Upload vintext.py with huggingface_hub

vintext.py

@@ -0,0 +1,235 @@
+# 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.
+
+"""
+Vintext is a challenging scene text dataset for Vietnamese, where some characters are equivocal in the visual form due to accent symbols.
+This dataset contains 1500 fully annotated images from the original format. Each text instance is delineated by a quadrilateral bounding box and associated with the ground truth sequence of characters.
+The dataset is randomly split into 2 subsets for training (1,200 images) and testing (300 images).
+"""
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@INPROCEEDINGS{vintext,
+    author={Nguyen, Nguyen and Nguyen, Thu and Tran, Vinh and Tran, Minh-Triet and Ngo, Thanh Duc and Huu Nguyen, Thien and Hoai, Minh},
+    booktitle={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    title={Dictionary-guided Scene Text Recognition},
+    year={2021},
+    pages={7379-7388},
+    keywords={Training;Visualization;Computer vision;Casting;Dictionaries;Codes;Text recognition},
+    doi={10.1109/CVPR46437.2021.00730}
+}
+"""
+
+_DATASETNAME = "vintext"
+
+_DESCRIPTION = """\
+Vintext is a challenging scene text dataset for Vietnamese, where some characters are equivocal in the visual form due to accent symbols.
+This dataset contains 2000 fully annotated images with 56,084 text instances. Each text instance is delineated by a quadrilateral bounding box and associated with the ground truth sequence of characters.
+The dataset is randomly split into three subsets for training (1,200 images), validation (300 images), and testing (500 images).
+"""
+
+_HOMEPAGE = "https://github.com/VinAIResearch/dict-guided"
+
+_LANGUAGES = ["vie"]
+
+_LICENSE = Licenses.AGPL_3_0.value
+
+_LOCAL = False
+
+_GDRIVE_ID = "1UUQhNvzgpZy7zXBFQp0Qox-BBjunZ0ml"
+
+_SUPPORTED_TASKS = [Tasks.OPTICAL_CHARACTER_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class VintextDataset(datasets.GeneratorBasedBuilder):
+    """
+    Vintext is a challenging scene text dataset for Vietnamese, where some characters are equivocal in the visual form due to accent symbols.
+    This dataset contains 1500 fully annotated images from the original format. Each text instance is delineated by a quadrilateral bounding box and associated with the ground truth sequence of characters.
+    The dataset is randomly split into 2 subsets for training (1,200 images) and testing (300 images).
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_imtext",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_imtext",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "image_path": datasets.Value("string"),
+                    "annotations": datasets.Sequence(
+                        {
+                            "x1": datasets.Value("int32"),
+                            "y1": datasets.Value("int32"),
+                            "x2": datasets.Value("int32"),
+                            "y2": datasets.Value("int32"),
+                            "x3": datasets.Value("int32"),
+                            "y3": datasets.Value("int32"),
+                            "x4": datasets.Value("int32"),
+                            "y4": datasets.Value("int32"),
+                            "transcript": datasets.Value("string"),
+                        }
+                    ),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_imtext":
+            features = schemas.image_text_features()
+            features["metadata"]["annotations"] = datasets.Sequence(
+                {
+                    "x1": datasets.Value("int32"),
+                    "y1": datasets.Value("int32"),
+                    "x2": datasets.Value("int32"),
+                    "y2": datasets.Value("int32"),
+                    "x3": datasets.Value("int32"),
+                    "y3": datasets.Value("int32"),
+                    "x4": datasets.Value("int32"),
+                    "y4": datasets.Value("int32"),
+                    "transcript": datasets.Value("string"),
+                }
+            )
+
+        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."""
+        try:
+            import gdown
+        except ImportError as err:
+            raise ImportError("You need to install gdown (`pip install gdown`) to downloads a public file/folder from Google Drive.") from err
+
+        zip_filepath = os.path.join(os.path.dirname(__file__), "vietnamese_original.zip")
+        if not os.path.exists(zip_filepath):
+            gdown.download(id=_GDRIVE_ID, output=zip_filepath)
+
+        data_dir = dl_manager.extract(zip_filepath)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "imagepath": Path(data_dir) / "vietnamese/train_images",
+                    "labelpath": Path(data_dir) / "vietnamese/labels",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "imagepath": Path(data_dir) / "vietnamese/test_image",
+                    "labelpath": Path(data_dir) / "vietnamese/labels",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, imagepath: Path, labelpath: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        df_list = []
+
+        for image in os.listdir(imagepath):
+            image_id = int(image.split(".")[0][2:])
+            label_file = os.path.join(labelpath, f"gt_{image_id}.txt")
+            with open(label_file, "r") as f:
+                label = f.read().strip()
+            df_list.append({"id": image_id, "image_path": os.path.join(imagepath, image), "label": label})
+
+        if self.config.schema == "source":
+            for i, row in enumerate(df_list):
+                labels = [label.split(",") for label in row["label"].split("\n")]
+
+                yield i, {
+                    "id": row["id"],
+                    "image_path": row["image_path"],
+                    "annotations": [
+                        {
+                            "x1": label[0],
+                            "y1": label[1],
+                            "x2": label[2],
+                            "y2": label[3],
+                            "x3": label[4],
+                            "y3": label[5],
+                            "x4": label[6],
+                            "y4": label[7],
+                            "transcript": label[8],
+                        }
+                        for label in labels
+                    ],
+                }
+
+        elif self.config.schema == "seacrowd_imtext":
+            for i, row in enumerate(df_list):
+                labels = [label.split(",") for label in row["label"].split("\n")]
+
+                yield i, {
+                    "id": row["id"],
+                    "image_paths": [row["image_path"]],
+                    "texts": None,
+                    "metadata": {
+                        "context": None,
+                        "labels": None,
+                        "annotations": [
+                            {
+                                "x1": label[0],
+                                "y1": label[1],
+                                "x2": label[2],
+                                "y2": label[3],
+                                "x3": label[4],
+                                "y3": label[5],
+                                "x4": label[6],
+                                "y4": label[7],
+                                "transcript": label[8],
+                            }
+                            for label in labels
+                        ],
+                    },
+                }