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",
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   "execution_count": 174,
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   "execution_count": 196,
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   "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",
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   "execution_count": 175,
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   "execution_count": 197,
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   "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 = 100\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)*0.5\n",
    "path_image = \"fruits.jpg\"\n",
    "# print(mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fonctions à utiliser pour le clustering"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 176,
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   "execution_count": 198,
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   "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",
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   "execution_count": 177,
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   "execution_count": 199,
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   "metadata": {},
   "outputs": [],
   "source": [
    "def distance(points,Pc):   \n",
    "    return scipy.spatial.distance.cdist(points[:,:], Pc[:,:])"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 178,
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   "execution_count": 200,
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   "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",
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   "execution_count": 179,
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   "execution_count": 201,
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   "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",
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   "execution_count": 180,
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   "execution_count": 202,
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   "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",
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   "execution_count": 181,
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   "execution_count": 203,
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   "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",
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   "execution_count": 182,
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   "execution_count": 204,
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   "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",
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   "execution_count": 183,
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   "execution_count": 205,
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   "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",
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   "execution_count": 184,
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   "execution_count": 206,
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   "metadata": {},
   "outputs": [],
   "source": [
    "dist = distance(points,points)\n",
    "# print(dist)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 185,
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   "execution_count": 207,
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   "metadata": {},
   "outputs": [],
   "source": [
    "Pc, index, clusters = kmeans(points,K=K)\n",
    "# print(index)\n",
    "# print(clusters)\n"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 186,
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   "execution_count": 208,
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   "metadata": {},
   "outputs": [
    {
     "data": {
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      "image/png": "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
=======
      "image/png": "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
>>>>>>> a3a978f4f7cba37939976062fd710d891e0bcc2b
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualisation(clusters, index, Pc, K=K)\n",
    "# print(Pc)\n",
    "# print(mean)"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 187,
   "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_348\\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_348\\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_348\\144608849.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)"
=======
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for k in range(1,50):\n",
    "#     Pc, index, img_seg = kmeans_image(path_image=path_image, K=k)"
>>>>>>> a3a978f4f7cba37939976062fd710d891e0bcc2b
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
=======
   "execution_count": 210,
>>>>>>> a3a978f4f7cba37939976062fd710d891e0bcc2b
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(index.shape)\n",
    "# print(Pc)\n",
    "# print(img_seg)"
   ]
  }
 ],
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
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