3H proficient in opencv (VIII) - shape detection

import cv2import numpy as np

We also need to know where OpenCV in , The direction of the coordinate axis is x Shaft to the right ,y Axis down , The coordinate origin is in the upper left corner , For example, the following one is 640 Pixels , Wide for 480 Pixel image .OK, Let's begin with this section .

1、 Implementation source code

First, convert the original image to a grayscale image , Gaussian smoothing is applied to it , Noise removal , Get a smooth background image , And then edge detection , Then the extracted shape is judged , Frame them separately and label them .

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 verdef getContours(img):    contours,hierarchy=cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)    for cnt in contours:        area=cv2.contourArea(cnt)        print(area)        cv2.drawContours(imgContour,cnt,-1,(255,0,0),3)        if area>500:            cv2.drawContours(imgContour,cnt,-1,(255,0,0),3)            peri=cv2.arcLength(cnt,True)            #print(peri)            approx=cv2.approxPolyDP(cnt,0.02*peri,True)            print(len(approx))            objCor=len(approx)            x,y,w,h=cv2.boundingRect(approx)            if objCor==3:objectType="Tri"            elif objCor==4:                aspRatio=w/float(h)                if aspRatio>0.98 and aspRatio<1.03: objectType="Square"                else:objectType="Rectangle"            elif objCor>4:                objectType="Circle"            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_SIMPLEX,0.7,                        (0,255,255),2)path='Resources/shapes.png'img=cv2.imread(path)img=cv2.resize(img,(400,400))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("imgStack",imgStack)cv2.waitKey(0)

2、 Running effect