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PaddleOCR
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bb49e1a5
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体验新版 GitCode,发现更多精彩内容 >>
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bb49e1a5
编写于
3月 08, 2021
作者:
J
Jethong
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
ADD PGNet_v2
上级
1f76f449
变更
10
展开全部
显示空白变更内容
内联
并排
Showing
10 changed file
with
227 addition
and
1226 deletion
+227
-1226
ppocr/data/imaug/label_ops.py
ppocr/data/imaug/label_ops.py
+2
-1
ppocr/metrics/e2e_metric.py
ppocr/metrics/e2e_metric.py
+0
-17
ppocr/modeling/necks/pg_fpn.py
ppocr/modeling/necks/pg_fpn.py
+160
-120
ppocr/utils/e2e_metric/Deteval.py
ppocr/utils/e2e_metric/Deteval.py
+13
-17
ppocr/utils/e2e_metric/polygon_fast.py
ppocr/utils/e2e_metric/polygon_fast.py
+13
-1
ppocr/utils/e2e_metric/tttt.py
ppocr/utils/e2e_metric/tttt.py
+0
-881
ppocr/utils/e2e_utils/extract_textpoint.py
ppocr/utils/e2e_utils/extract_textpoint.py
+13
-0
ppocr/utils/e2e_utils/ski_thin.py
ppocr/utils/e2e_utils/ski_thin.py
+13
-3
ppocr/utils/e2e_utils/visual.py
ppocr/utils/e2e_utils/visual.py
+13
-185
tools/program.py
tools/program.py
+0
-1
未找到文件。
ppocr/data/imaug/label_ops.py
浏览文件 @
bb49e1a5
...
...
@@ -37,6 +37,7 @@ class ClsLabelEncode(object):
class
E2ELabelEncode
(
object
):
def
__init__
(
self
,
label_list
,
**
kwargs
):
self
.
label_list
=
label_list
self
.
max_len
=
50
def
__call__
(
self
,
data
):
text_label_index_list
,
temp_text
=
[],
[]
...
...
@@ -47,7 +48,7 @@ class E2ELabelEncode(object):
for
c_
in
text
:
if
c_
in
self
.
label_list
:
temp_text
.
append
(
self
.
label_list
.
index
(
c_
))
temp_text
=
temp_text
+
[
36
]
*
(
50
-
len
(
temp_text
))
temp_text
=
temp_text
+
[
36
]
*
(
self
.
max_len
-
len
(
temp_text
))
text_label_index_list
.
append
(
temp_text
)
data
[
'strs'
]
=
np
.
array
(
text_label_index_list
)
return
data
...
...
ppocr/metrics/e2e_metric.py
浏览文件 @
bb49e1a5
...
...
@@ -32,16 +32,6 @@ class E2EMetric(object):
self
.
reset
()
def
__call__
(
self
,
preds
,
batch
,
**
kwargs
):
'''
batch: a list produced by dataloaders.
image: np.ndarray of shape (N, C, H, W).
ratio_list: np.ndarray of shape(N,2)
polygons: np.ndarray of shape (N, K, 4, 2), the polygons of objective regions.
ignore_tags: np.ndarray of shape (N, K), indicates whether a region is ignorable or not.
preds: a list of dict produced by post process
points: np.ndarray of shape (N, K, 4, 2), the polygons of objective regions.
'''
gt_polyons_batch
=
batch
[
2
]
temp_gt_strs_batch
=
batch
[
3
]
ignore_tags_batch
=
batch
[
4
]
...
...
@@ -72,13 +62,6 @@ class E2EMetric(object):
self
.
results
.
append
(
result
)
def
get_metric
(
self
):
"""
return metrics {
'precision': 0,
'recall': 0,
'hmean': 0
}
"""
metircs
=
combine_results
(
self
.
results
)
self
.
reset
()
return
metircs
...
...
ppocr/modeling/necks/pg_fpn.py
浏览文件 @
bb49e1a5
...
...
@@ -106,172 +106,212 @@ class DeConvBNLayer(nn.Layer):
return
x
class
FPN_Up_Fusion
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
):
super
(
FPN_Up_Fusion
,
self
).
