python+opencv 分水岭算法

基于距离的分水岭分割流程：输入图像——>灰度转换(如果有噪声，在这之前要先消去噪声)——>二值图像——>距离变换

——>寻找种子——>生成Marker——>分水岭变换——>输出图像——>END

import cv2 as cv
import numpy as np


def watershed_demo():
    print(src.shape)
    # remove noise if any
    blurred = cv.pyrMeanShiftFiltering(src, 10, 100)
    # gray/binary image
    gray = cv.cvtColor(blurred, cv.COLOR_BGR2GRAY)
    ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
    cv.imshow('binary-image', binary)

    # morphology operation
    kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
    # iterations=2 连续两次进行开操作
    mb = cv.morphologyEx(binary, cv.MORPH_OPEN, kernel, iterations=2)
    sure_bg = cv.dilate(mb, kernel, iterations=3)
    cv.imshow('mor-opt', sure_bg)

    # distance transform
    # 掩膜大小是3，cv.DIST_L2是距离的方法
    dist = cv.distanceTransform(mb, cv.DIST_L2, 3)
    dist_output = cv.normalize(dist, 0, 1.0, cv.NORM_MINMAX)
    cv.imshow('distance-t', dist_output*50)

    ret, surface = cv.threshold(dist, dist.max()*0.6, 255, cv.THRESH_BINARY)
    cv.imshow('surface-bin', surface)

    surface_fg = np.uint8(surface)
    unknown = cv.subtract(sure_bg, surface_fg)
    ret, markers = cv.connectedComponents(surface_fg)
    print(ret)

    # watershed transform
    markers = markers + 1
    markers[unknown == 255] = 0
    markers = cv.watershed(src, markers=markers)
    src[markers == -1] = [0, 0, 255]
    cv.imshow('result', src)


src = cv.imread('C:/Users/Y/Pictures/Saved Pictures/coins.jpg')
cv.namedWindow('input image', cv.WINDOW_AUTOSIZE)
cv.imshow('input image', src)
watershed_demo()
cv.waitKey(0)
cv.destroyAllWindows()