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