107 lines
3.8 KiB
Python
107 lines
3.8 KiB
Python
from __future__ import division, print_function, absolute_import
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import random
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import os.path as osp
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from torchreid.utils import read_json, write_json
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from ..dataset import ImageDataset
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class PRID(ImageDataset):
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"""PRID (single-shot version of prid-2011)
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Reference:
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Hirzer et al. Person Re-Identification by Descriptive and Discriminative
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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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- Two views.
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- View A captures 385 identities.
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- View B captures 749 identities.
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- 200 identities appear in both views.
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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.cam_a_dir = osp.join(
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self.dataset_dir, 'prid_2011', 'single_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', 'single_shot', 'cam_b'
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)
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self.split_path = osp.join(self.dataset_dir, 'splits_single_shot.json')
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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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self.prepare_split()
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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, query, gallery = self.process_split(split)
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super(PRID, self).__init__(train, query, gallery, **kwargs)
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def prepare_split(self):
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if not osp.exists(self.split_path):
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print('Creating splits ...')
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splits = []
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for _ in range(10):
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# randomly sample 100 IDs for train and use the rest 100 IDs for test
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# (note: there are only 200 IDs appearing in both views)
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pids = [i for i in range(1, 201)]
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train_pids = random.sample(pids, 100)
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train_pids.sort()
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test_pids = [i for i in pids if i not in train_pids]
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split = {'train': train_pids, 'test': test_pids}
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splits.append(split)
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print('Totally {} splits are created'.format(len(splits)))
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write_json(splits, self.split_path)
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print('Split file is saved to {}'.format(self.split_path))
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def process_split(self, split):
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train_pids = split['train']
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test_pids = split['test']
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train_pid2label = {pid: label for label, pid in enumerate(train_pids)}
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# train
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train = []
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for pid in train_pids:
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img_name = 'person_' + str(pid).zfill(4) + '.png'
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pid = train_pid2label[pid]
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img_a_path = osp.join(self.cam_a_dir, img_name)
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train.append((img_a_path, pid, 0))
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img_b_path = osp.join(self.cam_b_dir, img_name)
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train.append((img_b_path, pid, 1))
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# query and gallery
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query, gallery = [], []
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for pid in test_pids:
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img_name = 'person_' + str(pid).zfill(4) + '.png'
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img_a_path = osp.join(self.cam_a_dir, img_name)
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query.append((img_a_path, pid, 0))
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img_b_path = osp.join(self.cam_b_dir, img_name)
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gallery.append((img_b_path, pid, 1))
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for pid in range(201, 750):
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img_name = 'person_' + str(pid).zfill(4) + '.png'
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img_b_path = osp.join(self.cam_b_dir, img_name)
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gallery.append((img_b_path, pid, 1))
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return train, query, gallery
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