{
 "metadata": {
  "name": "",
  "signature": "sha256:78138dc17f10550cd721240f4580b77dd56bdba366ab9062c72f97239c746c50"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Motivating example: Parallel image processing with scikit-image"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To get a sense of what IPython.parallel might be used for,\n",
      "we start with an example of some batch processing of image files with [scikit-image](http://scikit-image.org/).\n",
      "We will revisit pieces of this example as we learn about the different components of IPython.\n",
      "\n",
      "You can download images with [this notebook](../images.ipynb), or get a zip [here](https://s3.amazonaws.com/ipython-parallel-data/images.zip), or find any images on your computer."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys,os,re,time\n",
      "import urllib\n",
      "\n",
      "import numpy as np\n",
      "\n",
      "from IPython import parallel"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from skimage.io import imread\n",
      "from skimage import measure"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Define a function to "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def find_contours(path, low=0.1, high=0.8):\n",
      "    \"\"\"Find contours in an image at path\n",
      "    \n",
      "    Returns the image and the contour lists.\n",
      "    \"\"\"\n",
      "    img = imread(path, flatten=True)\n",
      "    \n",
      "    # Find contours at a constant value of 0.1 and 0.8\n",
      "    dark = measure.find_contours(img, low)\n",
      "    light = measure.find_contours(img, high)\n",
      "    return img, dark, light\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_contours(img, dark, light, show=True):\n",
      "    \"\"\"Display the image and plot all contours found\"\"\"\n",
      "    plt.imshow(img, cmap='gray')\n",
      "    \n",
      "    for n, contour in enumerate(dark):\n",
      "        plt.plot(contour[:, 1], contour[:, 0], c='r', linewidth=1)\n",
      "    \n",
      "    for n, contour in enumerate(light):\n",
      "        plt.plot(contour[:, 1], contour[:, 0], c='b', linewidth=1)\n",
      "\n",
      "    plt.axis('image')\n",
      "    plt.xticks([])\n",
      "    plt.yticks([])\n",
      "    if show:\n",
      "        plt.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_contours_image(path):\n",
      "    \"\"\"Given a path, return a PNG of the image with contour lines\n",
      "    \n",
      "    Calls both find_contours and plot_contours\n",
      "    \"\"\"\n",
      "    from IPython.core.pylabtools import print_figure\n",
      "    \n",
      "    img, dark, light = find_contours(path)\n",
      "    plot_contours(img, dark, light, show=False)\n",
      "    fig = plt.gcf()\n",
      "    pngdata = print_figure(fig)\n",
      "    plt.close(fig)\n",
      "    return pngdata\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "pictures_dir = os.path.join('..', 'images', 'castle')\n",
      "\n",
      "pictures = []\n",
      "for directory, subdirs, files in os.walk(pictures_dir):\n",
      "    for fname in files:\n",
      "        if fname.lower().endswith(('.jpg', '.png')):\n",
      "            pictures.append(os.path.join(directory, fname))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's test our function locally, to see what it does."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for p in pictures[:3]:\n",
      "    img, dark, light = find_contours(p)\n",
      "    plot_contours(img, dark, light)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Now in parallel"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, we connect our parallel Client"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rc = parallel.Client()\n",
      "all_engines = rc[:]\n",
      "view = rc.load_balanced_view()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Then we initialize the namespace on all of the engines with imports"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%px import os; os.chdir(\"{os.getcwd()}\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px\n",
      "import matplotlib\n",
      "matplotlib.use('Agg')\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "from skimage.io import imread\n",
      "from skimage import measure"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "and make sure some functions are defined everywhere (this is only necessary for the `contours_in_url` case)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_engines.push(dict(\n",
      "    plot_contours=plot_contours,\n",
      "    find_contours=find_contours,\n",
      "))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we can iterate through all of our pictures, and detect and display any faces we find"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import display, Image\n",
      "\n",
      "amr = view.map_async(get_contours_image, pictures[:20], ordered=False)\n",
      "for pngdata in amr:\n",
      "    display(Image(data=pngdata))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}