as JSON objects in the following format: @@ -75,7 +74,7 @@ Messages should be encoded as JSON objects in the following format: "args": ["arg1", "arg2", "arg3"], // **Prefer kwargs to args** "kwargs": { "confidence": 0.87, // argument that should always be present - "source": "chloe" // or "human" + "source": "chloe" // or "human", should always be present "key1": 1, "key2": "kwarg2" }, @@ -86,7 +85,7 @@ Messages should be encoded as JSON objects in the following format: TODO(Alex) Revise Here is an example of a response for "say": -Topic: `nsf/ai/say` +Topic: `dev/chloe/explorer/12345/response` Payload: ```json { From 2d2bbad64e680cd194d6896a03493569b7809d1e Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 15 Dec 2017 18:27:23 -0800 Subject: [PATCH 106/174] - Added names to string templates. --- nlp/command/color.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/nlp/command/color.py b/nlp/command/color.py index a46cd6c..3c2a3b0 100644 --- a/nlp/command/color.py +++ b/nlp/command/color.py @@ -1,10 +1,9 @@ -from nlp import describe_state from nlp.dispatch import Dispatchable class DescribeColor(Dispatchable): - color_tmpl = "The {} is primarily {}" + color_tmpl = "The {obj_name} is primarily {color}" def __init__(self, state_q): self.state_q = state_q @@ -22,6 +21,11 @@ def __call__(self, payload): max_color = color_freq.idxmax() print(state) - self.send({'response': self.color_tmpl.format(obj_name, max_color)}, subtopic=['say']) + + payload = { + 'response': self.color_tmpl.format(obj_name=obj_name, color=max_color) + } + + self.send(payload, subtopic=['say']) From 1a2ce9ea4bf097b3a47ab9424fc0f5453157adc6 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 15 Dec 2017 18:30:06 -0800 Subject: [PATCH 107/174] - Now we can communicate with the dash + android device --- nlp/dispatch.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/nlp/dispatch.py b/nlp/dispatch.py index f4afee9..cbacec2 100644 --- a/nlp/dispatch.py +++ b/nlp/dispatch.py @@ -14,9 +14,10 @@ import typing import paho.mqtt.client as mqtt - -EXPLORER_TOPIC = 'nsf/explorer/command' -AI_TOPIC = 'nsf/ai/response' +USER_ID = 1234 +EXPLORER_SUB_TOPIC = 'dev/chloe/explorer/{}/statement'.format(USER_ID) +AGENT_TOPIC = 'dev/chloe/agent/{}/response'.format(USER_ID) +# EXPLORER_PUB_TOPIC = 'dev/chloe/explorer/{}/response'.format(USER_ID) # Define event callbacks @@ -68,7 +69,7 @@ def on_message(client, obj, msg): mqttc.connect(url_str, port, 60) -mqttc.subscribe(EXPLORER_TOPIC, 0) +mqttc.subscribe(EXPLORER_SUB_TOPIC, 0) def interp_command(cmd_str: str, actions: typing.List[str]) -> str: @@ -99,7 +100,7 @@ def interp_command(cmd_str: str, actions: typing.List[str]) -> str: class Dispatchable: client = mqttc - root_topic = AI_TOPIC + root_topic = AGENT_TOPIC def send(self, payload: typing.Dict, *, subtopic: typing.List[str] = list()): payload_json = json.dumps(payload) From 7b2f4e8395f07cb167acf0543569042c89ab65c6 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Fri, 15 Dec 2017 18:52:29 -0800 Subject: [PATCH 108/174] (Updated README) - Added all the program arguments that we have accumulated. --- README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/README.md b/README.md index 99137df..65be6b9 100644 --- a/README.md +++ b/README.md @@ -13,11 +13,16 @@ A real-time object recognition application using [Google's TensorFlow Object Det * `which python` and this time the place where you installed conda will show up 5. `python object_detection_app.py` Optional arguments (default value): + * Show all commands `--help` * Device index of the camera `--source=0` * Width of the frames in the video stream `--width=480` * Height of the frames in the video stream `--height=360` * Number of workers `--num-workers=2` * Size of the queue `--queue-size=5` + * URL for video stream `--url=` + * Turn on GUI (defaulted to run headless) `--gui` + * Turn on vocal commands on MacOS (defaulted to silent) `--say` + * State Buffer Size, how many "states" to capture `--state-queue-size=5` ## Development ### Updating the environment From b7f6675d05cd3ec9d504fe8d4dd0824846943a32 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Sat, 16 Dec 2017 17:52:15 -0800 Subject: [PATCH 109/174] object_vectors is a list of lists --- nlp/__init__.py | 3 +- nlp/command/describe.py | 6 +- nlp/core.py | 136 +++++---------------- object_detection/color_labeler/__init__.py | 2 +- object_detection/color_labeler/core.py | 74 +++-------- object_detection/constants.py | 2 +- object_detection_app.py | 22 ++-- 7 files changed, 65 insertions(+), 180 deletions(-) diff --git a/nlp/__init__.py b/nlp/__init__.py index 79fbff9..a629519 100644 --- a/nlp/__init__.py +++ b/nlp/__init__.py @@ -1,2 +1 @@ -from nlp.core import update_state, update_state_dict, describe_state, say - +from nlp.core import update_state, describe_scene, say diff --git a/nlp/command/describe.py b/nlp/command/describe.py index 76ca726..c870166 100644 --- a/nlp/command/describe.py +++ b/nlp/command/describe.py @@ -1,4 +1,4 @@ -from nlp import describe_state +from nlp import describe_scene from nlp.dispatch import Dispatchable @@ -11,6 +11,6 @@ def __call__(self, payload): state = self.state_q.get() if state: - description = describe_state(state) + description = describe_scene(state) - self.send({'response': description}, subtopic=['say']) \ No newline at end of file + self.send({'response': description}, subtopic=['say']) diff --git a/nlp/core.py b/nlp/core.py index 82e73ed..ecab9d3 100644 --- a/nlp/core.py +++ b/nlp/core.py @@ -3,7 +3,8 @@ import os import pandas as pd -import object_detection.color_labeler as color_labeler +# fix the antipattern of having a separate folder for every function/class +from object_detection.color_labeler import estimate as estimate_color from nlp.plurals import PLURALS from collections import defaultdict @@ -44,135 +45,58 @@ def pluralize(s): return word + 's' -def update_state(boxes, classes, scores, category_index, src_img=None, window=10, max_boxes_to_draw=None, min_score_thresh=.5): +def update_state(image, boxes, classes, scores, category_index, window=10, max_boxes_to_draw=None, min_score_thresh=.5): """ Revise state based on latest frame of information (object boxes) - TODO(Hobson | Alex) Finish docstring (Need to know all the args and the return val) - Args: - boxes (list): 2D numpy array of shape (N, 4): (ymin, xmin, ymax, xmax), in normalized format between [0, 1]. - classes, - Args (that should be class attributes): - category_index (dict of dicts): {1: {'id': 1, 'name': 'person'}, 2: {'id': 2, 'name': 'bicycle'},...} - Returns: - - Example Output: - { - "cup": - [ - { color: [] }, - { color: [] ), - ], - "person": - [ - { color: [] }, - { color: [] }, - { color: [] } - ], - ... - - } - """ - num_boxes = min([boxes.shape[0] if max_boxes_to_draw is None else max_boxes_to_draw, boxes.shape[0], len(classes)]) - - if update_state.states is None: - # Initialize a matrix of state vectors for the past 20 frames - update_state.i = 0 - update_state.window = 20 - update_state.columns = pd.DataFrame(list(category_index.values())).set_index('id', drop=True)['name'] - update_state.states = pd.DataFrame(pd.np.zeros((20, len(category_index)), dtype=int), columns=update_state.columns) - update_state.state0 = pd.Series(index=update_state.columns) - - state = [] # if state is None else state - for i in range(num_boxes): - if scores is None or scores[i] > min_score_thresh: - # box = tuple(boxes[i].tolist()) - class_name = category_index.get(classes[i], {'name': 'unknown object'})['name'] - display_str = '{}: {} {}%'.format(classes[i], class_name, int(100 * scores[i])) - print(display_str) # TODO(Alex) Convert to logging - state += [class_name] - state = collections.Counter(state) - update_state.states.iloc[i % len(update_state.states), :] = pd.Series(state) - state = sorted(list(state.items())) - i = (i + 1) % len(update_state.states) # update_state.window TODO(Hobs) Is `i` used after this? - return state - - -update_state.states = None - -def update_state_dict(image, boxes, classes, scores, category_index, src_img=None, window=10, max_boxes_to_draw=None, min_score_thresh=.5): - """ Revise state based on latest frame of information (object boxes) + TODO: complete docstring - TODO(Hobson | Alex) Finish docstring (Need to know all the args and the return val) Args: boxes (list): 2D numpy array of shape (N, 4): (ymin, xmin, ymax, xmax), in normalized format between [0, 1]. classes, Args (that should be class attributes): category_index (dict of dicts): {1: {'id': 1, 'name': 'person'}, 2: {'id': 2, 'name': 'bicycle'},...} Returns: - - Example Output: - { - "cup": - [ - { color: [], score: 0.95, ... }, - { color: [], score: 0.88, ... ), - ], - "person": - [ - { color: [], score: 0.99, ... }, - { color: [], score: 0.75, ... }, - { color: [], score: 0.85, ... } - ], - ... - - } + list: list of object vectors, for example: + LABEL_KEYS = '.split() + [ + ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] + ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] + ] + The object vector keys are defined in constants.OBJECT_VECTOR_KEYS: + [category instance confidence x y z width height depth + black white red orange yellow green cyan blue purple pink] """ num_boxes = min([boxes.shape[0] if max_boxes_to_draw is None else max_boxes_to_draw, boxes.shape[0], len(classes)]) - state_obj = defaultdict(list) - if update_state_dict.i is None: - update_state_dict.i = 0 - + object_vectors = [] for i in range(num_boxes): if scores is None or scores[i] > min_score_thresh: + # box = tuple(boxes[i].tolist()) class_name = category_index.get(classes[i], {'name': 'unknown object'})['name'] display_str = '{}: {} {}%'.format(classes[i], class_name, int(100 * scores[i])) - print(display_str) # TODO(Alex) Convert to logging - - obj_data = { - 'score': scores[i], - 'color': color_labeler.estimate(image, box=boxes[i]) - } - - state_obj[class_name] += [obj_data] - - update_state_dict.i += 1 - - return state_obj - - -update_state_dict.i = None - -# for i in range(update_state.window): -# update_state.states.append([]) + print(display_str) # TODO: Convert to logging + object_vectors.append([class_name, 0, scores[i], 0, 0, 0, 0, 0, 0] + + list(estimate_color(image, box=boxes[i]))) + return object_vectors -def describe_state(state): +def describe_scene(object_vectors): """ Convert a state vector dictionary of objects and their counts into a natural language string - >>> describe_state({'skis': [{'score': 0.99}, {'score': 0.88}]}) + >>> describe_scene({'skis': [{'score': 0.99}, {'score': 0.88}]}) '2 pairs of skis' - >>> statement = describe_state({'skis': [{'score': 0.88}], 'cup': [{'score': 0.87 }, {'score': 0.66}]} ) - >>> '2 cups' in statement and 'and' in statement and '1 skis' in statement - True + >>> object_vectors = [ + ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] + ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] + ... ] + >>> describe_scene(object_vectors) """ - def count_objects(state_dict): - new_dict = {k: len(v) for k, v in state_dict.items()} - return new_dict + def count_objects(object_vectors): + return collections.Counter(list(zip(*object_vectors))[0]) if len(object_vectors) else {} - state_counts = count_objects(state) + object_counts = count_objects(object_vectors) - plural_description_list = ['{} {}'.format(i, pluralize(s) if i > 1 else s) for (s, i) in state_counts.items()] + plural_description_list = ['{} {}'.format(i, pluralize(s) if i > 1 else s) for (s, i) in object_counts.items()] comma_list = ', '.join(plural_description_list[:-2]) conjunction = ' and '.join(plural_description_list[-2:]) diff --git a/object_detection/color_labeler/__init__.py b/object_detection/color_labeler/__init__.py index f3e380e..f6ff58e 100644 --- a/object_detection/color_labeler/__init__.py +++ b/object_detection/color_labeler/__init__.py @@ -1,3 +1,3 @@ -from object_detection.color_labeler.core import estimate_color as estimate +from object_detection.color_labeler.core import estimate_color as estimate # TODO: this is an anti-pattern, __init__.py should always be empty from object_detection.color_labeler.core import estimate_color diff --git a/object_detection/color_labeler/core.py b/object_detection/color_labeler/core.py index bbc4783..db5debd 100644 --- a/object_detection/color_labeler/core.py +++ b/object_detection/color_labeler/core.py @@ -2,12 +2,15 @@ Assigns a human understandable label to a color value +TODO: in python there's rarely a need for generic file or folder names like core.py or main.py """ from collections import OrderedDict import pandas as pd import numpy as np import cv2 +from object_detection.constants import COLOR_KEYS + def estimate_color(img, box=None): """ @@ -16,40 +19,23 @@ def estimate_color(img, box=None): img: source RGB image box: bounding box (ymin, xmin, ymax, xmax) - Examples: - >>> from skimage.data import coffee - >>> img = coffee() - >>> result = estimate_color(img) - >>> result.get('black', None) is not None - True - >>> result.get('white', None) is not None - True - - # Colors are within sensible range - >>> 0 <= result['black'] <= 0.4 - True - >>> 0.1 <= result['white'] <= 0.3 - True - >>> 0 <= result['blue'] <= 0.1 - True - >>> 0.4 <= result['red'] <= 1 - True - - # Order of output is enforced - >>> abs(result['black'] - result[0]) < 0.0001 - True - >>> abs(result['white'] - result[1]) < 0.0001 - True - >>> abs(result['purple'] - result[-2] < 0.0001) - True - >>> abs(result['pink'] - result[-1] < 0.0001) - True - >>> result.idxmax() - 'red' - Returns: - Dictionary of color-frequency pairs. + pd.Series: color histogram where the values are normalized pixel color frequencies + Examples: + >>> from skimage.data import coffee + >>> estimate_color(coffee()).round(1) + black 0.1 + white 0.3 + red 0.5 + orange 0.2 + yellow 0.0 + green 0.0 + cyan 0.0 + blue 0.0 + purple 0.0 + pink 0.0 + Name: color, dtype: float64 """ # Get center of image (via bounding box) @@ -93,23 +79,7 @@ def estimate_color(img, box=None): total = n_black + n_white + sum(hist_hue) - od = OrderedDict() - od['black'] = n_black / total - od['white'] = n_white / total - od['red'] = hist_hue[0] / total - od['orange'] = hist_hue[1] / total - od['yellow'] = hist_hue[2] / total - od['green'] = hist_hue[3] / total - od['cyan'] = hist_hue[4] / total - od['blue'] = hist_hue[5] / total - od['purple'] = hist_hue[6] / total - od['pink'] = hist_hue[7] / total - output = pd.Series(od) - - return output - - - + return pd.Series([n_black, n_white] + list(hist_hue), index=COLOR_KEYS, name='color') / total def _get_bbox_center_img(img, box=None): @@ -151,9 +121,3 @@ def _get_bbox_center_img(img, box=None): obj_center = img[ystart:yend, xstart:xend, :] return obj_center - - - - - - diff --git a/object_detection/constants.py b/object_detection/constants.py index 1578b2a..5b63776 100644 --- a/object_detection/constants.py +++ b/object_detection/constants.py @@ -20,6 +20,6 @@ CATEGORY_INDEX = label_map_util.create_category_index(CATEGORIES) LABEL_KEYS = 'category instance'.split() -COLOR_KEYS = 'red orange yellow green indigo violet black white pink'.split() +COLOR_KEYS = 'black white red orange yellow green cyan blue purple pink'.split() BB_KEYS = 'x y z width height depth'.split() OBJECT_VECTOR_KEYS = LABEL_KEYS + BB_KEYS + COLOR_KEYS diff --git a/object_detection_app.py b/object_detection_app.py index f6515d6..1800ada 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -9,16 +9,15 @@ from utils.app_utils import FPS, WebcamVideoStream from multiprocessing import Queue, Pool from object_detection.utils import visualization_utils as vis_util -from nlp import update_state_dict, describe_state, say +from nlp import describe_scene, say, update_state from nlp.dispatch import mqttc, dispatcher from nlp.command import Describe, DescribeColor - from object_detection.constants import CATEGORY_INDEX, PATH_TO_CKPT -def detect_objects(image_np, sess, detection_graph, _state_q, utterance_frames=20, voice_on=False): +def detect_objects(image_np, sess, detection_graph, _state_q, utterance_frames=1, voice_on=False): # Expand dimensions since the model expects images to have shape: [1, None, None, 3] image_np_expanded = np.expand_dims(image_np, axis=0) image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') @@ -48,17 +47,16 @@ def detect_objects(image_np, sess, detection_graph, _state_q, utterance_frames=2 line_thickness=8) # Describe the image - state = update_state_dict(image=image_np, boxes=np.squeeze(boxes), - classes=np.squeeze(classes).astype(np.int32), - scores=np.squeeze(scores), category_index=CATEGORY_INDEX) + object_vectors = update_state(image=image_np, boxes=np.squeeze(boxes), + classes=np.squeeze(classes).astype(np.int32), + scores=np.squeeze(scores), category_index=CATEGORY_INDEX) - # Persists image state in a queue - _state_q.put(state) + # Persists image state (list of object vectors for that image) in a queue + _state_q.put(object_vectors) - if not update_state_dict.i % utterance_frames: - description = describe_state(state) - if voice_on: - say(description) + description = describe_scene(object_vectors) + if voice_on: + say(description) return image_np From f3d214172b47f2ece46af60d7af6272103d45f14 Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Mon, 18 Dec 2017 09:57:14 -0800 Subject: [PATCH 110/174] updated to coding standards and changed all tabs to 4 spaces. --- nlp/transform.py | 302 ++++++++++++++++++++++------------------------- 1 file changed, 139 insertions(+), 163 deletions(-) diff --git a/nlp/transform.py b/nlp/transform.py index faf4e85..9d93ab4 100644 --- a/nlp/transform.py +++ b/nlp/transform.py @@ -1,163 +1,139 @@ -from PIL import Image - -def normalize_x_and_y(image,xmin,xmax,ymin,ymax): - """ - Takes in an image which will be provided and then computes the normalized bouding box information. - Args: - xmin - this is the left most point of the bounding box - xmax - this is the right most point of the bounding box - ymin - this is the lowest point of the bounding box - ymax - this is the highest point of the bouding box - From image, try to get image width and height such that we can scale it appropriately - - (xmin,ymax). (xmax,ymax) - --------------- - | | - | | - | | - | | - | | - ________________ - (xmin,ymin) (xmax,ymin) - - - - The output will be the following 6 parameters - x - the left most point and is scaled between -1 and 1. to scale, can do (xmin-image_width/2)/(image_width/2) - y - the bottom most point and is scaled between -1 and 1. to scale, can do (ymin-image_height/2)/(image_height/2) - width - this is defined as xmax-xmin. to scale, compute (xmax-xmin)/(image_width/2) - height - this is defined as