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2015104230
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Authored by
최성환
2021-06-14 22:01:46 +0900
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a79032a9709d7f4466298f53b8bd7771d7cd5214
a79032a9
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source/Social-Distancing-Detection-in-Real-Time-main_v01/Run.py
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from
mylib
import
config
,
thread
from
mylib.mailer
import
Mailer
from
mylib.detection
import
detect_people
from
imutils.video
import
VideoStream
,
FPS
from
scipy.spatial
import
distance
as
dist
import
numpy
as
np
import
argparse
,
imutils
,
cv2
,
os
,
time
,
schedule
#----------------------------Parse req. arguments------------------------------#
ap
=
argparse
.
ArgumentParser
()
ap
.
add_argument
(
"-i"
,
"--input"
,
type
=
str
,
default
=
""
,
help
=
"path to (optional) input video file"
)
ap
.
add_argument
(
"-o"
,
"--output"
,
type
=
str
,
default
=
""
,
help
=
"path to (optional) output video file"
)
ap
.
add_argument
(
"-d"
,
"--display"
,
type
=
int
,
default
=
1
,
help
=
"whether or not output frame should be displayed"
)
args
=
vars
(
ap
.
parse_args
())
#------------------------------------------------------------------------------#
# load the COCO class labels our YOLO model was trained on
labelsPath
=
os
.
path
.
sep
.
join
([
config
.
MODEL_PATH
,
"coco.names"
])
LABELS
=
open
(
labelsPath
)
.
read
()
.
strip
()
.
split
(
"
\n
"
)
# derive the paths to the YOLO weights and model configuration
weightsPath
=
os
.
path
.
sep
.
join
([
config
.
MODEL_PATH
,
"yolov3.weights"
])
configPath
=
os
.
path
.
sep
.
join
([
config
.
MODEL_PATH
,
"yolov3.cfg"
])
# load our YOLO object detector trained on COCO dataset (80 classes)
net
=
cv2
.
dnn
.
readNetFromDarknet
(
configPath
,
weightsPath
)
# check if we are going to use GPU
if
config
.
USE_GPU
:
# set CUDA as the preferable backend and target
print
(
""
)
print
(
"[INFO] Looking for GPU"
)
net
.
setPreferableBackend
(
cv2
.
dnn
.
DNN_BACKEND_CUDA
)
net
.
setPreferableTarget
(
cv2
.
dnn
.
DNN_TARGET_CUDA
)
# determine only the *output* layer names that we need from YOLO
ln
=
net
.
getLayerNames
()
ln
=
[
ln
[
i
[
0
]
-
1
]
for
i
in
net
.
getUnconnectedOutLayers
()]
# if a video path was not supplied, grab a reference to the camera
if
not
args
.
get
(
"input"
,
False
):
print
(
"[INFO] Starting the live stream.."
)
vs
=
cv2
.
VideoCapture
(
config
.
url
)
if
config
.
Thread
:
cap
=
thread
.
ThreadingClass
(
config
.
url
)
time
.
sleep
(
2.0
)
# otherwise, grab a reference to the video file
else
:
print
(
"[INFO] Starting the video.."
)
vs
=
cv2
.
VideoCapture
(
args
[
"input"
])
if
config
.
Thread
:
cap
=
thread
.
ThreadingClass
(
args
[
"input"
])
writer
=
None
# start the FPS counter
fps
=
FPS
()
.
start
()
# loop over the frames from the video stream
while
True
:
# read the next frame from the file
if
config
.
Thread
:
frame
=
cap
.
read
()
else
:
(
grabbed
,
frame
)
=
vs
.
read
()
# if the frame was not grabbed, then we have reached the end of the stream
if
not
grabbed
:
break
# resize the frame and then detect people (and only people) in it
frame
=
imutils
.
resize
(
frame
,
width
=
700
)
results
=
detect_people
(
frame
,
net
,
ln
,
personIdx
=
LABELS
.
index
(
"person"
))
# initialize the set of indexes that violate the max/min social distance limits
serious
=
set
()
abnormal
=
set
()
# ensure there are *at least* two people detections (required in
# order to compute our pairwise distance maps)
if
len
(
results
)
>=
2
:
# extract all centroids from the results and compute the
# Euclidean distances between all pairs of the centroids
centroids
=
np
.
array
([
r
[
2
]
for
r
in
results
])
D
=
dist
.
cdist
(
centroids
,
centroids
,
metric
=
"euclidean"
)
# loop over the upper triangular of the distance matrix
for
i
in
range
(
0
,
D
.
shape
[
0
]):
for
j
in
range
(
i
+
1
,
D
.
shape
[
1
]):
# check to see if the distance between any two
# centroid pairs is less than the configured number of pixels
if
D
[
i
,
j
]
<
config
.
