vincentsarago/mask_boundless_resampling.ipynb

## mask_boundless_resampling.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "resampling doesn't give the same result when `boundless=True`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "import rasterio\n",
    "from rasterio.plot import reshape_as_image\n",
    "from rasterio.enums import Resampling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x115050cc0>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f35080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "src = rasterio.open('s3://remotepixel/data/image/LC82330572016015LGN00.tif')\n",
    "data = src.read(indexes=[1,2,3], window=((-100,100), (-100,100)), boundless=True)\n",
    "imshow(reshape_as_image(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected mask\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1150991d0>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1119c8550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask_ok = src.dataset_mask(window=((-100,100), (-100,100)), boundless=True)\n",
    "print('expected mask')\n",
    "imshow(mask_ok)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "value are not modified even if we have Resampling.bilinear\n",
      "Are masks equals:  True\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x111924ba8>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1119f6630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reading the mask withing the image bounds \n",
    "mask = src.read_masks(1, window=((0,100), (0,100)), resampling=Resampling.bilinear)\n",
    "print()\n",
    "print('value are not modified even if we have Resampling.bilinear')\n",
    "print('Are masks equals: ', numpy.all(mask == mask_ok[100:,100:]))\n",
    "imshow(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "mask get modified when adding boudless=True if Resampling.bilinear\n",
      "Are masks equals:  False\n",
      "mask diff\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1148ec8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reading the mask with boundless=True, resampling=Resampling.bilinear\n",
    "mask = src.read_masks(1, window=((-100,100), (-100,100)), boundless=True, resampling=Resampling.bilinear)\n",
    "print()\n",
    "print('mask get modified when adding boudless=True if Resampling.bilinear')\n",
    "print('Are masks equals: ', numpy.all(mask == mask_ok))\n",
    "imshow(mask - mask_ok)\n",
    "print('mask diff')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "But it's ok if we choose Resampling.nearest\n",
      "Are masks equals:  True\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1152a6e10>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11516e390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reading the mask with boundless=True, resampling=Resampling.nearest\n",
    "mask = src.read_masks(1, window=((-100,100), (-100,100)), boundless=True, resampling=Resampling.nearest)\n",
    "print()\n",
    "print('But it\\'s ok if we choose Resampling.nearest')\n",
    "print('Are masks equals: ', numpy.all(mask == mask_ok))\n",
    "\n",
    "imshow(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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