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# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
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
@METRICS.register_module()
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)
# load from local file
if osp.isfile(occluded_ann) and not osp.isabs(occluded_ann):
occluded_ann = osp.join(self.data_root, occluded_ann)
if osp.isfile(separated_ann) and not osp.isabs(separated_ann):
separated_ann = osp.join(self.data_root, separated_ann)
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
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