khan_image.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import PIL.Image\n",
    "import numpy.testing as npt\n",
    "from numba import jit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def avg_transform(image):\n",
    "    m, n, c = image.shape\n",
    "    for i in range(m):\n",
    "        xi = image[i]\n",
    "        for j in range(n):\n",
    "            avg = xi[j].sum()/3\n",
    "            xi[j][:] = avg\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def affine_avg_transform(image):\n",
    "    affine_avg = np.ones((3,3))/3\n",
    "    image[:] = image.dot(affine_avg)\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fast_avg_transform = jit(avg_transform, nopython=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGQAAAAyCAIAAAAlV+npAAAAgklEQVR4nO3QQQkAIADAQLV/Z63g\nXiLcJRibe3BrvQ74iVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmB\nWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZwQFIjwFj\nyDtoQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x50 at 0x10CAC52B0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image = PIL.Image.new('RGB', (100, 50), (255, 0, 0))\n",
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGQAAAAyCAIAAAAlV+npAAAAhUlEQVR4nO3QQQ0AIRDAwOM8rn8r\nWKAvQjKjoOmamY8z/+2Al5gVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWY\nFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFWxH\nkAFjY983dgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x50 at 0x10FCC8BA8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_data = np.asarray(image).copy()\n",
    "PIL.Image.fromarray(avg_transform(image_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGQAAAAyCAIAAAAlV+npAAAAhUlEQVR4nO3QQQ0AIRDAwOM8rn8r\nWKAvQjKjoOmamY8z/+2Al5gVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWY\nFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFWxH\nkAFjY983dgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x50 at 0x10D7D3CF8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_data = np.asarray(image).copy()\n",
    "PIL.Image.fromarray(fast_avg_transform(image_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGQAAAAyCAIAAAAlV+npAAAAhUlEQVR4nO3QQQ0AIRDAwOM8rn8r\nWKAvQjKjoOmamY8z/+2Al5gVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWY\nFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFWxH\nkAFjY983dgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x50 at 0x10FCD9C88>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_data = np.asarray(image).copy()\n",
    "PIL.Image.fromarray(affine_avg_transform(image_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image = PIL.Image.new('RGB', (1280, 720), (255, 0, 0))\n",
    "image_data = np.asarray(image).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 25.3 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "fast_avg_transform(image_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 95.8 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "affine_avg_transform(image_data)"
   ]
  }
 ],
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