ITKDaskSkimageLocalMeanResizeComparison.ipynb

ITKDaskSkimageLocalMeanResizeComparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itk\n",
    "from itkwidgets import view\n",
    "import dask.array as da\n",
    "import skimage.transform\n",
    "import numpy as np\n",
    "from functools import reduce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 596 ms, sys: 5.7 s, total: 6.29 s\n",
      "Wall time: 13.5 s\n"
     ]
    }
   ],
   "source": [
    "# Available https://data.kitware.com/#folder/5ac69d1b8d777f0685792255, both huc01resa.mhd, huc01resa.raw required\n",
    "%time image = itk.imread('huc01resa.mhd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image (0x55a9a14b1ed0)\n",
      "  RTTI typeinfo:   itk::Image<unsigned char, 3u>\n",
      "  Reference Count: 1\n",
      "  Modified Time: 349\n",
      "  Debug: Off\n",
      "  Object Name: \n",
      "  Observers: \n",
      "    none\n",
      "  Source: (none)\n",
      "  Source output name: (none)\n",
      "  Release Data: Off\n",
      "  Data Released: False\n",
      "  Global Release Data: Off\n",
      "  PipelineMTime: 179\n",
      "  UpdateMTime: 348\n",
      "  RealTimeStamp: 0 seconds \n",
      "  LargestPossibleRegion: \n",
      "    Dimension: 3\n",
      "    Index: [0, 0, 0]\n",
      "    Size: [2048, 2048, 1600]\n",
      "  BufferedRegion: \n",
      "    Dimension: 3\n",
      "    Index: [0, 0, 0]\n",
      "    Size: [2048, 2048, 1600]\n",
      "  RequestedRegion: \n",
      "    Dimension: 3\n",
      "    Index: [0, 0, 0]\n",
      "    Size: [2048, 2048, 1600]\n",
      "  Spacing: [1, 1, 1]\n",
      "  Origin: [0, 0, 0]\n",
      "  Direction: \n",
      "1 0 0\n",
      "0 1 0\n",
      "0 0 1\n",
      "\n",
      "  IndexToPointMatrix: \n",
      "1 0 0\n",
      "0 1 0\n",
      "0 0 1\n",
      "\n",
      "  PointToIndexMatrix: \n",
      "1 0 0\n",
      "0 1 0\n",
      "0 0 1\n",
      "\n",
      "  Inverse Direction: \n",
      "1 0 0\n",
      "0 1 0\n",
      "0 0 1\n",
      "\n",
      "  PixelContainer: \n",
      "    ImportImageContainer (0x55a9a3bece50)\n",
      "      RTTI typeinfo:   itk::ImportImageContainer<unsigned long, unsigned char>\n",
      "      Reference Count: 1\n",
      "      Modified Time: 345\n",
      "      Debug: Off\n",
      "      Object Name: \n",
      "      Observers: \n",
      "        none\n",
      "      Pointer: 0x7ff769752010\n",
      "      Container manages memory: true\n",
      "      Size: 6710886400\n",
      "      Capacity: 6710886400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "shrink_factors = (10, 10, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#view(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loop, best of 3: 1.86 s per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit shrunk = itk.BinShrinkImageFilter(image, ShrinkFactors=shrink_factors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image (0x55a9a422a5e0)\n",
      "  RTTI typeinfo:   itk::Image<unsigned char, 3u>\n",
      "  Reference Count: 1\n",
      "  Modified Time: 476\n",
      "  Debug: Off\n",
      "  Object Name: \n",
      "  Observers: \n",
      "    none\n",
      "  Source: (none)\n",
      "  Source output name: (none)\n",
      "  Release Data: Off\n",
      "  Data Released: False\n",
      "  Global Release Data: Off\n",
      "  PipelineMTime: 465\n",
      "  UpdateMTime: 475\n",
      "  RealTimeStamp: 0 seconds \n",
      "  LargestPossibleRegion: \n",
      "    Dimension: 3\n",
      "    Index: [0, 0, 0]\n",
      "    Size: [204, 204, 160]\n",
      "  BufferedRegion: \n",
      "    Dimension: 3\n",
      "    Index: [0, 0, 0]\n",
      "    Size: [204, 204, 160]\n",
      "  RequestedRegion: \n",
      "    Dimension: 3\n",
