import cv2
import numpy as np
#用来进行图像的堆栈代码，可以复制使用
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


#轮廓contour
# 特征feature
# 面积area
# 弧长perimeter
# 图像矩image moment
def getContours(img):
    #img是二值图像
    contours,hierarchy = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)#返回值一个是轮廓本身，还有一个是每条轮廓对应的属性。

    for cnt in contours:
        area = cv2.contourArea(cnt)#求得轮廓的面积
        #输出轮廓的面积
        print('轮廓的面积',area)

        if area>500:
            cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 3)
            #计算轮廓周长
            peri = cv2.arcLength(cnt,True)#这个函数的第二参数可以用来指定对象的形状是闭合（True），还是打开的（一条曲线）

            print('轮廓的周长',peri)

            #轮廓近似
            approx = cv2.approxPolyDP(cnt,0.02*peri,True)#输出的是多边形的顶点坐标

            print(approx)
            #输出坐标的个数
            print(len(approx))
            objCor = len(approx)
            #获得图像的最小矩形信息
            x, y, w, h = cv2.boundingRect(approx)
            #判断输出坐标的个数
            if objCor ==3: objectType ="sjx"
            elif objCor == 4:
                aspRatio = w/float(h)

                if aspRatio >0.98 and aspRatio <1.02: objectType= "zfx"

                else:objectType="jx "

            elif objCor>4: objectType= "y"

            else:objectType="None"
            #画出三角形并和文本
            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)


img = cv2.imread('shapes.png')
imgContour = img.copy()
imgGray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
imgBlur = cv2.GaussianBlur(imgGray,(7,7),1)
imgCanny = cv2.Canny(imgBlur,50,50)
getContours(imgCanny)
imgBlank = np.zeros_like(img)
imgStack = stackImages(0.8,([img,imgGray,imgBlur],
                            [imgCanny,imgContour,imgBlank]))
cv2.imshow("Stack", imgStack)
cv2.waitKey(0)

通过对opencv基础知识的学习，我们利用简单的函数进行对物体形状及轮廓进行识别和显示，基本操作是：

对图片进行一系列操作（灰度化，模糊化，二值化，提取边缘轮廓），并进行轮廓的寻找和绘制，通过轮廓可以获得角点的坐标并进行形状的判断，最后输出相应的形状类型。

原图片如下：

 仅供学习参考，如有不足，敬请指正!