129 lines
4.9 KiB
Python
129 lines
4.9 KiB
Python
from __future__ import division, print_function, absolute_import
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import glob
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import numpy as np
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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 VIPeR(ImageDataset):
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"""VIPeR.
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Reference:
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Gray et al. Evaluating appearance models for recognition, reacquisition, and tracking. PETS 2007.
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URL: `<https://vision.soe.ucsc.edu/node/178>`_
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Dataset statistics:
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- identities: 632.
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- images: 632 x 2 = 1264.
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- cameras: 2.
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"""
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dataset_dir = 'viper'
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dataset_url = 'http://users.soe.ucsc.edu/~manduchi/VIPeR.v1.0.zip'
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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(self.dataset_dir, 'VIPeR', 'cam_a')
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self.cam_b_dir = osp.join(self.dataset_dir, 'VIPeR', 'cam_b')
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self.split_path = osp.join(self.dataset_dir, 'splits.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 {}, '
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'but expected between 0 and {}'.format(
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split_id,
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len(splits) - 1
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)
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)
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split = splits[split_id]
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train = split['train']
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query = split['query'] # query and gallery share the same images
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gallery = split['gallery']
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train = [tuple(item) for item in train]
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query = [tuple(item) for item in query]
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gallery = [tuple(item) for item in gallery]
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super(VIPeR, 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 10 random splits of train ids and test ids')
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cam_a_imgs = sorted(glob.glob(osp.join(self.cam_a_dir, '*.bmp')))
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cam_b_imgs = sorted(glob.glob(osp.join(self.cam_b_dir, '*.bmp')))
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assert len(cam_a_imgs) == len(cam_b_imgs)
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num_pids = len(cam_a_imgs)
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print('Number of identities: {}'.format(num_pids))
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num_train_pids = num_pids // 2
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"""
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In total, there will be 20 splits because each random split creates two
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sub-splits, one using cameraA as query and cameraB as gallery
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while the other using cameraB as query and cameraA as gallery.
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Therefore, results should be averaged over 20 splits (split_id=0~19).
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In practice, a model trained on split_id=0 can be applied to split_id=0&1
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as split_id=0&1 share the same training data (so on and so forth).
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"""
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splits = []
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for _ in range(10):
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order = np.arange(num_pids)
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np.random.shuffle(order)
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train_idxs = order[:num_train_pids]
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test_idxs = order[num_train_pids:]
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assert not bool(set(train_idxs) & set(test_idxs)), \
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'Error: train and test overlap'
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train = []
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for pid, idx in enumerate(train_idxs):
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cam_a_img = cam_a_imgs[idx]
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cam_b_img = cam_b_imgs[idx]
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train.append((cam_a_img, pid, 0))
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train.append((cam_b_img, pid, 1))
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test_a = []
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test_b = []
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for pid, idx in enumerate(test_idxs):
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cam_a_img = cam_a_imgs[idx]
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cam_b_img = cam_b_imgs[idx]
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test_a.append((cam_a_img, pid, 0))
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test_b.append((cam_b_img, pid, 1))
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# use cameraA as query and cameraB as gallery
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split = {
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'train': train,
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'query': test_a,
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'gallery': test_b,
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'num_train_pids': num_train_pids,
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'num_query_pids': num_pids - num_train_pids,
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'num_gallery_pids': num_pids - num_train_pids
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}
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splits.append(split)
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# use cameraB as query and cameraA as gallery
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split = {
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'train': train,
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'query': test_b,
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'gallery': test_a,
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'num_train_pids': num_train_pids,
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'num_query_pids': num_pids - num_train_pids,
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'num_gallery_pids': num_pids - num_train_pids
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}
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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 saved to {}'.format(self.split_path))
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