Source code for doctr.datasets.doc_artefacts

# Copyright (C) 2021-2022, 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 typing import Any, Dict, List, Tuple

import numpy as np

from .datasets import VisionDataset

__all__ = ["DocArtefacts"]

[docs]class DocArtefacts(VisionDataset): """Object detection dataset for non-textual elements in documents. The dataset includes a variety of synthetic document pages with non-textual elements. .. image:: :align: center >>> from doctr.datasets import DocArtefacts >>> train_set = DocArtefacts(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) **kwargs: keyword arguments from `VisionDataset`. """ URL = "" SHA256 = "13fab8ced7f84583d9dccd0c634f046c3417e62a11fe1dea6efbbaba5052471b" CLASSES = ["background", "qr_code", "bar_code", "logo", "photo"] def __init__( self, train: bool = True, use_polygons: bool = False, **kwargs: Any, ) -> None: super().__init__(self.URL, None, self.SHA256, True, **kwargs) self.train = train # Update root self.root = os.path.join(self.root, "train" if train else "val") # List images tmp_root = os.path.join(self.root, "images") with open(os.path.join(self.root, "labels.json"), "rb") as f: labels = json.load(f) List[Tuple[str, Dict[str, Any]]] = [] img_list = os.listdir(tmp_root) if len(labels) != len(img_list): raise AssertionError("the number of images and labels do not match") np_dtype = np.float32 for img_name, label in labels.items(): # File existence check if not os.path.exists(os.path.join(tmp_root, img_name)): raise FileNotFoundError(f"unable to locate {os.path.join(tmp_root, img_name)}") # xmin, ymin, xmax, ymax boxes: np.ndarray = np.asarray([obj["geometry"] for obj in label], dtype=np_dtype) classes: np.ndarray = np.asarray([self.CLASSES.index(obj["label"]) for obj in label], dtype=np.int64) if use_polygons: # (x, y) coordinates of top left, top right, bottom right, bottom left corners boxes = np.stack( [ np.stack([boxes[:, 0], boxes[:, 1]], axis=-1), np.stack([boxes[:, 2], boxes[:, 1]], axis=-1), np.stack([boxes[:, 2], boxes[:, 3]], axis=-1), np.stack([boxes[:, 0], boxes[:, 3]], axis=-1), ], axis=1, ), dict(boxes=boxes, labels=classes))) self.root = tmp_root def extra_repr(self) -> str: return f"train={self.train}"