02-alphashape.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alpha Shape\n",
    "\n",
    "Seguindo https://gist.github.com/dwyerk/10561690"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapely import geometry, wkt\n",
    "import alphashape\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pylab as pl\n",
    "from descartes import PolygonPatch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "points = wkt.loads(pontos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pts = [(pt.x, pt.y) for pt in points.geoms]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18306"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from descartes import PolygonPatch\n",
    "\n",
    "def plot_polygon(polygon):\n",
    "    fig = pl.figure(figsize=(10,10))\n",
    "    ax = fig.add_subplot(111)\n",
    "    margin = .3\n",
    "\n",
    "    x_min, y_min, x_max, y_max = polygon.bounds\n",
    "\n",
    "    ax.set_xlim([x_min-margin, x_max+margin])\n",
    "    ax.set_ylim([y_min-margin, y_max+margin])\n",
    "    patch = PolygonPatch(polygon, fc='#999999', ec='#000000', fill=True, zorder=-1)\n",
    "    ax.add_patch(patch)\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-0.4480000000000004 -3.248 82.89600000000002 87.69600000000001\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,81.2)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"1.7539200000000001\" opacity=\"0.6\" d=\"M 63.2,16.4 L 62.8,16.0 L 62.4,15.2 L 62.0,14.0 L 61.6,13.2 L 61.2,12.4 L 60.8,11.6 L 60.8,11.2 L 60.4,10.8 L 60.0,10.4 L 59.6,9.6 L 59.6,9.2 L 59.2,8.4 L 59.2,8.0 L 58.8,7.2 L 58.4,6.4 L 58.0,5.6 L 57.6,4.8 L 57.2,4.0 L 57.2,3.6 L 56.4,2.8 L 56.4,2.4 L 56.0,1.6 L 56.0,1.2 L 55.6,0.4 L 54.8,0.0 L 54.4,0.0 L 53.6,0.4 L 52.4,0.8 L 52.0,1.2 L 51.2,1.6 L 50.8,2.0 L 50.0,2.4 L 49.6,2.4 L 48.4,2.8 L 48.0,3.2 L 47.2,3.6 L 46.4,4.0 L 46.0,4.0 L 45.6,4.4 L 44.8,4.8 L 44.0,5.2 L 43.2,5.6 L 42.4,5.6 L 42.0,6.0 L 41.2,6.4 L 40.4,6.8 L 39.6,7.2 L 38.4,7.6 L 38.0,8.0 L 38.0,8.4 L 38.4,8.8 L 38.8,9.6 L 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      ],
      "text/plain": [
       "<POLYGON ((63.2 16.4, 62.8 16, 62.4 15.2, 62 14, 61.6 13.2, 61.2 12.4, 60.8 ...>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alphashape.alphashape(pts, alpha=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(*zip(*pts))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array: array is 0-dimensional, but 2 were indexed",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/home/feromes/dev/faveLiDAR/02-alphashape.ipynb Cell 10\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/feromes/dev/faveLiDAR/02-alphashape.ipynb#X30sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m ax\u001b[39m.\u001b[39mscatter(\u001b[39m*\u001b[39m\u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39mpts))\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/feromes/dev/faveLiDAR/02-alphashape.ipynb#X30sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m alpha_shape \u001b[39m=\u001b[39m alphashape\u001b[39m.\u001b[39malphashape(pts, alpha\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/feromes/dev/faveLiDAR/02-alphashape.ipynb#X30sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m ax\u001b[39m.\u001b[39madd_patch(PolygonPatch(alpha_shape, alpha\u001b[39m=\u001b[39;49m\u001b[39m0.2\u001b[39;49m))\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/feromes/dev/faveLiDAR/02-alphashape.ipynb#X30sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m plt\u001b[39m.\u001b[39mshow()\n",
