{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install itk-spcn and itkwidgets, if necessary\n",
    "import sys\n",
    "necessary = False\n",
    "if necessary:\n",
    "    !{sys.executable} -m pip install itk-spcn\n",
    "    !{sys.executable} -m pip install itkwidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import needed packages\n",
    "from urllib.request import urlretrieve\n",
    "import os\n",
    "\n",
    "# Import itk, which includes itk-spcn.\n",
    "import itk\n",
    "from itkwidgets import view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetch input images, if we don have them already.\n",
    "input_image_filename = 'Easy1.png'\n",
    "input_image_url = 'https://data.kitware.com/api/v1/file/576ad39b8d777f1ecd6702f2/download'\n",
    "if not os.path.exists(input_image_filename):\n",
    "    urlretrieve(input_image_url, input_image_filename)\n",
    "\n",
    "reference_image_filename = 'Hard.png'\n",
    "reference_image_url = 'https://data.kitware.com/api/v1/file/57718cc48d777f1ecd8a883f/download'\n",
    "if not os.path.exists(reference_image_filename):\n",
    "    urlretrieve(reference_image_url, reference_image_filename)\n",
    "    \n",
    "output_image_filename = 'HardWithEasy1Colors.png'\n",
    "\n",
    "# The pixels are RGB triplets of unsigned char.  The images are 2 dimensional.\n",
    "PixelType = itk.RGBPixel[itk.UC]\n",
    "ImageType = itk.Image[PixelType, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Invoke the functional, eager interface for ITK\n",
    "input_image = itk.imread(input_image_filename, PixelType)\n",
    "reference_image = itk.imread(reference_image_filename, PixelType)\n",
    "eager_normalized_image = itk.structure_preserving_color_normalization_filter(\n",
    "    input_image,\n",
    "    reference_image,\n",
    "    color_index_suppressed_by_hematoxylin=0,\n",
    "    color_index_suppressed_by_eosin=1)\n",
    "itk.imwrite(eager_normalized_image, output_image_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3f03f6a7e49440668e975aa026027775",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Viewer(geometries=[], gradient_opacity=0.22, point_sets=[], rendered_image=<itk.itkImagePython.itkImageRGBUC2;…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view(input_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "76f3dee08eb34e689538015114cee50e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Viewer(geometries=[], gradient_opacity=0.22, point_sets=[], rendered_image=<itk.itkImagePython.itkImageRGBUC2;…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view(reference_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "deb3b419fb6645ba84dcf8517aa1a93e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Viewer(geometries=[], gradient_opacity=0.22, point_sets=[], rendered_image=<itk.itkImagePython.itkImageRGBUC2;…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view(eager_normalized_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alternatively, invoke the ITK pipeline\n",
    "input_reader = itk.ImageFileReader[ImageType].New(FileName=input_image_filename)\n",
    "reference_reader = itk.ImageFileReader[ImageType].New(FileName=reference_image_filename)\n",
    "spcn_filter = itk.StructurePreservingColorNormalizationFilter.New(Input=input_reader.GetOutput())\n",
    "spcn_filter.SetColorIndexSuppressedByHematoxylin(0)\n",
    "spcn_filter.SetColorIndexSuppressedByEosin(1)\n",
    "spcn_filter.SetInput(0, input_reader.GetOutput())\n",
    "spcn_filter.SetInput(1, reference_reader.GetOutput())\n",
    "output_writer = itk.ImageFileWriter.New(spcn_filter.GetOutput())\n",
    "output_writer.SetInput(spcn_filter.GetOutput())\n",
    "output_writer.SetFileName(output_image_filename)\n",
    "output_writer.Write()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "73ea7d590f444c728aabb333b5c9ff43",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Viewer(geometries=[], gradient_opacity=0.22, point_sets=[], rendered_image=<itk.itkImagePython.itkImageRGBUC2;…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view(output_writer.GetInput())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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