Spaces:
Running
Running
File size: 5,213 Bytes
f3270e6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 |
# Copyright (C) 2021-2025, 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
import numpy as np
from tqdm import tqdm
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
detection_task: whether the dataset should be used for detection 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,
detection_task: bool = False,
**kwargs: Any,
) -> None:
super().__init__(
img_folder, pre_transforms=convert_target_to_relative if not recognition_task else None, **kwargs
)
# Task check
if recognition_task and detection_task:
raise ValueError(
"`recognition_task` and `detection_task` cannot be set to True simultaneously. "
+ "To get the whole dataset with boxes and labels leave both parameters to False."
)
# 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[str | Path | np.ndarray, str | dict[str, Any] | np.ndarray]] = []
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: list[float] | np.ndarray
for json_string in tqdm(
iterable=json_strings, desc="Preparing and Loading WILDRECEIPT", total=len(json_strings)
):
_targets = []
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))
elif detection_task:
self.data.append((img_path, np.asarray(box_targets, dtype=int).clip(min=0)))
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}"
|