import os
from collections import OrderedDict
import numpy as np
from mmpose.datasets.builder import DATASETS
from .fashion_base_dataset import FashionBaseDataset
[文档]@DATASETS.register_module()
class DeepFashionDataset(FashionBaseDataset):
"""DeepFashion dataset (full-body clothes) for fashion landmark detection.
`DeepFashion: Powering Robust Clothes Recognition
and Retrieval with Rich Annotations' CVPR'2016 and
`Fashion Landmark Detection in the Wild' ECCV'2016
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
The dataset contains 3 categories for full-body, upper-body and lower-body.
Fashion landmark indexes for upper-body clothes::
0: 'left collar',
1: 'right collar',
2: 'left sleeve',
3: 'right sleeve',
4: 'left hem',
5: 'right hem'
Fashion landmark indexes for lower-body clothes::
0: 'left waistline',
1: 'right waistline',
2: 'left hem',
3: 'right hem'
Fashion landmark indexes for full-body clothes::
0: 'left collar',
1: 'right collar',
2: 'left sleeve',
3: 'right sleeve',
4: 'left waistline',
5: 'right waistline',
6: 'left hem',
7: 'right hem'
Args:
ann_file (str): Path to the annotation file.
img_prefix (str): Path to a directory where images are held.
Default: None.
subset (str): The FLD dataset has 3 subsets, 'upper', 'lower',
and 'full', denoting different types of clothes.
data_cfg (dict): config
pipeline (list[dict | callable]): A sequence of data transforms.
test_mode (bool): Store True when building test or
validation dataset. Default: False.
"""
def __init__(self,
ann_file,
img_prefix,
subset,
data_cfg,
pipeline,
test_mode=False):
super().__init__(
ann_file, img_prefix, data_cfg, pipeline, test_mode=test_mode)
if subset == 'upper':
assert self.ann_info['num_joints'] == 6
self.ann_info['flip_pairs'] = [[0, 1], [2, 3], [4, 5]]
self.dataset_name = 'deepfashion_upper'
elif subset == 'lower':
assert self.ann_info['num_joints'] == 4
self.ann_info['flip_pairs'] = [[0, 1], [2, 3]]
self.dataset_name = 'deepfashion_lower'
elif subset == 'full':
assert self.ann_info['num_joints'] == 8
self.ann_info['flip_pairs'] = [[0, 1], [2, 3], [4, 5], [6, 7]]
self.dataset_name = 'deepfashion_full'
else:
NotImplementedError()
self.ann_info['use_different_joint_weights'] = False
self.ann_info['joint_weights'] = \
np.ones((self.ann_info['num_joints'], 1), dtype=np.float32)
self.dataset_name = 'deepfashion_' + subset
self.db = self._get_db()
print(f'=> num_images: {self.num_images}')
print(f'=> load {len(self.db)} samples')
def _get_db(self):
"""Load dataset."""
gt_db = []
bbox_id = 0
num_joints = self.ann_info['num_joints']
for img_id in self.img_ids:
ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=False)
objs = self.coco.loadAnns(ann_ids)
for obj in objs:
if max(obj['keypoints']) == 0:
continue
joints_3d = np.zeros((num_joints, 3), dtype=np.float32)
joints_3d_visible = np.zeros((num_joints, 3), dtype=np.float32)
keypoints = np.array(obj['keypoints']).reshape(-1, 3)
joints_3d[:, :2] = keypoints[:, :2]
joints_3d_visible[:, :2] = np.minimum(1, keypoints[:, 2:3])
# use 1.25bbox as input
center, scale = self._xywh2cs(*obj['bbox'][:4], 1.25)
image_file = os.path.join(self.img_prefix,
self.id2name[img_id])
gt_db.append({
'image_file': image_file,
'center': center,
'scale': scale,
'rotation': 0,
'joints_3d': joints_3d,
'joints_3d_visible': joints_3d_visible,
'dataset': self.dataset_name,
'bbox': obj['bbox'],
'bbox_score': 1,
'bbox_id': bbox_id
})
bbox_id = bbox_id + 1
gt_db = sorted(gt_db, key=lambda x: x['bbox_id'])
return gt_db
[文档] def evaluate(self, outputs, res_folder, metric='PCK', **kwargs):
"""Evaluate freihand keypoint results. The pose prediction results will
be saved in `${res_folder}/result_keypoints.json`.
Note:
batch_size: N
num_keypoints: K
heatmap height: H
heatmap width: W
Args:
outputs (list(preds, boxes, image_path, output_heatmap))
:preds (np.ndarray[N,K,3]): The first two dimensions are
coordinates, score is the third dimension of the array.
:boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
, scale[1],area, score]
:image_paths (list[str]): For example, [ 'img_00000001.jpg']
:output_heatmap (np.ndarray[N, K, H, W]): model outpus.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'PCK', 'AUC', 'EPE'.
Returns:
dict: Evaluation results for evaluation metric.
"""
metrics = metric if isinstance(metric, list) else [metric]
allowed_metrics = ['PCK', 'AUC', 'EPE']
for metric in metrics:
if metric not in allowed_metrics:
raise KeyError(f'metric {metric} is not supported')
res_file = os.path.join(res_folder, 'result_keypoints.json')
kpts = []
for output in outputs:
preds = output['preds']
boxes = output['boxes']
image_paths = output['image_paths']
bbox_ids = output['bbox_ids']
batch_size = len(image_paths)
for i in range(batch_size):
image_id = self.name2id[image_paths[i][len(self.img_prefix):]]
kpts.append({
'keypoints': preds[i].tolist(),
'center': boxes[i][0:2].tolist(),
'scale': boxes[i][2:4].tolist(),
'area': float(boxes[i][4]),
'score': float(boxes[i][5]),
'image_id': image_id,
'bbox_id': bbox_ids[i]
})
kpts = self._sort_and_unique_bboxes(kpts)
self._write_keypoint_results(kpts, res_file)
info_str = self._report_metric(res_file, metrics)
name_value = OrderedDict(info_str)
return name_value