screencp.py 文件源码

def k(screen):
        Z = screen.reshape((-1,3))

        # convert to np.float32
        Z = np.float32(Z)

        # define criteria, number of clusters(K) and apply kmeans()
        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
        K = 2
        ret,label,center=cv2.kmeans(Z,K,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)

        # Now convert back into uint8, and make original image
        center = np.uint8(center)
        res = center[label.flatten()]
        res2 = res.reshape((screen.shape))
        return res2