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 = '/home/pi/image.png'
img = cv2.imread(path)
imgContour = img.copy()
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(imgBlur,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)
