docs/20h_segmentation_post_processing/merging_labels_based_on_centroid_distance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "50f6743a-03a3-43b5-8e8a-5bb32ff41f6f",
   "metadata": {},
   "source": [
    "# Merging labels according to centroid-distances\n",
    "In this notebook we will merge labels in a label image according to their centroid-distances to each other. Labels close-by will be merged.\n",
    "\n",
    "See also\n",
    "* [Image.sc discussion](https://forum.image.sc/t/measure-distances-between-labels/79125)\n",
    "* [Merging labels using napari-accelerated-pixel-and-object-classifiers](https://github.com/haesleinhuepf/napari-accelerated-pixel-and-object-classification#merging-objects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "626d0d82-dbbb-4092-bf57-80eda98de375",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyclesperanto_prototype as cle\n",
    "from skimage.io import imread"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6585d60-e431-4902-bed9-901afcf4f6ba",
   "metadata": {},
   "source": [
    "For demonstration purposes, we use a modified version of the labels derived from the blobs example-image. We artificially introduce gaps between them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "878e3c57-593c-4ad3-9c3c-096f1eef2c6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<tr>\n",
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       "<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>(254,&nbsp;256)</td></tr>\n",
       "<tr><td>dtype</td><td>uint32</td></tr>\n",
       "<tr><td>size</td><td>254.0 kB</td></tr>\n",
       "<tr><td>min</td><td>0.0</td></tr><tr><td>max</td><td>47.0</td></tr>\n",
       "</table>\n",
       "\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "cl.OCLArray([[ 0,  0,  0, ..., 45, 45, 45],\n",
       "       [ 0,  0,  0, ..., 45, 45, 45],\n",
       "       [ 0,  0,  0, ..., 45, 45, 45],\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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image = imread(\"../../data/blobs.tif\")\n",
    "image[:, 80:150] = 0\n",
    "image[80:130, 100:] = 0\n",
    "\n",
    "image = cle.asarray(image)\n",
    "labels = cle.voronoi_otsu_labeling(image, spot_sigma=4, outline_sigma=3)\n",
    "labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9a0c583-ffd7-4e7a-b5e0-46c67d4013da",
