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#!/usr/bin/env python
# USAGE
# python ball_tracking.py --video ball_tracking_example.mp4
# python ball_tracking.py
# import the necessary packages
from collections import deque
import numpy as np
import argparse
import imutils
import cv2
turn_right = 0;
turn_left = 0;
x=-1;
y=-1;
# 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 "green"
# ball in the HSV color space, then initialize the
# list of tracked points
greenLower = (30, 86, 6)
greenUpper = (40, 255, 255)
#redLower = (-20, 86, 6)
#redUpper = (20, 255, 255)
pts = deque(maxlen=args["buffer"])
# if a video path was not supplied, grab the reference
# to the webcam
if not args.get("video", False):
camera = cv2.VideoCapture(1)
# otherwise, grab a reference to the video file
else:
camera = cv2.VideoCapture(args["video"])
# keep looping
while True:
#sarjakcount=1;
#while (sarjakcount<1000000):
# sarjakcount=sarjakcount+1;
# 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=600)
# 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)
#mask2 = cv2.inRange(hsv, redLower, redUpper)
#mask2 = cv2.erode(mask2, None, iterations=2)
#mask2 = cv2.dilate(mask2, 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
'''
cnts2 = cv2.findContours(mask2.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)[-2]
center2 = 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
#print "green frame"
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
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)
'''
if len(cnts2) > 0:
# find the largest contour in the mask, then use
# it to compute the minimum enclosing circle and
# centroid
#print "red frame"
c2 = max(cnts2, key=cv2.contourArea)
((x2, y2), radius2) = cv2.minEnclosingCircle(c2)
M2 = cv2.moments(c2)
center2 = (int(M2["m10"] / M2["m00"]), int(M2["m01"] / M2["m00"]))
# print "***",center2,"***";
# only proceed if the radius meets a minimum size
if radius2 > 10:
# draw the circle and centroid on the frame,
# then update the list of tracked points
cv2.circle(frame, (int(x2), int(y2)), int(radius2),
(0, 255, 255), 2)
cv2.circle(frame, center2, 5, (0, 0, 255), -1)
'''
#print "---",int(x),y,"---";
#print radius;
if(x > 400):
turn_right = turn_right+1;
else:
turn_right = 0;
if(x<200):
turn_left = turn_left + 1;
else:
turn_left = 0;
if(turn_right > 10):
print "right turn";
elif(turn_left > 10):
print "left turn";
# else:
# print "-";
if(radius > 120):
print " Too close, move away";
elif(radius < 80):
print " Too far, move closer";
# update the points queue
pts.appendleft(center)
# loop over the set of tracked points
for i in xrange(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
# otherwise, compute the thickness of the line and
# draw the connecting lines
thickness = int(np.sqrt(args["buffer"] / float(i + 1)) * 2.5)
cv2.line(frame, pts[i - 1], pts[i], (0, 0, 255), thickness)
# show the frame to our screen
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# 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()
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