OpenCV计算机视觉整理(二)图像的分割与修复

import cv2
import numpy as np

if __name__ == "__main__":

    cv2.namedWindow('img', cv2.WINDOW_NORMAL)
    img = cv2.imread("/Users/admin/Documents/9527.png")
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # 二值化，cv2.THRESH_OTSU表示自适应阈值
    ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
    # 进行两次开运算
    open = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
    # 进行一次膨胀
    bg = cv2.dilate(open, kernel, iterations=1)
    # 获取前景物体，计算非0值到离它距离最近的0值之间的距离
    # cv2.DIST_L2为距离的计算方式，这里为欧式距离
    # maskSize:扫描时的卷积核大小，L1时用3*3，L2时用5*5
    dist = cv2.distanceTransform(open, cv2.DIST_L2, 5)
    ret, fg = cv2.threshold(dist, 0.7 * dist.max(), 255, cv2.THRESH_BINARY)
    fg = np.uint8(fg)
    # 获取未知区域
    unknow = cv2.subtract(bg, fg)
    # 创建连通域，求所有非0相同像素的连通域，它还有一个参数为连通周围的4个还是
    # 8个，默认为8个
    ret, marker = cv2.connectedComponents(fg)
    marker += 1
    marker[unknow == 255] = 0
    # 进行图像分割
    result = cv2.watershed(img, marker)
    img[result == -1] = [0, 0, 255]
    while True:
        cv2.imshow('img', img)
        key = cv2.waitKey()
        if key & 0xFF == ord('q'):
            break
    cv2.destroyAllWindows()