Notes

# import the necessary packages
from collections import deque
import numpy as np
import argparse
import imutils
import cv2
import RPi.GPIO as GPIO

GPIO.setmode(GPIO.BOARD)
GPIO.setup(11, GPIO.OUT) #sol
GPIO.setup(13, GPIO.OUT) #atis
GPIO.setup(15, GPIO.OUT) #sag

# 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=32,
help="max buffer size")
args = vars(ap.parse_args())
# define the lower and upper boundaries of the "green"
# ball in the HSV color space
greenLower = (50, 60, 60)
greenUpper = (70, 150, 150)

# initialize the list of tracked points, the frame counter,
# and the coordinate deltas
pts = deque(maxlen=args["buffer"])
counter = 0
(dX, dY) = (0, 0)
direction = ""

# 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"])
# keep looping
while True:
# 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

# resize the frame, blur it, and convert it to the HSV
# color space
frame = imutils.resize(frame, width=400)
blurred = cv2.GaussianBlur(frame, (11, 11), 0)
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

# construct a mask for the color "green", then perform
# a series of dilations and erosions to remove any small
# blobs left in the mask
mask = cv2.inRange(hsv, greenLower, greenUpper)
mask = cv2.erode(mask, None, iterations=2)
mask = cv2.dilate(mask, None, iterations=2)

# 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"]))
print "x:"
print x
print "y:"
print y


# 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)
pts.appendleft(center)
# loop over the set of tracked points
for i in np.arange(1, len(pts)):
# if either of the tracked points are None, ignore
# them
if pts[i - 1] is None or pts[i] is None:
continue
# check to see if enough points have been accumulated in
# the buffer
if counter >= 10 and i == 1 and pts[-10] is not None:
# compute the difference between the x and y
# coordinates and re-initialize the direction
# text variables
dX = pts[-10][0] - pts[i][0]
dY = pts[-10][1] - pts[i][1]
(dirX, dirY) = ("", "")

# ensure there is significant movement in the
# x-direction
if np.abs(dX) > 20:
dirX = "East" if np.sign(dX) == 1 else "West"

# ensure there is significant movement in the
# y-direction
if np.abs(dY) > 20:
dirY = "North" if np.sign(dY) == 1 else "South"

# handle when both directions are non-empty
if dirX != "" and dirY != "":
direction = "{}-{}".format(dirY, dirX)

# otherwise, only one direction is non-empty
else:
direction = dirX if dirX != "" else dirY
# otherwise, compute the thickness of the line and
# draw the connecting lines
thickness = int(np.sqrt(args["buffer"] / float(i + 1)) * 2.5)

# show the movement deltas and the direction of movement on
# the frame
cv2.putText(frame, direction, (10, 30), cv2.FONT_HERSHEY_SIMPLEX,
0.65, (0, 0, 255), 3)
cv2.putText(frame, "dx: {}, dy: {}".format(dX, dY),
(10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX,
0.35, (0, 0, 255), 1)

# show the frame to our screen and increment the frame counter
cv2.imshow("Mask", mask)
key = cv2.waitKey(1) & 0xFF
counter += 1

# if the 'q' key is pressed, stop the loop
if key == ord("q"):
break

# cleanup the camera and close any open windows
camera.release()
cv2.destroyAllWindows()