__init__
()
in_channels
=
in_channels
[::
-
1
]
out_channels
=
[
256
,
256
,
192
,
192
,
128
]
class
PGFPN
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
,
**
kwargs
):
super
(
PGFPN
,
self
).
__init__
()
num_inputs
=
[
2048
,
2048
,
1024
,
512
,
256
]
num_outputs
=
[
256
,
256
,
192
,
192
,
128
]
self
.
out_channels
=
128
# print(in_channels)
self
.
conv_bn_layer_1
=
ConvBNLayer
(
in_channels
=
3
,
out_channels
=
32
,
kernel_size
=
3
,
stride
=
1
,
act
=
None
,
name
=
'FPN_d1'
)
self
.
conv_bn_layer_2
=
ConvBNLayer
(
in_channels
=
64
,
out_channels
=
64
,
kernel_size
=
3
,
stride
=
1
,
act
=
None
,
name
=
'FPN_d2'
)
self
.
conv_bn_layer_3
=
ConvBNLayer
(
in_channels
=
256
,
out_channels
=
128
,
kernel_size
=
3
,
stride
=
1
,
act
=
None
,
name
=
'FPN_d3'
)
self
.
conv_bn_layer_4
=
ConvBNLayer
(
in_channels
=
32
,
out_channels
=
64
,
kernel_size
=
3
,
stride
=
2
,
act
=
None
,
name
=
'FPN_d4'
)
self
.
conv_bn_layer_5
=
ConvBNLayer
(
in_channels
=
64
,
out_channels
=
64
,
kernel_size
=
3
,
stride
=
1
,
act
=
'relu'
,
name
=
'FPN_d5'
)
self
.
conv_bn_layer_6
=
ConvBNLayer
(
in_channels
=
64
,
out_channels
=
128
,
kernel_size
=
3
,
stride
=
2
,
act
=
None
,
name
=
'FPN_d6'
)
self
.
conv_bn_layer_7
=
ConvBNLayer
(
in_channels
=
128
,
out_channels
=
128
,
kernel_size
=
3
,
stride
=
1
,
act
=
'relu'
,
name
=
'FPN_d7'
)
self
.
conv_bn_layer_8
=
ConvBNLayer
(
in_channels
=
128
,
out_channels
=
128
,
kernel_size
=
1
,
stride
=
1
,
act
=
None
,
name
=
'FPN_d8'
)
self
.
h0_conv
=
ConvBNLayer
(
in_channels
[
0
],
out_channels
[
0
],
1
,
1
,
act
=
None
,
name
=
'conv_h0'
)
self
.
h1_conv
=
ConvBNLayer
(
in_channels
[
1
],
out_channels
[
1
],
1
,
1
,
act
=
None
,
name
=
'conv_h1'
)
self
.
h2_conv
=
ConvBNLayer
(
in_channels
[
2
],
out_channels
[
2
],
1
,
1
,
act
=
None
,
name
=
'conv_h2'
)
self
.
h3_conv
=
ConvBNLayer
(
in_channels
[
3
],
out_channels
[
3
],
1
,
1
,
act
=
None
,
name
=
'conv_h3'
)
self
.
h4_conv
=
ConvBNLayer
(
in_channels
[
4
],
out_channels
[
4
],
1
,
1
,
act
=
None
,
name
=
'conv_h4'
)
self
.
conv_h0
=
ConvBNLayer
(
in_channels
=
num_inputs
[
0
],
out_channels
=
num_outputs
[
0
],
kernel_size
=
1
,
stride
=
1
,
act
=
None
,
name
=
"conv_h{}"
.
format
(
0
))
self
.
conv_h1
=
ConvBNLayer
(
in_channels
=
num_inputs
[
1
],
out_channels
=
num_outputs
[
1
],
kernel_size
=
1
,
stride
=
1
,
act
=
None
,
name
=
"conv_h{}"
.
format
(
1
))
self
.
conv_h2
=
ConvBNLayer
(
in_channels
=
num_inputs
[
2
],
out_channels
=
num_outputs
[
2
],
kernel_size
=
1
,
stride
=
1
,
act
=
None
,
name
=
"conv_h{}"
.
