Spaces:
Runtime error
Runtime error
File size: 9,166 Bytes
51f6859 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 |
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import mmcv
import numpy as np
from mmcv.fileio import load
from mmcv.utils import print_log
from pycocotools import mask as coco_mask
from terminaltables import AsciiTable
from .builder import DATASETS
from .coco import CocoDataset
@DATASETS.register_module()
class OccludedSeparatedCocoDataset(CocoDataset):
"""COCO dataset with evaluation on 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.
""" # noqa
def __init__(
self,
*args,
occluded_ann='https://www.robots.ox.ac.uk/~vgg/research/tpod/datasets/occluded_coco.pkl', # noqa
separated_ann='https://www.robots.ox.ac.uk/~vgg/research/tpod/datasets/separated_coco.pkl', # noqa
**kwargs):
super().__init__(*args, **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)
def evaluate(self,
results,
metric=[],
score_thr=0.3,
iou_thr=0.75,
**kwargs):
"""Occluded and separated mask evaluation in COCO protocol.
Args:
results (list[tuple]): Testing results of the dataset.
metric (str | list[str]): Metrics to be evaluated. Options are
'bbox', 'segm', 'proposal', 'proposal_fast'. Defaults to [].
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.
Returns:
dict[str, float]: The recall of occluded and separated masks and
COCO style evaluation metric.
"""
coco_metric_res = super().evaluate(results, metric=metric, **kwargs)
eval_res = self.evaluate_occluded_separated(results, score_thr,
iou_thr)
coco_metric_res.update(eval_res)
return coco_metric_res
def evaluate_occluded_separated(self,
results,
score_thr=0.3,
iou_thr=0.75):
"""Compute the recall of occluded and separated masks.
Args:
results (list[tuple]): Testing results of the dataset.
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.
Returns:
dict[str, float]: The recall of occluded and separated masks.
"""
dict_det = {}
print_log('processing detection results...')
prog_bar = mmcv.ProgressBar(len(results))
for i in range(len(results)):
cur_img_name = self.data_infos[i]['filename']
if cur_img_name not in dict_det.keys():
dict_det[cur_img_name] = []
for cat_id in range(len(results[i][1])):
assert len(results[i][1][cat_id]) == len(results[i][0][cat_id])
for instance_id in range(len(results[i][1][cat_id])):
cur_binary_mask = coco_mask.decode(
results[i][1][cat_id][instance_id])
cur_det_bbox = results[i][0][cat_id][instance_id][:4]
dict_det[cur_img_name].append([
results[i][0][cat_id][instance_id][4],
self.CLASSES[cat_id], cur_binary_mask, cur_det_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...')
occluded_correct_num, occluded_recall = self.compute_recall(
dict_det,
gt_ann=self.occluded_ann,
score_thr=score_thr,
iou_thr=iou_thr,
is_occ=True)
print_log(f'\nCOCO occluded mask recall: {occluded_recall:.2f}%')
print_log(f'COCO occluded mask success num: {occluded_correct_num}')
print_log('computing separated mask recall...')
separated_correct_num, separated_recall = self.compute_recall(
dict_det,
gt_ann=self.separated_ann,
score_thr=score_thr,
iou_thr=iou_thr,
is_occ=False)
print_log(f'\nCOCO separated mask recall: {separated_recall:.2f}%')
print_log(f'COCO separated mask success num: {separated_correct_num}')
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)
return dict(
occluded_recall=occluded_recall, separated_recall=separated_recall)
def compute_recall(self,
result_dict,
gt_ann,
score_thr=0.3,
iou_thr=0.75,
is_occ=True):
"""Compute the recall of occluded or separated masks.
Args:
results (list[tuple]): Testing results of the dataset.
gt_ann (list): Occluded or separated coco annotations.
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.
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 = mmcv.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 < 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 >= 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, mask2):
"""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
|