ymax-ymin. to scale, compute (ymax-ymin)/(image_height/2) - z - set to 0 - depth - set to 0 - - (x,y+height). (x+width, y+height) - --------------- - | | - | | - | | - | | - | | - ________________ - (x,y) (x+width,y) - - these test cases are for a 100*100 image but generic code is built to run any height and width - >>> normalize_x_and_y(image,100,100,50,50) - 1.0 0.0 0.0 0.0 0.0 0.0 - >>> normalize_x_and_y(image,10,90,10,90) - -0.8 -0.8 1.6 1.6 0.0 0.0 - >>> normalize_x_and_y(image,90,10,10,90) - xmin is greater than xmax - >>> normalize_x_and_y(image,110,190,10,40) - xmin is greater than image width - >>> normalize_x_and_y(image,-10,10,10,90) - xmin < 0 - >>> normalize_x_and_y(image,10,90,90,10) - ymin is greater than ymax - >>> normalize_x_and_y(image,101,150,50,100) - xmin is greater than image width - >>> normalize_x_and_y(image,10,90,10,190) - ymax is greater than image height - >>> normalize_x_and_y(iamge,0,200,100,100) - xmax is greater than image width - >>> normalize_x_and_y(image,0,100,-10,100) - ymin < 0 - >>> normalize_x_and_y(image,50,50,50,50) - 0.0 0.0 0.0 0.0 0.0 0.0 - >>> normalize_x_and_y(image,50,100,50,100) - 0.0 0.0 1.0 1.0 0.0 0.0 - >>> normalize_x_and_y(image,0,100,0,100) - -1.0 -1.0 2.0 2.0 0.0 0.0 - - """ - - #TODO: Check if I really need this next line or not - im = Image.open(image) - im_width, im_height = im.size - if (xmin > xmax): - print ("xmin is greater than xmax") - return ("error") - - if (ymin>ymax): - print("ymin is greater than ymax") - return ("error") - - if (xmin > im_width): - print("xmin is greater than image width") - return ("error") - - if (xmax > im_width): - print("xmax is greater than image width") - return ("error") - - if (ymin > im_height): - print ("ymin is greater than image height") - return ("error") - if (ymax > im_height): - print ("ymax is greater than image height") - return ("error") - - if (xmin < 0): - print ("xmin < 0") - return ("error") - - if (ymin < 0): - print ("ymin < 0") - return ("error") - - x = (xmin-(im_width/2))/(im_width/2) - y = (ymin-(im_height/2))/(im_height/2) - width = ((xmax-xmin)/(im_width/2)) - height = ((ymax-ymin))/(im_height/2) - z = 0.0 - depth = 0.0 - return(x,y,width,height,z,depth) - -def position(a): - """ - this function takes in the image as its argument. - the function then calls the normalizing function to get the values of x,y, width and height from - normalize function. - Once the values of x,y,width and height are received, the function returns what it believes to be the - position of the object. - The position of the object has been defined in the follwing manner: - if the x position is less than half of the image widht, it is in the left side but to check if it is - centered or not, I check the value of x+width of box. If the value of x+width is greater than 0 and the - value of x is less than 0, i say that the object is centered. In the case that x < 0 and x+width < 0, - I say the object is to the left. In any case, since the width cannot be negative, if x >= 0, I say that it - is to the right as the position. - - Also have accounted for error messages. - - >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",0,60,0,100)) - >>> position(a) - center - >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",0,10,0,100)) - left - >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",50,60,0,100)) - right - >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",40,60,0,100)) - center - >>> a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",110,60,0,100)) - xmin is greater than xmax - see above error message - - """ - - if (a != "error"): - #print("test passed") - x,y,width,height,z,depth = a - position = "" - if (x <= (0)): - if (x+width <= 0): - position = "left" - else: - position = "center" - if (x >= (0)): - position = "right" - - print (position) - else: - print("see above error message") - -a = (normalize_x_and_y("/Users/ashwin/Documents/Coding/vish.jpg",110,60,0,100)) -position = position(a) \ No newline at end of file +from skimage.data import coffee + +def _normalize_x_and_y(image, box): + """ + Takes in an image which will be provided and then computes the normalized bouding box information. + Args: + xmin - this is the left most point of the bounding box + xmax - this is the right most point of the bounding box + ymin - this is the lowest point of the bounding box + ymax - this is the highest point of the bouding box + From image, try to get image width and height such that we can scale it appropriately + + (xmin,ymax). (xmax,ymax) + --------------- + | | + | | + | | + | | + | | + ________________ + (xmin,ymin) (xmax,ymin) + + + + The output will be the following 6 parameters + x - the left most point and is scaled between -1 and 1. to scale, can do (xmin-image_width/2)/(image_width/2) + y - the bottom most point and is scaled between -1 and 1. to scale, can do (ymin-image_height/2)/(image_height/2) + width - this is defined as xmax-xmin. to scale, compute (xmax-xmin)/(image_width/2) + height - this is defined as ymax-ymin. to scale, compute (ymax-ymin)/(image_height/2) + z - set to 0 + depth - set to 0 + + (x,y+height). (x+width, y+height) + --------------- + | | + | | + | | + | | + | | + ________________ + (x,y) (x+width,y) + + these test cases are for a 100*100 image but generic code is built to run any height and width + >>> from skimage.data import coffee + >>> img = coffee() + >>> _normalize_x_and_y(img,(100,100,50,50)) + (-0.5, -0.8333333333333334, 0.0, 0.0, 0.0, 0.0) + >>> _normalize_x_and_y(img,(10,90,10,90)) + (-0.95, -0.9666666666666667, 0.4, 0.26666666666666666, 0.0, 0.0) + >>> _normalize_x_and_y(img,(0,400,0,600)) + (-1.0, -1.0, 2.0, 2.0, 0.0, 0.0) + >>> _normalize_x_and_y(img,(100,50,0,600)) + Traceback (most recent call last): + ... + AssertionError: xmin is greater than xmax + >>> _normalize_x_and_y(img,(100,600,0,600)) + Traceback (most recent call last): + ... + AssertionError: xmax is greater than image width + >>> _normalize_x_and_y(img,(100,400,200,100)) + Traceback (most recent call last): + ... + AssertionError: ymin is greater than ymax + >>> _normalize_x_and_y(img,(-100,400,100,100)) + Traceback (most recent call last): + ... + AssertionError: xmin < 0 + """ + xmin, xmax, ymin, ymax = box + im_width, im_height = image.shape[:2] + + assert xmin <= xmax, 'xmin is greater than xmax' + assert ymin <= ymax, 'ymin is greater than ymax' + assert xmin <= im_width, 'xmin is greater than image width' + assert xmax <= im_width, 'xmax is greater than image width' + assert ymin <= im_height, 'ymin is greater than image height' + assert ymax <= im_height, 'ymax is greater than image height' + assert xmin >= 0, 'xmin < 0' + assert ymin >= 0, 'ymin < 0' + + x_center = im_width / 2 + y_center = im_height / 2 + x = (xmin - (x_center)) / x_center + y = (ymin - (y_center)) / y_center + width = (xmax - xmin) / x_center + height = (ymax - ymin) / y_center + z = 0.0 + depth = 0.0 + return (x, y, width, height, z, depth) + +def position(image,box): + """ + this function takes in the image as its argument. + the function then calls the normalizing function to get the values of x,y, width and height from + normalize function. + Once the values of x,y,width and height are received, the function returns what it believes to be the + position of the object. + The position of the object has been defined in the follwing manner: + if the x position is less than half of the image widht, it is in the left side but to check if it is + centered or not, I check the value of x+width of box. If the value of x+width is greater than 0 and the + value of x is less than 0, i say that the object is centered. In the case that x < 0 and x+width < 0, + I say the object is to the left. In any case, since the width cannot be negative, if x >= 0, I say that it + is to the right as the position. + + Also have accounted for error messages. + >>> from skimage.data import coffee + >>> img = coffee() + >>> position(img,(0,400,0,600)) + center + >>> position(img,(0,100,0,200)) + left + >>> position(img,(200,400,300,400)) + right + >>> position(img,(-100,200,300,400)) + Traceback (most recent call last): + ... + AssertionError: xmin < 0 + >>> position(img,(0,400,0,800)) + Traceback (most recent call last): + ... + AssertionError: ymax is greater than image height + + """ + + x,y,width,height,z,depth = _normalize_x_and_y(image,box) + position = "" + if (x <= (0)): + if (x+width <= 0): + position = "left" + else: + position = "center" + if (x >= (0)): + position = "right" + + print (position) + +if __name__ == '__main__': + import doctest + doctest.testmod() \ No newline at end of file From 98498e3cff4d0f62c4eadd798b931da02956ce27 Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Mon, 18 Dec 2017 10:00:15 -0800 Subject: [PATCH 111/174] changed print to return --- nlp/transform.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/nlp/transform.py b/nlp/transform.py index 9d93ab4..441ea24 100644 --- a/nlp/transform.py +++ b/nlp/transform.py @@ -106,11 +106,11 @@ def position(image,box): >>> from skimage.data import coffee >>> img = coffee() >>> position(img,(0,400,0,600)) - center + 'center' >>> position(img,(0,100,0,200)) - left + 'left' >>> position(img,(200,400,300,400)) - right + 'right' >>> position(img,(-100,200,300,400)) Traceback (most recent call last): ... @@ -132,7 +132,7 @@ def position(image,box): if (x >= (0)): position = "right" - print (position) + return (position) if __name__ == '__main__': import doctest From f6bd5316ae4c3542bb20f08c6105b6a5cc6eb9f4 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Mon, 18 Dec 2017 10:03:16 -0800 Subject: [PATCH 112/174] add singulars to plurals.py --- nlp/plurals.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/nlp/plurals.py b/nlp/plurals.py index ec30afe..324a8fb 100644 --- a/nlp/plurals.py +++ b/nlp/plurals.py @@ -1,3 +1,5 @@ + +# TODO: only list exceptions that are not easy to generate using simple "add s" rules. PLURALS = { 'apple': 'apples', 'backpack': 'backpacks', @@ -78,4 +80,7 @@ 'umbrella': 'umbrellas', 'vase': 'vases', 'wine glass': 'wine glasses', - 'zebra': 'zebras'} \ No newline at end of file + 'zebra': 'zebras'} + + +SINGULAR = dict(list(zip(PLURALS.values(), PLURALS.keys()))) From cb503cd856b1506408dd372533e013b237f8036d Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Mon, 18 Dec 2017 10:08:40 -0800 Subject: [PATCH 113/174] no more utterance_frames arg --- object_detection_app.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/object_detection_app.py b/object_detection_app.py index 1800ada..a4d22fd 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -17,7 +17,7 @@ from object_detection.constants import CATEGORY_INDEX, PATH_TO_CKPT -def detect_objects(image_np, sess, detection_graph, _state_q, utterance_frames=1, voice_on=False): +def detect_objects(image_np, sess, detection_graph, _state_q, voice_on=False): # Expand dimensions since the model expects images to have shape: [1, None, None, 3] image_np_expanded = np.expand_dims(image_np, axis=0) image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') @@ -54,6 +54,7 @@ def detect_objects(image_np, sess, detection_graph, _state_q, utterance_frames=1 # Persists image state (list of object vectors for that image) in a queue _state_q.put(object_vectors) + # FIXME: this should not be happening here, but should be happening in the commands.py executive logic description = describe_scene(object_vectors) if voice_on: say(description) From 17486ee7682ecbd2aa53d62f988394bfd5641fa5 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Mon, 18 Dec 2017 10:35:17 -0800 Subject: [PATCH 114/174] fixed say error handling (os.system doesn't raise exceptions) --- nlp/core.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/nlp/core.py b/nlp/core.py index ecab9d3..8cb0528 100644 --- a/nlp/core.py +++ b/nlp/core.py @@ -57,7 +57,6 @@ def update_state(image, boxes, classes, scores, category_index, window=10, max_b category_index (dict of dicts): {1: {'id': 1, 'name': 'person'}, 2: {'id': 2, 'name': 'bicycle'},...} Returns: list: list of object vectors, for example: - LABEL_KEYS = '.split() [ ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] @@ -86,8 +85,9 @@ def describe_scene(object_vectors): >>> describe_scene({'skis': [{'score': 0.99}, {'score': 0.88}]}) '2 pairs of skis' >>> object_vectors = [ - ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] - ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] + ... # categ instnc x y z wdth hght dpth blk wht red orng yel grn cyn blu purp pink + ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] + ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] ... ] >>> describe_scene(object_vectors) """ @@ -108,7 +108,7 @@ def count_objects(object_vectors): return delim_description -def say(s, rate=230): +def say(s, rate=250): """ Convert text to speech (TTS) and play resulting audio to speakers If "say" command is not available in os.system then print the text to stdout and return False. @@ -116,9 +116,13 @@ def say(s, rate=230): >>> say('hello') 'hello' """ + shell_cmd = 'say --rate={rate} "{s}"'.format(**dict(rate=rate, s=s)) try: - os.system('say --rate={rate} "{s}"'.format(**dict(rate=rate, s=s))) + status = os.system(shell_cmd) + if status > 0: + print('os.system({shell_cmd}) returned nonzero status: {status}'.format(**locals())) + raise OSError return s - except: + except OSError: print(s) return False From 95f6c96ccd1ebd48f0d149fd955dd33b94d8b7c7 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Mon, 18 Dec 2017 10:47:56 -0800 Subject: [PATCH 115/174] (NLP Functions) - Added template string to `Describe` command + started helper fxn - Updated nlp core `__init__.py` - Broke out functions in `nlp/core.py`. --- nlp/__init__.py | 3 +-- nlp/command/describe.py | 9 ++++++++- nlp/core.py | 45 ++++++++++++++++++++++++++++++++--------- 3 files changed, 44 insertions(+), 13 deletions(-) diff --git a/nlp/__init__.py b/nlp/__init__.py index 79fbff9..0eebe41 100644 --- a/nlp/__init__.py +++ b/nlp/__init__.py @@ -1,2 +1 @@ -from nlp.core import update_state, update_state_dict, describe_state, say - +from nlp.core import update_state, update_state_dict, describe_state, say, pluralize, compose_comma_series diff --git a/nlp/command/describe.py b/nlp/command/describe.py index 76ca726..ba06542 100644 --- a/nlp/command/describe.py +++ b/nlp/command/describe.py @@ -4,6 +4,8 @@ class Describe(Dispatchable): + obj_desc_tmpl = '{num_obj} {obj_color} {obj_name} to your {rel_pos}' + def __init__(self, state_q): self.state_q = state_q @@ -13,4 +15,9 @@ def __call__(self, payload): if state: description = describe_state(state) - self.send({'response': description}, subtopic=['say']) \ No newline at end of file + self.send({'response': description}, subtopic=['say']) + + +def count_obj_by_color(state): + new_state = {k: len(v) for k, v in state.items()} + return new_state diff --git a/nlp/core.py b/nlp/core.py index 435714c..3781e14 100644 --- a/nlp/core.py +++ b/nlp/core.py @@ -1,6 +1,7 @@ """ Natural Language Processing (Generation) utilities """ import collections import os +import typing import pandas as pd import object_detection.color_labeler as color_labeler @@ -166,31 +167,55 @@ def describe_state(state): >>> '2 cups' in statement and 'and' in statement and '1 skis' in statement True """ - def count_objects(state_dict): - new_dict = {k: len(v) for k, v in state_dict.items()} - return new_dict - - state_counts = count_objects(state) + state_counts = _count_objects(state) plural_description_list = ['{} {}'.format(i, pluralize(s) if i > 1 else s) for (s, i) in state_counts.items()] - comma_list = ', '.join(plural_description_list[:-2]) - conjunction = ' and '.join(plural_description_list[-2:]) + delim_description = compose_comma_series(plural_description_list) + + return delim_description + + +def compose_comma_series(noun_list: typing.List[str]) -> str: + """ Join a list of noun phrases into a comma delimited series + + Args: + noun_list: list of noun phrases (object + adjective descriptors) + + Returns: + string consisting of a comma delimited series of noun phrases + + Examples: + >>> compose_comma_series(['1 pair of skis', '2 cups']) + '1 pair of skis and 2 cups' + >>> compose_comma_series(['1 pair of skis', '2 cups', '1 laptop']) + '1 pair of skis, 2 cups and 1 laptop' + """ + + comma_list = ', '.join(noun_list[:-2]) + conjunction = ' and '.join(noun_list[-2:]) + if len(comma_list) > 0: - delim_description = comma_list + ',' + conjunction + delim_description = comma_list + ', ' + conjunction else: delim_description = conjunction return delim_description +def _count_objects(state_dict): + new_dict = {k: len(v) for k, v in state_dict.items()} + return new_dict + + def say(s, rate=230): """ Convert text to speech (TTS) and play resulting audio to speakers If "say" command is not available in os.system then print the text to stdout. - >>> say('hello') - 'hello' + >>> result = say('hello') # TODO(Ashwin | Alex | Hobbs) This test will fail on Jenkins + >>> result == 'hello' or result == False + True """ try: os.system('say --rate={rate} "{s}"'.format(**dict(rate=rate, s=s))) From 77989c8771d9b1a7191479034b94466d9c09a196 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Mon, 18 Dec 2017 10:48:35 -0800 Subject: [PATCH 116/174] Revert "(NLP Functions) - Added template string to `Describe` command + started helper fxn - Updated nlp core `__init__.py` - Broke out functions in `nlp/core.py`." This reverts commit 95f6c96 --- nlp/__init__.py | 3 ++- nlp/command/describe.py | 9 +-------- nlp/core.py | 45 +++++++++-------------------------------- 3 files changed, 13 insertions(+), 44 deletions(-) diff --git a/nlp/__init__.py b/nlp/__init__.py index 0eebe41..79fbff9 100644 --- a/nlp/__init__.py +++ b/nlp/__init__.py @@ -1 +1,2 @@ -from nlp.core import update_state, update_state_dict, describe_state, say, pluralize, compose_comma_series +from nlp.core import update_state, update_state_dict, describe_state, say + diff --git a/nlp/command/describe.py b/nlp/command/describe.py index ba06542..76ca726 100644 --- a/nlp/command/describe.py +++ b/nlp/command/describe.py @@ -4,8 +4,6 @@ class Describe(Dispatchable): - obj_desc_tmpl = '{num_obj} {obj_color} {obj_name} to your {rel_pos}' - def __init__(self, state_q): self.state_q = state_q @@ -15,9 +13,4 @@ def __call__(self, payload): if state: description = describe_state(state) - self.send({'response': description}, subtopic=['say']) - - -def count_obj_by_color(state): - new_state = {k: len(v) for k, v in state.items()} - return new_state + self.send({'response': description}, subtopic=['say']) \ No newline at end of file diff --git