用python实现对图像基于阈值的区域分裂代码

以下是基于阈值的图像区域分割的Python实现。该算法将图像分割为更小的区域，使得每个区域内像素的灰度值接近，并且不同区域之间的差异较大。

import numpy as np
from skimage import io

def region_growing(img, seed, threshold):
    # Create a binary output image with the same size as the input image
    out_img = np.zeros_like(img)
    # Mark the seed point as visited and add it to the queue
    visited = []
    visited.append((seed[0], seed[1]))
    # Loop through the queue until all pixels have been visited
    while len(visited) > 0:
        # Pop the first pixel from the queue and set it as the current pixel
        curr = visited.pop(0)
        # Add the current pixel to the output image
        out_img[curr[0], curr[1]] = 255
        # Check the neighboring pixels and add them to the queue if they meet the threshold criteria
        for i in range(curr[0]-1, curr[0]+2):
            for j in range(curr[1]-1, curr[1]+2):
                if i >= 0 and j >= 0 and i < img.shape[0] and j < img.shape[1]:
                    if (i,j) not in visited and abs(int(img[i,j])-int(img[curr[0],curr[1]])) < threshold:
                        visited.append((i,j))
    return out_img

# Load the input image
img = io.imread('input_image.png')
# Set the seed point for region growing
seed = (100, 100)
# Set the threshold for region growing
threshold = 20
# Apply region growing to the input image
out_img = region_growing(img, seed, threshold)
# Save the output image
io.imsave('output_image.png', out_img)

该代码使用skimage库来加载和保存图像，并使用numpy数组来表示图像和输出图像。`region_growing()`函数实现了基于阈值的区域生长算法，它从种子点开始将图像分割为更小的区域。在这里，我们使用8邻域搜索来查找与当前像素相邻的像素，并添加到访问队列中。如果相邻像素的灰度值与当前像素的灰度值之间的差小于给定的阈值，则将其添加到访问队列中，并将其设置为输出图像的一部分。最后，将输出图像保存到磁盘上。