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https://github.com/PaddlePaddle/PaddleOCR.git
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174 lines
6.9 KiB
Python
Executable File
174 lines
6.9 KiB
Python
Executable File
import json
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from ppstructure.table.table_master_match import deal_eb_token, deal_bb
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def distance(box_1, box_2):
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x1, y1, x2, y2 = box_1
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x3, y3, x4, y4 = box_2
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dis = abs(x3 - x1) + abs(y3 - y1) + abs(x4 - x2) + abs(y4 - y2)
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dis_2 = abs(x3 - x1) + abs(y3 - y1)
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dis_3 = abs(x4 - x2) + abs(y4 - y2)
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return dis + min(dis_2, dis_3)
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def compute_iou(rec1, rec2):
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"""
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computing IoU
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:param rec1: (y0, x0, y1, x1), which reflects
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(top, left, bottom, right)
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:param rec2: (y0, x0, y1, x1)
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:return: scala value of IoU
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"""
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# computing area of each rectangles
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S_rec1 = (rec1[2] - rec1[0]) * (rec1[3] - rec1[1])
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S_rec2 = (rec2[2] - rec2[0]) * (rec2[3] - rec2[1])
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# computing the sum_area
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sum_area = S_rec1 + S_rec2
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# find the each edge of intersect rectangle
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left_line = max(rec1[1], rec2[1])
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right_line = min(rec1[3], rec2[3])
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top_line = max(rec1[0], rec2[0])
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bottom_line = min(rec1[2], rec2[2])
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# judge if there is an intersect
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if left_line >= right_line or top_line >= bottom_line:
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return 0.0
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else:
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intersect = (right_line - left_line) * (bottom_line - top_line)
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return (intersect / (sum_area - intersect)) * 1.0
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class TableMatch:
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def __init__(self, filter_ocr_result=False, 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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if self.use_master:
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pred_html, pred = self.get_pred_html_master(pred_structures,
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matched_index, rec_res)
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else:
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pred_html, pred = self.get_pred_html(pred_structures, 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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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][
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0]] 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[
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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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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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