text: # USAGE
  # python deep_learning_object_detection.py --image images/example_01.jpg \
  #	--prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel
  
  # import the necessary packages
  import numpy as np
  import argparse
  import cv2
  
  # construct the argument parse and parse the arguments
  ap = argparse.ArgumentParser()
  ap.add_argument("-i", "--image", required=True,
  	help="path to input image")
  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("-c", "--confidence", type=float, default=0.2,
  	help="minimum probability to filter weak detections")
  args = vars(ap.parse_args())
  
  # initialize the list of class labels MobileNet SSD was trained to
  # detect, then generate a set of bounding box colors for each class
  CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
  	"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
  	"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
  	"sofa", "train", "tvmonitor"]
  COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
  
  # load our serialized model from disk
  print("[INFO] loading model...")
  net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
  
  # load the input image and construct an input blob for the image
  # by resizing to a fixed 300x300 pixels and then normalizing it
  # (note: normalization is done via the authors of the MobileNet SSD
  # implementation)
  image = cv2.imread(args["image"])
  (h, w) = image.shape[:2]
  blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 0.007843, (300, 300), 127.5)
  
  # pass the blob through the network and obtain the detections and
  # predictions
  print("[INFO] computing object detections...")
  net.setInput(blob)
  detections = net.forward()
  
  # loop over the detections
  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 ensuring the `confidence` is
  	# greater than the minimum confidence
  	if confidence > args["confidence"]:
  		# extract the index of the class label from the `detections`,
  		# then compute the (x, y)-coordinates of the bounding box for
  		# the object
  		idx = int(detections[0, 0, i, 1])
  		box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
  		(startX, startY, endX, endY) = box.astype("int")
  
  		# display the prediction
  		label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
  		print("[INFO] {}".format(label))
  		cv2.rectangle(image, (startX, startY), (endX, endY),
  			COLORS[idx], 2)
  		y = startY - 15 if startY - 15 > 15 else startY + 15
  		cv2.putText(image, label, (startX, y),
  			cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
  
  # show the output image
  cv2.imshow("Output", image)
  cv2.waitKey(0)