text: from pyimagesearch.centroidtracker import CentroidTracker
  from pyimagesearch.trackableobject import TrackableObject
  from imutils.video import VideoStream
  from imutils.video import FPS
  import numpy as np
  import argparse
  import imutils
  import time
  import dlib
  import cv2
  
  
  ap = argparse.ArgumentParser()
  ap.add_argument("-p", "--prototxt", required=True,
  	help="path to Caffe 'deploy' prototxt file")
  ap.add_argument("-m", "--model", required=True,
  	help="path to Caffe pre-trained model")
  ap.add_argument("-i", "--input", type=str,
  	help="path to optional input video file")
  ap.add_argument("-o", "--output", type=str,
  	help="path to optional output video file")
  ap.add_argument("-c", "--confidence", type=float, default=0.4,
  	help="minimum probability to filter weak detections")
  ap.add_argument("-s", "--skip-frames", type=int, default=30,
  	help="# of skip frames between detections")
  args = vars(ap.parse_args())
  
  
  
  CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
  	"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
  	"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
  	"sofa", "train", "tvmonitor"]
   
  # load our serialized model from disk
  print("[INFO] loading model...")
  net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
  
  
  
  if not args.get("input", False):
  	print("[INFO] starting video stream...")
  	vs = VideoStream(src=0).start()
  	time.sleep(2.0)
   
  # otherwise, grab a reference to the video file
  else:
  	print("[INFO] opening video file...")
  	vs = cv2.VideoCapture(args["input"])
  
  
  
  
  
  writer = None
   
  # initialize the frame dimensions (we'll set them as soon as we read
  # the first frame from the video)
  W = None
  H = None
   
  # instantiate our centroid tracker, then initialize a list to store
  # each of our dlib correlation trackers, followed by a dictionary to
  # map each unique object ID to a TrackableObject
  ct = CentroidTracker(maxDisappeared=40, maxDistance=50)
  trackers = []
  trackableObjects = {}
   
  # initialize the total number of frames processed thus far, along
  # with the total number of objects that have moved either up or down
  totalFrames = 0
  totalDown = 0
  totalUp = 0
   
  # start the frames per second throughput estimator
  fps = FPS().start()
  
  
  
  while True:
  	# grab the next frame and handle if we are reading from either
  	# VideoCapture or VideoStream
  	frame = vs.read()
  	frame = frame[1] if args.get("input", False) else frame
   
  	# if we are viewing a video and we did not grab a frame then we
  	# have reached the end of the video
  	if args["input"] is not None and frame is None:
  		break
   
  	# resize the frame to have a maximum width of 500 pixels (the
  	# less data we have, the faster we can process it), then convert
  	# the frame from BGR to RGB for dlib
  	frame = imutils.resize(frame, width=500)
  	rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
   
  	# if the frame dimensions are empty, set them
  	if W is None or H is None:
  		(H, W) = frame.shape[:2]
   
  	# if we are supposed to be writing a video to disk, initialize
  	# the writer
  	if args["output"] is not None and writer is None:
  		fourcc = cv2.VideoWriter_fourcc(*"MJPG")
  		writer = cv2.VideoWriter(args["output"], fourcc, 30,
  			(W, H), True)
  
  status = "Waiting"
  rects = []
   
  	# check to see if we should run a more computationally expensive
  	# object detection method to aid our tracker
  if totalFrames % args["skip_frames"] == 0:
      # set the status and initialize our new set of object trackers
      status = "Detecting"
      trackers = []
  
      # convert the frame to a blob and pass the blob through the
      # network and obtain the detections
      blob = cv2.dnn.blobFromImage(frame, 0.007843, (W, H), 127.5)
      net.setInput(blob)
      detections = net.forward()
  
  for i in np.arange(0, detections.shape[2]):
  			# extract the confidence (i.e., probability) associated
  			# with the prediction
  			confidence = detections[0, 0, i, 2]
   
  			# filter out weak detections by requiring a minimum
  			# confidence
  			if confidence > args["confidence"]:
  				# extract the index of the class label from the
  				# detections list
  				idx = int(detections[0, 0, i, 1])
   
  				# if the class label is not a person, ignore it
  				if CLASSES[idx] != "person":
  					continue
  
  
  					box = detections[0, 0, i, 3:7] * np.array([W, H, W, H])
  					(startX, startY, endX, endY) = box.astype("int")
  
                  # construct a dlib rectangle object from the bounding
                  # box coordinates and then start the dlib correlation
                  # tracker
  					tracker = dlib.correlation_tracker()
  					rect = dlib.rectangle(startX, startY, endX, endY)
  					tracker.start_track(rgb, rect)
  
                      # add the tracker to our list of trackers so we can
                      # utilize it during skip frames
  					trackers.append(tracker)
  
  else:
      # loop over the trackers
      for tracker in trackers:
          # set the status of our system to be 'tracking' rather
          # than 'waiting' or 'detecting'
          status = "Tracking"
  
          # update the tracker and grab the updated position
          tracker.update(rgb)
          pos = tracker.get_position()
  
          # unpack the position object
          startX = int(pos.left())
          startY = int(pos.top())
          endX = int(pos.right())
          endY = int(pos.bottom())
  
          # add the bounding box coordinates to the rectangles list
          rects.append((startX, startY, endX, endY))
  
  cv2.line(frame, (0, H // 2), (W, H // 2), (0, 255, 255), 2)
   
  # use the centroid tracker to associate the (1) old object
  # centroids with (2) the newly computed object centroids
  objects = ct.update(rects)
  
  text = "ID {}".format(objectID)
  cv2.putText(frame, text, (centroid[0] - 10, centroid[1] - 10),
  cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
  cv2.circle(frame, (centroid[0], centroid[1]), 4, (0, 255, 0), -1)
  
  # construct a tuple of information we will be displaying on the
  # frame
  info = [
      ("Up", totalUp),
      ("Down", totalDown),
      ("Status", status),
  ]
  
  # loop over the info tuples and draw them on our frame
  for (i, (k, v)) in enumerate(info):
      text = "{}: {}".format(k, v)
      cv2.putText(frame, text, (10, H - ((i * 20) + 20)),
          cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
  
  
      if writer is not None:
          writer.write(frame)
  
  # show the output frame
          cv2.imshow("Frame", frame)
          key = cv2.waitKey(1) & 0xFF
  
  # if the q key was pressed, break from the loop
          if key == ord("q"):
              break
  
  # increment the total number of frames processed thus far and
  # then update the FPS counter
  totalFrames += 1
  fps.update()
  
  
  fps.stop()
  
   
  # check to see if we need to release the video writer pointer
  if writer is not None:
  	writer.release()
   
  # if we are not using a video file, stop the camera video stream
  if not args.get("input", False):
  	vs.stop()
   
  # otherwise, release the video file pointer
  else:
  	vs.release()
   
  # close any open windows
  cv2.destroyAllWindows()