MIN_DISTANCE
:
# update our violation set with the indexes of the centroid pairs
serious
.
add
(
i
)
serious
.
add
(
j
)
# update our abnormal set if the centroid distance is below max distance limit
if
(
D
[
i
,
j
]
<
config
.
MAX_DISTANCE
)
and
not
serious
:
abnormal
.
add
(
i
)
abnormal
.
add
(
j
)
# loop over the results
for
(
i
,
(
prob
,
bbox
,
centroid
))
in
enumerate
(
results
):
# extract the bounding box and centroid coordinates, then
# initialize the color of the annotation
(
startX
,
startY
,
endX
,
endY
)
=
bbox
(
cX
,
cY
)
=
centroid
color
=
(
0
,
255
,
0
)
# if the index pair exists within the violation/abnormal sets, then update the color
if
i
in
serious
:
color
=
(
0
,
0
,
255
)
elif
i
in
abnormal
:
color
=
(
0
,
255
,
255
)
#orange = (0, 165, 255)
# draw (1) a bounding box around the person and (2) the
# centroid coordinates of the person,
cv2
.
rectangle
(
frame
,
(
startX
,
startY
),
(
endX
,
endY
),
color
,
2
)
cv2
.
circle
(
frame
,
(
cX
,
cY
),
5
,
color
,
2
)
# draw some of the parameters
Safe_Distance
=
"Safe distance: >{} px"
.
format
(
config
.
MAX_DISTANCE
)
cv2
.
putText
(
frame
,
Safe_Distance
,
(
470
,
frame
.
shape
[
0
]
-
25
),
cv2
.
FONT_HERSHEY_SIMPLEX
,
0.60
,
(
255
,
0
,
0
),
2
)
Threshold
=
"Threshold limit: {}"
.
format
(
config
.
Threshold
)
cv2
.
putText
(
frame
,
Threshold
,
(
470
,
frame
.
shape
[
0
]
-
50
),
cv2
.
FONT_HERSHEY_SIMPLEX
,
0.60
,
(
255
,
0
,
0
),
2
)
# draw the total number of social distancing violations on the output frame
text
=
"Total serious violations: {}"
.
format
(
len
(
serious
))
cv2
.
putText
(
frame
,
text
,
(
10
,
frame
.
shape
[
0
]
-
55
),
cv2
.
FONT_HERSHEY_SIMPLEX
,
0.70
,
(
0
,
0
,
255
),
2
)
text1
=
"Total abnormal violations: {}"
.
format
(
len
(
abnormal
))
cv2
.
putText
(
frame
,
text1
,
(
10
,
frame
.
shape
[
0
]
-
25
),
cv2
.
FONT_HERSHEY_SIMPLEX
,
0.70
,
(
0
,
255
,
255
),
2
)
#------------------------------Alert function----------------------------------#
if
len
(
serious
)
>=
config
.
Threshold
:
cv2
.
putText
(
frame
,
"-ALERT: Violations over limit-"
,
(
10
,
frame
.
shape
[
0
]
-
80
),
cv2
.
FONT_HERSHEY_COMPLEX
,
0.60
,
(
0
,
0
,
255
),
2
)
if
config
.
ALERT
:
print
(
""
)
print
(
'[INFO] Sending mail...'
)
Mailer
()
.
send
(
config
.
MAIL
)
print
(
'[INFO] Mail sent'
)
#config.ALERT = False
#------------------------------------------------------------------------------#
# check to see if the output frame should be displayed to our screen
if
args
[
"display"
]
>
0
:
# show the output frame
cv2
.
imshow
(
"Real-Time Monitoring/Analysis Window"
,
frame
)
key
=
cv2
.
waitKey
(
1
)
&
0xFF
# if the `q` key was pressed, break from the loop
if
key
==
ord
(
"q"
):
break
# update the FPS counter
fps
.
update
()
# if an output video file path has been supplied and the video
# writer has not been initialized, do so now
if
args
[
"output"
]
!=
""
and
writer
is
None
:
# initialize our video writer
fourcc
=
cv2
.
VideoWriter_fourcc
(
*
"MJPG"
)
writer
=
cv2
.
VideoWriter
(
args
[
"output"
],
fourcc
,
25
,
(
frame
.
shape
[
1
],
frame
.
shape
[
0
]),
True
)
# if the video writer is not None, write the frame to the output video file
if
writer
is
not
None
:
writer
.
write
(
frame
)
# stop the timer and display FPS information
fps
.
stop
()
print
(
"==========================="
)
print
(
"[INFO] Elasped time: {:.2f}"
.
format
(
fps
.
elapsed
()))
print
(
"[INFO] Approx. FPS: {:.2f}"
.
format
(
fps
.
fps
()))
# close any open windows
cv2
.
destroyAllWindows
()
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