diff --git a/D3/TP/TP_SETI_Kmeans/Kmeans.py b/D3/TP/TP_SETI_Kmeans/Kmeans.py index f635a67..e661843 100644 --- a/D3/TP/TP_SETI_Kmeans/Kmeans.py +++ b/D3/TP/TP_SETI_Kmeans/Kmeans.py @@ -21,7 +21,7 @@ def kmeans(points = [0,0], K = 1): Pc_index.append(np.random.randint(0,N)) Pc = points[Pc_index,:] - while (np.mean(distance(Pc,Pc_save)) > eps and iter < 3): + while (np.mean(distance(Pc,Pc_save)) > eps and iter < 10): iter += 1 Pc_save = Pc # print(Pc) @@ -74,9 +74,10 @@ def kmeans_image(path_image, K): # imgplot = plt.imshow(img_seg) return Pc, index, img_seg -path_image = "fruits.jpg" +path_image = "images/fruits.jpg" -start_time = time.time() -Pc, index, img_seg = kmeans_image(path_image=path_image, K=2) -end_time = time.time() -print(f"It took {end_time-start_time:.2f} seconds to compute") +for K in range(1,21): + start_time = time.time() + Pc, index, img_seg = kmeans_image(path_image=path_image, K=K) + end_time = time.time() + print(f"It took {end_time-start_time:.2f} seconds to compute for K =", K) diff --git a/D3/TP/TP_SETI_Kmeans/Kmeans_cuda.py b/D3/TP/TP_SETI_Kmeans/Kmeans_cuda.py deleted file mode 100644 index 48387b6..0000000 --- a/D3/TP/TP_SETI_Kmeans/Kmeans_cuda.py +++ /dev/null @@ -1,82 +0,0 @@ -import numpy as np -import pycuda.autoinit -import pycuda.driver as cuda -from pycuda.compiler import SourceModule -import time - -# Load the image and convert it to a NumPy array -from PIL import Image -im = Image.open('fruits.jpg') -im_data = np.array(im) - -# Convert the image data to float32 and normalize it -im_data = im_data.astype(np.float32) / 255 - -# Create a CUDA kernel to perform K-means clustering -kernel = """ -__global__ void kmeans(float *data, int *labels, float *centroids, int n, int k, int dim) -{ - int tid = blockIdx.x * blockDim.x + threadIdx.x; - if (tid >= n) - return; - - float min_dist = 10000; - int min_centroid = -1; - for (int i = 0; i < k; i++) - { - float dist = 0.0; - for (int j = 0; j < dim; j++) - { - float diff = data[tid * dim + j] - centroids[i * dim + j]; - dist += diff * diff; - } - if (dist < min_dist) - { - min_dist = dist; - min_centroid = i; - } - } - labels[tid] = min_centroid; -} -""" - -mod = SourceModule(kernel) -kmeans = mod.get_function("kmeans") - -# Set the number of clusters and the number of iterations -k = 2 -n_iter = 5 - -# Initialize the centroids and labels -centroids = np.random.rand(k, im_data.shape[-1]).astype(np.float32) -labels = np.zeros(im_data.shape[:2], dtype=np.int32) - -def replace_with_nearest_centroid(centroids, colors): - # Compute the distance between each color and each centroid - distances = np.sqrt(np.sum((colors[:, :] - centroids) ** 2, axis=2)) - - # Find the index of the centroid that is nearest to each color - nearest_centroids = np.argmin(distances, axis=1) - - # Replace each color with the nearest centroid - colors[:] = centroids[nearest_centroids] - - -start_time = time.time() - -# Run the K-means algorithm -for _ in range(n_iter): - kmeans(cuda.In(im_data), cuda.Out(labels), cuda.In(centroids), np.int32(im_data.shape[0] * im_data.shape[1]), np.int32(k), np.int32(im_data.shape[-1]), block=(1024,1,1), grid=(im_data.shape[0] * im_data.shape[1] // 1024 + 1, 1)) - - # Update the centroids - for i in range(k): - centroids[i] = np.mean(im_data[labels == i], axis=0) - -replace_with_nearest_centroid(centroids=centroids, colors=im_data) -# Convert the labels back to the original image format -labels = labels - -end_time = time.time() -print(f"It took {end_time-start_time:.2f} seconds to compute") - - diff --git a/D3/TP/TP_SETI_Kmeans/Kmeans_skcuda.py b/D3/TP/TP_SETI_Kmeans/Kmeans_skcuda.py index 2ce320c..74b2bce 100644 --- a/D3/TP/TP_SETI_Kmeans/Kmeans_skcuda.py +++ b/D3/TP/TP_SETI_Kmeans/Kmeans_skcuda.py @@ -1,23 +1,41 @@ -import numpy as np -import cv2 import cupy as cp +import numpy as np +from sklearn.cluster import KMeans +from skimage import io +import time -# Load the image and convert it to a NumPy array -image = cv2.imread("fruits.jpg") -image = image.astype(np.float32) +# Load the image using skimage +image = io.imread('fruits.jpg') -# Use cupy to transfer the image to the GPU -image_gpu = cp.asarray(image) +# Convert the image to a CuPy array +image_cp = cp.asarray(image) -# Perform k-means clustering on the GPU -cluster_centers_gpu, labels_gpu, _ = cp.cluster.kmeans(image_gpu.reshape(-1, 3), k=8) +# Flatten the image into a 2D array of pixels +image_flat = image_cp.get().reshape(image_cp.shape[0] * image_cp.shape[1], image_cp.shape[2]) -# Transfer the cluster centers and labels back to the CPU -cluster_centers = cp.asnumpy(cluster_centers_gpu) -labels = cp.asnumpy(labels_gpu) +def Kmeans_cuda(K=1): + # Use KMeans to cluster the pixels into a specified number of clusters + kmeans = KMeans(n_clusters=K, random_state=0).fit(image_flat) -# Convert the image pixels to the closest cluster -clustered_image = cluster_centers[labels].reshape(image.shape) + # Predict the cluster for each pixel + clusters = kmeans.predict(image_flat) -# Save the clustered image as a PNG file -cv2.imwrite("clustered_image.png", clustered_image) + # Create a new CuPy array to hold the modified image + new_image_cp = cp.empty_like(image_cp) + + # Iterate over each pixel and assign its value to the corresponding cluster center + for i, cluster in enumerate(clusters): + new_image_cp[i // image_cp.shape[1], i % image_cp.shape[1]] = cp.asarray(kmeans.cluster_centers_[cluster]) + + # Convert the CuPy array back to a NumPy array + new_image = cp.asnumpy(new_image_cp) + + # Save the modified image using skimage + io.imsave("fruits" + "_%d" % K + "_cuda.jpg", new_image) + + +for K in range(1,256): + start_time = time.time() + Kmeans_cuda(K=K) + end_time = time.time() + print(f"It took {end_time-start_time:.2f} seconds to compute for K =",K) \ No newline at end of file diff --git a/D3/TP/TP_SETI_Kmeans/TP1.ipynb b/D3/TP/TP_SETI_Kmeans/TP1.ipynb index 2a51a47..d6c8fa0 100644 --- a/D3/TP/TP_SETI_Kmeans/TP1.ipynb +++ b/D3/TP/TP_SETI_Kmeans/TP1.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 186, "metadata": {}, "outputs": [], "source": [ @@ -24,21 +24,17 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 187, "metadata": {}, "outputs": [], "source": [ - "# mean = [1,2,3,4]\n", - "# sd = [0.25, 0.25, 0.1, 0.2]\n", - "clusters = 5\n", + "clusters = 2\n", "dim = 2\n", "nb = 50\n", - "K= clusters\n", - "mean = np.random.randint(5, size=clusters)\n", + "K = clusters\n", + "mean = np.random.randint(5, size=clusters)*2\n", "mean = mean.T * np.random.random(size=clusters)\n", - "sd = np.random.random(size=clusters)\n", - "path_image = \"fruits.jpg\"\n", - "# print(mean)" + "sd = np.random.random(size=clusters)" ] }, { @@ -50,11 +46,18 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 188, "metadata": {}, "outputs": [], "source": [ "def gen_points(mean=1,sd=0.5, nb=100, dim=2, clusters=2):\n", + " \"\"\" Generates data\n", + " dim: dimension\n", + " nb: number of points\n", + " clusters: number of clusters\n", + " mean: mean\n", + " sd: standard deviation \n", + " \"\"\"\n", " size = []\n", " # for i in range(0,dim):\n", " size.append(nb)\n", @@ -68,21 +71,25 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 189, "metadata": {}, "outputs": [], "source": [ "def distance(points,Pc): \n", + " \"\"\" Returns spatial distance between two matrix\n", + " \"\"\"\n", " return scipy.spatial.distance.cdist(points[:,:], Pc[:,:])" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 195, "metadata": {}, "outputs": [], "source": [ "def kmeans(points = [0,0], K = 1):\n", + " \"\"\" Create K clusters from points\n", + " \"\"\"\n", " # Initialisation K prototypes\n", " dim = points.shape[1]\n", " N = points.shape[0]\n", @@ -128,50 +135,58 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 191, "metadata": {}, "outputs": [], "source": [ "colors=['red', 'green','yellow','blue','purple', 'orange']\n", "def visualisation(points, index, Pc=[0,0], K=1):\n", + " \"\"\"Visualisation function of a dataset and its K clusters\n", + " \"\"\"\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.plot(Pc[:,0],Pc[:,1],'ko')\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, + "execution_count": 