mirror of https://github.com/JDAI-CV/fast-reid.git
75 lines
2.1 KiB
Python
75 lines
2.1 KiB
Python
# coding: utf-8
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import os
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import logging
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import pandas as pd
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from tabulate import tabulate
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from termcolor import colored
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from fastreid.data.data_utils import read_image
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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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from fastreid.utils.env import seed_all_rng
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@DATASET_REGISTRY.register()
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class ExcelDataset(ImageDataset):
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_logger = logging.getLogger('fastreid.fastshoe')
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def __init__(self, img_root, anno_path, transform=None, **kwargs):
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self._logger.info('set with {} random seed: 12345'.format(self.__class__.__name__))
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seed_all_rng(12345)
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self.img_root = img_root
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self.anno_path = anno_path
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self.transform = transform
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df = pd.read_csv(self.anno_path)
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df = df[['内网crop图', '外网crop图', '确认是否撞款']]
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df['确认是否撞款'] = df['确认是否撞款'].map({'是': 1, '否': 0})
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self.df = df
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def __getitem__(self, idx):
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image_inner, image_outer, label = tuple(self.df.loc[idx])
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image_inner_path = os.path.join(self.img_root, image_inner)
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image_outer_path = os.path.join(self.img_root, image_outer)
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img1 = read_image(image_inner_path)
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img2 = read_image(image_outer_path)
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if self.transform:
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img1 = self.transform(img1)
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img2 = self.transform(img2)
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return {
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'img1': img1,
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'img2': img2,
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'target': label
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}
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def __len__(self):
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return len(self.df)
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#-------------下面是辅助信息------------------#
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@property
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def num_classes(self):
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return 2
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def show_test(self):
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num_pairs = len(self)
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num_images = num_pairs * 2
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headers = ['pairs', 'images']
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csv_results = [[num_pairs, num_images]]
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# tabulate it
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table = tabulate(
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csv_results,
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tablefmt="pipe",
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headers=headers,
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numalign="left",
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)
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self._logger.info(f"=> Loaded {self.__class__.__name__} in csv format: \n" + colored(table, "cyan"))
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