kmeans聚类代码
将像素进行聚类，得到每个像素的聚类标签，默认聚类簇数为3
def seg_kmeans(img,clusters=3):img_flat=img.reshape((-1,3))# print(img_flat.shape)img_flat=np.float32(img_flat)criteria=(cv.TERM_CRITERIA_MAX_ITER+cv.TERM_CRITERIA_EPS,20,0.5)flags=cv.KMEANS_RANDOM_CENTERSret,labels,centers=cv.kmeans(data=img_flat,K=clusters,bestLabels=None,criteria=criteria,attempts=10,flags=flags)return labels为显示分割后的图像，将标签进行颜色映射，下述代码中，将第一类标签映射为绿色，第二类标签映射为蓝色，第三类标签映射为红色等：
def img_res(labels):color = np.uint8([[255, 0, 0],[0, 0, 255],[0, 255, 0],[255,255,0]])res = color[labels.flatten()] # 像素映射result = res.reshape((img.shape))return result为探究不同聚类簇数的影响，分别采用聚类簇数clusters为2、3、4进行结果显示
RGB颜色模型下的聚类效果
HSV颜色模型下的聚类效果
完整源码
import cv2 as cv
import matplotlib.pyplot as plt
import os
import numpy as npdef img_read(pattern=cv.IMREAD_GRAYSCALE):#默认显示灰度图path=os.path.abspath(".")img_path=path+r"\data\twins.jpg"img=cv.imread(img_path,pattern)return imgdef seg_kmeans(img,clusters=3):img_flat=img.reshape((-1,3))# print(img_flat.shape)img_flat=np.float32(img_flat)criteria=(cv.TERM_CRITERIA_MAX_ITER+cv.TERM_CRITERIA_EPS,20,0.5)flags=cv.KMEANS_RANDOM_CENTERSret,labels,centers=cv.kmeans(data=img_flat,K=clusters,bestLabels=None,criteria=criteria,attempts=10,flags=flags)return labelsdef img_res(labels):color = np.uint8([[255, 0, 0],[0, 0, 255],[0, 255, 0],[255,255,0]])res = color[labels.flatten()] # 像素映射result = res.reshape((img.shape))return resultdef plot_res(img):clusters = [2, 3, 4] # 聚类簇数img_list = [img] * len(clusters)# 采取不同的聚类簇数进行聚类labels = map(seg_kmeans, img_list, clusters)#map函数返回一个迭代器对象labels = [np.array(label) for label in labels]# 对结果进行颜色映射，便于输出results = map(img_res, labels)plt.subplot(2, 2, 1), plt.imshow(img)for i, res in enumerate(results):plt.subplot(2, 2, i + 2)plt.imshow(res)plt.title('k=' + str(i + 2))plt.subplots_adjust(hspace=0.5)plt.show()if __name__=="__main__":img=img_read(pattern=None)img_RGB=cv.cvtColor(img,cv.COLOR_BGR2RGB)img_HSV=cv.cvtColor(img,cv.COLOR_BGR2HSV)plot_res(img_RGB)plot_res(img_HSV)
