from __future__ import division, print_function, absolute_import
import copy
import glob
import os.path as osp
from ..dataset import ImageDataset
[docs]class SenseReID(ImageDataset):
"""SenseReID.
This dataset is used for test purpose only.
Reference:
Zhao et al. Spindle Net: Person Re-identification with Human Body
Region Guided Feature Decomposition and Fusion. CVPR 2017.
URL: `<https://drive.google.com/file/d/0B56OfSrVI8hubVJLTzkwV2VaOWM/view>`_
Dataset statistics:
- query: 522 ids, 1040 images.
- gallery: 1717 ids, 3388 images.
"""
dataset_dir = 'sensereid'
dataset_url = None
def __init__(self, root='', **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.query_dir = osp.join(self.dataset_dir, 'SenseReID', 'test_probe')
self.gallery_dir = osp.join(
self.dataset_dir, 'SenseReID', 'test_gallery'
)
required_files = [self.dataset_dir, self.query_dir, self.gallery_dir]
self.check_before_run(required_files)
query = self.process_dir(self.query_dir)
gallery = self.process_dir(self.gallery_dir)
# relabel
g_pids = set()
for _, pid, _ in gallery:
g_pids.add(pid)
pid2label = {pid: i for i, pid in enumerate(g_pids)}
query = [
(img_path, pid2label[pid], camid) for img_path, pid, camid in query
]
gallery = [
(img_path, pid2label[pid], camid)
for img_path, pid, camid in gallery
]
train = copy.deepcopy(query) + copy.deepcopy(gallery) # dummy variable
super(SenseReID, self).__init__(train, query, gallery, **kwargs)
def process_dir(self, dir_path):
img_paths = glob.glob(osp.join(dir_path, '*.jpg'))
data = []
for img_path in img_paths:
img_name = osp.splitext(osp.basename(img_path))[0]
pid, camid = img_name.split('_')
pid, camid = int(pid), int(camid)
data.append((img_path, pid, camid))
return data