Good question!
This is an example of HoughCircles() function from opencv-python tutorials. Lets look at it in details
HoughCircles(image, method, dp, minDist[, param1[, param2[, minRadius[, maxRadius]]]]])
image
This is the input image you want to detect circles from. It's strongly recommended that the image is grayscaled because HoughCircles() uses Canny() function to detect edges in image.
method
This is the mathematical formula used to find circles. The only available formula in HoughCircle is cv2.HOUGH_GRADIENT so you don't have other choice but using it.
dp
Check out this answer here. If you couldn't understand it don't worry. Hough Transform is broad subjet and I advise you to research it more in detail if you want to know what this variable mean, but anyway, this variable should be between 0 and 2 and is of type double, so try using numbers like 0.6 or 1.3.
minDist
This is the minimum distance between the center of circles to be detected. How close are the circles in your image? Do you want the function to detect closely connected circles or far between circles?
param1 and param2
As mentioned before, HoughCircles() internally uses Canny() function. These parameters specify how aggressively you want to detect the edges.
The thresholder used in the Canny operator uses a method called
"hysteresis". Most thresholders used a single threshold limit, which
means if the edge values fluctuate above and below this value the line
will appear broken (commonly referred to as ``streaking''). Hysteresis
counters streaking by setting an upper and lower edge value limit.
Considering a line segment, if a value lies above the upper threshold
limit it is immediately accepted. If the value lies below the low
threshold it is immediately rejected. Points which lie between the two
limits are accepted if they are connected to pixels which exhibit
strong response.
minRadius and maxRadius
The size of circle is represented by its radius. The bigger the radius the bigger the circle and vice versa. These parameters specify the range of sizes of the circles you want to detect.
Finally
When you're using HoughCircles() and other similar functions, a lot of time you will spend will be on tuning these parameters to find the best combination of numbers to detect the circles in your image. So don't be frustrated if you think your parameters are wrong.
