05a_surface reconstruction/creating_surfaces.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ff11395c-5b0c-4df8-8b20-a5ab712d31ff",
   "metadata": {},
   "source": [
    "# Creating surfaces\n",
    "In this notebook we create a surface (mesh) from a 3D dataset of a simulated 3D binary image dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a43114eb-755f-485d-b602-4d0b6f5330da",
   "metadata": {},
   "outputs": [],
   "source": [
    "import napari_process_points_and_surfaces as nppas\n",
    "import pyclesperanto_prototype as cle\n",
    "import vedo\n",
    "\n",
    "from branchoid import branchoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0bec9a3c-4553-40e9-b51e-3a4d666cb855",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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\"></img>\n",
       "</td>\n",
       "<td style=\"text-align: center; vertical-align: top;\">\n",
       "\n",
       "<table>\n",
       "<tr><td>shape</td><td>(100,&nbsp;100,&nbsp;100)</td></tr>\n",
       "<tr><td>dtype</td><td>uint8</td></tr>\n",
       "<tr><td>size</td><td>976.6 kB</td></tr>\n",
       "<tr><td>min</td><td>0</td></tr><tr><td>max</td><td>1</td></tr>\n",
       "</table>\n",
       "<img src=\"data:image/png;base64,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\"></img>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "StackViewNDArray([[[0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   ...,\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0]],\n",
       "\n",
       "                  [[0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   ...,\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0]],\n",
       "\n",
       "                  [[0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   ...,\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0]],\n",
       "\n",
       "                  ...,\n",
       "\n",
       "                  [[0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   ...,\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0]],\n",
       "\n",
       "                  [[0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   ...,\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0]],\n",
       "\n",
       "                  [[0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   ...,\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0],\n",
       "                   [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_image = branchoid()\n",
    "binary_image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2491f7f-9650-4797-ae2e-7ee2bd64dfb7",
   "metadata": {},
   "source": [
    "## Generating surfaces\n",
    "We first generate a surface from the binary image. In this case, we take _all_ non-zero labeled pixels and turn them into a surface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8fb1ee00-5458-407b-addf-fc79297d9143",
   "metadata": {},
   "outputs": [],
   "source": [
    "surface = nppas.all_labels_to_surface(binary_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7affb339-e0a8-48d8-949c-f16083e0c154",
   "metadata": {},
   "source": [
    "The resulting object is visualized in Jupyter notebooks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "286341c1-8d10-4554-a709-68288eaa0f28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<tr>\n",
       "<td>\n",
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1cvjcMO5gLlRyAwDW2wfYeZg/Kad21KSScgXLMBOBOE8xic/JRGRyFIAFImApDdESxM0NVTn+d0nsPYw8Xh1SNhz758r+CQcB2Lh7iCV4ycSUKqdELiI7nbbdm5mzvYX9rwyvl9+eicmIhIxCoAiEZCM+xacnnKg+ypU5gN6WECtS4PrlCiXSs/71EREvra0CEREACvA+b4tyKjsxFEq27y7B83PW45yeLxKBxc/sLmHQbC8Id3ZOZ/pWVUARUQelwKgSARURlgLRbssFuiSdTBXgsvnYW4G6tthfBZOf2F79T5pCCwW4PxRuDsAyTZwY3D9PNzug2Js6UUmQWBz/woFC461W8WJiMjjUQAUiYDpuSTjU7YDyPTs4o9J1sOeN6wKePx9SKThwNtQyNq+vYXc47++X4ZLx2DgBqzfCZv2wPVzcOUUtPXAjpcfvMp4cBT6h6F/yFrJZJ7gXERERAFQJBLcxAp8v7rrx1IaW2D3S1DnwswwtHXD1p12vZh9/NcvF4EsrOqFTTuAAArj0NsNuw5Dqu7hx4h5VsUslqqLVURE5PFoEYhIBAROKys6rdWKEwbAhUOolWbMsVj4uPB217W5gKVw6Hi5u4EEgfXuKxTCRtRu9biJeLgqObD5gEsN67Y22VZw07OQTkJXe4yGeiVAEZHHpQAoEgGOY2HLcWwOYDYX7rlbtiHfsg/xKWjshYQ3/7mN3XDhLJz6Ag69A3WNjx4CgwBKBTh7BPoHYOteiKfAL81/TN8NuHXbQqjrhquVK5fwtVzXwmIqCYlEDM9TABQReVwKgCIR4Hr1DIxAVztMzVg/vYUhLl+Akx/Bxm0WCEvhItumNgt+R96Do+/Dnpdt+DWehkRq8TBYKkJu1gLdpVPQdxO27IVNey3IVY7v+9B3HS6fgUJ+6fNvbZ5/3XGcx9+XWEREFABFoqClbQWFkks259/bCcRzbXcOL6y4OeHXwizkHDh/0vYDbm6Hlk44/CNw5Idw6XOrwuWBXa/NrwgGgYW/s5+DP2vVu+FhC39b9oXhrwxXzljFb+V6yE1CZ4uFQT8Iv4ZVycr36SV2LxERkcejACgSAY2t2/CdNvqGRmlqgN6uxR8XBFbV27sHTnwER9+DQ++GIbDDQuDd8/aHY2zQ7t/9UnV3kSCAq+dtSHftaqsUbj0A63bY/r/lMlw6bgFw1QbYcRiuHwvnHD6koqeuLyIiT48CoEgEJFIdpOtXEBRHKZdtMUVtha32++FJaOiGA2/B8Q/vD4HNb1jQ6xqFYx/A1aOQDPcKDgKr+G3cZfP9XBccz8LdwvC3+xUYuA03b1qbGNe1x92bA+hYhdINb3OwY4iIyJNTABSJAMdL077iVbLT5yiWfEYnlq64FUsW/A68PT8E7n8D6uqrj2tqgYPvwNAV8GqGgLf0wpptUMxzr2wXADcuwtWz88Pf6c9skcijCICG9GO9fRERWUABUCQCPM9j31vfITs3Qv+138Fzq3306lLQUAdeDIIkDN2FXAaOvW8BrxICz38KLU3VY+aBbS/Btlfvb99y5TTMDsyvDN4dDMPfqzBwa374a2yxhR6VhSPlmrmAtXMDPc9WMfsaDxYReSJqBC0SAY7jkEi1su/Nv0NDU+u9OXtgIa2+zi67XoK1W606mM9aCJyesNsbW8CLVy+ZGVsUMjUK+Tm75Gbh8km7BM78x/eugW37YeDm/PDX1GpDzK3tUJ+GhnpobrDef+0t0NkG3e2wotMe3z8MdYkcpcIkgfaDExF5LKoAikSIl+jBcVtwnIl7t81lIR82eU7dsYUZALcuWQg89yl0ddjq3u7t0NkLOLBq1gLihU8ttFUMDEPXKlshHE9aRe/OVcjegutHoH+wJvyFLWYAbt20ELlwLqDjVucBZnN2W0vdKKc/+FkOfvs3STeswlFPGBGRZVEFUCRC6hpaWbfjz+K41SbKhSJkshauLhyH25dh+0FYvcnuLxZtK7dCDk59aqt/S3lrLL37JUilLeQFvi3m6Oi2Sp+DPa7vBpw/CvlcuCVcWLRraLbhZbD+gpPjkM1bIJ3N2EKViWkYn4TRCRges2C4stuqgqN97/HFD3/5K/z0RES+OVQBFIkQ142x/aVfJJuZ5e6F/wYIKJetyuZgW7ZdPAb5cUgGtv9uZeWt60B2Ds5/Dh1t1WPGnJo5gA7Efbhzqnr/4DAUCzaHz87BvtYlYfiS3T89Hh4rDh2dUMws0hcQ+xdrsWRzASnC8FDfs/qoRES+0RQARSLGi6XZ8/rfxi/00X/td3DdgFLZQlUlBGamrTef61q4C4Lq0GwprAguNurqED4+3OotCKp7Ci8MgKU8ZGfsK1j42/MqxHwYuXn/sVtXweCQzSGsBM569xzTY2dpat+lYWARkWXQELBIBCVSbex79x/Tu/FP4zjOvUqfH67ALRZtpNZxwipcEM7HoxrkHlUl8FWeV8lp5UogDK83NkB5Bvpvwsg4jE3C1Cx4yXAv4xTsewNWrKseu5i9ydE/+PNMj53RghARkWVQBVAkgmxVcDv73v1vAei/9j8T84J78wHzeWsN4wfVqp7r2FBx+XEDYJjPvPB6UAmE4fVyEaZHYHba5gC6rm0f19wAIzfsMYmkhUCA/vC26fGzHPmDP8/hH/uXNLXvfuqVwFIpx2j/l8xMjxGUZ/CYpFSYoVycprGhfO/9PEy+ANl8Ci/eQDzZSq7YSENjC+3dO6hrXKsKpoh8pRQARSJqsRCYTAT3evfVBj3ftz6BrmPfB8HDt26rqFT4Fg4BVwLhvfuD6v2V8LdxF1w+ZpXA7vCv1WIhcGb83FMLgbWVxHIpz/kvv8O1E79CqZQnHgMI7g2Jr+yq9lN8mLkMDI3Z947j2L7JZYeWju289G/beVcoDIrIs6YAKBJhi4XAhbvuBoThLWzFUi7f3/h56eNXA195wRBwbeCD6jFjXhj+dsKVU3DjMqzfAR2rq89/FiHQ933GBj5lauwi+Uw/xewAczN3GL79RxQKeUpl60eYTNgQdam8vIbUlTmWiQR0tATM5aB/KGBi5BxffP9P077iDeLJFoivoqVtJW0r3qG+sUdhUESeCQVAkYibFwIdh/6r/5ogCCwGhpW+UtlWBbuuDWUWShCrGdJ1nGqwC4LqCl6oVgyDBYFv4ZzAyvXGRqiLwaWjcPOqhb9tB5jXvNpxIJl6OiEwCAICv8ytS/+K0x/8FXLZCZvrGFirG8eBdMoqlZ5nQbaS+4qlR/+cS2XurYgJHAu6zY32OnNT15idvEapVJlz6dKz9lsc/Nav09S8ChxHQVBEnion0MxpkUc2PT1Nc3MzU1NTNDU1PfwJXyNBEFDIjfHx93+Oc8f/R9KpIjEvrLjFIZ20reA818ILDkxM2f3dHeFtWCgaHLGg1N4SLiTx7RjJBOTyMDBi3/d2Wf/BwVFIJWBFVzUQzmahZaWFP+8B/1TN5+Dkx9UQCNDYtpPDP/YvaGrfs2RwKpeL9F/7Xxi6+wlz0/1MDf4+pcI0QRAuiKkJgF1tUJe2Idxc3t5vKvHow+DVz9g+n4ERO3Z3O+QKMDRq95VK9rlaqHZobNtNc9dLNDStYN2OnybduEZB8Bn4Jv9eiyxFFUARAaqVwLf/5D+mq6eXayf/AX65eO/+SiWvPl37JAtw+QL44V+TUsmaSwdYK5lKMITqSuPKpXYuYRloXWMNosEWhzR3gVvz/MUsPRz8F+5VAhcql4pcOf3fcemLv0k2M4PnQrEMjXVW5SuVqxVJ3w8rnkUr4MVjFgJnM9DSYEO6j6JchvEp+zxi4XBwsVQdUnccm0/oOlbtLJUDhvtPMzl6mpgHo3ffY9+P/iaNLeuolBIVBkXkcakCKLIMUakUlEs5zn7+HY59/q/p65+EUj/1dT6NdRYAW5ts+HJ8CiZnoKPFhknBwszIuIWm3k5IJucfO/Bt67mYZyGqULRjxjxwUnDwXWhoWl51LQigkF+sEridLfv+Mo6bYHqmSDYzTSo+w9T4Lfqv/x6eM3fv+X5gO4w0N9jOI7OZ6nG62qvBNwisYpnLW8UyveD9LfmZlqFvyILxyq6wmTUWAvuGanobpu08ZuasMlhZFAMQS2+hc/W3aO/azPqdP40Xb1AIfAqi8nstUksBUGQZovQfinK5TD6fpVyc5eIXP8+tC/8SAh/HtZDSFYaU0QnoaK3ZDziwIc58wYZK69KLH79Ugv5hq2Wt7LEA1D8c7g/8rlUCl5ttFg4Hu65tHQcWsipz9rxwMUupbLfVp+212lugqcEC7GzGQm0iblXASmh1XSgUrErX1mz3P4ogsK3tMjlIhNXSfMGeH4vZa8zM2Wv2dFg7nqExG0rP5qzymIjbcXIFjx0H/zK7Xv+7xBJNCoFPKEq/1yIVGgIWkUV5nkddXQPQwL63fw3f6eb0qSOMjmfIZqYoFIrMZnxm5gJKpSJBuTBvcYTnQmODS31dCsddpFleAI11Bdb3DAM+8bhVBydH4eh7jxcCkynY/4aFtNy4hcvxSQt6pZLtVVwZUnZdSLgL2rgseK2GsOI5NGq9EVs7oL4uPP2aRS61i2Due5s1/8RubbLQCBbs+gYt3K3ssZeey1S33qusGnYdqEtVj++6kIiVuXHunzA1eoKW7lfoWvUWKzb8SRznIePlIiIhVQBFliHKlYIg8PH9AL9cpFzKkMuXKJbsz0e5XKZcKhEsaCHjOC7xeHzJCpVLjusn/p/cvfwvCcJJd5WHtnQ8XggMAhi4AQMXLPD1j1h4q8xJfNCx0kmb0+eFw67FklXpHMcWrTTWVQNjENgQeC4H7a2QWmIoOAhsV5NSyR4Xj1Vvn561oeRS2ap7lUUnpZLdVjsMvZhyuLI4mWxi55v/kDVb/zxeuGJGVcFHF+Xfa4kuVQBF5JE4jovnWWUwnkiRqnvyYwZBQNPbv0beX81A31XqvUvkZ04Dj1cJDAIYugOnPrWdRYLAAlw5gGR8fqua+e/Nvmbzdulss+rfyHi46rd9fuWvsoglk7NwWSo/+LwqO6zU19lrVVrnNDfaFnh9gzCXtQohjg1Xl2uOudT5OuFwdLk8zSff/1luXvgd2jq3sGbrn6G1+7BCoIgsSRVAkWVQpeDZsD9DAbm5fo794U8xcveP7933qJXAIIDBO3DiQ8hnw7YqZRtqjXvVRRdgt5VK4X0xC16phLWeyS0IgLMZWwTSUBMAR8ZtuLa+zubspZPz+xQuPK980YLi1Izd1t3BvR1X/AD6h+x8erstHPYN2e0tjdUK4lzW3kMqUV0UUjn+zFy1ehgAza0bOPjt36Bz5au4rv6d/zD6vZYoesRdLEVEnh3HcXAcl1T9Sg5++zfpXP0tKhPyKpXA2amldyAJAhi8vXj4q1TbanmuBbC6lAXAVNIqcfGlstKC1w0Cu6kuDY31S4c/e28W2poaLKTVtpghfJeuW32JynxCBzt2Q3j8xno738WmUzbWV29PxiE7c53Pf+9Pc+KHP0MhN770yYlIZCkAisgLw3EcUvW9HPzWb9C5+kfv3T45Ckd+uHgIvBf+Plo8/C0WAJey3PEQLwZenPsWjyw8puNCLFFtaH3fkG7t6zt2CSrPfcACE6jeX5nf6PsWBv3iKDfO/TO++MF/ytz0XYqFGXz/IWPVIhIZCoAi8kKZHwK/de/2qbH7Q+Cjhr/lToV7lMe3d8POt6BjkzWRfpCSC2sPwK7XIVW3xJy+cLu8yspfwt6EYR58oMrx4rGFFcKAWxf/J/7ot1/hj377dc4f+W8olQoPf3Mi8o2nACgiLxzHcUg3rAxD4LepRKCFIfBh4a9y20JNDdDTuXSPwodpaYe9r0E+A6c+s9eIpxe/JOpgehLOfA6NLbDnFasGzn+/1YrfwuuVAOs6Nhexq/3+YWDfry5gKdcML3supJMB2dk+RgfPcPGLX+DC0V9jZnqEfG4WTQEXiS7NDhaRF5ZVAv8Zx/7opxi588dAwNQYHP8ANmyHc0eWDn+VkLRYZ7x4zBZuzIVtVpZbIUzHoO8sDA1Duh52v209CJfSMwnH3oezH9pcw2ScefMKF65Orr1+r4IZtqJxwzBYk/PwPKhbogWg61ZbzECGa8f+FjfP/iPSdR3se/u/or33Ta0WFokgVQBF5IW11HAweRi9CkH5weFvKa4XNoR+xNwTT9nFCYeU/TIUsrZl3d5XrDegX1760tAIe1+1oFbMcS/8uR4k0hAPewj6ldudcKFJbUUwnB/oxuw8lqMSIoMAisU8pex1Zsa+5Ivf/48Yvv2DeXs+i0g0qAIoIi+0eyHw27/BsR/8JOMDPyTAeuv5/vIXfDS2wLZXoFSAvo8f/vqdK2HnGzAzDrfPQrLmr2bShTunH/29xF3uhb8ggGwRNu+FXgeOvr94BRDCVcIBuHWwaR+krsOlUxYuH1Wlmug51ePOTd/iw//lz7Dzpb/G1sN/C897xI2NReRrTwFQRF54juOQru9l056/RH/wQ8YnYWC6GgJrK3+VytliQbCxFQ69Y/ed+hRyWbs9Fp8/Ly+etIpfWxds3AdzU3Dy03Dv35p5g4WcNZx+FEFgFb94zVDx7ASc+BgOvGUVwjvnbSeSRBLIAOHq4VgcyMGd65BqhM17AAcunVw6BNalrW3MXNa2m6vseww2BB7zrNJYKs5y8civkM/lWLnlP6S+oYO6xtU4yy0zisjXigKgiHxtBLjWwDkMM5V9eN2aYd9iafF+frXh7+h7MD1ht3esgJ2vw/Q43OqrVvymRmHgMtw9DUMj0NoFe16FeMKKeIO3oe88pOL3v9ZSskVYsdOCJUBmzuYznv0IWpqsktmzHVa4NmcwV4Yde6EzC8c/gtwcXDllz928x74uFQLjYe/ActkCYDx2/+di7WLA9wvcOPP3uXXu10im29n37vfoWffjCoEi32D67RaRr43J6Rz9wzCTgWKxutK3tthX22S5Ysnw1wsH37FK4JkvoL4R9rwMs5N23fetd197N+w4YK9TKsDwXVvV63iQbHi0S6rRwtjJT2BixI6TiMOul+x1vTgU8nDyY3udva/Z9TNfQFMrHHrbWsj4voXAK6dh4y7Yui+cz7hA5XPwllgcAmGT6iQEPjgElEs5xsf6+PT7f4mb53+D0f7PyGeGH+MnJSIvOlUAReRro1ycoxwurKhsfbYw4Cwc+q0Nf0feg5kw/HX2woG3YW7aqm3ZOWiqg9snYWjUKn67XrGwN3sVbh2vHnN4zKp4e16rLuB4FPksHPsAzn8KbS3V20tFaN8Gaxqt0nf2I2hphoQHI/32nINv25Z4R9+3SuDl2kqgA5dOzK8EVtrBPGxBjONUdzJxXVsdncsO8en3/xKeF6d71QFe+rHfoq5pvVYLi3yDqAIoIl8bydgcftgguTLsu7DRc2VI2HHur/zdF/6mquEPx4Z3vRisWGsVuLEhOPsl1bl4CfASFv62H7BjFfOPfnFd2H0YGlrsOJVjlkpWGczlLOi1dtl5JMJwWQmBDU32ftL1VrW7XKkE7oSt++dXAmuHxx8m5lkgbay3uYKJGLiOj+fmGb77GUf+4M8xOXxEfQNFvkFUARSRr4VyqcjE6EVcx4ZelypGtbdYo2fHgZXbgAXDvvPC3wdh+ANWrofdr1pLF1wYuGkLRTpWVCt9QQD9N2H6Atw89vjvpVSEtq12bMepVgaPvW9D0tteAQIbmj7+IYwOVEPggbctJFbO/b45gWElsORD6zpY1w7Fz2FscOnzcVwLf4UCzISfx73KqgcTQ1/y+e/9KQ786D+la82PqRIo8g2gCqCIvPDK5SKXTn6Pa2f/R1z3wfPaXLe6yjUz/YjhbwPsfb06x2/gRjX87XvdHlPIwsAtOP2Z9eJL1j/+Bcd2MBntt+O6rgW/ugYLgePDdh7xhN3escLOYaTfFo3UN9vt6foFcwJrKoGlIpw/YYtiDr4D7T2Lf15tLRaYh8dgYvr++4PAVltnZ/s59kf/Z4Zv/4EqgSLfAE6g32SRRzY9PU1zczNTU1M0NTU979OJjL6rv8sXv/8TFPJzJBMPfqwbtoQBGyouh/Pilgp/qzZahW+kDwYv26rewRFo6rRQODoAA5es/9/IONS32e3xh5zHgxQLcOoTyIxDRzvkS7Bmt/UoPPY+lDLQ0Qq5EqzdA/VNdvvowIL3UjN/0XGtCrhlL1w/BxfDSmC6wYaN6xps/uDCSuC6zbB+G9y8Ajcv3b9PcRBYyCyUIJWAVH0PB7/1z75RlUD9XksUqQIoIi+0IAiYGL1IPjdHIm5hZGFIqd0vl8AWQJTDx3luNTDNPiD8nfrU2rt4CehdXw1/Jz+x48SSNjdw3xuQTFuVrVyEUv7RL+WiPS9VB/vehJ61NgfQ962yNzNZrdZ5CQuvxz6woLewEnjsfQuGlUpg4FsV8PIp2LATtoWVwOysBb/MrAXBhZXAOzdgdAR2HIZ1W+///CsLbRIxyBUgNzfIsT/6jxm6/fuqBIp8jakCKLIMqhR89Xy/zJE/+iv0XfwngA1pxrzq4oZ0yho0Q3VeoHPvf+zLun1QdixkLRr+PrH+f3tfC+cAOjbce/IT6OixMJhIADU7jQzdsZ1BltMHMF+C1Tuhe7VdDwILbqVi2B5m2IJqR0+4bVtYKZwYtXl/CyuBHSssANZWAt2wErh5GZVALwYbN1lYvnbFhnwXUy5XK4Hphl5e/vHfo7Vr79e+Eqjfa4kiVQBF5IXl+2VunP8XDF3/7XsVvcrq1grXqTY5jnlhOAwDohtWBseGLPwVclbNWrXRwt7EEJz7wsLfvtchkbIFJrXhr1Lxc8J9fPNztkDkxEc2xJyse/QL2PMGb9lxijk712Qa9r9hq3+PfwCjg9Xb970Bre1w6mPIZyzwda6w9zE+bBXC2kogwNWzy6sElkswPWbn9KD5lbWVwMxMP5//wc+Qmb6uSqDI15AqgCLLoErBV2ts8Agf/c63KRemKJWhu8MCYDppVarxKQt5nndvi10I5n9f+dLeY5XC3DRsPARNHXD9OEwNw4aD0BzuztF/Iwx/K6qhECwU9p236tfIGCSb4MCb1fsfRbFgjZ4zE9DZDvki9G6DFevC+/O2NVylEtjZa7eP98OtU9C9AXq3wsgtuHsOGrrgzi3bnu7g2zZXcWoI6jrh+iVYu/XR5wR2roDdL1uoPHvEqpKLCcI2PA5WDVy18cd47f/wu3ixZXwQLxj9XksUqQIoIi+s7Gw/xfz0vYpfIm6XeLxmzp9j8/18v2Z+YOWC3d/RawGp0lfPce3ucincozf8S9hXG/7eqIa7/ptw8iM7fixhQ7gH34ZUvQ2fPuolmYb9b9rzY+HQ8cmP7fhgr7f/DeszePwDG54Ge69BYOcL4Z7HQEMzHPoRqySe+shWDgP0rrNt625dml8JjMWsinivEviuVTndsEp65Sys3Ai7X7FFLouN7FY+d8exn8XIwBkK+Zkn/VGLyFdMfQBF5IU1N9OH51qSCwJrVQJW8atLQW/3/G3gcBZcD2/bsA+mJuDaVat0pRut0nf1CnStglSThb9TNeEvmar2/Tv5MbR2wp4wFDqLNKB+FI5jIXD7K9W5f6c/s+ODBbdEyl7/5MfWA/DAW9DcDgUPLp6F+g5oaIe8CxfPwMF3YetOGLwG8TSM34FLZ2x+455X7fgAm3ZCLvz88lR7Du44ADdOQKoZ+u7a43e/AtkxmJuEIGWfzcKxomLJ3k+pMMzgze+zbsdPau9gka8R/baKyAsrKI7j+7Y7heNY6AjC1i6Bb0HQW2TO38JwNjEMZz61ytrOV2Dobtjnr9fCznDf/PCXqGn6fCoMfwfeqlb8KhXDYt4qao96KeSqFUcvZmFv7+vQtbJaCQyCaghs67YQODVmq4brmsJVwTN2PZ6EEx9AJizANXfC3jesmnf6U5vbuOdVuH0Zrp6xP/jxuIXD9h4LgZMj9hm2dVkgHBu0EOgEtvBlxyFYv33+ritu+NnGPHCdEp//4OcZvPFvCIJFNmIWkReS5gCKLIPmCn01giCgVMxy/I//E25d+G1Kvs29A5v/19kG2TxMho2LF/0jFgCOVfha6iEzZXP9Zmfnr/odGbg//MH8yt+Bt8KFIE7l/GB8CG6cgtQyxlHyJZvz17Nm/rEqq32H++wcetfZfcW8DUmPD9k5NLZaAMzM2BxBPwd3zkFzD9y9bcPKB94JVwV/AO3dFgBvHLP9g5OtcOsK7HjJzuH0p1YJdbBjH3zXdiU5+n64QMWFLfth1Qa49KmdT+WcJ6dhLmvf5wvQ1rmRt//9T0jVdT/6B/KC0O+1RJEqgCLywimXcpz7/O8wcP1f4XmQjNdU+9zqnLjKgoTK99RenOo2bpX5dsN34Ozntrhi7+tWGTz7WXW173LC39H3IJcJq4+PeCmXqquAK//0dsI9iBdWAsEqfPtet+rcqU8s+B182xZwnPoIZsbtcXWNsP9tyOft9qYWe9z4EJz9wuYuAqzbAet3wPkvYeSuBeDe9XYuU+P2npJpWySSSNkexRePw80L4WKbsMl2zKv+DBzHWvEUskMUchPP9v8YIvLUaA6giLxQfL/MtXP/gptnfpVSqUjMs9W/8bA9Sb4A/cNQl7Y5gLVqh30dx/r/zUzC1XDun5+HrlbYuMeGN+eG7fqGPTVz/sKFIG1dtmBjqfCXTMOet6Gu/tHfW7FoQe7ER7APayxdGVathMBTn4RzAgMLZ4kUbN0D147CzKANC2/fB7dOgl+CHNW5gNt2wtB1m7vXsQI2b4XJQUj0wp274J2FTbsgPw7T/daIets+cHOQaoTRCXtvh96F7btttXG6A65fhKA0fx5gOWzH095igXB4Isfo4FkaW7d+7fsCikSBKoAi8sKwvn+/xcn3/3NKpTzlslWcXMcqaLGYfQ2w8BHzqj0Aa/sAxsJq4eSwVcTau2HXK1bh8yoVRCzEQLiqNrAK4ZnPHy38HXrXtm6LJR79kqqz47Z1WwgceEAl8MxnMHQ7rLJVtrarOV+vZkeRWCKcCzhbbS5deRzYquMdL8Hdq7ZvMEF1xbQbVvNSdVb5i8XhxIc2FOy61jNx9yt2Dn5QvTiVimD4WROUuPTFzzHa94H6Aop8DagCKCJPJAgCAr9EsTAJBDZE+1jHgdtXvs+5T/4qfmkGLwbNDdDean33XNcqgakkrOyuLkRY+oAw1Q8dzbBxd3V4t+LeKuCVkA6nfc0OQ1czrN/z8PBX3/R4q4ATKQuBxz+yEAj3VwK37YUrOTuf7tV2fkEaLl+Ehm6IV17XseHfbbttLqDjwEwBLpyCgy3QtAKuXILyKQtx6zdYs+dUm1X10pdh9WagDq5etsdv3wO3zlgwnMzA5TP23LkNNo9yoYlpmM3YcHF5+g4Xj/5/eW3Fm3gP6igtIs+dAqCIPLYgCCiX8lw+8avcOvOrJBMlutof71i+H3D77gzlYp5koloZC4LqtL5SuToPrRK+fN8qUrU8z0JopU9gpW9euWxVv8HbFoDae2D3q1ZBo1I5c6pVscrrjw/Bkfcg9QThr6ISAg+8aQFwsRDouHYelfcei9t5nvzY+hFu3Bq+l7AvYGVeXkOzzQU8+p497sDbViE8/oHteJIO/+Kv2QpuHC6dsNXIO1+2BSEnP4L1m+0x9U2w7y1bKXz+S0g88GdX3Xs5N9eHX87hecsYGxeRr5wCoIg8Flupm+Hise9y7fj/G9fJknChlH+8cFQq2U4VYM/3AxibZN4OIAMj1v+vo7VyDjA9C1OVPsThwo/2Jtvxo3qutvDi2lW738/Z3L9Ne6uVwcE7NlewtbNaEayt/KXS1iblScJfRW0l8MRHFrygGgKT9eAnbV/ehm5bsRtP2ny9q0eglIO8ZxXBxu75q6CbWmHbLhi8Wp0LuGWbzQWMd8Pt2+Cehc27ITtil9gm2L4fLn9mP7+8AxdO27zCzdtg/C4kuuHWbVuxXKs+DWt6YXDE2vT4xTECPw8oAIq8yDQHUEQeS6mY5dRHf4OrR/8O9fVZNm6Ajses/kG12uWF8/lSCWioD/v71fSgq2SvSn9AP6zagc2tq8yJK5WrVcThuzanri2s+CWSYZUw/As4ctcqYE1tNgcvHvYBnByx6lll2Leh+cnDX0VtCGzrhtOfwNAde914Ana/Bq3dYXuYu/Yc14GYa2F035tQ32jhcXKk5rOgZqVu+FqVeXo9a63ad/cqXD1d01MxqM4rTNdZ5S+egJMfWvsY34eedTaPMp6o/kwqPxffr/QEhFx2irm5RcaKReSFogAoIstilb8c5498lzsXfp2G5iKHfgS6t0C28PgBKQDS9bBjP6zssXl+q3ugp9MCTSIOK7ugrcUeP5uBviELLSu77Tnb9lgPvKtXbOeMdLM9dmYQOltsbt1ie/fODEFHE2zfO3/u39QAtNXDjn1Pp/K3UCUE7jxoFcmZwWpoTSTn375Qqs4+q9Z6Ww2cDWw18MwDOrE4DqxcD+vWQXkWUq1W1btxoRoewX4O2/ZCQ9z2GZ7Ow4UT0NUL69fZz6FyiXlW/Wtttp+N7+fwCyNP8VMSkWdBQ8AiskwB105/jyvHfpnm5iKH3rXQcuJjaG6EuubHO2oygHUHbAeKqb5w9e+Cf6LWriuo9KBzw2oXwNQoXL9slbp9b8DgJWuT4obz6hYer6Kyorh27h9YBS0W7uP7rDi1K5wXtLGpvLelFry4YWUv3QD7tlm18vgHsGr1/IrgQp5nn++67TYX8MopKGbuf23PtQbRB8J5hee+gLrY/J9DpbpYKNrXhJfHL449+QcjIs+UAqCILEvgFxm8/j+RimdZt95CxvEPbO7ZntfvX227HLPTFjQoQNtDgmRjPTTUVYdxAaYHoa0BNh60ClmU1DdZBfTOOaumVlYDH2hZ+jmOC+u2Qn7U9gleKiA3tcGmLTYXsLDgMQ111pNxLmNV2p5On0S8+LTelog8IxoCFpFlKZdLeEySTllLkQufQ3OrVdyS6eXtjFF7KRbg/OdQmKtW9B6kMmettjrmVip5Ef3L5oU9EBtbwp1BcjZHMJ9d+jmVat/DPjPXWfwxtXsDV66LyItPFUARWRbf92lp8inFbDVqQ8IWCCRST/Yf/1IO0i6kOp7aqUbavNXAE9ENxSKyOAVAEXks88Le01oZ+xSPFXWOU60IiogspH8TioiIiESMAqCILIvrusxmPKZnwfFgLgeTE9X9Zx+X40GmYI2dc/lqOxR5PEFQ7ZUoIrKQAqCILIvrxcgVG5iatZYvq3fCjctw9azt5BEEj3dJN9qxpjPW40+ezPS49QUsxaG+7XmfjYi8aDQHUESWxXFiNHX9CCP9xxgaKrN/B+x6Gc5+AX4emlse77iBA229sPtl6L/w8MeXwz53Xs2q3yCwPWmX6n/3Tef79rnMTMLF09YKZ8/rMHINlsrUfhmKRfvcvCVKAkEAuZz1+kuE+yZXFIuQK4TVxhJkcg6lsmoLIi86BUARWRbX9dj5yt8ik5lm6Np/y4mPfPa/aSHw1hnIjz/ecQtFuHEFNm2HdPrhQ8ozGZiYgvYWaGqw2xp77BiXTtuWblGSnYVLZywANgGNSVi3+8H9EMtFuHLG9ljeugdm+u9/TBDAnatw9TJs2A5BBvKz1fvncjA+CT0dNtw8NRsncB6zG7iIfGX0zzQRWbZkqoHDP/L/oXfDv8dIn8upD8HPQEvL4x/TAUYH4NTnMDYBE9MwNQMz4V60vg/ZsAoVBFatisfs+2LJqk/N7bD3DZgagzOfWm9BCOfDBQ+YVxjuQ8wi9wfB4rc/bUudW/CA867M88vNwYmPoFCwvYUbmh/cD7EUfl5XT8HgLdjzGnSvnn/cQh7yBRjtg6snYMMOWLvNfgZz4TD9zJw9tqnBHjuXAdw0TqzriT4LEXn2FABF5LEkkk0c+NFfZ8Xmv8TIoMfFUzA28viLNyptZeZmLfiNT8LdIfvqB1AoweCoVf3AdqDo7bKKV9+QPfbCaahvhI2bwM1DpvLYbhgeh2sXoLTIJhWpVrv/5pX59yebYWQSbl1b/HlPQ7kMfTfs9WMN1c/BL8PtazA0bue3UCEPV87CeLgYJxXA1p22ddtSKtW827ehvhvIw5rV0L1qflufiRE4cwy8ZqsgdjTDilVw8TjcuglDYzA8BiPjkMnaPsCzGZjLQirVSENT+9P8iETkGdAQsIg8FsdxiCfb2Pf23+ekE2f4+j/HLZfv20/3UXlOgOvm8MMJfAGQSlh1KZO18FKXgmSi8vrh88J9bQNgchhOfgztzfP733WthlQLnP7Unpd251f2Vqy3Cta5I4ALO1+yvXm710A+D2c+tzmKe16FWPzp7HZRqd5dPWOXTbth1RY7tl+GK6fh2jnYfhBWbODeZ+IHVuk78ymMD8OBd8ApwtxIdY/ecjgXsNJTsVS2y8BNuHkZNu2FVRvgwsfghXsqF4v2GcxOw60b0NZh8zFvnoF8CW6eh5E+qK+zwzpOeK6+hfJS2V4zkerA9VJP/gGJyDOlACgij81xHJKpBva/8/fIHvi/AHniscfr5RwEkLr8v3Lp2HfI5fJ4LjTUWwDsG7LhzN6uavCw17f7G+vDYwBOANnp+efgONC7DuaGYGYckh1WxXIvw47D4MVg7VbIT8L0qO2c0dxpe+Wu2WzHOPO5fX1aIfBe+Dtt4W/TnmqAy87AdD9s2GB79bquDdnevAyjk5BuBzKwZQd09MD4nepxp8fh0lmrhNa1wPBduHwBulZCOQsrOqF3DVw+CUOjtoVfLgtnj0CxDD3tUJiBFWutMnntMmzdB8VJ6Gyd/76DwKqAE9P2fhwHvGQPjvsEG0KLyFdCAVBEnlgikSbRsfOJj7Pl0C4cN8GR938Jz80xl7VKXn2dVfkcx6pbxaLdHovND4QL+WGFLwgsEMbjEPes4pdohPNH7bnbD1v4SiQgEQNqFqA8ixBYLlvwq1T+asMf2AKYhAfJZPieS3DxKNy5Zq9dn4bcmL3/yvv0/XD175nq6t/ZSat6dq20oHvjuFXqrp2GoTs296+tC85+BqU8HHwHZkZtVe9oPwz3w9a9sHIjnP0QMuGcv4BqBTWZgI5WmJyGWLKTtTt/Bs/Tf1pEXnSaAygiLwzXS7D5wF9n7+t/E9dzKfu20KCxHpob7TH5AgyOwOTMQw7mQEMXDE/Y3L9izRw+x7HK2qZNFqQyk3Z7ohlGJuDuzXAItfL4MATufsWGUU9/9vhzAivh78oS4a9chrs37LwTzfY+5iYhOwqbt8LK9cwrbxZycOUCzITn05iAreHq35kh6GiETTvtvG/dgvoucAqwepXN/SsVIFaENWttXuHZ45Bss4Ukbc3Q2gHnvoA7t2F0AsYmbV7mxJRV/uJxaEiD43psPPh3Wbvl38V5GmPkIvJM6Z9pIvJCcb0Ea7b8Ka6f/A4OOfwApmfCuX5eOBcwbd/nCvPno1W+J7zesRLq2uD0J3a9LlatCjoOxBNWEQzC4cveDTbn78JRwIVtB6rh7L5KYGAVtFj80d+b71fD3+Y9FgArx6/MCbxyyub+bT0AvRvDJwZ2nvG4vTfCVc2FLJz8yFYBH3oXZobtUln96/tWrRu8aQtcNu2Flevg4ifghuddKtjOK6URuHIZOnutWnjzDMzOwdkvrfLX0mTn6FYW62QtjE9O2WfnuglWrD6M62rzYZGvAwVAEXnhuG4C143jl3OUSrZCNhVOK0snoafTKoODI4QT/6rPdcL/cRwonrE5bps229w+v9OO5VyyvoWpFpvj5lyBna0W5tZvt2NeOGZftx1cOgQ6ZWjvePT3NTsLN6/dH/6gJvydtfC3YUd1QcjdmzAyBd3brPJ44wqMT1u49QqwdZeFs0vnwrl/rTb378ol6Fltc/962m3u38UTVl3ct8Pm/p38DMYHAdeqojsOQ991m0dYKkJjCxx4E4avQDFXPd9iyQJgrmCteeobW0ikWpbxUxaR50kBUERePGGga6y34Dc0Gu7u4dgctkpvwHSy2ievUtmD6m3Dd+HMZ9CYsqpYz1pINtmQJg7sPGwh5vwRC1s7DochcIcd58Ix+7pUCLx7ASaXMRScLdiQ78LwVy4vHf4un4Kbl2w1cHsvnP0chu7CvjchFkBmzJpAXzlfnfs3MWqrobtXw7ZDcO2IzZ28egrG+mHvazbEe+x9G16uS0Pnqmr4u3g8nHfpwO637FzHJ6yvYuWzzeaqn3XJT9O+/j8lWdezvJ+ziDw3CoAi8kIJgoBCbpZyuYjn2YKHVBKmZsOFINhctIY66O5Y+Nzwa/g/AeBkwW2BkVsQXLJ5fBCGQCwEgoVAWBACnXA4mMVDYFuH7abxqBwXGtofHv5c1xZ+XD4FNy5Y+Fu5wc554DYceMuaXp/62KpvjYHN/Vu3B6bGq+FvZxjobt2GLXsgNwKreqG+CY59YAFzz8swet2GfvuuWYVw+0EoTdlq5KAMxz6x5tq1n3MuD+mUrQRfveP/xsE3f55YLPHoH4aIPFcKgCLywgiCgOzcCKc//SX8spWYHAc62yxsQHUFsBez/oCV4d7K3LR78wFrVgd39kJ9K5z6xMLVrpcsSJ770r7WhsAgvF4ZDnaw1cJgcwLdmhDY+Jj9jitBtXbYd9sBC52VVb+14W/VBjvXwTsW/lo6bF5jZs5W7s4Ow/QQTAzZ8HBPTTXvwjE7Ru86ONcP5ZxV/sDmDc6OWxPnzBWYnLTnrVgDpz+0VcQDH8LcIgtuUmH11Yl3ceCN/4xEUr3/RL5OFABF5IXhl3Oc/uBnGbr5b0jEqwsjEnFrAu25tiNIOmnDwEM1VSnn3v/Mv+5gcwH3vg6btsD0CGSmrYKXG7e+gJkpWLfNnlepBFZC4Lrt4e1HbS5dUxMQh1Vbl7cApKJchoGrUMrYnMBbN+aHv8qwbyX8rd0KU0PWh2/7Hqvsjd21nU627QJq5v75Behogg3b5oe/lRvsfd2+Y59pa6eFv3wWTn5uQ8ixhLWYWbHW5jfeuc1Dt8DLFaC7awOpdMvyPwgRea4UAEXkhZHLTjDa9x6JuCWPmVlbbdrVZuFofNrmnjXWh3MCsWpgMomtjA1XvVbuq8xXG7pjw6ItDeEK2XD3i0RlFXBgtz8sBA5dhSBnrVAmJ8MdQ5azCjgMdwNXob3FAtSS4e8QrA13Bgl82/c4Eb5WULYwPDsFF85AOpz713fBAuaty3Dnxvzwd/NiGP664NA7Fv6OvmfhD6C7B5KOVRbvXOeh4a/sQ13TZva/8128WN2jfwgi8kJQABSRF0IQBGRmRymXC7iuDekGWCgCa4FSLFoALJerrU6SSQuI9x+v+rVpJVy7aFuZOQAXYNcrkGqzlbJchl3hKuAHhcCOHgtfjcNw/hhQgrZFXnsp0zNw6zps3w/tXeDGoL5lkTl/NeGvVIBbV23P31VN1vfv6kUYHYJgEDpWwP63bAeQK5esNYw7ZsPc94W/zvnhLzNbPTc/C4NXYWSYB4a/yoKbWLyJQ9/6Hq3dh9X3T+RrSAFQRJ67IAiYm+7n0he/gF+aobEeWpu5t5CjsgtIZ5tV/8anqs8tlWwOmxv2/3NrewKGz2vtsOBTCT23Lttjd74EWw/CmZpVwV5s8RDoxaAxDHtNHRbe+i6CU9Ma5WEyeTvWmi3zdxG5L/yFq4yLBZv7N9RnQ9gNLXD8Q5sLCNC1qhr+jn8IuYyF1SXD37uLh7/GFli72/oJ3h160M/Jwp8Xa2DvW9+hd93bCn8iX1MKgCLy3BWLOU5/9H9nrO/7JOJWEats3+Y41e3HEgkb8p2cqQ7z5gu2H22t2vmADjB33ALgoXfh6PuQmYFbl+z+nS/Z17NfADloaACSsDYclj33JZCHxkZwUzb3z4tZha69yyqCj8r1rOLnuNZj7+4l8HM21N13F3Ycgt71cP00BHmYmYHhEQt/rZ0W8obC8NddE/6OfQj5Svh7OVwx/KW9x4XDvkdqhn3Bwt+hd+3cLpyav2NKrdrwt++t/4q1O/8TXFf/CRH5utJvr4g8d8V8lomh4yQTAd3tVuG7Ozh/hw/XhZ41kPChp8PmoFXakUzXBJpE3PbIDXzb0jefh/EhC36H3gkrge/XhMAAdr4MONbsOFOG4TFboFFZHTx8FSjY3L+pqWqrmIbWx3u/pSKcOwLjd6pzASvh7+wXMD0IbS1WGVw0/K2G/W9WK3+PG/4A2lth4jbcuGEtZIKgGrrBQl+4+UhY+VP4E/km0G+wiDxXQRCQy9ylXJrGobqzhBdWAYNwYUf3Gth+GG4chWTYbq7S/qU2ANalwuFjoG013L4FI/1Lh8Cbl+2xO1+GnpVAAE134ewRe/1dL0FXbzj3byBsCVOE5pbHf89Tk1bx2/2SDU87LsTTFv6G7tjuJa1t4Hj23pcKf8c+sGD3uOEPYLAfxoatutrUYJ9lZfV1ZRW170Ms0ci+t/6ewp/IN4R+i0XkuQmCgLmpa5z58GcoF0YpBTAwAsk4rOyuNnR2PNh6GMYG4U4frFgNTh68OASl+ccsFK0/oOOAE4MDb8PxDxaEwHeXCIEv2fDumi2AY/vggoWrWBzWNVtYG7wMLGPu30LZgu3GsWJduNCjaK1Xhu7YsG5Xr4WwYh6Of3R/+Jsat/f0oPCXiMO69TA5CGePLx7+wCqovm/hj5pdVfzAFuA4DjS1bmTXa7/Eys3/vsKfyDeEfpNF5LkpFQsc/eFfZ2Lwc8CCSH2d9fmr3S3D9WDoFlw8ZcOkG3fBjSOQSMPOPZD/ECZH7bGjk5AJw9nkMTjQsEgIfG+R4eDL1uKlvh6cFKzfhYXAz8Ep2hxAsIBU3wKrtzxeH8B8DvquQG4MboR9DKemYGjIwl9zG1w5BhRhYgKGBuwxywl/YJ/fZJ99FkuFv4pCEW712+rqeMyqqq4H2ZJDa8dmXvl3fouWrkNa8CHyDaIAKCLPTRAUKGZu3gst6RSs6Lz/cX4ZJu7Aii7rbYdfva+xxbYzu/ylBZiJaVvI4Di2KvbY+3Dw7SUqge+EC0PesxCYz0DCtVW2s7MWrgLf5gC6NZXG0QkoObatXGwZf0ULeTh/AnJTFior8jkb9m1qtX6F5Qy0NNr5Q034Gwvn/D0k/EF1ZxS/5rNaiu/bkHss7Inok6Zn40/QteoNOlYcpKVzh8KfyDeMAqCIPBdBEDA3eYFiboBS2cKH41ifv8riD9etLgDxPIgn7Gu5NtQ4kEhCXdrCWqkMEzVtYvJZmyv3KCGw5MLa/dA6Bac+tRW6DXWQbrKKYGWr27ZhOP0ZUICWlkd/z2NjNv9v/+vQEM5TzGfg1jmYugM3T0OuBPvfsH5/fR89XviDap/E8iMEwFqOV8fKrf8FB9/66yQS2t5N5JtKAVBEvnJBEDA1eopjf/gXyWeHKYT7++YLMBgO5VZaubgurNsKXpElGxQn6iAPTI5ZAFzogSHwPdtfd++rcOIjOHfMVt7ueQUGLts8vJFRKAK7w7mAK9bZce+eg5nS/a+3+Hu2CuX+N6G9OzyvHJw/bk2Y3SYLuNtetvtOfw7peluEMjVqcwEr4W/3KzYUvlT4A6voQbUC6Ps2r6/SZLuW6zq09rzB6q1/gUTdGrrXvKvwJ/INpwAoIl+5IAi4ePx7TI1dxHFs0UdDXXUbt8pChCCwvXC37rc5f+Uleu4lUrDjVQtzc8OLP2bJEDgMZz+EhnqIuTBwC7ySzQWMpWDtXmifhpOfgFOYPxewLr2MPoAONKRh8hZM3rabRkdt1fOBN+38+i7B4AUYHoWZSejphFvHYWhkeeEPYDYL+WK1r58f2Fy/ZBzwah/p0L7qxzj4rX9KXcMKDfWKRIQCoIh85YLAJ5eZoFiyRQdNDdDVXnN/+D+OBxt3PHyeneNAfWN1KHfiISFw36uweSdkpmFu1vrtlWsqjIUcxB0YHbch2X2vWUWw/4L1Cbz3uq6tGn6k94y9Tmaq8hlY1XP/G3bfmS8h5YBbZ3MFK08qF223E4C1m6Cl1drF3L68dPgDe06ppjoZ8+ziB7ZKOgAam7rYsPfn2bDrJ6hr6Fb4E4kQBUAR+UoFQcDk0Odkxj/GD6C50RZ/QHV7tMrwb2Urt0dRCYGH37Wedw8KgddOQFMjuA8IUBWDt+BMybagSya4FxKDAMoObNwHqfTDjzM5CnfOQdytnm8qBoMXbbePmQlIhyE48KuPqVx3HChMwo3j0D/44PC3FD+AfMGhrj5NItXJ9le/y7ptfxK3MmFQRCJDAVBEvjJBEDA2eJQjv/8TZOcGSCagtana8qUSah63EOU4UPeAEFi7y0WwjMURxSwU3Oox3JhVJYeG4dxRq+Ilkks/f3oCTn8BTXWQTkMprPD5JRumLYRtaypz8yrnWfkc/KDme786PF77vivNm+/tnuLY9Vwe4nH7vlT22H7wp9m45z8jme4gWdet8CcSUQqAIvKVunnhXzMzdYvONluoMD4VLkxwLVQ1NQGPMK/Or+xWsaAStlgIrEvZThdjE4AD2XzYaDkcIl1uNS1Xgq0HoLtgK3NPf2AVxaUMDYftal6D0QEYuBjOxWP+69cGvsr1IKheZubC+31bLY1jPRMrSmULkZUhc8exhtA+SUqk2fXyT7Lrte8Qi9ct7w2LyDeOAqCIfKViztS9nSpingXASsVrw05obYTxuw8+RjwFqQ5bBNG00vYIrq0aVkLgvfYuc9DWbOFofMrmwGWyj/8eJsdsUcj+12HXYei/CIXM4o8NAmhutaHiiWE48wW01D84AAbh/ruuY/k2CG+bnK4+p27BsLPjVLfIq+zukUxAuuUgO179ZTq61lPfvA7Pe0CpUkQiQwFQRL5SxYIFwOEx2/WjrdnCSttK2HbA5ufNZW0V7lJcDzbtsR53pz6x2xYLgZU5gZePghfY1nGP4lEqguNDcO5jmxsYf8goqleG2ydgMFzN6yx4b7VDvvP24a2pAC5HIg7lIEHbiv0c/rd+k4aWLVrgISLzKACKyFcmCEpkZkdtNapfswct0N4ONy7AtUuwaTckfJidWPpYXszaw4BV4/axdCVw95s23y47Byc+hokRq7AtzFXLzUh+2Vb2PojjVBtUVx678HXuDfmG14Pwe8eZP98vCCz0ejWLY4qlcAu3uA0Ll31oalnJvjd+lbWb3iaRalf4E5H7KACKyFfC90tcOfVPGen7lEQCOlqriz8cB8b7YWwcNu2EzXvg9umaJztQCqw9Srlc/cPledUQeOoTIICOFYu/vuNaGNz7Glz8EtyyzambnoWxSZiaqQax+567ID8l4ta/b3zKeus1Ndhw9mKCwIZkZzM29zCdnN/WpjK864SrnoNwzLfsQ99wtYF0pRqYL0Aq3BGltnLoOjEaWjeQbt7H3ld+hp417yr4iciSFABF5CsxcP13Offp38Avz+ElIJWs7lYB4Bdt/l/3yuo2ZhWuB92brXnzhWO2DVosHM71PNi6z74/9zms6K5W0pZSn7CqXGXbufsWhDxgyNVxoKXJQt/AiG07l0xA7AGtYCanIZOzxs51qQWBMqjO+XPC16+8fGlBxdB1oT5dDZWOEwbCVJLNB3+eNTt/lobGTlzXVfgTkQdSABSRZy4IAobvvI/rz7CqxypoC7cjm8cBYjA7B/k8pLHK3oG34NiH9pB5ITBmITCdguL0EsesUS4v3gamucFCVqFocxSTCWhvsVYt+SnbrSQRr+5T3NRQrSI+aFFJqWw7jSQT81f6zszOHwYfmwLPgZZGC3kTUw/MoiRSLazaeJjutT/Ght0/ixfT9m0i8mgUAEXkmQqCgFIpz9xMP4lETY+6AJwF/e5q9W6B4SGb33foXWhoho5e28rt2Af2mIUhcN3Oh/f3Gx+Cy8egPm5DsW5NIPM8W9QxNmlDtumUhb6RcQt4yYTtXAJ2zo31dnlUlWoj2HlOz1rAW9Fpt49N2mu0NFtlrzI8nC/YuXquBcZMDuqb1/Paj/8GXatexXFiqviJyLIoAIrIM+WXC5z9/O+TGf3f6e6AoVEbbnUdiCdg1Vooz81/juNYY+V9b8DJ920V7543IZ60SmBtCNy049HPJTMLJz+17dRaesAbs5A3NQ1TszAxbXMBW1ugsRkyczbM29wEra0wM2MVv9ammmrecnJXUA16AdDVaYFuZNw+i64uwIe+ofkrf2uDaqls1cd1W/+PdK9+U8FPRB6LAqCIPDNBENB37d9w68zfpS6Rs6pWJdg4sHEvtLfBnTP3tzpxnDDwtdueveWSXXecMAS+Bac/AWbvnzO4lJk5aG6HPW/AaD9MXIemdPX5QQDlAPI+7H7ZFmIc/wBIwPbXYGwYznxutweB7du7bofNUXyYwdsw3R/O3yvA+p2wbgtcOgnBNOx8xaqcJz6yIeraz6F2gYnjQCyepLF9z6O9aRGRRSgAisgzNTZ0nqCcYS5rCxe6O20os74VNu6A6XC7tngdjIxAwzA0d1socxzwPavQjfdBMg3pFkg32HDwgbdhZuThRbjpMdvOra4OutfB4HU4fwLWbYaOLkiOwdRpa+sCMDMJxz+yoeeD78Cx9+HCCdvJw3Fg7KaFsv4+8NK2EvlBIXTgFlw9Dys67Pmb91gAvHwKhu7C/jehuc12FRkbWvwYlf6AyWSCLQf/Jht3/llV/0TksSkAisgz45fzlLLnqU9b1czBglPMs63ZnJrQ1NoDjb1w7kvba3fddgtLa3bC+Dj0XbLnzZZh35tWLWvpssuSAqu8jVy03TdcYOSaNZpevx227LPg1rEWvAScP1oNgVNjtotIbQg8/am1kelZY8duugNnv7Ah7PQS6y98H27chFWbYPPucM6jB5dOwJ2rNsxdCX8j/Uu/lbIPxSK09LzMloN/Qws+ROSJKACKyDNRLuW4dOSXyY3+Dl3tdlulb91iHAdWb7Lvz31pc+TWb7e5gPvfhJkxIICrFyyYbdv18KHXXBbOnQA3AGoWa9Q3QNqDvnPhaydg3TYoZWHoFhCDyfEwBP4QDv1IGAI/sEUpe16xRSc9a6BchIm7UFhiuW7Zh1XrYVN4vr4Pl5cKfw40N9v5eq4NWecLdhzXhWIZGjrexHtQzxkRkUegACgiz8TkyDGunPgHxL0CgQ/JFKQeYSu22hAIYQhMQftKC5D7OuDMp9B/eenmyxXZnG29ll6w/e3cDNy6Q7XHigNrx6C12Vqw9G6H/rtw8yJMjdeEwLdtTuDZD6oVP9+3ALn+kC3kqOX7cPM85Mbh2hd2W7FkC2EWC39rNsPKlTB0tdoGpiLwoa5hFRu2/Tsa+hWRJ6YAKCLPRGZ2BL+cJV8G34FduyE7DIXMg5+3sBIIFgIrcwITSdj7OsyOc29V7Wg/ZEYXqS6G1ys5r7KvbmXHj4Bw311sG7rhcJi4fgJ2HLbn37wQhsD3YN9rsH0fjN2BeLp6zIFbUD4KOw9VdzcJgFuX4eYlWLOm5vY8bNlj8xEXhr9dL8GNczA6YQtB5rLWMNsPwHc7efVP/DM6V762rJ+DiMhiFABF5JmYnZ0BoC4N2w5D9yo4exOCEiRq//Is2O8WlgiB26oBLxaHlm4LWYO34OZV6Gy1kFjbB3Dh7hr5PIxM2FZqK7ssYI1N2mODAGZn7fsLJ8CJwY5Ddv3mBRsOvvw5pFKQ9WHdfmsVA9C6Fk58CBc+sbmNYHMJB8dgx0uwcoOdRz4HZz+D0h0YvQGjQ1TD38tw6xJcChejBIH1HLTQ6rFpz1+gd522dxORp0MBUESeuiAIoDwO2C4XzS1w6jMYHYFdh2C6r/rYulbwk3DtMrT02grfSnPo1Zvs69kvID9p1bBaZR+uXbW5eJt3wZ0rkB2prsit9OnzfSgUbPh1LhO2e/GtypbJ2u4enlcNmOUynD9i398LgRchUQ9t3XDrpg0F73nZAl86BVv3wPhtKBcqnwFs2AItLZCZsNc7f8yGpNdvhIkRe9zazbAzDH8XjlUXoThOpWrosn7HT7H79b+D+6j9bkREHkIBUESeqiAIyGeHyI7+GzparIp14wzMjtpijvau+QEwnoBdr9riimMf2GNSNWscelZDqQCj16G44C+W78OK1baat+8GXDsPPR02jFurWLKGzoRVNc91iMVcYrGAZMK/txdwomaO4sIQ6ABDd2DNLji43s712lFrCF3h1RTnHAcyI3BjpHoOhaItJikW4dIFq/ztfOn+8Fcr3bSZ7a9+h3ii6VE+fhGRR6IAKCJPVSE/xbE/+ssUJn5Ia1O4/VkRurugc8X9IcdxbJHHvtctBJ7+wLZCmydYMGwccl2IFeHal7aoo1Ti3rzAfFjxAxvq9VwLa14sRffWv0HXmpcpFvPkT/wDZkY/prRI+KoNgdvDSuDxD2HHXlizFjIztpDjUcxMQlvK+h6eP2XNrHcetnmCS4U/gLqGblIphT8ReboUAEXkqZqdvMHonR/QVF+d1Oc41WbNjmPtTHI5m6/nePND4K1z4BTvP248aRW/3CzVVR1YdbBcDhdzhLeXyzA0Vt1Roy7t4bgpEsl6thz8f7Bx71/FdWMEQUB3736O/uF/xNCdjxZtUzMvBB60r/1XrJI4MQcd6yzMPcjkKFy5AE0pC6ntPQ8Pf4UCJNItrN7+Mziu/lSLyNOlvyoi8tQEQcDk5Chl36/NaPMWeMRTsGIbnPoEkudhw86aFb4p2Hzg/m3hwALetbOQHa22dQkCm9NXKNnCDjfsnecH1gImCKCurpn1+/9fdKx8l+bWDlJ13bhhoHIch3TjWg59+3/gy9//cwz3fUbMWzoEFmegPg6JTuheA6ULNhdwxz6IL9HiplSCi2egvRvWbYLhOzZUfPU4XLt0f/gLAsgXoViu49Bbv8rabf8hjqO5fyLydCkAishTEQQBmZm7XDv+yxAU8V1INkJh1rZLm5yEXAYaEtWdNE59agGoZ+XDjz/YBxdPQEdLNSAGwOSM7a27ssvm8PUPQ6kco7VjHcm6blZu/RnW7/hzeN7if+4sBK5h5ab/E2MDn+EHFtAScQuUvl9dMDIzZquAB0ch0Whbw535HIauWUVwMaWyDX1vPwz9N+DuLehqg9k5O2YsZq/juTZHsFSy6l9P7wpWb/p3cR9lo2ERkWVSABSRp6KQn+HoH/40c2MfUt8E+96C3BiMzdl2blfPwYmP4MA7UN8IPWvteZeOQnaJ/W9rjYxZWKz07ZvN2Dy/sg+lItzqtyDlumm2vfyLbN77M3ixOlwv8dAKmuM4uLHkvP18mxqgsR5woKkXLp+DIAUbDkP6Nlw6bo/b8ypkZ8BZYicQHEg3Q991uHActu2Htg5bFd3SCWvXwtSgDZH3D1sAbKyHeLId100scVARkSejACgiT8XM9DCTw0doqLNmye3d0Ddm98UTVi07/Skcf39+CEymID9N2LAPxgegmJk/DFs7N6/SM3AuA3NZh2RdJy0NrTheM/UNbXSu+XE27v7LeLHlhafeDX+KgWu/w8jdHwKQy1tVzotBSwccesdW/l49Z/P3HKcaAtdtXXqLuwBrT3PhmM0h7F4dVj59O+bUkPUnLPsWaB0H4qleNuz/23jxumW9BxGRR6UAKCJPLAgCyrk7xLwcsdji8+EWhsCD70BdI7R2A91AALevwNAgtDdXjmtz+WYz1hOvp8OC2cAIZLIOXev+NPvf+XskUq04Tpp4IgE4j9UsOVXfy8Fv/yZffP8/YHzoM2Yz9roA2SNw+F3bCu7YB3bbzsP29dIJKEwsPQewUIDbdyz89ayGk59Cbg4OvguFHJw/YVvTgYXARKKRnW/+I9Zu/rfV9FlEnhkFQBF5IkEQMDV6nguf/Rwr2nNLVsKgJgR+Bsfes4bIlcdnMnD1ImzaDZ09cPsCkLM5dLMZqK8LQ1Z8FU5dL92bDrDjlV8iVdf+VIKS4zik6lfS0vMuw3c/IxarntvEMBx5b0EIDGDrPmv8nBu3VjeLKRRh0y6bB3jyU5sHefBdKGTh6PvWSqbCdYB4L92r38ZR02cReYYUAEXkiRTyc1z87K8QZE/iNIchJlTZa9f3q6tdPc/2vL14FEZvV3ftKBQt/K3bZnPlBvugu70awuYyUN/+Lvt/9L8nme7GceM4jvdUq2SO41DftI7AieP7xXv798L9IfDERxDMYauG07Bhvw0X1yoV4PIJ2wP5wl1b3Xvw7cXDX2V3ku7u7SSSaUREniUFQBF5IqVSkZTXT2ph82agqQeuXoKrR+fv7gEQL0OhpsgVj0NxEs58AHfuWGicTDg4iQ10rFpHQ8smth3+RVL1vc90aHTdjp9gauI6d8791/eV9SohcM9h2LwDMpN23teuAS5s3l0zV9GHiydhahjWrgNmoXeVPefk5/eHv2IJ2lf+CPvf+Yd4nhZ/iMizpQAoIk+F44Dj2hy9Ypibmtpg7xswcKnaCLr6hPlXi0W4O2BVMGu94jDDt3j5nX9MR9eaMPQ93vy+5Ygn0ux9/b/EKY9z++J/Z92qa0yOwNVjEPcg58Ce16Gh24a1/RnuVQ2LRWsUfeBtC3j9n0P5qrV/WRj+fB/i6XXsf/d7pBtWae6fiDxzCoAi8tTUt0HSgwunId0KbV3Q2gkt7fObO5dLtip2LmMrbcECUGPPfppbV5BMd+F7K1m54U/Q3PpsK36LicVT7HnzVwj8DHcu/8v7QmC6xeb0XTpn8wF3HoANm2F2xFYpg+XbTduhMAdnjtgeyD2r4Mal+1+v5Kzi0Du/TlvnZoU/EflKKACKyFPjujaPD2yHjL2vQn3D/McEwI2Ldgl8cByP+pbtHHjln9PSsRvXfbrz+h6H4zjEky3seetXmZm4xOTwkXv3BQHcvQ1d662dzckP4dYpq/zVzn90HZi8a61dOlbAjsO2ynlkYc9DJ8aOV/8+G7f96HN/3yISHQqAIvJEKpGlUuFzXdi8x76/fASa6sP7seHhUglGJ8B3V7Prlf8rqfrVNHYepqVtwwsXgGLxRhKprvtuz2Us4B54y9rZzI0vMsQdcjxo7LTwd/6IVT+hup2x68ZYsWrLC/feReSbTQFQRJ5IIlUP9d9mdOg6BXxSV20+YGMdFFrAL1TD4fQszGVjpBtWc/Bbv0l77xsvdPBxXI+W7pcZufsDAt8mNsZjNk8xl7Hh34NvQ9eapY8RBHD9fDX8xeO2BVw5HFVubNtKY3PvV/BuRESqFABF5Il4XpxtL3+H49kio3f+e+L484ZCAeaykCn2sOnwL1LfsoW6pvXUN714Fb/7OWw+8HMUcpPcOvtrEBTp6YLmlbbCN5+FEx/Clm3MaxlTK5OFKxct/HWsgN5eG/6emYOmtl0c+vY/J5m+v8ooIvIsKQCKyBNxHIdUuokD7/zXnP3EJZb/XZwFG+Om3GY27f2HdK35sYfuy/sicRyHRKKeHS//ImP9f8zM2CkKRVixDhJ1tqtJIQ/TY9YPcDGzc9Xwd+At631YLoPjxtn5+q/Q1L77axCEReSbRgFQRJ6KZKqRfW9/l3LxF3CYHwAdN0ks2f21Cn+1Yok0iUQjAEMjcOZz2P8W7A13NYk12eKXhZt3FHJw8pMw/L0Nc1Nw/hTkcuDF4iTTXQp/IvJcKACKyFPhOA6xeJpYfO3zPpWnznXjrN3500yNnaZUmGbwju0EsmM/bNgKVy9AIgXrtnJvNUi5BGePQmYW9r0K06PVreBwXFZt+bM0tm1/nm9LRCJMAVBE5CEcx2XN1p8k8Muc+fjnKBWmGbkLV8tAYO1srpyC3Gi1FUw2B4MD0FAHAxdharoa/tZu/yn2vPldYvH65/m2RCTCFABFRB6B43qs3f4XATjz8c8RlKYJ/HCFcxgCywUIwmHgUs3q5yDc3aQS/na/+V1i4ZCyiMjzoAAoIvKIHDfG2u0/BY7D2Y//GqMT0xDY7h/egql8lfCXzcPwGBRL1fAXV/gTkedMAVBEZBkc12Pttr+I48S4cvIfUc4PEQum8NwcrudTWdPheOB6CbxEPSS76F31Lrtf+yVV/kTkhaAAKCKyTI7rsWbbT9K78c8Q+Dmy2RlKuWGS8dy9AFj2HTaVWmlo6sCL1dllqWaBIiJfMQVAEZHH4DgO8UQdUEci1QZ881Y/i8g319ezKZeIiIiIPDYFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIUQAUERERiRgFQBEREZGIiT3vExD5OgmCAIChoSEymcxzPhsReRpmZmaA6u+3SBQoAIosQ+U/FFu2bHnOZyIiT9vMzAzNzc3P+zREvhJOoH/yiDwy3/fp7++nsbERx3Ge9+mIyFMQBAEzMzP09vbiupoZJdGgACgiIiISMfqnjoiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRIwCoIiIiEjEKACKiIiIRMz/H6L41bVz4FH9AAAAAElFTkSuQmCC\"></img>\n",
       "</td>\n",
       "<td style=\"text-align: center; vertical-align: top;\">\n",
       "<b><a href=\"https://github.com/haesleinhuepf/napari-process-points-and-surfaces\" target=\"_blank\">nppas.SurfaceTuple</a></b><br/>\n",
       "<table>\n",
       "<tr><td>origin (z/y/x)</td><td>[0.&nbsp;0.&nbsp;0.]</td></tr>\n",
       "<tr><td>center of mass(z/y/x)</td><td>50.000,46.575,42.589</td></tr>\n",
       "<tr><td>scale(z/y/x)</td><td>1.000,1.000,1.000</td></tr>\n",
       "<tr><td>bounds (z/y/x)</td><td>25.500...74.500<br/>2.500...88.500<br/>2.500...83.500</td></tr>\n",
       "<tr><td>average size</td><td>31.277</td></tr>\n",
       "<tr><td>number of vertices</td><td>19040</td></tr>\n",
       "<tr><td>number of faces</td><td>38076</td></tr>\n",
       "\n",
       "</table>\n",
       "\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "(array([[25.5, 44. , 47. ],\n",
       "        [26. , 43.5, 47. ],\n",
       "        [26. , 44. , 46.5],\n",
       "        ...,\n",
       "        [74.5, 56. , 51. ],\n",
       "        [74.5, 56. , 52. ],\n",
       "        [74.5, 56. , 53. ]]),\n",
       " array([[    2,     1,     0],\n",
       "        [    4,     3,     0],\n",
       "        [    4,     0,     1],\n",
       "        ...,\n",
       "        [19038, 18870, 18872],\n",
       "        [19038, 18872, 19039],\n",
       "        [19039, 18872, 18852]], dtype=int64))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surface"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e55e713-e535-4f11-b19b-589b84505889",
   "metadata": {},
   "source": [
    "Technically, it is a tuple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ec3216dc-2ff6-460f-97c9-397eeff4c2e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(surface, tuple)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46a04221-c759-45be-a69f-90d738f92297",
   "metadata": {},
   "source": [
    "The tuple contains vertices and faces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9dbbb259-d444-428b-84b7-ed471dbaa9e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "vertices, faces = surface"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7cbfd62-c8c4-4f90-96a0-e09084396021",
   "metadata": {},
   "source": [
    "Vertices are lists of lists of Z/Y/X coordinates in 3D space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9af4fe17-5988-4006-8aa5-588eaef129d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[25.5, 44. , 47. ],\n",
       "       [26. , 43.5, 47. ],\n",
       "       [26. , 44. , 46.5],\n",
       "       ...,\n",
       "       [74.5, 56. , 51. ],\n",
       "       [74.5, 56. , 52. ],\n",
       "       [74.5, 56. , 53. ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vertices"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2c9e671-f65f-441f-93f7-8e31077dc98f",
   "metadata": {},
   "source": [
    "Faces are lists of lists of indices. Every triangle has three point coordinates indexed like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "02561534-d60b-4d78-855d-515acdf7d9a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    2,     1,     0],\n",
       "       [    4,     3,     0],\n",
       "       [    4,     0,     1],\n",
       "       ...,\n",
       "       [19038, 18870, 18872],\n",
       "       [19038, 18872, 19039],\n",
       "       [19039, 18872, 18852]], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faces"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2de7d766-c833-4775-8cf2-c8757dd0c434",
   "metadata": {},
   "source": [
    "## Surfaces from individual labels\n",
    "If we have a label image as starting point, we can also turn individual objects into surfaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6932814b-452e-4d71-8038-2ec78c928f56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr>\n",
       "<td>\n",
       "<img 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\"></img>\n",
       "</td>\n",
       "<td style=\"text-align: center; vertical-align: top;\">\n",
       "<b><a href=\"https://github.com/clEsperanto/pyclesperanto_prototype\" target=\"_blank\">cle._</a> image</b><br/>\n",
       "<table>\n",
       "<tr><td>shape</td><td>(100,&nbsp;100,&nbsp;100)</td></tr>\n",
       "<tr><td>dtype</td><td>uint32</td></tr>\n",
       "<tr><td>size</td><td>3.8 MB</td></tr>\n",
       "<tr><td>min</td><td>0.0</td></tr><tr><td>max</td><td>2.0</td></tr>\n",
       "</table>\n",
       "\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
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       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]],\n",
       "\n",
       "       [[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]]], dtype=uint32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = cle.voronoi_otsu_labeling(binary_image, spot_sigma=6)\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "79e8ca48-ecc9-435f-9edf-862c636aa0a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr>\n",
       "<td>\n",
       "<img 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\"></img>\n",
       "</td>\n",
       "<td style=\"text-align: center; vertical-align: top;\">\n",
       "<b><a href=\"https://github.com/haesleinhuepf/napari-process-points-and-surfaces\" target=\"_blank\">nppas.SurfaceTuple</a></b><br/>\n",
       "<table>\n",
       "<tr><td>origin (z/y/x)</td><td>[0.&nbsp;0.&nbsp;0.]</td></tr>\n",
       "<tr><td>center of mass(z/y/x)</td><td>50.000,53.374,48.714</td></tr>\n",
       "<tr><td>scale(z/y/x)</td><td>1.000,1.000,1.000</td></tr>\n",
       "<tr><td>bounds (z/y/x)</td><td>25.000...75.000<br/>25.000...89.000<br/>12.000...84.000</td></tr>\n",
       "<tr><td>average size</td><td>28.026</td></tr>\n",
       "<tr><td>number of vertices</td><td>8278</td></tr>\n",
       "<tr><td>number of faces</td><td>16552</td></tr>\n",
       "\n",
       "</table>\n",
       "\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "(array([[26., 42., 49.],\n",
       "        [26., 43., 48.],\n",
       "        [25., 43., 49.],\n",
       "        ...,\n",
       "        [75., 57., 49.],\n",
       "        [75., 57., 50.],\n",
       "        [75., 57., 51.]]),\n",
