基于python的区域生长算法代码

以下是基于 Python 的区域生长算法代码示例：

import numpy as np
import cv2

def region_growing(img, seed):
    # 获取图像的行数和列数
    rows, cols = img.shape[:2]
    # 创建一个与原始图像大小相同的标记矩阵，用于标记已经生长的区域
    visited = np.zeros((rows, cols), np.uint8)
    # 创建一个空的输出图像
    region = np.zeros((rows, cols), np.uint8)
    # 将种子点的像素值作为生长的起点
    region[seed] = img[seed]
    # 创建一个队列，用于存储待生长的像素点
    queue = []
    queue.append(seed)
    # 定义生长的阈值
    threshold = 10
    # 循环生长
    while queue:
        # 从队列中取出一个像素点
        pixel = queue.pop(0)
        # 获取该像素点的邻域像素点
        neighbors = get_neighbors(pixel, rows, cols)
        # 遍历邻域像素点
        for neighbor in neighbors:
            # 如果邻域像素点未被访问过，并且与种子点的像素值之差小于阈值，则将其加入到生长队列中
            if not visited[neighbor] and abs(int(img[neighbor]) - int(img[pixel])) < threshold:
                region[neighbor] = img[neighbor]
                visited[neighbor] = 1
                queue.append(neighbor)
    return region

def get_neighbors(pixel, rows, cols):
    # 定义邻域像素点的偏移量
    offsets = [[-1, 0], [0, -1], [0, 1], [1, 0]]
    neighbors = []
    # 遍历邻域像素点
    for offset in offsets:
        row = pixel[0] + offset[0]
        col = pixel[1] + offset[1]
        # 判断邻域像素点是否越界
        if row >= 0 and row < rows and col >= 0 and col < cols:
            neighbors.append((row, col))
    return neighbors

# 读取图像
img = cv2.imread('test.jpg', cv2.IMREAD_GRAYSCALE)
# 设置种子点
seed = (100, 100)
# 进行区域生长
region = region_growing(img, seed)
# 显示结果
cv2.imshow('Original Image', img)
cv2.imshow('Region Growing Result', region)
cv2.waitKey(0)
cv2.destroyAllWindows()

请注意，这只是一个示例代码，实际应用中可能需要根据具体情况进行修改和优化。