Count circle objects in an image using matlab

Based on well known image processing techniques, you can write your own processing tool:

img = imread('Mlj6r.jpg'); % read the image
imgGray = rgb2gray(img); % convert to grayscale
sigma = 1;
imgGray = imgaussfilt(imgGray, sigma); % filter the image (we will take derivatives, which are sensitive to noise)
imshow(imgGray) % show the image
[gx, gy] = gradient(double(imgGray)); % take the first derivative
[gxx, gxy] = gradient(gx); % take the second derivatives
[gxy, gyy] = gradient(gy); % take the second derivatives

k = 0.04; %0.04-0.15 (see wikipedia)
blob = (gxx.*gyy - gxy.*gxy - k*(gxx + gyy).^2); % Harris corner detector (high second derivatives in two perpendicular directions)
blob = blob .* (gxx < 0 & gyy < 0); % select the top of the corner (i.e. positive second derivative)

figure
imshow(blob) % show the blobs

blobThresshold = 1;
circles = imregionalmax(blob) & blob > blobThresshold; % find local maxima and apply a thresshold
figure
imshow(imgGray) % show the original image
hold on
[X, Y] = find(circles); % find the position of the circles
plot(Y, X, 'w.'); % plot the circle positions on top of the original figure
nCircles = length(X)

This code counts 2710 circles, which is probably a slight (but not so bad) overestimation.

The following figure shows the original image with the circle positions indicated as white dots. Some wrong detections are made at the border of the object. You can try to make some adjustments to the constants sigma, k and blobThresshold to obtain better results. In particular, higher k may be beneficial. See wikipedia, for more information about the Harris corner detector.