format
(
2
))
self
.
conv_h3
=
ConvBNLayer
(
in_channels
=
num_inputs
[
3
],
out_channels
=
num_outputs
[
3
],
kernel_size
=
1
,
stride
=
1
,
act
=
None
,
name
=
"conv_h{}"
.
format
(
3
))
self
.
conv_h4
=
ConvBNLayer
(
in_channels
=
num_inputs
[
4
],
out_channels
=
num_outputs
[
4
],
kernel_size
=
1
,
stride
=
1
,
act
=
None
,
name
=
"conv_h{}"
.
format
(
4
))
self
.
dconv0
=
DeConvBNLayer
(
in_channels
=
out_channel
s
[
0
],
out_channels
=
out_channels
[
1
],
in_channels
=
num_output
s
[
0
],
out_channels
=
num_outputs
[
0
+
1
],
name
=
"dconv_{}"
.
format
(
0
))
self
.
dconv1
=
DeConvBNLayer
(
in_channels
=
out_channel
s
[
1
],
out_channels
=
out_channels
[
2
],
in_channels
=
num_output
s
[
1
],
out_channels
=
num_outputs
[
1
+
1
],
act
=
None
,
name
=
"dconv_{}"
.
format
(
1
))
self
.
dconv2
=
DeConvBNLayer
(
in_channels
=
out_channel
s
[
2
],
out_channels
=
out_channels
[
3
],
in_channels
=
num_output
s
[
2
],
out_channels
=
num_outputs
[
2
+
1
],
act
=
None
,
name
=
"dconv_{}"
.
format
(
2
))
self
.
dconv3
=
DeConvBNLayer
(
in_channels
=
out_channel
s
[
3
],
out_channels
=
out_channels
[
4
],
in_channels
=
num_output
s
[
3
],
out_channels
=
num_outputs
[
3
+
1
],
act
=
None
,
name
=
"dconv_{}"
.
format
(
3
))
self
.
conv_g1
=
ConvBNLayer
(
in_channels
=
out_channel
s
[
1
],
out_channels
=
out_channel
s
[
1
],
in_channels
=
num_output
s
[
1
],
out_channels
=
num_output
s
[
1
],
kernel_size
=
3
,
stride
=
1
,
act
=
'relu'
,
name
=
"conv_g{}"
.
format
(
1
))
self
.
conv_g2
=
ConvBNLayer
(
in_channels
=
out_channel
s
[
2
],
out_channels
=
out_channel
s
[
2
],
in_channels
=
num_output
s
[
2
],
out_channels
=
num_output
s
[
2
],
kernel_size
=
3
,
stride
=
1
,
act
=
'relu'
,
name
=
"conv_g{}"
.
format
(
2
))
self
.
conv_g3
=
ConvBNLayer
(
in_channels
=
out_channel
s
[
3
],
out_channels
=
out_channel
s
[
3
],
in_channels
=
num_output
s
[
3
],
out_channels
=
num_output
s
[
3
],
kernel_size
=
3
,
stride
=
1
,
act
=
'relu'
,
name
=
"conv_g{}"
.
format
(
3
))
self
.
conv_g4
=
ConvBNLayer
(
in_channels
=
out_channel
s
[
4
],
out_channels
=
out_channel
s
[
4
],
in_channels
=
num_output
s
[
4
],
out_channels
=
num_output
s
[
4
],
kernel_size
=
3
,
stride
=
1
,
act
=
'relu'
,
name
=
"conv_g{}"
.
format
(
4
))
self
.
convf
=
ConvBNLayer
(
in_channels
=
out_channel
s
[
4
],
out_channels
=
out_channel
s
[
4
],
in_channels
=
num_output
s
[
4
],
out_channels
=
num_output
s
[
4
],
kernel_size
=
1
,
stride
=
1
,
act
=
None
,
name
=
"conv_f{}"
.
format
(
4
))
def
_add_relu
(
self
,
x1
,
x2
):
x
=
paddle
.
