Init

.gitignore

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+env
+load_data
+__pycache__

HOW_TO_RUN.md

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+# How to run
+
+## Test
+
+~~~bash
+datasets-cli test test.py  --save_info --all_configs
+~~~

generate_metadata.py

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+import os
+import glob
+import json
+from typing import Dict, List
+from tqdm import tqdm
+
+"""
+Generate Metadata for this dataset
+{
+    "data": [
+        {
+            "depth": "depths/COME_Train_1.png",
+            "rgb": "RGB/COME_Train_1.jpg",
+            "gt": "GT/COME_Train_1.png"
+        },
+        ...
+    ]
+}
+"""
+
+depths = glob.glob("depths/*")
+gts = glob.glob("GT/*")
+rgbs = glob.glob("RGB/*")
+
+metadata: List[Dict[str, str]] = []
+
+for depth in tqdm(depths):
+    name = os.path.basename(depth).split(".")[0]
+
+    gts = glob.glob(f"GT/{name}.*")
+    if len(gts) != 1:
+        raise Exception(f"Inconsitent corresponding GT of name {name}, gts = ", gts)
+    gt = gts[0]
+
+    rgbs = glob.glob(f"RGB/{name}.*")
+    if len(rgbs) != 1:
+        raise Exception(f"Inconsitent corresponding RGB of name {name}, rgbs = ", rgbs)
+    rgb = rgbs[0]
+
+    metadata.append(
+        {
+            "depth": depth,
+            "gt": gt,
+            "rgb": rgb,
+            "name": name,
+        }
+    )
+
+# Serializing json
+json_object = json.dumps(dict(metadata=metadata), indent=4)
+
+# Writing to sample.json
+with open("metadata.json", "w") as outfile:
+    outfile.write(json_object)
+    
+os.system('zip -r train.zip depths GT RGB metadata.json generate_metadata.py')
+

requirements.txt

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+# Python 3.10.6
+datasets
+Pillow

rgbdsod_datasets.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.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+from typing import Dict, List
+from PIL import Image
+
+import datasets
+from datasets import DownloadManager
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    # "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
+    # "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+}
+
+_BASE_URL = ""
+
+
+def get_download_url(config_name: str, partition: str) -> str:
+    """Get download URL based on config name and parition (train/dev/test)
+
+    Args:
+        config_name (str): can be "v1", "v2",...
+        partition (str): can be "train", "dev" or "test"
+
+    Returns:
+        str: URL to download file
+    """
+    return f"https://huggingface.co/datasets/RGBD-SOD/test/resolve/main/data/{config_name}/{partition}.zip"
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class Test(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="v1",
+            version=VERSION,
+            description="RGB-D SOD Set 1",
+        ),
+        # datasets.BuilderConfig(
+        #     name="v2",
+        #     version=VERSION,
+        #     description="RGB-D SOD Set 2",
+        # ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "v1"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=datasets.Features(
+                {
+                    "depth": datasets.Image(),
+                    "rgb": datasets.Image(),
+                    "gt": datasets.Image(),
+                    "name": datasets.Value("string"),
+                    # These are the features of your dataset like images, labels ...
+                }
+            ),  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        train_dir = dl_manager.download_and_extract(
+            get_download_url(self.config.name, "train")
+        )
+        dev_dir = dl_manager.download_and_extract(
+            get_download_url(self.config.name, "dev")
+        )
+        # test_dir = dl_manager.download_and_extract(
+        #     get_download_url(self.config.name, "test")
+        # )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"dir_path": train_dir},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"dir_path": dev_dir},
+            ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.TEST,
+            #     # These kwargs will be passed to _generate_examples
+            #     gen_kwargs={"dir_path": test_dir},
+            # ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, dir_path: str):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(os.path.join(dir_path, "metadata.json"), "r") as f:
+            json_object = json.load(f)
+
+        metadata: List[Dict[str, str]] = json_object["metadata"]
+
+        for key, row in enumerate(metadata):
+            yield key, {
+                "name": row["name"],
+                "rgb": Image.open(os.path.join(dir_path, row["rgb"])).convert("RGB"),
+                "gt": Image.open(os.path.join(dir_path, row["gt"])).convert("L"),
+                "depth": Image.open(os.path.join(dir_path, row["depth"])).convert("L"),
+            }