mmpose.datasets.datasets.body.jhmdb_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 ..base import BaseCocoStyleDataset


@DATASETS.register_module()
class JhmdbDataset(BaseCocoStyleDataset):
    """JhmdbDataset dataset for pose estimation.

    "Towards understanding action recognition", ICCV'2013.
    More details can be found in the `paper
    <https://openaccess.thecvf.com/content_iccv_2013/papers/\
    Jhuang_Towards_Understanding_Action_2013_ICCV_paper.pdf>`__

    sub-JHMDB keypoints::

        0: "neck",
        1: "belly",
        2: "head",
        3: "right_shoulder",
        4: "left_shoulder",
        5: "right_hip",
        6: "left_hip",
        7: "right_elbow",
        8: "left_elbow",
        9: "right_knee",
        10: "left_knee",
        11: "right_wrist",
        12: "left_wrist",
        13: "right_ankle",
        14: "left_ankle"

    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/jhmdb.py')

    def parse_data_info(self, raw_data_info: dict) -> Optional[dict]:
        """Parse raw COCO 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'])
        img_w, img_h = img['width'], img['height']

        # get bbox in shape [1, 4], formatted as xywh
        x, y, w, h = ann['bbox']
        # JHMDB uses matlab format, index is 1-based,
        # we should first convert to 0-based index
        x -= 1
        y -= 1
        x1 = np.clip(x, 0, img_w - 1)
        y1 = np.clip(y, 0, img_h - 1)
        x2 = np.clip(x + w, 0, img_w - 1)
        y2 = np.clip(y + h, 0, img_h - 1)

        bbox = np.array([x1, y1, x2, y2], dtype=np.float32).reshape(1, 4)

        # keypoints in shape [1, K, 2] and keypoints_visible in [1, K]
        _keypoints = np.array(
            ann['keypoints'], dtype=np.float32).reshape(1, -1, 3)
        # JHMDB uses matlab format, index is 1-based,
        # we should first convert to 0-based index
        keypoints = _keypoints[..., :2] - 1
        keypoints_visible = np.minimum(1, _keypoints[..., 2])

        num_keypoints = np.count_nonzero(keypoints.max(axis=2))
        area = np.clip((x2 - x1) * (y2 - y1) * 0.53, a_min=1.0, a_max=None)
        category_id = ann.get('category_id', [1] * len(keypoints))

        data_info = {
            'img_id': ann['image_id'],
            'img_path': img_path,
            'bbox': bbox,
            'bbox_score': np.ones(1, dtype=np.float32),
            'num_keypoints': num_keypoints,
            'keypoints': keypoints,
            'keypoints_visible': keypoints_visible,
            'area': np.array(area, dtype=np.float32),
            'iscrowd': ann.get('iscrowd', 0),
            'segmentation': ann.get('segmentation', None),
            'id': ann['id'],
            'category_id': category_id,
        }

        return data_info