【opencv-python 检测图片的轮廓形状】检测图片的轮廓为什么图形，包含三角形，圆形，矩形，正方形

# 检测图片的轮廓

def getContours(img):
    # contours：list结构，列表中每个元素代表一个边沿信息。每个元素是(x, 1, 2)的三维向量，x表示该条边沿里共有多少个像素点，第三维的那个“2”表示每个点的横、纵坐标；
    # hierarchy：返回类型是(x, 4)的二维ndarray。x和contours里的x是一样的意思。
    for cnt in contours:
        #传入轮廓计算面积
        area = cv2.contourArea(cnt)
        print(area)
        if area>500:
            # 第一个参数是指明在哪幅图像上绘制轮廓；image为三通道才能显示轮廓
            # 第二个参数是轮廓本身，在Python中是一个list;
            # 第三个参数指定绘制轮廓list中的哪条轮廓，如果是 - 1，则绘制其中的所有轮廓。后面的参数很简单。其中thickness表明轮廓线的宽度，如果是 - 1（cv2.FILLED），则为填充模式。
            cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 3)
            #  计算轮廓的周长
            peri = cv2.arcLength(cnt,True)
            #print(peri)
            # 用于获得轮廓的近似值，使用cv2.drawCountors进行画图操作 ， 参数说明：cnt为输入的轮廓值， epsilon为阈值T，通常使用轮廓的周长作为阈值，True表示的是轮廓是闭合的
            approx = cv2.approxPolyDP(cnt,0.02*peri,True)
            print(len(approx))
            # 提取拐点
            objCor = len(approx)
            # cv2.boundingRect矩阵边框，xy为左上，长宽
            x, y, w, h = cv2.boundingRect(approx)
            # 三角形 
            if objCor ==3: objectType ="Tri"
            elif objCor == 4:
                # 判断长方形还是正方形
                aspRatio = w/float(h)
                if aspRatio >0.98 and aspRatio <1.03: objectType= "Square"
                else:objectType="Rectangle"
                # 圆形
            elif objCor>4: objectType= "Circles"
            else:objectType="None"
            cv2.rectangle(imgContour,(x,y),(x+w,y+h),(0,255,0),2)
            cv2.putText(imgContour,objectType,
                        (x+(w//2)-10,y+(h//2)-10),cv2.FONT_HERSHEY_COMPLEX,0.7,
                        (0,0,0),2)




path = "Resources/majiang2.png"
img = cv2.imread(path)
imgContour = img.copy()

imgGray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
imgmedianBlur = cv2.medianBlur(img ,5)
imgBlur = cv2.GaussianBlur(imgGray,(7,7),2)
imgCanny = cv2.Canny(imgmedianBlur,50,300)
getContours(imgCanny)

imgBlank = np.zeros_like(img)
imgStack = stackImages(0.4,([img,imgGray,imgBlur],
                            [imgCanny,imgContour,imgBlank]))

cv2.imshow("Stack", imgStack)

cv2.waitKey(0)

输出图片的代码

# 图片叠加
def stackImages(scale, imgArray):
    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