remove unused code
WenmuZhou
parent
d5ea6f21f8
commit
c86c174062
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@ -1,6 +1,6 @@
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Global:
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use_gpu: true
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epoch_num: 400
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epoch_num: 100
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log_smooth_window: 20
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print_batch_step: 20
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save_model_dir: ./output/SLANet
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@ -28,7 +28,10 @@ Optimizer:
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beta2: 0.999
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clip_norm: 5.0
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lr:
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name: Piecewise
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learning_rate: 0.001
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decay_epochs : [40, 50]
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values : [0.001, 0.0001, 0.00005]
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regularizer:
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name: 'L2'
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factor: 0.00000
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@ -105,8 +108,8 @@ Train:
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Eval:
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dataset:
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name: PubTabDataSet
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data_dir: /home/zhoujun20/table/PubTabNe/pubtabnet/val/
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label_file_list: [/home/zhoujun20/table/PubTabNe/pubtabnet/val_500.jsonl]
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data_dir: train_data/table/pubtabnet/val/
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label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_val.jsonl]
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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@ -40,169 +40,6 @@ def compute_iou(rec1, rec2):
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return (intersect / (sum_area - intersect)) * 1.0
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def matcher_merge(ocr_bboxes, pred_bboxes):
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all_dis = []
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ious = []
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matched = {}
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for i, gt_box in enumerate(ocr_bboxes):
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distances = []
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for j, pred_box in enumerate(pred_bboxes):
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# compute l1 distence and IOU between two boxes
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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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sorted_distances = distances.copy()
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# select nearest cell
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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 #, sum(ious) / len(ious)
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def complex_num(pred_bboxes):
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complex_nums = []
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for bbox in pred_bboxes:
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distances = []
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temp_ious = []
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for pred_bbox in pred_bboxes:
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if bbox != pred_bbox:
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distances.append(distance(bbox, pred_bbox))
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temp_ious.append(compute_iou(bbox, pred_bbox))
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complex_nums.append(temp_ious[distances.index(min(distances))])
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return sum(complex_nums) / len(complex_nums)
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def get_rows(pred_bboxes):
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pre_bbox = pred_bboxes[0]
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res = []
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step = 0
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for i in range(len(pred_bboxes)):
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bbox = pred_bboxes[i]
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if bbox[1] - pre_bbox[1] > 2 or bbox[0] - pre_bbox[0] < 0:
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break
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else:
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res.append(bbox)
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step += 1
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for i in range(step):
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pred_bboxes.pop(0)
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return res, pred_bboxes
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def refine_rows(pred_bboxes): # 微调整行的框，使在一条水平线上
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ys_1 = []
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ys_2 = []
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for box in pred_bboxes:
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ys_1.append(box[1])
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ys_2.append(box[3])
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min_y_1 = sum(ys_1) / len(ys_1)
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min_y_2 = sum(ys_2) / len(ys_2)
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re_boxes = []
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for box in pred_bboxes:
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box[1] = min_y_1
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box[3] = min_y_2
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re_boxes.append(box)
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return re_boxes
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def matcher_refine_row(gt_bboxes, pred_bboxes):
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before_refine_pred_bboxes = pred_bboxes.copy()
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pred_bboxes = []
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while (len(before_refine_pred_bboxes) != 0):
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row_bboxes, before_refine_pred_bboxes = get_rows(
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before_refine_pred_bboxes)
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print(row_bboxes)
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pred_bboxes.extend(refine_rows(row_bboxes))
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all_dis = []
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ious = []
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matched = {}
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for i, gt_box in enumerate(gt_bboxes):
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distances = []
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#temp_ious = []
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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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#temp_ious.append(compute_iou(gt_box, pred_box))
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#all_dis.append(min(distances))
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#ious.append(temp_ious[distances.index(min(distances))])
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if distances.index(min(distances)) not in matched.keys():
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matched[distances.index(min(distances))] = [i]
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else:
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matched[distances.index(min(distances))].append(i)
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return matched #, sum(ious) / len(ious)
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#先挑选出一行，再进行匹配
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def matcher_structure_1(gt_bboxes, pred_bboxes_rows, pred_bboxes):
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gt_box_index = 0
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delete_gt_bboxes = gt_bboxes.copy()
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match_bboxes_ready = []
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matched = {}
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while (len(delete_gt_bboxes) != 0):
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row_bboxes, delete_gt_bboxes = get_rows(delete_gt_bboxes)
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row_bboxes = sorted(row_bboxes, key=lambda key: key[0])
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if len(pred_bboxes_rows) > 0:
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match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
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print(row_bboxes)
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for i, gt_box in enumerate(row_bboxes):
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#print(gt_box)
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pred_distances = []
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distances = []
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for pred_bbox in pred_bboxes:
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pred_distances.append(distance(gt_box, pred_bbox))
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for j, pred_box in enumerate(match_bboxes_ready):
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distances.append(distance(gt_box, pred_box))
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index = pred_distances.index(min(distances))
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#print('index', index)
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if index not in matched.keys():
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matched[index] = [gt_box_index]
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else:
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matched[index].append(gt_box_index)
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gt_box_index += 1
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return matched
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def matcher_structure(gt_bboxes, pred_bboxes_rows, pred_bboxes):
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'''
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gt_bboxes: 排序后
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pred_bboxes:
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'''
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pre_bbox = gt_bboxes[0]
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matched = {}
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match_bboxes_ready = []
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match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
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for i, gt_box in enumerate(gt_bboxes):
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pred_distances = []
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for pred_bbox in pred_bboxes:
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pred_distances.append(distance(gt_box, pred_bbox))
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distances = []
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gap_pre = gt_box[1] - pre_bbox[1]
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gap_pre_1 = gt_box[0] - pre_bbox[2]
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#print(gap_pre, len(pred_bboxes_rows))
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if (gap_pre_1 < 0 and len(pred_bboxes_rows) > 0):
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match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
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if len(pred_bboxes_rows) == 1:
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match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
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if len(match_bboxes_ready) == 0 and len(pred_bboxes_rows) > 0:
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match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
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if len(match_bboxes_ready) == 0 and len(pred_bboxes_rows) == 0:
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break
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#print(match_bboxes_ready)
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for j, pred_box in enumerate(match_bboxes_ready):
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distances.append(distance(gt_box, pred_box))
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index = pred_distances.index(min(distances))
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#print(gt_box, index)
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#match_bboxes_ready.pop(distances.index(min(distances)))
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print(gt_box, match_bboxes_ready[distances.index(min(distances))])
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if index not in matched.keys():
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matched[index] = [i]
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else:
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matched[index].append(i)
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pre_bbox = gt_box
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return matched
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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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@ -225,14 +62,13 @@ class TableMatch:
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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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# gt_box = [np.min(gt_box[:, 0]), np.min(gt_box[:, 1]), np.max(gt_box[:, 0]), np.max(gt_box[:, 1])]
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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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)) # 获取两两cell之间的L1距离和 1- IOU
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)) # compute iou and l1 distance
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sorted_distances = distances.copy()
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# 根据距离和IOU挑选最"近"的cell
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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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