first draft

dataset_infos.json

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+{"default": {"description": "TODO", "citation": "    @dataset{clerice_thibault_2022_6827706,\n  author       = {Cl\u00e9rice, Thibault},\n  title        = {YALTAi: Tabular Dataset},\n  month        = jul,\n  year         = 2022,\n  publisher    = {Zenodo},\n  version      = {1.0.0},\n  doi          = {10.5281/zenodo.6827706},\n  url          = {https://doi.org/10.5281/zenodo.6827706}\n}\n", "homepage": "https://doi.org/10.5281/zenodo.6827706", "license": "Creative Commons Attribution 4.0 International", "features": {"image": {"decode": true, "id": null, "_type": "Image"}, "objects": {"feature": {"label": {"num_classes": 4, "names": ["Header", "Col", "Marginal", "text"], "id": null, "_type": "ClassLabel"}, "bbox": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": 4, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "yalt_ai_tabular_dataset", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 60704, "num_examples": 196, "dataset_name": "yalt_ai_tabular_dataset"}, "validation": {"name": "validation", "num_bytes": 7537, "num_examples": 22, "dataset_name": "yalt_ai_tabular_dataset"}, "test": {"name": "test", "num_bytes": 47159, "num_examples": 135, "dataset_name": "yalt_ai_tabular_dataset"}}, "download_checksums": {"https://zenodo.org/record/6827706/files/yaltai-table.zip?download=1": {"num_bytes": 376190064, "checksum": "5b312faf097939302fb98ab0a8b35c007962d88978ea9dc28d2f560b89dc0657"}}, "download_size": 376190064, "post_processing_size": null, "dataset_size": 115400, "size_in_bytes": 376305464}}

yalt_ai_tabular_dataset.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Script for reading 'Object Detection for Chess Pieces' dataset."""
+
+
+import os
+from glob import glob
+
+import datasets
+from PIL import Image
+
+_CITATION = """\
+    @dataset{clerice_thibault_2022_6827706,
+  author       = {Clérice, Thibault},
+  title        = {YALTAi: Tabular Dataset},
+  month        = jul,
+  year         = 2022,
+  publisher    = {Zenodo},
+  version      = {1.0.0},
+  doi          = {10.5281/zenodo.6827706},
+  url          = {https://doi.org/10.5281/zenodo.6827706}
+}
+"""
+
+_DESCRIPTION = """TODO"""
+
+_HOMEPAGE = "https://doi.org/10.5281/zenodo.6827706"
+
+_LICENSE = "Creative Commons Attribution 4.0 International"
+
+_URL = "https://zenodo.org/record/6827706/files/yaltai-table.zip?download=1"
+
+_CATEGORIES = ["Header", "Col", "Marginal", "text"]
+
+
+class YaltAiTabularDataset(datasets.GeneratorBasedBuilder):
+    """Object Detection for historic manuscripts"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "objects": datasets.Sequence(
+                        {
+                            "label": datasets.ClassLabel(names=_CATEGORIES),
+                            "bbox": datasets.Sequence(
+                                datasets.Value("int32"), length=4
+                            ),
+                        }
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            description=_DESCRIPTION,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_dir": os.path.join(data_dir, "yaltai-table/", "train")
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"data_dir": os.path.join(data_dir, "yaltai-table/", "val")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_dir": os.path.join(data_dir, "yaltai-table/", "test")
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_dir):
+        image_dir = os.path.join(data_dir, "images")
+        label_dir = os.path.join(data_dir, "labels")
+        image_paths = sorted(glob(f"{image_dir}/*.jpg"))
+        label_paths = sorted(glob(f"{label_dir}/*.txt"))
+
+        for idx, (image_path, label_path) in enumerate(zip(image_paths, label_paths)):
+            im = Image.open(image_path)
+            width, height = im.size
+
+            with open(label_path, "r") as f:
+                lines = f.readlines()
+
+            objects = []
+            for line in lines:
+                line = line.strip().split()
+                bbox_class = int(line[0])
+                bbox_xcenter = int(float(line[1]) * width)
+                bbox_ycenter = int(float(line[2]) * height)
+                bbox_width = int(float(line[3]) * width)
+                bbox_height = int(float(line[4]) * height)
+                objects.append(
+                    {
+                        "label": bbox_class,
+                        "bbox": [bbox_xcenter, bbox_ycenter, bbox_width, bbox_height],
+                    }
+                )
+
+            yield idx, {"image": image_path, "objects": objects}