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yalta_ai_segmonto_manuscript_dataset.py

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+# Copyright 2022 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 = """Yalt AI Tabular Dataset"""
+
+_HOMEPAGE = "https://doi.org/10.5281/zenodo.6827706"
+
+_LICENSE = "Creative Commons Attribution 4.0 International"
+
+_URL = "https://zenodo.org/record/6814770/files/yaltai-segmonto-dataset.zip?download=1"
+
+_CATEGORIES = ['DamageZone', 'DigitizationArtefactZone', 'DropCapitalZone', 'GraphicZone', 'MainZone', 
+        'MarginTextZone', 'MusicZone', 'NumberingZone', 'QuireMarksZone', 'RunningTitleZone', 'SealZone', 
+        'StampZone', 'TableZone', 'TitlePageZone']
+
+class YaltAiTabularDatasetConfig(datasets.BuilderConfig):
+    """BuilderConfig for YaltAiTabularDataset."""
+
+    def __init__(self, name, **kwargs):
+        """BuilderConfig for YaltAiTabularDataset."""
+        super(YaltAiTabularDatasetConfig, self).__init__(
+            version=datasets.Version("1.0.0"), name=name, description=None, **kwargs
+        )
+
+
+class YaltAiTabularDataset(datasets.GeneratorBasedBuilder):
+    """Object Detection for historic manuscripts"""
+
+    BUILDER_CONFIGS = [
+        YaltAiTabularDatasetConfig("YOLO"),
+        YaltAiTabularDatasetConfig("COCO"),
+    ]
+
+    def _info(self):
+        if self.config.name == "COCO":
+            features = datasets.Features(
+                {
+                    "image_id": datasets.Value("int64"),
+                    "image": datasets.Image(),
+                    "width": datasets.Value("int32"),
+                    "height": datasets.Value("int32"),
+                }
+            )
+            object_dict = {
+                "category_id": datasets.ClassLabel(names=_CATEGORIES),
+                "image_id": datasets.Value("string"),
+                "id": datasets.Value("int64"),
+                "area": datasets.Value("int64"),
+                "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+                "segmentation": [[datasets.Value("float32")]],
+                "iscrowd": datasets.Value("bool"),
+            }
+            features["objects"] = [object_dict]
+        if self.config.name == "YOLO":
+            features = datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "objects": datasets.Sequence(
+                        {
+                            "label": datasets.ClassLabel(names=_CATEGORIES),
+                            "bbox": datasets.Sequence(
+                                datasets.Value("int32"), length=4
+                            ),
+                        }
+                    ),
+                }
+            )
+        return datasets.DatasetInfo(
+            features=features,
+            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-segmonto-dataset", "train")
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"data_dir": os.path.join(data_dir, "yaltai-segmonto-dataset", "val")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_dir": os.path.join(data_dir, "yaltai-segmonto-dataset", "test")
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_dir):
+        def create_annotation_from_yolo_format(
+            min_x,
+            min_y,
+            width,
+            height,
+            image_id,
+            category_id,
+            annotation_id,
+            segmentation=False,
+        ):
+            bbox = (float(min_x), float(min_y), float(width), float(height))
+            area = width * height
+            max_x = min_x + width
+            max_y = min_y + height
+            if segmentation:
+                seg = [[min_x, min_y, max_x, min_y, max_x, max_y, min_x, max_y]]
+            else:
+                seg = []
+            return {
+                "id": annotation_id,
+                "image_id": image_id,
+                "bbox": bbox,
+                "area": area,
+                "iscrowd": 0,
+                "category_id": category_id,
+                "segmentation": seg,
+            }
+
+        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"))
+        if self.config.name == "COCO":
+            for idx, (image_path, label_path) in enumerate(
+                zip(image_paths, label_paths)
+            ):
+                image_id = idx
+                annotations = []
+                image = Image.open(image_path)  # Possibly conver to RGB?
+                w, h = image.size
+                with open(label_path, "r") as f:
+                    lines = f.readlines()
+                for line in lines:
+                    line = line.strip().split()
+                    category_id = line[0]
+                    x_center = float(line[1])
+                    y_center = float(line[2])
+                    width = float(line[3])
+                    height = float(line[4])
+
+                    float_x_center = w * x_center
+                    float_y_center = h * y_center
+                    float_width = w * width
+                    float_height = h * height
+
+                    min_x = int(float_x_center - float_width / 2)
+                    min_y = int(float_y_center - float_height / 2)
+                    width = int(float_width)
+                    height = int(float_height)
+
+                    annotation = create_annotation_from_yolo_format(
+                        min_x,
+                        min_y,
+                        width,
+                        height,
+                        image_id,
+                        category_id,
+                        image_id,
+                    )
+                    annotations.append(annotation)
+
+                example = {
+                    "image_id": image_id,
+                    "image": image,
+                    "width": w,
+                    "height": h,
+                    "objects": annotations,
+                }
+                yield idx, example
+        if self.config.name == "YOLO":
+            for idx, (image_path, label_path) in enumerate(
+                zip(image_paths, label_paths)
+            ):
+                im = Image.open(image_path)
+                width, height = im.size
+                image_id = idx
+                annotations = []
+                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,
+                    "objects": objects,
+                }