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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) == 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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