add
(
x
=
x1
,
y
=
x2
)
x
=
F
.
relu
(
x
)
return
x
def
forward
(
self
,
x
):
f
=
x
[
2
:][::
-
1
]
h0
=
self
.
h0_conv
(
f
[
0
])
h1
=
self
.
h1_conv
(
f
[
1
])
h2
=
self
.
h2_conv
(
f
[
2
])
h3
=
self
.
h3_conv
(
f
[
3
])
h4
=
self
.
h4_conv
(
f
[
4
])
c0
,
c1
,
c2
,
c3
,
c4
,
c5
,
c6
=
x
# FPN_Down_Fusion
f
=
[
c0
,
c1
,
c2
]
g
=
[
None
,
None
,
None
]
h
=
[
None
,
None
,
None
]
h
[
0
]
=
self
.
conv_bn_layer_1
(
f
[
0
])
h
[
1
]
=
self
.
conv_bn_layer_2
(
f
[
1
])
h
[
2
]
=
self
.
conv_bn_layer_3
(
f
[
2
])
g0
=
self
.
dconv0
(
h0
)
g
[
0
]
=
self
.
conv_bn_layer_4
(
h
[
0
])
g
[
1
]
=
paddle
.
add
(
g
[
0
],
h
[
1
])
g
[
1
]
=
F
.
relu
(
g
[
1
])
g
[
1
]
=
self
.
conv_bn_layer_5
(
g
[
1
])
g
[
1
]
=
self
.
conv_bn_layer_6
(
g
[
1
])
g1
=
self
.
dconv2
(
self
.
conv_g2
(
self
.
_add_relu
(
g0
,
h1
)))
g2
=
self
.
dconv2
(
self
.
conv_g2
(
self
.
_add_relu
(
g1
,
h2
)))
g3
=
self
.
dconv3
(
self
.
conv_g2
(
self
.
_add_relu
(
g2
,
h3
)))
g4
=
self
.
dconv4
(
self
.
conv_g2
(
self
.
_add_relu
(
g3
,
h4
)))
return
g4
g
[
2
]
=
paddle
.
add
(
g
[
1
],
h
[
2
])
g
[
2
]
=
F
.
relu
(
g
[
2
])
g
[
2
]
=
self
.
conv_bn_layer_7
(
g
[
2
])
f_down
=
self
.
conv_bn_layer_8
(
g
[
2
])
# FPN UP Fusion
f1
=
[
c6
,
c5
,
c4
,
c3
,
c2
]
g
=
[
None
,
None
,
None
,
None
,
None
]
h
=
[
None
,
None
,
None
,
None
,
None
]
h
[
0
]
=
self
.
conv_h0
(
f1
[
0
])
h
[
1
]
=
self
.
conv_h1
(
f1
[
1
])
h
[
2
]
=
self
.
conv_h2
(
f1
[
2
])
h
[
3
]
=
self
.
conv_h3
(
f1
[
3
])
h
[
4
]
=
self
.
conv_h4
(
f1
[
4
])
class
FPN_Down_Fusion
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
):
super
(
FPN_Down_Fusion
,
self
).
__init__
()
out_channels
=
[
32
,
64
,
128
]
g
[
0
]
=
self
.
dconv0
(
h
[
0
])
g
[
1
]
=
paddle
.
add
(
g
[
0
],
h
[
1
])
g
[
1
]
=
F
.
relu
(
g
[
1
])
g
[
1
]
=
self
.
conv_g1
(
g
[
1
])
g
[
1
]
=
self
.
dconv1
(
g
[
1
])
self
.
h0_conv
=
ConvBNLayer
(
in_channels
[
0
],
out_channels
[
0
],
3
,
1
,
act
=
None
,
name
=
'FPN_d1'
)
self
.
h1_conv
=
ConvBNLayer
(
in_channels
[
1
],
out_channels
[
1
],
3
,
1
,
act
=
None
,
name
=
'FPN_d2'
)
self
.
h2_conv
=
ConvBNLayer
(
in_channels
[
2
],
out_channels
[
2
],
3
,
1
,
act
=
None
,
name
=
'FPN_d3'
)
g
[
2
]
=
paddle
.
