25、分水岭算法

应用于：图像分割

image.png image.png image.png image.png

def watershed_function(image):
    # 前提：降噪
    blurred = cv.pyrMeanShiftFiltering(image, 25, 100)
    # 第一步：灰度处理
    gray = cv.cvtColor(blurred, cv.COLOR_BGR2GRAY)
    # 第二步：二值化处理
    ret, binary = cv.threshold(
        gray, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)
    cv.imshow("binary", binary)

    # 第三步：距离变换
    kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
    mb = cv.morphologyEx(binary, cv.MORPH_OPEN, kernel,iterations=2)  # 连续两次开操作
    sure_bg = cv.dilate(mb, kernel, iterations=3) #对开操作的结果进行膨胀
    cv.imshow("open then dilate", sure_bg)
    '''
        distance_TYpe = cv.DIST_L2：欧几何距离
        maskSize = 3
    '''
    distance = cv.distanceTransform(mb, cv.DIST_L2, 3)
    dist_output = cv.normalize(distance, 0, 1.0, cv.NORM_MINMAX)
    cv.imshow("distance", dist_output*50)

    # 第四步：寻找种子
    ret, surface = cv.threshold(distance, distance.max()*0.6, 255, cv.THRESH_BINARY)
    cv.imshow("surface", surface)

    # 第五步：生成marker
    surface_fg = np.uint8(surface) #将 float 转为8位
    unknown = cv.subtract(sure_bg, surface_fg) 
    ret, markers = cv.connectedComponents(surface_fg) #连通区域
    print(ret)

    # 分水岭变换
    markers = markers + 1
    markers[unknown == 255] = 0
    markers = cv.watershed(image, markers=markers)
    image[markers == -1] = [0, 0, 255] #分水岭标记为红色
    cv.imshow("分水岭", image)

红色标记分水岭