mirror of https://github.com/JDAI-CV/fast-reid.git
60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
# encoding: utf-8
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"""
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@author: wangguanan
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@contact: guan.wang0706@gmail.com
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"""
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import glob
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import os
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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 WildTrackCrop(ImageDataset):
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"""WildTrack.
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Reference:
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WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection
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T. Chavdarova; P. Baqué; A. Maksai; S. Bouquet; C. Jose et al.
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URL: `<https://www.epfl.ch/labs/cvlab/data/data-wildtrack/>`_
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Dataset statistics:
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- identities: 313
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- images: 33979 (train only)
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- cameras: 7
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Args:
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data_path(str): path to WildTrackCrop dataset
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combineall(bool): combine train and test sets as train set if True
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"""
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dataset_url = None
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dataset_dir = 'Wildtrack_crop_dataset'
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dataset_name = 'wildtrack'
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def __init__(self, root='datasets', **kwargs):
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self.root = root
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self.dataset_dir = os.path.join(self.root, self.dataset_dir)
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self.train_dir = os.path.join(self.dataset_dir, "crop")
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train = self.process_dir(self.train_dir)
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query = []
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gallery = []
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super(WildTrackCrop, self).__init__(train, query, gallery, **kwargs)
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def process_dir(self, dir_path):
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r"""
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:param dir_path: directory path saving images
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Returns
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data(list) = [img_path, pid, camid]
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"""
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data = []
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for dir_name in os.listdir(dir_path):
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img_lists = glob.glob(os.path.join(dir_path, dir_name, "*.png"))
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for img_path in img_lists:
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pid = self.dataset_name + "_" + dir_name
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camid = img_path.split('/')[-1].split('_')[0]
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camid = self.dataset_name + "_" + camid
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data.append([img_path, pid, camid])
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return data
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