192, "metadata": {}, "outputs": [], "source": [ "def img_2_mat(my_img):\n", + " \"\"\" Reshaping 3D img NxMx3 to 2D matrix N*Mx3\n", + " \"\"\"\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, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def mat_2_img(mat,my_img):\n", + " \"\"\" Reshaping 2D matrix N*Mx3 to 3D img NxMx3 \n", + " \"\"\"\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, + "execution_count": 193, "metadata": {}, "outputs": [], "source": [ "def kmeans_image(path_image, K):\n", + " \"\"\" Clustering an image and changing pixels to its closest cluster\n", + " \"\"\"\n", " my_img = io.imread(path_image)\n", " imgplot = plt.imshow(my_img)\n", " Mat = img_2_mat(my_img)\n", @@ -189,46 +204,23 @@ ] }, { - "cell_type": "code", - "execution_count": 10, + "attachments": {}, + "cell_type": "markdown", "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)" + "## Exemple\n", + "### Clusterisation 2D\n", + "On fait la clusterisation d'un exemple simple avec 2 nuages de points éloignés" ] }, { "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, + "execution_count": 198, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -240,120 +232,143 @@ } ], "source": [ - "visualisation(clusters, index, Pc, K=K)\n", - "# print(Pc)\n", - "# print(mean)" + "points1, mean1 = gen_points(mean,sd,nb,dim,clusters)\n", + "Pc1, index1, clusters1 = kmeans(points1,K=K)\n", + "visualisation(clusters1, index1, Pc1, K=K)" ] }, { - "cell_type": "code", - "execution_count": 14, + "attachments": {}, + "cell_type": "markdown", "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\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 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\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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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], "source": [ - "Pc, index, img_seg = kmeans_image(path_image=path_image, K=250)" + "Avec un K ne correspondant pas au nombre réel de groupes" ] }, { "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" - ] - } - ], + "outputs": [], "source": [ - "print(index.shape)\n", - "print(Pc)\n", - "print(img_seg)" + "K = clusters+1\n", + "points, mean = gen_points(mean,sd,nb,dim,clusters)\n", + "Pc, index, clusters_ex = kmeans(points,K=K)\n", + "visualisation(clusters_ex, index, Pc, K=K)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Et un exemple un peu plus complexe avec des intensités différentes et relativement proche" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "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)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "points, mean = gen_points(mean,sd,nb,dim,clusters)\n", + "Pc, index, clusters_ex = kmeans(points,K=K)\n", + "visualisation(clusters_ex, index, Pc, K=K)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exemple de clusterisation sur une image\n", + "On souhaite pouvoir changer les pixels vers les le centre du cluster le plus proche.\n", + "\n", + "On observe ainsi pour un nombre différent de clusters :" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "path_image = \"images/fruits.jpg\"\n", + "for i in range(1,13):\n", + " Pc, index, img_seg = kmeans_image(path_image=path_image, K=i)\n", + "plt.figure()\n", + "plt.imshow(io.imread(\"images/fruits_2.jpg\"))\n", + "plt.figure()\n", + "plt.imshow(io.imread(\"images/fruits_4.jpg\"))\n", + "plt.figure()\n", + "plt.imshow(io.imread(\"images/fruits_10.jpg\"))\n", + "plt.figure()\n", + "plt.imshow(io.imread(\"images/fruits_255_cuda.jpg\"))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La clusterisation avec 255 couleurs à été réalisée à l'aide du script `Kmeans_skcuda.py` afin d'accélérer le temps d'exécution. \n", + "\n", + "Nous avons vu en A4 la puissance de CUDA pour le traitement de données importantes, ce qui est notre cas avec le nombre de pixels de l'image et le nombre d'itérations qui augmentent en fonction du nombre de clusters recherchés. J'ai ainsi décidé - pour voir à partir de combien de niveaux de couleurs peut-on apercevoir une image nette - d'observer le traitement Kmeans jusqu'à 255 clusters.