使用K均值聚类的图像压缩 _K均值聚类的图像压缩

先决条件：K均值聚类
互联网上以图像形式充满了大量数据。人们每天在社交媒体网站(如Instagram, Facebook)和云存储平台(如Google Drive等)上上传数百万张图片。由于海量数据, 图像压缩技术对于压缩图像和减少存储空间变得至关重要。
在本文中, 我们将研究使用无监督学习算法K-means聚类算法进行的图像压缩。
【使用K均值聚类的图像压缩】图像由称为像素的几个强度值组成。在彩色图像中, 每个像素为3个字节, 每个像素包含RGB(红-蓝-绿)值, 该值具有红色强度值, 然后是蓝色, 然后是绿色强度值。
方法：
K均值聚类会将相似的颜色归为不同颜色(RGB值)的” k” 个聚类(例如k = 64)。因此, 每个簇质心代表其各自簇的RGB颜色空间中的颜色矢量。现在, 这些” k” 簇质心将替换它们各自簇中的所有颜色矢量。因此, 我们只需要存储每个像素的标签, 就可以告诉该像素所属的集群。此外, 我们保留每个聚类中心的颜色向量的记录。
所需的库–

-> Numpy库：sudo pip3 install numpy。
-> Matplotlib库：sudo pip3 install matplotlib。
-> scipy库：sudo pip3 install scipy

以下是Python实现：

import numpy as np import matplotlib.pyplot as plt import matplotlib.image as img # from scipy.io import loadmat from scipy import miscdef read_image():# loading the png image as a 3d matrix img = misc.imread( 'bird_small.png' ) # uncomment the below code to view the loaded image # plt.imshow(A) # plotting the image # plt.show() # scaling it so that the values are small img = img /255 return imgdef initialize_means(img, clusters):# reshaping it or flattening it into a 2d matrix points = np.reshape(img, (img.shape[ 0 ] * img.shape[ 1 ], img.shape[ 2 ])) m, n = points.shape# clusters is the number of clusters # or the number of colors that we choose.# means is the array of assumed means or centroids. means = np.zeros((clusters, n)) # random initialization of means. for i in range (clusters): rand1 = int (np.random.random( 1 ) * 10 ) rand2 = int (np.random.random( 1 ) * 8 ) means[i, 0 ] = points[rand1, 0 ] means[i, 1 ] = points[rand2, 1 ]return points, means# Function to measure the euclidean # distance (distance formula) def distance(x1, y1, x2, y2):dist = np.square(x1 - x2) + np.square(y1 - y2) dist = np.sqrt(dist)return distdef k_means(points, means, clusters):iterations = 10 # the number of iterations m, n = points.shape# these are the index values that # correspond to the cluster to # which each pixel belongs to. index = np.zeros(m) # k-means algorithm. while (iterations> 0 ):for j in range ( len (points)):# initialize minimum value to a large value minv = 1000 temp = Nonefor k in range (clusters):x1 = points[j, 0 ] y1 = points[j, 1 ] x2 = means[k, 0 ] y2 = means[k, 1 ]if (distance(x1, y1, x2, y2) < minv): minv = distance(x1, y1, x2, y2) temp = k index[j] = k for k in range (clusters):sumx = 0 sumy = 0 count = 0for j in range ( len (points)):if (index[j] = = k): sumx + = points[j, 0 ] sumy + = points[j, 1 ] count + = 1if (count = = 0 ): count = 1means[k, 0 ] = float (sumx /count) means[k, 1 ] = float (sumy /count)iterations - = 1return means, indexdef compress_image(means, index, img):# recovering the compressed image by # assigning each pixel to its corresponding centroid. centroid = np.array(means) recovered = centroid[index.astype( int ), :]# getting back the 3d matrix (row, col, rgb(3)) recovered = np.reshape(recovered, (img.shape[ 0 ], img.shape[ 1 ], img.shape[ 2 ]))# plotting the compressed image. plt.imshow(recovered) plt.show()# saving the compressed image. misc.imsave( 'compressed_' + str (clusters) + '_colors.png' , recovered)# Driver Code if __name__ = = '__main__' :img = read_image()clusters = 16 clusters = int ( input ( 'Enter the number of colors in the compressed image. default = 16\n' ))points, means = initialize_means(img, clusters) means, index = k_means(points, means, clusters) compress_image(means, index, img)

输入图片：