Lecture 2.7 - Histograms.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histograms are a great way to visualize individual color components\n",
    "\n",
    "### NOTE:\n",
    "**The first time you run this block the diagrams won't be displayed, you will need to run this a second time!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "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]); 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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "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",
    "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": {},
   "outputs": [],
   "source": [
    "image = cv2.imread('images/tobago.jpg')\n",
    "cv2.imshow(\"Tobago\", image) \n",
    "\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}