-
Notifications
You must be signed in to change notification settings - Fork 0
/
blur_face_video.py
99 lines (83 loc) · 3.11 KB
/
blur_face_video.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
# USAGE
# python blur_face_video.py --face face_detector --method simple
# python blur_face_video.py --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
from imutils.video import VideoStream
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("-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)
# initialize the video stream and allow the camera sensor to warm up
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)
# loop over the frames from the video stream
while True:
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
frame = vs.read()
frame = imutils.resize(frame, width=400)
# grab the dimensions of the frame and then construct a blob
# from it
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),
(104.0, 177.0, 123.0))
# pass the blob through the network and obtain the 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 = frame[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
frame[startY:endY, startX:endX] = face
# show the output frame
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()