opencv之图像分割

原图：

最终分割得到的图：

def OpenCVSegmentation():
    cv2.namedWindow('origin', cv2.WINDOW_NORMAL)
    cv2.namedWindow('gray', cv2.WINDOW_NORMAL)
    cv2.namedWindow('closed', cv2.WINDOW_NORMAL)
    cv2.namedWindow('result', cv2.WINDOW_NORMAL)
    cv2.namedWindow('binary', cv2.WINDOW_NORMAL)
    cv2.namedWindow('DILATE', cv2.WINDOW_NORMAL)
    cv2.namedWindow('ERODE', cv2.WINDOW_NORMAL)

    img = cv2.imread("../data/imgs/20211126-Z2111001-4-1-1_0_1.png")  # 载入图像
    h, w = img.shape[:2]      #获取图像的高和宽
    #设置显示图片窗口大小
    cv2.resizeWindow('origin', int(w / 3), int(h / 3))
    cv2.resizeWindow('gray', int(w/3), int(h/3))
    cv2.resizeWindow('closed', int(w/3), int(h/3))
    cv2.resizeWindow('result', int(w/3), int(h/3))
    cv2.resizeWindow('binary', int(w / 3), int(h / 3))
    cv2.resizeWindow('DILATE', int(w / 3), int(h / 3))
    cv2.resizeWindow('ERODE', int(w / 3), int(h / 3))

    cv2.imshow("origin", img)     #显示原始图像

    # 滤波去噪
    blured = cv2.blur(img,(3,3))    #进行滤波去掉噪声
    # cv2.imshow("Blur", blured) #显示低通滤波后的图像

    #得到灰度图
    gray = cv2.cvtColor(blured,cv2.COLOR_BGR2GRAY)
    cv2.imshow("gray", gray)

    #定义结构元素
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(9, 9))
    #开闭运算，先开运算去除背景噪声，再继续闭运算填充目标内的孔洞
    opened = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel)
    closed = cv2.morphologyEx(opened, cv2.MORPH_CLOSE, kernel)
    cv2.imshow("closed", closed)

    #求二值图
    # ret, binary = cv2.threshold(closed,150,255,cv2.THRESH_BINARY)
    # cv2.imshow("binary", binary)
    # binary = cv2.adaptiveThreshold(closed, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 5)
    binary = cv2.adaptiveThreshold(closed, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,31, 5)
    cv2.imshow("binary", binary)

    #膨胀
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
    binary = cv2.morphologyEx(binary, cv2.MORPH_DILATE, kernel)
    cv2.imshow("DILATE", binary)

    #腐蚀
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 25))
    binary = cv2.morphologyEx(binary, cv2.MORPH_ERODE, kernel)
    cv2.imshow("ERODE", binary)

    #找到轮廓
    contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
    #绘制轮廓
    cv2.drawContours(img,contours,-1,(0,0,255),3)
    #绘制结果
    cv2.imshow("result", img)

    cv2.waitKey(0)
    cv2.destroyAllWindows()


if __name__ == "__main__":
    OpenCVSegmentation()

参考文章：

OpenCV-Python图像形态变换概述及morphologyEx函数介绍

OpenCV-Python图像处理：腐蚀和膨胀原理及erode、dilate函数介绍

Python OpenCV morphologyEx()函数

opencv 图像二值化的问题(cv2.adaptiveThreshold函数)

OpenCV adaptiveThreshold

opencv-python自适应阈值二值化函数cv2.adaptiveThreshold使用及效果