   "metadata": {},
   "source": [
    "From this image, we extract the coordinates of centroids. From these centroids, we can build a distance matrix. In this matrix, the distance from all centroids to all other centroids is computed. The diagonale is zero as it corresponds to the distance of one centroid to itself. Furthermore, the distance to background (first row and first colum) is also zero, as background is not considered for distance computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "faf523b6-2dc8-47ed-a89a-25b30c2f99b2",
   "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>(48,&nbsp;48)</td></tr>\n",
       "<tr><td>dtype</td><td>float32</td></tr>\n",
       "<tr><td>size</td><td>9.0 kB</td></tr>\n",
       "<tr><td>min</td><td>0.0</td></tr><tr><td>max</td><td>324.1848</td></tr>\n",
       "</table>\n",
       "<img src=\"data:image/png;base64,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\"></img>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "cl.OCLArray([[  0.      ,   0.      ,   0.      , ...,   0.      ,   0.      ,\n",
       "          0.      ],\n",
       "       [  0.      ,   0.      , 199.36461 , ..., 245.29527 , 274.9624  ,\n",
       "        306.2263  ],\n",
       "       [  0.      , 199.36461 ,   0.      , ..., 321.00064 , 250.97119 ,\n",
       "        249.23108 ],\n",
       "       ...,\n",
       "       [  0.      , 245.29527 , 321.00064 , ...,   0.      , 139.21213 ,\n",
       "        186.94691 ],\n",
       "       [  0.      , 274.9624  , 250.97119 , ..., 139.21213 ,   0.      ,\n",
       "         48.239468],\n",
       "       [  0.      , 306.2263  , 249.23108 , ..., 186.94691 ,  48.239468,\n",
       "          0.      ]], dtype=float32)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids = cle.centroids_of_labels(labels)\n",
    "\n",
    "distance_matrix = cle.generate_distance_matrix(centroids, centroids)\n",
    "distance_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a140beb6-94f2-4aef-9b4a-bb95a795c074",
   "metadata": {},
   "source": [
    "We can threshold this distance matrix with a given maximum distance. The result is a binary matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d15aff2c-1b37-4aba-9e0f-944ae1dbc257",
   "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>(48,&nbsp;48)</td></tr>\n",
