检测不规则图像中的形状

Question

Trying to find the circles and rectangle (or square) in an irregular object using contours,edge detection but not getting the output properly.

1. I tried changing values of canny values and epsilon(contour approx) but was not able to detect,

2. Another difficulty iam facing is lot hand written character are there in the metal object so my code is detecting that also as a shape

Can anyone please help me on detecting this required shape on this object using opencv-python.

Metal object

import imutils
import cv2
import numpy as np
import matplotlib.pyplot as plt

image = cv2.imread('part1.jpg')

#image = cv2.imread('C:\Python27\plates\plates2.1.jpg')#$episolon==0.04,len=5,6
#image = cv2.imread('C:\Python27\plates\plates4.jpg')
#image = cv2.imread('C:\Python27\plates\plates1.jpg')
#image = cv2.imread('C:\Python27\plates\plates3.jpg')#episilon=0.0370,len=5
#image = cv2.imread('C:\Python27\plates\plates5.jpg') #change the episilon to 0.01
#image = cv2.imread('C:\Python27\plates\plates6.jpg')#not working properly


cv2.namedWindow('Image')




#for angle in xrange(0, 360, 90):
# rotate the image and display it
#image = imutils.rotate(image, angle=angle)


#gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
#blurred = cv2.GaussianBlur(gray, (5, 5), 0)


#edges=cv2.Canny(image,200,650)#plates3.jpg,plates1.jpg,plates5.jpg,
#edges=cv2.Canny(image,200,500)#plates4.jpg
#edges=cv2.Canny(image,200,589)#plates2.1.jpg
#edges=cv2.Canny(image,100,450)
edges=cv2.Canny(image,300,589)
kernel = np.ones((5,5),np.uint8)


#thresh = cv2.erode(edges,kernel,iterations = 1)
#thresh = cv2.dilate(edges,kernel,iterations = 1)
#thresh = cv2.morphologyEx(edges, cv2.MORPH_OPEN, kernel)
thresh = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel)

cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
sd = ShapeDetector()
print len(cnts)
for c in cnts:

        shape = "unidentified"
        peri = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.0373* peri, True)





        if len(approx) == 4:

            (x, y, w, h) = cv2.boundingRect(approx)
            #ar = w / float(h)


            #shape = "slots" if ar >= 0.95 and ar <= 1.05 else "slots"
            shape="slots"
            #cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
            rect = cv2.minAreaRect(c)
            box = cv2.boxPoints(rect)
            box = np.int0(box)
            cv2.drawContours(image,[box],0,(0,0,255),2)
            #cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),2)
            cv2.putText(image, shape, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX,0.5, (255, 255, 255), 4)

        elif len(approx)==2:
            shape="nothing"

            (x,y),radius = cv2.minEnclosingCircle(c)
            center = (int(x),int(y))
            radius = int(radius)
            #cv2.circle(image,center,radius,(0,255,0),2)
            #cv2.putText(image, shape, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX,0.5, (255, 255, 255), 4)

        elif len(approx)==5:
            shape="nothing"
        elif len(approx)==3:
            shape="nothing"

        elif len(approx)==6:
            shape="nothing"





        else:
            shape = "c"+str(len(approx))
            (x,y),radius = cv2.minEnclosingCircle(c)
            center = (int(x),int(y))
            radius = int(radius)
            cv2.circle(image,center,radius,(0,255,0),2)
            cv2.putText(image, shape, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX,0.5, (255, 255, 255), 2)


cv2.imshow("Image",image)
cv2.imshow("edges", thresh)

cv2.waitKey(0)
cv2.destroyAllWindows()

Answer 1

Use binarization. You will get blobs that you can discriminate by size, location and other geometric criteria.