mirror of https://github.com/JDAI-CV/fast-reid.git
65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
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# encoding: utf-8
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"""
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@author: Jinkai Zheng
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@contact: 1315673509@qq.com
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"""
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import glob
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import os.path as osp
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import re
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from .bases import ImageDataset
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from ..datasets import DATASET_REGISTRY
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@DATASET_REGISTRY.register()
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class VeRi(ImageDataset):
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"""VeRi.
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Reference:
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Liu et al. A Deep Learning based Approach for Progressive Vehicle Re-Identification. ECCV 2016.
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URL: `<https://vehiclereid.github.io/VeRi/>`_
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Dataset statistics:
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- identities: 775.
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- images: 37778 (train) + 1678 (query) + 11579 (gallery).
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"""
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dataset_dir = 'veri'
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def __init__(self, root='/home/liuxinchen3/notespace/data', **kwargs):
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self.dataset_dir = osp.join(root, self.dataset_dir)
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self.train_dir = osp.join(self.dataset_dir, 'image_train')
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self.query_dir = osp.join(self.dataset_dir, 'image_query')
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self.gallery_dir = osp.join(self.dataset_dir, 'image_test')
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required_files = [
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self.dataset_dir,
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self.train_dir,
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self.query_dir,
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self.gallery_dir,
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]
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self.check_before_run(required_files)
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train = self.process_dir(self.train_dir)
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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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super(VeRi, 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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pattern = re.compile(r'([\d]+)_c(\d\d\d)')
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data = []
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for img_path in img_paths:
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pid, camid = map(int, pattern.search(img_path).groups())
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if pid == -1: continue # junk images are just ignored
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assert 1 <= pid <= 776
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assert 1 <= camid <= 20
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camid -= 1 # index starts from 0
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data.append((img_path, pid, camid))
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return data
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