Upload fMRI-openneuro.py

fMRI-openneuro.py

@@ -0,0 +1,157 @@
+import nibabel as nib
+import os
+import numpy as np
+
+import datasets
+
+import boto3
+
+
+_DESCRIPTION = """\
+fMIR dataset from openneuro.org
+"""
+
+class fMRIConfig(datasets.BuilderConfig):
+    """Builder Config for fMRI"""
+ 
+    def __init__(self, data_url, num_datasets=[10, 1, 1], num_frames=8, sampling_rate=1, **kwargs):
+        """BuilderConfig for fMRI.
+        Args:
+          data_url: `string`, url to download the zip file from.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(fMRIConfig, self).__init__(**kwargs)
+        self.data_url = data_url
+        self.num_datasets = num_datasets
+        self.num_frames = num_frames
+        self.sampling_rate = sampling_rate
+
+class fMRITest(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    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 = [
+        fMRIConfig(name="test1", data_url="openneuro.org", version=VERSION, description="fMRI test dataset 1", ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "test1"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        if self.config.name == "test1":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            # features = datasets.Features(
+            #     {
+            #         # "func": np.ndarray(shape=(65,77,65,self.config.duration)),
+            #         "func": datasets.Array4D(shape=(None,None,None,self.config.duration), dtype='float32'),
+            #     }
+            # )
+            features = None
+        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"),
+        )
+
+    def _split_generators(self, dl_manager):
+
+        # Connect to S3
+        s3 = boto3.client('s3')
+
+        bucket_name = self.config.data_url
+        
+        response = s3.list_objects_v2(Bucket=bucket_name, Prefix='', Delimiter='/')
+
+        folder_names = [x['Prefix'].split('/')[-2] for x in response.get('CommonPrefixes', [])]
+        print(len(folder_names))
+
+        ndatasets = self.config.num_datasets
+        if isinstance(ndatasets, int):
+            ndatasets = [ndatasets, 10, 10]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "bucket_name": bucket_name,
+                    "folder_names": folder_names[:ndatasets[0]],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "bucket_name": bucket_name,
+                    "folder_names": folder_names[ndatasets[0]:ndatasets[0] + ndatasets[1]],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "bucket_name": bucket_name,
+                    "folder_names": folder_names[ndatasets[0] + ndatasets[1]:ndatasets[0] + ndatasets[1] + ndatasets[2]],
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, bucket_name, folder_names):
+        # 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.
+        s3 = boto3.client('s3')
+        tmp_dir = os.path.join('tmp', folder_names[0]) if len(folder_names) > 0 else 'tmp'
+        if not os.path.exists(tmp_dir):
+            os.makedirs(tmp_dir)
+        anat_file = os.path.join(tmp_dir, 'T1w.nii.gz')
+        func_file = os.path.join(tmp_dir, 'bold.nii.gz')
+        
+        duration = self.config.num_frames * self.config.sampling_rate
+
+        for folder_name in folder_names:
+            response = s3.list_objects_v2(Bucket=bucket_name, Prefix=folder_name)
+            for obj in response.get('Contents', []):
+                obj_key = obj['Key']
+                if '_T1w.nii.gz' in obj_key: # Anatomical
+                    # Store subject number to verify anat/func match
+                    anat_subj = obj_key.split('/')[1]
+                    
+                    # Download the object to tmp location
+                    s3.download_file(bucket_name, obj_key, anat_file)
+
+                    # store the head of anat_subj
+                    anat_header = nib.load(anat_file).header
+                elif '_bold.nii.gz' in obj_key: # Functional
+                    func_subj = obj_key.split('/')[1]
+                    if func_subj == anat_subj:
+                        s3.download_file(bucket_name, obj_key, func_file)
+                        func = nib.load(func_file).get_fdata().astype('float16')
+                        func = np.transpose(func, (3, 0, 1, 2)) # T, X, Y, Z
+                        shape = func.shape
+                        # print(f"{obj_key}", shape)
+                        for i in range(0, shape[0] - duration + self.config.sampling_rate, duration):
+                            func_slice = func[i:i+duration:self.config.sampling_rate, :, :, :]
+                            # print(f"{obj_key}-{i}", func_slice.shape)
+                            yield f"{obj_key}-{i}", {
+                                "func": func_slice,
+                            }
+            
+
+