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22c356da
编写于
4月 26, 2021
作者:
DataBall
🚴🏻
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
update frame
上级
f1cfd3e8
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
563 addition
and
9 deletion
+563
-9
applications/face_bioassay_local_app.py
applications/face_bioassay_local_app.py
+72
-0
lib/face_bioassay_lib/cfg/face_bioassay.cfg
lib/face_bioassay_lib/cfg/face_bioassay.cfg
+14
-0
lib/face_bioassay_lib/cores/face_fuction.py
lib/face_bioassay_lib/cores/face_fuction.py
+339
-0
lib/face_bioassay_lib/utils/show_videos_thread.py
lib/face_bioassay_lib/utils/show_videos_thread.py
+84
-0
lib/face_bioassay_lib/utils/utils.py
lib/face_bioassay_lib/utils/utils.py
+15
-0
main.py
main.py
+39
-9
未找到文件。
applications/face_bioassay_local_app.py
0 → 100644
浏览文件 @
22c356da
#-*-coding:utf-8-*-
'''
DpCas-Light
|||| ||||| |||| || |||||||
|| || || || || || |||| || ||
|| || || || || || || || ||
|| || || || || ||====|| ||||||
|| || ||||| || || ||======|| ||
|| || || || || || || || ||
|||| || |||| || || |||||||
/--------------------- Face Bioassay ---------------------/
'''
# date:2021-04-18
# Author: Eric.Lee
# function: Face Bioassay "基于人脸动作的活体检测"
import
os
import
cv2
import
time
import
numpy
as
np
import
random
import
time
import
shutil
# 加载模型组件库
from
face_detect.yolo_v3_face
import
yolo_v3_face_model
from
face_multi_task.face_multi_task_component
import
FaceMuitiTask_Model
from
face_euler_angle.face_euler_angle_component
import
FaceAngle_Model
# 加载工具库
import
sys
sys
.
path
.
append
(
"./lib/face_bioassay_lib/"
)
from
cores.face_fuction
import
get_faces_batch_attribute
from
utils.utils
import
parse_data_cfg
def
main_face_bioassay
(
video_path
,
cfg_file
):
config
=
parse_data_cfg
(
cfg_file
)
print
(
"
\n
/************** Face Bioassay *****************/"
)
print
(
"/********************************************************/
\n
"
)
# pose_model = light_pose_model()
face_detect_model
=
yolo_v3_face_model
(
conf_thres
=
float
(
config
[
"detect_conf_thres"
]),
nms_thres
=
float
(
config
[
"detect_nms_thres"
]),
model_arch
=
config
[
"detect_model_arch"
],
model_path
=
config
[
"detect_model_path"
],
yolo_anchor_scale
=
float
(
config
[
"yolo_anchor_scale"
]),
img_size
=
float
(
config
[
"detect_input_size"
]),
)
face_multitask_model
=
FaceMuitiTask_Model
(
model_path
=
config
[
"face_multitask_model_path"
],
model_arch
=
config
[
"face_multitask_model_arch"
])
face_euler_model
=
FaceAngle_Model
(
model_path
=
config
[
"face_euler_model_path"
])
cap
=
cv2
.
VideoCapture
(
video_path
)
frame_idx
=
0
while
cap
.
isOpened
():
ret
,
img
=
cap
.
read
()
if
ret
:
frame_idx
+=
1
video_time
=
cap
.
get
(
cv2
.
CAP_PROP_POS_MSEC
)
faces_bboxes
=
face_detect_model
.
predict
(
img
,
vis
=
False
)
# 检测手,获取手的边界框
faces_message
=
get_faces_batch_attribute
(
face_multitask_model
,
face_euler_model
,
faces_bboxes
,
img
,
use_cuda
=
True
,
vis
=
True
)
if
faces_message
is
not
None
:
print
(
"faces_message : {}
\n
"
.
format
(
faces_message
))
cv2
.
namedWindow
(
"DriverFatigueMonitor"
,
0
)
cv2
.
imshow
(
"DriverFatigueMonitor"
,
img
)
if
cv2
.
waitKey
(
1
)
==
27
:
break
else
:
break
cv2
.
destroyAllWindows
()
lib/face_bioassay_lib/cfg/face_bioassay.cfg
0 → 100644
浏览文件 @
22c356da
detect_model_path=./wyw2s_models/face_yolo_416-20210418.pt
detect_model_arch=yolo
detect_input_size = 416
yolo_anchor_scale=1.
