Python image_reader.ImageReader类代码示例

 def test_operations(self):
     reader = ImageReader(['image'])
     reader.initialise(SINGLE_MOD_DATA, SINGLE_MOD_TASK, single_mod_list)
     idx, data, interp_order = reader()
     self.assertEqual(
         SINGLE_MOD_DATA['lesion'].interp_order, interp_order['image'][0])
     self.assertAllClose(data['image'].shape, (256, 168, 256, 1, 1))

    def initialise_dataset_loader(
            self, data_param=None, task_param=None, data_partitioner=None):
        self.data_param = data_param
        self.autoencoder_param = task_param

        if not self.is_training:
            self._infer_type = look_up_operations(
                self.autoencoder_param.inference_type, SUPPORTED_INFERENCE)
        else:
            self._infer_type = None

        file_lists = self.get_file_lists(data_partitioner)
        # read each line of csv files into an instance of Subject
        if self.is_evaluation:
            NotImplementedError('Evaluation is not yet '
                                'supported in this application.')
        if self.is_training:
            self.readers = []
            for file_list in file_lists:
                reader = ImageReader(['image'])
                reader.initialise(data_param, task_param, file_list)
                self.readers.append(reader)
        if self._infer_type in ('encode', 'encode-decode'):
            self.readers = [ImageReader(['image'])]
            self.readers[0].initialise(data_param,
                                       task_param,
                                       file_lists[0])
        elif self._infer_type == 'sample':
            self.readers = []
        elif self._infer_type == 'linear_interpolation':
            self.readers = [ImageReader(['feature'])]
            self.readers[0].initialise(data_param,
                                       task_param,
                                       [file_lists])

 def test_trainable_preprocessing(self):
     label_file = os.path.join('testing_data', 'label_reader.txt')
     if os.path.exists(label_file):
         os.remove(label_file)
     label_normaliser = DiscreteLabelNormalisationLayer(
         image_name='label',
         modalities=vars(LABEL_TASK).get('label'),
         model_filename=os.path.join('testing_data', 'label_reader.txt'))
     reader = ImageReader(['label'])
     with self.assertRaisesRegexp(AssertionError, ''):
         reader.add_preprocessing_layers(label_normaliser)
     reader.initialise(LABEL_DATA, LABEL_TASK, label_list)
     reader.add_preprocessing_layers(label_normaliser)
     reader.add_preprocessing_layers(
         [PadLayer(image_name=['label'], border=(10, 5, 5))])
     idx, data, interp_order = reader(idx=0)
     unique_data = np.unique(data['label'])
     expected_v1 = np.array(
         [0., 1., 2., 3., 4., 5., 6., 7., 8.,
          9., 10., 11., 12., 13., 14., 15., 16., 17.,
          18., 19., 20., 21., 22., 23., 24., 25., 26., 27.,
          28., 29., 30., 31., 32., 33., 34., 35., 36.,
          37., 38., 39., 40., 41., 42., 43., 44., 45.,
          46., 47., 48., 49., 50., 51., 52., 53., 54.,
          55., 56., 57., 58., 59., 60., 61., 62., 63.,
          64., 65., 66., 67., 68., 69., 70., 71., 72.,
          73., 74., 75., 76., 77., 78., 79., 80., 81.,
          82., 83., 84., 85., 86., 87., 88., 89., 90.,
          91., 92., 93., 94., 95., 96., 97., 98., 99.,
          100., 101., 102., 103., 104., 105., 106., 107., 108.,
          109., 110., 111., 112., 113., 114., 115., 116., 117.,
          118., 119., 120., 121., 122., 123., 124., 125., 126.,
          127., 128., 129., 130., 131., 132., 133., 134., 135.,
          136., 137., 138., 139., 140., 141., 142., 143., 144.,
          145., 146., 147., 148., 149., 150., 151., 152., 153.,
          154., 155., 156., 157.], dtype=np.float32)
     expected_v2 = np.array(
         [0., 1., 2., 3., 4., 5., 6., 7., 8.,
          9., 10., 11., 12., 13., 14., 15., 16., 17.,
          18., 20., 21., 22., 23., 24., 25., 26., 27.,
          28., 29., 30., 31., 32., 33., 34., 35., 36.,
          37., 38., 39., 40., 41., 42., 43., 44., 45.,
          46., 47., 48., 49., 50., 51., 52., 53., 54.,
          55., 56., 57., 58., 59., 60., 61., 62., 63.,
          64., 65., 66., 67., 68., 69., 70., 71., 72.,
          73., 74., 75., 76., 77., 78., 79., 80., 81.,
          82., 83., 84., 85., 86., 87., 88., 89., 90.,
          91., 92., 93., 94., 95., 96., 97., 98., 99.,
          100., 101., 102., 103., 104., 105., 106., 107., 108.,
          109., 110., 111., 112., 113., 114., 115., 116., 117.,
          118., 119., 120., 121., 122., 123., 124., 125., 126.,
          127., 128., 129., 130., 131., 132., 133., 134., 135.,
          136., 137., 138., 139., 140., 141., 142., 143., 144.,
          145., 146., 147., 148., 149., 150., 151., 152., 153.,
          154., 155., 156., 157.], dtype=np.float32)
     compatible_assert = \
         np.all(unique_data == expected_v1) or \
         np.all(unique_data == expected_v2)
     self.assertTrue(compatible_assert)
     self.assertAllClose(data['label'].shape, (103, 74, 93, 1, 1))

