243 lines
6.6 KiB
Text
243 lines
6.6 KiB
Text
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 63,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import scipy.spatial"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 64,
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"metadata": {},
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"outputs": [],
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"source": [
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"clusters = 3\n",
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"mean = np.random.randint(5, size=clusters)\n",
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"sd = [0.25, 0.25, 0.3]\n",
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"dim = 2\n",
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"nb = 50\n",
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"K= clusters"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 65,
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"metadata": {},
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"outputs": [],
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"source": [
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"def gen_points(mean=1,sd=0.5, nb=100, dim=2, clusters=2):\n",
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" size = []\n",
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" # for i in range(0,dim):\n",
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" size.append(nb)\n",
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" size.append(dim)\n",
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" points = np.random.normal(mean[0],sd[0],size=size)\n",
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" for i in range(1,clusters):\n",
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" points = np.concatenate((points,np.random.normal(mean[i],sd[i],size=size)),axis=0)\n",
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" \n",
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" return points"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 66,
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"metadata": {},
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"outputs": [],
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"source": [
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"def distance(points,Pc): \n",
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" return scipy.spatial.distance.cdist(points[:,:], Pc[:,:])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 67,
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"metadata": {},
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"outputs": [],
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"source": [
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"def kmeans(points = [0,0], K = 1, nb=1, dim=2):\n",
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" # Initialisation K prototypes\n",
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" Pc_index = []\n",
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" Pc_save = np.zeros([K,dim])\n",
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" clusters = []\n",
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" iter = 0\n",
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" eps = 0.1\n",
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"\n",
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" for i in range(0,K):\n",
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" Pc_index.append(np.random.randint(0,nb))\n",
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" Pc = points[Pc_index,:]\n",
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"\n",
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" # print(Pc.shape)\n",
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" # print(points.shape)\n",
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"\n",
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" while (np.mean(distance(Pc,Pc_save)) > eps and iter < 10):\n",
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" iter += 1\n",
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" Pc_save = Pc\n",
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" # print(Pc.shape[1])\n",
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" # toto = points[:,:Pc.shape[0]]\n",
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" # print(toto.shape)\n",
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" dist = distance(points=points[:,:Pc.shape[1]],Pc=Pc)\n",
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" clust = np.argmin(dist, axis=1)\n",
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" clust = np.expand_dims(clust, axis=0)\n",
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" points = np.append(points[:,:Pc.shape[1]], clust.T, axis=1)\n",
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" # print(points)\n",
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" Pc = np.zeros([K,dim])\n",
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" index = np.array([])\n",
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"\n",
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" for n in range(0,2*nb):\n",
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" for k in range(0,K):\n",
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" index = np.append(index, (clust==k).sum())\n",
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" if points[n,-1] == k:\n",
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" # print(points)\n",
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" # print(Pc)\n",
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" Pc[k,:] = np.add(Pc[k,:], points[n,:-1])\n",
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"\n",
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" for k in range(0,K):\n",
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" Pc[k,:] = np.divide(Pc[k,:],index[k])\n",
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"\n",
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" # print(Pc)\n",
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" return Pc, points\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 68,
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"metadata": {},
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"outputs": [],
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"source": [
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"points = gen_points(mean,sd,nb,dim,clusters)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 69,
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"metadata": {},
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"outputs": [],
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"source": [
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"Pc, clusters = kmeans(points,K=K,nb=nb,dim=dim)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 109,
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"metadata": {},
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"outputs": [],
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"source": [
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"def img_2_mat(my_img):\n",
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" mat = my_img.reshape(my_img.shape[0]*my_img.shape[1],my_img.shape[2])\n",
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" return mat\n",
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"\n",
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"def mat_2_img(mat,my_img):\n",
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" img_seg = mat.reshape(my_img.shape[0], my_img.shape[1], my_img.shape[2])\n",
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" return img_seg\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 97,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(103230, 3)\n"
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]
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}
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],
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"source": [
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"from skimage import io\n",
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"\n",
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"path_image = \"fruits.jpg\"\n",
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"my_img = io.imread(path_image)\n",
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"Mat = img_2_mat(my_img)\n",
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"print(Mat.shape)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 108,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[[0. 1. 2.]\n",
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" [0. 1. 2.]\n",
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" [0. 1. 2.]]\n",
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"\n",
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" [[0. 1. 2.]\n",
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" [0. 1. 2.]\n",
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" [0. 1. 2.]]]\n",
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"[[0. 1. 2.]\n",
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" [0. 1. 2.]\n",
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" [0. 1. 2.]\n",
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" [0. 1. 2.]\n",
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" [0. 1. 2.]\n",
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" [0. 1. 2.]]\n"
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]
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},
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{
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"ename": "IndexError",
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"evalue": "tuple index out of range",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
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"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",
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"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",
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"\u001b[0;31mIndexError\u001b[0m: tuple index out of range"
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]
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}
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],
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"source": [
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"A = np.zeros((2,3,3))\n",
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"for i in range(3):\n",
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" A[:,:,i] = i\n",
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"\n",
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"print(A)\n",
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"\n",
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"B = img_2_mat(A)\n",
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"\n",
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"print(B)\n",
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"\n",
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"A = mat_2_img(B,A)\n",
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"\n",
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"print(A)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.8.10 64-bit",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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