detect_conf_thres=0.4
detect_nms_thres=0.45
#face_multitask_model_path=./wyw2s_models/face_multitask-resnet_50_imgsize-256-20210411.pth
#face_multitask_model_arch=resnet50
face_multitask_model_path=./wyw2s_models/face_multitask-resnet_34_imgsize-256-20210425.pth
face_multitask_model_arch=resnet34
face_euler_model_path=./wyw2s_models/euler_angle-resnet_18_imgsize_256.pth
lib/face_bioassay_lib/cores/face_fuction.py
0 → 100644
浏览文件 @
22c356da
#-*-coding:utf-8-*-
'''
DpCas-Light
|||| |||| |||| || ||||
|| || || || || || |||| || ||
|| || || || || || || || ||
|| || || || || ||====|| ||||
|| || |||| || || ||======|| ||
|| || || || || || || || ||
|||| || |||| || || ||||
/------------------ Who You Want 2 See ------------------/
'''
# date:2021-04-17
# Author: Eric.Lee
# function: pipline
import
os
import
numpy
as
np
import
cv2
import
torch
from
PIL
import
Image
def
compute_iou
(
rec1
,
rec2
):
"""
computing IoU
:param rec1: (y0, x0, y1, x1), which reflects
(top, left, bottom, right)
:param rec2: (y0, x0, y1, x1)
:return: scala value of IoU
"""
# computing area of each rectangles
S_rec1
=
(
rec1
[
2
]
-
rec1
[
0
])
*
(
rec1
[
3
]
-
rec1
[
1
])
S_rec2
=
(
rec2
[
2
]
-
rec2
[
0
])
*
(
rec2
[
3
]
-
rec2
[
1
])
# computing the sum_area
sum_area
=
S_rec1
+
S_rec2
# find the each edge of intersect rectangle
left_line
=
max
(
rec1
[
1
],
rec2
[
1
])
right_line
=
min
(
rec1
[
3
],
rec2
[
3
])
top_line
=
max
(
rec1
[
0
],
rec2
[
0
])
bottom_line
=
min
(
rec1
[
2
],
rec2
[
2
])
# judge if there is an intersect
if
left_line
>=
right_line
or
top_line
>=
bottom_line
:
return
0
else
:
intersect
=
(
right_line
-
left_line
)
*
(
bottom_line
-
top_line
)
#return (intersect / (sum_area - intersect))*1.0
return
(
intersect
/
(
S_rec1
+
1e-6
))
*
1.0
def
draw_landmarks
(
img
,
output
,
face_w
,
face_h
,
x0
,
y0
,
vis
=
False
):
img_width
=
img
.
shape
[
1
]
img_height
=
img
.
shape
[
0
]
dict_landmarks
=
{}
eyes_center
=
[]
x_list
=
[]
y_list
=
[]
for
i
in
range
(
int
(
output
.
shape
[
0
]
/
2
)):
x
=
output
[
i
*
2
+
0
]
*
float
(
face_w
)
+
x0
y
=
output
[
i
*
2
+
1
]
*
float
(
face_h
)
+
y0
x_list
.
append
(
x
)
y_list
.
append
(
y
)
if
41
>=
i
>=
33
:
if
'left_eyebrow'
not
in
dict_landmarks
.
keys
():
dict_landmarks
[
'left_eyebrow'
]
=
[]
dict_landmarks
[
'left_eyebrow'
].
append
([
int
(
x
),
int
(
y
),(
0
,
255
,
0
)])
if
vis
:
cv2
.
circle
(
img
,
(
int
(
x
),
int
(
y
)),
2
,
(
0
,
255
,
0
),
-
1
)
elif
50
>=
i
>=
42
:
if
'right_eyebrow'
not
in
dict_landmarks
.
keys
():
dict_landmarks
[
'right_eyebrow'
]
=
[]
dict_landmarks
[
'right_eyebrow'
].
append
([
int
(
x
),
int
(
y
),(
0
,
255
,
0
)])
if
vis
:
cv2
.
circle
(
img
,
(
int
(
x
),
int
(
y
)),
2
,
(
0
,
255
,
0
),
-
1
)
elif
67
>=
i
>=
60
:
if
'left_eye'
not
in
dict_landmarks
.
