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