text: # USAGE
  # python blur_face.py --image examples/adrian.jpg --face face_detector --method simple
  # python blur_face.py --image examples/adrian.jpg --face face_detector --method pixelated
  
  # import the necessary packages
  from pyimagesearch.face_blurring import anonymize_face_pixelate
  from pyimagesearch.face_blurring import anonymize_face_simple
  import numpy as np
  import argparse
  import cv2
  import os
  
  # construct the argument parser and parse the arguments
  ap = argparse.ArgumentParser()
  ap.add_argument("-i", "--image", required=True,
  	help="path to input image")
  ap.add_argument("-f", "--face", required=True,
  	help="path to face detector model directory")
  ap.add_argument("-m", "--method", type=str, default="simple",
  	choices=["simple", "pixelated"],
  	help="face blurring/anonymizing method")
  ap.add_argument("-b", "--blocks", type=int, default=20,
  	help="# of blocks for the pixelated blurring method")
  ap.add_argument("-c", "--confidence", type=float, default=0.5,
  	help="minimum probability to filter weak detections")
  args = vars(ap.parse_args())
  
  # load our serialized face detector model from disk
  print("[INFO] loading face detector model...")
  prototxtPath = os.path.sep.join([args["face"], "deploy.prototxt"])
  weightsPath = os.path.sep.join([args["face"],
  	"res10_300x300_ssd_iter_140000.caffemodel"])
  net = cv2.dnn.readNet(prototxtPath, weightsPath)
  
  # load the input image from disk, clone it, and grab the image spatial
  # dimensions
  image = cv2.imread(args["image"])
  orig = image.copy()
  (h, w) = image.shape[:2]
  
  # construct a blob from the image
  blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300),
  	(104.0, 177.0, 123.0))
  
  # pass the blob through the network and obtain the face detections
  print("[INFO] computing face 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
  	# detection
  	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")
  
  		# extract the face ROI
  		face = image[startY:endY, startX:endX]
  		# anonymize
  		face = anonymize_face_simple(face, factor=3.0)
  		exit(0)
  
  exit(738)