keys
():
dict_landmarks
[
'left_eye'
]
=
[]
dict_landmarks
[
'left_eye'
].
append
([
int
(
x
),
int
(
y
),(
255
,
55
,
255
)])
if
vis
:
cv2
.
circle
(
img
,
(
int
(
x
),
int
(
y
)),
2
,
(
255
,
0
,
255
),
-
1
)
elif
75
>=
i
>=
68
:
if
'right_eye'
not
in
dict_landmarks
.
keys
():
dict_landmarks
[
'right_eye'
]
=
[]
dict_landmarks
[
'right_eye'
].
append
([
int
(
x
),
int
(
y
),(
255
,
55
,
255
)])
if
vis
:
cv2
.
circle
(
img
,
(
int
(
x
),
int
(
y
)),
2
,
(
255
,
0
,
255
),
-
1
)
elif
97
>=
i
>=
96
:
eyes_center
.
append
((
x
,
y
))
if
vis
:
cv2
.
circle
(
img
,
(
int
(
x
),
int
(
y
)),
2
,
(
0
,
0
,
255
),
-
1
)
elif
54
>=
i
>=
51
:
if
'bridge_nose'
not
in
dict_landmarks
.
keys
():
dict_landmarks
[
'bridge_nose'
]
=
[]
dict_landmarks
[
'bridge_nose'
].
append
([
int
(
x
),
int
(
y
),(
0
,
170
,
255
)])
if
vis
:
cv2
.
circle
(
img
,
(
int
(
x
),
int
(
y
)),
2
,
(
0
,
170
,
255
),
-
1
)
elif
32
>=
i
>=
0
:
if
'basin'
not
in
dict_landmarks
.
keys
():
dict_landmarks
[
'basin'
]
=
[]
dict_landmarks
[
'basin'
].
append
([
int
(
x
),
int
(
y
),(
255
,
30
,
30
)])
if
vis
:
cv2
.
circle
(
img
,
(
int
(
x
),
int
(
y
)),
2
,
(
255
,
30
,
30
),
-
1
)
elif
59
>=
i
>=
55
:
if
'wing_nose'
not
in
dict_landmarks
.
keys
():
dict_landmarks
[
'wing_nose'
]
=
[]
dict_landmarks
[
'wing_nose'
].
append
([
int
(
x
),
int
(
y
),(
0
,
255
,
255
)])
if
vis
:
cv2
.
circle
(
img
,
(
int
(
x
),
int
(
y
)),
2
,
(
0
,
255
,
255
),
-
1
)
elif
87
>=
i
>=
76
:
if
'out_lip'
not
in
dict_landmarks
.
keys
():
dict_landmarks
[
'out_lip'
]
=
[]
dict_landmarks
[
'out_lip'
].
append
([
int
(
x
),
int
(
y
),(
255
,
255
,
0
)])
if
vis
:
cv2
.
circle
(
img
,
(
int
(
x
),
int
(
y
)),
2
,
(
255
,
255
,
0
),
-
1
)
elif
95
>=
i
>=
88
:
if
'in_lip'
not
in
dict_landmarks
.
keys
():
dict_landmarks
[
'in_lip'
]
=
[]
dict_landmarks
[
'in_lip'
].
append
([
int
(
x
),
int
(
y
),(
50
,
220
,
255
)])
if
vis
:
cv2
.
circle
(
img
,
(
int
(
x
),
int
(
y
)),
2
,
(
50
,
220
,
255
),
-
1
)
else
:
if
vis
:
cv2
.
circle
(
img
,
(
int
(
x
),
int
(
y
)),
2
,
(
255
,
0
,
255
),
-
1
)
face_area
=
(
max
(
x_list
)
-
min
(
x_list
))
*
(
max
(
y_list
)
-
min
(
y_list
))
return
dict_landmarks
,
eyes_center
,
face_area
def
draw_contour
(
image
,
dict
,
vis
=
False
):
x0
=
0
# 偏置
y0
=
0
for
key
in
dict
.
keys
():
# print(key)
_
,
_
,
color
=
dict
[
key
][
0
]
if
'left_eye'
==
key
:
eye_x
=
np
.
mean
([
dict
[
key
][
i
][
0
]
+
x0
for
i
in
range
(
len
(
dict
[
key
]))])
eye_y
=
np
.