 def test_properties(self):
     reader = ImageReader(['image'])
     reader.initialise(SINGLE_MOD_DATA, SINGLE_MOD_TASK, single_mod_list)
     self.assertEqual(len(reader.output_list), 4)
     self.assertDictEqual(reader.shapes,
                          {'image': (256, 168, 256, 1, 1)})
     self.assertDictEqual(reader.tf_dtypes, {'image': tf.float32})
     self.assertEqual(reader.names, ['image'])
     self.assertDictEqual(reader.input_sources,
                          {'image': ('lesion',)})
     self.assertEqual(reader.get_subject_id(1)[:4], 'Fin_')

 def test_preprocessing_zero_padding(self):
     reader = ImageReader(['image'])
     reader.initialise(SINGLE_MOD_DATA, SINGLE_MOD_TASK, single_mod_list)
     idx, data, interp_order = reader()
     self.assertEqual(SINGLE_MOD_DATA['lesion'].interp_order,
                      interp_order['image'][0])
     self.assertAllClose(data['image'].shape, (256, 168, 256, 1, 1))
     reader.add_preprocessing_layers(
         [PadLayer(image_name=['image'], border=(0, 0, 0))])
     idx, data, interp_order = reader(idx=2)
     self.assertEqual(idx, 2)
     self.assertAllClose(data['image'].shape, (256, 168, 256, 1, 1))

    def test_errors(self):
        reader = ImageReader(['image'])
        reader.initialise(BAD_DATA, SINGLE_MOD_TASK, bad_data_list)
        with self.assertRaisesRegexp(ValueError, ''):
            reader = ImageReader(['image'])
            reader.initialise(SINGLE_MOD_DATA, BAD_TASK, single_mod_list)

        reader = ImageReader(['image'])
        reader.initialise(SINGLE_MOD_DATA, SINGLE_MOD_TASK, single_mod_list)
        idx, data, interp_order = reader(idx=100)
        self.assertEqual(idx, -1)
        self.assertEqual(data, None)
        idx, data, interp_order = reader(shuffle=True)
        self.assertEqual(data['image'].shape, (256, 168, 256, 1, 1))

    def initialise_dataset_loader(
            self, data_param=None, task_param=None, data_partitioner=None):
        self.data_param = data_param
        self.segmentation_param = task_param

