image.png

def contours_function(image):
    '''
        1、高斯模糊
        2、灰度图像
        3、全局二值化
        4、FindCoutours：找出轮廓
    '''
    dst = cv.GaussianBlur(image,(3,3),0)
    gray = cv.cvtColor(dst, cv.COLOR_BGR2GRAY)
    ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY| cv.THRESH_OTSU)
    cv.imshow("Binary", binary)

    '''
       cv.findContours：
            mode = cv.RETR_EXTERNAL：表示找出最外层的轮廓
                    cv.RETR_TREE：表示找出所有轮廓
    '''
    cloneImage, contours, hierachy = cv.findContours(
        binary, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
    for i, contour in enumerate(contours):
        cv.drawContours(image, contours, i, (0, 0, 255), 2)
        print(i)
    cv.imshow("Detect_contours", image)

mode = cv.RETR_TREE

---

mode = cv.RETR_EXTERNAL

轮廓填充

# thickness = -1
 for i, contour in enumerate(contours):
    cv.drawContours(image, contours, i, (0, 0, 255), -1)

image.png

---

基于Canny边缘提取

def canny_edge_function(image):
    '''
        Canny边缘提取算法：
            1、进行高斯模糊：因为Canny对噪声比较敏感，所以先高斯模糊降噪
            2、灰度转移：转化为单通道
            3、计算梯度：Sobel/Scharr
            4、
            5、高低阈值输出二值图像
    '''
    blurred = cv.GaussianBlur(image, (3, 3), 0)
    gray = cv.cvtColor(blurred, cv.COLOR_BGR2GRAY)

    grad_x = cv.Sobel(gray, cv.CV_16SC1, 1, 0)
    grad_y = cv.Sobel(gray, cv.CV_16SC1, 0, 1)

    #低阈值：50；高阈值：150
    edge_output = cv.Canny(grad_x,grad_y,50,150)
    cv.imshow("Binary",edge_output)
    return edge_output

def contours_function(image):
    binary = canny_edge_function(image)
    cloneImage, contours, hierachy = cv.findContours(
        binary, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
    for i, contour in enumerate(contours):
        cv.drawContours(image, contours, i, (0, 0, 255), -1)
        print(i)
    cv.imshow("Detect_contours", image)

基于Canny边缘提取