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| # Copyright (c) OpenMMLab. All rights reserved. | |
| from typing import Dict, List, Optional, Union | |
| import mmengine | |
| import numpy as np | |
| from mmengine.fileio import load | |
| from mmengine.logging import print_log | |
| from pycocotools import mask as coco_mask | |
| from terminaltables import AsciiTable | |
| from mmdet.registry import METRICS | |
| from .coco_metric import CocoMetric | |
| class CocoOccludedSeparatedMetric(CocoMetric): | |
| """Metric of separated and occluded masks which presented in paper `A Tri- | |
| Layer Plugin to Improve Occluded Detection. | |
| <https://arxiv.org/abs/2210.10046>`_. | |
| Separated COCO and Occluded COCO are automatically generated subsets of | |
| COCO val dataset, collecting separated objects and partially occluded | |
| objects for a large variety of categories. In this way, we define | |
| occlusion into two major categories: separated and partially occluded. | |
| - Separation: target object segmentation mask is separated into distinct | |
| regions by the occluder. | |
| - Partial Occlusion: target object is partially occluded but the | |
| segmentation mask is connected. | |
| These two new scalable real-image datasets are to benchmark a model's | |
| capability to detect occluded objects of 80 common categories. | |
| Please cite the paper if you use this dataset: | |
| @article{zhan2022triocc, | |
| title={A Tri-Layer Plugin to Improve Occluded Detection}, | |
| author={Zhan, Guanqi and Xie, Weidi and Zisserman, Andrew}, | |
| journal={British Machine Vision Conference}, | |
| year={2022} | |
| } | |
| Args: | |
| occluded_ann (str): Path to the occluded coco annotation file. | |
| separated_ann (str): Path to the separated coco annotation file. | |
| score_thr (float): Score threshold of the detection masks. | |
| Defaults to 0.3. | |
| iou_thr (float): IoU threshold for the recall calculation. | |
| Defaults to 0.75. | |
| metric (str | List[str]): Metrics to be evaluated. Valid metrics | |
| include 'bbox', 'segm', 'proposal', and 'proposal_fast'. | |
| Defaults to 'bbox'. | |
| """ | |
| default_prefix: Optional[str] = 'coco' | |
| def __init__( | |
| self, | |
| *args, | |
| occluded_ann: | |
| str = 'https://www.robots.ox.ac.uk/~vgg/research/tpod/datasets/occluded_coco.pkl', # noqa | |
| separated_ann: | |
| str = 'https://www.robots.ox.ac.uk/~vgg/research/tpod/datasets/separated_coco.pkl', # noqa | |
| score_thr: float = 0.3, | |
| iou_thr: float = 0.75, | |
| metric: Union[str, List[str]] = ['bbox', 'segm'], | |
| **kwargs) -> None: | |
| super().__init__(*args, metric=metric, **kwargs) | |
| self.occluded_ann = load(occluded_ann) | |
| self.separated_ann = load(separated_ann) | |
| self.score_thr = score_thr | |
| self.iou_thr = iou_thr | |
| def compute_metrics(self, results: list) -> Dict[str, float]: | |
| """Compute the metrics from processed results. | |
| Args: | |
| results (list): The processed results of each batch. | |
| Returns: | |
| Dict[str, float]: The computed metrics. The keys are the names of | |
| the metrics, and the values are corresponding results. | |
| """ | |
| coco_metric_res = super().compute_metrics(results) | |
| eval_res = self.evaluate_occluded_separated(results) | |
| coco_metric_res.update(eval_res) | |
| return coco_metric_res | |
| def evaluate_occluded_separated(self, results: List[tuple]) -> dict: | |
| """Compute the recall of occluded and separated masks. | |
| Args: | |
| results (list[tuple]): Testing results of the dataset. | |
| Returns: | |
| dict[str, float]: The recall of occluded and separated masks. | |
| """ | |
| dict_det = {} | |
| print_log('processing detection results...') | |
| prog_bar = mmengine.ProgressBar(len(results)) | |
| for i in range(len(results)): | |