        # read each line of csv files into an instance of Subject
        if self.is_training:
            file_lists = []
            if self.action_param.validation_every_n > 0:
                file_lists.append(data_partitioner.train_files)
                file_lists.append(data_partitioner.validation_files)
            else:
                file_lists.append(data_partitioner.all_files)
            self.readers = []
            for file_list in file_lists:
                reader = ImageReader(SUPPORTED_INPUT)
                reader.initialise(data_param, task_param, file_list)
                self.readers.append(reader)
        else:  # in the inference process use image input only
            inference_reader = ImageReader(['image'])
            file_list = data_partitioner.inference_files
            inference_reader.initialise(data_param, task_param, file_list)
            self.readers = [inference_reader]

        foreground_masking_layer = None
        if self.net_param.normalise_foreground_only:
            foreground_masking_layer = BinaryMaskingLayer(
                type_str=self.net_param.foreground_type,
                multimod_fusion=self.net_param.multimod_foreground_type,
                threshold=0.0)

        mean_var_normaliser = MeanVarNormalisationLayer(
            image_name='image', binary_masking_func=foreground_masking_layer)

        label_normaliser = DiscreteLabelNormalisationLayer(
            image_name='label',
            modalities=vars(task_param).get('label'),
            model_filename=self.net_param.histogram_ref_file)

        normalisation_layers = []
        normalisation_layers.append(mean_var_normaliser)
        if task_param.label_normalisation:
            normalisation_layers.append(label_normaliser)

        volume_padding_layer = []
        if self.net_param.volume_padding_size:
            volume_padding_layer.append(PadLayer(
                image_name=SUPPORTED_INPUT,
                border=self.net_param.volume_padding_size))
        for reader in self.readers:
            reader.add_preprocessing_layers(
                normalisation_layers + volume_padding_layer)

def get_label_reader():
    reader = ImageReader(['label'])
    reader.initialise(MOD_LABEL_DATA, MOD_LABEl_TASK, mod_label_list)
    label_normaliser = DiscreteLabelNormalisationLayer(
        image_name='label',
        modalities=vars(SINGLE_25D_TASK).get('label'),
        model_filename=os.path.join('testing_data', 'agg_test.txt'))
    reader.add_preprocessing_layers(label_normaliser)
    pad_layer = PadLayer(image_name=('label',), border=(5, 6, 7))
    reader.add_preprocessing_layers([pad_layer])
    return reader

    def initialise_dataset_loader(
            self, data_param=None, task_param=None, data_partitioner=None):
        RegressionApplication.initialise_dataset_loader(
            self, data_param, task_param, data_partitioner)
        if self.is_training:
            return
        if not task_param.error_map:
            return

        file_lists = self.get_file_lists(data_partitioner)
        # modifying the original readers in regression application
        # as we need ground truth labels to generate error maps
        self.readers=[]
        for file_list in file_lists:
            reader = ImageReader(['image', 'output'])
            reader.initialise(data_param, task_param, file_list)
            self.readers.append(reader)

        mean_var_normaliser = MeanVarNormalisationLayer(image_name='image')
        histogram_normaliser = None
        if self.net_param.histogram_ref_file:
            histogram_normaliser = HistogramNormalisationLayer(
                image_name='image',
                modalities=vars(task_param).get('image'),
                model_filename=self.net_param.histogram_ref_file,
                norm_type=self.net_param.norm_type,
                cutoff=self.net_param.cutoff,
                name='hist_norm_layer')

        preprocessors = []
        if self.net_param.normalisation:
            preprocessors.append(histogram_normaliser)
        if self.net_param.whitening:
            preprocessors.append(mean_var_normaliser)
        if self.net_param.volume_padding_size:
            preprocessors.append(PadLayer(
                image_name=SUPPORTED_INPUT,
                border=self.net_param.volume_padding_size))
        self.readers[0].add_preprocessing_layers(preprocessors)

 def test_existing_csv(self):
     reader_for_csv = ImageReader(['image'])
     reader_for_csv.initialise(
         SINGLE_MOD_DATA, SINGLE_MOD_TASK, single_mod_list)
     reader = ImageReader(['image'])
     reader.initialise(EXISTING_DATA, SINGLE_MOD_TASK, existing_list)
     self.assertEqual(len(reader.output_list), 4)
     self.assertDictEqual(reader.spatial_ranks, {'image': 3})
     self.assertDictEqual(reader.shapes,
                          {'image': (256, 168, 256, 1, 1)})
     self.assertDictEqual(reader.tf_dtypes, {'image': tf.float32})
     self.assertEqual(reader.names, ('image',))
     self.assertDictEqual(reader.input_sources,
                          {'image': ('lesion',)})
     self.assertEqual(reader.get_subject_id(1)[:4], 'Fin_')
     self.assertTrue(isinstance(reader.get_subject(1), dict))

