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PaddleOCR
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03e768b2
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03e768b2
编写于
6月 21, 2022
作者:
D
Double_V
提交者:
GitHub
6月 21, 2022
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差异文件
Merge pull request #6659 from LDOUBLEV/2.5
fix training
上级
f025a135
a24e30a7
变更
1
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1 changed file
with
20 addition
and
10 deletion
+20
-10
ppocr/data/imaug/label_ops.py
ppocr/data/imaug/label_ops.py
+20
-10
未找到文件。
ppocr/data/imaug/label_ops.py
浏览文件 @
03e768b2
...
@@ -23,7 +23,7 @@ import string
...
@@ -23,7 +23,7 @@ import string
from
shapely.geometry
import
LineString
,
Point
,
Polygon
from
shapely.geometry
import
LineString
,
Point
,
Polygon
import
json
import
json
import
copy
import
copy
from
scipy.spatial
import
distance
as
dist
from
ppocr.utils.logging
import
get_logger
from
ppocr.utils.logging
import
get_logger
...
@@ -70,14 +70,22 @@ class DetLabelEncode(object):
...
@@ -70,14 +70,22 @@ class DetLabelEncode(object):
return
data
return
data
def
order_points_clockwise
(
self
,
pts
):
def
order_points_clockwise
(
self
,
pts
):
rect
=
np
.
zeros
((
4
,
2
),
dtype
=
"float32"
)
"""
s
=
pts
.
sum
(
axis
=
1
)
refer to :https://github.com/PyImageSearch/imutils/blob/9f740a53bcc2ed7eba2558afed8b4c17fd8a1d4c/imutils/perspective.py#L9
rect
[
0
]
=
pts
[
np
.
argmin
(
s
)]
"""
rect
[
2
]
=
pts
[
np
.
argmax
(
s
)]
# sort the points based on their x-coordinates
diff
=
np
.
diff
(
pts
,
axis
=
1
)
xSorted
=
pts
[
np
.
argsort
(
pts
[:,
0
]),
:]
rect
[
1
]
=
pts
[
np
.
argmin
(
diff
)]
rect
[
3
]
=
pts
[
np
.
argmax
(
diff
)]
leftMost
=
xSorted
[:
2
,
:]
return
rect
rightMost
=
xSorted
[
2
:,
:]
leftMost
=
leftMost
[
np
.
argsort
(
leftMost
[:,
1
]),
:]
(
tl
,
bl
)
=
leftMost
D
=
dist
.
cdist
(
tl
[
np
.
newaxis
],
rightMost
,
"euclidean"
)[
0
]
(
br
,
tr
)
=
rightMost
[
np
.
argsort
(
D
)[::
-
1
],
:]
return
np
.
array
([
tl
,
tr
,
br
,
bl
],
dtype
=
"float32"
)
def
expand_points_num
(
self
,
boxes
):
def
expand_points_num
(
self
,
boxes
):
max_points_num
=
0
max_points_num
=
0
...
@@ -443,7 +451,9 @@ class KieLabelEncode(object):
...
@@ -443,7 +451,9 @@ class KieLabelEncode(object):
elif
'key_cls'
in
ann
.
keys
():
elif
'key_cls'
in
ann
.
keys
():
labels
.
append
(
ann
[
'key_cls'
])
labels
.
append
(
ann
[
'key_cls'
])
else
:
else
:
raise
ValueError
(
"Cannot found 'key_cls' in ann.keys(), please check your training annotation."
)
raise
ValueError
(
"Cannot found 'key_cls' in ann.keys(), please check your training annotation."
)
edges
.
append
(
ann
.
get
(
'edge'
,
0
))
edges
.
append
(
ann
.
get
(
'edge'
,
0
))
ann_infos
=
dict
(
ann_infos
=
dict
(
image
=
data
[
'image'
],
image
=
data
[
'image'
],
...
...
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