\n", + "\n", + "On observe ainsi qu'au dessus de 40 clusters on arrive à bien distinguer les fruits et légumes présents sur l'image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cpu = []\n", + "cuda = []\n", + "i = 0\n", + "with open(\"timing.txt\",'r') as data_file:\n", + " for line in data_file:\n", + " data = line.split()\n", + " if(len(data) < 4):\n", + " i=i+1\n", + " if(len(data) > 3):\n", + " if(i == 1):\n", + " cpu.append(float(data[2]))\n", + " if(i == 2):\n", + " cuda.append(float(data[2]))\n", + "\n", + "fig, ax1 = plt.subplots()\n", + "ax1.set_xlabel(\"K\")\n", + "ax1.set_ylabel(\"temps (s)\")\n", + "ax1.set_yscale('log')\n", + "ax1.plot(cpu,'b')\n", + "ax1.tick_params(axis ='y', labelcolor = 'blue') \n", + "plt.legend(['CPU'],loc='lower left')\n", + "\n", + "ax2 = ax1.twinx()\n", + "ax2.set_ylabel(\"temps (s)\")\n", + "ax2.plot(cuda,'r')\n", + "ax2.tick_params(axis ='y', labelcolor = 'red') \n", + "\n", + "plt.legend(['CUDA'],loc='lower right')\n", + "plt.title(\"Temps d'exécution Kmeans image\")\n", + "plt.show()\n" ] } ], @@ -373,7 +388,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.4 (tags/v3.9.4:1f2e308, Apr 6 2021, 13:40:21) [MSC v.1928 64 bit (AMD64)]" + "version": "3.9.4" }, "orig_nbformat": 4, "vscode": { diff --git a/D3/TP/TP_SETI_Kmeans/fruits.jpg b/D3/TP/TP_SETI_Kmeans/fruits.jpg deleted file mode 100644 index a9260c6..0000000 Binary files a/D3/TP/TP_SETI_Kmeans/fruits.jpg and /dev/null differ diff --git a/D3/TP/TP_SETI_Kmeans/fruits_1.jpg b/D3/TP/TP_SETI_Kmeans/fruits_1.jpg deleted file mode 100644 index 2eda92a..0000000 Binary files a/D3/TP/TP_SETI_Kmeans/fruits_1.jpg and /dev/null differ diff --git a/D3/TP/TP_SETI_Kmeans/fruits_10.jpg 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index 34b8299..0000000 Binary files a/D3/TP/TP_SETI_Kmeans/fruits_9.jpg and /dev/null differ diff --git a/D3/TP/TP_SETI_Kmeans/test.ipynb b/D3/TP/TP_SETI_Kmeans/test.ipynb deleted file mode 100644 index 668423d..0000000 --- a/D3/TP/TP_SETI_Kmeans/test.ipynb +++ /dev/null @@ -1,237 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import scipy.spatial" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "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": 4, - "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": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def distance(points,Pc): \n", - " return scipy.spatial.distance.cdist(points[:,:], Pc[:,:])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "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": 7, - "metadata": {}, - "outputs": [], - "source": [ - "points = gen_points(mean,sd,nb,dim,clusters)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "Pc, clusters = kmeans(points,K=K,nb=nb,dim=dim)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "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": 10, - "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": 11, - "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", - "[[[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" - ] - } - ], - "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", - "B[0,:] = np.array([0,0,0])\n", - "A = mat_2_img(B,A)\n", - "\n", - "print(A)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.9.4 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": "3.9.4" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "2ef431f6525756fa8a44688585fa332ef3b2e5fcfe8fe75df35bbf7028a8b511" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/D3/TP/TP_SETI_MLP/TP2.ipynb b/D3/TP/TP_SETI_MLP/TP2.ipynb index d3aac3e..c74956c 100644 --- a/D3/TP/TP_SETI_MLP/TP2.ipynb +++ b/D3/TP/TP_SETI_MLP/TP2.ipynb @@ -1851,7 +1851,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.4" + "version": "3.9.4 (tags/v3.9.4:1f2e308, Apr 6 2021, 13:40:21) [MSC v.1928 64 bit (AMD64)]" }, "orig_nbformat": 4, "vscode": { diff --git a/IR/Mimetic.docx b/IR/Mimetic.docx index 9fe4c18..80ac5db 100644 Binary files a/IR/Mimetic.docx and b/IR/Mimetic.docx differ