    def initialise_dataset_loader(
            self, data_param=None, task_param=None, data_partitioner=None):
        self.data_param = data_param
        self.autoencoder_param = task_param

        if not self.is_training:
            self._infer_type = look_up_operations(
                self.autoencoder_param.inference_type, SUPPORTED_INFERENCE)
        else:
            self._infer_type = None

        # read each line of csv files into an instance of Subject
        if self.is_training:
            file_lists = []
            if self.action_param.validation_every_n > 0:
                file_lists.append(data_partitioner.train_files)
                file_lists.append(data_partitioner.validation_files)
            else:
                file_lists.append(data_partitioner.train_files)

            self.readers = []
            for file_list in file_lists:
                reader = ImageReader(['image'])
                reader.initialise(data_param, task_param, file_list)
                self.readers.append(reader)
        if self._infer_type in ('encode', 'encode-decode'):
            self.readers = [ImageReader(['image'])]
            self.readers[0].initialise(data_param,
                                       task_param,
                                       data_partitioner.inference_files)
        elif self._infer_type == 'sample':
            self.readers = []
        elif self._infer_type == 'linear_interpolation':
            self.readers = [ImageReader(['feature'])]
            self.readers[0].initialise(data_param,
                                       task_param,
                                       data_partitioner.inference_files)

    def initialise_dataset_loader(
            self, data_param=None, task_param=None, data_partitioner=None):
        self.data_param = data_param
        self.registration_param = task_param

        file_lists = self.get_file_lists(data_partitioner)

        if self.is_evaluation:
            NotImplementedError('Evaluation is not yet '
                                'supported in this application.')

        self.readers = []
        for file_list in file_lists:
            fixed_reader = ImageReader({'fixed_image', 'fixed_label'})
            fixed_reader.initialise(data_param, task_param, file_list)
            self.readers.append(fixed_reader)

            moving_reader = ImageReader({'moving_image', 'moving_label'})
            moving_reader.initialise(data_param, task_param, file_list)
            self.readers.append(moving_reader)

    def test_volume_loader(self):
        expected_T1 = np.array(
            [0.0, 8.24277910972, 21.4917343731,
             27.0551695202, 32.6186046672, 43.5081573038,
             53.3535675285, 61.9058849776, 70.0929786194,
             73.9944243858, 77.7437509974, 88.5331971492,
             100.0])
        expected_FLAIR = np.array(
            [0.0, 5.36540863446, 15.5386130103,
             20.7431912042, 26.1536608309, 36.669150376,
             44.7821246138, 50.7930589961, 56.1703089214,
             59.2393548654, 63.1565641037, 78.7271261392,
             100.0])

        reader = ImageReader(['image'])
        reader.initialise(DATA_PARAM, TASK_PARAM, file_list)
        self.assertAllClose(len(reader._file_list), 4)

        foreground_masking_layer = BinaryMaskingLayer(
            type_str='otsu_plus',
            multimod_fusion='or')
        hist_norm = HistogramNormalisationLayer(
            image_name='image',
            modalities=vars(TASK_PARAM).get('image'),
            model_filename=MODEL_FILE,
            binary_masking_func=foreground_masking_layer,
            cutoff=(0.05, 0.95),
            name='hist_norm_layer')
        if os.path.exists(MODEL_FILE):
            os.remove(MODEL_FILE)
        hist_norm.train(reader.output_list)
        out_map = hist_norm.mapping

        self.assertAllClose(out_map['T1'], expected_T1)
        self.assertAllClose(out_map['FLAIR'], expected_FLAIR)

