import cv2
import numpy as np
import time

cap = cv2.VideoCapture(0)
upperOrange = np.array([10,255,255])
lowerOrange = np.array([0,150,150])
cnts = []
while(1):
    ret, img = cap.read()
    k = cv2.waitKey(30) & 0xff
    frame = cv2.resize(img, (0,0), fx=0.25, fy=0.25)
    if k == 27:
        break
    start = time.time()
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, lowerOrange, upperOrange)
    _, cnts,    _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    if len(cnts) > 0:
        # find the largest contour in the mask, then use
        # it to compute the minimum enclosing circle and
        # centroid
        c = max(cnts, key=cv2.contourArea)
        ((x, y), radius) = cv2.minEnclosingCircle(c)
        M = cv2.moments(c)
        if M["m00"] != 0:
            center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))

        # only proceed if the radius meets a minimum size
            if radius > 3:
        # draw the circle and centroid on the frame,
            # then update the list of tracked points
                cv2.circle(frame, (int(x), int(y)), int(radius),
                (0, 255, 255), 2)
                cv2.circle(frame, center, 5, (0, 0, 255), -1)
    end = time.time()
    print(end-start)
    cv2.imshow('frame',frame)


cap.release()
cv2.destroyAllWindows()
cv2.waitKey(1)