a/nlp/core.py b/nlp/core.py index 3781e14..435714c 100644 --- a/nlp/core.py +++ b/nlp/core.py @@ -1,7 +1,6 @@ """ Natural Language Processing (Generation) utilities """ import collections import os -import typing import pandas as pd import object_detection.color_labeler as color_labeler @@ -167,55 +166,31 @@ def describe_state(state): >>> '2 cups' in statement and 'and' in statement and '1 skis' in statement True """ - state_counts = _count_objects(state) + def count_objects(state_dict): + new_dict = {k: len(v) for k, v in state_dict.items()} + return new_dict - plural_description_list = ['{} {}'.format(i, pluralize(s) if i > 1 else s) for (s, i) in state_counts.items()] - - delim_description = compose_comma_series(plural_description_list) - - return delim_description - - -def compose_comma_series(noun_list: typing.List[str]) -> str: - """ Join a list of noun phrases into a comma delimited series - - Args: - noun_list: list of noun phrases (object + adjective descriptors) + state_counts = count_objects(state) - Returns: - string consisting of a comma delimited series of noun phrases - - Examples: - >>> compose_comma_series(['1 pair of skis', '2 cups']) - '1 pair of skis and 2 cups' - >>> compose_comma_series(['1 pair of skis', '2 cups', '1 laptop']) - '1 pair of skis, 2 cups and 1 laptop' - """ - - comma_list = ', '.join(noun_list[:-2]) - conjunction = ' and '.join(noun_list[-2:]) + plural_description_list = ['{} {}'.format(i, pluralize(s) if i > 1 else s) for (s, i) in state_counts.items()] + comma_list = ', '.join(plural_description_list[:-2]) + conjunction = ' and '.join(plural_description_list[-2:]) if len(comma_list) > 0: - delim_description = comma_list + ', ' + conjunction + delim_description = comma_list + ',' + conjunction else: delim_description = conjunction return delim_description -def _count_objects(state_dict): - new_dict = {k: len(v) for k, v in state_dict.items()} - return new_dict - - def say(s, rate=230): """ Convert text to speech (TTS) and play resulting audio to speakers If "say" command is not available in os.system then print the text to stdout. - >>> result = say('hello') # TODO(Ashwin | Alex | Hobbs) This test will fail on Jenkins - >>> result == 'hello' or result == False - True + >>> say('hello') + 'hello' """ try: os.system('say --rate={rate} "{s}"'.format(**dict(rate=rate, s=s))) From 4e38dcceae143b6b2dc06dc1eda75e0f23748553 Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Mon, 18 Dec 2017 10:50:35 -0800 Subject: [PATCH 117/174] fixed PR changes. --- nlp/transform.py | 26 ++++++++++++-------------- 1 file changed, 12 insertions(+), 14 deletions(-) diff --git a/nlp/transform.py b/nlp/transform.py index 441ea24..749d240 100644 --- a/nlp/transform.py +++ b/nlp/transform.py @@ -1,6 +1,4 @@ -from skimage.data import coffee - -def _normalize_x_and_y(image, box): +def normalize_position(image, box): """ Takes in an image which will be provided and then computes the normalized bouding box information. Args: @@ -43,25 +41,25 @@ def _normalize_x_and_y(image, box): these test cases are for a 100*100 image but generic code is built to run any height and width >>> from skimage.data import coffee >>> img = coffee() - >>> _normalize_x_and_y(img,(100,100,50,50)) + >>> normalize_position(img,(100,100,50,50)) (-0.5, -0.8333333333333334, 0.0, 0.0, 0.0, 0.0) - >>> _normalize_x_and_y(img,(10,90,10,90)) + >>> normalize_position(img,(10,90,10,90)) (-0.95, -0.9666666666666667, 0.4, 0.26666666666666666, 0.0, 0.0) - >>> _normalize_x_and_y(img,(0,400,0,600)) + >>> normalize_position(img,(0,400,0,600)) (-1.0, -1.0, 2.0, 2.0, 0.0, 0.0) - >>> _normalize_x_and_y(img,(100,50,0,600)) + >>> normalize_position(img,(100,50,0,600)) Traceback (most recent call last): ... AssertionError: xmin is greater than xmax - >>> _normalize_x_and_y(img,(100,600,0,600)) + >>> normalize_position(img,(100,600,0,600)) Traceback (most recent call last): ... AssertionError: xmax is greater than image width - >>> _normalize_x_and_y(img,(100,400,200,100)) + >>> normalize_position(img,(100,400,200,100)) Traceback (most recent call last): ... AssertionError: ymin is greater than ymax - >>> _normalize_x_and_y(img,(-100,400,100,100)) + >>> normalize_position(img,(-100,400,100,100)) Traceback (most recent call last): ... AssertionError: xmin < 0 @@ -122,18 +120,18 @@ def position(image,box): """ - x,y,width,height,z,depth = _normalize_x_and_y(image,box) + x,y,width,height,z,depth = normalize_position(image,box) position = "" - if (x <= (0)): + if (x <= 0): if (x+width <= 0): position = "left" else: position = "center" - if (x >= (0)): + if (x >= 0): position = "right" return (position) if __name__ == '__main__': import doctest - doctest.testmod() \ No newline at end of file + doctest.testmod() From be4052c3171c1ff9ca8107d5fefaa33afbb8a637 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Mon, 18 Dec 2017 11:05:22 -0800 Subject: [PATCH 118/174] - `nlp/core.py` doctests now pass --- nlp/core.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/nlp/core.py b/nlp/core.py index 4898d3a..cc1c2a5 100644 --- a/nlp/core.py +++ b/nlp/core.py @@ -83,14 +83,14 @@ def update_state(image, boxes, classes, scores, category_index, window=10, max_b def describe_scene(object_vectors): """ Convert a state vector dictionary of objects and their counts into a natural language string - >>> describe_scene({'skis': [{'score': 0.99}, {'score': 0.88}]}) - '2 pairs of skis' + categ instnc x y z wdth hght dpth blk wht red orng yel grn cyn blu purp pink >>> object_vectors = [ - ... # categ instnc x y z wdth hght dpth blk wht red orng yel grn cyn blu purp pink - ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] + ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01], ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] ... ] - >>> describe_scene(object_vectors) + >>> desc = describe_scene(object_vectors) + >>> '1 cup' in desc and ' and ' in desc and '1 ski' in desc + True """ def count_objects(object_vectors): return collections.Counter(list(zip(*object_vectors))[0]) if len(object_vectors) else {} From 6d16736cc78b4f95cd1b47cc15b9bf23b04f5e33 Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Mon, 18 Dec 2017 11:57:13 -0800 Subject: [PATCH 119/174] style changes for docstring and doctest --- nlp/transform.py | 71 ++++++++++++++++++++++++++---------------------- 1 file changed, 39 insertions(+), 32 deletions(-) diff --git a/nlp/transform.py b/nlp/transform.py index 749d240..36beda2 100644 --- a/nlp/transform.py +++ b/nlp/transform.py @@ -1,11 +1,17 @@ def normalize_position(image, box): - """ - Takes in an image which will be provided and then computes the normalized bouding box information. + """ Takes in an image which will be provided and then computes the normalized bouding box information. + Args: - xmin - this is the left most point of the bounding box - xmax - this is the right most point of the bounding box - ymin - this is the lowest point of the bounding box - ymax - this is the highest point of the bouding box + image (3D np.array): (rows, columns, channels) + rows (int): width of image + columns (int): height of image + channels (int): number of channels, if the image is in color + box (tuple): (xmin, xmax, ymin, ymax) + xmin (int): left most edge of the bounding box + xmax (int): right most edge of the bounding box + ymin (int): lowest edge of the bounding box + ymax (int): highest edge of the bouding box + From image, try to get image width and height such that we can scale it appropriately (xmin,ymax). (xmax,ymax) @@ -19,14 +25,14 @@ def normalize_position(image, box): (xmin,ymin) (xmax,ymin) - - The output will be the following 6 parameters - x - the left most point and is scaled between -1 and 1. to scale, can do (xmin-image_width/2)/(image_width/2) - y - the bottom most point and is scaled between -1 and 1. to scale, can do (ymin-image_height/2)/(image_height/2) - width - this is defined as xmax-xmin. to scale, compute (xmax-xmin)/(image_width/2) - height - this is defined as ymax-ymin. to scale, compute (ymax-ymin)/(image_height/2) - z - set to 0 - depth - set to 0 + Returns: + tuple: (x, y, z, widht, height, depth) + x (float): left most point and is scaled between -1 and 1. to scale, can do (xmin-image_width/2)/(image_width/2) + y (float): bottom most point and is scaled between -1 and 1. to scale, can do (ymin-image_height/2)/(image_height/2) + z (float): set to 0 + width (float):this is defined as xmax-xmin. to scale, compute (xmax-xmin)/(image_width/2) + height (float): this is defined as ymax-ymin. to scale, compute (ymax-ymin)/(image_height/2) + depth (float): set to 0 (x,y+height). (x+width, y+height) --------------- @@ -38,15 +44,14 @@ def normalize_position(image, box): ________________ (x,y) (x+width,y) - these test cases are for a 100*100 image but generic code is built to run any height and width >>> from skimage.data import coffee >>> img = coffee() >>> normalize_position(img,(100,100,50,50)) (-0.5, -0.8333333333333334, 0.0, 0.0, 0.0, 0.0) >>> normalize_position(img,(10,90,10,90)) - (-0.95, -0.9666666666666667, 0.4, 0.26666666666666666, 0.0, 0.0) + (-0.95, -0.9666666666666667, 0.0, 0.4, 0.26666666666666666, 0.0) >>> normalize_position(img,(0,400,0,600)) - (-1.0, -1.0, 2.0, 2.0, 0.0, 0.0) + (-1.0, -1.0, 0.0, 2.0, 2.0, 0.0) >>> normalize_position(img,(100,50,0,600)) Traceback (most recent call last): ... @@ -84,23 +89,25 @@ def normalize_position(image, box): height = (ymax - ymin) / y_center z = 0.0 depth = 0.0 - return (x, y, width, height, z, depth) + return (x, y, z, width, height, depth) def position(image,box): - """ - this function takes in the image as its argument. - the function then calls the normalizing function to get the values of x,y, width and height from - normalize function. - Once the values of x,y,width and height are received, the function returns what it believes to be the - position of the object. - The position of the object has been defined in the follwing manner: - if the x position is less than half of the image widht, it is in the left side but to check if it is - centered or not, I check the value of x+width of box. If the value of x+width is greater than 0 and the - value of x is less than 0, i say that the object is centered. In the case that x < 0 and x+width < 0, - I say the object is to the left. In any case, since the width cannot be negative, if x >= 0, I say that it - is to the right as the position. + """ takes an image and the bounding box, returns the position of the bounding box with respect to the image + + Args: + image (3D np.array): (rows, columns, channels) + rows (int): width of image + columns (int): height of image + channels (int): number of channels, if the image is in color + box (tuple): (xmin, xmax, ymin, ymax) + xmin (int): left most edge of the bounding box + xmax (int): right most edge of the bounding box + ymin (int): lowest edge of the bounding box + ymax (int): highest edge of the bouding box + + Returns: + string: 'left', 'right' or 'center' - Also have accounted for error messages. >>> from skimage.data import coffee >>> img = coffee() >>> position(img,(0,400,0,600)) @@ -120,7 +127,7 @@ def position(image,box): """ - x,y,width,height,z,depth = normalize_position(image,box) + x, y, z, width, height, depth = normalize_position(image,box) position = "" if (x <= 0): if (x+width <= 0): From 7bd3dcb599e515a68ae34c6ba9937a2844234ce5 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Mon, 18 Dec 2017 15:27:37 -0800 Subject: [PATCH 120/174] Update README.md --- README.md | 34 ++++++++++++++++++++++++++++------ 1 file changed, 28 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 65be6b9..bffa0e4 100644 --- a/README.md +++ b/README.md @@ -3,14 +3,36 @@ A real-time object recognition application using [Google's TensorFlow Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection) and [OpenCV](http://opencv.org/). ## Getting Started -1. `conda env create -f environment.yml` -2. `conda install pip`. - *If you already have pip, you will need to `source deactivate` first and then run `conda install pip` to make sure there are no errors which will occur in the OS -3. To see where the source of your pyhton files that you are running are, use `which python` -4. If it is not where you have installed the conda environment, you need to change the source for python - * `vim environment.yml` and look at the first line of the file which should say something like `name: object-detection`. We are interested in the name of the file. + +Install Anaconda according to the instructions [here](https://docs.anaconda.com/anaconda/install/). + +Make sure your python package installer, `pip`, is updated to use the Anaconda version: + +```bash +$ conda install pip +``` + +Clone the repo to your local machine in whatever folder you use to hold source code, like ~/src/ + +```bash +$ mkdir ~/src +$ cd ~/src +$ git clone https://github.com/aira/object_detector_app +$ cd object_detector_app +``` + +Create a new Anaconda environment on your machine to hold tensorflow, python 3.5, OpenCV, etc. This will take a while: + +`conda env create -f environment.yml` + +Check to make sure you're using the python that's in your conda environment: `which python` should have a path that indicates anaconda and the object-detection environment. + +If it is not where you have installed the conda environment, you need to change the source for python + + * `head environment.yml` and look at the first line of the file which should say something like `name: object-detection`. We are interested in the name of the file. * `source activate name` where name will be replaced with what was stated in your environment.yml file * `which python` and this time the place where you installed conda will show up + 5. `python object_detection_app.py` Optional arguments (default value): * Show all commands `--help` From e7357d817337069ee239e5d380a26bfeb96d7ad9 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Mon, 18 Dec 2017 16:13:15 -0800 Subject: [PATCH 121/174] Got it (mostly) working. - Broke down into composable, configurable functions. - Commented functions - Doctest (nearly) all passing. --- nlp/core.py | 185 ++++++++++++++++++++++++++++++++-- object_detection/constants.py | 30 +++++- 2 files changed, 203 insertions(+), 12 deletions(-) diff --git a/nlp/core.py b/nlp/core.py index cc1c2a5..628d268 100644 --- a/nlp/core.py +++ b/nlp/core.py @@ -1,14 +1,15 @@ """ Natural Language Processing (Generation) utilities """ -import collections import os import typing import pandas as pd +import object_detection.constants as constants + # fix the antipattern of having a separate folder for every function/class from object_detection.color_labeler import estimate as estimate_color - from nlp.plurals import PLURALS -from collections import defaultdict + +from collections import Counter def pluralize(s): @@ -89,21 +90,184 @@ def describe_scene(object_vectors): ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] ... ] >>> desc = describe_scene(object_vectors) - >>> '1 cup' in desc and ' and ' in desc and '1 ski' in desc + >>> 'A cup' in desc and ' and ' in desc and 'A ski' in desc True """ - def count_objects(object_vectors): - return collections.Counter(list(zip(*object_vectors))[0]) if len(object_vectors) else {} + feature_list = list(map(object_features, object_vectors)) - object_counts = count_objects(object_vectors) + plural_descriptions = aggregate_descriptions_by_features(feature_list) - plural_description_list = ['{} {}'.format(i, pluralize(s) if i > 1 else s) for (s, i) in object_counts.items()] - - delim_description = compose_comma_series(plural_description_list) + delim_description = compose_comma_series(plural_descriptions) return delim_description +def aggregate_descriptions_by_features(feature_list, *, + include_color: bool = True, include_position: bool = False) -> typing.List[str]: + """Produce a list of descriptions created through aggregations (counts) of objects in the scene. + + Can optionally aggregate by color and position. + + Args: + feature_list: + include_color: + include_position: + + Returns: + A list of strings with valid descriptions. + + Examples: + >>> obj_vectors = [ + ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01], + ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] + ... ] + >>> feature_list = list(map(object_features, obj_vectors)) + >>> aggregate_descriptions_by_features(feature_list) + ['A white cup', 'A white ski'] + # TODO(Alex) doctest with `include_*` parameters + """ + counts = Counter(feature_list) + + pluralized_feature_groups = [describe_object_from_feature(feature, count, + include_color=include_color, + include_position=include_position) + for feature, count in counts.items()] + + return pluralized_feature_groups + + +def describe_object(obj_vec) -> str: + """Creates formatted string description from object vector + + Args: + obj_vec: a vector representing a single object. + + Returns: + a description of the object. + + Examples: + + >>> obj_vec = ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] + >>> describe_object(obj_vec) + 'A white cup' + """ + feature = object_features(obj_vec) + return describe_object_from_feature(feature) + + +def describe_object_from_feature(feature, count: typing.Optional[int] = None, *, + include_color=True, include_position=False) -> str: + """Creates formatted string description from object features and counts, including pluralization. + + Args: + feature: tuple of strings (, ) + count: a count of the occurrence of the feature, or None + include_color: Optionally include the color feature in description + include_position: Optionally include the position in object description + + Returns: + A noun phrase describing the object. + + Raises: + - AssertionError: This is for development time. Should the feature tuple get more than + the expected number of items in the tuple, an assertion error should be thrown + - ValueError: Count cannot be zero, negative, or an non-integer less than 1. + + Examples: + >>> describe_object_from_feature(('cup', 'white')) + 'A white cup' + >>> describe_object_from_feature(('cup', 'orange')) + 'An orange cup' + >>> describe_object_from_feature(('cup', 'red'), 2) + '2 red cups' + + """ + name, color, *_ = feature + + assert len(_) == 0, 'Need to update string formatting function with new features!' + + if count is None: + count = 1 + + # Structure the string templates based on what features to include + base_tmpl = '{name}' + multiple_desc_tmpl = '' + single_desc_tmlp = '' + + if include_color: + multiple_desc_tmpl = '{color} ' + base_tmpl + single_desc_tmlp = '{color} ' + base_tmpl + + if include_position: + multiple_desc_tmpl += ' to your {position}' + single_desc_tmlp += ' to your {position}' + + multiple_desc_tmpl = '{amount} ' + multiple_desc_tmpl + single_desc_tmlp = '{article} ' + single_desc_tmlp + + if count > 1: + output = multiple_desc_tmpl.format(amount=count, + color=color, + name=pluralize(name)) + elif count == 1: + article = 'An' if _starts_with_vowel(color) else 'A' + + output = single_desc_tmlp.format(article=article, + color=color, + name=name) + else: + raise ValueError('There cannot be zero, negative, or fractional objects!') + + return output + + +def _starts_with_vowel(char) -> bool: + """Test to see if string starts with a vowel + + Args: + char: character or string + + Returns: + bool True if the character is a vowel, False otherwise + + Examples: + >>> _starts_with_vowel('a') + True + >>> _starts_with_vowel('b') + False + >>> _starts_with_vowel('cat') + False + >>> _starts_with_vowel('apple') + True + """ + if len(char) > 1: + char = char[0] + + return char in 'aeiou' + + +def object_features(obj_vec): + """Converts object vector to a tuple of labels (name, color, position, etc.) + + Args: + obj_vec: + + Returns: + Tuple of strings with the following format: + (, , ...) + + Examples: + >>> obj_vec = ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] + >>> object_features(obj_vec) + ('cup', 'white') + """ + if type(obj_vec) is list or type(obj_vec) is pd.Series: + obj_vec = constants.ObjectSeries(obj_vec, index=constants.ObjectSeries.OBJECT_VECTOR_KEYS) + + # Name, Color + return obj_vec['category'], obj_vec.obj_primary_color + + def compose_comma_series(noun_list: typing.List[str]) -> str: """ Join a list of noun phrases into a comma delimited series @@ -119,7 +283,6 @@ def compose_comma_series(noun_list: typing.List[str]) -> str: >>> compose_comma_series(['1 pair of skis', '2 cups', '1 laptop']) '1 pair of skis, 2 cups and 1 laptop' """ - comma_list = ', '.join(noun_list[:-2]) conjunction = ' and '.join(noun_list[-2:]) diff --git a/object_detection/constants.py b/object_detection/constants.py index 5b63776..1580b06 100644 --- a/object_detection/constants.py +++ b/object_detection/constants.py @@ -1,6 +1,7 @@ """ Constants that depend on the object detection model (tensorflow network) being used. """ import os - +import numpy as np +import pandas as pd from object_detection.utils import label_map_util @@ -23,3 +24,30 @@ COLOR_KEYS = 'black white red orange yellow green cyan blue purple pink'.split() BB_KEYS = 'x y z width height depth'.split() OBJECT_VECTOR_KEYS = LABEL_KEYS + BB_KEYS + COLOR_KEYS + + +class ObjectSeries(pd.Series): + LABEL_KEYS = LABEL_KEYS + COLOR_KEYS = COLOR_KEYS + BB_KEYS = BB_KEYS + OBJECT_VECTOR_KEYS = OBJECT_VECTOR_KEYS + + @property + def obj_colors(self): + return self[self.COLOR_KEYS] + + @property + def obj_primary_color(self): + return self.COLOR_KEYS[self.obj_colors.values.argmax()] + + @property + def obj_labels(self): + return self[self.LABEL_KEYS] + + @property + def obj_bbox(self): + return self[self.BB_KEYS] + + + + From 3a7f4ef1d8020ce70b808137fc891f6353c42c2c Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Mon, 18 Dec 2017 16:15:28 -0800 Subject: [PATCH 122/174] (NLP Doctests passing) --- nlp/core.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/nlp/core.py b/nlp/core.py index 628d268..193687f 100644 --- a/nlp/core.py +++ b/nlp/core.py @@ -90,7 +90,7 @@ def describe_scene(object_vectors): ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] ... ] >>> desc = describe_scene(object_vectors) - >>> 'A cup' in desc and ' and ' in desc and 'A ski' in desc + >>> 'A white cup' in desc and ' and ' in desc and 'A white ski' in desc True """ feature_list = list(map(object_features, object_vectors)) @@ -117,14 +117,16 @@ def aggregate_descriptions_by_features(feature_list, *, A list of strings with valid descriptions. Examples: + # TODO(Alex) doctest with `include_*` parameters + >>> obj_vectors = [ ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01], ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] ... ] >>> feature_list = list(map(object_features, obj_vectors)) - >>> aggregate_descriptions_by_features(feature_list) - ['A white cup', 'A white ski'] - # TODO(Alex) doctest with `include_*` parameters + >>> descs = aggregate_descriptions_by_features(feature_list) + >>> 'A white cup' in descs and 'A white ski' in descs + True """ counts = Counter(feature_list) From 685e78b9a29ec082ebd8fff1ddf791c723c86cfd Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Mon, 18 Dec 2017 16:53:33 -0800 Subject: [PATCH 123/174] - Doctests passing for `include_color` parameter. - Added feature tuple filtering. - articles are lowercase --- nlp/core.py | 59 +++++++++++++++++++++++++++++++++++++++++------------ 1 file changed, 46 insertions(+), 13 deletions(-) diff --git a/nlp/core.py b/nlp/core.py index 193687f..35a6870 100644 --- a/nlp/core.py +++ b/nlp/core.py @@ -90,7 +90,7 @@ def describe_scene(object_vectors): ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] ... ] >>> desc = describe_scene(object_vectors) - >>> 'A white cup' in desc and ' and ' in desc and 'A white ski' in desc + >>> 'a white cup' in desc and ' and ' in desc and 'a white ski' in desc True """ feature_list = list(map(object_features, object_vectors)) @@ -121,14 +121,36 @@ def aggregate_descriptions_by_features(feature_list, *, >>> obj_vectors = [ ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01], + ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01], ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] ... ] >>> feature_list = list(map(object_features, obj_vectors)) >>> descs = aggregate_descriptions_by_features(feature_list) - >>> 'A white cup' in descs and 'A white ski' in descs + >>> '2 white cups' in descs and 'a white ski' in descs + True + >>> no_color = aggregate_descriptions_by_features(feature_list, include_color=False) + >>> '2 cups' in no_color and 'a ski' in no_color True """ - counts = Counter(feature_list) + + def include_features(f): + """Filters feature tuple s.t. we include only the features we want to aggregate over. """ + assert len(f) == 2, 'Make sure to include all the features in this function!!' + + fs = list() + fs.append(f[0]) + + if include_color: + fs.append(f[1]) + + if include_position: + fs.append(f[2]) + + return tuple(fs) + + included = list(map(include_features, feature_list)) + + counts = Counter(included) pluralized_feature_groups = [describe_object_from_feature(feature, count, include_color=include_color, @@ -151,7 +173,7 @@ def describe_object(obj_vec) -> str: >>> obj_vec = ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] >>> describe_object(obj_vec) - 'A white cup' + 'a white cup' """ feature = object_features(obj_vec) return describe_object_from_feature(feature) @@ -177,28 +199,38 @@ def describe_object_from_feature(feature, count: typing.Optional[int] = None, *, Examples: >>> describe_object_from_feature(('cup', 'white')) - 'A white cup' + 'a white cup' >>> describe_object_from_feature(('cup', 'orange')) - 'An orange cup' + 'an orange cup' >>> describe_object_from_feature(('cup', 'red'), 2) '2 red cups' """ - name, color, *_ = feature + name, *rest = feature + + if include_color: + color, *rest = rest + else: + color = None + + if include_position: + position, *rest = rest + else: + position = None - assert len(_) == 0, 'Need to update string formatting function with new features!' + assert len(rest) == 0, 'Need to update string formatting function with new features!' if count is None: count = 1 # Structure the string templates based on what features to include base_tmpl = '{name}' - multiple_desc_tmpl = '' - single_desc_tmlp = '' + multiple_desc_tmpl = base_tmpl + single_desc_tmlp = base_tmpl if include_color: - multiple_desc_tmpl = '{color} ' + base_tmpl - single_desc_tmlp = '{color} ' + base_tmpl + multiple_desc_tmpl = '{color} ' + multiple_desc_tmpl + single_desc_tmlp = '{color} ' + single_desc_tmlp if include_position: multiple_desc_tmpl += ' to your {position}' @@ -212,7 +244,8 @@ def describe_object_from_feature(feature, count: typing.Optional[int] = None, *, color=color, name=pluralize(name)) elif count == 1: - article = 'An' if _starts_with_vowel(color) else 'A' + first_word = color if include_color else name + article = 'an' if _starts_with_vowel(first_word) else 'a' output = single_desc_tmlp.format(article=article, color=color, From 017128c591556cff47c8ccdbbbd770ed3be6ec80 Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Mon, 18 Dec 2017 17:03:41 -0800 Subject: [PATCH 124/174] changed output state vector to have location --- nlp/core.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/nlp/core.py b/nlp/core.py index 8cb0528..bab9a01 100644 --- a/nlp/core.py +++ b/nlp/core.py @@ -8,7 +8,7 @@ from nlp.plurals import PLURALS from collections import defaultdict - +from nlp.transform import position, estimate_distance def pluralize(s): """ Convert word to its plural form. @@ -66,7 +66,6 @@ def update_state(image, boxes, classes, scores, category_index, window=10, max_b black white red orange yellow green cyan blue purple pink] """ num_boxes = min([boxes.shape[0] if max_boxes_to_draw is None else max_boxes_to_draw, boxes.shape[0], len(classes)]) - object_vectors = [] for i in range(num_boxes): if scores is None or scores[i] > min_score_thresh: @@ -74,7 +73,9 @@ def update_state(image, boxes, classes, scores, category_index, window=10, max_b class_name = category_index.get(classes[i], {'name': 'unknown object'})['name'] display_str = '{}: {} {}%'.format(classes[i], class_name, int(100 * scores[i])) print(display_str) # TODO: Convert to logging - object_vectors.append([class_name, 0, scores[i], 0, 0, 0, 0, 0, 0] + + #change variable name later + estimate_distance = list(estimate_distance(boxes[i])) + object_vectors.append([class_name, 0, scores[i]] + position(estimate_distance) + list(estimate_color(image, box=boxes[i]))) return object_vectors From de17a4a053b9dc3df90902bf826e5c3d6bf3a20b Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Mon, 18 Dec 2017 17:04:49 -0800 Subject: [PATCH 125/174] added a estimate function to take in the box args and output that as x, y, z, width, height, depth --- nlp/transform.py | 93 +++++++++++++++++++++++++++++------------------- 1 file changed, 56 insertions(+), 37 deletions(-) diff --git a/nlp/transform.py b/nlp/transform.py index 36beda2..c9ff8e6 100644 --- a/nlp/transform.py +++ b/nlp/transform.py @@ -19,14 +19,14 @@ def normalize_position(image, box): | | | | | | - | | + | | | | ________________ (xmin,ymin) (xmax,ymin) - Returns: - tuple: (x, y, z, widht, height, depth) + Returns: + tuple: (x, y, z, widht, height, depth) x (float): left most point and is scaled between -1 and 1. to scale, can do (xmin-image_width/2)/(image_width/2) y (float): bottom most point and is scaled between -1 and 1. to scale, can do (ymin-image_height/2)/(image_height/2) z (float): set to 0 @@ -39,7 +39,7 @@ def normalize_position(image, box): | | | | | | - | | + | | | | ________________ (x,y) (x+width,y) @@ -69,7 +69,7 @@ def normalize_position(image, box): ... AssertionError: xmin < 0 """ - xmin, xmax, ymin, ymax = box + ymin, xmin, ymax, xmax = box im_width, im_height = image.shape[:2] assert xmin <= xmax, 'xmin is greater than xmax' @@ -80,7 +80,7 @@ def normalize_position(image, box): assert ymax <= im_height, 'ymax is greater than image height' assert xmin >= 0, 'xmin < 0' assert ymin >= 0, 'ymin < 0' - + x_center = im_width / 2 y_center = im_height / 2 x = (xmin - (x_center)) / x_center @@ -89,55 +89,74 @@ def normalize_position(image, box): height = (ymax - ymin) / y_center z = 0.0 depth = 0.0 - return (x, y, z, width, height, depth) + return x, y, z, width, height, depth + -def position(image,box): +def estimate_distance(box): + """ + Args: box (tuple) : (ymin, xmin, ymax, xmax) + + Returns : x (float): left most point and is scaled between 0 and 1. + y (float): bottom most point and is scaled between 0 and 1. + z (float): set to 0 + width (float):this is defined as xmax-xmin. + height (float): this is defined as ymax-ymin. + depth (float): set to 0 + + """ + ymin, xmin, ymax, xmax = box + x = (xmin + xmax) / 2.0 + y = (ymin + ymax) / 2.0 + z = 0.0 + width = xmax - xmin + height = ymax - ymin + depth = 0.0 + return x, y, z, width, height, depth + + +def position(normalized_box): """ takes an image and the bounding box, returns the position of the bounding box with respect to the image - + Args: - image (3D np.array): (rows, columns, channels) - rows (int): width of image - columns (int): height of image - channels (int): number of channels, if the image is in color - box (tuple): (xmin, xmax, ymin, ymax) - xmin (int): left most edge of the bounding box - xmax (int): right most edge of the bounding box - ymin (int): lowest edge of the bounding box - ymax (int): highest edge of the bouding box - + normalized_box (tuple): (x, y, z, width, height, depth) + x (float): left most point and is scaled between -1 and 1. to scale, can do (xmin-image_width/2)/(image_width/2) + y (float): bottom most point and is scaled between -1 and 1. to scale, can do (ymin-image_height/2)/(image_height/2) + z (float): set to 0 + width (float):this is defined as xmax-xmin. to scale, compute (xmax-xmin)/(image_width/2) + height (float): this is defined as ymax-ymin. to scale, compute (ymax-ymin)/(image_height/2) + depth (float): set to 0 + Returns: string: 'left', 'right' or 'center' >>> from skimage.data import coffee >>> img = coffee() - >>> position(img,(0,400,0,600)) + >>> normalized_box = normalize_position(img,(0,400,0,600)) + >>> position(normalized_box) 'center' - >>> position(img,(0,100,0,200)) + >>> normalized_box = normalize_position(img,(0,100,0,200)) + >>> position(normalized_box) 'left' - >>> position(img,(200,400,300,400)) - 'right' - >>> position(img,(-100,200,300,400)) + >>> normalized_box = normalize_position(img,(200,400,300,400)) + >>> position(normalized_box) + 'center' + >>> normalized_box = normalize_position(img,(-100,200,300,400)) Traceback (most recent call last): ... AssertionError: xmin < 0 - >>> position(img,(0,400,0,800)) + >>> normalized_box = normalize_position(img,(0,400,0,800)) Traceback (most recent call last): ... AssertionError: ymax is greater than image height """ - - x, y, z, width, height, depth = normalize_position(image,box) - position = "" - if (x <= 0): - if (x+width <= 0): - position = "left" - else: - position = "center" - if (x >= 0): - position = "right" - - return (position) + x, y, z, width, height, depth = normalized_box + if x > 0.6: + return 'right' + elif x < 0.4: + return 'left' + return 'center' + if __name__ == '__main__': import doctest From 27d468fc4554704c44cb59be891dfc29410c338c Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Mon, 18 Dec 2017 17:18:56 -0800 Subject: [PATCH 126/174] - Tests passing --- nlp/core.py | 38 ++++++++++++++++++++------------------ 1 file changed, 20 insertions(+), 18 deletions(-) diff --git a/nlp/core.py b/nlp/core.py index 22d8330..7aa4f34 100644 --- a/nlp/core.py +++ b/nlp/core.py @@ -107,7 +107,7 @@ def describe_scene(object_vectors): def aggregate_descriptions_by_features(feature_list, *, - include_color: bool = True, include_position: bool = False) -> typing.List[str]: + include_color: bool = True, include_position: bool = True) -> typing.List[str]: """Produce a list of descriptions created through aggregations (counts) of objects in the scene. Can optionally aggregate by color and position. @@ -129,17 +129,17 @@ def aggregate_descriptions_by_features(feature_list, *, ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] ... ] >>> feature_list = list(map(object_features, obj_vectors)) - >>> descs = aggregate_descriptions_by_features(feature_list) - >>> '2 white cups' in descs and 'a white ski' in descs + >>> descs = aggregate_descriptions_by_features(feature_list, include_position=True) + >>> '2 white cups to your right' in descs and 'a white ski to your right' in descs True >>> no_color = aggregate_descriptions_by_features(feature_list, include_color=False) - >>> '2 cups' in no_color and 'a ski' in no_color + >>> '2 cups to your right' in no_color and 'a ski to your right' in no_color True """ def include_features(f): """Filters feature tuple s.t. we include only the features we want to aggregate over. """ - assert len(f) == 2, 'Make sure to include all the features in this function!!' + assert len(f) == 3, 'Make sure to include all the features in this function!!' fs = list() fs.append(f[0]) @@ -177,14 +177,14 @@ def describe_object(obj_vec) -> str: >>> obj_vec = ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] >>> describe_object(obj_vec) - 'a white cup' + 'a white cup to your right' """ feature = object_features(obj_vec) return describe_object_from_feature(feature) def describe_object_from_feature(feature, count: typing.Optional[int] = None, *, - include_color=True, include_position=False) -> str: + include_color: bool = True, include_position: bool = True) -> str: """Creates formatted string description from object features and counts, including pluralization. Args: @@ -202,12 +202,12 @@ def describe_object_from_feature(feature, count: typing.Optional[int] = None, *, - ValueError: Count cannot be zero, negative, or an non-integer less than 1. Examples: - >>> describe_object_from_feature(('cup', 'white')) - 'a white cup' - >>> describe_object_from_feature(('cup', 'orange')) - 'an orange cup' - >>> describe_object_from_feature(('cup', 'red'), 2) - '2 red cups' + >>> describe_object_from_feature(('cup', 'white', 'left')) + 'a white cup to your left' + >>> describe_object_from_feature(('cup', 'orange', 'center')) + 'an orange cup to your center' + >>> describe_object_from_feature(('cup', 'red', 'right'), 2) + '2 red cups to your right' """ name, *rest = feature @@ -246,14 +246,16 @@ def describe_object_from_feature(feature, count: typing.Optional[int] = None, *, if count > 1: output = multiple_desc_tmpl.format(amount=count, color=color, - name=pluralize(name)) + name=pluralize(name), + position=position) elif count == 1: first_word = color if include_color else name article = 'an' if _starts_with_vowel(first_word) else 'a' output = single_desc_tmlp.format(article=article, color=color, - name=name) + name=name, + position=position) else: raise ValueError('There cannot be zero, negative, or fractional objects!') @@ -298,13 +300,13 @@ def object_features(obj_vec): Examples: >>> obj_vec = ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] >>> object_features(obj_vec) - ('cup', 'white') + ('cup', 'white', 'right') """ if type(obj_vec) is list or type(obj_vec) is pd.Series: obj_vec = constants.ObjectSeries(obj_vec, index=constants.ObjectSeries.OBJECT_VECTOR_KEYS) - # Name, Color - return obj_vec['category'], obj_vec.obj_primary_color + # Name, Color, Position + return obj_vec['category'], obj_vec.obj_primary_color, position(tuple(obj_vec.obj_bbox)) def compose_comma_series(noun_list: typing.List[str]) -> str: From 93d7ed11a97d4a0831e4e035a393970694753c74 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Mon, 18 Dec 2017 17:25:55 -0800 Subject: [PATCH 127/174] - wrote more doctests to test differentiation in aggregation. - will fix tomorrow. --- nlp/core.