        # normalise a uniformly sampled random image
        test_shape = (20, 20, 20, 3, 2)
        rand_image = np.random.uniform(low=-10.0, high=10.0, size=test_shape)
        norm_image = np.copy(rand_image)
        norm_image_dict, mask_dict = hist_norm({'image': norm_image})
        norm_image, mask = hist_norm(norm_image, mask_dict)
        self.assertAllClose(norm_image_dict['image'], norm_image)
        self.assertAllClose(mask_dict['image'], mask)

        # apply mean std normalisation
        mv_norm = MeanVarNormalisationLayer(
            image_name='image',
            binary_masking_func=foreground_masking_layer)
        norm_image, _ = mv_norm(norm_image, mask)
        self.assertAllClose(norm_image.shape, mask.shape)

        mv_norm = MeanVarNormalisationLayer(
            image_name='image',
            binary_masking_func=None)
        norm_image, _ = mv_norm(norm_image)

        # mapping should keep at least the order of the images
        rand_image = rand_image[:, :, :, 1, 1].flatten()
        norm_image = norm_image[:, :, :, 1, 1].flatten()

        order_before = rand_image[1:] > rand_image[:-1]
        order_after = norm_image[1:] > norm_image[:-1]
        self.assertAllClose(np.mean(norm_image), 0.0)
        self.assertAllClose(np.std(norm_image), 1.0)
        self.assertAllClose(order_before, order_after)
        if os.path.exists(MODEL_FILE):
            os.remove(MODEL_FILE)

    def initialise_dataset_loader(
            self, data_param=None, task_param=None, data_partitioner=None):
        self.data_param = data_param
        self.gan_param = task_param

        # read each line of csv files into an instance of Subject
        if self.is_training:
            file_lists = []
            if self.action_param.validation_every_n > 0:
                file_lists.append(data_partitioner.train_files)
                file_lists.append(data_partitioner.validation_files)
            else:
                file_lists.append(data_partitioner.train_files)
            self.readers = []
            for file_list in file_lists:
                reader = ImageReader(['image', 'conditioning'])
                reader.initialise(data_param, task_param, file_list)
                self.readers.append(reader)
        else:
            inference_reader = ImageReader(['conditioning'])
            file_list = data_partitioner.inference_files
            inference_reader.initialise(data_param, task_param, file_list)
            self.readers = [inference_reader]

        foreground_masking_layer = None
        if self.net_param.normalise_foreground_only:
            foreground_masking_layer = BinaryMaskingLayer(
                type_str=self.net_param.foreground_type,
                multimod_fusion=self.net_param.multimod_foreground_type,
                threshold=0.0)

        mean_var_normaliser = MeanVarNormalisationLayer(
            image_name='image',
            binary_masking_func=foreground_masking_layer)
        histogram_normaliser = None
        if self.net_param.histogram_ref_file:
            histogram_normaliser = HistogramNormalisationLayer(
                image_name='image',
                modalities=vars(task_param).get('image'),
                model_filename=self.net_param.histogram_ref_file,
                binary_masking_func=foreground_masking_layer,
                norm_type=self.net_param.norm_type,
                cutoff=self.net_param.cutoff,
                name='hist_norm_layer')

        normalisation_layers = []
        if self.net_param.normalisation:
            normalisation_layers.append(histogram_normaliser)
        if self.net_param.whitening:
            normalisation_layers.append(mean_var_normaliser)

        augmentation_layers = []
        if self.is_training:
            if self.action_param.random_flipping_axes != -1:
                augmentation_layers.append(RandomFlipLayer(
                    flip_axes=self.action_param.random_flipping_axes))
            if self.action_param.scaling_percentage:
                augmentation_layers.append(RandomSpatialScalingLayer(
                    min_percentage=self.action_param.scaling_percentage[0],
                    max_percentage=self.action_param.scaling_percentage[1]))
            if self.action_param.rotation_angle:
                augmentation_layers.append(RandomRotationLayer())
                augmentation_layers[-1].init_uniform_angle(
                    self.action_param.rotation_angle)

        for reader in self.readers:
            reader.add_preprocessing_layers(
                normalisation_layers + augmentation_layers)

def get_25d_reader():
    reader = ImageReader(['image'])
    reader.initialise(SINGLE_25D_DATA, SINGLE_25D_TASK, single_25d_list)
    return reader