How to use the miscnn.processing.data_augmentation.Data_Augmentation function in miscnn

return_slicer=False)
                seg_data = pad_patch(seg_data, self.patch_shape,
                                     return_slicer=False)
            # Run data augmentation
            if data_aug:
                img_data, seg_data = self.data_augmentation.run(img_data,
                                                                seg_data)
        # If skipping blank is not active -> random crop
        else:
            # Access image and segmentation data
            img = sample.img_data
            seg = sample.seg_data
            # If no data augmentation should be performed
            # -> create Data Augmentation instance without augmentation methods
            if not data_aug or self.data_augmentation is None:
                cropping_data_aug = Data_Augmentation(cycles=1,
                                            scaling=False, rotations=False,
                                            elastic_deform=False, mirror=False,
                                            brightness=False, contrast=False,
                                            gamma=False, gaussian_noise=False)
            else : cropping_data_aug = self.data_augmentation
            # Configure the Data Augmentation instance to cropping
            cropping_data_aug.cropping = True
            cropping_data_aug.cropping_patch_shape = self.patch_shape
            # Expand image dimension to simulate a batch with one image
            img_data = np.expand_dims(img, axis=0)
            seg_data = np.expand_dims(seg, axis=0)
            # Run data augmentation and cropping
            img_data, seg_data = cropping_data_aug.run(img_data, seg_data)
        # Create tuple of preprocessed data
        ready_data = list(zip(img_data, seg_data))
        # Return preprocessed data tuple

def __init__(self, data_io, batch_size, subfunctions=[],
                 data_aug=Data_Augmentation(), prepare_subfunctions=False,
                 prepare_batches=False, analysis="patchwise-crop",
                 patch_shape=None):
        # Parse Data Augmentation
        if isinstance(data_aug, Data_Augmentation):
            self.data_augmentation = data_aug
        else:
            self.data_augmentation = None
        # Exception: Analysis parameter check
        analysis_types = ["patchwise-crop", "patchwise-grid", "fullimage"]
        if not isinstance(analysis, str) or analysis not in analysis_types:
            raise ValueError('Non existent analysis type in preprocessing.')
        # Exception: Patch-shape parameter check
        if (analysis == "patchwise-crop" or analysis == "patchwise-grid") and \
            not isinstance(patch_shape, tuple):
            raise ValueError("Missing or wrong patch shape parameter for " + \
                             "patchwise analysis.")

def __init__(self, data_io, batch_size, subfunctions=[],
                 data_aug=Data_Augmentation(), prepare_subfunctions=False,
                 prepare_batches=False, analysis="patchwise-crop",
                 patch_shape=None):
        # Parse Data Augmentation
        if isinstance(data_aug, Data_Augmentation):
            self.data_augmentation = data_aug
        else:
            self.data_augmentation = None
        # Exception: Analysis parameter check
        analysis_types = ["patchwise-crop", "patchwise-grid", "fullimage"]
        if not isinstance(analysis, str) or analysis not in analysis_types:
            raise ValueError('Non existent analysis type in preprocessing.')
        # Exception: Patch-shape parameter check
        if (analysis == "patchwise-crop" or analysis == "patchwise-grid") and \
            not isinstance(patch_shape, tuple):
            raise ValueError("Missing or wrong patch shape parameter for " + \
                             "patchwise analysis.")
        # Parse parameter
        self.data_io = data_io
        self.batch_size = batch_size
        self.subfunctions = subfunctions