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| # -*- coding: utf-8 -*- | |
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| 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() # HWC -> NCHW | |
| 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) | |