      "File \u001b[0;32m~/miniconda3/envs/pdal/lib/python3.9/site-packages/descartes/patch.py:87\u001b[0m, in \u001b[0;36mPolygonPatch\u001b[0;34m(polygon, **kwargs)\u001b[0m\n\u001b[1;32m     73\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mPolygonPatch\u001b[39m(polygon, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m     74\u001b[0m     \u001b[39m\"\"\"Constructs a matplotlib patch from a geometric object\u001b[39;00m\n\u001b[1;32m     75\u001b[0m \n\u001b[1;32m     76\u001b[0m \u001b[39m    The `polygon` may be a Shapely or GeoJSON-like object with or without holes.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     85\u001b[0m \n\u001b[1;32m     86\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 87\u001b[0m     \u001b[39mreturn\u001b[39;00m PathPatch(PolygonPath(polygon), \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/miniconda3/envs/pdal/lib/python3.9/site-packages/descartes/patch.py:62\u001b[0m, in \u001b[0;36mPolygonPath\u001b[0;34m(polygon)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     59\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m     60\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mA polygon or multi-polygon representation is required\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m---> 62\u001b[0m vertices \u001b[39m=\u001b[39m concatenate([\n\u001b[1;32m     63\u001b[0m     concatenate([asarray(t\u001b[39m.\u001b[39mexterior)[:, :\u001b[39m2\u001b[39m]] \u001b[39m+\u001b[39m\n\u001b[1;32m     64\u001b[0m                 [asarray(r)[:, :\u001b[39m2\u001b[39m] \u001b[39mfor\u001b[39;00m r \u001b[39min\u001b[39;00m t\u001b[39m.\u001b[39minteriors])\n\u001b[1;32m     65\u001b[0m     \u001b[39mfor\u001b[39;00m t \u001b[39min\u001b[39;00m polygon])\n\u001b[1;32m     66\u001b[0m codes \u001b[39m=\u001b[39m concatenate([\n\u001b[1;32m     67\u001b[0m     concatenate([coding(t\u001b[39m.\u001b[39mexterior)] \u001b[39m+\u001b[39m\n\u001b[1;32m     68\u001b[0m                 [coding(r) \u001b[39mfor\u001b[39;00m r \u001b[39min\u001b[39;00m t\u001b[39m.\u001b[39minteriors]) \u001b[39mfor\u001b[39;00m t \u001b[39min\u001b[39;00m polygon])\n\u001b[1;32m     70\u001b[0m \u001b[39mreturn\u001b[39;00m Path(vertices, codes)\n",
      "File \u001b[0;32m~/miniconda3/envs/pdal/lib/python3.9/site-packages/descartes/patch.py:63\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     59\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m     60\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mA polygon or multi-polygon representation is required\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m     62\u001b[0m vertices \u001b[39m=\u001b[39m concatenate([\n\u001b[0;32m---> 63\u001b[0m     concatenate([asarray(t\u001b[39m.\u001b[39;49mexterior)[:, :\u001b[39m2\u001b[39;49m]] \u001b[39m+\u001b[39m\n\u001b[1;32m     64\u001b[0m                 [asarray(r)[:, :\u001b[39m2\u001b[39m] \u001b[39mfor\u001b[39;00m r \u001b[39min\u001b[39;00m t\u001b[39m.\u001b[39minteriors])\n\u001b[1;32m     65\u001b[0m     \u001b[39mfor\u001b[39;00m t \u001b[39min\u001b[39;00m polygon])\n\u001b[1;32m     66\u001b[0m codes \u001b[39m=\u001b[39m concatenate([\n\u001b[1;32m     67\u001b[0m     concatenate([coding(t\u001b[39m.\u001b[39mexterior)] \u001b[39m+\u001b[39m\n\u001b[1;32m     68\u001b[0m                 [coding(r) \u001b[39mfor\u001b[39;00m r \u001b[39min\u001b[39;00m t\u001b[39m.\u001b[39minteriors]) \u001b[39mfor\u001b[39;00m t \u001b[39min\u001b[39;00m polygon])\n\u001b[1;32m     70\u001b[0m \u001b[39mreturn\u001b[39;00m Path(vertices, codes)\n",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array: array is 0-dimensional, but 2 were indexed"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(*zip(*pts))\n",
    "alpha_shape = alphashape.alphashape(pts, alpha=1)\n",
    "ax.add_patch(PolygonPatch(alpha_shape, alpha=0.2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "convex_hull_polygon = points.convex_hull\n",
    "_ = plot_polygon(convex_hull_polygon)\n",
    "# _ = pl.plot(x,y,'o', color='#f16824')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapely.ops import cascaded_union, polygonize\n",
    "from scipy.spatial import Delaunay\n",
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def alpha_shape(points, alpha):\n",
    "    \"\"\"\n",
    "    Compute the alpha shape (concave hull) of a set\n",
    "    of points.\n",
    "    @param points: Iterable container of points.\n",
    "    @param alpha: alpha value to influence the\n",