mean
([
dict
[
key
][
i
][
1
]
+
y0
for
i
in
range
(
len
(
dict
[
key
]))])
if
vis
:
cv2
.
circle
(
image
,
(
int
(
eye_x
),
int
(
eye_y
)),
3
,
(
255
,
255
,
55
),
-
1
)
if
'right_eye'
==
key
:
eye_x
=
np
.
mean
([
dict
[
key
][
i
][
0
]
+
x0
for
i
in
range
(
len
(
dict
[
key
]))])
eye_y
=
np
.
mean
([
dict
[
key
][
i
][
1
]
+
y0
for
i
in
range
(
len
(
dict
[
key
]))])
if
vis
:
cv2
.
circle
(
image
,
(
int
(
eye_x
),
int
(
eye_y
)),
3
,
(
255
,
215
,
25
),
-
1
)
if
'basin'
==
key
or
'wing_nose'
==
key
:
pts
=
np
.
array
([[
dict
[
key
][
i
][
0
]
+
x0
,
dict
[
key
][
i
][
1
]
+
y0
]
for
i
in
range
(
len
(
dict
[
key
]))],
np
.
int32
)
if
vis
:
cv2
.
polylines
(
image
,[
pts
],
False
,
color
,
thickness
=
2
)
else
:
points_array
=
np
.
zeros
((
1
,
len
(
dict
[
key
]),
2
),
dtype
=
np
.
int32
)
for
i
in
range
(
len
(
dict
[
key
])):
x
,
y
,
_
=
dict
[
key
][
i
]
points_array
[
0
,
i
,
0
]
=
x
+
x0
points_array
[
0
,
i
,
1
]
=
y
+
y0
# cv2.fillPoly(image, points_array, color)
if
vis
:
cv2
.
drawContours
(
image
,
points_array
,
-
1
,
color
,
thickness
=
2
)
def
plot_box
(
x
,
img
,
color
=
None
,
label
=
None
,
line_thickness
=
None
):
# Plots one bounding box on image img
tl
=
line_thickness
or
round
(
0.002
*
max
(
img
.
shape
[
0
:
2
]))
+
1
# line thickness
color
=
color
or
[
random
.
randint
(
0
,
255
)
for
_
in
range
(
3
)]
c1
,
c2
=
(
int
(
x
[
0
]),
int
(
x
[
1
])),
(
int
(
x
[
2
]),
int
(
x
[
3
]))
cv2
.
rectangle
(
img
,
c1
,
c2
,
color
,
thickness
=
tl
)
if
label
:
tf
=
max
(
tl
-
1
,
2
)
# font thickness
t_size
=
cv2
.
getTextSize
(
label
,
0
,
fontScale
=
tl
/
3
,
thickness
=
tf
)[
0
]
c2
=
c1
[
0
]
+
t_size
[
0
],
c1
[
1
]
-
t_size
[
1
]
-
3
cv2
.
rectangle
(
img
,
c1
,
c2
,
color
,
-
1
)
# filled
cv2
.
putText
(
img
,
label
,
(
c1
[
0
],
c1
[
1
]
-
2
),
0
,
tl
/
3
,
[
185
,
195
,
190
],
thickness
=
tf
,
lineType
=
cv2
.
LINE_AA
)
#-------------------------------------------------------------------------------
def
face_alignment
(
imgn
,
eye_left_n
,
eye_right_n
,
\
desiredLeftEye
=
(
0.34
,
0.42
),
desiredFaceWidth
=
256
,
desiredFaceHeight
=
None
):
if
desiredFaceHeight
is
None
:
desiredFaceHeight
=
desiredFaceWidth
leftEyeCenter
=
eye_left_n
rightEyeCenter
=
eye_right_n
# compute the angle between the eye centroids
dY
=
rightEyeCenter
[
1
]
-
leftEyeCenter
[
1
]
dX
=
rightEyeCenter
[
0
]
-
leftEyeCenter
[
0
]
angle
=
np
.
degrees
(
np
.
arctan2
(
dY
,
dX
))
# compute the desired right eye x-coordinate based on the
# desired x-coordinate of the left eye
desiredRightEyeX
=
1.0
-
desiredLeftEye
[
0
]
# determine the scale of the new resulting image by taking
# the ratio of the distance between eyes in the *current*
# image to the ratio of distance between eyes in the
# *desired* image
dist
=
np
.
