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‎image-segmentation_widget.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<img src=\"header.jpg\">\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<h2> <span style=\"color:#0000C0\">Introduction</span></h2>\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<B>C</B>lustering is a Machine Learning technique and it can be used also in data mining that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. In theory, data points that are in the same group should have similar properties and/or features, while data points in different groups should have highly dissimilar properties and/or features. Clustering is a method of unsupervised learning and is a common technique for statistical data analysis used in many fields ( AI,ML,DATA MINING ,...) </br> \n",
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"\n",
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"<B>I</B>n our case Study , we will apply clustering on Images using k-means Algorithm then we will add a noise to image and reapply k-means Algorithm then filtring noise and use k-mean algorithm on the image After filtring \n",
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"<B>l</B>ibraries used : <B> Sklearn and Open Cv </B> "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## <span style=\"color:#0000C0\">Step 1 </span>: <B> Application of K-means on an Image ( Image Segmentation)</B> \n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: opencv-python in c:\\users\\user\\anaconda3\\lib\\site-packages (4.4.0.44)\n",
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"Requirement already satisfied: numpy>=1.17.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from opencv-python) (1.18.5)\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"pip install opencv-python"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
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"outputs": [],
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"source": [
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"from ipywidgets import interact,interactive,fixed,interact_manual\n",
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"import ipywidgets as widgets"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.cluster import KMeans\n",
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"import cv2\n",
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"import pandas as pd\n",
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"import numpy as np \n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"def segm(k):\n",
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" image = cv2.imread('boy.jpg')\n",
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" (h1, w1) = image.shape[:2]\n",
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" image = image.reshape((image.shape[0] * image.shape[1], 3))\n",
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"\n",
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"\n",
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"\n",
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" clt = KMeans(n_clusters = k)\n",
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" labels = clt.fit_predict(image)\n",
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" quant = clt.cluster_centers_.astype(\"uint8\")[labels]\n",
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"\n",
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" \n",
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"\n",
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"#reshape the feature vectors to images\n",
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" quant = quant.reshape((h1, w1, 3))\n",
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" image = image.reshape((h1, w1, 3))\n",
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" \n",
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" plt.figure(figsize=(8,8))\n",
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" \n",
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" plt.imshow(image)\n",
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"\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 60,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "5c4fac37a2eb4a438528ca109148fe02",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"interactive(children=(IntSlider(value=2, description='k', max=30, min=2), Output()), _dom_classes=('widget-int…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"interact(segm,k=widgets.IntSlider(min=2,max=30, step=1, value=2));"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## <span style=\"color:#0000C0\">Step 2 </span> : <B>Add Noise to image</B> \n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 62,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Figure size 1296x1728 with 0 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "9b96b564cc654f3f9c3e8c3b5032ed81",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"interactive(children=(Dropdown(description='mode', options=('gaussian', 'localvar', 'poisson', 'salt', 'pepper…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import skimage.io\n",
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"import matplotlib.pyplot as plt\n",
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"img_path=\"eleph.jpg\"\n",
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"img = skimage.io.imread('boy.jpg')/255.0\n",
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"\n",
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"def plotnoise(mode):\n",
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" img_path=\"eleph.jpg\"\n",
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" img = skimage.io.imread('boy.jpg')/255.0\n",
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" \n",
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" if mode is not None:\n",
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" gimg = skimage.util.random_noise(img, mode=mode)\n",
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" plt.imshow(gimg)\n",
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" else:\n",
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" plt.imshow(img)\n",
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" plt.title(mode)\n",
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" plt.axis(\"off\")\n",
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"\n",
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"plt.figure(figsize=(18,24))\n",
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"r=4\n",
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"c=2\n",
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"interact(plotnoise,mode=[\"gaussian\",\"localvar\",\"poisson\",\"salt\",\"pepper\",\"s&p\",\"speckle\"])\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## <span style=\"color:#0000C0\">Step 3 </span> : <B>Denoising</B> \n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 94,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "0f105e4e681349f2bcae8877d2ef661d",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Button(description='denoising image', style=ButtonStyle())"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "7cf5c17503404757b9146e6132c308aa",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Output()"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import cv2 as cv\n",
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"from matplotlib import pyplot as plt\n",
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" \n",
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"def blur_demo(image):\n",
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" blur = cv.blur(image,(3,3))\n",
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" return blur\n",
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" \n",
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"def boxFilter_demo(image):\n",
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" boxFilter = cv.boxFilter(image,-1,(3,3),normalize=True)\n",
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" return boxFilter\n",
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" \n",
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"def boxFilterF_demo(image):\n",
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" boxFilterF = cv.boxFilter(image,-1,(3,3),normalize=False)\n",
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" return boxFilterF\n",
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" \n",
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"def Gaussian_demo(image):\n",
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" gaussian = cv.GaussianBlur(image,(5,5),1)\n",
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" return gaussian\n",
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" \n",
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"def medianBulr(image):\n",
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" medianbulr = cv.medianBlur(image,5)\n",
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" return medianbulr\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"from IPython.display import display\n",
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"button = widgets.Button(description=\"denoising image\")\n",
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"output = widgets.Output()\n",
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"\n",
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"display(button, output)\n",
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"\n",
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"def on_button_clicked(b):\n",
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" with output:\n",
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" src = cv.imread(\"pepper.JPG\")\n",
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" src = src[:,:,[2,1,0]]\n",
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" img1 = blur_demo(src)\n",
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" img2 = boxFilter_demo(src)\n",
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" img3 = boxFilterF_demo(src)\n",
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" img4 = Gaussian_demo(src)\n",
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" img5 = medianBulr(src)\n",
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" plt.figure(figsize=(30,20))\n",
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"\n",
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" \n",
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" plt.subplot(2,3,1),plt.imshow(src)\n",
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" plt.title(\"noised image\"),plt.xticks([]),plt.yticks([])\n",
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" plt.subplot(232),plt.imshow(img1)\n",
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" plt.title(\"blur_image\"),plt.xticks([]),plt.yticks([])\n",
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" plt.subplot(233),plt.imshow(img2)\n",
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" plt.title(\"boxFilter_image\"),plt.xticks([]),plt.yticks([])\n",
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" plt.subplot(234),plt.imshow(img3)\n",
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" plt.title(\"boxFilterF_image\"),plt.xticks([]),plt.yticks([])\n",
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" plt.subplot(235),plt.imshow(img4)\n",
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" plt.title(\"gaussian_image\"),plt.xticks([]),plt.yticks([])\n",
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" plt.subplot(236),plt.imshow(img5)\n",
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" plt.title(\"medianBulr_image\"),plt.xticks([]),plt.yticks([])\n",
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" plt.show()\n",
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" \n",
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" \n",
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"button.on_click(on_button_clicked)\n",
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"\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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