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/nlp/core.py b/nlp/core.py index 7aa4f34..13832eb 100644 --- a/nlp/core.py +++ b/nlp/core.py @@ -129,12 +129,25 @@ def aggregate_descriptions_by_features(feature_list, *, ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] ... ] >>> feature_list = list(map(object_features, obj_vectors)) - >>> descs = aggregate_descriptions_by_features(feature_list, include_position=True) + >>> descs = aggregate_descriptions_by_features(feature_list) >>> '2 white cups to your right' in descs and 'a white ski to your right' in descs True >>> no_color = aggregate_descriptions_by_features(feature_list, include_color=False) >>> '2 cups to your right' in no_color and 'a ski to your right' in no_color True + >>> obj_vectors_color = [ + ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .51, .01, .01, .01, .01, .01], + ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01], + ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] + ... ] + >>> feature_list = list(map(object_features, obj_vectors)) + >>> diff_colors = aggregate_descriptions_by_features(feature_list) + >>> diff_colors + >>> 'a red cup to your right' in diff_colors and 'a white cup to your right' in diff_colors + True + >>> diff_no_colors = aggregate_descriptions_by_features(feature_list, include_color=False) + >>> '2 cups to your right' in diff_no_colors + True """ def include_features(f): From 212b13e811ab4b4de32dd8aae1ea2ed3450dd6cf Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Tue, 19 Dec 2017 00:11:28 -0800 Subject: [PATCH 128/174] no same variable names and all docstring plus doctests updated --- nlp/transform.py | 89 +++++++++++++++++++++++++----------------------- 1 file changed, 47 insertions(+), 42 deletions(-) diff --git a/nlp/transform.py b/nlp/transform.py index c9ff8e6..38475ad 100644 --- a/nlp/transform.py +++ b/nlp/transform.py @@ -6,7 +6,8 @@ def normalize_position(image, box): rows (int): width of image columns (int): height of image channels (int): number of channels, if the image is in color - box (tuple): (xmin, xmax, ymin, ymax) + box (tuple): (ymin, xmin, ymax, xmax) + (xmin, xmax, ymin, ymax) xmin (int): left most edge of the bounding box xmax (int): right most edge of the bounding box ymin (int): lowest edge of the bounding box @@ -44,27 +45,28 @@ def normalize_position(image, box): ________________ (x,y) (x+width,y) + >>> from skimage.data import coffee >>> img = coffee() - >>> normalize_position(img,(100,100,50,50)) + >>> normalize_position(img,(50,100,50,100)) (-0.5, -0.8333333333333334, 0.0, 0.0, 0.0, 0.0) - >>> normalize_position(img,(10,90,10,90)) + >>> normalize_position(img,(10,10,90,90)) (-0.95, -0.9666666666666667, 0.0, 0.4, 0.26666666666666666, 0.0) - >>> normalize_position(img,(0,400,0,600)) + >>> normalize_position(img,(0,0,600,400)) (-1.0, -1.0, 0.0, 2.0, 2.0, 0.0) - >>> normalize_position(img,(100,50,0,600)) + >>> normalize_position(img,(0,100,600,50)) Traceback (most recent call last): ... AssertionError: xmin is greater than xmax - >>> normalize_position(img,(100,600,0,600)) + >>> normalize_position(img,(0,100,600,600)) Traceback (most recent call last): ... AssertionError: xmax is greater than image width - >>> normalize_position(img,(100,400,200,100)) + >>> normalize_position(img,(200,100,100,400)) Traceback (most recent call last): ... AssertionError: ymin is greater than ymax - >>> normalize_position(img,(-100,400,100,100)) + >>> normalize_position(img,(100,-100,100,400)) Traceback (most recent call last): ... AssertionError: xmin < 0 @@ -83,26 +85,39 @@ def normalize_position(image, box): x_center = im_width / 2 y_center = im_height / 2 - x = (xmin - (x_center)) / x_center - y = (ymin - (y_center)) / y_center - width = (xmax - xmin) / x_center - height = (ymax - ymin) / y_center - z = 0.0 - depth = 0.0 - return x, y, z, width, height, depth + x_scaled = (xmin - (x_center)) / x_center + y_scaled = (ymin - (y_center)) / y_center + width_scaled = (xmax - xmin) / x_center + height_scaled = (ymax - ymin) / y_center + z_scaled = 0.0 + depth_scaled = 0.0 + return x_scaled, y_scaled, z_scaled, width_scaled, height_scaled, depth_scaled def estimate_distance(box): """ Args: box (tuple) : (ymin, xmin, ymax, xmax) - - Returns : x (float): left most point and is scaled between 0 and 1. - y (float): bottom most point and is scaled between 0 and 1. - z (float): set to 0 - width (float):this is defined as xmax-xmin. - height (float): this is defined as ymax-ymin. - depth (float): set to 0 - + ymin (float): between value of 0 to 1 for the bounding box y value + xmin (float): between value of 0 to 1 for the bounding box x value + ymax (float): between value of 0 to 1 for the bounding box y value + xmax (float): between value of 0 to 1 for the bounding box x value + + Returns : tuple : (x, y, z, width, height, depth) + x (float): x-center of the bounding box + y (float): y-center of the bounding box + z (float): set to 0 + width (float):this is defined as xmax-xmin. + height (float): this is defined as ymax-ymin. + depth (float): set to 0 + + >>> estimate_distance((0.0,0.0,1.0,1.0)) + (0.5, 0.5, 0.0, 1.0, 1.0, 0.0) + >>> estimate_distance((0.0, 0.0, 0.5, 1.0)) + (0.5, 0.25, 0.0, 1.0, 0.5, 0.0) + >>> estimate_distance((1.0, 1.0, 1.0, 1.0)) + (1.0, 1.0, 0.0, 0.0, 0.0, 0.0) + >>> estimate_distance((0.0,0.0, 0.5, 0.75)) + (0.375, 0.25, 0.0, 0.75, 0.5, 0.0) """ ymin, xmin, ymax, xmax = box x = (xmin + xmax) / 2.0 @@ -119,36 +134,26 @@ def position(normalized_box): Args: normalized_box (tuple): (x, y, z, width, height, depth) - x (float): left most point and is scaled between -1 and 1. to scale, can do (xmin-image_width/2)/(image_width/2) - y (float): bottom most point and is scaled between -1 and 1. to scale, can do (ymin-image_height/2)/(image_height/2) + x (float): x-center of the bounding box + y (float): y-center of the bounding box z (float): set to 0 - width (float):this is defined as xmax-xmin. to scale, compute (xmax-xmin)/(image_width/2) - height (float): this is defined as ymax-ymin. to scale, compute (ymax-ymin)/(image_height/2) + width (float):this is defined as xmax-xmin. + height (float): this is defined as ymax-ymin. depth (float): set to 0 Returns: string: 'left', 'right' or 'center' - >>> from skimage.data import coffee - >>> img = coffee() - >>> normalized_box = normalize_position(img,(0,400,0,600)) + + >>> normalized_box = estimate_distance((0.0,0.0,1.0,1.0)) >>> position(normalized_box) 'center' - >>> normalized_box = normalize_position(img,(0,100,0,200)) + >>> normalized_box = estimate_distance((0.0,0.0,1.0,0.4)) >>> position(normalized_box) 'left' - >>> normalized_box = normalize_position(img,(200,400,300,400)) + >>> normalized_box = estimate_distance((0.0,0.5,1.0,1.0)) >>> position(normalized_box) - 'center' - >>> normalized_box = normalize_position(img,(-100,200,300,400)) - Traceback (most recent call last): - ... - AssertionError: xmin < 0 - >>> normalized_box = normalize_position(img,(0,400,0,800)) - Traceback (most recent call last): - ... - AssertionError: ymax is greater than image height - + 'right' """ x, y, z, width, height, depth = normalized_box if x > 0.6: From 1366d0d3dc9554f3728e7806c20a9ff6f145bfec Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Tue, 19 Dec 2017 00:13:27 -0800 Subject: [PATCH 129/174] minor changes --- nlp/core.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/nlp/core.py b/nlp/core.py index bab9a01..9e69442 100644 --- a/nlp/core.py +++ b/nlp/core.py @@ -10,6 +10,7 @@ from collections import defaultdict from nlp.transform import position, estimate_distance + def pluralize(s): """ Convert word to its plural form. @@ -25,7 +26,6 @@ def pluralize(s): Or, even better, just create pluralized versions of all the class names by hand! """ word = str.lower(s) - # `.get()` rather than `word in PLURALS` so that we only look up the word once pluralized_word = PLURALS.get(word, None) if pluralized_word is not None: @@ -73,7 +73,7 @@ def update_state(image, boxes, classes, scores, category_index, window=10, max_b class_name = category_index.get(classes[i], {'name': 'unknown object'})['name'] display_str = '{}: {} {}%'.format(classes[i], class_name, int(100 * scores[i])) print(display_str) # TODO: Convert to logging - #change variable name later + # change variable name later estimate_distance = list(estimate_distance(boxes[i])) object_vectors.append([class_name, 0, scores[i]] + position(estimate_distance) + list(estimate_color(image, box=boxes[i]))) From 6d19fd1e2e81644a689bdb02c80734ef60a3453e Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Tue, 19 Dec 2017 00:18:15 -0800 Subject: [PATCH 130/174] no variables have the same name --- nlp/transform.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/nlp/transform.py b/nlp/transform.py index 38475ad..7afd165 100644 --- a/nlp/transform.py +++ b/nlp/transform.py @@ -134,12 +134,12 @@ def position(normalized_box): Args: normalized_box (tuple): (x, y, z, width, height, depth) - x (float): x-center of the bounding box - y (float): y-center of the bounding box - z (float): set to 0 - width (float):this is defined as xmax-xmin. - height (float): this is defined as ymax-ymin. - depth (float): set to 0 + x_normalized (float): x-center of the bounding box + y_normalized (float): y-center of the bounding box + z_normalized (float): set to 0 + width_normalized (float):this is defined as xmax-xmin. + height_normalized (float): this is defined as ymax-ymin. + depth_normalized (float): set to 0 Returns: string: 'left', 'right' or 'center' @@ -155,10 +155,10 @@ def position(normalized_box): >>> position(normalized_box) 'right' """ - x, y, z, width, height, depth = normalized_box - if x > 0.6: + x_normalized, y_normalized, z_normalized, width_normalized, height_normalized, depth_normalized = normalized_box + if x_normalized > 0.6: return 'right' - elif x < 0.4: + elif x_normalized < 0.4: return 'left' return 'center' From 8460c1d2b2b063f0692dcc321f726b0216d75345 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 19 Dec 2017 08:44:04 -0800 Subject: [PATCH 131/174] - doctested `include_color` and `include_position` and tests all pass - Had to change constants/ObjectVector to include `confidence` key. This led me to update all my doctests :) --- nlp/core.py | 64 ++++++++++++++++++++--------------- object_detection/constants.py | 2 +- 2 files changed, 38 insertions(+), 28 deletions(-) diff --git a/nlp/core.py b/nlp/core.py index 13832eb..cfac1a9 100644 --- a/nlp/core.py +++ b/nlp/core.py @@ -62,8 +62,8 @@ def update_state(image, boxes, classes, scores, category_index, window=10, max_b Returns: list: list of object vectors, for example: [ - ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] - ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] + ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01, .01] + ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01, .01] ] The object vector keys are defined in constants.OBJECT_VECTOR_KEYS: [category instance confidence x y z width height depth @@ -88,13 +88,13 @@ def update_state(image, boxes, classes, scores, category_index, window=10, max_b def describe_scene(object_vectors): """ Convert a state vector dictionary of objects and their counts into a natural language string - categ instnc x y z wdth hght dpth blk wht red orng yel grn cyn blu purp pink + categ inst, conf, x y z wdth hght dpth blk wht red orng yel grn cyn blu purp pink >>> object_vectors = [ - ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01], - ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] + ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01, .01], + ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01, .01] ... ] >>> desc = describe_scene(object_vectors) - >>> 'a white cup' in desc and ' and ' in desc and 'a white ski' in desc + >>> 'a black cup' in desc and ' and ' in desc and 'a black ski' in desc True """ feature_list = list(map(object_features, object_vectors)) @@ -113,40 +113,50 @@ def aggregate_descriptions_by_features(feature_list, *, Can optionally aggregate by color and position. Args: - feature_list: - include_color: - include_position: + feature_list: list of tuples with description features [(, , ), ...] + include_color: flag to aggregate by color + include_position: flag to aggregate by position Returns: A list of strings with valid descriptions. Examples: - # TODO(Alex) doctest with `include_*` parameters - + TODO(Hobbs, Ashwin): Please review these test cases >>> obj_vectors = [ - ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01], - ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01], - ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] + ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01, .01], + ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01, .01], + ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01, .01] ... ] >>> feature_list = list(map(object_features, obj_vectors)) >>> descs = aggregate_descriptions_by_features(feature_list) - >>> '2 white cups to your right' in descs and 'a white ski to your right' in descs + >>> '2 black cups to your left' in descs and 'a black ski to your left' in descs True >>> no_color = aggregate_descriptions_by_features(feature_list, include_color=False) - >>> '2 cups to your right' in no_color and 'a ski to your right' in no_color + >>> '2 cups to your left' in no_color and 'a ski to your left' in no_color True >>> obj_vectors_color = [ - ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .51, .01, .01, .01, .01, .01], - ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01], - ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] + ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .51, .01, .01, .01, .01, .01, .01], + ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01, .01], + ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01, .01] ... ] - >>> feature_list = list(map(object_features, obj_vectors)) + >>> feature_list = list(map(object_features, obj_vectors_color)) >>> diff_colors = aggregate_descriptions_by_features(feature_list) - >>> diff_colors - >>> 'a red cup to your right' in diff_colors and 'a white cup to your right' in diff_colors + >>> 'an orange cup to your left' in diff_colors and 'a black cup to your left' in diff_colors True >>> diff_no_colors = aggregate_descriptions_by_features(feature_list, include_color=False) - >>> '2 cups to your right' in diff_no_colors + >>> '2 cups to your left' in diff_no_colors + True + >>> obj_vectors_pos = [ + ... ['cup', 0, .95, 0.7, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01, .01], + ... ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01, .01], + ... ['ski', 0, .80, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01, .01] + ... ] + >>> feature_list = list(map(object_features, obj_vectors_pos)) + >>> diff_pos = aggregate_descriptions_by_features(feature_list) + >>> 'a black cup to your left' in diff_pos and 'a black cup to your right' in diff_pos + True + >>> diff_no_pos = aggregate_descriptions_by_features(feature_list, include_position=False) + >>> '2 black cups' in diff_no_pos True """ @@ -188,9 +198,9 @@ def describe_object(obj_vec) -> str: Examples: - >>> obj_vec = ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] + >>> obj_vec = ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01, .01] >>> describe_object(obj_vec) - 'a white cup to your right' + 'a black cup to your left' """ feature = object_features(obj_vec) return describe_object_from_feature(feature) @@ -311,9 +321,9 @@ def object_features(obj_vec): (, , ...) Examples: - >>> obj_vec = ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01] + >>> obj_vec = ['cup', 0, .95, -.5, .1, 0, .1, .1, 0, .5, .3, .14, .01, .01, .01, .01, .01, .01, .01] >>> object_features(obj_vec) - ('cup', 'white', 'right') + ('cup', 'black', 'left') """ if type(obj_vec) is list or type(obj_vec) is pd.Series: obj_vec = constants.ObjectSeries(obj_vec, index=constants.ObjectSeries.OBJECT_VECTOR_KEYS) diff --git a/object_detection/constants.py b/object_detection/constants.py index 1580b06..0141a8a 100644 --- a/object_detection/constants.py +++ b/object_detection/constants.py @@ -20,7 +20,7 @@ CATEGORIES = label_map_util.convert_label_map_to_categories(LABEL_MAP, max_num_classes=90, use_display_name=True) CATEGORY_INDEX = label_map_util.create_category_index(CATEGORIES) -LABEL_KEYS = 'category instance'.split() +LABEL_KEYS = 'category instance confidence'.split() COLOR_KEYS = 'black white red orange yellow green cyan blue purple pink'.split() BB_KEYS = 'x y z width height depth'.split() OBJECT_VECTOR_KEYS = LABEL_KEYS + BB_KEYS + COLOR_KEYS From 7b904e341a6df76d74845b4a256bb864b5daab8d Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 19 Dec 2017 09:00:22 -0800 Subject: [PATCH 132/174] (Fixed position bugs) - Changes pulled from branch were not prod ready, needed fixing. --- nlp/core.