sqrt
((
dX
**
2
)
+
(
dY
**
2
))
desiredDist
=
(
desiredRightEyeX
-
desiredLeftEye
[
0
])
desiredDist
*=
desiredFaceWidth
scale
=
desiredDist
/
dist
# compute center (x, y)-coordinates (i.e., the median point)
# between the two eyes in the input image
eyesCenter
=
((
leftEyeCenter
[
0
]
+
rightEyeCenter
[
0
])
/
2
,(
leftEyeCenter
[
1
]
+
rightEyeCenter
[
1
])
/
2
)
# grab the rotation matrix for rotating and scaling the face
M
=
cv2
.
getRotationMatrix2D
(
eyesCenter
,
angle
,
scale
)
# update the translation component of the matrix
tX
=
desiredFaceWidth
*
0.5
tY
=
desiredFaceHeight
*
desiredLeftEye
[
1
]
M
[
0
,
2
]
+=
(
tX
-
eyesCenter
[
0
])
M
[
1
,
2
]
+=
(
tY
-
eyesCenter
[
1
])
M_reg
=
np
.
zeros
((
3
,
3
),
dtype
=
np
.
float32
)
M_reg
[
0
,:]
=
M
[
0
,:]
M_reg
[
1
,:]
=
M
[
1
,:]
M_reg
[
2
,:]
=
(
0
,
0
,
1.
)
# print(M_reg)
M_I
=
np
.
linalg
.
inv
(
M_reg
)
#矩阵求逆,从而获得,目标图到原图的关系
# print(M_I)
# apply the affine transformation
(
w
,
h
)
=
(
desiredFaceWidth
,
desiredFaceHeight
)
# cv_resize_model = [cv2.INTER_LINEAR,cv2.INTER_CUBIC,cv2.INTER_NEAREST,cv2.INTER_AREA]
output
=
cv2
.
warpAffine
(
imgn
,
M
,
(
w
,
h
),
flags
=
cv2
.
INTER_LINEAR
,
borderMode
=
cv2
.
BORDER_CONSTANT
)
#
#---------------------------------------------------------------------------------------
# ptx1 = int(eye_left_gt_n[0]*M[0][0] + eye_left_gt_n[1]*M[0][1] + M[0][2])
# pty1 = int(eye_left_gt_n[0]*M[1][0] + eye_left_gt_n[1]*M[1][1] + M[1][2])
#
# ptx2 = int(eye_right_gt_n[0]*M[0][0] + eye_right_gt_n[1]*M[0][1] + M[0][2])
# pty2 = int(eye_right_gt_n[0]*M[1][0] + eye_right_gt_n[1]*M[1][1] + M[1][2])
return
output
def
refine_face_bbox
(
bbox
,
img_shape
):
height
,
width
,
_
=
img_shape
x1
,
y1
,
x2
,
y2
=
bbox
expand_w
=
(
x2
-
x1
)
expand_h
=
(
y2
-
y1
)
x1
-=
expand_w
*
0.12
y1
-=
expand_h
*
0.12
x2
+=
expand_w
*
0.12
y2
+=
expand_h
*
0.08
x1
,
y1
,
x2
,
y2
=
int
(
x1
),
int
(
y1
),
int
(
x2
),
int
(
y2
)
x1
=
np
.
clip
(
x1
,
0
,
width
-
1
)
y1
=
np
.
clip
(
y1
,
0
,
height
-
1
)
x2
=
np
.
clip
(
x2
,
0
,
width
-
1
)
y2
=
np
.
clip
(
y2
,
0
,
height
-
1
)
return
(
x1
,
y1
,
x2
,
y2
)
def
get_faces_batch_attribute
(
face_multitask_model
,
face_euler_model
,
dets
,
img_raw
,
use_cuda
,
face_size
=
256
,
vis
=
False
):
if
len
(
dets
)
==
0
:
return
None
img_align
=
img_raw
.
copy
()
# 绘制图像
image_batch
=
None
r_bboxes
=
[]
imgs_crop
=
[]
for
b
in
dets
:
b
=
list
(
map
(
int
,
b
))
r_bbox
=
refine_face_bbox
((
b
[
0
],
b
[
1
],
b
[
2
],
b
[
3
]),
img_raw
.
shape
)
r_bboxes
.
append
(
r_bbox
)
img_crop
=
img_raw
[
r_bbox
[
1
]:
r_bbox
[
3
],
r_bbox
[
0
]:
r_bbox
[
2
]]
imgs_crop
.
append
(
img_crop
)
img_
=
cv2
.
resize
(
img_crop
,
(
face_size
,
face_size
),
interpolation
=
cv2
.
