# import the necessary packages
from threading import Thread
import sys
import cv2
import time
# import the Queue class from Python 3
if sys.version_info >= (3, 0):
    from queue import Queue

# otherwise, import the Queue class for Python 2.7
else:
    from Queue import Queue


class FileVideoStream:
    def __init__(self,stream_config, transform=None):
        # initialize the file video stream along with the boolean
        # used to indicate if the thread should be stopped or not
        self.transform = transform
        self.stream_config =stream_config
        # initialize the queue used to store frames read from
        # the video file
        self.build_pipeline()


    def start(self):
        # start a thread to read frames from the file video stream
        self.thread.start()
        return self

    def build_cv_obj(self):
        self.stream = cv2.VideoCapture(self.stream_config["uri"])
        self.stopped = False

    def build_pipeline(self):
        self.build_cv_obj()
        if "queueSize" not in self.stream_config:
            self.stream_config["queueSize"] =128

        self.Q = Queue(maxsize=int(self.stream_config["queueSize"]))
        # intialize thread
        self.thread = Thread(target=self.update, args=())
        self.thread.daemon = True


    def is_opened(self):
        return self.stream.isOpened()

    def update(self):
        # keep looping infinitely
        while True:
            # if the thread indicator variable is set, stop the
            # thread
            if self.stopped:
                break

            # otherwise, ensure the queue has room in it
            if not self.Q.full():
                # read the next frame from the file
                (grabbed, frame) = self.stream.read()

                # if the `grabbed` boolean is `False`, then we have
                # reached the end of the video file
                if grabbed is False or frame is None:
                    #self.stopped = True
                    self.build_cv_obj()
                    continue

                # if there are transforms to be done, might as well
                # do them on producer thread before handing back to
                # consumer thread. ie. Usually the producer is so far
                # ahead of consumer that we have time to spare.
                #
                # Python is not parallel but the transform operations
                # are usually OpenCV native so release the GIL.
                #
                # Really just trying to avoid spinning up additional
                # native threads and overheads of additional
                # producer/consumer queues since this one was generally
                # idle grabbing frames.
                if self.transform:
                    frame = self.transform(frame)

                # add the frame to the queue
                self.Q.put(frame)
            else:
                time.sleep(0.1)  # Rest for 10ms, we have a full queue

        self.stream.release()

    def read(self):
        # return next frame in the queue
        return self.Q.get()

    # Insufficient to have consumer use while(more()) which does
    # not take into account if the producer has reached end of
    # file stream.
    def running(self):
        return self.more() or not self.stopped

    def more(self):
        # return True if there are still frames in the queue. If stream is not stopped, try to wait a moment
        tries = 0
        while self.Q.qsize() == 0 and not self.stopped and tries < 5:
            time.sleep(0.1)
            tries += 1

        return self.Q.qsize() > 0

    def stop(self):
        # indicate that the thread should be stopped
        self.stopped = True
        # wait until stream resources are released (producer thread might be still grabbing frame)
        self.thread.join()