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| # Copyright (C) 2021-2024, Mindee. | |
| # This program is licensed under the Apache License 2.0. | |
| # See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details. | |
| import json | |
| import os | |
| from pathlib import Path | |
| from typing import Any, Dict, List, Tuple, Union | |
| import numpy as np | |
| from .datasets import AbstractDataset | |
| from .utils import convert_target_to_relative, crop_bboxes_from_image | |
| __all__ = ["WILDRECEIPT"] | |
| class WILDRECEIPT(AbstractDataset): | |
| """WildReceipt dataset from `"Spatial Dual-Modality Graph Reasoning for Key Information Extraction" | |
| <https://arxiv.org/abs/2103.14470v1>`_ | | |
| `repository <https://download.openmmlab.com/mmocr/data/wildreceipt.tar>`_. | |
| .. image:: https://doctr-static.mindee.com/models?id=v0.7.0/wildreceipt-dataset.jpg&src=0 | |
| :align: center | |
| >>> # NOTE: You need to download the dataset first. | |
| >>> from doctr.datasets import WILDRECEIPT | |
| >>> train_set = WILDRECEIPT(train=True, img_folder="/path/to/wildreceipt/", | |
| >>> label_path="/path/to/wildreceipt/train.txt") | |
| >>> img, target = train_set[0] | |
| >>> test_set = WILDRECEIPT(train=False, img_folder="/path/to/wildreceipt/", | |
| >>> label_path="/path/to/wildreceipt/test.txt") | |
| >>> img, target = test_set[0] | |
| Args: | |
| ---- | |
| img_folder: folder with all the images of the dataset | |
| label_path: path to the annotations file of the dataset | |
| train: whether the subset should be the training one | |
| use_polygons: whether polygons should be considered as rotated bounding box (instead of straight ones) | |
| recognition_task: whether the dataset should be used for recognition task | |
| **kwargs: keyword arguments from `AbstractDataset`. | |
| """ | |
| def __init__( | |
| self, | |
| img_folder: str, | |
| label_path: str, | |
| train: bool = True, | |
| use_polygons: bool = False, | |
| recognition_task: bool = False, | |
| **kwargs: Any, | |
| ) -> None: | |
| super().__init__( | |
| img_folder, pre_transforms=convert_target_to_relative if not recognition_task else None, **kwargs | |
| ) | |
| # File existence check | |
| if not os.path.exists(label_path) or not os.path.exists(img_folder): | |
| raise FileNotFoundError(f"unable to locate {label_path if not os.path.exists(label_path) else img_folder}") | |
| tmp_root = img_folder | |
| self.train = train | |
| np_dtype = np.float32 | |
| self.data: List[Tuple[Union[str, Path, np.ndarray], Union[str, Dict[str, Any]]]] = [] | |
| with open(label_path, "r") as file: | |
| data = file.read() | |
| # Split the text file into separate JSON strings | |
| json_strings = data.strip().split("\n") | |
| box: Union[List[float], np.ndarray] | |
| _targets = [] | |
| for json_string in json_strings: | |
| json_data = json.loads(json_string) | |
| img_path = json_data["file_name"] | |
| annotations = json_data["annotations"] | |
| for annotation in annotations: | |
| coordinates = annotation["box"] | |
| if use_polygons: | |
| # (x, y) coordinates of top left, top right, bottom right, bottom left corners | |
| box = np.array( | |
| [ | |
| [coordinates[0], coordinates[1]], | |
| [coordinates[2], coordinates[3]], | |
| [coordinates[4], coordinates[5]], | |
| [coordinates[6], coordinates[7]], | |
| ], | |
| dtype=np_dtype, | |
| ) | |
| else: | |
| x, y = coordinates[::2], coordinates[1::2] | |
| box = [min(x), min(y), max(x), max(y)] | |
| _targets.append((annotation["text"], box)) | |
| text_targets, box_targets = zip(*_targets) | |
| if recognition_task: | |
| crops = crop_bboxes_from_image( | |
| img_path=os.path.join(tmp_root, img_path), geoms=np.asarray(box_targets, dtype=int).clip(min=0) | |
| ) | |
| for crop, label in zip(crops, list(text_targets)): | |
| if label and " " not in label: | |
| self.data.append((crop, label)) | |
| else: | |
| self.data.append(( | |
| img_path, | |
| dict(boxes=np.asarray(box_targets, dtype=int).clip(min=0), labels=list(text_targets)), | |
| )) | |
| self.root = tmp_root | |
| def extra_repr(self) -> str: | |
| return f"train={self.train}" | |