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": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AJ4BbgM9S9fp8cFaOagqemoTtnXO1S0mSDkpzMldJKeWV4yzbBGya5DN7qeZd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\n/6+/lWqa/oepek22JTmVOfy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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d85fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cvOTijPdVV1mXX49IUBFREBERkTxoaiYEQUWksjK716VOzaT2iGQj7xvfaWpG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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa99c208>"
      ]
     },
     "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",
    "#redundanter code\n",
    "#histogram = cv2.calcHist([image], [0], None, [256], [0, 256])\n",
    "\n",
    "# We plot a histogram, ravel() flatens our 2-dim image to 1-dim array \n",
    "plt.hist(image.ravel(), 256, [0, 256]); 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": "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": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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",
    "#redundanter code\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]); 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()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:Python2.7]",
   "language": "python",
   "name": "conda-env-Python2.7-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}