# [OpenCV] It's better to use a minimum circumscribed circle than to use Hough Circle Transform for circle detection

2020/08/09

This is a tip I found when I tried to detect a red ball.

One of the popular methods is using Hough Circle Transform.
This method is difficult to explain, so I list some references.↓↓

However, the Hough transform is heavy, slow, and inaccurate. Besides, the size of the detected circle is not stable.

I was wondering how to deal with it, then an experienced friend told me that "it is better to use a minimum circumscribed circle".
I actually used it, and I found it was much more accurate (it doesn't mean truly accurate, though) and smooth.
it would be more accurate to say that it seems to recognize circles with good accuracy because it's actually not recognizing circles. It only detects certain colors, so the detection is fast and accurate.

I recognized a red ball displayed on my smartphone as a demo.
It seems good, isn't it?

Of course, there are cases of the Hough Circle Transform is a suitable choice, but this time I want to recognize only one red ball, so using the minimum circumscribed circle was comfortable enough.

An example code is shown below.

```# -*- coding: utf-8 -*-
import numpy as np
import cv2

def getCircle(frame, lower_color, upper_color):

# Convert color space of an image to HSV
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

# Apply Gaussian blur to improve recognition accuracy
blur = cv2.GaussianBlur(hsv, (9, 9), 0)

# Extract color in specified range
color = cv2.inRange(blur, lower_color, upper_color)

# Noise reduction with opening and closing
element8 = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]], np.uint8)
oc = cv2.morphologyEx(color, cv2.MORPH_OPEN, element8)
oc = cv2.morphologyEx(oc, cv2.MORPH_CLOSE, element8)

# Extract contours
img, contours, hierarchy = cv2.findContours(oc, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
print("{} contours.".format(len(contours)))

if len(contours) > 0:
# Get the most biggest red area
contours.sort(key=cv2.contourArea, reverse=True)
cnt = contours[0]

# Detect a circle using a minimum circumscribed circle
center = (int(x), int(y))

# Omit a too small circle
return None
else:
else:
return None

if __name__ == '__main__':
# Start an internal camera (set 0 as an argument if only one camera is connected)
cap = cv2.VideoCapture(0)

while True:
# Extract an red circle
getframe = getCircle(frame, np.array([130, 80, 80]), np.array([200, 255, 255]))

if getframe is not None:
# Draw a blue circle on a circle we found
cv2.circle(frame, getframe[0], getframe[1], (255, 0, 0), 2)
print(getframe[1])

# Show movie with the result of detection
cv2.imshow('Circle Detect', frame)

if cv2.waitKey(1) & 0xFF == ord('q'):
break

# Release the camera
cap.release()
cv2.destroyAllWindows()
```

Everyone, let's use the minimum circumscribed circle!

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