       "<tr><td>dtype</td><td>uint8</td></tr>\n",
       "<tr><td>size</td><td>2.2 kB</td></tr>\n",
       "<tr><td>min</td><td>0.0</td></tr><tr><td>max</td><td>1.0</td></tr>\n",
       "</table>\n",
       "<img src=\"data:image/png;base64,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\"></img>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "cl.OCLArray([[1, 1, 1, ..., 1, 1, 1],\n",
       "       [1, 1, 0, ..., 0, 0, 0],\n",
       "       [1, 0, 1, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [1, 0, 0, ..., 1, 0, 0],\n",
       "       [1, 0, 0, ..., 0, 1, 0],\n",
       "       [1, 0, 0, ..., 0, 0, 1]], dtype=uint8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maximum_distance = 40\n",
    "\n",
    "merge_matrix = distance_matrix <= maximum_distance\n",
    "merge_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2207fcf0-dad3-4b2d-bbc8-3471e071f20f",
   "metadata": {},
   "source": [
    "If we werged labels with the background, all labels would be merged because all touch the background. In order to prevent this, we set the first row and column to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c6a202c4-1edd-494b-83aa-cf3171195c3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr>\n",
       "<td>\n",
       "<img 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EAAAAxHiMBruAQQAALAMFUAAAAzDFDDcEAAx6UWzdUg8W5aM9v1e//GNZ7sVWr0A8UcAhBumgAEAACxDBRAAAMNQAYQbKoAAAACWIQACAABYhilgAAAMwxQw3BAAAQAwDAEQbpgCBgAAsAwVQFjNSy8+U/vd2XregEmoAMINARAAAMMQAOGGAAgAgGEIgHDDPYAAAACWoQIIAIBhqADCDQEQAADDEADhhilgAAAAyxAAAQAALMMUMCa90aYiotmTLtr97qJ5Xl6+O57HBhAZTAHDDRVAAAAAy1ABBADAMMFgUMFg0NP+MBsBEAAAwzAFDDdMAQMAAFiGAAgAgGGuVgC9LBOxa9cuZWdnKzk5WXl5eTp+/Pio2/f396uyslLz5s2T3+/XN77xDe3Zs2dCx8b4MAUMAIBh4jEFfODAAZWVlWnXrl269dZb9cILL6ikpERnz57V9ddfP+w+9957rz7++GO99NJLuvHGG9XV1aUrV65MeNwYOwIgJr3RWoe4/SMVzbYjXo8drxY2Xscdz2sOYGwcx/H0IMdEAuCOHTu0fv16bdiwQZK0c+dOvfXWW6qrq1NNTc2Q7Y8cOaKGhgZ98MEHmjlzpiTp61//+oTHjPFhChgAAAyrt7c3bOnv7x92u4GBATU1Nam4uDhsfXFxsU6cODHsPocPH1Z+fr62b9+ur33ta5o/f74eeeQRXbp0KeLngaGoAAIAYJhITQFnZWWFrd+6dauqqqqGbN/d3a3BwUGlp6eHrU9PT1dnZ+ewx/jggw/0zjvvKDk5WYcOHVJ3d7c2btyoP//5z9wHGAMEQAAADBOpANje3q7U1NTQer/fP+p+X7wFxHGcEW8LCQaD8vl8evXVV5WWlibps2nkv/3bv9Vzzz2nadOmTXj8cMcUMAAAGFZqamrYMlIAnD17tqZMmTKk2tfV1TWkKnjVnDlz9LWvfS0U/iRpwYIFchxHf/zjHyN3EhgWARAAAMPEug1MUlKS8vLyFAgEwtYHAgEVFhYOu8+tt96qjz