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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import numpy as np | |
| import mmocr.utils as utils | |
| from . import utils as eval_utils | |
| def eval_hmean_iou(pred_boxes, | |
| gt_boxes, | |
| gt_ignored_boxes, | |
| iou_thr=0.5, | |
| precision_thr=0.5): | |
| """Evaluate hmean of text detection using IOU standard. | |
| Args: | |
| pred_boxes (list[list[list[float]]]): Text boxes for an img list. Each | |
| box has 2k (>=8) values. | |
| gt_boxes (list[list[list[float]]]): Ground truth text boxes for an img | |
| list. Each box has 2k (>=8) values. | |
| gt_ignored_boxes (list[list[list[float]]]): Ignored ground truth text | |
| boxes for an img list. Each box has 2k (>=8) values. | |
| iou_thr (float): Iou threshold when one (gt_box, det_box) pair is | |
| matched. | |
| precision_thr (float): Precision threshold when one (gt_box, det_box) | |
| pair is matched. | |
| Returns: | |
| hmean (tuple[dict]): Tuple of dicts indicates the hmean for the dataset | |
| and all images. | |
| """ | |
| assert utils.is_3dlist(pred_boxes) | |
| assert utils.is_3dlist(gt_boxes) | |
| assert utils.is_3dlist(gt_ignored_boxes) | |
| assert 0 <= iou_thr <= 1 | |
| assert 0 <= precision_thr <= 1 | |
| img_num = len(pred_boxes) | |
| assert img_num == len(gt_boxes) | |
| assert img_num == len(gt_ignored_boxes) | |
| dataset_gt_num = 0 | |
| dataset_pred_num = 0 | |
| dataset_hit_num = 0 | |
| img_results = [] | |
| for i in range(img_num): | |
| gt = gt_boxes[i] | |
| gt_ignored = gt_ignored_boxes[i] | |
| pred = pred_boxes[i] | |
| gt_num = len(gt) | |
| gt_ignored_num = len(gt_ignored) | |
| pred_num = len(pred) | |
| hit_num = 0 | |
| # get gt polygons. | |
| gt_all = gt + gt_ignored | |
| gt_polys = [eval_utils.points2polygon(p) for p in gt_all] | |
| gt_ignored_index = [gt_num + i for i in range(len(gt_ignored))] | |
| gt_num = len(gt_polys) | |
| pred_polys, _, pred_ignored_index = eval_utils.ignore_pred( | |
| pred, gt_ignored_index, gt_polys, precision_thr) | |
| # match. | |
| if gt_num > 0 and pred_num > 0: | |
| sz = [gt_num, pred_num] | |
| iou_mat = np.zeros(sz) | |
| gt_hit = np.zeros(gt_num, np.int8) | |
| pred_hit = np.zeros(pred_num, np.int8) | |
| for gt_id in range(gt_num): | |
| for pred_id in range(pred_num): | |
| gt_pol = gt_polys[gt_id] | |
| det_pol = pred_polys[pred_id] | |
| iou_mat[gt_id, | |
| pred_id] = eval_utils.poly_iou(det_pol, gt_pol) | |
| for gt_id in range(gt_num): | |
| for pred_id in range(pred_num): | |
| if (gt_hit[gt_id] != 0 or pred_hit[pred_id] != 0 | |
| or gt_id in gt_ignored_index | |
| or pred_id in pred_ignored_index): | |
| continue | |
| if iou_mat[gt_id, pred_id] > iou_thr: | |
| gt_hit[gt_id] = 1 | |
| pred_hit[pred_id] = 1 | |
| hit_num += 1 | |
| gt_care_number = gt_num - gt_ignored_num | |
| pred_care_number = pred_num - len(pred_ignored_index) | |
| r, p, h = eval_utils.compute_hmean(hit_num, hit_num, gt_care_number, | |
| pred_care_number) | |
| img_results.append({'recall': r, 'precision': p, 'hmean': h}) | |
| dataset_hit_num += hit_num | |
| dataset_gt_num += gt_care_number | |
| dataset_pred_num += pred_care_number | |
| dataset_r, dataset_p, dataset_h = eval_utils.compute_hmean( | |
| dataset_hit_num, dataset_hit_num, dataset_gt_num, dataset_pred_num) | |
| dataset_results = { | |
| 'num_gts': dataset_gt_num, | |
| 'num_dets': dataset_pred_num, | |
| 'num_match': dataset_hit_num, | |
| 'recall': dataset_r, | |
| 'precision': dataset_p, | |
| 'hmean': dataset_h | |
| } | |
| return dataset_results, img_results | |