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'''

Created on Sep 9, 2017

@author: inayat

'''

# import the required packages

from imutils.video import FileVideoStream

#from imutils.video import FPS

import numpy as np

import argparse

import imutils

import time

import cv2

import sys

from utils.fps2 import FPS2

from matplotlib import tight_bbox

if __name__ == '__main__':

# Initialize the argument parse which is used to parse the arguments

ap = argparse.ArgumentParser()

ap.add_argument("-v", "--video", required=True,

help="path to input video file")

ap.add_argument("-t", "--type", required=True,

help="input from [0..5] for selection of type of tracker from ['BOOSTING', 'MIL','KCF', 'TLD', 'MEDIANFLOW', 'GOTURN'] ")

args = vars(ap.parse_args())

print("[info] tracker selected is ", args["type"])

# a list of trackers type available in OpenCV3.2

#

trackerTypes = ['BOOSTING', 'MIL','KCF', 'TLD', 'MEDIANFLOW', 'GOTURN']

trackerType = trackerTypes[int(args["type"])]

# initialize multiple Tracker object with tracking algo

multipleTrackerOpenCV = cv2.MultiTracker(trackerType)

# Since the read method of the openCV cv2.VideoCapture

# is blocking IO operation

# Therefore I am going to use thread enable version of reading

# the video. It is implemented in imutil package (pyImagesearch.com)

# start and open a pointer to the file video stream thread

# and allow the buffer to start to fill

print("[info] starting to read a video file ...")

fvs = FileVideoStream(args["video"]).start()

time.sleep(1.0)

# read the first frame

frame = fvs.read()

#if not success:

# print("[info]: Failed to read the video")

# sys.exit(1)

#

init = False

bboxes = [] # contains bounding boxes around the objects to be tracked

# initialize various objects in the first frame of the video

while True:

# select the ROI around the object to be tracked

box = cv2.selectROI('tracking', frame, showCrossair=False, fromCenter=False)

print(box)

bboxes.append(box)

print("[info] print q to quit object selection and anyother key for next object selection")

k = cv2.waitKey(0) & 0xff

if(k== 113):

break

print("[info] selecting objects {}".format(bboxes))

# start the frame per second (FPS) counter

fps = FPS2().start()

# loop over the frames obtained from the video file stream

while fvs.more():

# grab each frame from the threaded video file stream,

# resize

# it, and convert it to grayscale (while still retaining 3

# channels)

frame = fvs.read()

#frame = imutils.resize(frame, width=450)

#frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

#frame = np.dstack([frame, frame, frame])

# display the size of the queue on the frame

#cv2.putText(frame, "Queue Size: {}".format(fvs.Q.qsize()),

# (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)

# initialize the trackers only once

if not init:

success = multipleTrackerOpenCV.add(frame,bboxes)

init = True

success, boxes = multipleTrackerOpenCV.update(frame)

print("[info] no boxes {}".format(len(boxes)))

for box in boxes:

p1= (int(box[0]), int(box[1]))

p2 = (int(box[0] + box[2]), int(box[1] + box[3]))

cv2.rectangle(frame, p1, p2, (200, 0, 0))

fps.update()

cv2.putText(frame, "FPS: {:.2f}".format(fps.fps()),

(10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)

cv2.putText(frame, trackerType + " Tracker", (20,80), cv2.FONT_HERSHEY_SIMPLEX, 0.75,(0,0,255),2)

# show the frame and update the FPS counter

cv2.imshow("Frame", frame)

k = cv2.waitKey(1) & 0xff

if k == 27:

break

# stop the timer and display FPS information

fps.stop()

print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))

print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

# do a bit of cleanup

cv2.destroyAllWindows()

fvs.stop()