131 lines
4.5 KiB
Python
131 lines
4.5 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 scipy.io import loadmat
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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 GRID(ImageDataset):
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"""GRID.
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Reference:
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Loy et al. Multi-camera activity correlation analysis. CVPR 2009.
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URL: `<http://personal.ie.cuhk.edu.hk/~ccloy/downloads_qmul_underground_reid.html>`_
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Dataset statistics:
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- identities: 250.
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- images: 1275.
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- cameras: 8.
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"""
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dataset_dir = 'grid'
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dataset_url = 'http://personal.ie.cuhk.edu.hk/~ccloy/files/datasets/underground_reid.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.probe_path = osp.join(
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self.dataset_dir, 'underground_reid', 'probe'
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)
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self.gallery_path = osp.join(
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self.dataset_dir, 'underground_reid', 'gallery'
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)
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self.split_mat_path = osp.join(
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self.dataset_dir, 'underground_reid', 'features_and_partitions.mat'
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)
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self.split_path = osp.join(self.dataset_dir, 'splits.json')
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required_files = [
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self.dataset_dir, self.probe_path, self.gallery_path,
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self.split_mat_path
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]
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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']
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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(GRID, 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')
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split_mat = loadmat(self.split_mat_path)
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trainIdxAll = split_mat['trainIdxAll'][0] # length = 10
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probe_img_paths = sorted(
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glob.glob(osp.join(self.probe_path, '*.jpeg'))
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)
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gallery_img_paths = sorted(
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glob.glob(osp.join(self.gallery_path, '*.jpeg'))
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)
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splits = []
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for split_idx in range(10):
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train_idxs = trainIdxAll[split_idx][0][0][2][0].tolist()
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assert len(train_idxs) == 125
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idx2label = {
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idx: label
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for label, idx in enumerate(train_idxs)
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}
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train, query, gallery = [], [], []
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# processing probe folder
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for img_path in probe_img_paths:
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img_name = osp.basename(img_path)
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img_idx = int(img_name.split('_')[0])
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camid = int(
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img_name.split('_')[1]
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) - 1 # index starts from 0
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if img_idx in train_idxs:
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train.append((img_path, idx2label[img_idx], camid))
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else:
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query.append((img_path, img_idx, camid))
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# process gallery folder
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for img_path in gallery_img_paths:
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img_name = osp.basename(img_path)
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img_idx = int(img_name.split('_')[0])
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camid = int(
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img_name.split('_')[1]
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) - 1 # index starts from 0
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if img_idx in train_idxs:
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train.append((img_path, idx2label[img_idx], camid))
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else:
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gallery.append((img_path, img_idx, camid))
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split = {
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'train': train,
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'query': query,
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'gallery': gallery,
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'num_train_pids': 125,
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'num_query_pids': 125,
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'num_gallery_pids': 900
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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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