K-means聚类实现图片分割

    import numpy as np
    import matplotlib.pyplot as plt
    import cv2
    
    %matplotlib inline
    
    # Read in the image
    ## TODO: Check out the images directory to see other images you can work with
    # And select one!
    image = cv2.imread('images/monarch.jpg')
    
    # Change color to RGB (from BGR)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    
    plt.imshow(image)

    # Reshape image into a 2D array of pixels and 3 color values (RGB)
    pixel_vals = image.reshape((-1,3))
    
    # Convert to float type
    pixel_vals = np.float32(pixel_vals)

    ## TODO: Visualize one segment, try to find which is the leaves, background, etc!
    plt.imshow(labels_reshape==0, cmap='gray')

    # mask an image segment by cluster
    
    cluster = 0                            # the first cluster
    
    masked_image = np.copy(image)
    # turn the mask green!
    masked_image[labels_reshape == cluster] = [0, 255, 0]
    
    plt.imshow(masked_image)

    # define stopping criteria  
    # you can change the number of max iterations for faster convergence!
    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.2)
    
    ## TODO: Select a value for k
    # then perform k-means clustering
    k = 3
    retval, labels, centers = cv2.kmeans(pixel_vals, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
    
    # convert data into 8-bit values
    centers = np.uint8(centers)
    segmented_data = centers[labels.flatten()]
    
    # reshape data into the original image dimensions
    segmented_image = segmented_data.reshape((image.shape))
    labels_reshape = labels.reshape(image.shape[0], image.shape[1])
    
    plt.imshow(segmented_image)