mirror of https://github.com/JDAI-CV/fast-reid.git
49 lines
1.3 KiB
Python
49 lines
1.3 KiB
Python
# 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 fastreid.data.datasets import DATASET_REGISTRY
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from fastreid.data.datasets.bases import ImageDataset
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__all__ = ['Shinpuhkan', ]
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@DATASET_REGISTRY.register()
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class Shinpuhkan(ImageDataset):
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"""shinpuhkan
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"""
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dataset_dir = "shinpuhkan"
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dataset_name = 'shinpuhkan'
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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)
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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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for root, dirs, files in os.walk(train_path):
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img_names = list(filter(lambda x: x.endswith(".jpg"), files))
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# fmt: off
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if len(img_names) == 0: continue
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# fmt: on
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for img_name in img_names:
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img_path = os.path.join(root, img_name)
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split_path = img_name.split('_')
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pid = self.dataset_name + "_" + split_path[0]
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camid = self.dataset_name + "_" + split_path[2]
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data.append((img_path, pid, camid))
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return data
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