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

[文档]@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 <\ 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/')
[文档] 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)) 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, 'iscrowd': ann.get('iscrowd', 0), 'segmentation': ann.get('segmentation', None), 'id': ann['id'], } return data_info
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