How to use the miscnn.data_loading.interfaces.Dictionary_interface function in miscnn

def test_IOI_DICTIONARY_loading(self):
        my_dict = dict()
        my_dict["dict_sample"] = (self.img, self.seg)
        interface = Dictionary_interface(my_dict)
        sample_list = interface.initialize("")
        img = interface.load_image(sample_list[0])
        seg = interface.load_segmentation(sample_list[0])
        self.assertTrue(np.array_equal(img, self.img))
        self.assertTrue(np.array_equal(seg, self.seg))
    # NIFTI_interface - Loading and Storage of Predictions

def setUpClass(self):
        np.random.seed(1234)
        # Create 2D imgaging and segmentation data set
        self.dataset2D = dict()
        for i in range(0, 6):
            img = np.random.rand(16, 16) * 255
            self.img = img.astype(int)
            seg = np.random.rand(16, 16) * 3
            self.seg = seg.astype(int)
            self.dataset2D["TEST.sample_" + str(i)] = (self.img, self.seg)
        # Initialize Dictionary IO Interface
        io_interface2D = Dictionary_interface(self.dataset2D, classes=3,
                                              three_dim=False)
        # Initialize temporary directory
        self.tmp_dir2D = tempfile.TemporaryDirectory(prefix="tmp.miscnn.")
        tmp_batches = os.path.join(self.tmp_dir2D.name, "batches")
        # Initialize Data IO
        self.data_io2D = Data_IO(io_interface2D,
                                 input_path=os.path.join(self.tmp_dir2D.name),
                                 output_path=os.path.join(self.tmp_dir2D.name),
                                 batch_path=tmp_batches, delete_batchDir=False)
        # Initialize Preprocessor
        self.pp2D = Preprocessor(self.data_io2D, batch_size=2,
                                 data_aug=None, analysis="fullimage")
        # Get sample list
        self.sample_list2D = self.data_io2D.get_indiceslist()
        # Create 3D imgaging and segmentation data set
        self.dataset3D = dict()

tmp_batches = os.path.join(self.tmp_dir2D.name, "batches")
        # Initialize Data IO
        self.data_io2D = Data_IO(io_interface2D, input_path="", output_path="",
                              batch_path=tmp_batches, delete_batchDir=False)
        # Create 3D imgaging and segmentation data set
        self.dataset3D = dict()
        for i in range(0, 10):
            img = np.random.rand(16, 16, 16) * 255
            self.img = img.astype(int)
            seg = np.random.rand(16, 16, 16) * 3
            self.seg = seg.astype(int)
            if i in range(8,10): sample = (self.img, None)
            else : sample = (self.img, self.seg)
            self.dataset3D["TEST.sample_" + str(i)] = sample
        # Initialize Dictionary IO Interface
        io_interface3D = Dictionary_interface(self.dataset3D, classes=3,
                                              three_dim=True)
        # Initialize temporary directory
        self.tmp_dir3D = tempfile.TemporaryDirectory(prefix="tmp.miscnn.")
        tmp_batches = os.path.join(self.tmp_dir3D.name, "batches")
        # Initialize Data IO
        self.data_io3D = Data_IO(io_interface3D, input_path="", output_path="",
                              batch_path=tmp_batches, delete_batchDir=False)

def test_SUBFUNCTIONS_postprocessing(self):
        ds = dict()
        for i in range(0, 10):
            img = np.random.rand(16, 16, 16) * 255
            img = img.astype(int)
            seg = np.random.rand(16, 16, 16) * 3
            seg = seg.astype(int)
            sample = (img, seg)
            ds["TEST.sample_" + str(i)] = sample
        io_interface = Dictionary_interface(ds, classes=3, three_dim=True)
        self.tmp_dir = tempfile.TemporaryDirectory(prefix="tmp.miscnn.")
        tmp_batches = os.path.join(self.tmp_dir.name, "batches")
        dataio = Data_IO(io_interface, input_path="", output_path="",
                         batch_path=tmp_batches, delete_batchDir=False)
        sf = [Resize((9,9,9)), Normalization(), Clipping(min=-1.0, max=0.0)]
        pp = Preprocessor(dataio, batch_size=1, prepare_subfunctions=False,
                          analysis="patchwise-grid", subfunctions=sf,
                          patch_shape=(4,4,4))
        sample_list = dataio.get_indiceslist()
        for index in sample_list:
            sample = dataio.sample_loader(index)
            for sf in pp.subfunctions:
                sf.preprocessing(sample, training=False)
            pp.cache["shape_" + str(index)] = sample.img_data.shape
            sample.seg_data = np.random.rand(9, 9, 9) * 3
            sample.seg_data = sample.seg_data.astype(int)

def test_IOI_DICTIONARY_predictionhandling(self):
        my_dict = dict()
        my_dict["dict_sample"] = (self.img, self.seg)
        interface = Dictionary_interface(my_dict)
        sample_list = interface.initialize("")
        interface.save_prediction(self.seg, "dict_sample", "")
        pred = interface.load_prediction("dict_sample", "")
        self.assertTrue(np.array_equal(pred, self.seg))

def setUpClass(self):
        # Create imgaging and segmentation data set
        np.random.seed(1234)
        self.dataset = dict()
        for i in range(0, 10):
            img = np.random.rand(16, 16, 16) * 255
            self.img = img.astype(int)
            seg = np.random.rand(16, 16, 16) * 3
            self.seg = seg.astype(int)
            if i == 3 : sample = (self.img, self.seg, self.seg)
            elif i == 5 : sample = (self.img, None, self.seg)
            else : sample = (self.img, self.seg)
            self.dataset["TEST.sample_" + str(i)] = sample
        # Initialize Dictionary IO Interface
        self.io_interface = Dictionary_interface(self.dataset)
        # Initialize temporary directory
        self.tmp_dir = tempfile.TemporaryDirectory(prefix="tmp.miscnn.")
        self.tmp_batches = os.path.join(self.tmp_dir.name, "batches")

