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200 lines
7.2 KiB
Python
200 lines
7.2 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, 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, pred_structures, cell_bboxes, dt_boxes, rec_res):
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if self.filter_ocr_result:
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dt_boxes, rec_res = self._filter_ocr_result(cell_bboxes, dt_boxes, rec_res)
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matched_index = self.match_result(dt_boxes, cell_bboxes)
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pred_html, pred = self.get_pred_html(pred_structures, matched_index, rec_res)
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return pred_html
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def match_result(self, dt_boxes, cell_bboxes, min_iou=0.1**8):
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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(cell_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]),
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np.min(pred_box[1::2]),
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np.max(pred_box[0::2]),
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np.max(pred_box[1::2]),
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]
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distances.append(
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(distance(gt_box, pred_box), 1.0 - 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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)
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# must > min_iou
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if sorted_distances[0][1] >= 1 - min_iou:
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continue
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if distances.index(sorted_distances[0]) not in matched:
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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>" not in tag:
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end_html.append(tag)
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continue
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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 (
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"<b>" in ocr_contents[matched_index[td_index][0]]
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and len(matched_index[td_index]) > 1
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):
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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 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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# Filter <thead></thead><tbody></tbody> elements
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filter_elements = ["<thead>", "</thead>", "<tbody>", "</tbody>"]
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end_html = [v for v in end_html if v not in filter_elements]
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return "".join(end_html), end_html
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def decode_logic_points(self, pred_structures):
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logic_points = []
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current_row = 0
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current_col = 0
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max_rows = 0
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max_cols = 0
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occupied_cells = {} # 用于记录已经被占用的单元格
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def is_occupied(row, col):
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return (row, col) in occupied_cells
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def mark_occupied(row, col, rowspan, colspan):
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for r in range(row, row + rowspan):
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for c in range(col, col + colspan):
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occupied_cells[(r, c)] = True
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i = 0
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while i < len(pred_structures):
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token = pred_structures[i]
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if token == "<tr>":
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current_col = 0 # 每次遇到 <tr> 时，重置当前列号
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elif token == "</tr>":
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current_row += 1 # 行结束，行号增加
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elif token.startswith("<td"):
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colspan = 1
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rowspan = 1
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j = i
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if token != "<td></td>":
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j += 1
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# 提取 colspan 和 rowspan 属性
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while j < len(pred_structures) and not pred_structures[
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j
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].startswith(">"):
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if "colspan=" in pred_structures[j]:
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colspan = int(pred_structures[j].split("=")[1].strip("\"'"))
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elif "rowspan=" in pred_structures[j]:
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rowspan = int(pred_structures[j].split("=")[1].strip("\"'"))
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j += 1
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# 跳过已经处理过的属性 token
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i = j
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# 找到下一个未被占用的列
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while is_occupied(current_row, current_col):
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current_col += 1
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# 计算逻辑坐标
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r_start = current_row
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r_end = current_row + rowspan - 1
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col_start = current_col
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col_end = current_col + colspan - 1
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# 记录逻辑坐标
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logic_points.append([r_start, r_end, col_start, col_end])
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# 标记占用的单元格
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mark_occupied(r_start, col_start, rowspan, colspan)
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# 更新当前列号
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current_col += colspan
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# 更新最大行数和列数
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max_rows = max(max_rows, r_end + 1)
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max_cols = max(max_cols, col_end + 1)
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i += 1
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return logic_points
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def _filter_ocr_result(self, cell_bboxes, dt_boxes, rec_res):
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y1 = cell_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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