test.png

import cv2
import numpy as np

#定义形状检测函数
def ShapeDetection(img):
    contours,hierarchy = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)  #寻找轮廓点
    for obj in contours:
        area = cv2.contourArea(obj)  #计算轮廓内区域的面积
        cv2.drawContours(imgContour, obj, -1, (255, 0, 0), 4)  #绘制轮廓线
        perimeter = cv2.arcLength(obj,True)  #计算轮廓周长
        approx = cv2.approxPolyDP(obj,0.02*perimeter,True)  #获取轮廓角点坐标
        CornerNum = len(approx)   #轮廓角点的数量
        x, y, w, h = cv2.boundingRect(approx)  #获取坐标值和宽度、高度

        #轮廓对象分类
        if CornerNum ==3: objType ="triangle"
        elif CornerNum == 4:
            if w==h: objType= "Square"
            else:objType="Rectangle"
        elif CornerNum>4: objType= "Circle"
        else:objType="N"

        cv2.rectangle(imgContour,(x,y),(x+w,y+h),(0,0,255),2)  #绘制边界框
        cv2.putText(imgContour,objType,(x+(w//2),y+(h//2)),cv2.FONT_HERSHEY_COMPLEX,0.6,(0,0,0),1)  #绘制文字

path = "./test.png"
img = cv2.imread(path)
imgContour = img

#imgGray = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)  #转灰度图
#imgBlur = cv2.GaussianBlur(imgGray,(5,5),1)  #高斯模糊
imgCanny = cv2.Canny(img,60,60)  #Canny算子边缘检测
ShapeDetection(imgCanny)  #形状检测

cv2.imshow("Original img", img)
#cv2.imshow("imgGray", imgGray)
#cv2.imshow("imgBlur", imgBlur)
cv2.imshow("imgCanny", imgCanny)
cv2.imshow("shape Detection", imgContour)

cv2.waitKey(0)

运行结果：