add
(
g
[
1
],
h
[
2
])
g
[
2
]
=
F
.
relu
(
g
[
2
])
g
[
2
]
=
self
.
conv_g2
(
g
[
2
])
g
[
2
]
=
self
.
dconv2
(
g
[
2
])
self
.
g0_conv
=
ConvBNLayer
(
out_channels
[
0
],
out_channels
[
1
],
3
,
2
,
act
=
None
,
name
=
'FPN_d4'
)
g
[
3
]
=
paddle
.
add
(
g
[
2
],
h
[
3
])
g
[
3
]
=
F
.
relu
(
g
[
3
])
g
[
3
]
=
self
.
conv_g3
(
g
[
3
])
g
[
3
]
=
self
.
dconv3
(
g
[
3
])
self
.
g1_conv
=
nn
.
Sequential
(
ConvBNLayer
(
out_channels
[
1
],
out_channels
[
1
],
3
,
1
,
act
=
'relu'
,
name
=
'FPN_d5'
),
ConvBNLayer
(
out_channels
[
1
],
out_channels
[
2
],
3
,
2
,
act
=
None
,
name
=
'FPN_d6'
))
self
.
g2_conv
=
nn
.
Sequential
(
ConvBNLayer
(
out_channels
[
2
],
out_channels
[
2
],
3
,
1
,
act
=
'relu'
,
name
=
'FPN_d7'
),
ConvBNLayer
(
out_channels
[
2
],
out_channels
[
2
],
1
,
1
,
act
=
None
,
name
=
'FPN_d8'
))
def
forward
(
self
,
x
):
f
=
x
[:
3
]
h0
=
self
.
h0_conv
(
f
[
0
])
h1
=
self
.
h1_conv
(
f
[
1
])
h2
=
self
.
h2_conv
(
f
[
2
])
g0
=
self
.
g0_conv
(
h0
)
g1
=
paddle
.
add
(
x
=
g0
,
y
=
h1
)
g1
=
F
.
relu
(
g1
)
g1
=
self
.
g1_conv
(
g1
)
g2
=
paddle
.
add
(
x
=
g1
,
y
=
h2
)
g2
=
F
.
relu
(
g2
)
g2
=
self
.
g2_conv
(
g2
)
return
g2
class
PGFPN
(
nn
.
Layer
):
def
__init__
(
self
,
in_channels
,
with_cab
=
False
,
**
kwargs
):
super
(
PGFPN
,
self
).
__init__
()
self
.
in_channels
=
in_channels
self
.
with_cab
=
with_cab
self
.
FPN_Down_Fusion
=
FPN_Down_Fusion
(
self
.
in_channels
)
self
.
FPN_Up_Fusion
=
FPN_Up_Fusion
(
self
.
in_channels
)
self
.
out_channels
=
128
def
forward
(
self
,
x
):
# down fpn
f_down
=
self
.
FPN_Down_Fusion
(
x
)
# up fpn
f_up
=
self
.
FPN_Up_Fusion
(
x
)
# fusion
f_common
=
paddle
.
add
(
x
=
f_down
,
y
=
f_up
)
g
[
4
]
=
paddle
.
add
(
x
=
g
[
3
],
y
=
h
[
4
])
g
[
4
]
=
F
.
relu
(
g
[
4
])
g
[
4
]
=
self
.
conv_g4
(
g
[
4
])
f_up
=
self
.
convf
(
g
[
4
])
f_common
=
paddle
.
add
(
f_down
,
f_up
)
f_common
=
F
.
relu
(
f_common
)
return
f_common
ppocr/utils/e2e_metric/Deteval.py
浏览文件 @
bb49e1a5
from
os
import
listdir
import
os
,
sys
from
scipy
import
io
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
numpy
as
np
from
ppocr.utils.e2e_metric.polygon_fast
import
iod
,
area_of_intersection
,
area
from
tqdm
import
tqdm
try
:
# python2
range
=
xrange
...
...
