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体验新版 GitCode,发现更多精彩内容 >>
提交
dfba983c
编写于
6月 08, 2021
作者:
W
WenmuZhou
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
remove unused code
上级
3b81e304
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
10 addition
and
30 deletion
+10
-30
ppocr/postprocess/rec_postprocess.py
ppocr/postprocess/rec_postprocess.py
+1
-17
ppstructure/table/eval_table.py
ppstructure/table/eval_table.py
+3
-3
ppstructure/table/matcher.py
ppstructure/table/matcher.py
+6
-10
未找到文件。
ppocr/postprocess/rec_postprocess.py
浏览文件 @
dfba983c
...
...
@@ -382,23 +382,12 @@ class TableLabelDecode(object):
"""convert text-label into text-index.
"""
if
char_or_elem
==
"char"
:
max_len
=
self
.
max_text_length
current_dict
=
self
.
dict_idx_character
else
:
max_len
=
self
.
max_elem_length
current_dict
=
self
.
dict_idx_elem
ignored_tokens
=
self
.
get_ignored_tokens
(
'elem'
)
beg_idx
,
end_idx
=
ignored_tokens
# select_td_tokens = []
# select_span_tokens = []
# for elem in self.dict_elem:
# # if elem == '<td>' or elem == '<td' or elem == '<tr>'\
# # or 'rowspan' in elem or 'colspan' in elem:
# if elem == '<td>' or elem == '<td' or elem == '<tr>':
# select_td_tokens.append(self.dict_elem[elem])
# if 'rowspan' in elem or 'colspan' in elem:
# select_span_tokens.append(self.dict_elem[elem])
result_list
=
[]
result_pos_list
=
[]
result_score_list
=
[]
...
...
@@ -415,12 +404,7 @@ class TableLabelDecode(object):
break
if
tmp_elem_idx
in
ignored_tokens
:
continue
# if tmp_elem_idx in select_td_tokens:
# total_td_score += structure_probs[batch_idx, idx]
# total_td_num += 1
# if tmp_elem_idx in select_span_tokens:
# total_span_score += structure_probs[batch_idx, idx]
# total_span_num += 1
char_list
.
append
(
current_dict
[
tmp_elem_idx
])
elem_pos_list
.
append
(
idx
)
score_list
.
append
(
structure_probs
[
batch_idx
,
idx
])
...
...
ppstructure/table/eval_table.py
浏览文件 @
dfba983c
...
...
@@ -38,15 +38,15 @@ def main(gt_path, img_root, args):
pred_htmls
=
[]
gt_htmls
=
[]
for
img_name
in
tqdm
(
jsons_gt
):
#
读取信息
#
read image
img
=
cv2
.
imread
(
os
.
path
.
join
(
img_root
,
img_name
))
pred_html
=
text_sys
(
img
)
pred_htmls
.
append
(
pred_html
)
gt_structures
,
gt_bboxes
,
gt_contents
,
contents_with_block
=
jsons_gt
[
img_name
]
gt_html
,
gt
=
get_gt_html
(
gt_structures
,
contents_with_block
)
# 获取HTMLgt
gt_html
,
gt
=
get_gt_html
(
gt_structures
,
contents_with_block
)
gt_htmls
.
append
(
gt_html
)
scores
=
teds
.
batch_evaluate_html
(
gt_htmls
,
pred_htmls
)
# 计算teds
scores
=
teds
.
batch_evaluate_html
(
gt_htmls
,
pred_htmls
)
print
(
'teds:'
,
sum
(
scores
)
/
len
(
scores
))
...
...
ppstructure/table/matcher.py
浏览文件 @
dfba983c
...
...
@@ -2,14 +2,9 @@ import json
def
distance
(
box_1
,
box_2
):
x1
,
y1
,
x2
,
y2
=
box_1
x3
,
y3
,
x4
,
y4
=
box_2
# min_x = (x1 + x2) / 2
# min_y = (y1 + y2) / 2
# max_x = (x3 + x4) / 2
# max_y = (y3 + y4) / 2
dis
=
abs
(
x3
-
x1
)
+
abs
(
y3
-
y1
)
+
abs
(
x4
-
x2
)
+
abs
(
y4
-
y2
)
dis_2
=
abs
(
x3
-
x1
)
+
abs
(
y3
-
y1
)
dis_3
=
abs
(
x4
-
x2
)
+
abs
(
y4
-
y2
)
#dis = pow(min_x - max_x, 2) + pow(min_y - max_y, 2) + pow(x3 - x1, 2) + pow(y3 - y1, 2) + pow(x4- x2, 2) + pow(y4 - y2, 2) + abs(x3 - x1) + abs(y3 - y1) + abs(x4- x2) + abs(y4 - y2)
return
dis
+
min
(
dis_2
,
dis_3
)
def
compute_iou
(
rec1
,
rec2
):
...
...
@@ -21,7 +16,6 @@ def compute_iou(rec1, rec2):
:return: scala value of IoU
"""
# computing area of each rectangles
rec1
,
rec2
=
rec1
*
1000
,
rec2
*
1000
S_rec1
=
(
rec1
[
2
]
-
rec1
[
0
])
*
(
rec1
[
3
]
-
rec1
[
1
])
S_rec2
=
(
rec2
[
2
]
-
rec2
[
0
])
*
(
rec2
[
3
]
-
rec2
[
1
])
...
...
@@ -36,29 +30,31 @@ def compute_iou(rec1, rec2):
# judge if there is an intersect
if
left_line
>=
right_line
or
top_line
>=
bottom_line
:
return
0
return
0
.0
else
:
intersect
=
(
right_line
-
left_line
)
*
(
bottom_line
-
top_line
)
return
(
intersect
/
(
sum_area
-
intersect
))
*
1.0
def
matcher_merge
(
ocr_bboxes
,
pred_bboxes
):
# ocr_bboxes: OCR pred_bboxes:端到端
def
matcher_merge
(
ocr_bboxes
,
pred_bboxes
):
all_dis
=
[]
ious
=
[]
matched
=
{}
for
i
,
gt_box
in
enumerate
(
ocr_bboxes
):
distances
=
[]
for
j
,
pred_box
in
enumerate
(
pred_bboxes
):
distances
.
append
((
distance
(
gt_box
,
pred_box
),
1.
-
compute_iou
(
gt_box
,
pred_box
)))
#获取两两cell之间的L1距离和 1- IOU
# compute l1 distence and IOU between two boxes
distances
.
append
((
distance
(
gt_box
,
pred_box
),
1.
-
compute_iou
(
gt_box
,
pred_box
)))
sorted_distances
=
distances
.
copy
()
#
根据距离和IOU挑选最"近"的
cell
#
select nearest
cell
sorted_distances
=
sorted
(
sorted_distances
,
key
=
lambda
item
:
(
item
[
1
],
item
[
0
]))
if
distances
.
index
(
sorted_distances
[
0
])
not
in
matched
.
keys
():
matched
[
distances
.
index
(
sorted_distances
[
0
])]
=
[
i
]
else
:
matched
[
distances
.
index
(
sorted_distances
[
0
])].
append
(
i
)
return
matched
#, sum(ious) / len(ious)
def
complex_num
(
pred_bboxes
):
complex_nums
=
[]
for
bbox
in
pred_bboxes
:
...
...
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