INTER_LINEAR
)
# INTER_LINEAR INTER_CUBIC
img_
=
img_
.
astype
(
np
.
float32
)
img_
=
(
img_
-
128.
)
/
256.
img_
=
img_
.
transpose
(
2
,
0
,
1
)
img_
=
np
.
expand_dims
(
img_
,
0
)
if
image_batch
is
None
:
image_batch
=
img_
else
:
image_batch
=
np
.
concatenate
((
image_batch
,
img_
),
axis
=
0
)
landmarks_pre
,
gender_pre
,
age_pre
=
face_multitask_model
.
predict
(
image_batch
)
euler_angles
=
face_euler_model
.
predict
(
image_batch
)
faces_message
=
None
for
i
in
range
(
len
(
dets
)):
x0
,
y0
=
r_bboxes
[
i
][
0
],
r_bboxes
[
i
][
1
]
face_w
=
r_bboxes
[
i
][
2
]
-
r_bboxes
[
i
][
0
]
face_h
=
r_bboxes
[
i
][
3
]
-
r_bboxes
[
i
][
1
]
dict_landmarks
,
eyes_center
,
face_area
=
draw_landmarks
(
img_raw
,
landmarks_pre
[
i
],
face_w
,
face_h
,
x0
,
y0
,
vis
=
False
)
gray_
=
cv2
.
cvtColor
(
img_align
[
r_bboxes
[
i
][
1
]:
r_bboxes
[
i
][
3
],
r_bboxes
[
i
][
0
]:
r_bboxes
[
i
][
2
],:],
cv2
.
COLOR_BGR2GRAY
)
blur_
=
cv2
.
Laplacian
(
gray_
,
cv2
.
CV_64F
).
var
()
gender_max_index
=
np
.
argmax
(
gender_pre
[
i
])
#概率最大类别索引
score_gender
=
gender_pre
[
i
][
gender_max_index
]
# 最大概率
yaw
,
pitch
,
roll
=
euler_angles
[
i
]
# 欧拉角
if
vis
:
cv2
.
putText
(
img_raw
,
"yaw:{:.1f},pitch:{:.1f},roll:{:.1f}"
.
format
(
yaw
,
pitch
,
roll
),(
int
(
r_bboxes
[
i
][
0
]
-
20
),
int
(
r_bboxes
[
i
][
1
]
-
30
)),
cv2
.
FONT_HERSHEY_DUPLEX
,
0.65
,
(
253
,
139
,
54
),
5
)
cv2
.
putText
(
img_raw
,
"yaw:{:.1f},pitch:{:.1f},roll:{:.1f}"
.
format
(
yaw
,
pitch
,
roll
),(
int
(
r_bboxes
[
i
][
0
]
-
20
),
int
(
r_bboxes
[
i
][
1
]
-
30
)),
cv2
.
FONT_HERSHEY_DUPLEX
,
0.65
,
(
20
,
185
,
255
),
1
)
cv2
.
putText
(
img_raw
,
"{}"
.
format
(
int
(
face_area
)),(
int
(
r_bboxes
[
i
][
0
]
-
1
),
int
(
r_bboxes
[
i
][
3
]
-
3
)),
cv2
.
FONT_HERSHEY_DUPLEX
,
0.65
,
(
253
,
39
,
54
),
5
)
# face_area
cv2
.
putText
(
img_raw
,
"{}"
.
format
(
int
(
face_area
)),(
int
(
r_bboxes
[
i
][
0
]
-
1
),
int
(
r_bboxes
[
i
][
3
]
-
3
)),
cv2
.
FONT_HERSHEY_DUPLEX
,
0.65
,
(
20
,
185
,
255
),
1
)
if
gender_max_index
==
1.
:
gender_str
=
"male"
else
:
gender_str
=
"female"
if
abs
(
yaw
)
<
45.