@@ -862,16 +871,3 @@ def combine_results(all_data):
'f_score_e2e'
:
f_score_e2e
}
return
final
# a = [1526, 642, 1565, 629, 1579, 627, 1593, 625, 1607, 623, 1620, 622, 1634, 620, 1659, 620, 1654, 681, 1631, 680, 1618,
# 681, 1606, 681, 1594, 681, 1584, 682, 1573, 685, 1542, 694]
# gt_dict = [{'points': np.array(a).reshape(-1, 2), 'text': 'MILK'}]
# pred_dict = [{'points': np.array(a), 'text': 'ccc'},
# {'points': np.array(a), 'text': 'ccf'}]
# result = []
# for i in range(2):
# result.append(get_socre(gt_dict, pred_dict))
# print(111)
# a = combine_results(result)
# print(a)
ppocr/utils/e2e_metric/polygon_fast.py
浏览文件 @
bb49e1a5
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
numpy
as
np
from
shapely.geometry
import
Polygon
#import Polygon
"""
:param det_x: [1, N] Xs of detection's vertices
:param det_y: [1, N] Ys of detection's vertices
...
...
ppocr/utils/e2e_metric/tttt.py
已删除
100644 → 0
浏览文件 @
1f76f449
此差异已折叠。
点击以展开。
ppocr/utils/e2e_utils/extract_textpoint.py
浏览文件 @
bb49e1a5
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Contains various CTC decoders."""
from
__future__
import
absolute_import
from
__future__
import
division
...
...
ppocr/utils/e2e_utils/ski_thin.py
浏览文件 @
bb49e1a5
"""
Algorithms for computing the skeleton of a binary image
"""
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
numpy
as
np
from
scipy
import
ndimage
as
ndi
...
...
ppocr/utils/e2e_utils/visual.py
浏览文件 @
bb49e1a5
import
os
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
numpy
as
np
import
cv2
import
time
def
visualize_e2e_result
(
im_fn
,
poly_list
,
seq_strs
,
src_im
):
"""
"""
result_path
=
'./out'
im_basename
=
os
.
path
.
basename
(
im_fn
)
im_prefix
=
im_basename
[:
im_basename
.
rfind
(
'.'
)]
vis_det_img
=
src_im
.
copy
()
valid_set
=
'partvgg'
gt_dir
=
"/Users/hongyongjie/Downloads/part_vgg_synth/train"
text_path
=
os
.
path
.
join
(
gt_dir
,
im_prefix
+
'.txt'
)
fid
=
open
(
text_path
,
'r'
)
lines
=
[
line
.
strip
()
for
line
in
fid
.
readlines
()]
for
line
in
lines
:
if
valid_set
==
'partvgg'
:
tokens
=
line
.
strip
().
split
(
'
\t
'
)[
0
].
split
(
','
)
# tokens = line.strip().split(',')
coords
=
tokens
[:]
coords
=
list
(
map
(
float
,
coords
))
gt_poly
=
np
.
array
(
coords
).
reshape
(
1
,
4
,
2
)
elif
valid_set
==
'totaltext'
:
tokens
=
line
.
strip
().
split
(
'
\t
'
)[
0
].
split
(
','
)
coords
=
tokens
[:]
coords_len
=
len
(
coords
)
/
2
coords
=
list
(
map
(
float
,
coords
))
gt_poly
=
np
.
array
(
coords
).
reshape
(
1
,
coords_len
,
2
)
cv2
.
polylines
(
vis_det_img
,
np
.
array
(
gt_poly
).
astype
(
np
.
int32
),
isClosed
=
True
,
color
=
(
255
,
0
,
0
),
thickness
=
2
)
for
detected_poly
,
recognized_str
in
zip
(
poly_list
,
seq_strs
):
cv2
.
polylines
(
vis_det_img
,
np
.
array
(
detected_poly
[
np
.
newaxis
,
...]).
astype
(
np
.
int32
),
isClosed
=
True
,
color
=
(
0
,
0
,
255
),
thickness
=
2
)
cv2
.
putText
(
vis_det_img
,
recognized_str
,
org
=
(
int
(
detected_poly
[
0
,
0
]),
int
(
detected_poly
[
0
,
1
])),
fontFace
=
cv2
.
