python - Opencv divide contacted circles into single

Question

I have an image to process.I need detect all the circles in the image.Here is it.

And here is my code.

import cv2
import cv2.cv as cv
img = cv2.imread(imgpath)
cv2.imshow("imgorg",img)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2.imshow("gray",gray)
ret,thresh = cv2.threshold(gray, 199, 255, cv.CV_THRESH_BINARY_INV)
cv2.imshow("thresh",thresh)
cv2.waitKey(0)
cv2.destrotAllWindows()

Then,I got a image like this.

And I tried to use erode and dilate to divided them into single.But it doesnt work.My question is how to divide these contacted circles into single,so i can detect them.

According to @Micka's idea,I tried to process the image in following way,and here is my code.

import cv2
import cv2.cv as cv
import numpy as np

def findcircles(img,contours):
    minArea = 300;
    minCircleRatio = 0.5;
    for  contour  in contours:

        area = cv2.contourArea(contour)
        if area < minArea: 
            continue

        (x,y),radius = cv2.minEnclosingCircle(contour)
        center = (int(x),int(y))
        radius = int(radius)
        circleArea = radius*radius*cv.CV_PI;

        if area/circleArea < minCircleRatio:
             continue;
        cv2.circle(img, center, radius, (0, 255, 0), 2)
        cv2.imshow("imggg",img)

img = cv2.imread("a.png")
cv2.imshow("org",img)

gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,threshold = cv2.threshold(gray, 199, 255,cv. CV_THRESH_BINARY_INV)
cv2.imshow("threshold",threshold)

blur = cv2.medianBlur(gray,5)
cv2.imshow("blur",blur)

laplacian=cv2.Laplacian(blur,-1,ksize = 5,delta = -50)
cv2.imshow("laplacian",laplacian)

kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(7,7))
dilation = cv2.dilate(laplacian,kernel,iterations = 1)
cv2.imshow("dilation", dilation)

result= cv2.subtract(threshold,dilation) 
cv2.imshow("result",result)

contours, hierarchy = cv2.findContours(result,cv2.RETR_LIST,cv2.CHAIN_APPROX_NONE)
findcircles(gray,contours)

But I dont get the same effect as @Micka's.I dont know which step is wrong.

Answer 1

Adapting the idea of @jochen I came to this:

1. extract the full circle mask as you've done (I called it fullForeground )

2. from your colored image, compute grayscale, blur (median blur size 7) it and and extract edges, for example with cv::Laplacian This laplacian thresholded > 50 gives:

cv::Laplacian(blurred, lap, 0, 5); // no delta lapMask = lap > 50; // thresholding to values > 50

This one dilated once gives:

cv::dilate(lapMask, dilatedThresholdedLaplacian, cv::Mat()); // dilate the edge mask once

Now subtraction fullForeground - dilatedThresholdedLaplacian (same as and_not operator for this type of masks) gives:

from this you can compute contours. For each contour you can compute the area and compare it to the area of an enclosing circle, giving this code and result:

std::vector<std::vector<cv::Point> > contours;
cv::findContours(separated.clone(), contours, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);

double minArea = 500;
double minCircleRatio = 0.5;
for(unsigned int i=0; i<contours.size(); ++i)
{
    double cArea = cv::contourArea(contours[i]);
    if(cArea < minArea) continue;

    //filteredContours.push_back(contours[i]);
    //cv::drawContours(input, contours, i, cv::Scalar(0,255,0), 1);
    cv::Point2f center;
    float radius;
    cv::minEnclosingCircle(contours[i], center, radius);

    double circleArea = radius*radius*CV_PI;

    if(cArea/circleArea < minCircleRatio) continue;

    cv::circle(input, center, radius, cv::Scalar(0,0,255),2);
}

here is another image showing the coverage:

hope this helps