2024.07.17 Release

.gitattributes

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+data.tar.gz filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text

.gitignore

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+env
+.idea
+*.json
+*.arrow

README.md

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+---
+task_categories:
+- object-detection
+version: 2024.07.17
+license: cc-by-4.0
+pretty_name: LADaS
+size_categories:
+- 1K<n<10K
+---
+
+# LADaS: Layout Analysis Dataset with Segmonto
+
+## Dataset Details
+
+LADaS, created by the [ALMANaCH team-project](https://almanach.inria.fr/index-en.html) at Inria,
+continued in partnership with other researchers, is a multidocuments diachronic layout analysis 
+dataset. This dataset includes:
+
+- Monographs from the Bibliothèque Nationale de France (17th century - today);
+- PhD Thesis, in various fields (not only STEM, 20th-21st century);
+- Selling Catalogs (for manuscripts and art pieces), in various fields (18th-20th century);
+- Noisy digitization (with fingers for example, 20th-21st century);
+- Academic papers (mostly Humanities and Social Sciences) (19th-21st century);
+- Magazines about technologies and video games, from 1920s to 2010;
+- Misc stuff found here and there.
+
+The data are in YoloV8 bbox format (center_x center_y width height).
+
+The script in document is mostly Latin script, and language is mostly French with some representation of the main
+western academic languages.
+
+### Annotations
+
+Label Annotation have been conducted using the [SegmOnto](https://segmonto.github.io/) vocabulary. 
+An annotation guide is available [here](https://github.com/DEFI-COLaF/LADaS/blob/25d4cf3f850cf79d18af572153cfbc73deff4160/AnnotationGuide.md).
+
+
+### Dataset Description
+
+
+- **Curated by:** Thibault Clérice & Juliette Janès
+- **Funded by:** Défi COLaF, Inria
+- **License:** CC-BY
+
+## Uses
+
+### Direct Use
+
+- Layout Analysis
+
+## Dataset Structure
+
+- Data contains the main `split` that is loaded through `load_dataset("CATMuS/medieval")`
+- Data can be split with each manuscript inside train, val and test using the `gen_split` columns which results in a 90/5/5 split
+- The image is in the `im` column, and the text in the `text` column
+
+### Annotations [optional]
+
+#### Annotation process
+
+The annotation process is described in the [dataset paper](https://inria.hal.science/hal-04453952).
+
+#### Who are the annotators?
+
+
+## Citation
+
+**BibTeX:**
+
+```tex
+@misc{Clerice_Layout_Analysis_Dataset,
+  author = {Clérice, Thibault and Janès, Juliette and Scheithauer, Hugo and Bénière, Sarah and Langlais, Pierre-Carl and Romary, Laurent and Sagot, Benoit and Bougrelle, Roxane},
+  title = {{Layout Analysis Dataset with SegmOnto (LADaS)}},
+  url = {https://github.com/DEFI-COLaF/LADaS}
+}
+```
+
+## Dataset Card Contact
+
+Thibault Clérice or Juliette Janes (first.last@inria.fr)

build.py

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+import os
+from datasets import load_dataset
+from datasets import config
+from datasets.utils.py_utils import convert_file_size_to_int
+from datasets.table import embed_table_storage
+from tqdm import tqdm
+
+
+def build_parquet(split):
+    # Source: https://discuss.huggingface.co/t/how-to-save-audio-dataset-with-parquet-format-on-disk/66179
+    dataset = load_dataset("./src/LADaS.py", split=split, trust_remote_code=True)
+    max_shard_size = '500MB'
+
+    dataset_nbytes = dataset._estimate_nbytes()
+    max_shard_size = convert_file_size_to_int(max_shard_size or config.MAX_SHARD_SIZE)
+    num_shards = int(dataset_nbytes / max_shard_size) + 1
+    num_shards = max(num_shards, 1)
+    shards = (dataset.shard(num_shards=num_shards, index=i, contiguous=True) for i in range(num_shards))
+
+    def shards_with_embedded_external_files(shards):
+        for shard in shards:
+            format = shard.format
+            shard = shard.with_format("arrow")
+            shard = shard.map(
+                embed_table_storage,
+                batched=True,
+                batch_size=1000,
+                keep_in_memory=True,
+            )
+            shard = shard.with_format(**format)
+            yield shard
+
+    shards = shards_with_embedded_external_files(shards)
+
+    os.makedirs("data", exist_ok=True)
+
+    for index, shard in tqdm(
+        enumerate(shards),
+        desc="Save the dataset shards",
+        total=num_shards,
+    ):
+        shard_path = f"data/{split}-{index:05d}-of-{num_shards:05d}.parquet"
+        shard.to_parquet(shard_path)
+
+
+if __name__ == "__main__":
+    build_parquet("train")
+    build_parquet("validation")
+    build_parquet("test")
+

data/test-00000-of-00001.parquet

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data/train-00000-of-00004.parquet

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data/validation-00000-of-00001.parquet

