DynaSlum/satsense

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conversions between binary mask and multipolygon \n",
    "## Simple example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook illustrates via a simle example the conversions between a binary mask and a multipolygonal shape and the loading and saving to image and shape files using `satsense` python library.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate a toy shape as multipolygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multipolygon is valid?:  True\n"
     ]
    }
   ],
   "source": [
    "# Python package imports\n",
    "from shapely.geometry import MultiPolygon # for multipolygon data structure\n",
    "\n",
    "# Create a multipolygon\n",
    "a = [(2,6), (4,6), (4,9), (2,9), (2,6)]\n",
    "b = [(6,9), (7,6), (9,4),(9,8), (7,9),(6,9)]\n",
    "c = [(1,1), (5,1),(5,4),(3,4),(1,1)]\n",
    "d = [(6,1), (10,1),(7,3),(6,1)]\n",
    "\n",
    "multi = MultiPolygon([[a, []], [b, []] ,[c, []], [d, []]])\n",
    "\n",
    "print('Multipolygon is valid?: ', multi.is_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display the mulipolygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oFosUi1N4kXiEc46hoSHy+Xxg66rcP3pKhzntpfsoJbrATPtHK6l47XD6wZ2c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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01bc5df0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The area of the multi-polygon is 27.5\n"
     ]
    }
   ],
   "source": [
    "# Python package imports\n",
    "from matplotlib import pyplot # for plots\n",
    "from satsense.util.shapefile import show_multipolygon as shmp # visualization of multipolygons\n",
    "\n",
    "# Visualization parameters\n",
    "ORANGE = '#FF6600'\n",
    "al = 0.8\n",
    "show_verticies = True\n",
    "extent = [0, 0, 10, 10] # format of extent is [xmin, ymin, xmax, ymax]\n",
    "\n",
    "# Display the multipolygon\n",
    "fig = pyplot.figure(1, dpi=90)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "shmp(multi, ax, show_verticies, extent, ORANGE, al, 'multipolygon')\n",
    "\n",
    "pyplot.show()\n",
    "\n",
    "# area\n",
    "area = multi.area\n",
    "print(\"The area of the multi-polygon is\", area)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Convert the multipolygon to a binary mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   0   0   0   0   0   0   0   0   0]\n",
      " [  0 255 255 255 255   0 255 255 255   0]\n",
      " [  0   0 255 255 255   0   0 255   0   0]\n",
      " [  0   0   0 255 255   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0 255   0]\n",
      " [  0   0 255 255   0   0   0 255 255   0]\n",
      " [  0   0 255 255   0   0   0 255 255   0]\n",
      " [  0   0 255 255   0   0 255 255   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0]]\n",
      "(10, 10)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0186e529b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Python package imports\n",
    "from satsense.util.conversions import multipolygon2mask as mp2m # conversion from multipolygon to mask\n",
    "\n",
    "# parameters\n",
    "rows = 10\n",
    "cols = 10\n",
    "default_val = 255\n",
    "all_touched = False\n",
    "binary_mask1 = mp2m(multi, rows, cols, default_val, all_touched)\n",
    "\n",
    "# print and display mask\n",
    "print(binary_mask1)\n",
    "print(binary_mask1.shape)\n",
    "pyplot.imshow(binary_mask1, cmap='gray', origin='lower')\n",
    "pyplot.title('Binary mask with all_touched = False')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   0   0   0   0   0   0   0   0   0]\n",
      " [  0 255 255 255 255 255 255 255 255 255]\n",
      " [  0 255 255 255 255 255 255 255 255   0]\n",
      " [  0   0 255 255 255 255   0 255   0   0]\n",
      " [  0   0   0 255 255 255   0   0 255 255]\n",
      " [  0   0   0   0   0   0   0 255 255 255]\n",
      " [  0   0 255 255 255   0 255 255 255 255]\n",
      " [  0   0 255 255 255   0 255 255 255 255]\n",
      " [  0   0 255 255 255   0 255 255 255 255]\n",
      " [  0   0 255 255 255   0 255 255   0   0]]\n",
      "(10, 10)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01bc5dac18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# parameters\n",
    "all_touched = True\n",
    "binary_mask2 = mp2m(multi, rows, cols, default_val, all_touched)\n",
    "\n",
    "# print and display mask\n",
    "print(binary_mask2)\n",
    "print(binary_mask2.shape)\n",
    "pyplot.imshow(binary_mask2, cmap='gray', origin='lower')\n",
    "pyplot.title('Binary mask with all_touched = True')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving binary mask to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Binary mask 1 is saved at  /home/elena/DynaSlum/Data/misc/simple_example_multipolygon_mask1.tif\n",
      "Binary mask 2 is saved at  /home/elena/DynaSlum/Data/misc/simple_example_multipolygon_mask2.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/elena/anaconda3/envs/satelite/lib/python3.5/site-packages/rasterio/__init__.py:193: UserWarning: Dataset has no geotransform set.  Default transform will be applied (Affine.identity())\n",
      "  s.start()\n"
     ]
    }
   ],
   "source": [
    "# Python and satsense imports\n",
    "import os.path\n",
    "from satsense.util.mask import save_mask2file\n",
    "\n",
    "# raster filenames\n",
    "path = '/home/elena/DynaSlum/Data/misc/'\n",
