mirror of https://github.com/JDAI-CV/fast-reid.git
45 lines
1.1 KiB
Python
45 lines
1.1 KiB
Python
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# encoding: utf-8
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"""
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@author: xingyu liao
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@contact: sherlockliao01@gmail.com
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"""
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import os
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from glob import glob
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from fastreid.data.datasets import DATASET_REGISTRY
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from fastreid.data.datasets.bases import ImageDataset
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__all__ = ['GRID', ]
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@DATASET_REGISTRY.register()
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class GRID(ImageDataset):
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"""GRID
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"""
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dataset_dir = "underground_reid"
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dataset_name = 'grid'
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def __init__(self, root='datasets', **kwargs):
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self.root = root
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self.train_path = os.path.join(self.root, self.dataset_dir, 'images')
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required_files = [self.train_path]
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self.check_before_run(required_files)
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train = self.process_train(self.train_path)
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super().__init__(train, [], [], **kwargs)
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def process_train(self, train_path):
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data = []
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img_paths = glob(os.path.join(train_path, "*.jpeg"))
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for img_path in img_paths:
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img_name = os.path.basename(img_path)
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img_info = img_name.split('_')
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pid = self.dataset_name + "_" + img_info[0]
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camid = self.dataset_name + "_" + img_info[1]
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data.append([img_path, pid, camid])
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return data
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