mirror of https://github.com/JDAI-CV/fast-reid.git
59 lines
1.5 KiB
Python
59 lines
1.5 KiB
Python
# encoding: utf-8
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"""
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@author: liaoxingyu
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@contact: sherlockliao01@gmail.com
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"""
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from torch.utils.data import Dataset
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from .data_utils import read_image
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class CommDataset(Dataset):
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"""Image Person ReID Dataset"""
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def __init__(self, img_items, transform=None, relabel=True):
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self.img_items = img_items
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self.transform = transform
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self.relabel = relabel
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pid_set = set()
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cam_set = set()
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for i in img_items:
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pid_set.add(i[1])
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cam_set.add(i[2])
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self.pids = sorted(list(pid_set))
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self.cams = sorted(list(cam_set))
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if relabel:
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self.pid_dict = dict([(p, i) for i, p in enumerate(self.pids)])
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self.cam_dict = dict([(p, i) for i, p in enumerate(self.cams)])
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def __len__(self):
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return len(self.img_items)
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def __getitem__(self, index):
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img_item = self.img_items[index]
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img_path = img_item[0]
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pid = img_item[1]
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camid = img_item[2]
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img = read_image(img_path)
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if self.transform is not None: img = self.transform(img)
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if self.relabel:
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pid = self.pid_dict[pid]
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camid = self.cam_dict[camid]
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return {
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"images": img,
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"targets": pid,
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"camids": camid,
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"img_paths": img_path,
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}
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@property
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def num_classes(self):
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return len(self.pids)
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@property
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def num_cameras(self):
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return len(self.cams)
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