FONT_HERSHEY_COMPLEX
,
fontScale
=
0.7
,
color
=
(
0
,
255
,
0
),
thickness
=
1
)
if
not
os
.
path
.
exists
(
result_path
):
os
.
makedirs
(
result_path
)
cv2
.
imwrite
(
"{}/{}_detection.jpg"
.
format
(
result_path
,
im_prefix
),
vis_det_img
)
def
visualization_output
(
src_image
,
f_tcl
,
f_chars
,
output_dir
,
image_prefix
=
None
):
"""
"""
# restore BGR image, CHW -> HWC
im_mean
=
[
0.485
,
0.456
,
0.406
]
im_std
=
[
0.229
,
0.224
,
0.225
]
im_mean
=
np
.
array
(
im_mean
).
reshape
((
3
,
1
,
1
))
im_std
=
np
.
array
(
im_std
).
reshape
((
3
,
1
,
1
))
src_image
*=
im_std
src_image
+=
im_mean
src_image
=
src_image
.
transpose
([
1
,
2
,
0
])
src_image
=
src_image
[:,
:,
::
-
1
]
*
255
# BGR -> RGB
H
,
W
,
_
=
src_image
.
shape
file_prefix
=
image_prefix
if
image_prefix
is
not
None
else
str
(
int
(
time
.
time
()
*
1000
))
if
not
os
.
path
.
exists
(
output_dir
):
os
.
makedirs
(
output_dir
)
# visualization f_tcl
tcl_file_name
=
os
.
path
.
join
(
output_dir
,
file_prefix
+
'_0_tcl.jpg'
)
vis_tcl_img
=
src_image
.
copy
()
f_tcl_resized
=
cv2
.
resize
(
f_tcl
,
dsize
=
(
W
,
H
))
vis_tcl_img
[:,
:,
1
]
=
f_tcl_resized
*
255
cv2
.
imwrite
(
tcl_file_name
,
vis_tcl_img
)
# visualization char maps
vis_char_img
=
src_image
.
copy
()
# CHW -> HWC
char_file_name
=
os
.
path
.
join
(
output_dir
,
file_prefix
+
'_1_chars.jpg'
)
f_chars
=
np
.
argmax
(
f_chars
,
axis
=
2
)[:,
:,
np
.
newaxis
].
astype
(
'float32'
)
f_chars
[
f_chars
<
95
]
=
1.0
f_chars
[
f_chars
==
95
]
=
0.0
f_chars_resized
=
cv2
.
resize
(
f_chars
,
dsize
=
(
W
,
H
))
vis_char_img
[:,
:,
1
]
=
f_chars_resized
*
255
cv2
.
imwrite
(
char_file_name
,
vis_char_img
)
def
visualize_point_result
(
im_fn
,
point_list
,
point_pair_list
,
src_im
,
gt_dir
,
result_path
):
"""
"""
im_basename
=
os
.
path
.
basename
(
im_fn
)
im_prefix
=
im_basename
[:
im_basename
.
rfind
(
'.'
)]
vis_det_img
=
src_im
.
copy
()
# draw gt bbox on the image.
text_path
=
os
.
path
.
join
(
gt_dir
,
im_prefix
+
'.txt'
)
fid
=
open
(
text_path
,
'r'
)
lines
=
[
line
.
strip
()
for
line
in
fid
.
readlines
()]
for
line
in
lines
:
tokens
=
line
.
strip
().
split
(
'
\t
'
)
coords
=
tokens
[
0
].
split
(
','
)
coords_len
=
len
(
coords
)
coords
=
list
(
map
(
float
,
coords
))
gt_poly
=
np
.
array
(
coords
).
reshape
(
1
,
coords_len
/
2
,
2
)
cv2
.
polylines
(
vis_det_img
,
np
.
array
(
gt_poly
).
astype
(
np
.
int32
),
isClosed
=
True
,
color
=
(
255
,
255
,
255
),
thickness
=
1
)
for
point
,
point_pair
in
zip
(
point_list
,
point_pair_list
):
cv2
.
