Opencv-Python Contour Shape Detection 轮廓形状检测

    import cv2
    import numpy as np
    
    def stackImages(scale,imgArray):  # 堆栈操作
    rows = len(imgArray)
    cols = len(imgArray[0])
    rowsAvailable = isinstance(imgArray[0], list)
    width = imgArray[0][0].shape[1]
    height = imgArray[0][0].shape[0]
    if rowsAvailable:
        for x in range(0, rows):
            for y in range(0, cols):
                if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:
                    imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)
                else:
                    imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)
                if len(imgArray[x][y].shape) == 2 : imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)
        imageBlank = np.zeros((height, width, 3), np.uint8)
        hor = [imageBlank]*rows
        hor_con = [imageBlank]*rows
        for x in range(0, rows):
            hor[x] = np.hstack(imgArray[x])
        ver = np.vstack(hor)
    else:
        for x in range(0, rows):
            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
                imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
            else:
                imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)
            if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
        hor= np.hstack(imgArray)
        ver = hor
    return ver
    
    def getContours(img):  #读取的图像要先转成灰度的
    contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)  # 参数二为轮廓的检索模式（四种），cv2.RETR_EXTERNAL表示只检测外轮廓
    # 第三个参数method为轮廓的近似办法，cv2.CHAIN_APPROX_NONE存储所有的轮廓点，相邻的两个点的像素位置差不超过1
    for cnt in contours:  # 遍历每一个轮廓
        area = cv2.contourArea(cnt)  # 计算面积
        # print(area)
        if area > 500:  # 消除噪点，要求面积合格
            cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 3)  # 到此处为画边框
            peri = cv2.arcLength(cnt, True)  # permimeter周长，true意味着封闭的
            #print(peri)
            approx = cv2.approxPolyDP(cnt, 0.02*peri, True)  # 拐点 做一个近似0.02该值越小，拐弯处近似后越不明显
            # print(len(approx))
            objCor = len(approx)  # 多边形拟合后的点数，4方形，3三角，8圆
            x, y, w, h = cv2.boundingRect(approx)  # 边界框
    
            if objCor == 3:
                objectType ="Tri"
            elif objCor == 4:
                aspRatio = w/float(h)
                if aspRatio >0.98 and aspRatio <1.03: objectType= "Square" # 确定正方形
                else:objectType="Rectangle"
            elif objCor > 4: objectType= "Circles"
            else:objectType="None"
    
    
    
            cv2.rectangle(imgContour,(x,y),(x+w,y+h),(0,255,0),2)  # 画图形的矩形轮廓
            cv2.putText(imgContour, objectType,
                        (x+(w//2)-10, y+(h//2)-10), cv2.FONT_HERSHEY_COMPLEX, 0.7,  # 宽中心点，高中心点-10偏差，0.7 比例
                        (0, 0, 0), 2)
    
    # 主函数
    path = 'shapes.png'
    img = cv2.imread(path)
    imgContour = img.copy()  # 因为要修改参数，故需要复制原图
    
    imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    imgBlur = cv2.GaussianBlur(imgGray, (7, 7), 1)  # 模糊为了降噪
    imgCanny = cv2.Canny(imgBlur, 50, 50)
    getContours(imgCanny)
    
    imgBlank = np.zeros_like(img)  # 制作一个黑色空图像ones为白色
    imgStack = stackImages(0.5, ([img, imgGray, imgBlur],
                             [imgCanny, imgContour, imgBlank]))
    
    cv2.imshow("Stack", imgStack)
    
    cv2.waitKey(0)