AeroPath.py

# 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

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@misc{støverud2023aeropath,
title={AeroPath: An airway segmentation benchmark dataset with challenging pathology}, 
author={Karen-Helene Støverud and David Bouget and Andre Pedersen and Håkon Olav Leira and Thomas Langø and Erlend Fagertun Hofstad},
year={2023},
eprint={2311.01138},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
AeroPath: An airway segmentation benchmark dataset with challenging pathology.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://github.com/raidionics/AeroPath"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "MIT"

# 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",
    "zenodo": "https://zenodo.org/records/10069289/files/AeroPath.zip?download=1"
}


# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class AeroPath(datasets.GeneratorBasedBuilder):
    """An airway segmentation benchmark dataset with challenging pathology."""

    VERSION = datasets.Version("1.0.0")

    # 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="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
        #datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
        datasets.BuilderConfig(name="zenodo", version=VERSION, description="This includes all 27 CTs stored as a single zip on Zenodo"),
    ]

    DEFAULT_CONFIG_NAME = "zenodo"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.DATA_DIR = None

    def get_patient(self, patient_id):
        if (patient_id < 1) or (patiend_id > 27):
            raise ValueError("patient_id should be an integer in range [1, 27].")

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        if self.config.name == "zenodo":  # This is the name of the configuration selected in BUILDER_CONFIGS above
            features = datasets.Features(
                {
                    "ct": datasets.Value("string"),
                    "airways": datasets.Value("string"),
                    "lungs": datasets.Value("string")
                    # These are the features of your dataset like images, labels ...
                }
            )
        else:  
            raise ValueError("Only 'zenodo' is supported.")# This is an example to show how to have different features for "first_domain" and "second_domain"

        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=features,  # 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 get_data_dir(self):
        return self.DATA_DIR
    
    def _split_generators(self, dl_manager):
        # 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
        urls = _URLS[self.config.name]
        self.DATA_DIR = dl_manager.download_and_extract(urls)

        # append AeroPath
        self.DATA_DIR = os.path.join(self.DATA_DIR, "AeroPath")

        print("data is downloaded to:", self.DATA_DIR)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "split": "test",
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, split):
        # 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.
        for patient_id in os.listdir(self.DATA_DIR):
            curr_path = os.path.join(self.DATA_DIR, patient_id)
            if patient_id in ["README.md", "license.md"]:
                continue
            yield patient_id, {
                "ct": os.path.join(curr_path, patient_id + "_CT_HR.nii.gz"),
                "airways": os.path.join(curr_path, patient_id + "_CT_HR_label_airways.nii.gz"),
                "lungs": os.path.join(curr_path, patient_id + "_CT_HR_label_lungs.nii.gz"),
            }