achabotl/CountingPixels.ipynb

## CountingPixels.ipynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:ab5b0f97916b85ea193d953e6c593b6fd2b30e9de172c905cd1af1eec9558bd0"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division\n",
      "import numpy as np\n",
      "from matplotlib import pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create the image."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_white, n_gray, n_black = 20, 50, 30\n",
      "in_white = np.zeros(n_white).reshape((n_white / 10, 10))\n",
      "in_gray = 125 * np.ones(n_gray).reshape((n_gray / 10, 10))\n",
      "in_black = 255 * np.ones(n_black).reshape((n_black / 10, 10))\n",
      "img = np.vstack([in_white, in_gray, in_black])\n",
      "plt.imshow(img, cmap='bone', interpolation='nearest')\n",
      "plt.colorbar()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0x10df31200>"
       ]
      },
      {
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        "' width=296 height=251/>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x10a4d8dd0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Calculate how many pixels are in the white, gray, and black ranges."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lower = 255 / 3\n",
      "upper = 2 * lower\n",
      "black = np.sum(img >= upper)\n",
      "gray = np.sum(np.logical_and(lower <= img, img < upper))\n",
      "white = np.sum(img < lower)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all = img.size\n",
      "print \"Total pixels: %d\" % all\n",
      "print \"White pixels: %d (%5.2f%%)\" % (white, 100.0*white/all)\n",
      "print \"Black pixels: %d (%5.2f%%)\" % (black, 100.0*black/all)\n",
      "print \"Gray pixels:  %d (%5.2f%%)\" % (gray, 100.0*gray/all)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Total pixels: 100\n",
        "White pixels: 20 (20.00%)\n",
        "Black pixels: 30 (30.00%)\n",
        "Gray pixels:  50 (50.00%)\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "assert(n_white == white)\n",
      "assert(n_gray == gray)\n",
      "assert(n_black == black)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    }
   ],
   "metadata": {}
  }
 ]
}
	{
	"metadata": {
	"name": "",
	"signature": "sha256:ab5b0f97916b85ea193d953e6c593b6fd2b30e9de172c905cd1af1eec9558bd0"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"from __future__ import division\n",
	"import numpy as np\n",
	"from matplotlib import pyplot as plt"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 1
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"Create the image."
	]
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"n_white, n_gray, n_black = 20, 50, 30\n",
	"in_white = np.zeros(n_white).reshape((n_white / 10, 10))\n",
	"in_gray = 125 * np.ones(n_gray).reshape((n_gray / 10, 10))\n",
	"in_black = 255 * np.ones(n_black).reshape((n_black / 10, 10))\n",
	"img = np.vstack([in_white, in_gray, in_black])\n",
	"plt.imshow(img, cmap='bone', interpolation='nearest')\n",
	"plt.colorbar()"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 2,
	"text": [
	"<matplotlib.colorbar.Colorbar instance at 0x10df31200>"
	]
	},
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	"Fs8AAAAASUVORK5CYII=\n",
	"' width=296 height=251/>"
	],
	"metadata": {},
	"output_type": "display_data",
	"text": [
	"<matplotlib.figure.Figure at 0x10a4d8dd0>"
	]
	}
	],
	"prompt_number": 2
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"Calculate how many pixels are in the white, gray, and black ranges."
	]
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"lower = 255 / 3\n",
	"upper = 2 * lower\n",
	"black = np.sum(img >= upper)\n",
	"gray = np.sum(np.logical_and(lower <= img, img < upper))\n",
	"white = np.sum(img < lower)"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 3
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"all = img.size\n",
	"print \"Total pixels: %d\" % all\n",
	"print \"White pixels: %d (%5.2f%%)\" % (white, 100.0*white/all)\n",
	"print \"Black pixels: %d (%5.2f%%)\" % (black, 100.0*black/all)\n",
	"print \"Gray pixels: %d (%5.2f%%)\" % (gray, 100.0*gray/all)"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": [
	"Total pixels: 100\n",
	"White pixels: 20 (20.00%)\n",
	"Black pixels: 30 (30.00%)\n",
	"Gray pixels: 50 (50.00%)\n"
	]
	}
	],
	"prompt_number": 4
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"assert(n_white == white)\n",
	"assert(n_gray == gray)\n",
	"assert(n_black == black)"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 5
	}
	],
	"metadata": {}
	}
	]
	}