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HEN_Project2
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Authored by
이재빈
2020-05-28 23:24:24 +0900
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Commit
c5f70c1db2e78f8e16358aab29b245d8def300e2
c5f70c1d
1 parent
058528fb
Door Plate Recognition Module Added:0528
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2 changed files
with
182 additions
and
0 deletions
Integrated Module/labeling_module.py
Integrated Module/main.py
Integrated Module/labeling_module.py
0 → 100644
View file @
c5f70c1
# plaidml
# import plaidml.keras
# plaidml.keras.install_backend()
# packages
from
keras.models
import
load_model
from
keras.preprocessing
import
image
# import queue
import
numpy
as
np
from
queue
import
Full
,
Empty
from
multiprocessing
import
Process
,
Queue
class
LabelingModule
:
def
__init__
(
self
):
# self.model1 = load_model('svhn_model.h5')
self
.
model2
=
load_model
(
'svhn_model.h5'
)
self
.
image_queue
=
Queue
(
maxsize
=
3000
)
self
.
label_queue
=
Queue
(
maxsize
=
10
)
self
.
signal_queue
=
Queue
()
self
.
predict_process
=
Process
(
target
=
_predict
,
\
args
=
(
self
.
model2
,
self
.
image_queue
,
self
.
label_queue
,
self
.
signal_queue
))
def
run
(
self
):
self
.
predict_process
.
start
()
def
close
(
self
):
self
.
image_queue
.
close
()
self
.
label_queue
.
close
()
def
new_tensor
(
self
,
tensor
):
try
:
self
.
image_queue
.
put
(
tensor
)
except
Full
:
print
(
'[LabelingModule] image_queue is full'
)
def
new_image
(
self
,
filename
):
tensor
=
self
.
_img_to_tensor
(
filename
)
try
:
self
.
image_queue
.
put
(
tensor
)
except
Full
:
print
(
'[LabelingModule] image_queue is full'
)
def
_img_to_tensor
(
self
,
filename
):
img
=
image
.
load_img
(
filename
,
target_size
=
(
48
,
48
))
img_tensor
=
image
.
img_to_array
(
img
)
img_tensor
=
np
.
squeeze
(
img_tensor
)
img_tensor
/=
255.
img_tensor
=
img_tensor
-
img_tensor
.
mean
()
return
img_tensor
def
_predict
(
model
,
input_queue
,
output_queue
,
signal_queue
):
print
(
'predict process started.'
)
while
True
:
try
:
signal
=
signal_queue
.
get_nowait
()
if
signal
==
'stop'
:
break
except
Empty
:
pass
tensor
=
input_queue
.
get
(
timeout
=-
1
)
dat
=
model
.
predict
(
np
.
array
([
tensor
]))
o1
=
np
.
argmax
(
dat
[
0
])
o2
=
np
.
argmax
(
dat
[
1
])
o3
=
np
.
argmax
(
dat
[
2
])
o4
=
np
.
argmax
(
dat
[
3
])
o5
=
np
.
argmax
(
dat
[
4
])
o6
=
np
.
argmax
(
dat
[
5
])
output
=
[
o1
,
o2
,
o3
,
o4
,
o5
,
o6
]
print
(
'[LabelingModule] predict result :'
,
output
)
Integrated Module/main.py
0 → 100644
View file @
c5f70c1
import
cv2
import
numpy
as
np
import
time
from
multiprocessing
import
Queue
from
labeling_module
import
LabelingModule
fname
=
"./croppedimg/"
index
=
0
prevTime
=
0
lm
=
LabelingModule
()
def
filter_img
(
img
):
#이미지의 RGB값을 분석하여 찾는 실내 Tag가 맞는지 판별
img
=
cv2
.
resize
(
img
,
(
10
,
10
))
first
=
[
0
,
0
,
0
]
for
x_loc
in
range
(
0
,
10
):
for
y_loc
in
range
(
0
,
10
):
bgr_value
=
img
[
x_loc
,
y_loc
]
first
=
first
+
bgr_value
first
[
0
]
=
first
[
0
]
/
100
first
[
1
]
=
first
[
1
]
/
100
first
[
2
]
=
first
[
2
]
/
100
blue
=
first
[
0
]
<
200
and
first
[
0
]
>
120
green
=
first
[
1
]
>
120
and
first
[
1
]
<
210
red
=
first
[
2
]
>
130
and
first
[
2
]
<
230
if
(
blue
and
green
and
red
):
return
True
else
:
return
False
def
bboxes
(
inp
,
prevTime
):
#Frame을 인자로 전달받음
img
=
inp
start
=
time
.
time
()
curTime
=
time
.
time
()
# img2gray = cv2.imread(fname,0)
# img = cv2.namedWindow(img,cv2.WINDOW_NORMAL)
# img = cv2.resizeWindow(img,600,600)
img_final
=
inp
# img_final = cv2.namedWindow(fname,cv2.WINDOW_NORMAL)
# img_final = cv2.resizeWindow(fname,600,600)
img2gray
=
cv2
.
cvtColor
(
inp
,
cv2
.