       " array([[   2,    1,    0],\n",
       "        [   2,    4,    3],\n",
       "        [   0,    4,    2],\n",
       "        ...,\n",
       "        [8095, 8276, 8094],\n",
       "        [8277, 8276, 8095],\n",
       "        [8083, 8277, 8095]], dtype=int64))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nppas.largest_label_to_surface(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "31beeea3-9184-4fad-8910-595794f34db9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr>\n",
       "<td>\n",
       "<img 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DzEVDGBZEICA56y1GrtzVW9//9eVm7jS8ISPTFpqy7oADAJ3ibPqz5l2adfe1uNv0zZp//NuuleKp2fDSakwPt+xSsO33dfJlLTnGenhXfEIXzNtVjr8knT01TgCb3widbTP/1ojE1LHGjcaGP1O2vulY0daj8AdT0ozI1Juwk1xh6FUDt36wHLo1j0Wi1KhWPtc1RF49cJbOvqjX9XhL/9bIhDAsmAKGPCYtVZjI1f1zvd/TZO3f9z0bN90Slo3IK3pkwZ6pd5uqbvTBVRb1oWKCRYRf8/Vxd9PpMELra/ni+LvoRbiT3Lvqb3LRWD/+hZepKJclN7/mXTxrHtdY6SB9dKhl6X2zsaPiSIwmg42QSWgM1JHm9TVIfV2Sf297ve5rl/q625+3Jm0+zx09XUd/dGvanriEtPBAO4aAQh4LDc1rHd++Lc0MfRjheH8Jz4YLXxiRMvxl3XPFcXftcqU7kJnGVvrRhmj+GvlrOTZ46+LwFYH0cqlOAKj39FiInChY1roaifGuJHXUlkaHnxdR3/0a5rJ3Wrt4AGgCaaAAY/duXVGU8NvSFrcPn+NTIxKJ3/a+rRvfkY6WRn5k9z3e9fFW8c0ZKX1u6WN2+IRx6kxKSwscHCB1NnnpmSjCLzxsWQWGEibKkmTuTgCpfjkkIH1bqr36ALTwQdfWODYWhCNAkrS8PU3NXTjjB7asYihTACoQwACnrLWKixelVSaHWWaTxjGl3OLtmwJK581LV17VZpssn5v0yNzp31r4i/r4nDjQ2ppHtgYdwy3rkofHZW62+a//0xB6t4s7drvTmZp75K2H1j4dTY85iJu+EbjCOxvJQJflbraJFt2ayZN4LbACYJ4VNWGcx9brWak04YqzbdQEgBaQAACHrLWqjAzomvn/0yyoYolFyf11++NFIvS4FAcf9ZKanBpt0Y2PSLt/7n54+/A89LGh1sfhYzi7/jrUspIWiAASyXpo1PucY8daP4+67V1xIG31AicnnQf1aItcKIp4MWt6AtlCxdlreVkEABLxhpAwFOfnv62blz4S0nx2b3NWLmIKpfdiF+j6/o2MjvyV7XmbznjbybX+uPCsnT+lHTuROtbshgTR+CaDe57jdYERhHY3tX6ewitOwu4VF74eKIR18otXfzwT1QsTLT2YgDQAAEIeCo3fk5Rxt2LgaT6ad85a/4WiL+aqeaqj+r4k9zzp7LzfyTT7r5h6EYCz51wIVf/3I1Oro0i8NDLbk9AaZ4IfKn5iSF3w1p3ubhIYWZI5cIYZwMDWDKmgAHPWGs1Mz2ukaHzs9/LF9yJBgupnrqUqVrLVvkc3e7qc1f0WOq0r7VSqShd/lCy+fj7oZUufBrHX3e/tPdFqaPJNiqRQl4qvyHdvOrW250/JZUmpfaqqeNyKK3ZLvWvm3tM1SOBC00HH3he+uBN9/P6tZLVU+hWVdPpC7DW7RMY/RvNTA3qg7f/ofa98C2lMj0LPwEA1CEAAY9Ya1XIj+uDt35X40Nvuevxyu3nl0lVIi6ojbnqr6M1a4Gp3cKkupeMkToHquIvGvm76G5nohM+Foi/996SJm66PfOi+5VD93yS1DPgRty6+hYewUwkpYMvSsdfcxEYhlJuVLIz8X2m89KlS9LBl9wZvvNF4LFXpdvXqyLQStsed7+jzm63V2JYN60bhV5NBNr4xJrqUJz9sPH3osvrSZINQ10++13JWu178VtKZXrn/wUAQB0CEPBIIT+h99/8XV058y9lFM8pdrZL/XUDScsxLVwff+lFxN/l8w02SK6MnPUMuBDr6m3tOI2RMm21EdjI1IS7ysehlxeIwJfc2b2zEfiO+/m2x2vvW//YxageGbRWujksTVeC1UoqFkJd+egVSdK+F/8PRgIBLAprAAFPWGt18+Jf6sqZP1I+X6o6qSAewTPVI3t3qVH8HVxE/MnGI42z95Gb9l1M/EWqI3D91uaPzU26CBy+2XxNYLYSgWs2uu9FEXjxzMJbuizmeJv+e1g3YhmWQ1396E917ZPvLc+LAvAGI4CAR4ZvnZMNi0qlWrvsWr2F1qtF05qFvHT6LWnoqruMXDIl7fmCtGER8ZdMSRu3S11Vo4BWUtc6t+ZvKZE6G4EvSHcGazeCzufdtX/z03EEHnrJnfjRaDQvisBjR6TxETdVe/a4VMxVpm/LC8f0UkPbGKm7I1q7WdbM1HW2hQGwKAQg4A2r4vQV91XorooR/6RykoIarFEL69apRber16xVrWdLZ6Ttm6TEuLS5crGKdFZau3Fx8ffk0/G6uuVkjJRtlzY+Ovc40l3SiTeqIvDVhSPw4e3S7aRm98Up5aSgS7p2Pr7f7NrJ6rWVpm5TaDN3reXsCTeau84yuk5wviCNj48t7y8JwGceAQh4Ij89pKk770lysbN+ID7JY3JKunZrbvwtVjorPfmM1N0lTd2Mv99sYKpp/H0uPrP2fjFG2vCQGx08/nprESi5MAsqJ8NIUiop7TzkevDCmaX9HusD0Bipt9t9RPE9OhGfvT02ck1huaREMrXUtw/AM6wBBDxgrdXQ1SMavX1CUuWauG1SW9YFy3R+7kbPi5XOSgeec9f8be2YVk/8RYxx6wMPvuCmiqU4AodvtP57id7HI48vbZo3Gm2NNoouliojjhmpPev+7VKV/3wPAinM/UylIqOAAFpHAAKeGB2+0vQMhfAu9xOO4m/jttaDJ4q/K+fdSFdqheMvUh2B2TZ3ezqKwCYnhjSSSN5dBNabL8zDUk5hOd/4hwDQAFPAgCfK+YtNfxZUBUr9Zs/1U5Gz69eq9gl89MDi4k+SRq5KpTFp49rK3oF90rbHVjb+IsZI67dIO3dLI4PxlPiNj6S+NS7uWnmOaCQwLEh3bsbrJmf3/6u6bSW3zU2TTaLzBamro/FrlQhAAItEAAIesNZqevKqEgkXc+WqgcAwlPp66k5AkGav9KHqIGzABFJPC5sxzzmmUMpWXX0kldLSTk2+V4w7puorpCSXcHyJpDQwIJmZ5veZLwCjj+m6xycTbnNoWcmooJmZKXWwFSCAFhGAgAfK5ZLa0hPavN411sRU/DMrF2KJRLNHYznc7XYwhWLt7d5uqafLfW2NVbk0T2ECQB0CEPBEYKxMJfLMKphmxdLV7y/oRgzvciEnAK8QgACWVbS+rVSOpy9N3fSutW7j5emZ+D6Z3tU1AyxJNpCmcnFw2aSbMq8ZLA3i0TljpFR5aWdRA8D9RAACHonCJKxsLyIrFctSepn+ElgrFfPSuVPSjcq2Kcm0tGe3+xzd5+YV6YMTbq89SepdI21+TKuuADfskK4PSjcuu9vBsJQ6Je0+4Nb2GSP1b5UufiLdvuEOPzksdayXHtq5vCOt5bCyHYziE3BmRwFX2e8NwOpHAAIesNZq+E5RhTE3Ojc9I41PuKnDclnavOHu/xhE8XfiJ9LgBfe9ZFra84y0dUdlBK0Sf9FGy5LUM+AuqdbZuzzbpSyX6LJxB16QTrzuIjAMpfPvSbLS7kNu3WRbh7u+8NFX3V6BhYLb3kZyEbhccTY+KY1N1J6VnUxWvk5YrSmWl+eFAHiBlUCAB4zNq5gfUW4mPpu0HFbtLbcMU5aN4m/fF6StlZGwZvH39MurL/4ixkiZrIvADQ+579lQOn9aOntMKlc2aG7rdBG7ZoO7T/UG18s1HVx9Kb5yKI1Nun/L6bw0PR3K2InleSEAXiAAAQ8UClMqztyWVFl/V1jm56+Lv1Ra2vestPXR5iN/vWukp7+4euMv0iwCPz4tnT1eG4GHXpobgVeWKQLrz9Ku/Z2FUkgAAmgdU8CAB/LTwypVtgkxRkqnqvb6C+4ywKx09YyUH5UGet1zbd4Vx58klQrS9XNSb4dkOt1r7jwsdfas7viLRBG453NSsuiiL7TS+KA0ulka2BxH4P7npPffcO/ZWunqh1LPMuzP15Zx0/WSJOsuCRextqxifvTuXwSANwhAwAOBHVZ/d95tGmzcJsKzmz5Xbt8NU473pJOkru7aEyCsdSeaBJXr6xojpTIPRvxFoit7ZDNSWPWX05Zr75PJSl2dUrlqlLW8DCOu/T1Sb1e8UXQ5dNdvtnIjksZOylor8yD9UgGsGAIQ8EAyCNXTaRWGD1Z0IZZINN6sO5peDoLS/T0gAA80AhDwCPH32cO/KYCl4CQQAAAAzxCAAAAAniEAAQAAPEMAAgAAeIaTQADcN7MbIj/gJy5E74MTMAA8qAhAwANWbuPiMHQ3wrCyf5yt7NGXcvsCLvn5k9KdYbcfnZWUvCH1b423LQkSUkHS8J04ntqvSh19d/e695O10u2b0o0hzV46L52RtqZq7zM6LA3dlsJyZb/DtNTbK4XLfPUVALgbBCDggUIpq2s3EyqXi5J1mwhHo1dhKG3eIGXTS3xyI219XCp9JF352H1r8gPJJKXHDlT2r0tKuw5LR49IIzfdfT485jaL3v7k6o9Aa6XBi9Kpn7rL3kku7HYeknrXx/e5fd1d7m56yn0vmZL2HJDMjDR6fUUOHQAaWuV/dgEsB5tYo1KYUbksFUvS1HQ8+metZke0liqVlvZ9wV3+TXJRef6UdO6Eu3yZMVJHl3T4Zam/EkxhWfrgXemT9ysjk6tUFH8n36iNv6c+Lz20y8VrFH/HXq2Nv73PSg/tZKoYwOpDAAIe6OrqUzrTIcnFSPV1ZJdLMi3tbRaBlYtUtHfOjcAPj67eCLRWGrzQQvwNEn8AHiwEIOCBIJFVIr1W0r0Lkmi9294vSFt3uKlfY6QLH0ifVgLPmDgCB9ZLyaSUMNLHp6QrH1WdJLIKWCsNXZM++Jm73m8qKWUyc+Nv9FZlanjGved0pir+lvEvbPWIbc3oLQAsAWsAAQ9YpdTR2a2sdRE2MSX1dLmTccth42vMLkUUgbv2SMGMCxQjKT8ilYtSkIkjcOdu6fp5zZ4RnK+cILKaRsxyt6U1PZJ63O1Mh7RlR+2axcnbUn+H+7CS2rvdfZYz/iRpfFLKTVduGPd7Cqp+V10l/pwDaB1/MQAPGGPU1ZFQWBkxspI62lxEFEu1IXH3r+U+ElUBFBjVrDM0xgVSdXiupvCL1L+PRDD3OI1q30ej+yyHiSkpX4ifu1x2IepuGyVSPTKr8ZcIYFViChjwSBRnePAUS7W3p6bjr40JlEx13t8DAvBAIwAB4AGUqdm2J5BJdK/UoQB4ADEFDHjAGKNSOaF8ZdSoUJDUEf2sdjRJanxygbUNdoupfKP/jtS1ZnGji0FKujPuzgSWpKmitDkntXWs/Cilte6kjls3pYk7lW8aaV3X4o7NWncG9MiIdGes8WONmjynqb1gijFSV0f8vO1tkir/JsYEsoYABNA6AhDwQCKRVDHs1Y3b7nZopTV97uvAuBMMCsWlP/97P5MSKWnjttYDac0WacOwdO6ki8CxSen4a9KhF6XsCkZgFH8nfyINXoq/v3aTtGNf6yd3RPH3wTvSxY/v/ozdjnZpbX+THxqjZJIJHQCt4y8G4Il0+5bGP1iG0CrMSCfecBsmtxo6QULatc9dLSSonEQxNCgde83tp7cSW5zUxN/F+PtrNy0+TMsl6f13pAtnlue9RFdqidZxVn8AwGIRgIAnEpmtjX/QaGp3CQp5t2Hy4MXWny9ISLv2SrvrIvD4CkTgcsZfqeji7+IyxZ8U76sIAMuBKWDAE339m+X+my9005NhPPjXnpUmynf/GqWidPpN6dEn3PNHJRiU442L6yMmSEg797q7njvhpoOjCDzwvNt8eZZx1xW+2z32rHX7ElaXarEovffW4uMvDN2WLJFSSTpzVLr80fKN0Bmt/uslA3iwEICAB4wx6t/0RWW792h67JTCULp6I/55d4e0dcPyvV5xSipnpOuXXGwFgZT8UNq1v/Gm09FIoJF0tioCTx2Ruqt2Nwmt1LFBeuSJpQdRdHm3oU+k6mVzM3np+mB8e6H4s1YavS2dOyPlJtz3kklpxzrJDktb1i/t+JohAAEsJwIQ8ER75xr1rt2jsdunlE7NvfZusIwbGKcz0t5D7jUGL7rPH51yP9u1z43i1Ws0ElgsSKW6zY/PHHM/2/FUPG3cKmula5+6S7cNdEttVddELhXj6dpW4+/oEWlyzH0vkZQef1pav0n65M6yLK0EgHuG/6YEvGGUzGxUEEgTudppy3shnZX2Py9t2uZuh2UXgR+dcidINFK/JtDWRaqMe54Pj0kfvx9vIdOKKP5O/tStV2zEGGntZunQSwvE39Dc+Hvq89K23ct/CTgAuBcYAQQ80jewWVeTbkSuWdwsp3TGRaCMm3aNIlByI4FBgxMbopFASbrx8dznNMYF5Jlj7vaje1obuYzir1iJv/q3auRG/g6+KGXbF4i/V+fG3yN18bfY3+V876HZc3FSCIClIgABTxhjtO6hL6uzb7d052zNz6ampWK5+abEzTYwbjTPaYxUkLQl7wIwnZH2P+d+NicC9zdfE7hzr9TbJ5Vn4u9bK02WpOGb7nnOHJPCgtS/Zv73PjUpfXAijr9URlr7kNTeXvXcRhrYImXuIv6sdT+7M+Yiu9mG2vW/r741kilr3tOnxyakfLRXo3VrI+MpbCtrS7LWcj1gAC0hAAGPdPU9rqe/8ore+f4va/T2WaVS7vtjk1JbZvlGlEbHJfuGG/3LZN1H0wicZ03g+m2137NW6tviImz4hnuewU+k/PD8xzM5VRt/e56Rtj7a+okV88Xftqr4G74pnfhpfFJIKzY9Ij2yR7ry3vxT2tN5aboqhjuq4jUsF/TJqW+pf+1upbPNdosGgBirVQCPGGPUs/aAnv5rr6izb/vs97PpeR60RIMX3b6A+Uq0RBG46RF3O4rAcyebrwmsZ4zU1ikdfkkaqJy13NJUayVso/h7aJHxd2dIerfJmr+gKv6Ovbr4+Nv/c1KmrfXHRIKqWLeyunb+z3Xy1b+jwszI4p8MgHcIQMAzxhj1rj2gTdu+UvW9e/Na1RFobTwdXB2B56sisJWYm43