| gt, dt = results[i] | |
| img_id = dt['img_id'] | |
| cur_img_name = self._coco_api.imgs[img_id]['file_name'] | |
| if cur_img_name not in dict_det.keys(): | |
| dict_det[cur_img_name] = [] | |
| for bbox, score, label, mask in zip(dt['bboxes'], dt['scores'], | |
| dt['labels'], dt['masks']): | |
| cur_binary_mask = coco_mask.decode(mask) | |
| dict_det[cur_img_name].append([ | |
| score, self.dataset_meta['classes'][label], | |
| cur_binary_mask, bbox | |
| ]) | |
| dict_det[cur_img_name].sort( | |
| key=lambda x: (-x[0], x[3][0], x[3][1]) | |
| ) # rank by confidence from high to low, avoid same confidence | |
| prog_bar.update() | |
| print_log('\ncomputing occluded mask recall...', logger='current') | |
| occluded_correct_num, occluded_recall = self.compute_recall( | |
| dict_det, gt_ann=self.occluded_ann, is_occ=True) | |
| print_log( | |
| f'\nCOCO occluded mask recall: {occluded_recall:.2f}%', | |
| logger='current') | |
| print_log( | |
| f'COCO occluded mask success num: {occluded_correct_num}', | |
| logger='current') | |
| print_log('computing separated mask recall...', logger='current') | |
| separated_correct_num, separated_recall = self.compute_recall( | |
| dict_det, gt_ann=self.separated_ann, is_occ=False) | |
| print_log( | |
| f'\nCOCO separated mask recall: {separated_recall:.2f}%', | |
| logger='current') | |
| print_log( | |
| f'COCO separated mask success num: {separated_correct_num}', | |
| logger='current') | |
| table_data = [ | |
| ['mask type', 'recall', 'num correct'], | |
| ['occluded', f'{occluded_recall:.2f}%', occluded_correct_num], | |
| ['separated', f'{separated_recall:.2f}%', separated_correct_num] | |
| ] | |
| table = AsciiTable(table_data) | |
| print_log('\n' + table.table, logger='current') | |
| return dict( | |
| occluded_recall=occluded_recall, separated_recall=separated_recall) | |
| def compute_recall(self, | |
| result_dict: dict, | |
| gt_ann: list, | |
| is_occ: bool = True) -> tuple: | |
| """Compute the recall of occluded or separated masks. | |
| Args: | |
| result_dict (dict): Processed mask results. | |
| gt_ann (list): Occluded or separated coco annotations. | |
| is_occ (bool): Whether the annotation is occluded mask. | |
| Defaults to True. | |
| Returns: | |
| tuple: number of correct masks and the recall. | |
| """ | |
| correct = 0 | |
| prog_bar = mmengine.ProgressBar(len(gt_ann)) | |
| for iter_i in range(len(gt_ann)): | |
| cur_item = gt_ann[iter_i] | |
| cur_img_name = cur_item[0] | |
| cur_gt_bbox = cur_item[3] | |
| if is_occ: | |
| cur_gt_bbox = [ | |
| cur_gt_bbox[0], cur_gt_bbox[1], | |
| cur_gt_bbox[0] + cur_gt_bbox[2], | |
| cur_gt_bbox[1] + cur_gt_bbox[3] | |
| ] | |
| cur_gt_class = cur_item[1] | |
| cur_gt_mask = coco_mask.decode(cur_item[4]) | |
| assert cur_img_name in result_dict.keys() | |
| cur_detections = result_dict[cur_img_name] | |
| correct_flag = False | |
| for i in range(len(cur_detections)): | |
| cur_det_confidence = cur_detections[i][0] | |
| if cur_det_confidence < self.score_thr: | |
| break | |
| cur_det_class = cur_detections[i][1] | |
| if cur_det_class != cur_gt_class: | |
| continue | |
| cur_det_mask = cur_detections[i][2] | |
| cur_iou = self.mask_iou(cur_det_mask, cur_gt_mask) | |
| if cur_iou >= self.iou_thr: | |
| correct_flag = True | |
| break | |
| if correct_flag: | |
| correct += 1 | |
| prog_bar.update() | |
| recall = correct / len(gt_ann) * 100 | |
| return correct, recall | |
| def mask_iou(self, mask1: np.ndarray, mask2: np.ndarray) -> np.ndarray: | |
| """Compute IoU between two masks.""" | |
| mask1_area = np.count_nonzero(mask1 == 1) | |
| mask2_area = np.count_nonzero(mask2 == 1) | |
| intersection = np.count_nonzero(np.logical_and(mask1 == 1, mask2 == 1)) | |
| iou = intersection / (mask1_area + mask2_area - intersection) | |
| return iou | |