Update LyNoS.py

LyNoS.py

@@ -1,7 +1,9 @@
 """LyNoS: Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding."""
 
 
-import datasets
+import os
+import csv
+import json
 
 _DESCRIPTION = """\
 LyNoS: Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding.
@@ -25,51 +27,113 @@ _CITATION = """\
 
 """
 
-_URLS = [
-    {
-        "ct": f"data/Pat{i}/Pat{i}_data.nii.gz",
-        "azygos": f"data/Pat{i}/Pat{i}_labels_Azygos.nii.gz",
-        "brachiocephalicveins": f"data/Pat{i}/Pat{i}_labels_BrachiocephalicVeins.nii.gz",
-        "esophagus": f"data/Pat{i}/Pat{i}_labels_Esophagus.nii.gz",
-        "lymphnodes": f"data/Pat{i}/Pat{i}_labels_LymphNodes.nii.gz",
-        "subclaviancarotidarteries": f"data/Pat{i}/Pat{i}_labels_SubCarArt.nii.gz",
-    }
-    for i in range(1, 15)
-]
-
+#_URLS = [
+#    {
+#        "ct": f"data/Pat{i}/Pat{i}_data.nii.gz",
+#        "azygos": f"data/Pat{i}/Pat{i}_labels_Azygos.nii.gz",
+#        "brachiocephalicveins": f"data/Pat{i}/Pat{i}_labels_BrachiocephalicVeins.nii.gz",
+#        "esophagus": f"data/Pat{i}/Pat{i}_labels_Esophagus.nii.gz",
+#        "lymphnodes": f"data/Pat{i}/Pat{i}_labels_LymphNodes.nii.gz",
+#        "subclaviancarotidarteries": f"data/Pat{i}/Pat{i}_labels_SubCarArt.nii.gz",
+#    }
+#    for i in range(1, 15)
+#]
+_URLS = {"zenodo": "https://zenodo.org/records/10102261/files/LyNoS.zip?download=1"}
 
 class LyNoS(datasets.GeneratorBasedBuilder):
     """A segmentation benchmark dataset for enlarged lymph nodes in patients with primary lung cancer."""
 
     VERSION = datasets.Version("1.0.0")
+    DEFAULT_CONFIG_NAME = "zenodo"
+    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 15 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 > 15):
+            raise ValueError("patient_id should be an integer in range [1, 15].")
 
     def _info(self):
-        features = datasets.Features(
-            {
-                "ct": datasets.Value("string"),
-                "lymphnodes": datasets.Value("string"),
-            }
-        )
-        return datasets.DatasetInfo(
+       # 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"),
+                   "lymphnodes": datasets.Value("string"),
+                   "azygos": datasets.Value("string"),
+                   "brachiocephalicveins": datasets.Value("string"),
+                   "esophagus": datasets.Value("string"),
+                   "subclaviancarotidarteries": datasets.Value("string")
+               }
+           )
+       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, "LyNoS")
+
+        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", "stations_sto.csv"]:
+                continue
+            yield patient_id, {
+                "ct": os.path.join(curr_path, patient_id.lower() + "_data.nii.gz"),
+                "lymphnodes": os.path.join(curr_path, patient_id.lower() + "_labels_LymphNodes.nii.gz"),
+                "azygos": os.path.join(curr_path, patient_id.lower() + "_labels_Azygos.nii.gz"),
+                "brachiocephalicveins": os.path.join(curr_path, patient_id.lower() + "_labels_BrachiocephalicVeins.nii.gz"),
+                "esophagus": os.path.join(curr_path, patient_id.lower() + "_labels_Esophagus.nii.gz"),
+                "subclaviancarotidarteries": os.path.join(curr_path, patient_id.lower() + "_labels_SubCarArt.nii.gz")
+            }