实现区域生长图像分割技术代码

区域生长（Region Growing）是一种基于像素相似性的图像分割技术，它通过初始化种子点，然后逐步将与其颜色或灰度值相近的邻域像素归并到同一区域。以下是一个简单的Python和OpenCV实现区域生长算法的基本步骤：

import cv2
import numpy as np

def region_growing(image, seed_point, threshold):
    # 1. 初始化
    mask = np.zeros(image.shape, dtype=np.uint8)
    seed_mask = np.zeros(image.shape, dtype=np.bool)
    seed_mask[seed_point] = True
    current_region = seed_point
    seed_color = image[seed_point]

    # 2. 遍历邻域像素
    while True:
        neighbors = [(x, y) for x, y in zip(*np.where(mask == 0))]
        if not neighbors:  # 如果没有未标记的邻居，则结束
            break

        # 3. 找到与种子颜色最接近的邻居
        min_diff = float('inf')
        closest_neighbor = None
        for neighbor in neighbors:
            diff = np.abs(image[neighbor] - seed_color).mean()
            if diff < min_diff:
                min_diff = diff
                closest_neighbor = neighbor

        # 4. 更新区域和掩码
        mask[closest_neighbor] = 255  # 标记为已处理
        current_region = np.append(current_region, closest_neighbor)
        seed_color = np.mean(image[current_region])

        # 5. 检查阈值条件（如果超过阈值则停止生长）
        if np.abs(np.mean(image[current_region]) - seed_color) > threshold:
            break

    return mask, current_region

# 示例用法
image = cv2.imread('your_image_path', 0)  # 读取灰度图像
seed_point = (50, 50)  # 选择初始种子点
threshold = 5  # 设置阈值
mask, region = region_growing(image, seed_point, threshold)
cv2.imshow('Segmented Image', mask)