How to detect circles in noisy images

p | This was a request from a(href=“") /r/computervision. | A reddit member was asking on how to count number of eggs on quite | noisy image like you may see below. | I’ve decided to write a simple algorithm that does the job and explain how it works.



h2 Step 1 - Filter image

p img(src=“source.jpg”,alt=“Source image”) | The original image has noticeable color noise and therefore it must be filtered before we pass it to further stages. | Ideally you should choose filter algorithm based on your task and noise model. For the sake of simplicity, | I will not use Weiner filter or deconvolution to deal with blur and artifacts that are present on source image.

p | Instead, I will use pyramidal mean-shift filter. | This algorithm works quite well on this problem and helps to get rid of artifacts.

    cv::Mat inputRgbImage = cv::imread("input.jpg");
    cv::Mat filtered;

strong Result of filtering div.beforeafter.twentytwenty-container img(src=“source.jpg”,alt=“before”) img(src=“filtered.jpg”,alt=“after”)

h2 Step 2 - Increase sharpness

p | As you may see - eggs edges are not too sharp. For circle detection we will use Hough transform. OpenCV’ Hough algorithm implementation use Canny edge detector to | detect edges. Unfortunately it’s not possible to pass manually computed binary image. | Instead we have to pass 8-bit grayscale image for circle detection. | Therefore we may want to increase their sharpness to make the image more friendly for Canny detector.

p | Sharpening can be easily done via unsharp mask.

    // Perform in-place unsharp masking operation
    void unsharpMask(cv::Mat& im) 
        cv::Mat tmp;
        cv::GaussianBlur(im, tmp, cv::Size(5,5), 5);
        cv::addWeighted(im, 1.5, tmp, -0.5, 0, im);

| However, unsharp mask can cause artifacts on edge borders which may lead to double edge.
| I'm dealing with it by adding laplaccian component to final result:

    cv::cvtColor(filtered, grayImg, cv::COLOR_BGR2GRAY);
    grayImg.convertTo(grayscale, CV_32F);

    cv::GaussianBlur(grayscale, blurred, cv::Size(5,5), 0);

    cv::Laplacian(blurred, laplaccian, CV_32F);

    cv::Mat sharpened = 1.5f * grayscale
                      - 0.5f * blurred
                      - weight * grayscale.mul(scale * laplaccian);

strong Result of sharpening

h3 Step 3 - Detect circles

p | After we got nicely filtered and enchanced image we can pass it to cv::HoughCircles to detect circles on the image. | According to problem task, we can limit the maximum radius of circles that we are interested in (we need only small circles). | In addition eggs can be close to each other, so it make sense to set minimal distance between two circles to minimal | allowed diameter of the circle.

                    2,   // Accumulator resolution
                    12,  // Minimum distance between the centers of the detected circles.
                    5,   // Minimum circle radius
                    20); // Maximum circle radius

h4 Step 4 - Validate eggs

p | I’ve skipped validation step since I don’t know what are the requirements to detected eggs. | But I suppose that after detection of possible candidates using Hough the algorithm should validate each contour | to verify it is exactly what we were looking for. Here are some hints what we can check:

    li Contour defects - how egg shape is close to ideal circle
    li Contour breaks - is there any breaks in egg boundary or not
    li Egg color - perhaps we need to count objects only of particular color
    li Neighbours - maybe (or maybe not) we need only isolated objects.

h4 Step 5 - GPU Speed-up

p | The given implementation is very slow since it use CPU and doing a lot of stuff that can be efficiently computed on GPU. | Fortunately, OpenCV has GPU implementations for mean-shift segmentation and hough transform and image blending. | You can easily speed-up this algorithm by using cv::gpu types and functions instead.

h4 Bonus content

p | Readed to the end of article? I’m impressed :) Here you go, the full source code of the algorithm and small playground to | tweak setting in runtime:

Eugene Khvedchenya avatar
About Eugene Khvedchenya
Eugene Khvedchenya is a computer vision developer skilled in high-performance image processing. In his spare time he loves to share his knowledge and thoughts about computer vision and augmented reality
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