      "    Index: [0, 0, 0]\n",
      "    Size: [204, 204, 160]\n",
      "  Spacing: [10, 10, 10]\n",
      "  Origin: [4.5, 4.5, 4.5]\n",
      "  Direction: \n",
      "1 0 0\n",
      "0 1 0\n",
      "0 0 1\n",
      "\n",
      "  IndexToPointMatrix: \n",
      "10 0 0\n",
      "0 10 0\n",
      "0 0 10\n",
      "\n",
      "  PointToIndexMatrix: \n",
      "0.1 0 0\n",
      "0 0.1 0\n",
      "0 0 0.1\n",
      "\n",
      "  Inverse Direction: \n",
      "1 0 0\n",
      "0 1 0\n",
      "0 0 1\n",
      "\n",
      "  PixelContainer: \n",
      "    ImportImageContainer (0x55a9a42a70f0)\n",
      "      RTTI typeinfo:   itk::ImportImageContainer<unsigned long, unsigned char>\n",
      "      Reference Count: 1\n",
      "      Modified Time: 473\n",
      "      Debug: Off\n",
      "      Object Name: \n",
      "      Observers: \n",
      "        none\n",
      "      Pointer: 0x55a9a44b9460\n",
      "      Container manages memory: true\n",
      "      Size: 6658560\n",
      "      Capacity: 6658560\n",
      "\n"
     ]
    }
   ],
   "source": [
    "shrunk = itk.BinShrinkImageFilter(image, ShrinkFactors=shrink_factors)\n",
    "print(shrunk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('shrinking with: ', [2, 2, 1])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d159660490e54eb2993ac0596e33feb1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Vmlld2VyKGdyYWRpZW50X29wYWNpdHk9MC4yMiwgcmVuZGVyZWRfaW1hZ2U9PGl0a0ltYWdlUHl0aG9uLml0a0ltYWdlVUMzOyBwcm94eSBvZiA8U3dpZyBPYmplY3Qgb2YgdHlwZSAnaXRrSW3igKY=\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view(shrunk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_array = itk.GetArrayViewFromImage(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loop, best of 3: 44.4 s per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit skshrunk = skimage.transform.downscale_local_mean(image_array, shrink_factors[::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('shrinking with: ', [2, 2, 1])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a989561a9e0145b4ac8166e7fb3b17ca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Vmlld2VyKGdyYWRpZW50X29wYWNpdHk9MC4yMiwgcmVuZGVyZWRfaW1hZ2U9PGl0a0ltYWdlUHl0aG9uLml0a0ltYWdlVUMzOyBwcm94eSBvZiA8U3dpZyBPYmplY3Qgb2YgdHlwZSAnaXRrSW3igKY=\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skshrunk = skimage.transform.downscale_local_mean(image_array, shrink_factors[::-1])\n",
    "view(skshrunk.astype(image_array.dtype))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loop, best of 3: 10.2 s per loop\n"
     ]
    }
   ],
   "source": [
    "chunks = (256, 256, 256)\n",
    "%timeit da.from_array(image_array, chunks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loop, best of 3: 3.09 s per loop\n"
     ]
    }
   ],
   "source": [
    "da_image = da.from_array(image_array, chunks)\n",
    "def dask_shrink(da_image):\n",
    "    dashrunk = da.coarsen(np.mean,\n",
    "                          da_image,\n",
    "                          {index: factor for index, factor in enumerate(shrink_factors[::-1])},\n",
    "                          trim_excess=True)\n",
    "    return dashrunk.compute().astype(image_array.dtype)\n",
    "%timeit da_shrunk = dask_shrink(da_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('shrinking with: ', [2, 2, 1])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a2da51cb85a46528bfe405c7a783505",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Vmlld2VyKGdyYWRpZW50X29wYWNpdHk9MC4yMiwgcmVuZGVyZWRfaW1hZ2U9PGl0a0ltYWdlUHl0aG9uLml0a0ltYWdlVUMzOyBwcm94eSBvZiA8U3dpZyBPYmplY3Qgb2YgdHlwZSAnaXRrSW3igKY=\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "da_shrunk = dask_shrink(da_image)\n",
    "view(da_shrunk)"
   ]
  }
 ],
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