Python中常用的圖像分割算法有基于閾值的分割算法、基于邊緣的分割算法和基于區域的分割算法。以下是使用這些算法的示例代碼：

1. 基于閾值的分割算法（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()

2. 基于邊緣的分割算法（Canny邊緣檢測）：

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()

3. 基于區域的分割算法（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：以上示例代碼中，image.jpg是待分割的圖像文件名，可以根據實際情況修改。同時，還需要安裝OpenCV庫，可以使用pip install opencv-python命令進行安裝。