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K-means clustering and disabling clusters

img = path
img = cv2.imread(img)
img = cv2.cvtColor(img,cv2.COLOR_BGR2LAB)
pixVal = img.reshape((-1,3))
pixVal = np.float(pixVal)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.2)
# number of clusters (K)
k = 3
_, labels, (centers) = cv2.kmeans(pixVal, k, None,
                                  criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
# convert back to 8 bit values
centers = np.uint8(centers)

# flatten the labels array
labels = labels.flatten()

# convert all pixels to the color of the centroids
segmented_image = centers[labels.flatten()]
# reshape back to the original image dimension
segmented_image = segmented_image.reshape(image.shape)
# show the image
plt.imshow(segmented_image)
plt.show()

# disable only the cluster number 2 (turn the pixel into black)
masked_image = np.copy(image)
# convert to the shape of a vector of pixel values
masked_image = masked_image.reshape((-1, 3))
# color (i.e cluster) to disable
cluster = 2
masked_image[labels == cluster] = [0, 0, 0]
# convert back to original shape
masked_image = masked_image.reshape(image.shape)
# show the image
plt.imshow(masked_image)
plt.show()

Source: thepythoncode.com

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