    "ext = 'tif'\n",
    "fname1 = 'simple_example_multipolygon_mask1' + '.' + ext\n",
    "mask_fullfname1 = os.path.join(path, fname1)\n",
    "\n",
    "fname2 = 'simple_example_multipolygon_mask2' + '.' + ext\n",
    "mask_fullfname2 = os.path.join(path, fname2)\n",
    "\n",
    "# save\n",
    "save_mask2file(binary_mask1, mask_fullfname1)\n",
    "print(\"Binary mask 1 is saved at \", mask_fullfname1)\n",
    "save_mask2file(binary_mask2, mask_fullfname2)\n",
    "print(\"Binary mask 2 is saved at \", mask_fullfname2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading binary mask from file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0186d4a7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# satsense import\n",
    "from satsense.util.mask import load_mask_from_file\n",
    "\n",
    "saved_mask1 = load_mask_from_file(mask_fullfname1)\n",
    "\n",
    "f, (ax1, ax2) = pyplot.subplots(1, 2, sharey=True)\n",
    "\n",
    "ax1.imshow(saved_mask1, cmap='gray', origin = 'lower')\n",
    "ax1.set_title('Saved binary mask1')\n",
    "\n",
    "saved_mask2 = load_mask_from_file(mask_fullfname2)\n",
    "\n",
    "ax2.imshow(saved_mask2, cmap='gray', origin = 'lower')\n",
    "ax2.set_title('Saved binary mask2')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conversion of binary mask to multipolygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Ring Self-intersection at or near point 8 4\n",
      "Ring Self-intersection at or near point 8 4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valid multi-polygon1 with connectivity 4? True\n",
      "Valid multi-polygon1 with connectivity 8? True\n",
      "Not a valid polygon!\n",
      "Valid multi-polygon2 with connectivity 4? True\n",
      "Valid multi-polygon2 with connectivity 8? False\n"
     ]
    },
    {
     "data": {
      "image/png": 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GN3BrSZVyDcVmu6PPoUfEh4EPd6kWMzPrgL8pamaWCDd0M7NEuKGbmSXCDd3MLBFu6GZm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\nEuGGbmaWCDd0M7NEuKGbmSXi/wP5WSg7gsy5MgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0186a2a630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01868a69e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The area of the multi-polygon1 is 26.0\n",
      "The area of the multi-polygon2 is 56.0\n",
      "The difference of areas for multi-polygon1 with the original multipolygon is -1.5\n",
      "The difference of areas for multi-polygon2 with the original multipolygon is 28.5\n"
     ]
    }
   ],
   "source": [
    "# satsense import\n",
    "from satsense.util.conversions import mask2multipolygon as m2m\n",
    "\n",
    "# parameters\n",
    "t=(1.0, 0.0, 0.0, 0.0, 1.0, 0.0)\n",
    "conn8 = 8\n",
    "conn4 = 4\n",
    "\n",
    "# convert \n",
    "multi1_4 = m2m(saved_mask1, saved_mask1 ,t, conn4);\n",
    "multi1_8 = m2m(saved_mask1, saved_mask1 ,t, conn8);\n",
    "\n",
    "print(\"Valid multi-polygon1 with connectivity 4?\", multi1_4.is_valid)\n",
    "print(\"Valid multi-polygon1 with connectivity 8?\", multi1_8.is_valid)\n",
    "\n",
    "multi2_4 = m2m(saved_mask2, saved_mask2 ,t, conn4);\n",
    "multi2_8 = m2m(saved_mask2, saved_mask2 ,t, conn8);\n",
    "\n",
    "print(\"Valid multi-polygon2 with connectivity 4?\", multi2_4.is_valid)\n",
    "print(\"Valid multi-polygon2 with connectivity 8?\", multi2_8.is_valid)\n",
    "\n",
    "# display the multipolygons\n",
    "_, (ax1, ax2) = pyplot.subplots(1, 2, sharey=True)\n",
    "\n",
    "shmp(multi1_4, ax1, show_verticies, extent, ORANGE, al, 'multipolygon1 \\n connectivity 4')\n",
    "shmp(multi1_8, ax2, show_verticies, extent, ORANGE, al, 'multipolygon1 \\n connectivity 8')\n",
    "\n",
    "pyplot.show()\n",
    "\n",
    "_, (ax1, ax2) = pyplot.subplots(1, 2, sharey=True)\n",
    "\n",
    "shmp(multi2_4, ax1, show_verticies, extent, ORANGE, al, 'multipolygon 2 \\n connectivity 4')\n",
    "shmp(multi2_8, ax2, show_verticies, extent, ORANGE, al, 'multipolygon 2 \\n connectivity 8')\n",
    "\n",
    "pyplot.show()\n",
    "\n",
    "# areas\n",
    "print(\"The area of the multi-polygon1 is\", multi1_4.area)\n",
    "print(\"The area of the multi-polygon2 is\", multi2_4.area)\n",
    "\n",
    "print(\"The difference of areas for multi-polygon1 with the original multipolygon is\", multi1_8.area - area)\n",
    "print(\"The difference of areas for multi-polygon2 with the original multipolygon is\", multi2_8.area - area)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving multipolygons to a shapefile\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please, check with a GIS weather this file contains a valid polygon! \n",
      " /home/elena/DynaSlum/Data/misc/simple_example_multipolygon.shp\n"
     ]
    }
   ],
   "source": [
    "# satsense import\n",
    "from satsense.util.shapefile import save_multipolygon2shapefile as sm2sh # saving of multipolygon to shapefile\n",
    "\n",
    "# parameters\n",
    "# filename\n",
    "ext = 'shp'\n",
    "fname = 'simple_example_multipolygon' + '.' + ext\n",
    "\n",
    "# full filename\n",
    "shapefname = os.path.join(path, fname)\n",
    "# saving\n",
    "sm2sh(multi1_4, shapefname)\n",
    "\n",
    "print(\"Please, check with a GIS weather this file contains a valid polygon! \\n\", shapefname)"
   ]
  }
 ],
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