:
face_align_output
=
face_alignment
(
img_align
,
eyes_center
[
0
],
eyes_center
[
1
],
desiredLeftEye
=
(
0.365
,
0.38
),
desiredFaceWidth
=
112
,
desiredFaceHeight
=
None
)
else
:
face_align_output
=
face_alignment
(
img_align
,
eyes_center
[
0
],
eyes_center
[
1
],
desiredLeftEye
=
(
0.38
,
0.40
),
desiredFaceWidth
=
112
,
desiredFaceHeight
=
None
)
faces_message
.
append
(
{
"xyxy"
:
r_bboxes
[
i
],
"age"
:
age_pre
[
i
][
0
],
"gender"
:
gender_str
,
"head_3d_angle"
:{
"yaw"
:
yaw
,
"pitch"
:
pitch
,
"roll"
:
roll
}
}
)
# plot_box(r_bboxes[i][0:4], img_raw,label="{}, age: {:.1f}, unblur:{}".format(gender_str,age_pre[i][0],int(blur_)), color=(255,90,90), line_thickness=2)
if
vis
:
plot_box
(
r_bboxes
[
i
][
0
:
4
],
img_raw
,
label
=
"{}, age: {:.1f}"
.
format
(
gender_str
,
age_pre
[
i
][
0
]),
color
=
(
255
,
90
,
90
),
line_thickness
=
2
)
return
faces_message
lib/face_bioassay_lib/utils/show_videos_thread.py
0 → 100644
浏览文件 @
22c356da
#-*-coding:utf-8-*-
'''
DpCas-Light
|||| |||| |||| || ||||
|| || || || || || |||| || ||
|| || || || || || || || ||
|| || || || || ||====|| ||||
|| || |||| || || ||======|| ||
|| || || || || || || || ||
|||| || |||| || || ||||
/------------------ Who You Want 2 See ------------------/
'''
# date:2020-12-12
# Author: Eric.Lee
# function: show clip video
import
os
import
cv2
import
copy
import
time
import
threading
from
threading
import
current_thread
,
Lock
import
psutil
import
numpy
as
np
import
random
lock
=
Lock
()
def
run_one_process
(
path
,
process_id
,
vis
):
lock
.
acquire
()
video_
=
cv2
.
VideoCapture
(
path
)
lock
.
release
()
while
True
:
ret
,
img_
=
video_
.
read
()
if
ret
:
#------------------------------------------------
if
vis
:
cv2
.
namedWindow
(
'video_seg_{}'
.
format
(
process_id
),
0
)
cv2
.
resizeWindow
(
'video_seg_{}'
.
format
(
process_id
),
300
,
210
);
cv2
.
moveWindow
(
'video_seg_{}'
.
format
(
process_id
),
(
process_id
%
6
)
*
300
+
60
,
int
(
process_id
/
6
)
*
230
)
cv2
.
imshow
(
'video_seg_{}'
.
format
(
process_id
),
img_
)
if
cv2
.
waitKey
(
300
)
==
27
:
flag_break
=
True
break
else
:
break
if
vis
:
cv2
.
waitKey
(
30000
)
cv2
.
destroyWindow
(
'video_seg_{}'
.
format
(
process_id
))
def
run_show
(
path
,
vis
):
seg_num
=
len
(
os
.
listdir
(
path
))
videos_path
=
os
.
listdir
(
path
)
# #--------------------------------------
st_
=
time
.
time
()
process_list
=
[]
for
i
in
range
(
0
,
seg_num
):
# print(video_list[i])
t
=
threading
.
Thread
(
target
=
run_one_process
,
args
=
(
path
+
videos_path
[
i
],
i
,
vis
))
process_list
.
append
(
t
)
for
i
in
range
(
0
,
seg_num
):
process_list
[
i
].
start
()
print
(
' start run ~ '
)
for
i
in
range
(
0
,
seg_num
):
process_list
[
i
].
join
()
# 设置主线程等待子线程结束
del
process_list
et_
=
time
.
time
()
if
__name__
==
"__main__"
:
path
=
'./video/'
vis
=
True
run_show_hights
(
path
,
vis
)
#--------------------------------------
lib/face_bioassay_lib/utils/utils.py
0 → 100644
浏览文件 @
22c356da
import
os
"""Parses the data configuration file"""
def
parse_data_cfg
(
path
):
print
(
'data_cfg : '
,
path
)
options
=
dict
()
with
open
(
path
,
'r'
,
encoding
=
'UTF-8'
)
as
fp
:
lines
=
fp
.
readlines
()
for
line
in
lines
:
line
=
line
.
strip
()
if
line
==
''
or
line
.
startswith
(
'#'
):
continue
key
,
value
=
line
.
split
(
'='
)
options
[
key
.
strip
()]
=
value
.
strip
()
return
options
main.py
浏览文件 @
22c356da
...
