Red color detection opencv python

#!/usr/bin/python
import cv2
import numpy as np

cap = cv2.VideoCapture(0)
while True:
    _, frame = cap.read()
    hsv = cv2.cvtColor(frame , cv2.COLOR_BGR2HSV)
    l_red = np.array([150,150,50])
    u_red = np.array([180,255,150])

    mask = cv2.inRange(hsv , l_red , u_red)
    res = cv2.bitwise_and(frame , frame , mask = mask)



    cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
        cv2.CHAIN_APPROX_SIMPLE)[-2]
    # print frame
    # print cnts
    center = None
        # only proceed if at least one contour was found
    # if len(cnts) > 0:
        # find the largest contour in the mask, then use
        # it to compute the minimum enclosing circle and
        # centroid
    try:
        areas = [cv2.contourArea(c) for c in cnts]
        max_index = np.argmax(areas)
        # print max_index
        cnt=cnts[max_index]
        x,y,w,h = cv2.boundingRect(cnt)
        print "X = ",x," Y=",y," w=",w," h= ",h
        cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)
        pass
    except:
        pass
    else:
        pass

        # # only proceed if the radius meets a minimum size
        # if radius > 10:
        #     # 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)
    # cnts = cnts[0] if imutils.is_cv2() else cnts[1]
    k = cv2.waitKey(5) & 0xFF
    if k == 27:
        break
    cv2.imshow('frame' , frame)
    # cv2.imshow('mask' , mask)
    # cv2.imshow('res' , res)

cv2.destroyAllWindows()
cap.release()







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