Al10EtvqYdFX8LeZybncqJ3xMjbvvJZLSnsq0b038HZk//uoPM4q/dLbh3Rc8rmJ9NNtQ187/n7MRaFfiUioAHhhMAQOe6uw/IBOkZMOiZvJSKun2sWvEGCmdcgNp1VlhK1cRMXJB0qg5oiuDHHi+yZrA0EWgJO1s4YSOIOnCq63DReC5d6VE/dnCdbJG2vOkm/aNpmvLJc275k6Sxu+4ad/6+Kuf9j12RMpNNn4OY+Lfa/TW1mx2v4N0ZZPrYtHtQxiWa3+H0ZfWSumklGiPn7OtYTi6CEy2bdeBF35PDRdpAoAIQMBLxhg98uTXNH77HV368NvKpOcPr1RS2rCmMuIlzQmnVFZKdEsfNVnHdv2i+3yg2ZrAUPr4Pak05l6rGWulVK+0/SkXY+2d0p7nKlf1WEA6G0fb0DXpxnkptcBI4I1btfHXaM3fsVfnib9A2vGEFOSlQi7+/qYdcfzN5KTTP5OGbqjpZfmMpHUDUntbK6O1oYq5TyqXXVnovgB8RQACnkok2zSw6Xld+vBfy5gFhtAUX9as0dm/QSDteFJKpt1l0BrtMdhKBOZzkk01P4YwlC5fkQoFafdBF2XJlPtohbXSrWvuMnO97S6o5rtvqeC+bnS27/AN6dhrzad9TeBGNHftkz59VypVxWYUcTM56fjr7pjmE1qpUKw98WM+pcKorCz9B6Ap1gACHguDtZIJFIbuLNO7YQJ3dY7HDzfe2kVyEXiibk3ggeekzdGawAU61Bj3uPOnpbPHWz95RKqNv5lc6/v0JZJu7eCc+Ht1gfjbG0dqIzM5dyy3ri58DMbUXRN5AYWZEdmFfpkAvMYIIOCx3oEdymTXaCZ3Q5l0ZdawybCRtW4kar5RpaASgZL04dGFp4PTGXdm7r7n5K7y0WQqtZox7goh50+76dLH54ms6mOvjr9WJZLSnoPStsfc8dXE3zzTvjsrVzNJJCvr+lQbnDNT0kevuano6vclVf1+q37RQeDWALYqn59RsVRQInkPTu8G8JlAAAIe6x3Yqf0v/3OdOPLrGrszpETCrcGrj8B8QRoccluPVE8BR1PCybS0bsSdldtyBIbSw4/Ez7VhvTTVJvWtnSdCJZWz0uVPXAR+fFpSWdq4Zf73OZ2TTh+N4y+VljbskDo6mj+mVJSSQ1I2Id34xH2vWJTOnGo9/qx1J5Jcvy4VZuIQHLwdb4+TTEkP75Bm7sz93c7eNFJiEfM1U2MXdP3Tv9C2x/8LGbPICyYD8AIBCHjMmEAbt/+CJOnEj39dE+NDKpWlZN0l2mYKUqk8zwkaM2493OGXpf51tRHYbE3g8HUpU47347NWGpuSuja4KeFmV+MYeMiF1qXzLgIHP3Enj8w3Mjk9Uxt/+35O2rKj+WuUS9Lpt6XcbalcNWKYL0jTU41fo1H8jQ1L7/443j+wXjLlppd7e6VLJ+d5A4sUaFIf/OS/USpptHnnf04EApiDNYCA56II3P/yv1B7R7fC0MVe9ZRlNu2icD65Cbdf3sgt99hggTWB0RYy8XFIxYJ08ifStQuN1+gZU4mmZ6WHd8pNG1stuJ1LZKH4k+L4u3SutUu5SXcRf89KD+9qfV/CxSjm7+jkq7+la+f/XNbOc4kRAF4iAAHImEBrtvw1ZTofVzqlhhHYitkIvFkbgU88PTcCbYNwCwJ3ybaTP5GufdpCBO5q9f259YYLjfyVii7+Lp5t/b0vNf72ViK2Ov6sbf6x4PtLuyn6IHAfYSgV86M6+erXdfPi/8vG0ABqMAUMQJKUzrTpqWf+W5348W9IGlGhKJUUj/yFYfOze6vlJtzmyfufdfv0SdK6DdLko9KFc/H9wgZ73kWXNyvmpZM/lWSl9fOs79t9QEpIsk2mZSOJhPTEIWn9ZqnU5Gznclk6c1y6eK7xz5vZtFXa8rCUq8TezLR08s2F4++hnfFZxeOj0p3xqtiz8XpBa6W+dVJGza8VHBhpywbN7sptrXTluvtZMT+qwUuvav22/2hxbwzAZxoBCECS2xx6046/IWtDnTzyddVHYCvxF8lNSOfekXq74++Vii5Uwkr1NRrZqh4NK+al99+WRjfNv/lxIpTKC2x4l0pKuZvS+VvN75PPS5evquXp5Iidli6diG9P5hYXf9cvSaffkfLTjR+zdpP02GHpyqnmAWhM7frMclj7O5scH5a1VuZeXfMPwAOHAAQwy5hAmx/9TyWpYQQuph/KZTdqWH82a3Vg1W9VF9Q9f7kkFWfiE0XuRrSp87w/X8Isaf0WL82232sWfyd/Mn/8HXrRrV28G+nEmKRQrPoBECEAAdSIItDI6MSR39RSI7BcF0ImqHtsgxHA+tC7n8vWlm1wrMExN4y/i26au2n8bXbxl22fe6m7BdcEKl4HKEn53A3ZsOgupAwAIgABNGBMoE2P/pIk6eSrX5c0vOgInBOAciN80Sym1dzRsvrnDVs4AeJ+mjNlXTkhpWYj6rqITaalvc9UxV9YGflrMf4a/a6NkYpWmhiLN+iuXjNow9pjHb11TOdP/IF2Hfx7ChJsDg2AAATQRBSBVtKpJURgo6nQ+hG+sH4EsO4567eKWWk1AWikTY+4bW6Slb+k01PSrTfiu9yL+JPccz3ylPT+u27bnYWEYUHn3v09SSICAUhiQQiAebjp4F/S/pf+udJt/YvaIqb+JA9j5gZN/UhVw/uspgKssmW721amvVtKt7uYff9daWzE/TyZqos/uzzxJ0kyUntXvPF2K8JyXufe/T19dPx/VVheYEEkgM88AhDAvKKRwAMv/0tl2gZajsBWRgDLDaZUq7WyB979ZozbS3DfF9zegpK7tu+x16ShQXc7mZL2fmHpa/5aXY+4pAh853/SR8f+FyIQ8BwBCGBBxgTatONvaP/L/2LBCIyiLQyrPir3mTPFW38WcDD3km73NQAXCC9jpM3bXfylKvE33Sj+npW2PuqeLwwXjr91C8SftVKpJBVLctPwpXi0NIrAgfVSNiO1ZaX2Nqm9vfFruengf0QEAp5jDSCAlkQRKEknj/ym5lsTWChKU9PSRM41VSrlLidX33INTwKp3ipmlY0AZrNS71pp+LK7ba306cdx/ElST7cUTkmXT7nbxZL08bn54+/gPPFXLkuffCBduuK+DhLSY/uldJv7eRSBO3ZKQxfjxw08LA0OSoMX5j5nGBZ07ujvS5J2HfoGawIBDxGAAFrWSgQaI2XS7qNaqcEmxnOmgFUbQfV77K04K41eq7pppbHbtXcJi9LYzfh95AtSfqbx07USf+dPSedOuFgOAmnXPunRvS4EI9Hvvfo5MlnpwHPu64YRWM7r3Lv/SBIRCPiIAASwKE0j0LqzYRezn179FLBM7VYx0twzhVeTRoHa6vtfSvw9dkDaua/1q7KkMgtEYGU6OAyL6t/yS1VXaUnKBGklkhkFiYyCIFCipjil9rYOpdJpri4CPKAIQACLFkeg0ckjv6HZCCwtLgLr465+BFBqEImrTYNAXehM5lbi76OT7iMM42nfXftqR/7qX6McanbavFyp6PRCI4FhQWff+X2d+3d/oInJ2XegchhoOp9Qe1tSnR0p9fel1dUR/dRoy8NPau9z/0R9a3cRgcADiAAEsCQuAn9RkpYcgWHo+mn2rg22gVnNI4BLir8t0sEXFh9/O+eJP0lq65eGT0kzucrUdF7qGJC6+haOQGNCHX58Smv742MqFKVrN+Pjb8tKG9bEPy+WLuj4/zeoA196Rf1rHyMCgQcMZwEDWLIoAvdXbxFTOWO1lbV71qomohqOAN6nAGy0B+FyqD7+u4m/haZ9+9ZJe5+TEmk3Ejg2Ir17RJq4444hisBNjzR+/Jwrt9T9Psp1azgDI02PHtfRH/6yRobOya6qxZoAFkIAArgrcQT+0aIjMLqEWbWFrhay2jWLyHWbFxl/QevxF73uus3SoZekTOUM4fG6CIzWBG5uEIH1Z2QHpnbbnuiSc9WvZwIpd4cIBB5EBCCAu+Yi8D/R/pf/SNnObWpra2spAm1lzVrtczW4zwPMqjLyt9g1fwfdmr9WT/iQmkfg0UoESi4C9/2ctGmbe61iUZrJS+OT0siYdGfcfYxNSpM5aXRcujUsXb4ujU3E/57GSInK/4Pk7hzXB29+U6VSfkm/IwD3H2sAASyLKAK71z6j8aG3dOq1v6OJsZuS4o2M80W3PUyq8pcntFJupjZyopEnq/hztWij6ervB0uYvg3r9hgMw8rzBG46NAgaXLmk7N7D7LGEbvubIKgcdyClU7WPaWuT1m2XpkelmdG5x2El3bgmnT9dFX8HpF1751/z10x1BB571e0/ODYiHf2xtHVLfL+ujLRxnQvAYtl9ztftC93V6fY+LJfjf8NCMX6Ps+sDu3boscN/X8lkZvEHDGBFEIAAlo0xgbp6Nqmz+xeVSGZ1/K/+K+VzN2UrW8QkkrUX2whD6ebtOOaMpP5eafP6OAAbhd2tERcikf5uqbNjccc6lXMjXZFEIK1f4z5HAZisC7CZvHR9KD7eZELast69t2aMlYY+mf9Ybg7H8bf7wNx9/hYrisDDL0lHX3UROD0pTY7URm1Hm6S21p+3VJZu3JYGel0ElkOprWu7DnzpO1q3+RlOBAEeIEwBA1h2xgRa//Bf18Gf/7Yy7etnTyioDzpTGTVLVD6iOEmlXGAkk42nQMOyG42KPupPYGhFGNY+R6nsXiuZjEcpG/VM/fGaoHYj5sU2kLVuJDEa+du5d3HTvs0Y464vfPglNx1cP2q6FEHl0na3hqXctJTt3K6DX/ou8Qc8gAhAAPdEHIF/rP6NX1BX3+PKdm6VCVLzPq7+ZISGz133l+tBP/fABG7kb6nTvk2ftyoC0226+wKsrPsrl6Xh0UDb9vxdrSX+gAcSU8AA7pkoAtdueVnWllUu5XXjwl9o5NYpXTn/fU1PnHNnDYeSjFQuuWnWYqnqOeRGxOpHDiPWuse3Eo71rK16rrotaZrdPwzd6GAi0XyUcN7nkNTeIyVT8e2+bdLWXcsbf5EoAg++II1dm/94cxNSaSa+T7TeMhqZNYq/DsNQg+df0bYnfkWpdPfyHziAe4oABHBPGWOUSLqFZslUpx5+4m/pocettj/1X+vdH/5NTQyfdtOxZRcXuWm3AXH8eGn9gJStOr8gk3L3k1ygTOYWfxm66bw7ESRRd+3h+bRlpU3rpXSTqelWFIvSRF7ac0BKRZffvUd7EEaMkQY2SgMbGv/cShq5IV19W+quWhNorTszuK87fr/xiR8P64kv/M9Kprru3YEDuGcIQAD3nTFG3QNP6nNf/VO9+4O/qfHh05JcBKbSc0fz5lxvt2oKODrztqtjcREVmDgipdZmRxMJqf0uR+msla587F5v37NSMn1v4y9ijGrPwKk6njs3pWOvS+W81JWtPZ6ZvDR0R1rb575fLrv4O/Tlf6M1m15g+hd4QLEGEMCKMMaoq/9JPf3VP1PP2s8p09avbFuXSmFmTozN2YB4ha4WshyiadUr56VTb0qlwsodv7XSyE13pnBuIr40XyQ6SSc37SKwVJYyHQ/pMPEHPPAYAQSwYlwEPqFnf+EHyk/flrHTmpmZ0PC1I5ocv6apyTFlEuPqbp9QNhMPC5p2qZScVCGfU6Ewo3Q2r2S61HCEq5lUKCWzaRmTVSbbpky2S51rUnO2fqmXm7ik3MTl1l8nUbtNTHXsXfnYfX7q87X3MYFbD7icfWVDt8ay2uiwdOw1F3+SO5u6PkZnN3uelmTatf/n/5kGiD/ggUcAAlhRxhi1tfeqrb1XktQjad3mZyW5Ew2MaXC5EEk2DFUqFZXPzyiVmFHClObcZz6hlXaXMzJBVplMVon6M02amBr9WEd/9DWN3jre0usM9Eo9Vcvk6gPr6idSUHAbLkeKVnpkn9TVuzwRGJalCx9KuaHatYu3bsXxJ7lInJ6ROquuWFL9+tneZ9W99nPEH/AZQAACWHWiwEgkEpKaDMkFUiKZVSa79JMQsgvfZY7Ovsd1+Cuv6N0f/LLGhhaOwDlXMmlwh3LeRZ9UmZYdlYZH3PYtXX13F4FhKH3ygXT2mLSuv/ZkGluee2zDo+71Oiong0R7LPZtfFkHv/THyravXfrBAFg1WAMIAItgjFFn72N6+quvqHfd05UzUpqcYaEW1is2eGhopbFhtzZv4s7S1wiGofTJ+9KZo42vy9xwk+1Quj3izv4NK9ve9G18WYe+/Cfq7N7K6B/wGcEIIAAsUhSBz/zH/0Hjw2eUm5rUzPSwUmZMY8Pv6eKHf6rpmYIyKalQcNc7jtRfb9fI7QmYqNofOzrLOYrApYwEhmU38nfmqDtzV5p7dnX9tY5nH2ul0XH3uO51L2vfF4k/4LOGAASAJTDGqK1jk9o6NtV8v1yaUVvnFp07+o9lw4LGJlxMNds3MLSS2qUdB+KNoAeGpBNvuGv4LiUCG8VfEEjd66Xuyoy5lVS8Lmmk2fuTpmY69Nief0j8AZ9BTAEDwDJKJLN67PDvavfhbypIpGf33yuVm0/lXjovfXpWSmakTLu04SHp0ItStrIObzHTwWFZ+vh96cO6+Nu1X3r8GWnrHvfRNiDdHm7+PCbI6KGn/q42PvQF4g/4DCIAAWCZJZJZ7Xr6H+ixw99UOtunVCqjwBiVQ9Nww2kbSuffk84ed1u1GCOt2yIdfFHKtrv7tBKBUfydOea+luL427U/nvK9PSgdf12anqo8LnSxGD2vi79vaM+zv6NkKlP/MgA+A4y1D9IWqsDKGh8fV09Pj8bGxtTdzfVPMb+wnNfU2AUVC6PKz4xpbGxUty99T0OXv+eqr44JpJ17pd0HpETSBdmtqy7WZnLuPj0D0qGXpO666eCF4i+RcM93e9Bd9WN6Mn6stW5tYiopJVNR/H1TqXTVdeE+w/jfNXzEGkAAuEeCREZd/btnb2+wVtt2flmnXs9q8ONX5kRgNBIoxRG4bov01CHp4vtupC4sSOePSntfkNJV+9jcuixd+sBtPG0Dt8Zvx56F409yIZlJS8VyRo889U3tefYb3sQf4CsCEADuE2OMMm192vfCH0pWGvxkgQg8WLn+cIfU3RlP0QaSykXVbGQY5t31eiV3vyApbX8ijr+hQen4a/G0b70gkdHO/d/Unme+qVR6KTskAniQEIAAcB/NRuBL/7tkTGUksHZH5tkItC4C48dGXzR77vhzInCfW4k/E2T08J5v6qlnvsmaP8ATBCAA3GfGGGWyvdr34h9KUvMIPO2+XrtuiS+0yPhj5A/wBwEIACtgNgJf+ENJ80fgxEYpGzQd+JO17kofpbIUVLadCSrxd/pnxB+AuQhAAFghbjq4eiTwuw3XBA7dlLZuUMMCtFYaviGdOe02jg6MO/s3EUiXr0uFfJPXJv4ArxGAALCCaqeDTSUCa0cCC4WGD3Vn9l6Xjr0Wn9lbbnzX2tcMMnp4z+/oqWe/oWSSNX+Aj9gIGgBWWBSBe5//A02U9tds9GytNN1kFK86/lrd0NUEWRd/z3xDqVSWq3wAniIAAWAVMMYone3WsbM96u6SNqyR1vZL/b0N7lyZ9l1S/O2txB/TvoDXmAIGgFXEWimTktor