步骤:
- 输入图像
- 去噪,采用边缘滤波算法
- 灰度
- 二值化
- 去除小的干扰块,进行开操作和膨胀操作
- 距离变换
- 归一化
- 寻找种子
- 生成marker
- 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()