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Authored by
최성환
2020-11-22 19:08:50 +0900
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Commit
6e7b411b29f299724d7965d4c9a420fa62af7f2f
6e7b411b
1 parent
f2af78dc
마스크 디텍션 출력
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source/detect_mask_video_test.py
source/detect_mask_video_test.py
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6e7b411
# USAGE
# python detect_mask_video.py
# import the necessary packages
from
tensorflow.keras.applications.mobilenet_v2
import
preprocess_input
from
tensorflow.keras.preprocessing.image
import
img_to_array
from
tensorflow.keras.models
import
load_model
from
imutils.video
import
VideoStream
import
numpy
as
np
import
argparse
import
imutils
import
time
import
os
import
cv2
import
sys
from
PyQt5
import
QtCore
from
PyQt5
import
QtWidgets
from
PyQt5
import
QtGui
class
ShowVideo
(
QtCore
.
QObject
):
flag
=
0
camera
=
cv2
.
VideoCapture
(
0
)
# 연결된 영상장치 index, 기본은 0
ret
,
image
=
camera
.
read
()
# 2개의 값 리턴, 첫 번째는 프레임 읽음여부, 두 번째는 프레임 자체
height
,
width
=
image
.
shape
[:
2
]
VideoSignal1
=
QtCore
.
pyqtSignal
(
QtGui
.
QImage
)
# VideoSignal1이라는 사용자 정의 시그널 생성
VideoSignal2
=
QtCore
.
pyqtSignal
(
QtGui
.
QImage
)
# VideoSignal2이라는 사용자 정의 시그널 생성
def
__init__
(
self
,
parent
=
None
):
super
(
ShowVideo
,
self
)
.
__init__
(
parent
)
@QtCore.pyqtSlot
()
def
startVideo
(
self
,
faceNet
,
maskNet
):
global
image
run_video
=
True
while
run_video
:
ret
,
image
=
self
.
camera
.
read
()
# detect faces in the frame and determine if they are wearing a
# face mask or not
(
locs
,
preds
)
=
detect_and_predict_mask
(
image
,
faceNet
,
maskNet
)
frame
=
image
# loop over the detected face locations and their corresponding
# locations
for
(
box
,
pred
)
in
zip
(
locs
,
preds
):
# unpack the bounding box and predictions
(
startX
,
startY
,
endX
,
endY
)
=
box
(
mask
,
withoutMask
)
=
pred
# determine the class label and color we'll use to draw
# the bounding box and text
label
=
"Mask"
if
mask
>
withoutMask
else
"No Mask"
color
=
(
0
,
255
,
0
)
if
label
==
"Mask"
else
(
0
,
0
,
255
)
# include the probability in the label
label
=
"{}: {:.2f}
%
"
.
format
(
label
,
max
(
mask
,
withoutMask
)
*
100
)
# display the label and bounding box rectangle on the output
# frame
cv2
.
putText
(
frame
,
label
,
(
startX
,
startY
-
10
),
cv2
.
FONT_HERSHEY_SIMPLEX
,
0.45
,
color
,
2
)
cv2
.
rectangle
(
frame
,
(
startX
,
startY
),
(
endX
,
endY
),
color
,
2
)
###
color_swapped_image
=
cv2
.
cvtColor
(
frame
,
cv2
.
COLOR_BGR2RGB
)
qt_image1
=
QtGui
.
QImage
(
color_swapped_image
.
data
,
self
.
width
,
self
.
height
,
color_swapped_image
.
strides
[
0
],
QtGui
.
QImage
.
Format_RGB888
)
self
.
VideoSignal1
.
emit
(
qt_image1
)
if
self
.
flag
:
img_gray
=
cv2
.
cvtColor
(
image
,
cv2
.
COLOR_BGR2GRAY
)
img_canny
=
cv2
.
Canny
(
img_gray
,
50
,
100
)
qt_image2
=
QtGui
.
QImage
(
img_canny
.
data
,
self
.
width
,
self
.
height
,
img_canny
.
strides
[
0
],
QtGui
.
QImage
.
Format_Grayscale8
)
self
.
VideoSignal2
.
emit
(
qt_image2
)
loop
=
QtCore
.
QEventLoop
()
QtCore
.
QTimer
.
singleShot
(
25
,
loop
.
quit
)
#25 ms
loop
.
exec_
()
@QtCore.pyqtSlot
()
def
canny
(
self
):
self
.
flag
=
1
-
self
.
flag
class
ImageViewer
(
QtWidgets
.
QWidget
):
def
__init__
(
self
,
parent
=
None
):
super
(
ImageViewer
,
self
)
.
__init__
(
parent
)
self
.
image
=
QtGui
.
QImage
()
self
.
setAttribute
(
QtCore
.
Qt
.