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/nlp/core.py b/nlp/core.py index cfac1a9..a0aa8b0 100644 --- a/nlp/core.py +++ b/nlp/core.py @@ -79,9 +79,11 @@ def update_state(image, boxes, classes, scores, category_index, window=10, max_b display_str = '{}: {} {}%'.format(classes[i], class_name, int(100 * scores[i])) print(display_str) # TODO: Convert to logging #change variable name later - estimate_distance = list(estimate_distance(boxes[i])) - object_vectors.append([class_name, 0, scores[i]] + position(estimate_distance) + + estimate_dist = list(estimate_distance(boxes[i])) + object_vectors.append([class_name, 0, scores[i]] + estimate_dist + list(estimate_color(image, box=boxes[i]))) + print(estimate_dist) + print(position(estimate_dist)) return object_vectors From 593a6f599d3b1547c7f4fb261e7eda5aa99e7b20 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 19 Dec 2017 09:00:55 -0800 Subject: [PATCH 133/174] - Cleaned up the `describe.py` file/command. --- nlp/command/describe.py | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/nlp/command/describe.py b/nlp/command/describe.py index 9738420..9f7465c 100644 --- a/nlp/command/describe.py +++ b/nlp/command/describe.py @@ -4,8 +4,6 @@ class Describe(Dispatchable): - obj_desc_tmpl = '{num_obj} {obj_color} {obj_name} to your {rel_pos}' - def __init__(self, state_q): self.state_q = state_q @@ -15,10 +13,6 @@ def __call__(self, payload): if state: description = describe_scene(state) - self.send({'response': description}, subtopic=['say']) - + self.send({'response': description}) -def count_obj_by_color(state): - new_state = {k: len(v) for k, v in state.items()} - return new_state From d53767de8e81ced138dd86a8cc64a841ceba5ddb Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Tue, 19 Dec 2017 09:08:27 -0800 Subject: [PATCH 134/174] Redone the variable names to be correct --- nlp/transform.py | 32 ++++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/nlp/transform.py b/nlp/transform.py index 7afd165..4d9dd67 100644 --- a/nlp/transform.py +++ b/nlp/transform.py @@ -85,13 +85,13 @@ def normalize_position(image, box): x_center = im_width / 2 y_center = im_height / 2 - x_scaled = (xmin - (x_center)) / x_center - y_scaled = (ymin - (y_center)) / y_center - width_scaled = (xmax - xmin) / x_center - height_scaled = (ymax - ymin) / y_center - z_scaled = 0.0 - depth_scaled = 0.0 - return x_scaled, y_scaled, z_scaled, width_scaled, height_scaled, depth_scaled + x = (xmin - (x_center)) / x_center + y = (ymin - (y_center)) / y_center + width = (xmax - xmin) / x_center + height = (ymax - ymin) / y_center + z = 0.0 + depth = 0.0 + return x, y, z, width, height, depth def estimate_distance(box): @@ -134,12 +134,12 @@ def position(normalized_box): Args: normalized_box (tuple): (x, y, z, width, height, depth) - x_normalized (float): x-center of the bounding box - y_normalized (float): y-center of the bounding box - z_normalized (float): set to 0 - width_normalized (float):this is defined as xmax-xmin. - height_normalized (float): this is defined as ymax-ymin. - depth_normalized (float): set to 0 + x (float): x-center of the bounding box + y (float): y-center of the bounding box + z (float): set to 0 + width (float):this is defined as xmax-xmin. + height (float): this is defined as ymax-ymin. + depth (float): set to 0 Returns: string: 'left', 'right' or 'center' @@ -155,10 +155,10 @@ def position(normalized_box): >>> position(normalized_box) 'right' """ - x_normalized, y_normalized, z_normalized, width_normalized, height_normalized, depth_normalized = normalized_box - if x_normalized > 0.6: + x, y, z, widht, height, depth = normalized_box + if x > 0.6: return 'right' - elif x_normalized < 0.4: + elif x < 0.4: return 'left' return 'center' From 72fcdefe9e8af97de13fbb8cbb2fb3864261a8a1 Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Tue, 19 Dec 2017 09:09:09 -0800 Subject: [PATCH 135/174] changed variable name to not be conflicting with function name --- nlp/core.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/nlp/core.py b/nlp/core.py index 9e69442..09684c3 100644 --- a/nlp/core.py +++ b/nlp/core.py @@ -74,8 +74,8 @@ def update_state(image, boxes, classes, scores, category_index, window=10, max_b display_str = '{}: {} {}%'.format(classes[i], class_name, int(100 * scores[i])) print(display_str) # TODO: Convert to logging # change variable name later - estimate_distance = list(estimate_distance(boxes[i])) - object_vectors.append([class_name, 0, scores[i]] + position(estimate_distance) + + loc = list(estimate_distance(boxes[i])) + object_vectors.append([class_name, 0, scores[i]] + position(loc) + list(estimate_color(image, box=boxes[i]))) return object_vectors From 0a9bbb9a0f143e7fbccc9189d40e8cd0591f0eed Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 19 Dec 2017 09:29:51 -0800 Subject: [PATCH 136/174] (Deployment) - Drafting test scripts for deployment --- bin/build.sh | 2 +- bin/deploy.sh | 7 +++++++ 2 files changed, 8 insertions(+), 1 deletion(-) create mode 100644 bin/deploy.sh diff --git a/bin/build.sh b/bin/build.sh index 3024611..383de3a 100644 --- a/bin/build.sh +++ b/bin/build.sh @@ -1,4 +1,4 @@ #!/usr/bin/env bash -conda env create -f environment.yml +conda env create -q --force -f environment.yml source activate object-detection diff --git a/bin/deploy.sh b/bin/deploy.sh new file mode 100644 index 0000000..8074f54 --- /dev/null +++ b/bin/deploy.sh @@ -0,0 +1,7 @@ +#!/usr/bin/env bash + +git pull origin master + +./build.sh + + From b21d7ca7e8741053eb93cb09a3a6d1465ad84f87 Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Tue, 19 Dec 2017 09:40:31 -0800 Subject: [PATCH 137/174] removed loc variable --- nlp/core.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/nlp/core.py b/nlp/core.py index 09684c3..d2ec2c1 100644 --- a/nlp/core.py +++ b/nlp/core.py @@ -74,8 +74,8 @@ def update_state(image, boxes, classes, scores, category_index, window=10, max_b display_str = '{}: {} {}%'.format(classes[i], class_name, int(100 * scores[i])) print(display_str) # TODO: Convert to logging # change variable name later - loc = list(estimate_distance(boxes[i])) - object_vectors.append([class_name, 0, scores[i]] + position(loc) + + + object_vectors.append([class_name, 0, scores[i]] + list(estimate_distance(boxes[i])) + list(estimate_color(image, box=boxes[i]))) return object_vectors From 146a62d24ed8d99216d48beaf8efae6c8a1e5764 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 19 Dec 2017 10:27:24 -0800 Subject: [PATCH 138/174] - renamed Describe --> DescribeScene --- nlp/command/__init__.py | 4 ++-- nlp/command/describe.py | 2 +- object_detection_app.py | 6 +++--- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/nlp/command/__init__.py b/nlp/command/__init__.py index 08b8fc9..9de2490 100644 --- a/nlp/command/__init__.py +++ b/nlp/command/__init__.py @@ -1,2 +1,2 @@ -from nlp.command.color import DescribeColor -from nlp.command.describe import Describe \ No newline at end of file +from nlp.command.color import DescribeObjectColor +from nlp.command.describe import DescribeScene \ No newline at end of file diff --git a/nlp/command/describe.py b/nlp/command/describe.py index 9f7465c..c692956 100644 --- a/nlp/command/describe.py +++ b/nlp/command/describe.py @@ -2,7 +2,7 @@ from nlp.dispatch import Dispatchable -class Describe(Dispatchable): +class DescribeScene(Dispatchable): def __init__(self, state_q): self.state_q = state_q diff --git a/object_detection_app.py b/object_detection_app.py index a4d22fd..2fbc408 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -11,7 +11,7 @@ from object_detection.utils import visualization_utils as vis_util from nlp import describe_scene, say, update_state from nlp.dispatch import mqttc, dispatcher -from nlp.command import Describe, DescribeColor +from nlp.command import DescribeScene, DescribeObjectColor from object_detection.constants import CATEGORY_INDEX, PATH_TO_CKPT @@ -122,8 +122,8 @@ def worker(input_q, output_q, state_q, voice_on=False): output_q = Queue(maxsize=args.queue_size) state_q = Queue(maxsize=args.state_queue_size) - dispatcher['color'] = DescribeColor(state_q) - dispatcher['describe'] = Describe(state_q) + dispatcher['color'] = DescribeObjectColor(state_q) + dispatcher['describe'] = DescribeScene(state_q) pool = Pool(args.num_workers, worker, (input_q, output_q, state_q, args.voice_on)) From 6ff918335d0e6986abe845e6bcf1e7008c56d3b2 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 19 Dec 2017 10:27:32 -0800 Subject: [PATCH 139/174] - Fixed color command - added default response (no color detected) - will prefer objects in center to other objects --- nlp/command/color.py | 50 ++++++++++++++++++++++++++++++++++---------- 1 file changed, 39 insertions(+), 11 deletions(-) diff --git a/nlp/command/color.py b/nlp/command/color.py index 3c2a3b0..f8a80f8 100644 --- a/nlp/command/color.py +++ b/nlp/command/color.py @@ -1,9 +1,12 @@ from nlp.dispatch import Dispatchable +import object_detection.constants as constants +from nlp.transform import position -class DescribeColor(Dispatchable): +class DescribeObjectColor(Dispatchable): - color_tmpl = "The {obj_name} is primarily {color}" + color_tmpl = 'The {obj_name} is primarily {color}' + color_fail_tmpl = 'I cannot determine the color' def __init__(self, state_q): self.state_q = state_q @@ -13,19 +16,44 @@ def __call__(self, payload): if state: - obj_name, object_data = state.popitem() + if type(state[0]) is not constants.ObjectSeries: + vecs = to_object_series_list(state) + else: + vecs = state - if object_data: - color_freq = object_data[0]['color'] + center_vecs = list(filter(lambda v: position(v.obj_bbox) is 'center', vecs)) - max_color = color_freq.idxmax() + if len(center_vecs) > 0: + obj_vec = center_vecs.pop(0) + else: + obj_vec = vecs.pop(0) - print(state) + max_color = obj_vec.obj_primary_color + obj_name = obj_vec['category'] - payload = { - 'response': self.color_tmpl.format(obj_name=obj_name, color=max_color) - } + print(state) - self.send(payload, subtopic=['say']) + payload = { + 'response': self.color_tmpl.format(obj_name=obj_name, color=max_color) + } + self.send(payload) + return + + payload = { + 'response': self.color_fail_tmpl + } + + self.send(payload) + + +class DescribeSceneColor(Dispatchable): + # TODO: implement + + def get_scene_color(self): + pass + + +def to_object_series_list(state): + return [constants.ObjectSeries(obj, index=constants.OBJECT_VECTOR_KEYS) for obj in state] From e8210d1638922aca69f9acea97e39d8a89c0d3e0 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 19 Dec 2017 10:33:40 -0800 Subject: [PATCH 140/174] Revert "(Deployment)" This reverts commit 0a9bbb9 --- bin/build.sh | 2 +- bin/deploy.sh | 7 ------- 2 files changed, 1 insertion(+), 8 deletions(-) delete mode 100644 bin/deploy.sh diff --git a/bin/build.sh b/bin/build.sh index 383de3a..3024611 100644 --- a/bin/build.sh +++ b/bin/build.sh @@ -1,4 +1,4 @@ #!/usr/bin/env bash -conda env create -q --force -f environment.yml +conda env create -f environment.yml source activate object-detection diff --git a/bin/deploy.sh b/bin/deploy.sh deleted file mode 100644 index 8074f54..0000000 --- a/bin/deploy.sh +++ /dev/null @@ -1,7 +0,0 @@ -#!/usr/bin/env bash - -git pull origin master - -./build.sh - - From e02a0d1052f70ef335f0c92455ce7ae684cde713 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 19 Dec 2017 10:40:41 -0800 Subject: [PATCH 141/174] (Deployment start) --- bin/build.sh | 2 +- bin/deploy.sh | 5 +++++ bin/update_env.sh | 5 +++++ 3 files changed, 11 insertions(+), 1 deletion(-) create mode 100644 bin/deploy.sh create mode 100644 bin/update_env.sh diff --git a/bin/build.sh b/bin/build.sh index 3024611..383de3a 100644 --- a/bin/build.sh +++ b/bin/build.sh @@ -1,4 +1,4 @@ #!/usr/bin/env bash -conda env create -f environment.yml +conda env create -q --force -f environment.yml source activate object-detection diff --git a/bin/deploy.sh b/bin/deploy.sh new file mode 100644 index 0000000..9308cab --- /dev/null +++ b/bin/deploy.sh @@ -0,0 +1,5 @@ +#!/usr/bin/env bash + +git clone origin master + +./update_env.sh diff --git a/bin/update_env.sh b/bin/update_env.sh new file mode 100644 index 0000000..d907555 --- /dev/null +++ b/bin/update_env.sh @@ -0,0 +1,5 @@ +#!/usr/bin/env bash + +conda env update -q -f environment.yml + +source activate object-detection \ No newline at end of file From 11ac8aec6128f057acd593a2c068178d12445a9d Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 19 Dec 2017 11:25:31 -0800 Subject: [PATCH 142/174] (Creating Deploy Scripts) - can't activate environments unless scripts are ran with `source`. Thus, `source activate ` was removed from many scripts. - deploy pulls (correctly), runs `update_env.sh`, then `run.sh` in bg - `run.sh` added. Activated env before running with hardcoded url --- bin/build.sh | 2 +- bin/deploy.sh | 6 ++++-- bin/run.sh | 6 ++++++ bin/update_env.sh | 1 - 4 files changed, 11 insertions(+), 4 deletions(-) mode change 100644 => 100755 bin/build.sh mode change 100644 => 100755 bin/deploy.sh create mode 100644 bin/run.sh mode change 100644 => 100755 bin/update_env.sh diff --git a/bin/build.sh b/bin/build.sh old mode 100644 new mode 100755 index 383de3a..db81909 --- a/bin/build.sh +++ b/bin/build.sh @@ -1,4 +1,4 @@ #!/usr/bin/env bash conda env create -q --force -f environment.yml -source activate object-detection + diff --git a/bin/deploy.sh b/bin/deploy.sh old mode 100644 new mode 100755 index 9308cab..ab0fce4 --- a/bin/deploy.sh +++ b/bin/deploy.sh @@ -1,5 +1,7 @@ #!/usr/bin/env bash -git clone origin master +git pull origin master -./update_env.sh +/bin/bash ./bin/update_env.sh + +/bin/bash ./bin/run.sh & \ No newline at end of file diff --git a/bin/run.sh b/bin/run.sh new file mode 100644 index 0000000..43b2f56 --- /dev/null +++ b/bin/run.sh @@ -0,0 +1,6 @@ +#!/usr/bin/env bash + + +source activate object-detection + +python object_detection_app.py -u=rtsp://52.39.224.108:1935/live/myStream &>> log.txt \ No newline at end of file diff --git a/bin/update_env.sh b/bin/update_env.sh old mode 100644 new mode 100755 index d907555..69a2710 --- a/bin/update_env.sh +++ b/bin/update_env.sh @@ -2,4 +2,3 @@ conda env update -q -f environment.yml -source activate object-detection \ No newline at end of file From efbb32582b410dc8aef25d50c6a11add8486de26 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 19 Dec 2017 11:42:39 -0800 Subject: [PATCH 143/174] (Creating Deploy Scripts) - can't activate environments unless scripts are ran with `source` - Keeping track of run PIDs - Keeping track of dates in log --- bin/deploy.sh | 4 +++- bin/run.sh | 1 + 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/bin/deploy.sh b/bin/deploy.sh index ab0fce4..67179db 100755 --- a/bin/deploy.sh +++ b/bin/deploy.sh @@ -4,4 +4,6 @@ git pull origin master /bin/bash ./bin/update_env.sh -/bin/bash ./bin/run.sh & \ No newline at end of file +date &>> pids.txt +/bin/bash ./bin/run.sh & +$! &>> pids.txt \ No newline at end of file diff --git a/bin/run.sh b/bin/run.sh index 43b2f56..fa6ba9b 100644 --- a/bin/run.sh +++ b/bin/run.sh @@ -3,4 +3,5 @@ source activate object-detection +date &>> log.txt python object_detection_app.py -u=rtsp://52.39.224.108:1935/live/myStream &>> log.txt \ No newline at end of file From 8193982d9288ebaaa0d9a7025858110c6ae6d9b5 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 19 Dec 2017 11:43:27 -0800 Subject: [PATCH 144/174] (gitignoring log) --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 0e8f8c0..cc51a57 100644 --- a/.gitignore +++ b/.gitignore @@ -90,6 +90,7 @@ ENV/ # Custom .idea/ +log.txt # tensorflow model weights *.tar.gz From 36087e43b44c1e6fac012ea3a57438bd19736ee2 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 19 Dec 2017 11:46:06 -0800 Subject: [PATCH 145/174] (gitignoring process id log) --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index cc51a57..6097a28 100644 --- a/.gitignore +++ b/.gitignore @@ -91,6 +91,7 @@ ENV/ # Custom .idea/ log.txt +pids.txt # tensorflow model weights *.tar.gz From 355cca2316c1d359c5358bfb3d5fcd9d156454f3 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 19 Dec 2017 12:32:59 -0800 Subject: [PATCH 146/174] - Added shutdown script - included it in the deploy script --- bin/deploy.sh | 4 ++-- bin/shutdown.sh | 4 ++++ 2 files changed, 6 insertions(+), 2 deletions(-) create mode 100644 bin/shutdown.sh diff --git a/bin/deploy.sh b/bin/deploy.sh index 67179db..33a67b6 100755 --- a/bin/deploy.sh +++ b/bin/deploy.sh @@ -1,9 +1,9 @@ #!/usr/bin/env bash +/bin/bash ./bin/shutdown.sh + git pull origin master /bin/bash ./bin/update_env.sh -date &>> pids.txt /bin/bash ./bin/run.sh & -$! &>> pids.txt \ No newline at end of file diff --git a/bin/shutdown.sh b/bin/shutdown.sh new file mode 100644 index 0000000..1d032a0 --- /dev/null +++ b/bin/shutdown.sh @@ -0,0 +1,4 @@ +#!/usr/bin/env bash + +pkill -9 -f run.sh +pkill -9 -f python \ No newline at end of file From 209fc8aaed25e71354e6903c0bc009091ce3d691 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Tue, 19 Dec 2017 12:39:07 -0800 Subject: [PATCH 147/174] (Revering weird merge change) - adding `typing` import. --- nlp/core.py | 1 + 1 file changed, 1 insertion(+) diff --git a/nlp/core.py b/nlp/core.py index 03d9065..10a2aac 100644 --- a/nlp/core.py +++ b/nlp/core.py @@ -1,5 +1,6 @@ """ Natural Language Processing (Generation) utilities """ import os +import typing import pandas as pd import object_detection.constants as constants From 89f072e942cfd3616673d349e08866e62b997d37 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Tue, 19 Dec 2017 17:29:26 -0800 Subject: [PATCH 148/174] Update CONTRIBUTING.md --- CONTRIBUTING.md | 29 ++++++++++++++++++++++------- 1 file changed, 22 insertions(+), 7 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index ae8192b..8d245cf 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,16 +1,16 @@ ## Style Guide The primary goal is to produce readable code. -This means being consistent with patterns used by many people, except when the "crowd" favors less readable layout or syntax. +This means being consistent with patterns and style prefered by most python developers at corporations that build great stuff with python. ### [PEP8](https://www.python.org/dev/peps/pep-0008/) with these exceptions Follow the [Hettinger interpretation of PEP8](https://www.youtube.com/watch?v=wf-BqAjZb8M) for beautiful, readable code. -* max line length is "about" 120 chars -* max complexity: mccabe_threshold": 12, # threshold limit for McCabe complexity checker. -* type hints are encouraged but not required +* max line length is "about" 120 chars (if you go a little over, don't worry about it) +* max complexity: "mccabe_threshold": 12, # McCabe complexity checker limits the number of conditional branches +* type hints are a good idea on conditional branches that are rarely run, but duck-typing is preferred for mainline code Here's my Sublime Anaconda plugin configuration. @@ -55,6 +55,21 @@ def add(value, num=0) ### Workflow -* branch off master whenever you begin a new feature/task -* commit often, mentioning the Jira ticket number in the comment, where possible (e.g. NSF-4) -* brief active voice comments, with optional Jira transition commands: `git commit -am 'NSF-4 #start-review integrate location and color vectors'`) +#### Branch off master + +whenever you begin a new feature/task: + +`git checkout master -b feature/my-awesome-new-feature` + +#### Commit often + +Mentioning the Jira ticket number in your, brief, active verb-tense comment (message) + +`git commit -am 'NSF-4 #start-review integrate location into description'` + +#### Transition your Jira Tickets + +Whenever you can, so save yourself the Jira GUI shuffle. But send it to "#start-review" (the QA stage) rather than #done: + +`git commit -am 'NSF-4 #start-review add color vectors'` + From 35a2bd34a11abe65c9de56da976503d53953c400 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Tue, 19 Dec 2017 17:30:16 -0800 Subject: [PATCH 149/174] Update CONTRIBUTING.md --- CONTRIBUTING.