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import logging
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import numpy as np
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from typing import Any, Callable, Dict, List, Optional, Union
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import torch
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from torch.utils.data.dataset import Dataset
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from detectron2.data.detection_utils import read_image
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ImageTransform = Callable[[torch.Tensor], torch.Tensor]
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class ImageListDataset(Dataset):
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"""
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Dataset that provides images from a list.
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"""
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_EMPTY_IMAGE = torch.empty((0, 3, 1, 1))
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def __init__(
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self,
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image_list: List[str],
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category_list: Union[str, List[str], None] = None,
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transform: Optional[ImageTransform] = None,
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):
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"""
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Args:
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image_list (List[str]): list of paths to image files
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category_list (Union[str, List[str], None]): list of animal categories for
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each image. If it is a string, or None, this applies to all images
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"""
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if type(category_list) is list:
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self.category_list = category_list
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else:
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self.category_list = [category_list] * len(image_list)
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assert len(image_list) == len(
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self.category_list
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), "length of image and category lists must be equal"
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self.image_list = image_list
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self.transform = transform
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def __getitem__(self, idx: int) -> Dict[str, Any]:
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"""
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Gets selected images from the list
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Args:
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idx (int): video index in the video list file
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Returns:
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A dictionary containing two keys:
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images (torch.Tensor): tensor of size [N, 3, H, W] (N = 1, or 0 for _EMPTY_IMAGE)
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categories (List[str]): categories of the frames
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"""
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categories = [self.category_list[idx]]
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fpath = self.image_list[idx]
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transform = self.transform
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try:
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image = torch.from_numpy(np.ascontiguousarray(read_image(fpath, format="BGR")))
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image = image.permute(2, 0, 1).unsqueeze(0).float()
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if transform is not None:
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image = transform(image)
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return {"images": image, "categories": categories}
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except (OSError, RuntimeError) as e:
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logger = logging.getLogger(__name__)
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logger.warning(f"Error opening image file container {fpath}: {e}")
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return {"images": self._EMPTY_IMAGE, "categories": []}
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def __len__(self):
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return len(self.image_list)
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