    "        gooeyness of the border. Smaller numbers\n",
    "        don't fall inward as much as larger numbers.\n",
    "        Too large, and you lose everything!\n",
    "    \"\"\"\n",
    "    if len(points) < 4:\n",
    "        # When you have a triangle, there is no sense\n",
    "        # in computing an alpha shape.\n",
    "        return geometry.MultiPoint(list(points)).convex_hull\n",
    "\n",
    "    coords = np.array([point.coords[0] for point in points])\n",
    "    tri = Delaunay(coords)\n",
    "    triangles = coords[tri.vertices]\n",
    "    a = ((triangles[:,0,0] - triangles[:,1,0]) ** 2 + (triangles[:,0,1] - triangles[:,1,1]) ** 2) ** 0.5\n",
    "    b = ((triangles[:,1,0] - triangles[:,2,0]) ** 2 + (triangles[:,1,1] - triangles[:,2,1]) ** 2) ** 0.5\n",
    "    c = ((triangles[:,2,0] - triangles[:,0,0]) ** 2 + (triangles[:,2,1] - triangles[:,0,1]) ** 2) ** 0.5\n",
    "    s = ( a + b + c ) / 2.0\n",
    "    areas = (s*(s-a)*(s-b)*(s-c)) ** 0.5\n",
    "    circums = a * b * c / (4.0 * areas)\n",
    "    filtered = triangles[circums < (1.0 / alpha)]\n",
    "    edge1 = filtered[:,(0,1)]\n",
    "    edge2 = filtered[:,(1,2)]\n",
    "    edge3 = filtered[:,(2,0)]\n",
    "    edge_points = np.unique(np.concatenate((edge1,edge2,edge3)), axis = 0).tolist()\n",
    "    m = geometry.MultiLineString(edge_points)\n",
    "    triangles = list(polygonize(m))\n",
    "    \n",
    "#     print(triangles)\n",
    "    print(list(map(lambda x: x.buffer(-0.20).intersection(points).type == 'MultiPoint', triangles)))\n",
    "    return cascaded_union(list(filter(lambda x: x.buffer(-0.20).intersection(points).type == 'MultiPoint', triangles)))\n",
    "#     return triangles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'Polygon' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/home/feromes/dev/faveLiDAR/02-alphashape.ipynb Cell 14\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/feromes/dev/faveLiDAR/02-alphashape.ipynb#X13sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m concave_hull \u001b[39m=\u001b[39m alpha_shape(points, alpha\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m)\n",
      "\u001b[0;31mTypeError\u001b[0m: 'Polygon' object is not callable"
     ]
    }
   ],
   "source": [
    "concave_hull = alpha_shape(points, alpha=1)\n",
    "\n",
    "_ = plot_polygon(concave_hull)\n",
    "# _ = pl.plot(x,y,'o', color='#f16824')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-0.4480000000000004 -3.248 82.89600000000002 87.69600000000001\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,81.2)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"1.7539200000000001\" opacity=\"0.6\" d=\"M 63.2,16.4 L 55.6,0.4 L 54.4,0.0 L 38.0,8.0 L 41.2,16.0 L 44.0,20.4 L 44.0,21.2 L 41.2,23.2 L 38.8,22.8 L 32.8,10.8 L 20.4,16.4 L 26.4,28.8 L 26.0,30.4 L 22.8,32.0 L 21.6,31.6 L 15.6,18.8 L 2.8,24.4 L 10.0,40.0 L 7.6,43.6 L 9.6,46.8 L 10.4,50.8 L 22.4,75.2 L 26.0,75.2 L 29.2,81.2 L 46.8,72.4 L 48.4,72.8 L 49.6,75.2 L 50.4,74.4 L 50.8,71.6 L 53.2,73.6 L 58.0,71.6 L 58.8,70.8 L 57.2,68.4 L 58.0,67.6 L 59.6,66.8 L 61.2,67.6 L 62.0,65.2 L 79.2,57.6 L 68.0,35.2 L 70.0,32.8 L 70.0,31.2 L 63.2,16.4 z\" /></g></svg>"
      ],
      "text/plain": [
       "<POLYGON ((63.2 16.4, 55.6 0.4, 54.4 0, 38 8, 41.2 16, 44 20.4, 44 21.2, 41....>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha_shape.simplify(tolerance=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: Shapely\n",
      "Version: 1.8.1.post1\n",
      "Summary: Geometric objects, predicates, and operations\n",
      "Home-page: https://github.com/shapely/shapely\n",
      "Author: Sean Gillies\n",
      "Author-email: sean.gillies@gmail.com\n",
      "License: BSD\n",
      "Location: /home/feromes/miniconda3/envs/pdal/lib/python3.9/site-packages\n",
      "Requires: \n",
      "Required-by: geopandas, alphashape\n"
     ]
    }
   ],
   "source": [
    "!pip show shapely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}