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