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baybayin.py

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+# coding=utf-8
+# 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.
+
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+from scipy.io import loadmat
+
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import (SCHEMA_TO_FEATURES, TASK_TO_SCHEMA,
+                                      Licenses, Tasks)
+
+_CITATION = """\
+@article{Pino2021,
+    title = {Optical character recognition system for Baybayin scripts using support vector machine},
+    volume = {7},
+    ISSN = {2376-5992},
+    url = {http://dx.doi.org/10.7717/peerj-cs.360},
+    DOI = {10.7717/peerj-cs.360},
+    journal = {PeerJ Computer Science},
+    publisher = {PeerJ},
+    author = {Pino,  Rodney and Mendoza,  Renier and Sambayan,  Rachelle},
+    year = {2021},
+    month = feb,
+    pages = {e360}
+}
+"""
+
+_DATASETNAME = "baybayin"
+
+_DESCRIPTION = """\
+The Baybayin dataset contains binary images of Baybayin characters, Latin
+characters, and 4 character symbols of Baybayin diacritics in MATLAB format. It
+consisted of 17000 images for Baybayin (1000 per character), 18200 images for
+Latin (700 per character), and 2000 images for Baybayin diacritics (500 per
+symbol). Each character image is strictly center-fitted with a size 56x56
+pixels. This dataset was initially used to discriminate Latin script from
+Baybayin script in character recognition.
+
+This is local dataset, please download the dataset from the `_HOMEPAGE` URL.
+"""
+
+_HOMEPAGE = "https://www.kaggle.com/datasets/rodneypino/baybayin-and-latin-binary-images-in-mat-format"
+
+_LANGUAGES = ["tgl"]
+_SUBSETS = ["baybayin", "latin", "diacritic"]
+
+_LICENSE = Licenses.CC_BY_4_0.value
+
+_LOCAL = True  # kaggle dataset need to register to download
+
+_URLS = {}
+
+_SUPPORTED_TASKS = [Tasks.OPTICAL_CHARACTER_RECOGNITION]
+_SEACROWD_SCHEMA = f"seacrowd_{TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()}"  # imtext
+
+_SOURCE_VERSION = "4.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class BaybayinDataset(datasets.GeneratorBasedBuilder):
+    """Binary images of Baybayin and Latin characters, and 4 character symbols of Baybayin diacritics"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = []
+    for subset in _SUBSETS:
+        BUILDER_CONFIGS += [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{subset}_source",
+                version=SOURCE_VERSION,
+                description=f"{_DATASETNAME} {subset} source schema",
+                schema="source",
+                subset_id=subset,
+            ),
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{subset}_{_SEACROWD_SCHEMA}",
+                version=SEACROWD_VERSION,
+                description=f"{_DATASETNAME} {subset} SEACrowd schema",
+                schema=_SEACROWD_SCHEMA,
+                subset_id=subset,
+            ),
+        ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_{_SUBSETS[0]}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "image": datasets.Array2D(shape=(56, 56), dtype="uint8"),
+                    "character": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == _SEACROWD_SCHEMA:
+            features = SCHEMA_TO_FEATURES[TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]]]  # image_text_features()
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        if self.config.data_dir is None:
+            raise ValueError("This is a local dataset. Please pass the `data_dir` kwarg (where the .pdf is located) to load_dataset.")
+        else:
+            data_dir = Path(self.config.data_dir)
+
+        subset_path = {
+            "baybayin": "Baybayin/Baybayin.mat",
+            "latin": "Latin/Latin.mat",
+            "diacritic": "Baybayin Diacritics/Baybayin_Diacritics.mat",
+        }
+
+        mat_file = data_dir / subset_path[self.config.subset_id]
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "mat_file": mat_file,
+                },
+            )
+        ]
+
+    def _generate_examples(self, mat_file: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        try:
+            from PIL import Image
+        except ImportError as err:
+            raise ImportError("You need to install PIL (`pip install pillow`) to store the image from MATLAB structs to .png files.") from err
+
+        raw_data = loadmat(str(mat_file))
+        contained_data = raw_data[str(mat_file.stem)][0, 0]
+
+        characters = list(contained_data.dtype.fields.keys())
+        data = {char: contained_data[char] for char in characters}
+
+        if self.config.schema == "source":
+            key = 0
+            for char, char_data in data.items():
+                for i in range(char_data.shape[0]):
+                    image = char_data[i].reshape((56, 56))
+                    yield key, {
+                        "image": image,
+                        "character": char,
+                    }
+                    key += 1
+
+        elif self.config.schema == _SEACROWD_SCHEMA:
+            key = 0
+            for char, char_data in data.items():
+                # prepare path for saving images
+                image_dir = mat_file.parent / char
+                image_dir.mkdir(exist_ok=True)
+
+                image_paths = []
+                for i in range(char_data.shape[0]):
+                    image = (char_data[i].reshape((56, 56)) * 255).astype("uint8")
+                    image_path = str(image_dir / f"{char}_{i}.png")
+
+                    # save image
+                    Image.fromarray(image).save(image_path)
+                    image_paths.append(image_path)
+
+                yield key, {"id": str(key), "image_paths": image_paths, "texts": char, "metadata": None}
+                key += 1