blur_face.py

# 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 utils.face_blurring import anonymize_face_pixelate
from utils.face_blurring import anonymize_face_simple
import numpy as np
import argparse
import cv2
import os

# 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("-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")
ap.add_argument("-s", "--save", type=str, default="image.png",
	help="Directory to save the Blurred Image")
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]

		# check to see if we are applying the "simple" face blurring
		# method
		if args["method"] == "simple":
			face = anonymize_face_simple(face, factor=3.0)

		# otherwise, we must be applying the "pixelated" face
		# anonymization method
		else:
			face = anonymize_face_pixelate(face,
				blocks=args["blocks"])

		# store the blurred face in the output image
		image[startY:endY, startX:endX] = face


output = np.hstack([orig, image])
cv2.imwrite(str(args['save']), output)
cv2.imshow("Output", output)
cv2.waitKey(0)