Reverted to working load script

AeroPath.py

@@ -1,16 +1,29 @@
-"""AeroPath: An airway segmentation benchmark dataset with challenging pathology."""
+# 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 datasets
-
-_DESCRIPTION = """\
-AeroPath: An airway segmentation benchmark dataset with challenging pathology.
-"""
+import csv
+import json
+import os
 
-_HOMEPAGE = "https://github.com/raidionics/AeroPath"
+import datasets
 
-_LICENSE = "MIT"
 
+# 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}, 
@@ -22,49 +35,129 @@ primaryClass={cs.CV}
 }
 """
 
-_URLS = [
-    {
-        "ct": f"data/{i}/{i}_CT_HR.nii.gz",
-        "airways": f"data/{i}/{i}_CT_HR_label_airways.nii.gz",
-        "lungs": f"data/{i}/{i}_CT_HR_label_lungs.nii.gz",
-    }
-    for i in range(1, 28)
-]
+# 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):
-        features = datasets.Features(
-            {
-                "ct": datasets.Value("string"),
-                "airways": datasets.Value("string"),
-                "lungs": datasets.Value("string"),
-            }
-        )
+        # 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,
-            features=features,
+            # 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):
-        data_dirs = dl_manager.download(_URLS)
+        # 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={
-                    "data_dirs": data_dirs,
+                    "split": "test",
                 },
             ),
         ]
 
-    def _generate_examples(self, data_dirs):
-        for key, patient in enumerate(data_dirs):
-            yield key, patient
+    # 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"),
+            }