Source code for doctr.datasets.cord

# Copyright (C) 2021-2023, Mindee.

# This program is licensed under the Apache License 2.0.
# See LICENSE or go to <> 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 tqdm import tqdm

from .datasets import VisionDataset
from .utils import convert_target_to_relative, crop_bboxes_from_image

__all__ = ["CORD"]

[docs]class CORD(VisionDataset): """CORD dataset from `"CORD: A Consolidated Receipt Dataset forPost-OCR Parsing" <>`_. .. image:: :align: center >>> from doctr.datasets import CORD >>> train_set = CORD(train=True, download=True) >>> img, target = train_set[0] Args: 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 `VisionDataset`. """ TRAIN = ( "", "45f9dc77f126490f3e52d7cb4f70ef3c57e649ea86d19d862a2757c9c455d7f8", "", ) TEST = ( "", "8c895e3d6f7e1161c5b7245e3723ce15c04d84be89eaa6093949b75a66fb3c58", "", ) def __init__( self, train: bool = True, use_polygons: bool = False, recognition_task: bool = False, **kwargs: Any, ) -> None: url, sha256, name = self.TRAIN if train else self.TEST super().__init__( url, name, sha256, True, pre_transforms=convert_target_to_relative if not recognition_task else None, **kwargs, ) # List images tmp_root = os.path.join(self.root, "image") List[Tuple[Union[str, np.ndarray], Union[str, Dict[str, Any]]]] = [] self.train = train np_dtype = np.float32 for img_path in tqdm(iterable=os.listdir(tmp_root), desc="Unpacking CORD", total=len(os.listdir(tmp_root))): # File existence check if not os.path.exists(os.path.join(tmp_root, img_path)): raise FileNotFoundError(f"unable to locate {os.path.join(tmp_root, img_path)}") stem = Path(img_path).stem _targets = [] with open(os.path.join(self.root, "json", f"{stem}.json"), "rb") as f: label = json.load(f) for line in label["valid_line"]: for word in line["words"]: if len(word["text"]) > 0: x = word["quad"]["x1"], word["quad"]["x2"], word["quad"]["x3"], word["quad"]["x4"] y = word["quad"]["y1"], word["quad"]["y2"], word["quad"]["y3"], word["quad"]["y4"] box: Union[List[float], np.ndarray] if use_polygons: # (x, y) coordinates of top left, top right, bottom right, bottom left corners box = np.array( [ [x[0], y[0]], [x[1], y[1]], [x[2], y[2]], [x[3], y[3]], ], dtype=np_dtype, ) else: # Reduce 8 coords to 4 -> xmin, ymin, xmax, ymax box = [min(x), min(y), max(x), max(y)] _targets.append((word["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)):, label)) else: (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}"