Circle Detector.py

import numpy as np
import cv2
import math

cap = cv2.VideoCapture(0)
ret, frame_large = cap.read()
scale = 0.5
frame = cv2.resize(frame_large, None, fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
y, x, c = np.shape(frame)
center = (y/2, x/2)

while (True):
    # Capture frame
    _, frame_large = cap.read()

    frame = cv2.resize(frame_large, None, fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)

    # Grayscale
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Smooth
    frame = cv2.GaussianBlur(frame, (5, 5), 0)
    frame = cv2.medianBlur(frame, 15)

    circles = cv2.HoughCircles(frame, cv2.cv.CV_HOUGH_GRADIENT, 1, 20,
                               param1=50, param2=60, minRadius=0, maxRadius=0)

    average_center = [-1, -1]
    if circles != None:
        for i in circles[0, :]:
            average_center[0] += i[0]
            average_center[1] += i[1]
            # cv2.circle(frame, (i[0], i[1]), i[2], (0, 255, 0), 2)
            # cv2.circle(frame, (i[0], i[1]), 2, (0, 0, 255), 3)

        average_center[0] /= len(circles[0, :])
        average_center[1] /= len(circles[0, :])

    if average_center[0] != -1:
        cv2.circle(frame, (int(average_center[0]), int(average_center[1])), 2, (0, 0, 255), 3)

    cv2.imshow('frame', frame)

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

# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()