{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.spatial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters = 3\n",
    "mean = np.random.randint(5, size=clusters)\n",
    "sd = [0.25, 0.25, 0.3]\n",
    "dim = 2\n",
    "nb = 50\n",
    "K= clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance(points,Pc):   \n",
    "    return scipy.spatial.distance.cdist(points[:,:], Pc[:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kmeans(points = [0,0], K = 1, nb=1, dim=2):\n",
    "    # Initialisation K prototypes\n",
    "    Pc_index = []\n",
    "    Pc_save = np.zeros([K,dim])\n",
    "    clusters = []\n",
    "    iter = 0\n",
    "    eps = 0.1\n",
    "\n",
    "    for i in range(0,K):\n",
    "        Pc_index.append(np.random.randint(0,nb))\n",
    "    Pc = points[Pc_index,:]\n",
    "\n",
    "    # print(Pc.shape)\n",
    "    # print(points.shape)\n",
    "\n",
    "    while (np.mean(distance(Pc,Pc_save)) > eps and iter < 10):\n",
    "        iter += 1\n",
    "        Pc_save = Pc\n",
    "        # print(Pc.shape[1])\n",
    "        # toto = points[:,:Pc.shape[0]]\n",
    "        # print(toto.shape)\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(0,2*nb):\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",
    "    return Pc, points\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "points = gen_points(mean,sd,nb,dim,clusters)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "Pc, clusters = kmeans(points,K=K,nb=nb,dim=dim)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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\n",
    "\n",
    "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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(103230, 3)\n"
     ]
    }
   ],
   "source": [
    "from skimage import io\n",
    "\n",
    "path_image = \"fruits.jpg\"\n",
    "my_img = io.imread(path_image)\n",
    "Mat = img_2_mat(my_img)\n",
    "print(Mat.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0. 1. 2.]\n",
      "  [0. 1. 2.]\n",
      "  [0. 1. 2.]]\n",
      "\n",
      " [[0. 1. 2.]\n",
      "  [0. 1. 2.]\n",
      "  [0. 1. 2.]]]\n",
      "[[0. 1. 2.]\n",
      " [0. 1. 2.]\n",
      " [0. 1. 2.]\n",
      " [0. 1. 2.]\n",
      " [0. 1. 2.]\n",
      " [0. 1. 2.]]\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "tuple index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [108], line 11\u001b[0m\n\u001b[1;32m      7\u001b[0m B \u001b[39m=\u001b[39m img_2_mat(A)\n\u001b[1;32m      9\u001b[0m \u001b[39mprint\u001b[39m(B)\n\u001b[0;32m---> 11\u001b[0m A \u001b[39m=\u001b[39m mat_2_img(B,A)\n\u001b[1;32m     13\u001b[0m \u001b[39mprint\u001b[39m(A)\n",
      "Cell \u001b[0;32mIn [106], line 6\u001b[0m, in \u001b[0;36mmat_2_img\u001b[0;34m(mat, my_img)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mmat_2_img\u001b[39m(mat,my_img):\n\u001b[0;32m----> 6\u001b[0m     img_seg \u001b[39m=\u001b[39m mat\u001b[39m.\u001b[39mreshape(my_img\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m], my_img\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m], my_img\u001b[39m.\u001b[39;49mshape[\u001b[39m3\u001b[39;49m])\n\u001b[1;32m      7\u001b[0m     \u001b[39mreturn\u001b[39;00m img_seg\n",
      "\u001b[0;31mIndexError\u001b[0m: tuple index out of range"
     ]
    }
   ],
   "source": [
    "A = np.zeros((2,3,3))\n",
    "for i in range(3):\n",
    "    A[:,:,i] = i\n",
    "\n",
    "print(A)\n",
    "\n",
    "B = img_2_mat(A)\n",
    "\n",
    "print(B)\n",
    "\n",
    "A = mat_2_img(B,A)\n",
    "\n",
    "print(A)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "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": "3.8.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}