mirror of
https://github.com/RapidAI/RapidOCR.git
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159 lines
6.5 KiB
Python
159 lines
6.5 KiB
Python
# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# -*- encoding: utf-8 -*-
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import numpy as np
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from .utils import compute_iou, deal_bb, distance
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class TableMatch():
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def __init__(self, filter_ocr_result=True, use_master=False):
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self.filter_ocr_result = filter_ocr_result
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self.use_master = use_master
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def __call__(self, structure_res, dt_boxes, rec_res):
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pred_structures, pred_bboxes = structure_res
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if self.filter_ocr_result:
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dt_boxes, rec_res = self._filter_ocr_result(pred_bboxes, dt_boxes,
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rec_res)
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matched_index = self.match_result(dt_boxes, pred_bboxes)
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pred_html, pred = self.get_pred_html(pred_structures,
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matched_index,
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rec_res)
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return pred_html
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def match_result(self, dt_boxes, pred_bboxes):
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matched = {}
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for i, gt_box in enumerate(dt_boxes):
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distances = []
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for j, pred_box in enumerate(pred_bboxes):
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if len(pred_box) == 8:
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pred_box = [
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np.min(pred_box[0::2]), np.min(pred_box[1::2]),
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np.max(pred_box[0::2]), np.max(pred_box[1::2])
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]
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distances.append((distance(gt_box, pred_box),
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1. - compute_iou(gt_box, pred_box)
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)) # compute iou and l1 distance
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sorted_distances = distances.copy()
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# select det box by iou and l1 distance
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sorted_distances = sorted(
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sorted_distances, key=lambda item: (item[1], item[0]))
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if distances.index(sorted_distances[0]) not in matched.keys():
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matched[distances.index(sorted_distances[0])] = [i]
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else:
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matched[distances.index(sorted_distances[0])].append(i)
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return matched
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def get_pred_html(self, pred_structures, matched_index, ocr_contents):
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end_html = []
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td_index = 0
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for tag in pred_structures:
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if '</td>' in tag:
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if '<td></td>' == tag:
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end_html.extend('<td>')
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if td_index in matched_index.keys():
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b_with = False
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if '<b>' in ocr_contents[matched_index[td_index][0]] \
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and len(matched_index[td_index]) > 1:
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b_with = True
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end_html.extend('<b>')
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for i, td_index_index in enumerate(matched_index[td_index]):
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content = ocr_contents[td_index_index][0]
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if len(matched_index[td_index]) > 1:
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if len(content) == 0:
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continue
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if content[0] == ' ':
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content = content[1:]
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if '<b>' in content:
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content = content[3:]
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if '</b>' in content:
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content = content[:-4]
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if len(content) == 0:
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continue
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if i != len(matched_index[td_index]) - 1 \
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and ' ' != content[-1]:
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content += ' '
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end_html.extend(content)
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if b_with:
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end_html.extend('</b>')
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if '<td></td>' == tag:
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end_html.append('</td>')
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else:
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end_html.append(tag)
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td_index += 1
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else:
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end_html.append(tag)
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return ''.join(end_html), end_html
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def get_pred_html_master(self, pred_structures, matched_index,
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ocr_contents):
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end_html = []
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td_index = 0
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for token in pred_structures:
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if '</td>' in token:
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txt = ''
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b_with = False
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if td_index in matched_index.keys():
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if '<b>' in ocr_contents[matched_index[td_index][
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0]] and len(matched_index[td_index]) > 1:
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b_with = True
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for i, td_index_index in enumerate(matched_index[td_index]):
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content = ocr_contents[td_index_index][0]
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if len(matched_index[td_index]) > 1:
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if len(content) == 0:
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continue
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if content[0] == ' ':
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content = content[1:]
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if '<b>' in content:
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content = content[3:]
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if '</b>' in content:
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content = content[:-4]
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if len(content) == 0:
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continue
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if i != len(matched_index[
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td_index]) - 1 and ' ' != content[-1]:
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content += ' '
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txt += content
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if b_with:
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txt = '<b>{}</b>'.format(txt)
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if '<td></td>' == token:
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token = '<td>{}</td>'.format(txt)
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else:
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token = '{}</td>'.format(txt)
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td_index += 1
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token = deal_eb_token(token)
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end_html.append(token)
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html = ''.join(end_html)
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html = deal_bb(html)
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return html, end_html
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def _filter_ocr_result(self, pred_bboxes, dt_boxes, rec_res):
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y1 = pred_bboxes[:, 1::2].min()
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new_dt_boxes = []
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new_rec_res = []
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for box, rec in zip(dt_boxes, rec_res):
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if np.max(box[1::2]) < y1:
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continue
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new_dt_boxes.append(box)
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new_rec_res.append(rec)
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return new_dt_boxes, new_rec_res
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