exercise_1_identify_leaf_contour.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 1: identify the contour of the maple leaf\n",
    "set by Hao-Chun Hsu<p>\n",
    "Write a script to identify the contour of the maple leaf in image ‘Maple.jpg’. The program should be able to exclude the petiole from the contour. Draw the contour on top of the maple leaf in the image. Please also calculate the ratio of leaf contour and leaf area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import PIL.Image\n",
    "import numpy as np\n",
    "from io import BytesIO\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import clear_output, Image, display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The function to display numpy-array images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def showarray(a, fmt='jpeg'):\n",
    "    a = np.uint8(np.clip(a, 0, 255))\n",
    "    f = BytesIO()\n",
    "    PIL.Image.fromarray(a).save(f, fmt)\n",
    "    display(Image(data=f.getvalue()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(607, 460, 3)\n"
     ]
    }
   ],
   "source": [
    "img = cv2.imread(\"Maple.jpg\")\n",
    "showarray(img[:,:,::-1]) # note that bgr image need to be transformed to rgb\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert the image to a binary image based on threshold\n",
    "### Convert the BGR-image to a grayscale image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(607, 460)\n"
     ]
    }
   ],
   "source": [
    "gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "showarray(gray_img)\n",
    "print(gray_img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use Otsu's method to determine the threshold and convert the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "169.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(gray_img.ravel(), 256, [0,256])\n",
    "ret, thresh = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n",
    "print(ret)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Complement the binary image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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T+8kzkyKc4LIBkKQT824Y2ZH1PXxh8SPHVx488US32Z49Ni/d2NrKwPlJgZOBxuYjcevYZIUVx9FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFSQTzWtxFcW8skM8Th45I2KsjA5BBHIIPOa+j/hN8Z2164TQPFM8Y1OR8Wl7tVFuCTxGwAAV+ykABun3sbvJLz4Q+ObTXBpI0Ge4kbcY7iEgwOozz5pwq52kgMVbkcZIrl9b0TUfDmsT6Tq1v9nvoNvmRb1fbuUMOVJB4IPBrPooooooooooooooooooooooooooooooooooooooooooooooooooooooorsNG+KfjbQNOSw07X50tUwESaOObYAAAqmRWKqABhRwPSuXv7641PUbm/vJPMurqV5pn2gbnYkscDgZJPSq9FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFf/9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#showarray(thresh)\n",
    "compleBW = 255 - thresh\n",
    "showarray(compleBW)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exclude the petiole by image opening"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "se = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(8, 8)) # structuring element\n",
    "opening = cv2.morphologyEx(compleBW, cv2.MORPH_OPEN, kernel=se)\n",
    "showarray(opening)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove the remaining part of the petiole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x, y, width, height, area\n",
      "[[     0      0    460    607 175134]\n",
      " [    13     16    430    385 103770]\n",
      " [    22    567     25     28    316]]\n"
     ]
    }
   ],
   "source": [
    "num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(opening, connectivity=8, ltype=cv2.CV_32S)\n",
    "print(\"x, y, width, height, area\")\n",
    "print(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "opening[labels==2] = 0\n",
    "showarray(opening)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identify the contour and draw it on top of the leaf in the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_, contours, hierarchy = cv2.findContours(image=opening, mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_NONE)\n",
    "img_contour = img.copy()\n",
    "_ = cv2.drawContours(image=img_contour, contours=contours, contourIdx=-1, color=(0, 255, 0), thickness=1)\n",
    "showarray(img_contour[:,:,::-1])\n",
    "cv2.imwrite(\"Maple_with_contour.jpg\", img_contour)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the ratio of leaf contour and area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area: 26461350\n",
      "Contour length: 1692\n",
      "length-area ratio: 6.394231586823801e-05\n"
     ]
    }
   ],
   "source": [
    "area = opening.sum()\n",
    "length = contours[0].shape[0]\n",
    "print(f\"Area: {area}\")\n",
    "print(f\"Contour length: {length}\")\n",
    "print(f\"length-area ratio: {length/area}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}