Added loading script

ex-dark.py

@@ -0,0 +1,154 @@
+# 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.
+"""Exclusively Dark Image Dataset"""
+
+
+import os
+
+import datasets
+import pandas as pd
+
+
+_CITATION = """\
+@article{Exdark,
+  title = {Getting to Know Low-light Images with The Exclusively Dark Dataset},
+  author = {Loh, Yuen Peng and Chan, Chee Seng},
+  journal = {Computer Vision and Image Understanding},
+  volume = {178},
+  pages = {30-42},
+  year = {2019},
+  doi = {https://doi.org/10.1016/j.cviu.2018.10.010}
+}
+"""
+
+_DESCRIPTION = """\
+The Exclusively Dark (ExDARK) dataset is a collection of low-light
+images from very low-light environments to twilight (i.e 10 different
+conditions) with 12 object classes (similar to PASCAL VOC) annotated on both
+image class level and local object bounding boxes.
+
+The object classes are as follows:
+
+- Dog
+- Motorbike
+- People
+- Cat
+- Chair
+- Table
+- Car
+- Bicycle
+- Bottle
+- Bus
+- Cup
+- Boat
+
+For more information about the original Exclusively Dark Image dataset,
+please visit the official dataset page:
+
+https://github.com/cs-chan/Exclusively-Dark-Image-Dataset
+
+Please refer to the original dataset source for any additional details,
+citations, or specific usage guidelines provided by the dataset creators.
+"""
+
+_HOMEPAGE = "https://github.com/cs-chan/Exclusively-Dark-Image-Dataset"
+
+_LICENSE = "bsd-3-clause"
+
+_LABEL_NAMES = [
+    "Dog",
+    "Motorbike",
+    "People",
+    "Cat",
+    "Chair",
+    "Table",
+    "Car",
+    "Bicycle",
+    "Bottle",
+    "Bus",
+    "Cup",
+    "Boat",
+]
+
+
+class ExDark(datasets.GeneratorBasedBuilder):
+    """Exclusively Dark (ExDARK) dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="exdark",
+            version=VERSION,
+            description="Exclusively Dark (ExDARK) dataset",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "exdark"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "img": datasets.Image(),
+                    "label": datasets.Sequence(
+                        feature={
+                            "class": datasets.ClassLabel(names=_LABEL_NAMES),
+                            "x": datasets.Value("int32"),
+                            "y": datasets.Value("int32"),
+                            "w": datasets.Value("int32"),
+                            "h": datasets.Value("int32"),
+                        }
+                    ),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract("ExDark.zip")
+
+        metadata_path = os.path.join(data_dir, "ExDark", "metadata.csv")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "metadata_path": metadata_path,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_dir, metadata_path, split):
+        df = pd.read_csv(metadata_path)
+        classes = df["class"].unique()
+
+        df["class"] = df["class"].apply(lambda x: classes.tolist().index(x))
+
+        for idx, file_name in enumerate(df.file_name.unique()):
+            sample = df[df.file_name == file_name]
+            bboxs = sample[["x", "y", "w", "h"]].to_numpy()
+            labels = sample["class"].to_numpy()
+            yield idx, {
+                "img": file_name,
+                "label": {
+                    "labels": labels,
+                    "bboxes": bboxs,
+                },
+            }