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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import json | |
| import logging | |
| import os.path as osp | |
| import warnings | |
| from typing import List, Union | |
| import mmcv | |
| from mmengine.dist import get_rank | |
| from mmengine.fileio import dump, get, get_text, load | |
| from mmengine.logging import print_log | |
| from mmengine.utils import ProgressBar | |
| from mmdet.registry import DATASETS | |
| from .base_det_dataset import BaseDetDataset | |
| class CrowdHumanDataset(BaseDetDataset): | |
| r"""Dataset for CrowdHuman. | |
| Args: | |
| data_root (str): The root directory for | |
| ``data_prefix`` and ``ann_file``. | |
| ann_file (str): Annotation file path. | |
| extra_ann_file (str | optional):The path of extra image metas | |
| for CrowdHuman. It can be created by CrowdHumanDataset | |
| automatically or by tools/misc/get_crowdhuman_id_hw.py | |
| manually. Defaults to None. | |
| """ | |
| METAINFO = { | |
| 'classes': ('person', ), | |
| # palette is a list of color tuples, which is used for visualization. | |
| 'palette': [(220, 20, 60)] | |
| } | |
| def __init__(self, data_root, ann_file, extra_ann_file=None, **kwargs): | |
| # extra_ann_file record the size of each image. This file is | |
| # automatically created when you first load the CrowdHuman | |
| # dataset by mmdet. | |
| if extra_ann_file is not None: | |
| self.extra_ann_exist = True | |
| self.extra_anns = load(extra_ann_file) | |
| else: | |
| ann_file_name = osp.basename(ann_file) | |
| if 'train' in ann_file_name: | |
| self.extra_ann_file = osp.join(data_root, 'id_hw_train.json') | |
| elif 'val' in ann_file_name: | |
| self.extra_ann_file = osp.join(data_root, 'id_hw_val.json') | |
| self.extra_ann_exist = False | |
| if not osp.isfile(self.extra_ann_file): | |
| print_log( | |
| 'extra_ann_file does not exist, prepare to collect ' | |
| 'image height and width...', | |
| level=logging.INFO) | |
| self.extra_anns = {} | |
| else: | |
| self.extra_ann_exist = True | |
| self.extra_anns = load(self.extra_ann_file) | |
| super().__init__(data_root=data_root, ann_file=ann_file, **kwargs) | |
| def load_data_list(self) -> List[dict]: | |
| """Load annotations from an annotation file named as ``self.ann_file`` | |
| Returns: | |
| List[dict]: A list of annotation. | |
| """ # noqa: E501 | |
| anno_strs = get_text( | |
| self.ann_file, backend_args=self.backend_args).strip().split('\n') | |
| print_log('loading CrowdHuman annotation...', level=logging.INFO) | |
| data_list = [] | |
| prog_bar = ProgressBar(len(anno_strs)) | |
| for i, anno_str in enumerate(anno_strs): | |
| anno_dict = json.loads(anno_str) | |
| parsed_data_info = self.parse_data_info(anno_dict) | |
| data_list.append(parsed_data_info) | |
| prog_bar.update() | |
| if not self.extra_ann_exist and get_rank() == 0: | |
| # TODO: support file client | |
| try: | |
| dump(self.extra_anns, self.extra_ann_file, file_format='json') | |
| except: # noqa | |
| warnings.warn( | |
| 'Cache files can not be saved automatically! To speed up' | |
| 'loading the dataset, please manually generate the cache' | |
| ' file by file tools/misc/get_crowdhuman_id_hw.py') | |
| print_log( | |
| f'\nsave extra_ann_file in {self.data_root}', | |
| level=logging.INFO) | |
| del self.extra_anns | |
| print_log('\nDone', level=logging.INFO) | |
| return data_list | |
| def parse_data_info(self, raw_data_info: dict) -> Union[dict, List[dict]]: | |
| """Parse raw annotation to target format. | |
| Args: | |
| raw_data_info (dict): Raw data information load from ``ann_file`` | |
| Returns: | |
| Union[dict, List[dict]]: Parsed annotation. | |
| """ | |
| data_info = {} | |
| img_path = osp.join(self.data_prefix['img'], | |
| f"{raw_data_info['ID']}.jpg") | |
| data_info['img_path'] = img_path | |
| data_info['img_id'] = raw_data_info['ID'] | |
| if not self.extra_ann_exist: | |
| img_bytes = get(img_path, backend_args=self.backend_args) | |
| img = mmcv.imfrombytes(img_bytes, backend='cv2') | |
| data_info['height'], data_info['width'] = img.shape[:2] | |
| self.extra_anns[raw_data_info['ID']] = img.shape[:2] | |
| del img, img_bytes | |
| else: | |
| data_info['height'], data_info['width'] = self.extra_anns[ | |
| raw_data_info['ID']] | |
| instances = [] | |
| for i, ann in enumerate(raw_data_info['gtboxes']): | |
| instance = {} | |
| if ann['tag'] not in self.metainfo['classes']: | |
| instance['bbox_label'] = -1 | |
| instance['ignore_flag'] = 1 | |
| else: | |
| instance['bbox_label'] = self.metainfo['classes'].index( | |
| ann['tag']) | |
| instance['ignore_flag'] = 0 | |
| if 'extra' in ann: | |
| if 'ignore' in ann['extra']: | |
| if ann['extra']['ignore'] != 0: | |
| instance['bbox_label'] = -1 | |
| instance['ignore_flag'] = 1 | |
| x1, y1, w, h = ann['fbox'] | |
| bbox = [x1, y1, x1 + w, y1 + h] | |
| instance['bbox'] = bbox | |
| # Record the full bbox(fbox), head bbox(hbox) and visible | |
| # bbox(vbox) as additional information. If you need to use | |
| # this information, you just need to design the pipeline | |
| # instead of overriding the CrowdHumanDataset. | |
| instance['fbox'] = bbox | |
| hbox = ann['hbox'] | |
| instance['hbox'] = [ | |
| hbox[0], hbox[1], hbox[0] + hbox[2], hbox[1] + hbox[3] | |
| ] | |
| vbox = ann['vbox'] | |
| instance['vbox'] = [ | |
| vbox[0], vbox[1], vbox[0] + vbox[2], vbox[1] + vbox[3] | |
| ] | |
| instances.append(instance) | |
| data_info['instances'] = instances | |
| return data_info | |