{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db2cc66a-3e23-4f4e-a1c0-81dcaf5d3ca2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<NVIDIA GeForce RTX 3050 Ti Laptop GPU on Platform: NVIDIA CUDA (1 refs)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pyclesperanto_prototype as cle\n",
    "\n",
    "cle.select_device(\"RTX\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9147857e-cea5-4062-8392-5f7a2df93a76",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_centroid_distance_matrix(label_image1, label_image2):\n",
    "    centroids1 = cle.centroids_of_labels(label_image1)\n",
    "    \n",
    "    if label_image1 is label_image2:\n",
    "        centroids2 = centroids1\n",
    "    else:\n",
    "        centroids2 = cle.centroids_of_labels(label_image1)\n",
    "\n",
    "    return cle.generate_distance_matrix(centroids1, centroids2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0d6e3cbf-1fb8-4d73-a219-7d890987a35a",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = np.asarray([[0,2,1,4,4],\n",
    "                    [0,2,3,4,4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "295d1bb9-d658-4c0f-96cd-085646ba6623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cl.Array([[0.       , 0.       , 0.       , 0.       , 0.       ],\n",
       "       [0.       , 0.       , 1.1180339, 1.       , 1.5811388],\n",
       "       [0.       , 1.1180339, 0.       , 1.1180339, 2.4999998],\n",
       "       [0.       , 1.       , 1.1180339, 0.       , 1.5811388],\n",
       "       [0.       , 1.5811388, 2.4999998, 1.5811388, 0.       ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generate_centroid_distance_matrix(image, image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3acb4d8-1808-4f3f-bd16-33ca02596d59",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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