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 8d245cf..75409e4 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -15,7 +15,7 @@ Follow the [Hettinger interpretation of PEP8](https://www.youtube.com/watch?v=wf Here's my Sublime Anaconda plugin configuration. -```json +```javascript { // Maximum McCabe complexity (number of conditional branches within a function). "mccabe_threshold": 7, From b36f9469f62e9d3dd44362437799232b93cc8a2e Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Tue, 19 Dec 2017 17:39:42 -0800 Subject: [PATCH 150/174] get rid of the annoying red json comment highlighting --- README.md | 70 +++++++++++++++++++++++++++++++------------------------ 1 file changed, 39 insertions(+), 31 deletions(-) diff --git a/README.md b/README.md index bffa0e4..0b64885 100644 --- a/README.md +++ b/README.md @@ -4,15 +4,15 @@ A real-time object recognition application using [Google's TensorFlow Object Det ## Getting Started -Install Anaconda according to the instructions [here](https://docs.anaconda.com/anaconda/install/). +1. Install Anaconda according to the instructions [here](https://docs.anaconda.com/anaconda/install/). -Make sure your python package installer, `pip`, is updated to use the Anaconda version: +2. Make sure your python package installer, `pip`, is updated to use the Anaconda version: ```bash $ conda install pip ``` -Clone the repo to your local machine in whatever folder you use to hold source code, like ~/src/ +3. Clone the repo to your local machine in whatever folder you use to hold source code, like ~/src/ ```bash $ mkdir ~/src @@ -21,55 +21,55 @@ $ git clone https://github.com/aira/object_detector_app $ cd object_detector_app ``` -Create a new Anaconda environment on your machine to hold tensorflow, python 3.5, OpenCV, etc. This will take a while: +4. Create a new Anaconda environment on your machine to hold tensorflow, python 3.5, OpenCV, etc. This will take a while: `conda env create -f environment.yml` -Check to make sure you're using the python that's in your conda environment: `which python` should have a path that indicates anaconda and the object-detection environment. +5. Check to make sure you're using the python that's in your conda environment -If it is not where you have installed the conda environment, you need to change the source for python +`which python` should have a path that indicates anaconda and the object-detection environment. - * `head environment.yml` and look at the first line of the file which should say something like `name: object-detection`. We are interested in the name of the file. - * `source activate name` where name will be replaced with what was stated in your environment.yml file - * `which python` and this time the place where you installed conda will show up +If it is not in a folder with your conda environment name (`object-detection` is the default), you need to activate your environment: -5. `python object_detection_app.py` - Optional arguments (default value): - * Show all commands `--help` - * Device index of the camera `--source=0` - * Width of the frames in the video stream `--width=480` - * Height of the frames in the video stream `--height=360` - * Number of workers `--num-workers=2` - * Size of the queue `--queue-size=5` - * URL for video stream `--url=` - * Turn on GUI (defaulted to run headless) `--gui` - * Turn on vocal commands on MacOS (defaulted to silent) `--say` - * State Buffer Size, how many "states" to capture `--state-queue-size=5` +5.1 `head environment.yml` -- 1st line should say something like `name: object-detection` +5.2 `source activate object-detection` -- replace `object-detection` with your environment name from the environment.yml +5.3 `which python` -- make sure this is now correctly pointing to your conda environment path + +6. Start the app! + +6.1 `python object_detection_app.py --help` to see all the options +6.2 `python object_detection_app.py` without any arguments to run it using your webcam without a guid and without verbalizing the responses ## Development + ### Updating the environment + `conda env update -f environment.yml` ### Tests -``` -pytest -vs utils/ -python -m pytest -python -m unittest discover -s object_detection -p "*_test.py" + +```bash +$ pytest -vs utils/ +$ python -m pytest +$ python -m unittest discover -s object_detection -p "*_test.py" ``` ### Requirements + - [Anaconda / Python 3.5](https://www.continuum.io/downloads) - [TensorFlow 1.2](https://www.tensorflow.org/) - [OpenCV 3.0](http://opencv.org/) ### API + Our API is accessible via the MQTT protocol. #### Android --> Chloe `dev/chloe/explorer//statement` + We subscribe to a topic coming from an Android client. Incoming messages should be encoded as JSON objects that match the following format: -```json +```javascript { "messageId": 123, "serviceId": 53453, @@ -80,12 +80,11 @@ the following format: } ``` - #### Any --> Explorer `dev/chloe/explorer//response` Messages should be encoded as JSON objects in the following format: -```json +```javascript { "messageId": 124, // ID for the current payload "statementId": 123, // ID of statement payload (payload this is in response to, see above) @@ -110,11 +109,15 @@ Messages should be encoded as JSON objects in the following format: } ``` -TODO(Alex) Revise +#### Example Chloe Response . **TODO:** Revise + Here is an example of a response for "say": + Topic: `dev/chloe/explorer/12345/response` -Payload: -```json + +Payload: + +```javascript { "messageId": 124, "statementId": 123, @@ -134,17 +137,22 @@ Payload: } } ``` + #### Chloe --> Test Harness/Agent `dev/chloe/agent//response` + The test harness gets the same message as the above section. ### Agent-Chloe Experiment Configuration Discussion + - Should be configured on dashboard. - Response to explorer should have a delay, whether they come from Chloe or the AI. The explorer should not be able to distinguish between human and machine. - Want to design intentional fallback from the AI to the Human agent. Thus, we need two buttons: the random send (either AI or Human), and a **SEND!** that forcibly sends the human response over the AI. ## Notes + - ~~OpenCV 3.1 might crash on OSX after a while, so that's why I had to switch to version 3.0. See open issue and solution [here](https://github.com/opencv/opencv/issues/5874).~~ - Moving the `.read()` part of the video stream in a multiple child processes did not work. However, it was possible to move it to a separate thread. ## Copyright + See [LICENSE](LICENSE) for details. From b1b894137cee7f7846a019fc3d72855d580ac9d1 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Tue, 19 Dec 2017 17:40:38 -0800 Subject: [PATCH 151/174] indent --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 0b64885..69341f2 100644 --- a/README.md +++ b/README.md @@ -31,14 +31,14 @@ $ cd object_detector_app If it is not in a folder with your conda environment name (`object-detection` is the default), you need to activate your environment: -5.1 `head environment.yml` -- 1st line should say something like `name: object-detection` -5.2 `source activate object-detection` -- replace `object-detection` with your environment name from the environment.yml -5.3 `which python` -- make sure this is now correctly pointing to your conda environment path + 5.1 `head environment.yml` -- 1st line should say something like `name: object-detection` + 5.2 `source activate object-detection` -- replace `object-detection` with your environment name from the environment.yml + 5.3 `which python` -- make sure this is now correctly pointing to your conda environment path 6. Start the app! -6.1 `python object_detection_app.py --help` to see all the options -6.2 `python object_detection_app.py` without any arguments to run it using your webcam without a guid and without verbalizing the responses + 6.1 `python object_detection_app.py --help` to see all the options + 6.2 `python object_detection_app.py` without any arguments to run it using your webcam without a guid and without verbalizing the responses ## Development From e7ddfe00b0740c506fc17a7ba88a2a3c4484e92b Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Tue, 19 Dec 2017 17:43:21 -0800 Subject: [PATCH 152/174] Update README.md --- README.md | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 69341f2..9d39bb8 100644 --- a/README.md +++ b/README.md @@ -37,8 +37,8 @@ If it is not in a folder with your conda environment name (`object-detection` is 6. Start the app! - 6.1 `python object_detection_app.py --help` to see all the options - 6.2 `python object_detection_app.py` without any arguments to run it using your webcam without a guid and without verbalizing the responses + 6.1 `python object_detection_app.py --help` to see all the options + 6.2 `python object_detection_app.py` without any arguments to run it using your webcam without a guid and without verbalizing the responses ## Development @@ -144,14 +144,12 @@ The test harness gets the same message as the above section. ### Agent-Chloe Experiment Configuration Discussion -- Should be configured on dashboard. -- Response to explorer should have a delay, whether they come from Chloe or the AI. The explorer should not be able to distinguish between human and machine. -- Want to design intentional fallback from the AI to the Human agent. Thus, we need two buttons: the random send (either AI or Human), and a **SEND!** that forcibly sends the human response over the AI. +* Should be configured on dashboard. +* Responses to the explorer should have a similar delay, whether they come from Chloe or the AI. The explorer should not be able to distinguish between human and machine. +* Want to design intentional fallback from the AI to the Human agent. Thus, we need two buttons: -## Notes - -- ~~OpenCV 3.1 might crash on OSX after a while, so that's why I had to switch to version 3.0. See open issue and solution [here](https://github.com/opencv/opencv/issues/5874).~~ -- Moving the `.read()` part of the video stream in a multiple child processes did not work. However, it was possible to move it to a separate thread. +1. the random send (either AI or Human) +2. **SEND!** that forcibly sends the human response over the AI. ## Copyright From 98bbf5a4064999733ad7330be10c9297a82a6864 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Tue, 19 Dec 2017 17:44:19 -0800 Subject: [PATCH 153/174] Update README.md --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 9d39bb8..8faf829 100644 --- a/README.md +++ b/README.md @@ -33,12 +33,12 @@ If it is not in a folder with your conda environment name (`object-detection` is 5.1 `head environment.yml` -- 1st line should say something like `name: object-detection` 5.2 `source activate object-detection` -- replace `object-detection` with your environment name from the environment.yml - 5.3 `which python` -- make sure this is now correctly pointing to your conda environment path +    5.3 `which python` -- make sure this is now correctly pointing to your conda environment path 6. Start the app! - 6.1 `python object_detection_app.py --help` to see all the options - 6.2 `python object_detection_app.py` without any arguments to run it using your webcam without a guid and without verbalizing the responses + 6.1 `python object_detection_app.py --help` to see all the options +    6.2 `python object_detection_app.py` without any arguments to run it using your webcam without a guid and without verbalizing the responses . ## Development From 547f0a91f7e0c1887c0389f9ccc448aae42dc21d Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Tue, 19 Dec 2017 18:23:46 -0800 Subject: [PATCH 154/174] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 8faf829..02a08c6 100644 --- a/README.md +++ b/README.md @@ -32,8 +32,8 @@ $ cd object_detector_app If it is not in a folder with your conda environment name (`object-detection` is the default), you need to activate your environment: 5.1 `head environment.yml` -- 1st line should say something like `name: object-detection` - 5.2 `source activate object-detection` -- replace `object-detection` with your environment name from the environment.yml -    5.3 `which python` -- make sure this is now correctly pointing to your conda environment path +    5.2 `source activate object-detection` -- replace `object-detection` with your environment name   +    5.3 `which python` -- make sure this is now correctly pointing to your conda environment path   6. Start the app! From 5a9c5206827e00f30061e64c22743509a95d25a3 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Tue, 19 Dec 2017 18:24:54 -0800 Subject: [PATCH 155/174] Update README.md --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 02a08c6..4898da6 100644 --- a/README.md +++ b/README.md @@ -31,9 +31,11 @@ $ cd object_detector_app If it is not in a folder with your conda environment name (`object-detection` is the default), you need to activate your environment: - 5.1 `head environment.yml` -- 1st line should say something like `name: object-detection` +```markdown + 5.1 `head environment.yml` -- 1st line should say something like `name: object-detection`    5.2 `source activate object-detection` -- replace `object-detection` with your environment name      5.3 `which python` -- make sure this is now correctly pointing to your conda environment path   +``` 6. Start the app! From c3b3505c8cbbc89d5e8c9531bc24954929b6a396 Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Tue, 19 Dec 2017 18:26:17 -0800 Subject: [PATCH 156/174] Update README.md --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 4898da6..a880bca 100644 --- a/README.md +++ b/README.md @@ -39,8 +39,10 @@ If it is not in a folder with your conda environment name (`object-detection` is 6. Start the app! +```markdown 6.1 `python object_detection_app.py --help` to see all the options -    6.2 `python object_detection_app.py` without any arguments to run it using your webcam without a guid and without verbalizing the responses . +    6.2 `python object_detection_app.py` to run it using your webcam but no GUI or verbalization +``` ## Development From 7323eac38189c652413199cdcb0aca578e52f160 Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Wed, 20 Dec 2017 15:44:45 -0800 Subject: [PATCH 157/174] mqtt fixes --- object_detection_app.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/object_detection_app.py b/object_detection_app.py index 2fbc408..bf49b7a 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -156,9 +156,9 @@ def worker(input_q, output_q, state_q, voice_on=False): break if rc is 0: - rc = mqttc.loop() - else: - print('MQTT Connection error!') + rc = mqttc.loop_start() + #else: + # print('MQTT Connection error!') fps.stop() print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed())) From e174a3e685cc58a82f8bfa9e56d03e4eab1da7d0 Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Wed, 20 Dec 2017 15:46:28 -0800 Subject: [PATCH 158/174] fixes to make the server run the shell scripts properly as paths need to be corrected --- bin/build.sh | 2 +- bin/run.sh | 2 +- bin/update_env.sh | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/bin/build.sh b/bin/build.sh index db81909..4fb3e04 100755 --- a/bin/build.sh +++ b/bin/build.sh @@ -1,4 +1,4 @@ #!/usr/bin/env bash -conda env create -q --force -f environment.yml +conda env create -q --force -f ../environment.yml diff --git a/bin/run.sh b/bin/run.sh index fa6ba9b..10cbc46 100644 --- a/bin/run.sh +++ b/bin/run.sh @@ -4,4 +4,4 @@ source activate object-detection date &>> log.txt -python object_detection_app.py -u=rtsp://52.39.224.108:1935/live/myStream &>> log.txt \ No newline at end of file +python ../object_detection_app.py -u=rtsp://52.39.224.108:1935/live/myStream &>> log.txt \ No newline at end of file diff --git a/bin/update_env.sh b/bin/update_env.sh index 69a2710..4be8d74 100755 --- a/bin/update_env.sh +++ b/bin/update_env.sh @@ -1,4 +1,4 @@ #!/usr/bin/env bash -conda env update -q -f environment.yml +conda env update -q -f ../environment.yml From 866df19f5d7475cc636a20c5227e095328d42477 Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Wed, 20 Dec 2017 17:00:54 -0800 Subject: [PATCH 159/174] simple fix. --- nlp/dispatch.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nlp/dispatch.py b/nlp/dispatch.py index cbacec2..0414ce2 100644 --- a/nlp/dispatch.py +++ b/nlp/dispatch.py @@ -68,7 +68,7 @@ def on_message(client, obj, msg): port = int(os.environ.get('AIRAMQTT_PORT', '1883')) -mqttc.connect(url_str, port, 60) +mqttc.connect(url_str, port, 15) mqttc.subscribe(EXPLORER_SUB_TOPIC, 0) From f65c108df8618a482d067910402f499ca68c40ab Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Wed, 20 Dec 2017 17:15:55 -0800 Subject: [PATCH 160/174] revert path --- bin/build.sh | 2 +- bin/run.sh | 2 +- bin/update_env.sh | 2 +- docs/image-datasets.md | 3 +++ 4 files changed, 6 insertions(+), 3 deletions(-) diff --git a/bin/build.sh b/bin/build.sh index 4fb3e04..db81909 100755 --- a/bin/build.sh +++ b/bin/build.sh @@ -1,4 +1,4 @@ #!/usr/bin/env bash -conda env create -q --force -f ../environment.yml +conda env create -q --force -f environment.yml diff --git a/bin/run.sh b/bin/run.sh index 10cbc46..8eb1905 100644 --- a/bin/run.sh +++ b/bin/run.sh @@ -4,4 +4,4 @@ source activate object-detection date &>> log.txt -python ../object_detection_app.py -u=rtsp://52.39.224.108:1935/live/myStream &>> log.txt \ No newline at end of file +python object_detection_app.py -u=rtsp://52.39.224.108:1935/live/myStream &>> log.txt diff --git a/bin/update_env.sh b/bin/update_env.sh index 4be8d74..69a2710 100755 --- a/bin/update_env.sh +++ b/bin/update_env.sh @@ -1,4 +1,4 @@ #!