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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}"
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