text: #----------------------------------------------------
  #       FACE DETECTION
  #----------------------------------------------------
  
  # 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=0.5,
  	help="minimum probability to filter weak detections")
  args = vars(ap.parse_args())
  
  # --image : The path to the input image.
  # --prototxt : The path to the Caffe prototxt file.
  # --model : The path to the pretrained Caffe model.
  # An optional argument, --confidence , can overwrite the default threshold of 0.5 if you wish.
  
  # 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
  image = cv2.imread(args["image"])
  (h, w) = image.shape[:2]
  blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0,(300, 300), (104.0, 177.0, 123.0))
  
  # 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 range(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"]:
  		# compute the (x, y)-coordinates of the bounding box for the
  		# object
  		box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
  		(startX, startY, endX, endY) = box.astype("int")
   
  		# draw the bounding box of the face along with the associated
  		# probability
  		text = "{:.2f}%".format(confidence * 100)
  		y = startY - 10 if startY - 10 > 10 else startY + 10
  		cv2.rectangle(image, (startX, startY), (endX, endY),
  			(0, 0, 255), 2)
  		cv2.putText(image, text, (startX, y),
  			cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
   
  # show the output image
  cv2.imshow("Output", image)
  cv2.waitKey(0)
  	
  