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src/LADaS.py

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+import glob
+import os
+import datasets
+from PIL import Image
+import csv
+
+
+_VERSION = "2024-07-17"
+_URL = f"https://github.com/DEFI-COLaF/LADaS/archive/refs/tags/{_VERSION}.tar.gz"
+_HOMEPAGE = "https://github.com/DEFI-COLaF/LADaS"
+_LICENSE = "CC BY 4.0"
+_CITATION = """\
+@misc{Clerice_Layout_Analysis_Dataset,
+    author = {Clérice, Thibault and Janès, Juliette and Scheithauer, Hugo and Bénière, Sarah and Romary, Laurent and Sagot, Benoit and Bougrelle, Roxane},
+    title = {{Layout Analysis Dataset with SegmOnto (LADaS)}},
+    url = {https://github.com/DEFI-COLaF/LADaS}
+}
+"""
+
+_CATEGORIES: list[str] = ["AdvertisementZone", "DigitizationArtefactZone", "DropCapitalZone", "FigureZone",
+                          "FigureZone-FigDesc", "FigureZone-Head", "GraphicZone", "GraphicZone-Decoration",
+                          "GraphicZone-FigDesc", "GraphicZone-Head", "GraphicZone-Maths", "GraphicZone-Part",
+                          "GraphicZone-TextualContent", "MainZone-Date", "MainZone-Entry", "MainZone-Entry-Continued",
+                          "MainZone-Form", "MainZone-Head", "MainZone-Lg", "MainZone-Lg-Continued", "MainZone-List",
+                          "MainZone-List-Continued", "MainZone-Other", "MainZone-P", "MainZone-P-Continued",
+                          "MainZone-Signature", "MainZone-Sp", "MainZone-Sp-Continued",
+                          "MarginTextZone-ManuscriptAddendum", "MarginTextZone-Notes", "MarginTextZone-Notes-Continued",
+                          "NumberingZone", "TitlePageZone", "TitlePageZone-Index", "QuireMarksZone", "RunningTitleZone",
+                          "StampZone", "StampZone-Sticker", "TableZone", "TableZone-Continued", "TableZone-Head"]
+
+
+class LadasConfig(datasets.BuilderConfig):
+    """Builder Config for LADaS"""
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+
+class LadasDataset(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version(_VERSION.replace("-", "."))
+    BUILDER_CONFIGS = [
+        LadasConfig(
+            name="full",
+            description="Full version of the dataset"
+        )
+    ]
+
+    def _info(self) -> datasets.DatasetInfo:
+        features = datasets.Features({
+            "image_path": datasets.Value("string"),
+            "year": datasets.Value("int32"),
+            "dating-certainty": datasets.Value("bool"),
+            "set": datasets.Value("string"),
+            "image": datasets.Image(),
+            "width": datasets.Value("int32"),
+            "height": datasets.Value("int32"),
+            "objects": datasets.Sequence(
+                {
+                    "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+                    "category": datasets.Value("string"),
+                }
+            )
+        })
+        return datasets.DatasetInfo(
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE
+        )
+
+    def _split_generators(self, dl_manager):
+        urls_to_download = _URL
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "local_dir": downloaded_files,
+                    "split": "train"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "local_dir": downloaded_files,
+                    "split": "valid"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "local_dir": downloaded_files,
+                    "split": "test"
+                },
+            ),
+        ]
+
+    def _generate_examples(self, local_dir: str, split: str):
+        idx = 0
+
+        df = {}
+        for file in glob.glob(os.path.join(local_dir, "*", "metadata.csv")):
+            with open(file) as f:
+                reader = csv.DictReader(f)
+                for line in reader:
+                   df[line["file"]] = line
+
+        for file in glob.glob(os.path.join(local_dir, "*", "data", "*", split, "labels", "*.txt")):
+            objects = []
+            with open(file) as f:
+                for line in f:
+                    cls, *bbox = line.strip().split()
+                    objects.append({"category": _CATEGORIES[int(cls)], "bbox": list(map(float, bbox))})
+
+            image_path = os.path.normpath(file).split(os.sep)
+            image_path = os.path.join(*image_path[:-2], "images", image_path[-1].replace(".txt", ".jpg"))
+            if file.startswith("/") and not image_path.startswith("/"):
+                image_path = "/" + image_path
+
+            with open(image_path, "rb") as f:
+                image_bytes = f.read()
+
+            with Image.open(image_path) as im:
+                width, height = im.size
+
+            filename = os.path.basename(image_path)
+            line = df[filename]
+
+            yield idx, {
+                "image_path": f"{line['subset']}/{filename}",
+                "image": {"path": image_path, "bytes": image_bytes},
+                "year": line["year"] or None,
+                "dating-certainty": line["dating-certainty"],
+                "set": line["subset"],
+                "width": width,
+                "height": height,
+                "objects": objects,
+            }
+            idx += 1
+
+if __name__ == "__main__":
+    LadasDataset().download_and_prepare()