+zCHoXSj7i+1DaUbH0mdaamt1135o1iSctPNYzCOv7+vVIr4A3xHAALAAyiVkFIdtdcFHhmTxibm3tfF3z/QU8/8PeIPgCSmgAFgFTFKZzKyqg27eR9h4o++bnfFkJqfE38AGmAEEABWkUymXXfG3NRvdDWPciun9spt/9Lf474enyT+ADRHAALAKhEYo83rM5rKuQCU3EjgRN1GztZK+YILwyBR2fvPuPV/5bKUSRt19u3W2m1f05Of/23iD8AcBCAArBZG6uhsVyIXfyusxF69REIaHnU/M8btER1NHQ9s/pKe+eK/UnvXJiUS/JkHMBdrAAFgFelozyhR/ZfZStMztfcxRkompHX9Uibtoi+0krVGA5u/ooNf+tfq7NlK/AFoir8OALBqGLW3pdW91t0qh+4qIOl0g3saKZl0EXhrRMoXjAY2f1kHv/TH6ujaxAbPAOZFAALAKhIYqS3rRvgkN7p38InKSR3GTf1GI4Tl0K356+l/WJm+L2v35/4H4g9ASwhAAFjFjJF6utz+fqNj8fYw0Zq/ju7tOvTcd9S3/vMyJiD+ALSENYAAsIok0x2SavcBDCoR2NvtglBy8dfevV2HvvId9W94VkGQIP4AtIwABIBVwhijpw7+oqZLD2ts0m3/kpuRZvLuMm9GUlvnZiXTvero2aHDX/mu+jc8S/gBWDSmgAFgFVm/+YBKn/s3OvlXv6ri9OWan3X07NaBr/xb5aeuq7N7o3rXHSb+ACwJAQgAq4gxRpu3vSD9/Hd08q++NhuB7T27dfDLr2hgw4Ga+wLAUjAFDACrjIvA53Xg57+jdPvDau/ZrUOV+DPGzH4AwFIxAggAq5AxRpu2Pa/kV/9cqVRGfev2EH0Alg0BCACrlDFG67c8vdKHAeAziClgAAAAzxCAAAAAniEAAQAAPEMAAgAAeIYABAAA8AwBCAAA4BkCEAAAwDMEIAAAgGcIQAAAAM8QgAAAAJ4hAAEAADxDAAIAAHiGAAQAAPAMAQgAAOAZAhAAAMAzBCAAAIBnCEAAAADPEIAAAACeIQABAAA8QwACAAB4hgAEAADwDAEIAADgGQIQAADAMwQgAACAZwhAAAAAzxCAAAAAniEAAQAAPEMAAgAAeIYABAAA8AwBCAAA4BkCEAAAwDMEIAAAgGcIQAAAAM8QgAAAAJ4hAAEAADxDAAIAAHiGAAQAAPAMAQgAAOAZAhAAAMAzBCAAAIBnCEAAAADPEIAAAACeIQABAAA8QwACAAB4hgAEAADwDAEIAADgGQIQAADAMwQgAACAZwhAAAAAzxCAAAAAniEAAQAAPEMAAgAAeIYABAAA8AwBCAAA4BkCEAAAwDMEIAAAgGcIQAAAAM8QgAAAAJ4hAAEAADxDAAIAAHiGAAQAAPAMAQgAAOAZAhAAAMAzBCAAAIBnCEAAAADPEIAAAACeIQABAAA8QwACAAB4hgAEAADwDAEIAADgGQIQAADAMwQgAACAZwhAAAAAzxCAAAAAniEAAQAAPEMAAgAAeIYABAAA8AwBCAAA4BkCEAAAwDMEIAAAgGcIQAAAAM8QgAAAAJ4hAAEAADxDAAIAAHiGAAQAAPAMAQgAAOAZAhAAAMAzBCAAAIBnCEAAAADPEIAAAACeIQABAAA8QwACAAB4hgAEAADwDAEIAADgGQIQAADAMwQgAACAZwhAAAAAzxCAAAAAniEAAQAAPEMAAgAAeIYABAAA8AwBCAAA4BkCEAAAwDMEIAAAgGcIQAAAAM8QgAAAAJ4hAAEAADyTXOkDAB4k1lpJ0s2bN5XL5Vb4aAAsh4mJCUnx/74BHxCAwCJE/0exa9euFT4SAMttYmJCPT09K30YwH1hLP/JA7QsDEMNDg6qq6tLxpiVPhwAy8Baq4mJCW3atElBwMoo+IEABAAA8Az/qQMAAOAZAhAAAMAzBCAAAIBnCEAAAADPEIAAAACeIQABAAA8QwACAAB4hgAEAADwDAEIAADgGQIQAADAMwQgAACAZwhAAAAAzxCAAAAAniEAAQAAPEMAAgAAeIYABAAA8AwBCAAA4BkCEAAAwDMEIAAAgGcIQAAAAM8QgAAAAJ4hAAEAADxDAAIAAHiGAAQAAPAMAQgAAOAZAhAAAMAzBCAAAIBnCEAAAADPEIAAAACeIQABAAA8QwACAAB4hgAEAADwDAEIAADgGQIQAADAMwQgAACAZwhAAAAAzxCAAAAAniEAAQAAPEMAAgAAeIYABAAA8AwBCAAA4BkCEAAAwDMEIAAAgGcIQAAAAM8QgAAAAJ4hAAEAADxDAAIAAHiGAAQAAPAMAQgAAOAZAhAAAMAz/z/+UcVa0H5GqwAAAABJRU5ErkJggg==\"></img>\n",
       "</td>\n",
       "<td style=\"text-align: center; vertical-align: top;\">\n",
       "<b><a href=\"https://github.com/haesleinhuepf/napari-process-points-and-surfaces\" target=\"_blank\">nppas.SurfaceTuple</a></b><br/>\n",
       "<table>\n",
       "<tr><td>origin (z/y/x)</td><td>[0.&nbsp;0.&nbsp;0.]</td></tr>\n",
       "<tr><td>center of mass(z/y/x)</td><td>50.000,18.136,18.136</td></tr>\n",
       "<tr><td>scale(z/y/x)</td><td>1.000,1.000,1.000</td></tr>\n",
       "<tr><td>bounds (z/y/x)</td><td>36.000...64.000<br/>2.000...38.000<br/>2.000...38.000</td></tr>\n",
       "<tr><td>average size</td><td>15.361</td></tr>\n",
       "<tr><td>number of vertices</td><td>2472</td></tr>\n",
       "<tr><td>number of faces</td><td>4952</td></tr>\n",
       "\n",
       "</table>\n",
       "\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "(array([[37., 10., 15.],\n",
       "        [37., 11., 14.],\n",
       "        [36., 11., 15.],\n",
       "        ...,\n",
       "        [64., 21., 15.],\n",
       "        [64., 21., 16.],\n",
       "        [64., 21., 17.]]),\n",
       " array([[   2,    1,    0],\n",
       "        [   2,    4,    3],\n",
       "        [   0,    4,    2],\n",
       "        ...,\n",
       "        [2376, 2470, 2375],\n",
       "        [2471, 2470, 2376],\n",
       "        [2367, 2471, 2376]], dtype=int64))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nppas.label_to_surface(labels, label_id=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5a797ea5-1e25-4449-90df-7270a3e9aa85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr>\n",
       "<td>\n",
       "<img 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\"></img>\n",
       "</td>\n",
       "<td style=\"text-align: center; vertical-align: top;\">\n",
       "<b><a href=\"https://github.com/haesleinhuepf/napari-process-points-and-surfaces\" target=\"_blank\">nppas.SurfaceTuple</a></b><br/>\n",
       "<table>\n",
       "<tr><td>origin (z/y/x)</td><td>[0.&nbsp;0.&nbsp;0.]</td></tr>\n",
       "<tr><td>center of mass(z/y/x)</td><td>50.000,53.374,48.714</td></tr>\n",
       "<tr><td>scale(z/y/x)</td><td>1.000,1.000,1.000</td></tr>\n",
       "<tr><td>bounds (z/y/x)</td><td>25.000...75.000<br/>25.000...89.000<br/>12.000...84.000</td></tr>\n",
       "<tr><td>average size</td><td>28.026</td></tr>\n",
       "<tr><td>number of vertices</td><td>8278</td></tr>\n",
       "<tr><td>number of faces</td><td>16552</td></tr>\n",
       "\n",
       "</table>\n",
       "\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "(array([[26., 42., 49.],\n",
       "        [26., 43., 48.],\n",
       "        [25., 43., 49.],\n",
       "        ...,\n",
       "        [75., 57., 49.],\n",
       "        [75., 57., 50.],\n",
       "        [75., 57., 51.]]),\n",
       " array([[   2,    1,    0],\n",
       "        [   2,    4,    3],\n",
       "        [   0,    4,    2],\n",
       "        ...,\n",
       "        [8095, 8276, 8094],\n",
       "        [8277, 8276, 8095],\n",
       "        [8083, 8277, 8095]], dtype=int64))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nppas.label_to_surface(labels, label_id=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "402d4776-697d-4fa9-8956-352176d878a8",
   "metadata": {},
   "source": [
    "## Creating surfaces using vedo\n",
    "\n",
    "Vedo also offers functions for creating surfaces such as `iso_surface()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9dad1e20-9d4f-4967-8b3c-6fbab650b810",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Mesh(0x000001FA8D537C50) at 0x000001FA9EC62760>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volume = vedo.Volume(binary_image)\n",
    "\n",
    "iso_surface = volume.isosurface()\n",
    "iso_surface"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c910826-35c3-4b2f-baf2-e93e117d5207",
   "metadata": {},
   "source": [
    "The resulting data structure is a vedo Mesh. You can access its points and faces as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "59f05510-b48a-41d8-b58b-4298cabcb0ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[49.       , 11.       ,  2.3333333],\n",
       "       [50.       , 11.       ,  2.3333333],\n",
       "       [51.       , 11.       ,  2.3333333],\n",
       "       ...,\n",
       "       [52.666668 , 55.       , 83.       ],\n",
       "       [52.       , 55.666668 , 83.       ],\n",
       "       [52.       , 55.       , 83.666664 ]], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iso_surface.points()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2adc890b-3599-4f7a-a685-e173725d6842",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 95, 108],\n",
       " [0, 1, 97],\n",
       " [95, 0, 97],\n",
       " [1, 2, 99],\n",
       " [97, 1, 99],\n",
       " [99, 2, 111],\n",
       " [3, 106, 120],\n",
       " [3, 4, 109],\n",
       " [106, 3, 109],\n",
       " [108, 109, 4]]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iso_surface.faces()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ddae3fa-21d0-45f0-92e8-8a38f8ec1bd1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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