Polygon2Mask.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Relevant issue: https://github.com/scikit-image/scikit-image/issues/1103"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "from skimage import draw\n",
    "from matplotlib import path\n",
    "\n",
    "\n",
    "def get_mask_from_polygon_mpl(image_shape, polygon):\n",
    "  \"\"\"Get a mask image of pixels inside the polygon.\n",
    "\n",
    "  Args:\n",
    "    image_shape: tuple of size 2.\n",
    "    polygon: Numpy array of dimension 2 (2xN).\n",
    "  \"\"\"\n",
    "  xx, yy = np.meshgrid(np.arange(image_shape[1]), np.arange(image_shape[0]))\n",
    "  xx, yy = xx.flatten(), yy.flatten()\n",
    "  indices = np.vstack((xx, yy)).T\n",
    "  mask = path.Path(polygon).contains_points(indices)\n",
    "  mask = mask.reshape(image_shape)\n",
    "  mask = mask.astype('bool')\n",
    "  return mask\n",
    "\n",
    "\n",
    "def get_mask_from_polygon_skimage(image_shape, polygon):\n",
    "  \"\"\"Get a mask image of pixels inside the polygon.\n",
    "\n",
    "  Args:\n",
    "    image_shape: tuple of size 2.\n",
    "    polygon: Numpy array of dimension 2 (2xN).\n",
    "  \"\"\"\n",
    "  vertex_row_coords = polygon[:, 1]\n",
    "  vertex_col_coords = polygon[:, 0]\n",
    "  fill_row_coords, fill_col_coords = draw.polygon(vertex_row_coords, vertex_col_coords, image_shape)\n",
    "  mask = np.zeros(image_shape, dtype=np.bool)\n",
    "  mask[fill_row_coords, fill_col_coords] = True\n",
    "  return mask\n",
    "\n",
    "image_shape = (2000, 2000)\n",
    "polygon = np.array([[80, 111, 146, 234, 407, 300, 187, 45], [465, 438, 499, 380, 450, 287, 210, 167]]).T\n",
    "#polygon = np.array([[200, 300, 300, 200], [400, 390, 383, 394]]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc974846630>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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329uBzUnWJlkHrGdw4WuaLWot3WnIi0ne3l3l/1DvPVNjGHqdaxn8d4MZX9fIlvsq6XGuMF8DfJ/B1eKblns+i5z7+QyueH8beGo4f+BXgZ3A093jmb333NStdQ9TdhUcuJvB4fbPGPyLed0oawE2MviB2wd8mu43eadsXf8IPAHsZhAK58zaupbyy1+1ltQ0racYkqaAASGpyYCQ1GRASGoyICQ1GRCSmgwISU3/DwhS/0F1qWvDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# matplotlib\n",
    "mask1 = get_mask_from_polygon_mpl(image_shape, polygon).astype('uint')\n",
    "plt.imshow(mask1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc9747a2748>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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329uBzUnWJlkHrGdw4WuaLWot3WnIi0ne3l3l/1DvPVNjGHqdaxn8d4MZX9fIlvsq6XGuMF8DfJ/B1eKblns+i5z7+QyueH8beGo4f+BXgZ3A093jmb333NStdQ9TdhUcuJvB4fbPGPyLed0oawE2MviB2wd8mu43eadsXf8IPAHsZhAK58zaupbyy1+1ltQ0racYkqaAASGpyYCQ1GRASGoyICQ1GRCSmgwISU3/DwhS/0F1qWvDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scikit-image\n",
    "mask2 = get_mask_from_polygon_skimage(image_shape, polygon).astype('uint')\n",
    "plt.imshow(mask2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fc97473a1d0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(mask1 - mask2)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "283 ms ± 2.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "# Benchmark matplotlib implementation\n",
    "get_mask_from_polygon_mpl(image_shape, polygon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.8 ms ± 334 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "# Benchmark scikit-image implementation\n",
    "get_mask_from_polygon_skimage(image_shape, polygon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:ws]",
   "language": "python",
   "name": "conda-env-ws-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}