Binary Image Processing/Connected_Component_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font style=\"color:rgb(50,120,229)\">Connected Component Analysis</font>\n",
    "In this section, we will dip our toes into a simple computer vision algorithm called **Connected Component Analysis (CCA)**. It is a fancy name for labeling blobs in a binary image. So, it can also be used to count the number of blobs ( also called connected components ) in a binary image. Lets understand it by way of an example. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from dataPath import DATA_PATH\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] = (6.0, 6.0)\n",
    "matplotlib.rcParams['image.cmap'] = 'gray'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11facec50>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read image as grayscale\n",
    "im = cv2.imread(\"../data/images/truth.png\", cv2.IMREAD_GRAYSCALE)\n",
    "plt.imshow(im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the figure above, we see a binary image with five blobs corresponding to the letters T, R, U, T and H. Connected Component Analysis (CCA) allows us to apply a label to every blob. In other words, CCA will create a mask where all pixels corresponding to the background are 0, all pixels corresponding to the first blob, (i.e. the letter T) are 1, those corresponding to R are 2 and so on and so forth. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc3a17625f8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Threshold Image\n",
    "th, imThresh = cv2.threshold(im, 127, 255, cv2.THRESH_BINARY)\n",
    "\n",
    "# Find connected components\n",
    "_, imLabels = cv2.connectedComponents(imThresh)\n",
    "plt.imshow(imLabels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font style=\"color:rgb(50,120,229)\">Display each label </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the labels\n",
    "nComponents = imLabels.max()\n",
    "\n",
    "displayRows = np.ceil(nComponents/3.0)\n",
    "plt.figure(figsize=[20,12])\n",
    "for i in range(nComponents+1):\n",
    "    plt.subplot(displayRows,3,i+1)\n",
    "    plt.imshow(imLabels==i)\n",
    "    if i == 0:\n",
    "        plt.title(\"Background, Component ID : {}\".format(i))\n",
    "    else:\n",
    "        plt.title(\"Component ID : {}\".format(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font style=\"color:rgb(50,120,229)\">Color Coding of Components</font>\n",
    "Since it is a bit difficult to visualize the difference in intensity value in grayscale images, we apply a colormap so that grayscale values are converted to color for the purpose of display."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we normalize the image pixel values to 0 to 255. To achieve this we first find the min and max values in the image, and then normalize the image by subtracting the min and dividing by (max - min). This normalizes the image to be between 0 and 1. Finally 255 is multiplied to we get an image with values between 0 and 255. Finally, we apply a colormap on the labelled image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font style=\"color:rgb(50,120,229)\">What is a colormap ?</font>\n",
    "\n",
    "Let’s say we want to show the temperature in different parts of the United States on a map. We could overlay temperature data on top of a US map as a grayscale image — darker regions representing cooler temperatures, and brighter regions representing hotter regions. Not only is such a representation incredibly boring, it is a bad representation for two big reasons. \n",
    "- First, the human visual system is not optimized to measure small changes in grayscale intensities. We are way better at perceiving changes in color. \n",
    "- Second, we associate different meanings with different colors. It is much more meaningful to represent cooler temperatures using blue and warmer temperatures using red.\n",
    "\n",
    "Temperature data is just one example, but there are several other cases where the data is single valued (grayscale), but it makes sense to convert it to color data for visualization. Other examples of data that are better visualized by pseudo-coloring are height, pressure, density, humidity so on and so forth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OpenCV defines 12 colormaps that can be applied to a grayscale image using the function `applyColorMap` to produce a pseudocolored image. We will use COLORMAP_JET for our example. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc3a1529f98>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The following line finds the min and max pixel values\n",
    "# and their locations on an image.\n",
    "(minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(imLabels)\n",
    "\n",
    "# Normalize the image so that the min value is 0 and max value is 255.\n",
    "imLabels = 255 * (imLabels - minVal)/(maxVal-minVal)\n",
    "\n",
    "# Convert image to 8-bits unsigned type\n",
    "imLabels = np.uint8(imLabels)\n",
    "\n",
    "# Apply a color map\n",
    "imColorMap = cv2.applyColorMap(imLabels, cv2.COLORMAP_JET)\n",
    "plt.imshow(imColorMap[:,:,::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 [3.6]",
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}