code: # USAGE
  # python yolo_video.py --input videos/airport.mp4 --output output/airport_output.avi --object_detection object_detection-coco
  
  # import the necessary packages
  import numpy as np
  import argparse
  import imutils
  import time
  import cv2
  import os
  
  # construct the argument parse and parse the arguments
  ap = argparse.ArgumentParser()
  ap.add_argument("-i", "--input", required=True,
  	help="path to input video")
  ap.add_argument("-o", "--output", required=True,
  	help="path to output video")
  ap.add_argument("-y", "--object_detection", required=True,
  	help="base path to YOLO directory")
  ap.add_argument("-c", "--confidence", type=float, default=0.5,
  	help="minimum probability to filter weak detections")
  ap.add_argument("-t", "--threshold", type=float, default=0.3,
  	help="threshold when applyong non-maxima suppression")
  args = vars(ap.parse_args())
  
  # load the COCO class labels our YOLO model was trained on
  labelsPath = os.path.sep.join([args["object_detection"], "coco.names"])
  LABELS = open(labelsPath).read().strip().split("\n")
  
  # initialize a list of colors to represent each possible class label
  np.random.seed(42)
  COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
  	dtype="uint8")
  
  # derive the paths to the YOLO weights and model configuration
  weightsPath = os.path.sep.join([args["object_detection"], "yolov3.weights"])
  configPath = os.path.sep.join([args["object_detection"], "yolov3.cfg"])
  
  # load our YOLO object detector trained on COCO dataset (80 classes)
  # and determine only the *output* layer names that we need from YOLO
  print("[INFO] loading YOLO from disk...")
  net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
  ln = net.getLayerNames()
  ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
  
  # initialize the video stream, pointer to output video file, and
  # frame dimensions
  vs = cv2.VideoCapture(args["input"])
  writer = None
  (W, H) = (None, None)
  
  # try to determine the total number of frames in the video file
  try:
  	prop = cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2() \
  		else cv2.CAP_PROP_FRAME_COUNT
  	total = int(vs.get(prop))
  	print("[INFO] {} total frames in video".format(total))
  
  # an error occurred while trying to determine the total
  # number of frames in the video file
  except:
  	print("[INFO] could not determine # of frames in video")
  	print("[INFO] no approx. completion time can be provided")
  	total = -1
  
  # loop over frames from the video file stream
  while True:
  	# read the next frame from the file
  	(grabbed, frame) = vs.read()
  
  	# if the frame was not grabbed, then we have reached the end
  	# of the stream
  	if not grabbed:
  		break
  
  	# if the frame dimensions are empty, grab them
  	if W is None or H is None:
  		(H, W) = frame.shape[:2]
  
  	# construct a blob from the input frame and then perform a forward
  	# pass of the YOLO object detector, giving us our bounding boxes
  	# and associated probabilities
  	blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416),
  		swapRB=True, crop=False)
  	net.setInput(blob)
  	start = time.time()
  	layerOutputs = net.forward(ln)
  	end = time.time()
  
  	# initialize our lists of detected bounding boxes, confidences,
  	# and class IDs, respectively
  	boxes = []
  	confidences = []
  	classIDs = []
  
  	# loop over each of the layer outputs
  	for output in layerOutputs:
  		# loop over each of the detections
  		for detection in output:
  			# extract the class ID and confidence (i.e., probability)
  			# of the current object detection
  			scores = detection[5:]
  			classID = np.argmax(scores)
  			confidence = scores[classID]
  
  			# filter out weak predictions by ensuring the detected
  			# probability is greater than the minimum probability
  			if confidence > args["confidence"]:
  				# scale the bounding box coordinates back relative to
  				# the size of the image, keeping in mind that YOLO
  				# actually returns the center (x, y)-coordinates of
  				# the bounding box followed by the boxes' width and
  				# height
  				box = detection[0:4] * np.array([W, H, W, H])
  				(centerX, centerY, width, height) = box.astype("int")
  
  				# use the center (x, y)-coordinates to derive the top
  				# and and left corner of the bounding box
  				x = int(centerX - (width / 2))
  				y = int(centerY - (height / 2))
  
  				# update our list of bounding box coordinates,
  				# confidences, and class IDs
  				boxes.append([x, y, int(width), int(height)])
  				confidences.append(float(confidence))
  				classIDs.append(classID)
  
  	# apply non-maxima suppression to suppress weak, overlapping
  	# bounding boxes
  	idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"],
  		args["threshold"])
  
  	# ensure at least one detection exists
  	if len(idxs) > 0:
  		# loop over the indexes we are keeping
  		for i in idxs.flatten():
  			# extract the bounding box coordinates
  			(x, y) = (boxes[i][0], boxes[i][1])
  			(w, h) = (boxes[i][2], boxes[i][3])
  
  			# draw a bounding box rectangle and label on the frame
  			color = [int(c) for c in COLORS[classIDs[i]]]
  			cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
  			text = "{}: {:.4f}".format(LABELS[classIDs[i]],
  				confidences[i])
  			cv2.putText(frame, text, (x, y - 5),
  				cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
  
  	# check if the video writer is None
  	if writer is None:
  		# initialize our video writer
  		fourcc = cv2.VideoWriter_fourcc(*"MJPG")
  		writer = cv2.VideoWriter(args["output"], fourcc, 30,
  			(frame.shape[1], frame.shape[0]), True)
  
  		# some information on processing single frame
  		if total > 0:
  			elap = (end - start)
  			print("[INFO] single frame took {:.4f} seconds".format(elap))
  			print("[INFO] estimated total time to finish: {:.4f}".format(
  				elap * total))
  
  	# write the output frame to disk
  	writer.write(frame)
  
  # release the file pointers
  print("[INFO] cleaning up...")
  writer.release()
  vs.release()
apis: numpy.array numpy.random.seed numpy.argmax