76SBcuXAit+/3vf69rrrlGc+fOHf9JY1wIgAAAGCYefQDLy8v14osvas+ePTp37pwefvhhtbW1qbS0VJJUUVGhtWvXhrZfvXq1Zs2apR/96Ec6e/asjh07pp/85Cf68Y9/zPRvDHAPIAAA8GzVqlU6f/68tm3bpo6ODuXm5qq+vl7z5s2TJHV0dKitrS20/YwZMxQIBPTggw8qPz9fs2bN0r333quf/vSn8ToFq/gcXvjnWU1NjQ4ePKj/+q//0rRp01RYWKif/exnuummm0LbOI6jp556Srt379Ynn3yiJUuW6LnnntPNN988pmP09vaG3SeB6JvM/fL4biBx9fT0hD1YEUlXf1bs27dP06dPn/D3fPrpp1q9enVUx4r4Ygo4AhoaGrRp0yadPHlSgUBAV65cUXFxsS5evBjaZvv27dqxY4dqa2vV2NiojIwMLV++XH19fXEcOQDARPF6FRwSB1PAEXDkyJGwP7/88su67rrr1NTUpO9+97tyHEc7d+5UZWWl7rnnHknS3r17lZ6ern379umBBx6Ix7ABAIClqABGQU9PjySFXm3T2tqqzs7OsA7pfr9fS5cuHbFDen9//5AO7AAAjAUVQLghAEaY4zgqLy/XbbfdptzcXEkK9UUaT4f0mpoapaWlhZYvdmMHAGAkBEC4IQBG2ObNm/X+++/rtddeG/LZeDqkV1RUqKenJ7S0t7dHZbwAAPMEg0HPC8zGPYAR9OCDD+rw4cM6duxYWBPLjIwMSZ9VAufMmRNaP1qHdL/f7/rKHQAAgImgAhgBjuNo8+bNOnjwoH71q18pOzs77PPs7GxlZGSEdUgfGBhQQ0PDiB3SAQCYKKaA4YYKYARs2rRJ+/bt05tvvqmUlJTQfX1paWmaNm2afD6fysrKVF1drZycHOXk5Ki6ulrTp0/X6tWr4zx6jCSeff7cxLMXn5f96fMHxIbXEEcANB8BMALq6uokScuWLQtb//LLL+v++++XJD366KO6dOmSNm7cGGoEffToUaWkpMR4tAAAwHYEwAgYy29KPp9PVVVVqqqqiv6AAABWowIINwRAAAAMQwCEGx4CAQAAsAwVQAAADOO1lx99AM1HAAQAwDBMAcMNU8AAAACWoQIITJDXnnaj/YY9mb/bS59A+gACsUMVD6MhAAIAYBimgOGGAAgAgGF4CARuuAcQAADAMlQAAQAwDFPAcEMABADAMARAuGEKGAAAwDJUAIEocfsNOpotUbx8t9dxj/Z5PK8JYBMqgHBDAAQAwDAEQLhhChgAAMAyVAABADAMfQDhhgAIAIBhmAKGG6aAAQAALEMFEAAAw1ABhBsCIAAAhiEAwg0BEIiSRO1pF8/+hNHsE0gPQtiEh0DghnsAAQAALEMFEAAAwzAFDDcEQAAADEMAhBumgAEAACxDBRAAAMNQAYQbAiAAAAYixGE0TAEDAABYhgoggEnDS5/ARO7j5+W86G+I4TAFDDcEQAAADEMAhBumgAEAACxDBRAAAMNQAYQbAiAAAIYhAMINARAAAMMQAOGGewABAAAsQwUQQMIYraXJZG6HEs2x0eYFwwkGgwoGg572h9kIgAAAGIYpYLhhChgAAMAyVAABADAMFUC4IQACAGAYAiDcMAUMAABgGSqAAAAYhgog3BAAAQAwDAEQbgiAACYNLz903PrhxbNPoNexRfPYAOxEAAQAwDBUAOGGAAgAgGEIgHBDAAQAwDAEQLihDQwAAIBlqAACAGAYKoBwQwAEAMAwjuMoGAx62h9mYwoYAADAMlQAAUwaidqLz+u46dWHSGMKGG4IgAAAGIYACDdMAQMAgIjYtWuXsrOzlZycrLy8PB0/fnxM+/3mN7/R1KlT9a1vfSu6A0QIARAAAMNcrQB6WcbrwIEDKisrU2VlpZqbm1VUVKSSkhK1tbWNul9PT4/Wrl2rO+64Y6KniwkgAAIAYJh4BMAdO3Zo/fr12rBhgxYsWKCdO3cqKytLdXV1o+73wAMPaPXq1SooKJjo6WICCIAAABgmUgGwt7c3bOnv7x/2eAMDA2pqalJxcXHY+uLiYp04cWLEcb788sv6n//5H23dujVyJ48xIQACAIBhZWVlKS0tLbTU1NQMu113d7cGBweVnp4etj49PV2dnZ3D7vPf//3fevzxx/Xqq69q6lSeSY01rjgAAIaJ1FPA7e3tSk1NDa33+/2j7vfFlkaO4wzb5mhwcFCrV6/WU089pfnz5094nJg4AiAAGGy0EED/QXNFKgCmpqaGBcCRzJ49W1OmTBlS7evq6hpSFZSkvr4+nTp1Ss3Nzdq8ebMkKRgMynEcTZ06VUePHtXtt98+4fHDHVPAAADAk6SkJOXl5SkQCIStDwQCKiwsHLJ9amqqTp8+rZaWltBSWlqqm266SS0tLVqyZEmshm4tKoAAABgmHo2gy8vLtWbNGuXn56ugoEC7d+9WW1ubSktLJUkVFRX605/+pJ///Oe65pprlJubG7b/ddddp+Tk5CHrER0EQAAADBOPALhq1SqdP39e27ZtU0dHh3Jzc1VfX6958+ZJkjo6Olx7AiJ2fA7ve0kIvb29SktLi/cwAGPF813A0cQ9gJNPT0/PmO6rm4irPyseeugh1wc2RtPf369nnnkmqmNFfFEBBADAMLwLGG4IgAAAGCYYDCoYDHraH2bjKeAIqKur08KFC0OPyxcUFOiXv/xl6HPHcVRVVaXMzExNmzZNy5Yt05kzZ+I4YgBf5PP5JrzE+pVbsZKo4wbgjgAYAXPnztXTTz+tU6dO6dSpU7r99tt19913h0Le9u3btWPHDtXW1qqxsVEZGRlavny5+vr64jxyAICJ4vEuYCQWAmAErFixQnfeeafmz5+v+fPn65/+6Z80Y8YMnTx5Uo7jaOfOnaqsrNQ999yj3Nxc7d27V59++qn27dsX76EDAAxEAIQbAmCEDQ4Oav/+/bp48aIKCgrU2tqqzs7OsBdk+/1+LV26dNQXZPf39w95CTcAAGNBAIQbAmCEnD59WjNmzJDf71dpaakOHTqkb37zm6HX4oznBdmSVFNTE/YC7qysrKiOHwAA2IMAGCFXX19z8uRJ/f3f/73WrVuns2fPhj4f6wuyr6qoqFBPT09oaW9vj9rYAQBmoQIIN7SBiZCkpCTdeOONkqT8/Hw1NjbqmWee0WOPPSZJ6uzs1Jw5c0Lbj/SC7Kv8fr+nJp4AAHvRBxBuqABGieM46u/vV3Z2tjIyMsJekD0wMKCGhoZhX5ANAAAQbVQAI+CJJ55QSUmJsrKy1NfXp/379+vtt9/WkSNH5PP5VFZWpurqauXk5CgnJ0fV1dWaPn26Vq9eHe+hA8Zwq1jY+tozW88bVPEwOgJgBHz88cdas2aNOjo6lJaWpoULF+rIkSNavny5JOnRRx/VpUuXtHHjRn3yySdasmSJjh49qpSUlDiPHABgIqaA4cbn8LecEK6+4BvA8OJZAfT6z+hkrdJRVY2Onp4epaamRuW7r/6s+Lu/+zslJSVN+HsGBga0e/fuqI4V8UUFEAAAw1ABhBsCIAAAhiEAwg1PAQMAAFiGCiAAAIahAgg3BEAAAAxDAIQbAiCAhDHaDyW3J1Kj+USr16dh43VePOVrLgIg3HAPIAAAgGWoAAIAYBgqgHBDAAQAwDAEQLhhChgAAMAyVAABADAMFUC4IQACAGAYAiDcEAABJAxT25J4adUSreOOBW1kgMRFAAQAwDBUAOGGAAgAgGEIgHDDU8AAAACWoQIIAIBhqADCDQEQAADDEADhhgAIAIBhCIBwwz2AAAAAlqECCCBhjFaVSOSec/GqtsSzjx89BKOLCiDcEAABADAMARBumAIGAACwDBVAAAAMRBUPoyEAAgBgGKaA4YYpYAAAAMtQAQQAwDDBYFDBYNDT/jAbARAAAMMwBQw3BEAACcNLbzivfeW8/EB0O3a8+u25HTeavfq8HtvLdwMgAAIAYBwqgHBDAAQAwDAEQLghAAIAYBgCINzQBgYAAMAyVAABADAMFUC4IQACAGAYAiDcEAABYAxMbNUSzTYvbmw9NjBZEAABADAMFUC4IQACAGAYAiDc8BQwAACAZagAAgBgmGAwqGAw6Gl/mI0ACACAYZgChhumgAEAACxDBRAAAMMwBQw3BEAA8MjrdJmXvnNe+gRGs8dgvI8drX0lb+cVK0wBww0BEAAAwziO46mKRwA0H/cAAgAAWIYKIAAAhmEKGG4IgAAAGIYACDdMAQMAAFiGCiAAAIahDQzcUAEEAMAwV6eAvSwTsWvXLmVnZys5OVl5eXk6fvz4iNsePHhQy5cv11e/+lWlpqaqoKBAb7311kRPGeNEAAQAj3w+n6dlNF5/iE/0uGM5Ly/H9rJvtHvtRfOamuzAgQMqKytTZWWlmpubVVRUpJKSErW1tQ27/bFjx7R8+XLV19erqalJ3/ve97RixQo1NzfHeOR28jnc6ZkQent7lZaWFu9hAIixeDaZduOlWXM0Gz17Fe2x9fT0KDU11dN3jOTqz4qioiJNnTrxu7yuXLmi48ePj2usS5Ys0S233KK6urrQugULFmjlypWqqakZ03fcfPPNWrVqlZ588skJjRtjRwUQAADDRGoKuLe3N2zp7+8f9ngDAwNqampScXFx2Pri4mKdOHFiTGMOBoPq6+vTzJkzvZ08xoQACAAAhpWVlaW0tLTQMlIlr7u7W4