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Python 中常用的图像分割算法有基于阈值的分割算法、基于边缘的分割算法和基于区域的分割算法。以下是使用这些算法的示例代码：
- 基于阈值的分割算法（二值化）：
import cv2
def threshold_segmentation(image, threshold):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
_, binary = cv2.threshold(gray, threshold, 255, cv2.THRESH_BINARY)
return binary
image = cv2.imread('image.jpg')
threshold = 127
segmented_image = threshold_segmentation(image, threshold)
cv2.imshow('Segmented Image', segmented_image)
cv2.waitKey(0)
cv2.destroyAllWindows()- 基于边缘的分割算法（Canny 边缘检测）：
import cv2
def edge_segmentation(image, min_threshold, max_threshold):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, min_threshold, max_threshold)
return edges
image = cv2.imread('image.jpg')
min_threshold = 100
max_threshold = 200
segmented_image = edge_segmentation(image, min_threshold, max_threshold)
cv2.imshow('Segmented Image', segmented_image)
cv2.waitKey(0)
cv2.destroyAllWindows()- 基于区域的分割算法（Felzenszwalb 算法）：
import cv2
import numpy as np
def region_segmentation(image, scale, min_size):
segments = cv2.ximgproc.segmentation.createGraphSegmentation()
segments.setSigma(0.5)
segments.setK(500)
segments.processImage(image)
result = segments.createSuperpixelMask()
return result
image = cv2.imread('image.jpg')
scale = 0.1
min_size = 100
segmented_image = region_segmentation(image, scale, min_size)
cv2.imshow('Segmented Image', segmented_image)
cv2.waitKey(0)
cv2.destroyAllWindows()注意：以上示例代码中，image.jpg是待分割的图像文件名，可以根据实际情况修改。同时，还需要安装 OpenCV 库，可以使用 pip install opencv-python 命令进行安装。
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