43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
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# This file is part of COAT, and is distributed under the
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# OSI-approved BSD 3-Clause License. See top-level LICENSE file or
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# https://github.com/Kitware/COAT/blob/master/LICENSE for details.
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import torch
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from PIL import Image
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class BaseDataset:
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"""
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Base class of person search dataset.
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"""
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def __init__(self, root, transforms, split):
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self.root = root
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self.transforms = transforms
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self.split = split
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assert self.split in ("train", "gallery", "query")
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self.annotations = self._load_annotations()
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def _load_annotations(self):
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"""
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For each image, load its annotation that is a dictionary with the following keys:
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img_name (str): image name
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img_path (str): image path
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boxes (np.array[N, 4]): ground-truth boxes in (x1, y1, x2, y2) format
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pids (np.array[N]): person IDs corresponding to these boxes
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cam_id (int): camera ID (only for PRW dataset)
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"""
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raise NotImplementedError
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def __getitem__(self, index):
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anno = self.annotations[index]
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img = Image.open(anno["img_path"]).convert("RGB")
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boxes = torch.as_tensor(anno["boxes"], dtype=torch.float32)
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labels = torch.as_tensor(anno["pids"], dtype=torch.int64)
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target = {"img_name": anno["img_name"], "boxes": boxes, "labels": labels}
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if self.transforms is not None:
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img, target = self.transforms(img, target)
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return img, target
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def __len__(self):
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return len(self.annotations)
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