/usr/bin/env bash -conda env update -q -f ../environment.yml +conda env update -q -f environment.yml diff --git a/docs/image-datasets.md b/docs/image-datasets.md index 14bb4fe..c7339fc 100644 --- a/docs/image-datasets.md +++ b/docs/image-datasets.md @@ -3,3 +3,6 @@ - [Open Images](https://github.com/openimages/dataset) -- 9M images, thousands of classes - Common Objects in Context (COCO) [competition](https://places-coco2017.github.io/#winners) and [data set](http://cocodataset.org/#overview) -- 200K images - Pascal VOC 2010 [dataset](http://www.cs.stanford.edu/~roxozbeh/pascal-context/#statistics) -- 10k images +- IIIT's [500 semantically segmented images](https://cvit.iiit.ac.in/research/projects/cvit-projects/iiit-seed-dataset) +- NYU [Kinect labeled depth map videos](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html) + From 0125a1ea60f5f0a0463bdcd6fa3ead2fb40ca33b Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Thu, 21 Dec 2017 01:12:55 -0800 Subject: [PATCH 161/174] basic JSON format being output. Need to change the confidence parameter --- nlp/dispatch.py | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/nlp/dispatch.py b/nlp/dispatch.py index 0414ce2..54005cd 100644 --- a/nlp/dispatch.py +++ b/nlp/dispatch.py @@ -14,6 +14,9 @@ import typing import paho.mqtt.client as mqtt +import numpy as np +from datetime import datetime + USER_ID = 1234 EXPLORER_SUB_TOPIC = 'dev/chloe/explorer/{}/statement'.format(USER_ID) AGENT_TOPIC = 'dev/chloe/agent/{}/response'.format(USER_ID) @@ -103,7 +106,18 @@ class Dispatchable: root_topic = AGENT_TOPIC def send(self, payload: typing.Dict, *, subtopic: typing.List[str] = list()): - payload_json = json.dumps(payload) + random_number = int(np.random.random_integers(0, 1000)) + timestamp = int((datetime.utcnow() - datetime(1970, 1, 1, 0, 0, 0, 0)).total_seconds()) + code = "ch-vis-000" + message = "success" + #FIXME: Fix this with actual value + confidence = 90 + + # to be replaced with actual values + data = {"messageID": random_number, "statementID": random_number, "timestamp": timestamp, + "status": {"code": code, "message": message}, "action": "say", "args": [], "kwargs": {"confidence": confidence, "source": "chloe", "text": str(payload)}} + + payload_json = json.dumps(data) if not subtopic: self.client.publish(self.root_topic, payload=payload_json) else: @@ -128,6 +142,7 @@ def __call__(self, msg): def _test_mqtt_loop(): + rc = 0 while rc == 0: rc = mqttc.loop() From 0380176fb0701414c909f7d326f08be2f6ccc626 Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Thu, 21 Dec 2017 11:57:06 -0800 Subject: [PATCH 162/174] fixed messageId to be incremented each time send() is called. Made the JSON text more readable --- nlp/dispatch.py | 23 ++++++++++++++++++++--- 1 file changed, 20 insertions(+), 3 deletions(-) diff --git a/nlp/dispatch.py b/nlp/dispatch.py index 54005cd..e1b7f79 100644 --- a/nlp/dispatch.py +++ b/nlp/dispatch.py @@ -104,18 +104,35 @@ def interp_command(cmd_str: str, actions: typing.List[str]) -> str: class Dispatchable: client = mqttc root_topic = AGENT_TOPIC + #TODO: __init__(self) + message_id = 0 def send(self, payload: typing.Dict, *, subtopic: typing.List[str] = list()): random_number = int(np.random.random_integers(0, 1000)) timestamp = int((datetime.utcnow() - datetime(1970, 1, 1, 0, 0, 0, 0)).total_seconds()) code = "ch-vis-000" message = "success" - #FIXME: Fix this with actual value + # FIXME: Fix this with actual value confidence = 90 + message_id = Dispatchable.message_id + 1 + Dispatchable.message_id += 1 # to be replaced with actual values - data = {"messageID": random_number, "statementID": random_number, "timestamp": timestamp, - "status": {"code": code, "message": message}, "action": "say", "args": [], "kwargs": {"confidence": confidence, "source": "chloe", "text": str(payload)}} + data = {"messageID": message_id, + "statementID": random_number, + "timestamp": timestamp, + "status": + {"code": code, + "message": message + }, + "action": "say", + "args": [], + "kwargs": + {"confidence": confidence, + "source": "chloe", + "text": str(payload) + } + } payload_json = json.dumps(data) if not subtopic: From 8f6df5ba314491a09643b8a08381b2a4a163fc8c Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 27 Dec 2017 09:23:43 -0800 Subject: [PATCH 163/174] (Fixed CONTRIBUTING.md minor error) - Python syntax in codeblock had minor error. --- CONTRIBUTING.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 75409e4..fc0de82 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -37,7 +37,7 @@ Use [Google/Numpy/Napolean/Markdown](http://sphinxcontrib-napoleon.readthedocs.i ```python -def add(value, num=0) +def add(value, num=0): """ Add a float to an integer Args: @@ -50,6 +50,7 @@ def add(value, num=0) >>> add(1., 2) 3.0 """ + pass ``` From 966f050b5067b19c83c62f5928386ae1cef72c8b Mon Sep 17 00:00:00 2001 From: Ashwin Kannan Date: Wed, 27 Dec 2017 10:51:38 -0800 Subject: [PATCH 164/174] milliseconds fix and command changed to statement --- nlp/dispatch.py | 43 ++++++++++++++++++++++--------------------- 1 file changed, 22 insertions(+), 21 deletions(-) diff --git a/nlp/dispatch.py b/nlp/dispatch.py index e1b7f79..d049eec 100644 --- a/nlp/dispatch.py +++ b/nlp/dispatch.py @@ -48,7 +48,7 @@ def on_message(client, obj, msg): except json.decoder.JSONDecodeError: return - cmd = payload.get('command', 'default').lower(), + cmd = payload.get('statement', 'default').lower(), action = interp_command(cmd, list(dispatcher.keys())) @@ -66,7 +66,6 @@ def on_message(client, obj, msg): mqttc.on_subscribe = on_subscribe # mqttc.on_log = on_log - url_str = os.environ.get('AIRAMQTT_URL', 'preprod-mqtt.aira.io') port = int(os.environ.get('AIRAMQTT_PORT', '1883')) @@ -104,35 +103,38 @@ def interp_command(cmd_str: str, actions: typing.List[str]) -> str: class Dispatchable: client = mqttc root_topic = AGENT_TOPIC - #TODO: __init__(self) message_id = 0 def send(self, payload: typing.Dict, *, subtopic: typing.List[str] = list()): random_number = int(np.random.random_integers(0, 1000)) - timestamp = int((datetime.utcnow() - datetime(1970, 1, 1, 0, 0, 0, 0)).total_seconds()) + timestamp = int((datetime.utcnow() - datetime(1970, 1, 1, 0, 0, 0, 0)).total_seconds() * 1000) code = "ch-vis-000" message = "success" # FIXME: Fix this with actual value + # TODO: assert that there is a key in the payload dictionary called confidence confidence = 90 message_id = Dispatchable.message_id + 1 Dispatchable.message_id += 1 - # to be replaced with actual values - data = {"messageID": message_id, - "statementID": random_number, - "timestamp": timestamp, - "status": - {"code": code, - "message": message - }, - "action": "say", - "args": [], - "kwargs": - {"confidence": confidence, - "source": "chloe", - "text": str(payload) - } - } + data = { + "messageID": message_id, + # FIXME: update with actual id from Android App + "statementID": random_number, + "timestamp": timestamp, + "status": + { + "code": code, + "message": message + }, + "action": "say", + "args": [], + "kwargs": + { + "confidence": confidence, + "source": "chloe", + "text": str(payload) + } + } payload_json = json.dumps(data) if not subtopic: @@ -159,7 +161,6 @@ def __call__(self, msg): def _test_mqtt_loop(): - rc = 0 while rc == 0: rc = mqttc.loop() From b3eb6465667cd6625b28221f79ab58549547b18b Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 27 Dec 2017 11:36:33 -0800 Subject: [PATCH 165/174] - Fixed bug, need to refactor. - solution: if stream is not opened, try to open the stream. --- object_detection_app.py | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/object_detection_app.py b/object_detection_app.py index bf49b7a..6cc600b 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -140,21 +140,26 @@ def worker(input_q, output_q, state_q, voice_on=False): rc = 0 # mqtt client status. Error if not zero while True: # fps._numFrames < 120 - frame = video_capture.read() - input_q.put(frame) t = time.time() - output_rgb = cv2.cvtColor(output_q.get(), cv2.COLOR_RGB2BGR) - if disp_graphics: - cv2.imshow('Video', output_rgb) - fps.update() + if video_capture.stream.isOpened(): + frame = video_capture.read() + input_q.put(frame) + + output_rgb = cv2.cvtColor(output_q.get(), cv2.COLOR_RGB2BGR) + if disp_graphics: + cv2.imshow('Video', output_rgb) + fps.update() + else: + video_capture.stream.open(source) print('[INFO] elapsed time: {:.2f}'.format(time.time() - t)) if cv2.waitKey(1) & 0xFF == ord('q'): break + if rc is 0: rc = mqttc.loop_start() #else: From ae78e927e84eb138b90bb255d887a9427079c1f1 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 27 Dec 2017 11:50:54 -0800 Subject: [PATCH 166/174] - Added `loop_stop` to stop the mqtt client before the python program exits. --- object_detection_app.py | 1 + 1 file changed, 1 insertion(+) diff --git a/object_detection_app.py b/object_detection_app.py index 6cc600b..2891aeb 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -169,6 +169,7 @@ def worker(input_q, output_q, state_q, voice_on=False): print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed())) print('[INFO] approx. FPS: {:.2f}'.format(fps.fps())) + mqttc.loop_stop() pool.terminate() video_capture.stop() if disp_graphics: From c8b0496f2ab341473fb8ebb0e52ba175a33abe55 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 27 Dec 2017 13:25:05 -0800 Subject: [PATCH 167/174] Revert "- Added `loop_stop` to stop the mqtt client before the python program exits." This reverts commit ae78e92 --- object_detection_app.py | 1 - 1 file changed, 1 deletion(-) diff --git a/object_detection_app.py b/object_detection_app.py index 2891aeb..6cc600b 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -169,7 +169,6 @@ def worker(input_q, output_q, state_q, voice_on=False): print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed())) print('[INFO] approx. FPS: {:.2f}'.format(fps.fps())) - mqttc.loop_stop() pool.terminate() video_capture.stop() if disp_graphics: From f0834e6196ed24848787398da73a88a726c39f8b Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 27 Dec 2017 14:58:58 -0800 Subject: [PATCH 168/174] (Threading potential fixes) - pushing to dev test on server. --- object_detection_app.py | 13 ++++++++++--- utils/app_utils.py | 6 ++++-- 2 files changed, 14 insertions(+), 5 deletions(-) diff --git a/object_detection_app.py b/object_detection_app.py index 6cc600b..445cedf 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -132,26 +132,33 @@ def worker(input_q, output_q, state_q, voice_on=False): if source is None: source = args.video_source - + print('before video capture') video_capture = WebcamVideoStream(src=source, width=args.width, - height=args.height).start() + height=args.height) + print('after video capture') fps = FPS().start() rc = 0 # mqtt client status. Error if not zero while True: # fps._numFrames < 120 t = time.time() - if video_capture.stream.isOpened(): + video_capture.start() + print('before frame = video_capture.read()') frame = video_capture.read() + print('after frame = video_capture.read()') input_q.put(frame) + print('after input_q put') output_rgb = cv2.cvtColor(output_q.get(), cv2.COLOR_RGB2BGR) if disp_graphics: cv2.imshow('Video', output_rgb) + print('after disp graphics ') fps.update() + else: + print('video stream is not open') video_capture.stream.open(source) print('[INFO] elapsed time: {:.2f}'.format(time.time() - t)) diff --git a/utils/app_utils.py b/utils/app_utils.py index 83911cb..6a84462 100644 --- a/utils/app_utils.py +++ b/utils/app_utils.py @@ -52,11 +52,13 @@ def __init__(self, src, width, height): # initialize the variable used to indicate if the thread should # be stopped - self.stopped = False + self.stopped = True def start(self): # start the thread to read frames from the video stream - Thread(target=self.update, args=()).start() + if self.stopped: + self.stopped = False + Thread(target=self.update, args=()).start() return self def update(self): From cf90269006e4fae7086e002548d197c9d55c1d9e Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 27 Dec 2017 15:38:57 -0800 Subject: [PATCH 169/174] (Dev test main while loop) - `loop_start()` --> `loop` - null check on frame - print statements. --- object_detection_app.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/object_detection_app.py b/object_detection_app.py index 445cedf..8ca7563 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -145,9 +145,15 @@ def worker(input_q, output_q, state_q, voice_on=False): t = time.time() if video_capture.stream.isOpened(): video_capture.start() + print('before frame = video_capture.read()') frame = video_capture.read() print('after frame = video_capture.read()') + + if frame is None: + print('frame is None') + continue + input_q.put(frame) print('after input_q put') @@ -166,11 +172,10 @@ def worker(input_q, output_q, state_q, voice_on=False): if cv2.waitKey(1) & 0xFF == ord('q'): break - if rc is 0: - rc = mqttc.loop_start() - #else: - # print('MQTT Connection error!') + rc = mqttc.loop() + else: + print('MQTT Connection error!') fps.stop() print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed())) From 1cb274c28f7ac5d03d33c4f7209863dc6551da77 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 27 Dec 2017 15:41:59 -0800 Subject: [PATCH 170/174] Revert "(Threading potential fixes)" This reverts commit f0834e6 --- object_detection_app.py | 12 ++++-------- utils/app_utils.py | 6 ++---- 2 files changed, 6 insertions(+), 12 deletions(-) diff --git a/object_detection_app.py b/object_detection_app.py index 8ca7563..a659eb4 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -132,28 +132,23 @@ def worker(input_q, output_q, state_q, voice_on=False): if source is None: source = args.video_source - print('before video capture') + video_capture = WebcamVideoStream(src=source, width=args.width, - height=args.height) - print('after video capture') + height=args.height).start() fps = FPS().start() rc = 0 # mqtt client status. Error if not zero while True: # fps._numFrames < 120 t = time.time() + if video_capture.stream.isOpened(): video_capture.start() print('before frame = video_capture.read()') frame = video_capture.read() print('after frame = video_capture.read()') - - if frame is None: - print('frame is None') - continue - input_q.put(frame) print('after input_q put') @@ -172,6 +167,7 @@ def worker(input_q, output_q, state_q, voice_on=False): if cv2.waitKey(1) & 0xFF == ord('q'): break + if rc is 0: rc = mqttc.loop() else: diff --git a/utils/app_utils.py b/utils/app_utils.py index 6a84462..83911cb 100644 --- a/utils/app_utils.py +++ b/utils/app_utils.py @@ -52,13 +52,11 @@ def __init__(self, src, width, height): # initialize the variable used to indicate if the thread should # be stopped - self.stopped = True + self.stopped = False def start(self): # start the thread to read frames from the video stream - if self.stopped: - self.stopped = False - Thread(target=self.update, args=()).start() + Thread(target=self.update, args=()).start() return self def update(self): From a6ec82730121c23493558c90de1fb1a162f39519 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 27 Dec 2017 16:41:32 -0800 Subject: [PATCH 171/174] Hard Revert "(Threading potential fixes)" This reverts commit f0834e6 --- object_detection_app.py | 14 +++----------- 1 file changed, 3 insertions(+), 11 deletions(-) diff --git a/object_detection_app.py b/object_detection_app.py index a659eb4..6cc600b 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -144,22 +144,14 @@ def worker(input_q, output_q, state_q, voice_on=False): t = time.time() if video_capture.stream.isOpened(): - video_capture.start() - - print('before frame = video_capture.read()') frame = video_capture.read() - print('after frame = video_capture.read()') input_q.put(frame) - print('after input_q put') output_rgb = cv2.cvtColor(output_q.get(), cv2.COLOR_RGB2BGR) if disp_graphics: cv2.imshow('Video', output_rgb) - print('after disp graphics ') fps.update() - else: - print('video stream is not open') video_capture.stream.open(source) print('[INFO] elapsed time: {:.2f}'.format(time.time() - t)) @@ -169,9 +161,9 @@ def worker(input_q, output_q, state_q, voice_on=False): if rc is 0: - rc = mqttc.loop() - else: - print('MQTT Connection error!') + rc = mqttc.loop_start() + #else: + # print('MQTT Connection error!') fps.stop() print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed())) From 773ff548e089485b9cecfca9a2d50b63740ce045 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 27 Dec 2017 16:46:22 -0800 Subject: [PATCH 172/174] (MQTT loop fix) - `loop_start()` --> `loop()` - fixed whitespace - added back console print --- object_detection_app.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/object_detection_app.py b/object_detection_app.py index 6cc600b..c3ac541 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -159,11 +159,10 @@ def worker(input_q, output_q, state_q, voice_on=False): if cv2.waitKey(1) & 0xFF == ord('q'): break - if rc is 0: - rc = mqttc.loop_start() - #else: - # print('MQTT Connection error!') + rc = mqttc.loop() + else: + print('[ERROR] MQTT Connection error!') fps.stop() print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed())) From ce7513a685cd2b4e7f24d4438466694e6ce5b4b6 Mon Sep 17 00:00:00 2001 From: Alex Rosengarten Date: Wed, 27 Dec 2017 16:48:39 -0800 Subject: [PATCH 173/174] (Null Check Frame) - continue in the loop if the video capture class doesn't read anything. --- object_detection_app.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/object_detection_app.py b/object_detection_app.py index c3ac541..636c5df 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -145,6 +145,10 @@ def worker(input_q, output_q, state_q, voice_on=False): if video_capture.stream.isOpened(): frame = video_capture.read() + + if frame is None: + continue + input_q.put(frame) output_rgb = cv2.cvtColor(output_q.get(), cv2.COLOR_RGB2BGR) From 30185b79749ec121c1b395571f22e395427cff4e Mon Sep 17 00:00:00 2001 From: Hobson Lane Date: Fri, 13 Apr 2018 13:04:50 -0700 Subject: [PATCH 174/174] NSF science contribution docs --- docs/community-contributions.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 docs/community-contributions.md diff --git a/docs/community-contributions.md b/docs/community-contributions.md new file mode 100644 index 0000000..e6c8723 --- /dev/null +++ b/docs/community-contributions.md @@ -0,0 +1,21 @@ +# NSF Grant 1722399 + +Contributions to the scientific community. + +## Publications + +- [Natural Language Processing in Action](https://www.manning.com/books/natural-language-processing-in-action?a_aid=totalgood&a_bid=19bd201b) +- [Open Data Science Conference West 2017](https://learnai.odsc.com/) [tutorial](https://www.youtube.com/watch?v=wI63y3LTOM8) and [open source code]() + +## Open Source Software Contributions + +- [aichat](https://github.com/aira/aichat): ~1000 lines of source code for new open source chatbot framework including AIRS (AI Response Specification a.k.a Chloe Language) parsers +- [nlpia](https://github.com/aira/nlpia): ~5000 lines of source code for NLP +- [object_detector_app](https://github.com/aira/object_detector): bug fixes, updates, added functionality +- [opencv](https://github.com/opencv/opencv): testing and bug reports +- [colorbalance](https://gist.github.com/hobson/e3b8805a558d974d48336e133dfb2bbd): improved color balance algorithm () + +## Mentoring and Teaching + +- Mentored undergrad and graduate students at Universities through [Springboard](springboard.com) sharing chatbot and object detection algorithms and software +- [Tutorial at ODSC 2017 West](https://www.youtube.com/watch?v=wI63y3LTOM8) \ No newline at end of file