Function opencv::imgproc::hough_circles

pub fn hough_circles(
    image: &dyn ToInputArray, 
    circles: &mut dyn ToOutputArray, 
    method: i32, 
    dp: f64, 
    min_dist: f64, 
    param1: f64, 
    param2: f64, 
    min_radius: i32, 
    max_radius: i32
) -> Result<()>

Finds circles in a grayscale image using the Hough transform.

The function finds circles in a grayscale image using a modification of the Hough transform.

Example: : @include snippets/imgproc_HoughLinesCircles.cpp

Note: Usually the function detects the centers of circles well. However, it may fail to find correct radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if you know it. Or, in the case of #HOUGH_GRADIENT method you may set maxRadius to a negative number to return centers only without radius search, and find the correct radius using an additional procedure.

It also helps to smooth image a bit unless it's already soft. For example, GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help.

Parameters

• image: 8-bit, single-channel, grayscale input image.
• circles: Output vector of found circles. Each vector is encoded as 3 or 4 element floating-point vector or .
• method: Detection method, see #HoughModes. The available methods are #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT.
• dp: Inverse ratio of the accumulator resolution to the image resolution. For example, if dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has half as big width and height. For #HOUGH_GRADIENT_ALT the recommended value is dp=1.5, unless some small very circles need to be detected.
• minDist: Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed.
• param1: First method-specific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT, it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value shough normally be higher, such as 300 or normally exposed and contrasty images.
• param2: Second method-specific parameter. In case of #HOUGH_GRADIENT, it is the accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure. The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles.
• minRadius: Minimum circle radius.
• maxRadius: Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, #HOUGH_GRADIENT returns centers without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses.

See also

fitEllipse, minEnclosingCircle

C++ default parameters

• param1: 100
• param2: 100
• min_radius: 0
• max_radius: 0