{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "## Image processing"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": "import numpy as np\nimport skimage\nfrom skimage import img_as_float\nimport skimage.filters as skif\nfrom skimage.color import rgb2gray\nimport skimage.data as skid\nimport skimage.exposure as skie\nfrom IPython.html.widgets import interact\nimport matplotlib.pyplot as plt\nimport seaborn\n%matplotlib inline"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": "chelsea = skid.chelsea()"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": "((300, 451, 3), dtype('uint8'))"
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": "chelsea.shape, chelsea.dtype"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": "plt.imshow(chelsea)\nplt.axis('off')"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": "img = rgb2gray(chelsea)"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": "((300, 451), dtype('float64'))"
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": "img.shape, img.dtype"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": "array([[ 0.4852, 0.4852, ..., 0.1169, 0.1169],\n       [ 0.4969, 0.4930, ..., 0.1225, 0.1272 ],\n       ...,\n       [ 0.4248, 0.3688, ..., 0.5544, 0.5583]])"
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": "img"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": "p2, p98 = np.percentile(img, (2, 98))"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": "img_rescale = skie.rescale_intensity(img, in_range=(p2, p98))"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": "img_eq = skie.equalize_hist(img)"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": "img_adapteq = img_as_float(skie.equalize_adapthist(img, clip_limit=0.03))"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": "hist_types = dict([('Contrast stretching', img_rescale),\n                   ('Histogram equalization', img_eq),\n                   ('Adaptive equalization', img_adapteq)])"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": "@interact(hist_type=list(hist_types.keys()))\ndef display_result(hist_type):\n    result = hist_types[hist_type]\n\n    # We display the processed grayscale image on the left.\n    plt.subplot(121)\n    plt.imshow(result, cmap='gray')\n    plt.axis('off')\n\n    # We display the histogram on the right.\n    plt.subplot(122)\n    plt.hist(result.ravel(), bins=np.linspace(0., 1., 256),\n             histtype='step', color='black')\n\n    plt.show()"
    }
  ],
  "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 0
}