ODg0pPTw9bn56ers7OzjEd61/+5V908eJF3XvvvZ7HDXc8BQwAgGEi1Qewvb09bArY7/ePut8Xp8ev3jPp5rXXXlNVVZXefPNNXXfddRMYMcaLAAgAgGEi1QYmNTV1TPcAzp49W1OmTBlS7evq6hpSFfyiAwcOaP369frFL36h73//+xMeM8aHKWAAAAwT6zYwSUlJysvLUyAQCFsfCARUWFg44n6vvfaa7r//fu3bt0933XXXhM4VE0MFEAAAeFZeXq41a9YoPz9fBQUF2r17t9ra2lRaWipJqqio0J/+9Cf9/Oc/l/RZ+Fu7dq2eeeYZ/fVf/3Woejht2jS6XsQAARAAJrFEbocy2v6TuWeeCecVjzeBrFq1SufPn9e2bdvU0dGh3Nxc1dfXa968eZKkjo6OsJ6AL7zwgq5cuaJNmzZp06ZNofXr1q3TK6+8MuGxY2zoA5gg6AMIINZsDYBuvJ5XLPoAfvvb3/bcB7CxsTGqY0V8cQ8gAACAZZgCBgDAMJFqAwNzEQABADCM4zie7gEkAJqPKWAAAADLUAEEAMAwTAHDDRXACKupqZHP51NZWVloneM4qqqqUmZmpqZNm6Zly5bpzJkz8RskAGN4aebrtq/P5xt18bJ/JJsQR/qaeTmvyeJqGxgvC8xGAIygxsZG7d69WwsXLgxbv337du3YsUO1tbVqbGxURkaGli9frr6+vjiNFAAA2IwAGCEXLlzQfffdp3/7t3/TV77yldB6x3G0c+dOVVZW6p577lFubq727t2rTz/9VPv27YvjiAEApor1q+CQeAiAEbJp0ybdddddQ15k3draqs7OThUXF4fW+f1+LV26VCdOnBjx+/r7+9Xb2xu2AAAwFkwBww0PgUTA/v379d5776mxsXHIZ1ffbZienh62Pj09XR9++OGI31lTU6OnnnoqsgMFAFiBh0DghgqgR+3t7XrooYf07//+70pOTh5xuy/eHHz1RuKRVFRUqKenJ7S0t7dHbMwAAMBuVAA9ampqUldXl/Ly8kLrBgcHdezYMdXW1up3v/udpM8qgXPmzAlt09XVNaQq+Hl+v19+vz96AwcAGIsKINxQAfTojjvu0OnTp9XS0hJa8vPzdd9996mlpUU33HCDMjIyFAgEQvsMDAyooaFBhYWFcRw5AMBU3AMIN1QAPUpJSVFubm7YumuvvVazZs0KrS8rK1N1dbVycnKUk5Oj6upqTZ8+XatXr47HkAEYxK333GiVHC/7jmV/L/vG89huJlod6+3tVVpamqdjA5FCAIyBRx99VJcuXdLGjRv1ySefaMmSJTp69KhSUlLiPTQAgIGYAoYbn8PfckLgN0cAEzFZK4Bu4nlsN14rgD09PUpNTY3wqMKPMX/+fE2ZMmXC3zM4OKjf//73UR0r4ot7AAEAACzDFDAAAIZhChhuCIAAABiGAAg3TAEDAABYhgogAACGCQaDnh6UoQ+g+QiAAIBhxfNJW69PKHv5bi/7T6ap08k0Fkw+BEAAAAzDPYBwwz2AAAAAlqECCACAYagAwg0BEAAAwxAA4YYpYAAAAMtQAQQAwDBe27jQBsZ8BEAAMFg8W7nYaLJcb6aA4YYpYAAAAMtQAQQAwDBUAOGGAAgAgGEIgHDDFDAAAIBlqAACAGAYKoBwQwAEAMAwBEC4IQACAGAY+gDCDQEQACzlVuWJZk87r8f2MrZ4njcwWRAAAQAwDFPAcEMABADAMARAuKENDAAAgGWoAAIAYBgqgHBDAAQAwDAEQLhhChgAAMAyVAABADAMFUC4IQACgKW89rtL1H56buNK1PP6PMdxPDVzJgCajylgAAAAy1ABBADAMF4reFQAzUcABADAMARAuCEAAgBgGAIg3HAPIAAAgGWoAAIAYBgqgHBDAAQAwDAEQLghAAIAhuW1H95o+0/mXnzRPC9gsiAAAgBgmGAw6CmMUgE0HwEQAADDMAUMNzwFDAAAYBkqgAAAGIYKINwQAAEAMAwBEG6YAgYAALAMFUAAwLC8tjSJZkuUeLZiGe37RxtXb2+v0tLSojGkcY0jFvtj8iMAAgBgGAIg3BAAAQAwDH0A4YZ7AAEAACxDAAQAwDCO43heJmLXrl3Kzs5WcnKy8vLydPz48VG3b2hoUF5enpKTk3XDDTfo+eefn9BxMX4EQAAADBOPAHjgwAGVlZWpsrJSzc3NKioqUklJidra2obdvrW1VXfeeaeKiorU3NysJ554Qlu2bNHrr7/u9fQxBj6Hif6EEMunxwAg3rz8aIr2U8CjGctTwD09PUpNTY3K8T//syIS9wCOZ6xLlizRLbfcorq6utC6BQsWaOXKlaqpqRmy/WOPPabDhw/