mmpose.datasets.datasets.face.face_300w_dataset 源代码

# 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

[文档]@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 ``. 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/')
[文档] 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
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