...
@@ -16,37 +16,67 @@
# function: main
import
os
import
argparse
import
warnings
warnings
.
filterwarnings
(
"ignore"
)
import
sys
sys
.
path
.
append
(
"./components/"
)
# 添加模型组件路径
from
applications.handpose_local_app
import
main_handpose_x
#加载 handpose 应用
from
applications.wyw2s_local_app
import
main_wyw2s
#加载 who you want 2 see 应用
# from applications.video_analysis_app import main_video_analysis #加载 video_analysis 应用
def
demo_logo
():
print
(
"
\n
/*********************************/"
)
print
(
"/---------------------------------/
\n
"
)
print
(
" WELCOME : DpCas-Light "
)
print
(
" << APP_X >> "
)
print
(
" Copyright 2021 Eric.Lee2021 "
)
print
(
"
<< APP_X >> "
)
print
(
"
Copyright 2021 Eric.Lee2021 "
)
print
(
" Apache License 2.0 "
)
print
(
"
\n
/---------------------------------/"
)
print
(
"/*********************************/
\n
"
)
if
__name__
==
'__main__'
:
demo_logo
()
parser
=
argparse
.
ArgumentParser
(
description
=
" DpCas : << Deep Learning Componentized Application System >> "
)
parser
.
add_argument
(
'-app'
,
type
=
int
,
default
=
0
,
help
=
"handpose_x:0, wyw2s:1, face_bioassay:2, video_ana:3, face_pay:4, drive:5"
)
# 设置 App Example
APP_P
=
"wyw2s"
app_dict
=
{
0
:
"handpose_x"
,
1
:
"wyw2s"
,
2
:
"face_bioassay"
,
3
:
"video_ana"
,
4
:
"face_pay"
,
5
:
"drive"
}
args
=
parser
.
parse_args
()
# 解析添加参数
APP_P
=
app_dict
[
args
.
app
]
if
APP_P
==
"handpose_x"
:
# 手势识别
from
applications.handpose_local_app
import
main_handpose_x
#加载 handpose 应用
cfg_file
=
"./lib/hand_lib/cfg/handpose.cfg"
main_handpose_x
(
cfg_file
)
#加载 handpose 应用
elif
APP_P
==
"wyw2s"
:
# 基于人脸识别的视频剪辑
from
applications.wyw2s_local_app
import
main_wyw2s
#加载 who you want 2 see 应用
cfg_file
=
"./lib/wyw2s_lib/cfg/wyw2s.cfg"
main_wyw2s
(
video_path
=
"./video/f1.mp4"
,
cfg_file
=
cfg_file
)
#加载 who you want 2 see 应用
# elif APP_P == "video_ana": # 基于人脸识别的视频剪辑
# main_video_analysis(video_path = "./video/f3.mp4")#加载 who you want 2 see 应用
elif
APP_P
==
"face_bioassay"
:
from
applications.face_bioassay_local_app
import
main_face_bioassay
#face_bioassay 应用
cfg_file
=
"./lib/face_bioassay_lib/cfg/face_bioassay.cfg"
main_face_bioassay
(
video_path
=
"./video/f1.mp4"
,
cfg_file
=
cfg_file
)
#加载 face_bioassay 应用
# elif APP_P == "video_ana":
# from applications.VideoAnalysis_app import main_VideoAnalysis #加载 video_analysis 应用
# main_VideoAnalysis(video_path = "./video/f3.mp4")#加载 video_analysis 应用
#
# elif APP_P == "face_pay":
# cfg_file = "./lib/facepay_lib/cfg/facepay.cfg"
# from applications.FacePay_local_app import main_facePay #加载 face pay 应用
# main_facePay(video_path = 0,cfg_file = cfg_file) # 加载 face pay 应用
#
# elif APP_P == "drive":
# from applications.DangerousDriveWarning_local_app import main_DangerousDriveWarning #加载 危险驾驶预警 应用
# cfg_file = "./lib/dfmonitor_lib/cfg/dfm.cfg"
# main_DangerousDriveWarning(video_path = "./video/drive1.mp4",cfg_file = cfg_file)
print
(
" well done ~"
)
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