r3LlzoXWlpaX67W9/q3fffXfCY8fY8BBIgiCnA7BJb29vvIcwIaON++pnsfr3PBLH+eL5+P1++f3+IdsNDAyoqalJjz/+eNj64uJinThxYtjvfvfdd1VcXBy27gc/+IFeeukl/eUvf9GXvvQlj6PHaAiACaKvry/eQwCAmEnUGY+xjLuvry9q55eUlKSMjAx1dnZ6/q4ZM2YoKysrbN3WrVtVVVU1ZNvu7m4NDg4qPT09bH16evqIY+ns7Bx2+ytXrqi7u1tz5szxdgIYFQEwQWRmZqq9vV0pKSny+Xzq7e1VVlaW2tvbozaVYBqu2fhxzcaPazZ+tlwzx3HU19enzMzMqB0jOTlZra2tGhgY8PxdjuMMmUYervr3eV/cfrjvcNt+uPWIPAJggrjmmms0d+7cIetTU1ON/gczGrhm48c1Gz+u2fjZcM1iUdlMTk5WcnJy1I/zebNnz9aUKVOGVPu6urqGVPmuGq5S2dXVpalTp2rWrFlRGys+w1PAAADAk6SkJOXl5SkQCIStDwQCKiwsHHafgoKCIdsfPXpU+fn53P8XAwRAAADgWXl5uV588UXt2bNH586d08MPP6y2tjaVlpZKkioqKrR27drQ9qWlpfrwww9VXl6uc+fOac+ePXrppZf0yCOPxOsUrMIUcILy+/3aunWr6/0Y+D9cs/Hjmo0f12z8uGZmWLVqlc6fP69t27apo6NDubm5qq+v17x58yRJHR0dYT0Bs7OzVV9fr4cffljPPfecMjMz9eyzz+qHP/xhvE7BKvQBBAAAsAxTwAAAAJYhAAIAAFiGAAgAAGAZAiAAAIBlCIAJaNeuXcrOzlZycrLy8vJ0/PjxeA9pUjl27JhWrFihzMxM+Xw+vfHGG2GfO46jqqoqZWZmatq0aVq2bJnOnDkTn8FOAjU1Nfr2t7+tlJQUXXfddVq5cqV+97vfhW3DNQtXV1enhQsXhhoXFxQU6Je//GXoc66Xu5qaGvl8PpWVlYXWcd2A2CEAJpgDBw6orKxMlZWVam5uVlFRkUpKSsIerbfdxYsXtWjRItXW1g77+fbt27Vjxw7V1taqsbFRGRkZWr58ubXvW25oaNCmTZt08uRJBQIBXblyRcXFxbp48WJoG65ZuLlz5+rpp5/WqVOndOrUKd1+++26++67Q2GF6zW6xsZG7d69WwsXLgxbz3UDYshBQvnOd77jlJaWhq37q7/6K+fxxx+P04gmN0nOoUOHQn8OBoNORkaG8/TTT4fWXb582UlLS3Oef/75OIxw8unq6nIkOQ0NDY7jcM3G6itf+Yrz4osvcr1c9PX1OTk5OU4gEHCWLl3qPPTQQ47j8N8ZEGtUABPIwMCAmpqaVFxcHLa+uLhYJ06ciNOoEktra6s6OzvDrqHf79fSpUu5hv9fT0+PJGnmzJmSuGZuBgcHtX//fl28eFEFBQVcLxebNm3SXXfdpe9///th67luQGzxJpAE0t3drcHBwSEv1k5PTx/yQm0M7+p1Gu4afvjhh/EY0qTiOI7Ky8t12223KTc3VxLXbCSnT59WQUGBLl++rBkzZujQoUP65je/GQorXK+h9u/fr/fee0+NjY1DPuO/MyC2CIAJyOfzhf3ZcZwh6zA6ruHwNm/erPfff1/vvPPOkM+4ZuFuuukmtbS06H//93/1+uuva926dWpoaAh9zvUK197eroceekhHjx5VcnLyiNtx3YDYYAo4gcyePVtTpkwZUu3r6uoa8lszhpeRkSFJXMNhPPjggzp8+LB+/etfa+7cuaH1XLPhJSUl6cYbb1R+fr5qamq0aNEiPfPMM1yvETQ1Namrq0t5eXmaOnWqpk6dqoaGBj377LOaOnVq6Npw3YDYIAAmkKSkJOXl5SkQCIStDwQCKiwsjNOoEkt2drYyMjLCruHAwIAaGhqsvYaO42jz5s06ePCgfvWrXyk7Ozvsc67Z2DiOo/7+fq7XCO644w6dPn1aLS0toSU/P1/33XefWlpadMMNN3DdgBhiCjjBlJeXa82aNcrPz1dBQYF2796ttrY2lZaWxntok8aFCxf0hz/8IfTn1tZWtbS0aObMmbr++utVVlam6upq5eTkKCcnR9XV1Zo+fbpWr14dx1HHz6ZNm7Rv3z69+eabSklJCVVg0tLSNG3atFCvNq7Z/3niiSdUUlKirKws9fX1af/+/Xr77bd15MgRrtcIUlJSQveVXnXttddq1qxZofVcNyCG4vcAMibqueeec+bNm+ckJSU5t9xyS6hdBz7z61//2pE0ZFm3bp3jOJ+1m9i6dauTkZHh+P1+57vf/a5z+vTp+A46joa7VpKcl19+ObQN1yzcj3/849D/g1/96