Source code for mmpose.datasets.datasets.face.face_300w_dataset
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from typing import Optional
import numpy as np
from mmpose.registry import DATASETS
from mmpose.structures.bbox import bbox_cs2xyxy
from ..base import BaseCocoStyleDataset
[docs]@DATASETS.register_module()
class Face300WDataset(BaseCocoStyleDataset):
"""300W dataset for face keypoint localization.
"300 faces In-the-wild challenge: Database and results",
Image and Vision Computing (IMAVIS) 2019.
The landmark annotations follow the 68 points mark-up. The definition
can be found in `https://ibug.doc.ic.ac.uk/resources/300-W/`.
Args:
ann_file (str): Annotation file path. Default: ''.
bbox_file (str, optional): Detection result file path. If
``bbox_file`` is set, detected bboxes loaded from this file will
be used instead of ground-truth bboxes. This setting is only for
evaluation, i.e., ignored when ``test_mode`` is ``False``.
Default: ``None``.
data_mode (str): Specifies the mode of data samples: ``'topdown'`` or
``'bottomup'``. In ``'topdown'`` mode, each data sample contains
one instance; while in ``'bottomup'`` mode, each data sample
contains all instances in a image. Default: ``'topdown'``
metainfo (dict, optional): Meta information for dataset, such as class
information. Default: ``None``.
data_root (str, optional): The root directory for ``data_prefix`` and
``ann_file``. Default: ``None``.
data_prefix (dict, optional): Prefix for training data. Default:
``dict(img=None, ann=None)``.
filter_cfg (dict, optional): Config for filter data. Default: `None`.
indices (int or Sequence[int], optional): Support using first few
data in annotation file to facilitate training/testing on a smaller
dataset. Default: ``None`` which means using all ``data_infos``.
serialize_data (bool, optional): Whether to hold memory using
serialized objects, when enabled, data loader workers can use
shared RAM from master process instead of making a copy.
Default: ``True``.
pipeline (list, optional): Processing pipeline. Default: [].
test_mode (bool, optional): ``test_mode=True`` means in test phase.
Default: ``False``.
lazy_init (bool, optional): Whether to load annotation during
instantiation. In some cases, such as visualization, only the meta
information of the dataset is needed, which is not necessary to
load annotation file. ``Basedataset`` can skip load annotations to
save time by set ``lazy_init=False``. Default: ``False``.
max_refetch (int, optional): If ``Basedataset.prepare_data`` get a
None img. The maximum extra number of cycles to get a valid
image. Default: 1000.
"""
METAINFO: dict = dict(from_file='configs/_base_/datasets/300w.py')
[docs] def parse_data_info(self, raw_data_info: dict) -> Optional[dict]:
"""Parse raw Face300W annotation of an instance.
Args:
raw_data_info (dict): Raw data information loaded from
``ann_file``. It should have following contents:
- ``'raw_ann_info'``: Raw annotation of an instance
- ``'raw_img_info'``: Raw information of the image that
contains the instance
Returns:
dict: Parsed instance annotation
"""
ann = raw_data_info['raw_ann_info']
img = raw_data_info['raw_img_info']
img_path = osp.join(self.data_prefix['img'], img['file_name'])
# 300w bbox scales are normalized with factor 200.
pixel_std = 200.
# center, scale in shape [1, 2] and bbox in [1, 4]
center = np.array([ann['center']], dtype=np.float32)
scale = np.array([[ann['scale'], ann['scale']]],
dtype=np.float32) * pixel_std
bbox = bbox_cs2xyxy(center, scale)
# keypoints in shape [1, K, 2] and keypoints_visible in [1, K]
_keypoints = np.array(
ann['keypoints'], dtype=np.float32).reshape(1, -1, 3)
keypoints = _keypoints[..., :2]
keypoints_visible = np.minimum(1, _keypoints[..., 2])
num_keypoints = ann['num_keypoints']
data_info = {
'img_id': ann['image_id'],
'img_path': img_path,
'bbox': bbox,
'bbox_center': center,
'bbox_scale': scale,
'bbox_score': np.ones(1, dtype=np.float32),
'num_keypoints': num_keypoints,
'keypoints': keypoints,
'keypoints_visible': keypoints_visible,
'iscrowd': ann['iscrowd'],
'id': ann['id'],
}
return data_info