line
(
vis_det_img
,
tuple
(
point_pair
[
0
]),
tuple
(
point_pair
[
1
]),
(
0
,
255
,
255
),
thickness
=
1
)
cv2
.
circle
(
vis_det_img
,
tuple
(
point
),
2
,
(
0
,
0
,
255
))
cv2
.
circle
(
vis_det_img
,
tuple
(
point_pair
[
0
]),
2
,
(
255
,
0
,
0
))
cv2
.
circle
(
vis_det_img
,
tuple
(
point_pair
[
1
]),
2
,
(
0
,
255
,
0
))
if
not
os
.
path
.
exists
(
result_path
):
os
.
makedirs
(
result_path
)
cv2
.
imwrite
(
"{}/{}_border_points.jpg"
.
format
(
result_path
,
im_prefix
),
vis_det_img
)
def
resize_image
(
im
,
max_side_len
=
512
):
"""
resize image to a size multiple of max_stride which is required by the network
...
...
@@ -295,49 +169,3 @@ def norm2(x, axis=None):
def
cos
(
p1
,
p2
):
return
(
p1
*
p2
).
sum
()
/
(
norm2
(
p1
)
*
norm2
(
p2
))
def
generate_direction_info
(
image_fn
,
H
,
W
,
ratio_h
,
ratio_w
,
max_length
=
640
,
out_scale
=
4
,
gt_dir
=
None
):
"""
"""
im_basename
=
os
.
path
.
basename
(
image_fn
)
im_prefix
=
im_basename
[:
im_basename
.
rfind
(
'.'
)]
instance_direction_map
=
np
.
zeros
(
shape
=
[
H
//
out_scale
,
W
//
out_scale
,
3
])
if
gt_dir
is
None
:
gt_dir
=
'/home/vis/huangzuming/data/SYNTH_DATA/part_vgg_synth_icdar/processed/val/poly'
# get gt label map
text_path
=
os
.
path
.
join
(
gt_dir
,
im_prefix
+
'.txt'
)
fid
=
open
(
text_path
,
'r'
)
lines
=
[
line
.
strip
()
for
line
in
fid
.
readlines
()]
for
label_idx
,
line
in
enumerate
(
lines
,
start
=
1
):
coords
,
txt
=
line
.
strip
().
split
(
'
\t
'
)
if
txt
==
'###'
:
continue
tokens
=
coords
.
strip
().
split
(
','
)
coords
=
list
(
map
(
float
,
tokens
))
poly
=
np
.
array
(
coords
).
reshape
(
4
,
2
)
*
np
.
array
(
[
ratio_w
,
ratio_h
]).
reshape
(
1
,
2
)
/
out_scale
mid_idx
=
poly
.
shape
[
0
]
//
2
direct_vector
=
(
(
poly
[
mid_idx
]
+
poly
[
mid_idx
-
1
])
-
(
poly
[
0
]
+
poly
[
-
1
]))
/
2.0
direct_vector
/=
len
(
txt
)
# l2_distance = norm2(direct_vector)
# avg_char_distance = l2_distance / len(txt)
avg_char_distance
=
1.0
direct_label
=
(
direct_vector
[
0
],
direct_vector
[
1
],
avg_char_distance
)
cv2
.
fillPoly
(
instance_direction_map
,
poly
.
round
().
astype
(
np
.
int32
)[
np
.
newaxis
,
:,
:],
direct_label
)
instance_direction_map
=
instance_direction_map
.
transpose
([
2
,
0
,
1
])
return
instance_direction_map
[:
2
,
...]
tools/program.py
浏览文件 @
bb49e1a5
...
...
@@ -44,7 +44,6 @@ class ArgsParser(ArgumentParser):
def
parse_args
(
self
,
argv
=
None
):
args
=
super
(
ArgsParser
,
self
).
parse_args
(
argv
)
args
.
config
=
'/Users/hongyongjie/project/PaddleOCR/configs/e2e/e2e_r50_vd_pg.yml'
assert
args
.
config
is
not
None
,
\
"Please specify --config=configure_file_path."
args
.
opt
=
self
.
_parse_opt
(
args
.
opt
)
...
...
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