{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> This is one of the 100 recipes of the [IPython Cookbook](http://ipython-books.github.io/), the definitive guide to high-performance scientific computing and data science in Python.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11.3. Segmenting an image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Let's import the packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from skimage.data import coins\n",
    "from skimage.filter import threshold_otsu\n",
    "from skimage.segmentation import clear_border\n",
    "from skimage.morphology import closing, square\n",
    "from skimage.measure import regionprops, label\n",
    "from skimage.color import lab2rgb\n",
    "import matplotlib as mpl\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. We create a function to display a grayscale image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def show(img, cmap=None):\n",
    "    cmap = cmap or plt.cm.gray\n",
    "    plt.figure(figsize=(4,2));\n",
    "    plt.imshow(img, cmap=cmap);\n",
    "    plt.axis('off');\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. We retrieve a test image bundled in scikit-image, showing various coins on a plain background."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "img = coins()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "show(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. The first step to segment the image consists in finding an intensity threshold separating the (bright) coins from the (dark) background. **Otsu's method** defines a simple algorithm to find such a threshold automatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "threshold_otsu(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "show(img>107)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. There appears to be a problem in the top left corner of the image, with part of the background being too bright. Let's use the notebook widgets to find a better threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from IPython.html import widgets\n",
    "@widgets.interact(t=(10, 240))\n",
    "def threshold(t):\n",
    "    show(img>t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. The threshold 120 looks better. The next step consists in cleaning the binary image by smoothing the coins and removing the border. Scikit-image contains a few functions for these purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "img_bin = clear_border(closing(img>120, square(5)))\n",
    "show(img_bin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Next, we perform the segmentation task itself with the `label` function. This function detects the connected components in the image, and attributes a unique label to every component. Here, we color-code the labels in the binary image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labels = label(img_bin)\n",
    "show(labels, cmap=plt.cm.rainbow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. Small artifacts in the image result in spurious labels that do not correspond to coins. Therefore we only keep components with more than 100 pixels. The `regionprops` function allows us to retrieve specific properties of the components (here, the area and the bounding box)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "regions = regionprops(labels)\n",
    "boxes = np.array([label['BoundingBox'] for label in regions \n",
    "                                       if label['Area'] > 100])\n",
    "print(\"There are {0:d} coins.\".format(len(boxes)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9. Finally, we show the label number on top of each component in the original image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(6,4));\n",
    "plt.imshow(img, cmap=plt.cm.gray);\n",
    "plt.axis('off');\n",
    "xs = boxes[:,[1,3]].mean(axis=1)\n",
    "ys = boxes[:,[0,2]].mean(axis=1)\n",
    "for i, box in enumerate(boxes):\n",
    "    plt.text(xs[i]-5, ys[i]+5, str(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> You'll find all the explanations, figures, references, and much more in the book (to be released later this summer).\n",
    "\n",
    "> [IPython Cookbook](http://ipython-books.github.io/), by [Cyrille Rossant](http://cyrille.rossant.net), Packt Publishing, 2014 (500 pages)."
   ]
  }
 ],
 "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": "3.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}