Traceback (most recent call last): File "D:\Documation\Coding\py_test_grow\main.py", line 97, in <module> disjoint_set = parallel_connected_components(image, num_processes) File "D:\Documation\Coding\py_test_grow\main.py", line 57, in parallel_connected_components result.get() File "D:\Download\python38\lib\multiprocessing\pool.py", line 771, in get raise self._value File "D:\Download\python38\lib\multiprocessing\pool.py", line 537, in _handle_tasks put(task) File "D:\Download\python38\lib\multiprocessing\connection.py", line 206, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "D:\Download\python38\lib\multiprocessing\reduction.py", line 51, in dumps cls(buf, protocol).dump(obj) File "D:\Download\python38\lib\multiprocessing\synchronize.py", line 101, in __getstate__ context.assert_spawning(self) File "D:\Download\python38\lib\multiprocessing\context.py", line 359, in assert_spawning raise RuntimeError( RuntimeError: Lock objects should only be shared between processes through inheritance Process finished with exit code 1 这次又是这样的报错

这个报错是因为在使用 `multiprocessing.Pool` 进行并行处理时，锁对象 `lock` 不能直接传递给子进程。根据报错信息，锁对象只能通过继承的方式在子进程之间共享。

为了解决这个问题，你可以使用 `multiprocessing.Manager` 模块中的 `Lock` 对象来创建一个可在多个进程之间共享的锁。以下是修改后的示例代码：

import numpy as np
import multiprocessing as mp

def find_connected_components(image, start_row, end_row, disjoint_set, lock):
    rows, cols = image.shape
    for i in range(start_row, end_row):
        for j in range(cols):
            if image[i, j] == 1:
                # 如果当前像素点是前景点
                neighbors = []
                if i > 0 and image[i-1, j] == 1:
                    neighbors.append((i-1, j))
                if i < rows-1 and image[i+1, j] == 1:
                    neighbors.append((i+1, j))
                if j > 0 and image[i, j-1] == 1:
                    neighbors.append((i, j-1))
                if j < cols-1 and image[i, j+1] == 1:
                    neighbors.append((i, j+1))
                
                if len(neighbors) == 0:
                    # 如果当前像素点没有相邻的前景点，则创建一个新的连通分量
                    with lock:
                        disjoint_set.make_set((i, j))
                else:
                    # 如果当前像素点有相邻的前景点，则将其与相邻点合并为同一连通分量
                    with lock:
                        for neighbor in neighbors:
                            disjoint_set.union((i, j), neighbor)

def parallel_connected_components(image, num_processes):
    rows, cols = image.shape
    
    # 使用并查集数据结构来记录连通分量
    disjoint_set = DisjointSet()
    
    # 创建进程池
    pool = mp.Pool(processes=num_processes)
    
    # 创建锁
    manager = mp.Manager()
    lock = manager.Lock()
    
    # 计算每个进程处理的行数
    chunk_size = rows // num_processes
    
    # 分配任务给不同的进程
    results = []
    for i in range(num_processes):
        start_row = i * chunk_size
        end_row = start_row + chunk_size if i < num_processes-1 else rows
        result = pool.apply_async(find_connected_components, (image, start_row, end_row, disjoint_set, lock))
        results.append(result)
    
    # 等待所有进程完成
    for result in results:
        result.get()
    
    # 关闭进程池
    pool.close()
    pool.join()
    
    return disjoint_set

# 示例使用的并查集数据结构
class DisjointSet:
    def __init__(self):
        self.parent = {}
    
    def make_set(self, x):
        self.parent[x] = x
    
    def find(self, x):
        if self.parent[x] != x:
            self.parent[x] = self.find(self.parent[x])
        return self.parent[x]
    
    def union(self, x, y):
        root_x = self.find(x)
        root_y = self.find(y)
        if root_x != root_y:
            self.parent[root_x] = root_y

# 示例
if __name__ == '__main__':
    # 二值图片示例
    image = np.array([[0, 1, 0, 0, 1],
                      [1, 1, 1, 0, 0],
                      [0, 0, 1, 1, 0],
                      [0, 0, 0, 0, 1],
                      [1, 0, 1, 0, 1]])
    
    # 并行处理连通分量
    num_processes = 2
    disjoint_set = parallel_connected_components(image, num_processes)
    
    # 输出每个连通分量的像素点集合
    connected_components = {}
    for pixel in disjoint_set.parent:
        root = disjoint_set.find(pixel)
        if root not in connected_components:
            connected_components[root] = []
        connected_components[root].append(pixel)
    
    print("Connected components:")
    for component in connected_components.values():
        print(component)

通过使用 `multiprocessing.Manager` 中的 `Lock` 对象，并在访问和修改 `disjoint_set` 数据结构时使用 `with lock` 块来保护共享资源的访问，可以确保并行处理过程中的线程安全性。

希望这次修改能解决你遇到的问题。如果还有其他问题，请随时提问。