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Circle Detection

Description​

Circle Detection tool is used to detect circles in an image. The tool uses cannny edge detection to detect edges in the image and then uses the Hough Transform to detect circles in the image.

This tool finds applications in various fields, including computer vision, image processing, robotics, and quality control. It provides a reliable means of automatically detecting circles in an image, enabling tasks such as object recognition, shape analysis.

Settings​

Blur Size​

The size of the Gaussian blur kernel to apply to the image before performing edge detection. The larger the kernel size, the more the image is blurred.

Accumulator Resolution​

The step size used when discretising the parameter space. It determines the distance between adjacent cells in the accumulator and influences the precision of circle detection.

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.

Minimum Distance​

The minimum distance (in pixels) between the centres of the detected circles. If the parameter is too small, multiple neighbour circles may be detected instead of a single one. If it is too large, some circles may be missed.

Canny Edge Threshold​

The threshold for the internal Canny edge detector. The smaller the threshold, the more circles are detected (including false circles). The larger the threshold, the more circles are rejected.

Accumulator threshold​

The minimum number of votes (intersections in Hough space) needed to detect a circle. The larger the threshold, the more circles will be returned.

Detectable Circle Radius Range​

The range of circle radius (in pixels) to detect. The minimum and maximum radius of the circles to detect.

Display Results​

Overlay Results​

Whether to draw the results on top of the image frame.

Overlay Opacity​

The opacity of the drawn overlay.