Detect object with openCV and python

问题

I'm trying to detect the white dots in the following image using OpenCV and Python.

I tried using the function cv2.HoughCircles but without any success.

Do I need to use a different method?

This is my code:

import cv2, cv
import numpy as np
import sys

if len(sys.argv)>1:
    filename = sys.argv[1]
else:
    filename = 'p.png'

img_gray = cv2.imread(filename,cv2.CV_LOAD_IMAGE_GRAYSCALE)

if img_gray==None:
    print "cannot open ",filename

else:
    img = cv2.GaussianBlur(img_gray, (0,0), 2)
    cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
    circles = cv2.HoughCircles(img,cv2.cv.CV_HOUGH_GRADIENT,4,10,param1=200,param2=100,minRadius=3,maxRadius=100)
if circles:
    circles = np.uint16(np.around(circles))
    for i in circles[0,:]:
        cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),1) 
        cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)   

cv2.imshow('detected circles',cimg)
cv2.waitKey(0)
cv2.destroyAllWindows()

回答1:

If you can reproduce a morphological reconstruction in OpenCV, you can easily build a h-dome transform which simplifies the task significantly. Otherwise, a simple threshold on a gaussian filtering might be enough too.

Binarize[FillingTransform[GaussianFilter[f, 2], 0.4, Padding -> 1]]

The gaussian filtering was done in the code above to effectively suppress the noise around the border of the input, which would remain after the h-dome transform otherwise.

Next there is the result of a simple threshold after a gaussian filtering (Binarize[GaussianFilter[f, 2], 0.5]) as well another result that is given by a direct binarization using Kapur's thresholding method (see the paper "A new method for gray-level picture thresholding using the entropy of the histogram" (which is no longer a new method, it is from 1985)):

The right image above has a lot of small points all over the border (which cannot be seen at this image resolution), but is fully automatic. From these 3 options, only the second one is already present in OpenCV.

回答2:

I think a median filter will improve your image. Try to experiment with some kernels, 3x3 or 7x7. Then after that some (local) thresholding algorithm will get you shapes. You can either you HoughCircles, or just find contours and check them for roundness.

回答3:

Convert the image to binary image using a suitable threshold technique (Otsu might help). Then use morphological operations like erosion to make circles smaller and then you can easily find their centers.

来源：https://stackoverflow.com/questions/14777545/detect-object-with-opencv-and-python