python-opencv 图像处理基础 （十一）传统图像分割算法：分水岭算法

步骤：

1. 输入图像
2. 去噪，采用边缘滤波算法
3. 灰度
4. 二值化
5. 去除小的干扰块，进行开操作和膨胀操作
6. 距离变换
7. 归一化
8. 寻找种子
9. 生成marker
10. 分水岭变换
    10.输出图像

结果展示：

import cv2 as cv
import numpy as np


def watershed_demo():
    # remove noise if any
    print(src.shape)
    blurred = cv.pyrMeanShiftFiltering(src, 10, 100)# 去噪 采用边缘保留滤波
    # 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 去除小的干扰块 进行开操作和膨胀操作
    kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
    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
    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)

    surface_fg = np.uint8(surface)
    cv.imshow("surface-bin", surface_fg)
    unknown = cv.subtract(sure_bg, surface_fg)
    ret, markers = cv.connectedComponents(surface_fg)

    # 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)



print("--------- Python OpenCV Tutorial ---------")
src = cv.imread("../opencv-python-img/coins.jpg")
cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
cv.imshow("input image", src)
watershed_demo()
cv.waitKey(0)

cv.destroyAllWindows()