import cv2
import numpy as np
from sklearn.cluster import KMeans
import time# 中文路径读取
def cv_imread(filePath, cv2_falg=cv2.COLOR_BGR2RGB):   cv_img = cv2.imdecode(np.fromfile(filePath, dtype=np.uint8), cv2_falg)    return cv_img# 自定义装饰器计算时间
def compute_time(func):def compute(*args, **kwargs):st = time.time()result = func(*args, **kwargs)et = time.time()print('消费时间 %.6f s' % (et - st))return resultreturn compute@compute_time
def kmeans_img(image,  num_clusters, show=False):# 如果图像是灰度图（单通道），将其转换为三通道if len(image.shape) == 2:image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)# 将图像的形状进行调整以便进行 K 均值聚类,提高训练速度pixels = cv2.resize(image.copy(), None, fx=0.05, fy=0.05, interpolation=cv2.INTER_LINEAR)pixels = np.float32(pixels.reshape((-1, 3)))segmented_pixels = np.float32(image.reshape((-1, 3)))# 初始化 KMeans 模型并拟合数据kmeans = KMeans(n_clusters=num_clusters)kmeans.fit(pixels)# 获取每个像素所属的簇标签labels = kmeans.predict(segmented_pixels)# 根据簇标签，将图像像素值转换为簇中心值segmented_image = kmeans.cluster_centers_[labels]segmented_image = np.uint8(segmented_image.reshape(image.shape))if show:plt.figure(figsize=(10, 5))plt.subplot(1, 2, 1)plt.title('Original Image')plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))plt.axis('off')plt.subplot(1, 2, 2)plt.title('Segmented Image')plt.imshow(segmented_image)plt.axis('off')plt.tight_layout()plt.show()return segmented_image

image_path =r"C:\Users\pc\Pictures\test\快.png"
image = cv_imread(image_path)
kmeans_img(image,4, show=True)

 使用opencv内设的kmeans函数：直接原图进行训练，然后获取每个像素点的类，速度慢。上述方法对图像进行一个缩放后，训练模型，然后用模型再预测原图的每个像素点，速度快。

def kmeans_img(image, num_clusters, show=True):# 如果图像是灰度图（单通道），将其转换为三通道if len(image.shape) == 2:image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)# image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)print(image.shape)# 将图像的形状进行调整以便进行 K 均值聚类pixels = image.reshape((-1, 3))pixels = np.float32(pixels)# 设定 kmeans 参数并运行算法criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.2)_, labels, centers = cv2.kmeans(pixels, num_clusters, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)# 将图像像素值转换为簇中心值centers = np.uint8(centers)segmented_image = centers[labels.flatten()]segmented_image = segmented_image.reshape(image.shape)if show:# 显示原始图像和分割后的图像plt.figure(figsize=(10, 5))plt.subplot(1, 2, 1)plt.title('Original Image')plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))plt.axis('off')plt.subplot(1, 2, 2)plt.title('Segmented Image')plt.imshow(segmented_image)plt.axis('off')plt.tight_layout()plt.show()return segmented_image