from collections import deque
from picamera.array import PiRGBArray
from picamera import PiCamera
import numpy as np
import argparse
import imutils
import cv2

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video",
    help="path to the (optional) video file")
ap.add_argument("-b", "--buffer", type=int, default=64,
    help="max buffer size")
args = vars(ap.parse_args())

# define the lower and upper boundaries of the colors in the HSV color space
lower = {'red':(166, 84, 141), 'green':(25, 81, 147), 'blue':(97, 100, 117)}
upper = {'red':(186,255,255), 'green':(65,101,147), 'blue':(117,255,255)}

# define standard colors for circle around the object
colors = {'red':(0,0,255), 'green':(0,255,0), 'blue':(255,0,0)}

#pts = deque(maxlen=args["buffer"])

# enable picamera
#camera = PiCamera()
#camera.resolution = (320, 240)
#camera.framerate = 60
#rawCapture = PiRGBArray(camera, size=(320, 240))

# if a video path was not supplied, grab the reference
# to the webcam
# if not args.get("video", False):
camera = cv2.VideoCapture(0)


# otherwise, grab a reference to the video file
# else:
#    camera = cv2.VideoCapture(args["video"])

while True:
#for image  in camera.capture_continuous(rawCapture, format="bgr"):
    # grab the current frame
    (grabbed, frame) = camera.read()
    # if we are viewing a video and we did not grab a frame,
    # then we have reached the end of the video
    if args.get("video") and not grabbed:
        break

    #Picamera version
    #frame = image.array

    #IP webcam image stream
    #URL = 'http://10.254.254.102:8080/shot.jpg'
    #urllib.urlretrieve(URL, 'shot1.jpg')
    #frame = cv2.imread('shot1.jpg')


    # resize the frame, blur it, and convert it to the HSV
    # color space
    frame = imutils.resize(frame, width=320)

    blurred = cv2.GaussianBlur(frame, (11, 11), 0)
    hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)
    #for each color in dictionary check object in frame
    for key, value in upper.items():
        # construct a mask for the color from dictionary`1, then perform
        # a series of dilations and erosions to remove any small
        # blobs left in the mask
        kernel = np.ones((9,9),np.uint8)
        mask = cv2.inRange(hsv, lower[key], upper[key])
        mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
        mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)

        cv2.imshow("Frame"+key, mask)

        # find contours in the mask and initialize the current
        # (x, y) center of the ball
        cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
            cv2.CHAIN_APPROX_SIMPLE)[-2]
        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
            c = max(cnts, key=cv2.contourArea)
            ((x, y), radius) = cv2.minEnclosingCircle(c)
            M = cv2.moments(c)
            center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))

            # only proceed if the radius meets a minimum size. Correct this value for your obect's size
            if radius > 0.5:
                # draw the circle and centroid on the frame,
                # then update the list of tracked points
                cv2.circle(frame, (int(x), int(y)), int(radius), colors[key], 2)
                cv2.putText(frame,key + " ball", (int(x-radius),int(y-radius)), cv2.FONT_HERSHEY_SIMPLEX, 0.6,colors[key],2)


    # show the frame to our screen

    key = cv2.waitKey(1) & 0xFF
    # if the 'q' key is pressed, stop the loop
    if key == ord("q"):
        break