File size: 7,972 Bytes
a89d9fd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 |
# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
#
# 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 ppstructure.table.table_master_match import deal_eb_token, deal_bb
def distance(box_1, box_2):
x1, y1, x2, y2 = box_1
x3, y3, x4, y4 = box_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)
return dis + min(dis_2, dis_3)
def compute_iou(rec1, rec2):
"""
computing IoU
:param rec1: (y0, x0, y1, x1), which reflects
(top, left, bottom, right)
:param rec2: (y0, x0, y1, x1)
:return: scala value of IoU
"""
# computing area of each rectangles
S_rec1 = (rec1[2] - rec1[0]) * (rec1[3] - rec1[1])
S_rec2 = (rec2[2] - rec2[0]) * (rec2[3] - rec2[1])
# computing the sum_area
sum_area = S_rec1 + S_rec2
# find the each edge of intersect rectangle
left_line = max(rec1[1], rec2[1])
right_line = min(rec1[3], rec2[3])
top_line = max(rec1[0], rec2[0])
bottom_line = min(rec1[2], rec2[2])
# judge if there is an intersect
if left_line >= right_line or top_line >= bottom_line:
return 0.0
else:
intersect = (right_line - left_line) * (bottom_line - top_line)
return (intersect / (sum_area - intersect)) * 1.0
class TableMatch:
def __init__(self, filter_ocr_result=False, use_master=False):
self.filter_ocr_result = filter_ocr_result
self.use_master = use_master
def __call__(self, structure_res, dt_boxes, rec_res):
pred_structures, pred_bboxes = structure_res
if self.filter_ocr_result:
dt_boxes, rec_res = self._filter_ocr_result(pred_bboxes, dt_boxes,
rec_res)
matched_index = self.match_result(dt_boxes, pred_bboxes)
if self.use_master:
pred_html, pred = self.get_pred_html_master(pred_structures,
matched_index, rec_res)
else:
pred_html, pred = self.get_pred_html(pred_structures, matched_index,
rec_res)
return pred_html
def match_result(self, dt_boxes, pred_bboxes):
matched = {}
for i, gt_box in enumerate(dt_boxes):
distances = []
for j, pred_box in enumerate(pred_bboxes):
if len(pred_box) == 8:
pred_box = [
np.min(pred_box[0::2]), np.min(pred_box[1::2]),
np.max(pred_box[0::2]), np.max(pred_box[1::2])
]
distances.append((distance(gt_box, pred_box),
1. - compute_iou(gt_box, pred_box)
)) # compute iou and l1 distance
sorted_distances = distances.copy()
# select det box by iou and l1 distance
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
def get_pred_html(self, pred_structures, matched_index, ocr_contents):
end_html = []
td_index = 0
for tag in pred_structures:
if '</td>' in tag:
if '<td></td>' == tag:
end_html.extend('<td>')
if td_index in matched_index.keys():
b_with = False
if '<b>' in ocr_contents[matched_index[td_index][
0]] and len(matched_index[td_index]) > 1:
b_with = True
end_html.extend('<b>')
for i, td_index_index in enumerate(matched_index[td_index]):
content = ocr_contents[td_index_index][0]
if len(matched_index[td_index]) > 1:
if len(content) == 0:
continue
if content[0] == ' ':
content = content[1:]
if '<b>' in content:
content = content[3:]
if '</b>' in content:
content = content[:-4]
if len(content) == 0:
continue
if i != len(matched_index[
td_index]) - 1 and ' ' != content[-1]:
content += ' '
end_html.extend(content)
if b_with:
end_html.extend('</b>')
if '<td></td>' == tag:
end_html.append('</td>')
else:
end_html.append(tag)
td_index += 1
else:
end_html.append(tag)
return ''.join(end_html), end_html
def get_pred_html_master(self, pred_structures, matched_index,
ocr_contents):
end_html = []
td_index = 0
for token in pred_structures:
if '</td>' in token:
txt = ''
b_with = False
if td_index in matched_index.keys():
if '<b>' in ocr_contents[matched_index[td_index][
0]] and len(matched_index[td_index]) > 1:
b_with = True
for i, td_index_index in enumerate(matched_index[td_index]):
content = ocr_contents[td_index_index][0]
if len(matched_index[td_index]) > 1:
if len(content) == 0:
continue
if content[0] == ' ':
content = content[1:]
if '<b>' in content:
content = content[3:]
if '</b>' in content:
content = content[:-4]
if len(content) == 0:
continue
if i != len(matched_index[
td_index]) - 1 and ' ' != content[-1]:
content += ' '
txt += content
if b_with:
txt = '<b>{}</b>'.format(txt)
if '<td></td>' == token:
token = '<td>{}</td>'.format(txt)
else:
token = '{}</td>'.format(txt)
td_index += 1
token = deal_eb_token(token)
end_html.append(token)
html = ''.join(end_html)
html = deal_bb(html)
return html, end_html
def _filter_ocr_result(self, pred_bboxes, dt_boxes, rec_res):
y1 = pred_bboxes[:, 1::2].min()
new_dt_boxes = []
new_rec_res = []
for box, rec in zip(dt_boxes, rec_res):
if np.max(box[1::2]) < y1:
continue
new_dt_boxes.append(box)
new_rec_res.append(rec)
return new_dt_boxes, new_rec_res
|