| | |
| | |
| |
|
| | 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) |
| |
|