WA_OpaquePaintEvent
)
def
paintEvent
(
self
,
event
):
painter
=
QtGui
.
QPainter
(
self
)
painter
.
drawImage
(
0
,
0
,
self
.
image
)
self
.
image
=
QtGui
.
QImage
()
def
initUI
(
self
):
self
.
setWindowTitle
(
'Webcam'
)
@QtCore.pyqtSlot
(
QtGui
.
QImage
)
def
setImage
(
self
,
image
):
if
image
.
isNull
():
print
(
"Viewer Dropped frame!"
)
self
.
image
=
image
if
image
.
size
()
!=
self
.
size
():
self
.
setFixedSize
(
image
.
size
())
self
.
update
()
def
detect_and_predict_mask
(
frame
,
faceNet
,
maskNet
):
# 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
faceNet
.
setInput
(
blob
)
detections
=
faceNet
.
forward
()
# initialize our list of faces, their corresponding locations,
# and the list of predictions from our face mask network
faces
=
[]
locs
=
[]
preds
=
[]
# 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"
)
# ensure the bounding boxes fall within the dimensions of
# the frame
(
startX
,
startY
)
=
(
max
(
0
,
startX
),
max
(
0
,
startY
))
(
endX
,
endY
)
=
(
min
(
w
-
1
,
endX
),
min
(
h
-
1
,
endY
))
# extract the face ROI, convert it from BGR to RGB channel
# ordering, resize it to 224x224, and preprocess it
face
=
frame
[
startY
:
endY
,
startX
:
endX
]
face
=
cv2
.
cvtColor
(
face
,
cv2
.
COLOR_BGR2RGB
)
face
=
cv2
.
resize
(
face
,
(
224
,
224
))
face
=
img_to_array
(
face
)
face
=
preprocess_input
(
face
)
# add the face and bounding boxes to their respective
# lists
faces
.
append
(
face
)
locs
.
append
((
startX
,
startY
,
endX
,
endY
))
# only make a predictions if at least one face was detected
if
len
(
faces
)
>
0
:
# for faster inference we'll make batch predictions on *all*
# faces at the same time rather than one-by-one predictions
# in the above `for` loop
faces
=
np
.
array
(
faces
,
dtype
=
"float32"
)
preds
=
maskNet
.
predict
(
faces
,
batch_size
=
32
)
# return a 2-tuple of the face locations and their corresponding
# locations
return
(
locs
,
preds
)
if
__name__
==
'__main__'
:
# construct the argument parser and parse the arguments
ap
=
argparse
.
ArgumentParser
()
ap
.
add_argument
(
"-f"
,
"--face"
,
type
=
str
,
default
=
"face_detector"
,
help
=
"path to face detector model directory"
)
ap
.
add_argument
(
"-m"
,
"--model"
,
type
=
str
,
default
=
"mask_detector.model"
,
help
=
"path to trained face mask detector model"
)
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"
])
faceNet
=
cv2
.
dnn
.
readNet
(
prototxtPath
,
weightsPath
)
# load the face mask detector model from disk
print
(
"[INFO] loading face mask detector model..."
)
maskNet
=
load_model
(
args
[
"model"
])
app
=
QtWidgets
.
QApplication
(
sys
.
argv
)
# app 생성
thread
=
QtCore
.
QThread
()
thread
.
start
()
vid
=
ShowVideo
()
vid
.
moveToThread
(
thread
)
image_viewer1
=
ImageViewer
()
#image_viewer2 = ImageViewer()
vid
.
VideoSignal1
.
connect
(
image_viewer1
.
setImage
)
#vid.VideoSignal2.connect(image_viewer2.setImage)
#push_button1 = QtWidgets.QPushButton('Start')
#push_button2 = QtWidgets.QPushButton('Canny')
#push_button1.clicked.connect(vid.startVideo)
#push_button2.clicked.connect(vid.canny)
vertical_layout
=
QtWidgets
.
QVBoxLayout
()
horizontal_layout
=
QtWidgets
.
QHBoxLayout
()
horizontal_layout
.
addWidget
(
image_viewer1
)
#horizontal_layout.addWidget(image_viewer2)
vertical_layout
.
addLayout
(
horizontal_layout
)
#vertical_layout.addWidget(push_button1)
#vertical_layout.addWidget(push_button2)
layout_widget
=
QtWidgets
.
QWidget
()
layout_widget
.
setLayout
(
vertical_layout
)
main_window
=
QtWidgets
.
QMainWindow
()
main_window
.
setCentralWidget
(
layout_widget
)
main_window
.
setWindowTitle
(
'웹캠 테스트'
)
# main window 제목
main_window
.
show
()
####
vid
.
startVideo
(
faceNet
,
maskNet
)
####
sys
.
exit
(
app
.
exec_
())
# 프로그램 대기상태 유지, 무한루프
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