M2_SETI/D3/TP/TP_SETI_Kmeans/TP1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TP1 KMEANS\n",
    "\n",
    "On nous propose de coder l'algorithme des kmeans afin de faire du clustering sur 2 classes puis plus de 2 classes.\n",
    "Plus tard, on utilisera notre algorithme pour segmenter une image sur l'information de couleur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.spatial\n",
    "from skimage import io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean = [1,2,3,4]\n",
    "# sd = [0.25, 0.25, 0.1, 0.2]\n",
    "clusters = 5\n",
    "dim = 2\n",
    "nb = 50\n",
    "K= clusters\n",
    "mean = np.random.randint(5, size=clusters)\n",
    "mean = mean.T * np.random.random(size=clusters)\n",
    "sd = np.random.random(size=clusters)\n",
    "path_image = \"fruits.jpg\"\n",
    "# print(mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fonctions à utiliser pour le clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_points(mean=1,sd=0.5, nb=100, dim=2, clusters=2):\n",
    "    size = []\n",
    "    # for i in range(0,dim):\n",
    "    size.append(nb)\n",
    "    size.append(dim)\n",
    "    points = np.random.normal(mean[0],sd[0],size=size)\n",
    "    for i in range(1,clusters):\n",
    "        points = np.concatenate((points,np.random.normal(mean[i],sd[i],size=size)),axis=0)\n",
    "    \n",
    "    return points, mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance(points,Pc):   \n",
    "    return scipy.spatial.distance.cdist(points[:,:], Pc[:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kmeans(points = [0,0], K = 1):\n",
    "    # Initialisation K prototypes\n",
    "    dim = points.shape[1]\n",
    "    N = points.shape[0]\n",
    "    iter = 0\n",
    "    eps = 0.1\n",
    "    Pc_index = []\n",
    "    Pc_save = np.zeros([K,dim])\n",
    "    clusters = []\n",
    "\n",
    "    for i in range(0,K):\n",
    "        Pc_index.append(np.random.randint(0,N))\n",
    "    Pc = points[Pc_index,:]\n",
    "\n",
    "    while (np.mean(distance(Pc,Pc_save)) > eps and iter < 3):\n",
    "        iter += 1\n",
    "        Pc_save = Pc\n",
    "        # print(Pc)\n",
    "        # print(points[:,:Pc.shape[0]])\n",
    "        dist = distance(points=points[:,:Pc.shape[1]],Pc=Pc)\n",
    "        clust = np.argmin(dist, axis=1)\n",
    "        clust = np.expand_dims(clust, axis=0)\n",
    "        points = np.append(points[:,:Pc.shape[1]], clust.T, axis=1)\n",
    "        # print(points)\n",
    "        Pc = np.zeros([K,dim])\n",
    "        index = np.array([])\n",
    "\n",
    "        for n in range(0,N):\n",
    "            for k in range(0,K):\n",
    "                index = np.append(index, (clust==k).sum())\n",
    "                if points[n,-1] == k:\n",
    "                    # print(points)\n",
    "                    # print(Pc)\n",
    "                    Pc[k,:] = np.add(Pc[k,:], points[n,:-1])\n",
    "\n",
    "        for k in range(0,K):\n",
    "            Pc[k,:] = np.divide(Pc[k,:],index[k])\n",
    "\n",
    "        # print(Pc)\n",
    "        indice = points[:,-1]\n",
    "        points = points[:,:-1]\n",
    "    return Pc, indice, points\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors=['red', 'green','yellow','blue','purple', 'orange']\n",
