rm prid450s

torchreid/data/datasets/__init__.py

@@ -14,7 +14,6 @@ __image_datasets = {
     'viper': VIPeR,
     'grid': GRID,
     'cuhk01': CUHK01,
-    'prid450s': PRID450S,
     'ilids': iLIDS,
     'sensereid': SenseReID,
     'prid': PRID

torchreid/data/datasets/image/__init__.py

@@ -8,7 +8,6 @@ from .msmt17 import MSMT17
 from .viper import VIPeR
 from .grid import GRID
 from .cuhk01 import CUHK01
-from .prid450s import PRID450S
 from .ilids import iLIDS
 from .sensereid import SenseReID
 from .prid import PRID

torchreid/data/datasets/image/prid450s.py

@@ -1,111 +0,0 @@
-from __future__ import absolute_import
-from __future__ import print_function
-from __future__ import division
-
-import sys
-import os
-import os.path as osp
-import glob
-import numpy as np
-
-from torchreid.data.datasets import ImageDataset
-from torchreid.utils import read_json, write_json
-
-
-class PRID450S(ImageDataset):
-    """PRID450S.
-
-    Reference:
-        Roth et al. Mahalanobis Distance Learning for Person Re-Identification. PR 2014.
-
-    URL: `<https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/prid450s/>`_
-    
-    Dataset statistics:
-        - identities: 450.
-        - images: 900.
-        - cameras: 2.
-    """
-    dataset_dir = 'prid450s'
-    dataset_url = 'https://files.icg.tugraz.at/f/8c709245bb/?raw=1'
-
-    def __init__(self, root='', split_id=0, min_seq_len=0, **kwargs):
-        self.root = osp.abspath(osp.expanduser(root))
-        self.dataset_dir = osp.join(self.root, self.dataset_dir)
-        self.download_dataset(self.dataset_dir, self.dataset_url)
-
-        self.split_path = osp.join(self.dataset_dir, 'splits.json')
-        self.cam_a_dir = osp.join(self.dataset_dir, 'cam_a')
-        self.cam_b_dir = osp.join(self.dataset_dir, 'cam_b')
-        
-        required_files = [
-            self.dataset_dir,
-            self.cam_a_dir,
-            self.cam_b_dir
-        ]
-        self.check_before_run(required_files)
-
-        self.prepare_split()
-        splits = read_json(self.split_path)
-        if split_id >= len(splits):
-            raise ValueError('split_id exceeds range, received {}, but expected between 0 and {}'.format(split_id, len(splits)-1))
-        split = splits[split_id]
-
-        train = split['train']
-        query = split['query']
-        gallery = split['gallery']
-
-        train = [tuple(item) for item in train]
-        query = [tuple(item) for item in query]
-        gallery = [tuple(item) for item in gallery]
-
-        super(PRID450S, self).__init__(train, query, gallery, **kwargs)
-
-    def prepare_split(self):
-        if not osp.exists(self.split_path):
-            cam_a_imgs = sorted(glob.glob(osp.join(self.cam_a_dir, 'img_*.png')))
-            cam_b_imgs = sorted(glob.glob(osp.join(self.cam_b_dir, 'img_*.png')))
-            assert len(cam_a_imgs) == len(cam_b_imgs)
-
-            num_pids = len(cam_a_imgs)
-            num_train_pids = num_pids // 2
-
-            splits = []
-            for _ in range(10):
-                order = np.arange(num_pids)
-                np.random.shuffle(order)
-                train_idxs = np.sort(order[:num_train_pids])
-                idx2label = {idx: label for label, idx in enumerate(train_idxs)}
-
-                train, test = [], []
-
-                # processing camera a
-                for img_path in cam_a_imgs:
-                    img_name = osp.basename(img_path)
-                    img_idx = int(img_name.split('_')[1].split('.')[0])
-                    if img_idx in train_idxs:
-                        train.append((img_path, idx2label[img_idx], 0))
-                    else:
-                        test.append((img_path, img_idx, 0))
-
-                # processing camera b
-                for img_path in cam_b_imgs:
-                    img_name = osp.basename(img_path)
-                    img_idx = int(img_name.split('_')[1].split('.')[0])
-                    if img_idx in train_idxs:
-                        train.append((img_path, idx2label[img_idx], 1))
-                    else:
-                        test.append((img_path, img_idx, 1))
-
-                split = {
-                    'train': train,
-                    'query': test,
-                    'gallery': test,
-                    'num_train_pids': num_train_pids,
-                    'num_query_pids': num_pids - num_train_pids,
-                    'num_gallery_pids': num_pids - num_train_pids
-                }
-                splits.append(split)
-
-            print('Totally {} splits are created'.format(len(splits)))
-            write_json(splits, self.split_path)
-            print('Split file saved to {}'.format(self.split_path))