openCV编程基础25分水岭分割算法

基于距离的分水岭分割流程
输入图像>灰度>二值>距离变换>寻找种子>生成Marker>分水岭变换>输出图像

import cv2 as cv
import numpy as np# 分水岭算法
def watershed_image():print(src.shape)blurred &＃61; cv.pyrMeanShiftFiltering(src, 10, 100) # 利用边缘滤波&＃xff0c;去除噪点# gray\binary image 灰度二值图像gray &＃61; cv.cvtColor(blurred, cv.COLOR_BGR2GRAY)ret, binary &＃61; cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)cv.imshow("binary_image", binary)# morphology operation 形态学操作kernel &＃61; cv.getStructuringElement(cv.MORPH_RECT, (3, 3)) #结构元素mb &＃61; cv.morphologyEx(binary, cv.MORPH_OPEN, kernel, iterations&＃61;2) #连续两次开操作sure_bg &＃61; cv.dilate(mb, kernel, iterations&＃61;3)cv.imshow("mor_opt", sure_bg)# distance transform 对上面的mb进行距离变换dist &＃61; cv.distanceTransform(mb, cv.DIST_L2, 3)dist_output &＃61; cv.normalize(dist, 0, 1.0, cv.NORM_MINMAX)cv.imshow("distance", dist_output * 70)ret, surface &＃61; cv.threshold(dist, dist.max() * 0.6, 255, cv.THRESH_BINARY)cv.imshow("surface-bin", surface)surface_fg &＃61; np.uint8(surface)unknown &＃61; cv.subtract(sure_bg, surface_fg)ret, markers &＃61; cv.connectedComponents(surface_fg)print(ret)# watershed transfrom 分水岭变换markers &＃43;&＃61; 1markers[unknown &＃61;&＃61; 255] &＃61; 0markers &＃61; cv.watershed(src, markers&＃61;markers)src[markers &＃61;&＃61; -1] &＃61; [0, 0, 255]cv.imshow("result", src)src &＃61; cv.imread(&＃39;F:00.jpg&＃39;)
#cv.namedWindow(&＃39;input_image&＃39;, cv.WINDOW_AUTOSIZE)
cv.imshow("0", src)
watershed_image()
cv.waitKey(0)
cv.destroyAllWindows()

输出结果&＃xff1a;