    "def visualisation(points, index, Pc=[0,0], K=1):\n",
    "    if(points.shape[1]==2):\n",
    "        # for k in range(0,K):\n",
    "        for n in range(0,len(points)):\n",
    "            plt.plot(points[n,0], points[n,1], 'o', color=colors[int(index[n])])\n",
    "        plt.plot(Pc[:,0],Pc[:,1],'r+')\n",
    "        plt.grid(True)\n",
    "        plt.axis([min(mean)-1,max(mean)+1,min(mean)-1,max(mean)+1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def img_2_mat(my_img):\n",
    "    mat = my_img.reshape(my_img.shape[0]*my_img.shape[1],my_img.shape[2])\n",
    "    return mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mat_2_img(mat,my_img):\n",
    "    img_seg = mat.reshape(my_img.shape[0], my_img.shape[1], my_img.shape[2])\n",
    "    return img_seg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kmeans_image(path_image, K):\n",
    "    my_img = io.imread(path_image)\n",
    "    imgplot = plt.imshow(my_img)\n",
    "    Mat = img_2_mat(my_img)\n",
    "    \n",
    "    Pc, index, clusters = kmeans(Mat, K)\n",
    "\n",
    "    for k in range(Mat.shape[0]):\n",
    "        Mat[k,:] = np.floor(Pc[index[k],:])\n",
    "\n",
    "    img_seg = mat_2_img(Mat, my_img)\n",
    "\n",
    "    io.imsave(path_image.split('.')[0] + \"_%d.jpg\" % K, img_seg)\n",
    "    imgplot = plt.imshow(img_seg)\n",
    "    return Pc, index, img_seg\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "points, mean = gen_points(mean,sd,nb,dim,clusters)\n",
    "# print(points.shape)\n",
    "# print(points.mean(axis=0))\n",
    "# print(points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist = distance(points,points)\n",
    "# print(dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Pc, index, clusters = kmeans(points,K=K)\n",
    "# print(index)\n",
    "# print(clusters)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD4CAYAAADvsV2wAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAzZUlEQVR4nO2df3QcZ3nvP4+EIkcRKMTG25wQrZIS6OWiEpBP8G1zb+TIPScxddL2pvcqdxMcwFVtLiVum9NSdEtsDqK01xQHQgDjAA7aE9ELgSTUORzsWAZ6MWBzkyghTWISSSRN5cQGJYoS2ZGe+8fMSqPVzP6cnZ1dPZ9z5mh35p2ZZ0e733nneZ73eUVVMQzDMOqfhmobYBiGYUSDCb5hGMYywQTfMAxjmWCCbxiGsUwwwTcMw1gmvKbaBgSxatUq7ejomH//0ksvcdZZZ1XPoAKIu41xtw/ib6PZBydfPskzLzzDqdlTnNF4Bue97jzOOfOcgve3a1ge+ew7evTo86r6Bt+NqhrLpaurS70cPHhQ407cbYy7farxt3G52zf40KC2DLQo25lfWgZadPChwYKPsdyvYbnksw84ogG6WrZLR0RWiMhPRORBEXlERHb4tLlBRJ4TkQfcZXO55zUMI3r6D/QzfXp60brp09P0H+ivkkVGMYThw58BLlfVtwMXA1eIyFqfdl9X1YvdZU8I5zUMI2LGJ8eLWm8UT3okTceuDhp2NNCxq4P0SDq0Y5ct+O5TxJT7tsldbPiuYdQh7W3tRa03iiM9kqbv3j7GJsdQlLHJMfru7QtN9EVDKK0gIo3AUeBNwOdU9a+ztt8A/B3wHPA48Oeq+kuf4/QBfQCJRKJraGhoftvU1BStra1l21pJ4m5j3O2D+Nu43O3bP7GfnY/vZGZuZn5dc0MzN735JtYn1hd0jOV+DXPRe7iXiZmJJesTzQmG1jp6mM++devWHVXVNb4bg5z7pSzA2cBB4G1Z61cCze7rPwXuz3csC9qGT9ztU42/jWafE7hNfjqpsl00+elkUQFbVbuGuZDtsiggnllkuxRsHzmCtqGmZarqr0XkIHAF8LBn/QlPsz3AP4R5XsMwoiPVmSLVmaq2GXVJe1s7Y5NjvuvDIIwsnTeIyNnu6zOB3wP+NavNuZ63VwGPlntewzCMemOgZ4CWppZF61qaWhjoGQjl+GH08M8F9rp+/Abgn1T1OyLyMZxHi3uAD4nIVcCrwEnghhDOaxiGUVdknpz6D/QzPjlOe1s7Az0DoT1RlS34qvoQ8A6f9R/1vP4b4G/KPZdhGEa9U0mXmdXSMQzDWCaY4BuGYSwTTPANwzCWCSb4hmEYywQTfMMwKkp2bZj9E/urbdKyxQTfMIyK4VcbZufjO0MtCGYUjgm+YRgVw6+c8szcjJVTrhIm+IZhVAwrpxwvTPANw6gYVk45XpjgG4ZRMfxqwzQ3NIdWG8YojthOYm4YRu3jVxvmunOvs2qbVcIE3zCMipJdG2Z4eLh6xixzzKVjGEZVqeQcrsZiTPCNusGEo/ao9ByuxmJM8I26wISjNvHL058+PW15+hXCBN+oC0w4apM45+nX4xOjCb5RF8RZOIxg4pqnX69PjGHMabtCRH4iIg+KyCMissOnTbOIfF1EjonIj0Wko9zzGoaXuAqHkZtKz+FaKvX6xBhGD38GuFxV3w5cDFwhImuz2rwf+JWqvgn4NPD3IZzXMOaJq3BUm7i7JVKdKXZv3E2yLYkgJNuS7N64u+p5+qU8Mcb9WkM4c9oqMOW+bXIXzWp2NbDdff0N4FYREXdfwyibSk/+XItk3BKZnmrGLQHE6rpUcg7XUmlva2dscsx3vR+1cq1D8eGLSKOIPAAcB76nqj/OanIe8EsAVX0VmARWhnFuw8iQ6kwxum2UuZvnGN02GqsfWjWoV7dEFBT7xFgr11rC7GSLyNnAt4A/U9WHPesfBq5Q1afd978A3qWqz2ft3wf0ASQSia6hoaH5bVNTU7S2toZmayWIu41xtw/ib2Mt2Xf5ocvRJQ/bIAj3X3Z/1KbNUyvXcP/EfvY8tYfjM8dZ3byazRdsZn1ive8+UV7rfNdv3bp1R1V1jd+2UAUfQEQ+Ckyr6k7Puu8C21X1RyLyGuDfgTfkcumsWbNGjxw5Mv9+eHiY7u7uUG0Nm7jbGHf7IP421pJ9Hbs6fN0SybYko9tGozXMQy1dw0KJ8lrns09EAgU/jCydN7g9e0TkTOD3gH/NanYPsMl9fQ1wv/nvDaOyWCA7OmrlWofhwz8XOCgiDwE/xfHhf0dEPiYiV7ltbgdWisgx4C+AD4dwXsMwchDXDJh6pFaudRhZOg8B7/BZ/1HP61eAPy73XIZhFEccM2DqlVq41jbS1jAMY5lggm8YhrFMMME3DMNYJpjgG4ZhLBNM8A3DMJYJJviGUQa1UDDLMDLYJOaGUSK1UjDLMDJYD98wSqRWCmYZRgYTfMMoEZtlKxrMbRYeJviGUSI2y1blqdepBquFCb5hlEitFMyqZcxtFi4m+HXOSHqEXR272NGwg8O9hxlJj1TbpLqhVgpm1TK53Gb15uqJ4vNYlk4dM5Ie4d6+ezk9fRqAmYkZ7u27F4DOVGc1Tasb4l4wKz2SrulpH4OmGjznzHPqKkMqqowv6+HXMQf6D8yLfYbT06c50H+gShYZ2VSyV1cP/u8gtxlQV66eqFxXJvh1zOT4ZFHrjWiptCDXg/87yG128uWTvu1rNUMqqowvE/w6pq29raj1RrRUWpDrJW3Ub3L6esuQiurzmODXMT0DPTS1NC1a19TSRM9AT5UsMrxUWpDrTRS91FuGVFSfxwS/julMdbJx90bakm0g0JxoZuPujRawrTIZv73iP61zWIJcb6Lopd4ypKL6PGVn6YjI+cAdQAJQYLeq3pLVphu4G3jKXXWXqn6s3HMb+elMdc4L/PDwMJ3dJvbVJDsbI5swBTkjFrWcpZOLuGdIFUsUnyeMtMxXgb9U1Z+JyGuBoyLyPVX9eVa7H6jq74dwPsOoWfz89hmSbcnQBbneRNEojzAmMX8WeNZ9/aKIPAqcB2QLvmEse4L884Iwum00WmOMZYeo+vsRSzqYSAfwfeBtqvqCZ3038E3gaeDfgJtU9RGf/fuAPoBEItE1NDQ0v21qaorW1tbQbK0Ecbcx7vZB/G0s177ew71MzEwsWZ9oTjC0dshnj+KI+/WD+NtY6/atW7fuqKqu8d2oqqEsQCtwFPgjn22vA1rd1xuAJ/Idr6urS70cPHhQ407cbYy7farxt7Fc+wYfGtSWgRZlO/NLy0CLDj40GAv7oiDuNta6fcARDdDVULJ0RKQJpwefVtW7fG4qL6jqlPt6H9AkIqvCOLdhVJtiRsvWW3ZJpYm6Xk56JE3v4d66qc+TTRhZOgLcDjyqqv8Y0OY3gAlVVRG5BCcd9ES55zaMalNKDRQLpBZG1DOKLYcZzMLo4f8ucD1wuYg84C4bRGSLiGxx21wDPCwiDwKfAXrdRw/DqGnqoXxBXIn62i6H/2UYWTo/BCRPm1uBW8s9l2HEjXopXxBHor62y+F/aSNtDaMMwixfUCl/da3WjY+6NEQ9l6LIYIJvGGUQVvmCSlXODPW427eXZUuxRF0aop5LUWQwwTeMMggr66ZS/uNQj7tjR1m2FEvUGU2Z8yWaE3WbQWUzXhlGmYSRdVMp/3Gt+6WjzmhKdaY478R5dHd3R3bOKLEevmHEgEr5j8s+7vbtIOIssPA6YveO4U+x8RkTfMPIohpBzkr5j8s+7vbtoOossPDaBL/qBMVnaOGcoH1M8A3DQ7Xmga2Uv9pG9tYvQfEZXst5QfuYD98wPOQKclZaJCvlrw7tuDffXP4xjNAIjMM0cEbQPtbDNwwPtR7krCjmxokVgXGYOU4F7WOCbxgeanHwjTfm0Hu4t2YGVhmlsX9iPx27OhibHEOyihy0NLXAizwTtK8JvmF4qLXBN9kxh4mZiUUxh1odZRsX4nb90iNpdj6+k7HJMQAUnRf9THyGaU4G7W8+fMPwUGvzwOYbWPW+u9/HqVnnCX9scoz33f0+oH6qP1a
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualisation(clusters, index, Pc, K=K)\n",
    "# print(Pc)\n",
    "# print(mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_20188\\152532697.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mPc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimg_seg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkmeans_image\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_image\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpath_image\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mK\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m250\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_20188\\1699160287.py\u001b[0m in \u001b[0;36mkmeans_image\u001b[1;34m(path_image, K)\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mMat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimg_2_mat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmy_img\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mPc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclusters\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkmeans\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mK\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_20188\\1194225063.py\u001b[0m in \u001b[0;36mkmeans\u001b[1;34m(points, K)\u001b[0m\n\u001b[0;32m     28\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mK\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 30\u001b[1;33m                 \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mclust\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     31\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mpoints\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     32\u001b[0m                     \u001b[1;31m# print(points)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mappend\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Sasa\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36mappend\u001b[1;34m(arr, values, axis)\u001b[0m\n\u001b[0;32m   4815\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4816\u001b[0m         \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4817\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4818\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4819\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mconcatenate\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Pc, index, img_seg = kmeans_image(path_image=path_image, K=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(103230,)\n",
      "[[252.69560225 251.67998184 249.86355505]\n",
      " [ 69.54698482  50.76095445  17.63930586]\n",
      " [228.4210873  174.61140875  69.5397857 ]\n",
      " [154.87751731  83.70097198  45.64258942]\n",
      " [         nan          nan          nan]]\n",
      "[[[252 251 249]\n",
      "  [252 251 249]\n",
      "  [252 251 249]\n",
      "  ...\n",
      "  [252 251 249]\n",
      "  [252 251 249]\n",
      "  [252 251 249]]\n",
      "\n",
      " [[252 251 249]\n",
      "  [252 251 249]\n",
      "  [252 251 249]\n",
      "  ...\n",
      "  [252 251 249]\n",
      "  [252 251 249]\n",
      "  [252 251 249]]\n",
      "\n",
      " [[252 251 249]\n",
      "  [252 251 249]\n",
      "  [252 251 249]\n",
      "  ...\n",
      "  [252 251 249]\n",
      "  [252 251 249]\n",
      "  [252 251 249]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[252 251 249]\n",
      "  [252 251 249]\n",
      "  [252 251 249]\n",
      "  ...\n",
      "  [252 251 249]\n",
      "  [252 251 249]\n",
      "  [252 251 249]]\n",
      "\n",
      " [[252 251 249]\n",
      "  [252 251 249]\n",
      "  [252 251 249]\n",
      "  ...\n",
      "  [252 251 249]\n",
      "  [252 251 249]\n",
      "  [252 251 249]]\n",
      "\n",
      " [[252 251 249]\n",
      "  [252 251 249]\n",
      "  [252 251 249]\n",
      "  ...\n",
      "  [252 251 249]\n",
      "  [252 251 249]\n",
      "  [252 251 249]]]\n"
     ]
    }
   ],
   "source": [
    "print(index.shape)\n",
    "print(Pc)\n",
    "print(img_seg)"
   ]
  }
 ],
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