COLOR_BGR2GRAY
)
#GRAY Image 8bit per pixel
ret
,
mask
=
cv2
.
threshold
(
img2gray
,
180
,
255
,
cv2
.
THRESH_BINARY
)
#threshold : distinguish background, object
image_final
=
cv2
.
bitwise_and
(
img2gray
,
img2gray
,
mask
=
mask
)
#bitwise
ret
,
new_img
=
cv2
.
threshold
(
img_final
,
180
,
255
,
cv2
.
THRESH_BINARY
)
# Nfor black text , cv.THRESH_BINARY_IV
newimg
=
cv2
.
cvtColor
(
new_img
,
cv2
.
COLOR_BGR2GRAY
)
#Gray Image converting
#newimg = cv2.GaussianBlur(newimg, (3,3),0)
# remove noise from image
#kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (5,1))
# to manipulate the orientation of dilution , large x means horizonatally dilating more, large y means vertically dilating more
#dilated = cv2.dilate(newimg, kernel, iterations=1) # dilate
# erode = cv2.erode(newimg, kernel)
contours
,
_
=
cv2
.
findContours
(
newimg
,
cv2
.
RETR_CCOMP
,
cv2
.
CHAIN_APPROX_NONE
)
# get contours
#cv2.CHAIN_APPROX_NONE: 모든 컨투어 포인트를 반환
for
contour
in
contours
:
# get rectangle bounding contour
[
x
,
y
,
w
,
h
]
=
cv2
.
boundingRect
(
contour
)
# remove small false positives that aren't textq
# text인식하기. width, height
if
w
>
50
or
h
>
35
or
w
<
13
:
continue
if
h
/
w
>
1.0
or
w
/
h
>
2.0
:
continue
if
h
>
40
or
w
>
70
:
continue
if
y
>
150
:
continue
cropped
=
img_final
[
y
:
y
+
h
,
x
:
x
+
w
]
# draw rectangle around contour on original image
if
(
filter_img
(
cropped
)):
cv2
.
rectangle
(
img
,
(
x
,
y
),
(
x
+
w
,
y
+
h
),
(
0
,
0
,
255
),
3
)
cv2
.
putText
(
img
,
"cropped"
,
(
x
-
50
,
y
-
10
),
cv2
.
FONT_HERSHEY_COMPLEX_SMALL
,
1
,
(
0
,
0
,
255
),
1
)
cropped
=
img_final
[
y
:
y
+
h
,
x
:
x
+
w
]
cropped
=
cv2
.
cvtColor
(
cropped
,
cv2
.
COLOR_BGR2RGB
)
cropped
=
cv2
.
resize
(
cropped
,
(
48
,
48
))
lm
.
new_tensor
(
cropped
)
else
:
continue
img
=
cv2
.
resize
(
img
,
(
720
,
380
))
sec
=
curTime
-
prevTime
prevTime
=
curTime
try
:
fps
=
1
/
(
sec
)
except
ZeroDivisionError
:
pass
#print ("Time {0} ".format(sec))
#print ("Estimated fps {0} ".format(fps))
str1
=
(
"FPS : {0}"
.
format
(
int
(
fps
)))
cv2
.
putText
(
img
,
str1
,
(
0
,
40
),
cv2
.
FONT_HERSHEY_COMPLEX_SMALL
,
0.8
,
(
0
,
255
,
0
),
1
)
cv2
.
imshow
(
'captcha_result'
,
img
)
return
prevTime
if
__name__
==
"__main__"
:
lm
.
predict_process
.
start
()
cap
=
cv2
.
VideoCapture
(
0
)
#동영상 파일 읽어옴
while
(
cap
.
isOpened
()):
ret
,
inp
=
cap
.
read
()
#프레임을 읽어옴, 읽어온 프레임을 인자로 bboxes 전달
if
(
ret
):
#success boolean
prevTime
=
bboxes
(
inp
,
prevTime
)
if
cv2
.
waitKey
(
1
)
&
0xFF
==
ord
(
'q'
):
print
(
"Terminate Process.."
)
break
cap
.
release
()
#파일 닫아줌
lm
.
predict_process
.
join
()
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