在Python中如何找到图像分块的边界顶点？

import cv2
import numpy as np

# 读取图像 (替换为你的图像路径)
image = cv2.imread('image.png', cv2.IMREAD_GRAYSCALE)

# 定义块的行列数 (可根据需要调整)
rows, cols = 3, 3
block_height = image.shape[0] // rows
block_width = image.shape[1] // cols

# 创建块值数组
blocks = np.zeros_like(image)

# 为每个块分配唯一值
for i in range(rows):
    for j in range(cols):
        blocks[i*block_height:(i+1)*block_height, j*block_width:(j+1)*block_width] = (i*cols + j + 1)

# 查找边界顶点函数
def find_boundary_vertices(block_id):
    y, x = np.where(blocks == block_id)
    if not y.size:  # 处理空块的情况
        return []
    min_y, max_y = y.min(), y.max()
    min_x, max_x = x.min(), x.max()
    vertices = [(min_y, min_x), (min_y, max_x), (max_y, min_x), (max_y, max_x)]
    return vertices

# 获取所有块的边界顶点
all_vertices = {}
for block_id in range(1, rows * cols + 1):
    all_vertices[block_id] = find_boundary_vertices(block_id)

# 可视化结果 (可选)
for block_id, vertices in all_vertices.items():
    for vertex in vertices:
        cv2.circle(image, (vertex[1], vertex[0]), 3, (255, 0, 0), -1)

cv2.imshow('Boundary Vertices', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 打印结果
print(all_vertices)