# 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 '' in tag:
if ' | ' == tag:
end_html.extend('')
if td_index in matched_index.keys():
b_with = False
if '' in ocr_contents[matched_index[td_index][
0]] and len(matched_index[td_index]) > 1:
b_with = True
end_html.extend('')
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 '' in content:
content = content[3:]
if '' 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('')
if ' | | ' == tag:
end_html.append('')
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 '' in token:
txt = ''
b_with = False
if td_index in matched_index.keys():
if '' 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 '' in content:
content = content[3:]
if '' 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 = '{}'.format(txt)
if ' | ' == token:
token = '{} | '.format(txt)
else:
token = '{}'.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