81 lines
2.8 KiB
Python
81 lines
2.8 KiB
Python
from __future__ import division, print_function, absolute_import
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import glob
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import os.path as osp
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from torchreid.utils import read_json
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from ..dataset import VideoDataset
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class PRID2011(VideoDataset):
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"""PRID2011.
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Reference:
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Hirzer et al. Person Re-Identification by Descriptive and
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Discriminative Classification. SCIA 2011.
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URL: `<https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/>`_
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Dataset statistics:
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- identities: 200.
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- tracklets: 400.
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- cameras: 2.
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"""
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dataset_dir = 'prid2011'
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dataset_url = None
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def __init__(self, root='', split_id=0, **kwargs):
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self.root = osp.abspath(osp.expanduser(root))
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self.dataset_dir = osp.join(self.root, self.dataset_dir)
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self.download_dataset(self.dataset_dir, self.dataset_url)
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self.split_path = osp.join(self.dataset_dir, 'splits_prid2011.json')
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self.cam_a_dir = osp.join(
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self.dataset_dir, 'prid_2011', 'multi_shot', 'cam_a'
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)
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self.cam_b_dir = osp.join(
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self.dataset_dir, 'prid_2011', 'multi_shot', 'cam_b'
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)
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required_files = [self.dataset_dir, self.cam_a_dir, self.cam_b_dir]
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self.check_before_run(required_files)
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splits = read_json(self.split_path)
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if split_id >= len(splits):
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raise ValueError(
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'split_id exceeds range, received {}, but expected between 0 and {}'
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.format(split_id,
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len(splits) - 1)
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)
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split = splits[split_id]
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train_dirs, test_dirs = split['train'], split['test']
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train = self.process_dir(train_dirs, cam1=True, cam2=True)
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query = self.process_dir(test_dirs, cam1=True, cam2=False)
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gallery = self.process_dir(test_dirs, cam1=False, cam2=True)
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super(PRID2011, self).__init__(train, query, gallery, **kwargs)
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def process_dir(self, dirnames, cam1=True, cam2=True):
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tracklets = []
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dirname2pid = {dirname: i for i, dirname in enumerate(dirnames)}
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for dirname in dirnames:
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if cam1:
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person_dir = osp.join(self.cam_a_dir, dirname)
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img_names = glob.glob(osp.join(person_dir, '*.png'))
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assert len(img_names) > 0
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img_names = tuple(img_names)
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pid = dirname2pid[dirname]
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tracklets.append((img_names, pid, 0))
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if cam2:
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person_dir = osp.join(self.cam_b_dir, dirname)
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img_names = glob.glob(osp.join(person_dir, '*.png'))
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assert len(img_names) > 0
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img_names = tuple(img_names)
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pid = dirname2pid[dirname]
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tracklets.append((img_names, pid, 1))
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return tracklets
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