OpenCV圆/轮廓检测Python

Question

I am currently having issues correctly detecting circles in the image bellow (pre-processing), the output can be sporadic for the times that it will show the circles semi correctly (post-processing). The image is taken live via a webcam feed at 800*600 resolution, then passed through a bilateralFilter which helps get rid of a few false negatives (I tried a GaussianBlur but it would go extremely slow at times....).

After that it gets changed to grey then passed through the HoughCircles function to provide the output provided.

I have looked at the contours function, but i haven't found that good of documentation to figure out what each variable pertains to, if that makes sense (at least for the python functions).

I would appreciate any and all help to make this more accurate as the end goal is to take known sizes of the holes and convert that to see if the distance between the circles is off for quality control testing. (and to check if a circle is not removed ie not there).

CODE:

import cv2
import os
import math
import numpy

minRad = 50
maxRad = 75

b1 = 2
b2 = 5
b3 = 5

c1 = 5
c2 = 200
c3 = 50
c4 = 100

bw = 1

vc =cv2.VideoCapture(0)
if vc.isOpened():
    vc.set(3,800)
    vc.set(4,600)
#       vc.set(10,10)
    rval, frame = vc.read()
else:
    rval = False

while rval:
    rval, frame = vc.read()
    blur = cv2.bilateralFilter(frame,b1,b2,b3)
#       blur = cv2.GaussianBlur(frame,(5,5),1)
    gray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)#frame
#       edges = cv2.Canny(gray, 200, 20, apertureSize=3)#80 120 3
    edges = gray

    circles = cv2.HoughCircles(edges,cv2.cv.CV_HOUGH_GRADIENT,c1,c2,param1=c3,param2=c4,minRadius=minRad,maxRadius=maxRad)
    print "\n\n"
    print circles

    if circles != None:
        circles = numpy.uint16(numpy.around(circles),decimals=1)
        for cir in circles[0,:]:
            if bw == 1:
                cv2.circle(edges,(cir[0],cir[1]),cir[2],(0,255,0),2)#frame
                cv2.circle(edges,(cir[0],cir[1]),2,(0,0,255),)#frame
            else:           
                #draw outer circle
                cv2.circle(blur,(cir[0],cir[1]),cir[2],(0,255,0),2)#frame
                #draw center
                cv2.circle(blur,(cir[0],cir[1]),2,(0,0,255),)#frame
    if bw == 1:
        cv2.imwrite('/home/kasper/test/test.jpg', edges, [int(cv2.IMWRITE_JPEG_QUALITY), 90])   
    else:
        cv2.imwrite('/home/kasper/test/test.jpg', blur, [int(cv2.IMWRITE_JPEG_QUALITY), 90])
    ch = cv2.waitKey(10)

    if ch != -1:
        print "keypressed"
        print ch
        break
    cv2.destroyAllWindows()

Circle Detection Output:

[[[ 652.5         507.5          62.45398331]
  [ 282.5         522.5          57.36288071]
  [ 102.5         342.5          52.84410858]
  [ 462.5         327.5          67.7089386 ]
  [ 697.5         242.5          52.52142334]
  [  82.5         547.5          52.50238037]
  [ 307.5         167.5          63.04363632]
  [  92.5         137.5          67.79749298]]]

[[[ 287.5         522.5          52.616539  ]
  [ 647.5         507.5          57.50217438]
  [ 472.5         337.5          67.7089386 ]
  [  87.5         512.5          67.78273773]
  [  82.5         292.5          67.64983368]
  [ 687.5         212.5          52.5594902 ]
  [ 302.5         162.5          67.88593292]]]

Answer 1

在我看来，输出的格式为（x，y，radius），其中（x，y）是每个圆的中心。

Answer 2

you can detect holes using this code:

import numpy as np 

import cv2

from matplotlib import pyplot as plt

plt.ion()

filteredContour = []

img = cv2.imread('circle.png')

grayImage = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)

binaryImage = np.uint8((grayImage < 100) *1)

binaryForContour = binaryImage*1

contour,hierarchy=cv2.findContours(binaryForContour,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
for iteration in range (0,len(contour)):

    areaOfContour = cv2.contourArea(contour[iteration])


    if areaOfContour >= 5000:

        filteredContour.append(contour[iteration])

        cv2.drawContours(img,filteredContour, -1, (0,255,0), 2)

        plt.imshow(img)

The image is not clear. If the lighting is proper it will work .