def setUpClass(self):
        np.random.seed(1234)
        # Create 2D imgaging and segmentation data set
        self.dataset = dict()
        for i in range(0, 6):
            img = np.random.rand(16, 16) * 255
            self.img = img.astype(int)
            seg = np.random.rand(16, 16) * 2
            self.seg = seg.astype(int)
            self.dataset["TEST.sample_" + str(i)] = (self.img, self.seg)
        # Initialize Dictionary IO Interface
        io_interface = Dictionary_interface(self.dataset, classes=3,
                                              three_dim=False)
        # Initialize temporary directory
        self.tmp_dir = tempfile.TemporaryDirectory(prefix="tmp.miscnn.")
        tmp_batches = os.path.join(self.tmp_dir.name, "batches")
        # Initialize Data IO
        self.data_io = Data_IO(io_interface,
                               input_path=os.path.join(self.tmp_dir.name),
                               output_path=os.path.join(self.tmp_dir.name),
                               batch_path=tmp_batches, delete_batchDir=False)
        # Initialize Preprocessor
        self.pp = Preprocessor(self.data_io, batch_size=2,
                               data_aug=None, analysis="fullimage")
        # Initialize Neural Network
        self.model = Neural_Network(self.pp)
        # Get sample list
        self.sample_list = self.data_io.get_indiceslist()

def setUpClass(self):
        np.random.seed(1234)
        # Create 2D imgaging and segmentation data set
        self.dataset2D = dict()
        for i in range(0, 10):
            img = np.random.rand(16, 16) * 255
            self.img = img.astype(int)
            seg = np.random.rand(16, 16) * 2
            self.seg = seg.astype(int)
            self.dataset2D["TEST.sample_" + str(i)] = (self.img, self.seg)
        # Initialize Dictionary IO Interface
        io_interface2D = Dictionary_interface(self.dataset2D, classes=3,
                                              three_dim=False)
        # Initialize temporary directory
        self.tmp_dir2D = tempfile.TemporaryDirectory(prefix="tmp.miscnn.")
        tmp_batches = os.path.join(self.tmp_dir2D.name, "batches")
        # Initialize Data IO
        self.data_io2D = Data_IO(io_interface2D, input_path="", output_path="",
                              batch_path=tmp_batches, delete_batchDir=False)
        # Create 3D imgaging and segmentation data set
        self.dataset3D = dict()
        for i in range(0, 10):
            img = np.random.rand(16, 16, 16) * 255
            self.img = img.astype(int)
            seg = np.random.rand(16, 16, 16) * 3
            self.seg = seg.astype(int)
            if i in range(8,10): sample = (self.img, None)
            else : sample = (self.img, self.seg)

batch_path=tmp_batches, delete_batchDir=False)
        # Initialize Preprocessor
        self.pp2D = Preprocessor(self.data_io2D, batch_size=2,
                                 data_aug=None, analysis="fullimage")
        # Get sample list
        self.sample_list2D = self.data_io2D.get_indiceslist()
        # Create 3D imgaging and segmentation data set
        self.dataset3D = dict()
        for i in range(0, 6):
            img = np.random.rand(16, 16, 16) * 255
            self.img = img.astype(int)
            seg = np.random.rand(16, 16, 16) * 3
            self.seg = seg.astype(int)
            self.dataset3D["TEST.sample_" + str(i)] = (self.img, self.seg)
        # Initialize Dictionary IO Interface
        io_interface3D = Dictionary_interface(self.dataset3D, classes=3,
                                              three_dim=True)
        # Initialize temporary directory
        self.tmp_dir3D = tempfile.TemporaryDirectory(prefix="tmp.miscnn.")
        tmp_batches = os.path.join(self.tmp_dir3D.name, "batches")
        # Initialize Data IO
        self.data_io3D = Data_IO(io_interface3D,
                                 input_path=os.path.join(self.tmp_dir3D.name),
                                 output_path=os.path.join(self.tmp_dir3D.name),
                                 batch_path=tmp_batches, delete_batchDir=False)
        # Initialize Preprocessor
        self.pp3D = Preprocessor(self.data_io3D, batch_size=2,
                                 data_aug=None, analysis="fullimage")
        # Get sample list
        self.sample_list3D = self.data_io3D.get_indiceslist()

def setUpClass(self):
        np.random.seed(1234)
        # Create imgaging and segmentation data set
        self.dataset = dict()
        for i in range(0, 10):
            img = np.random.rand(16, 16, 16) * 255
            self.img = img.astype(int)
            seg = np.random.rand(16, 16, 16) * 3
            self.seg = seg.astype(int)
            sample = (self.img, self.seg)
            self.dataset["TEST.sample_" + str(i)] = sample
        # Initialize Dictionary IO Interface
        io_interface = Dictionary_interface(self.dataset, classes=3,
                                            three_dim=True)
        # Initialize temporary directory
        self.tmp_dir = tempfile.TemporaryDirectory(prefix="tmp.miscnn.")
        tmp_batches = os.path.join(self.tmp_dir.name, "batches")
        # Initialize Data IO
        self.data_io = Data_IO(io_interface, input_path="", output_path="",
                               batch_path=tmp_batches, delete_batchDir=False)
        # Initialize Data Augmentation
        self.data_aug = Data_Augmentation()
        # Get sample list
        self.sample_list = self.data_io.get_indiceslist()