Finds circles in a grayscale image using the Hough transform

circles = cv.HoughCircles(image)
circles = cv.HoughCircles(..., 'OptionName', optionValue, ...)

Input

• image 8-bit, single-channel, grayscale input image.

Output

• circles Output vector of found circles. A cell-array of 3-element floating-point vectors {[x, y, radius], ...}.

Options

• Method Detection method. Currently, the only implemented method is 'Gradient' (default). One of:
  • Standard classical or standard Hough transform. Every line is represented by two floating-point numbers (rho,theta), where rho is a distance between (0,0) point and the line, and theta is the angle between x-axis and the normal to the line. Thus, the matrix must be (the created sequence will be) of single type with 2-channels.
  • Probabilistic probabilistic Hough transform (more efficient in case if the picture contains a few long linear segments). It returns line segments rather than the whole line. Each segment is represented by starting and ending points, and the matrix must be (the created sequence will be) of the int32 type with 4-channels.
  • MultiScale multi-scale variant of the classical Hough transform. The lines are encoded the same way as 'Standard'.
  • Gradient basically 21HT, described in [Yuen90].
• 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. default 1.
• 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. default size(image,1)/8.
• Param1 First method-specific parameter. In case of 'Gradient', it is the higher threshold of the two passed to the cv.Canny edge detector (the lower one is twice smaller). default 100.
• Param2 Second method-specific parameter. In case of '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. default 100.
• MinRadius Minimum circle radius. default 0.
• MaxRadius Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, returns centers without finding the radius. default 0.

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

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, you may set MaxRadius to a negative number to return centers only without radius search, and find the correct radius using an additional procedure.

References

[Yuen90]:

HK Yuen, John Princen, John Illingworth, and Josef Kittler. "Comparative study of hough transform methods for circle finding". Image and Vision Computing, 8(1):71-77, 1990.