Lecture 2.7 - Histograms.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histograms are a great way to visualize individual color components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xac3cdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xad08da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc8d3cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "g\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbfe1be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xafb5ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "# We need to import matplotlib to create our histogram plots\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "image = cv2.imread('images/input.jpg')\n",
    "\n",
    "#histogram = cv2.calcHist([image], [0], None, [256], [0, 256])\n",
    "\n",
    "# We plot a histogram, ravel() flatens our image array \n",
    "plt.hist(image.ravel(), 256, [0, 256]) \n",
    "plt.show()\n",
    "\n",
    "plt.plot(histogram, color = 'red')    \n",
    "plt.show()\n",
    "\n",
    "# Viewing Separate Color Channels\n",
    "color = ('b', 'g', 'r')\n",
    "\n",
    "# We now separate the colors and plot each in the Histogram\n",
    "for i, col in enumerate(color):\n",
    "    histogram2 = cv2.calcHist([image], [i], None, [256], [0, 256])\n",
    "    plt.plot(histogram2, color = col)    \n",
    "    plt.xlim([0,100])\n",
    "    print col\n",
    "    plt.show()\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**cv2.calcHist(images, channels, mask, histSize, ranges[, hist[, accumulate]])**\n",
    "\n",
    "- images : it is the source image of type uint8 or float32. it should be given in square brackets, ie, \"[img]\".\n",
    "- channels : it is also given in square brackets. It is the index of channel for which we calculate histogram. For example, if input is grayscale image, its value is [0]. For color image, you can pass [0], [1] or [2] to calculate histogram of blue, green or red channel respectively.\n",
    "- mask : mask image. To find histogram of full image, it is given as \"None\". But if you want to find histogram of particular region of image, you have to create a mask image for that and give it as mask. (I will show an example later.)\n",
    "- histSize : this represents our BIN count. Need to be given in square brackets. For full scale, we pass [256].\n",
    "- ranges : this is our RANGE. Normally, it is [0,256]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Required argument 'mat' (pos 2) not found",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-41-07d635daca68>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'images/tobago.jpg'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[0mhistogram\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcalcHist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m256\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m256\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: Required argument 'mat' (pos 2) not found"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "# We need to import matplotlib to create our histogram plots\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "image = cv2.imread('images/tobago.jpg')\n",
    "\n",
    "cv2.imshow(image)\n",
    "\n",
    "histogram = cv2.calcHist([image], [0], None, [256], [0, 256])\n",
    "\n",
    "# We plot a histogram, ravel() flatens our image array \n",
    "plt.hist(image.ravel(), 256, [0, 256])\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Viewing Separate Color Channels\n",
    "color = ('b', 'g', 'r')\n",
    "\n",
    "# We now separate the colors and plot each in the Histogram\n",
    "for i, col in enumerate(color):\n",
    "    histogram2 = cv2.calcHist([image], [i], None, [256], [0, 256])\n",
    "    plt.plot(histogram2, color = col)\n",
    "    plt.xlim([0,256])\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image = cv2.imread('images/tobago.jpg')\n",
    "cv2.imshow(\"Tobago\", image) \n",
    "\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  }
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