ledO+64wzl69Gjoc67X2Hy+DYzjcN2AWPI5juPEKXsCAAAgDrgHEAAAwDIEQAAAAMsQAAEAACxDAAQAALAMARAAAMAyBEAAAADLEAABAAAsQwAEAACwDAEQAADAMgRAAAAAyxAAAQAALEMABAAAsAwBEAAAwDIEQAAAAMsQAAEAACxDAAQAALAMARAAAMAyBEAAAADLEAABAAAsQwAEAACwDAEQAADAMgRAAAAAyxAAAQAALEMABAAAsAwBEAAAwDIEQAAAAMsQAAEAACxDAAQAALAMARAAAMAyBEAAAADLEAABAAAs8/8A2ihMizfQtqQAAAAASUVORK5CYII=\"></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>(48,&nbsp;48)</td></tr>\n",
       "<tr><td>dtype</td><td>uint8</td></tr>\n",
       "<tr><td>size</td><td>2.2 kB</td></tr>\n",
       "<tr><td>min</td><td>0.0</td></tr><tr><td>max</td><td>1.0</td></tr>\n",
       "</table>\n",
       "<img src=\"data:image/png;base64,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\"></img>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "cl.OCLArray([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 1, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 1, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 1, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 1, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 1]], dtype=uint8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cle.set_column(merge_matrix, 0, 0)\n",
    "cle.set_row(merge_matrix, 0, 0)\n",
    "\n",
    "merge_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80b121d7-5c03-4382-bd8b-3372f223666d",
   "metadata": {},
   "source": [
    "Using the binary matrix above we can now merge the labels accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e7f50e94-60e2-421c-8a50-b739fa51d9f2",
   "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>(254,&nbsp;256)</td></tr>\n",
       "<tr><td>dtype</td><td>uint32</td></tr>\n",
       "<tr><td>size</td><td>254.0 kB</td></tr>\n",
       "<tr><td>min</td><td>0.0</td></tr><tr><td>max</td><td>4.0</td></tr>\n",
       "</table>\n",
       "\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "cl.OCLArray([[0, 0, 0, ..., 3, 3, 3],\n",
       "       [0, 0, 0, ..., 3, 3, 3],\n",
       "       [0, 0, 0, ..., 3, 3, 3],\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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_labels = cle.merge_labels_according_to_touch_matrix(labels, merge_matrix)\n",
    "merged_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20258bd4-9f74-479e-a7bd-1d5399a6ef21",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}