本文整理汇总了Python中niftynet.io.image_reader.ImageReader类的典型用法代码示例。如果您正苦于以下问题：Python ImageReader类的具体用法？Python ImageReader怎么用？Python ImageReader使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了ImageReader类的20个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: test_operations
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))
开发者ID:nhu2000，项目名称:NiftyNet，代码行数:7，代码来源:image_reader_test.py
示例2: initialise_dataset_loader
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])
开发者ID:fepegar，项目名称:NiftyNet，代码行数:34，代码来源:autoencoder_application.py
示例3: test_trainable_preprocessing
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))
开发者ID:nhu2000，项目名称:NiftyNet，代码行数:60，代码来源:image_reader_test.py
示例4: test_properties
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_')
开发者ID:nhu2000，项目名称:NiftyNet，代码行数:11，代码来源:image_reader_test.py
示例5: test_preprocessing_zero_padding
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))
开发者ID:nhu2000，项目名称:NiftyNet，代码行数:12，代码来源:image_reader_test.py
示例6: test_errors
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))
开发者ID:fepegar，项目名称:NiftyNet，代码行数:14，代码来源:image_reader_test.py
示例7: initialise_dataset_loader
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)
开发者ID:nhu2000，项目名称:NiftyNet，代码行数:52，代码来源:brats_segmentation.py
示例8: get_label_reader
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
开发者ID:nhu2000，项目名称:NiftyNet，代码行数:11，代码来源:windows_aggregator_resize_test.py
示例9: initialise_dataset_loader
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)
开发者ID:fepegar，项目名称:NiftyNet，代码行数:39，代码来源:isample_regression.py
示例10: test_existing_csv
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))
开发者ID:fepegar，项目名称:NiftyNet，代码行数:16，代码来源:image_reader_test.py
示例11: initialise_dataset_loader
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)
开发者ID:nhu2000，项目名称:NiftyNet，代码行数:37，代码来源:autoencoder_application.py
示例12: initialise_dataset_loader
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)
开发者ID:fepegar，项目名称:NiftyNet，代码行数:20，代码来源:label_driven_registration.py
示例13: test_volume_loader
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)
开发者ID:nhu2000，项目名称:NiftyNet，代码行数:68，代码来源:histogram_normalisation_test.py
示例14: initialise_dataset_loader
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)
开发者ID:nhu2000，项目名称:NiftyNet，代码行数:68，代码来源:gan_application.py
示例15: get_25d_reader
def get_25d_reader():
reader = ImageReader(['image'])
reader.initialise(SINGLE_25D_DATA, SINGLE_25D_TASK, single_25d_list)
return reader
开发者ID:nhu2000，项目名称:NiftyNet，代码行数:4，代码来源:windows_aggregator_resize_test.py
示例16: initialise_dataset_loader
def initialise_dataset_loader(
self, data_param=None, task_param=None, data_partitioner=None):
self.data_param = data_param
self.classification_param = task_param
file_lists = self.get_file_lists(data_partitioner)
# read each line of csv files into an instance of Subject
if self.is_training:
self.readers = []
for file_list in file_lists:
reader = ImageReader(['image', 'label', 'sampler'])
reader.initialise(data_param, task_param, file_list)
self.readers.append(reader)
elif self.is_inference:
# in the inference process use image input only
inference_reader = ImageReader(['image'])
inference_reader.initialise(data_param, task_param, file_lists[0])
self.readers = [inference_reader]
elif self.is_evaluation:
reader = ImageReader({'image', 'label', 'inferred'})
reader.initialise(data_param, task_param, file_lists[0])
self.readers = [reader]
else:
raise ValueError('Action `{}` not supported. Expected one of {}'
.format(self.action, self.SUPPORTED_ACTIONS))
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')
label_normaliser = None
if self.net_param.histogram_ref_file:
label_normaliser = DiscreteLabelNormalisationLayer(
image_name='label',
modalities=vars(task_param).get('label'),
model_filename=self.net_param.histogram_ref_file)
normalisation_layers = []
if self.net_param.normalisation:
normalisation_layers.append(histogram_normaliser)
if self.net_param.whitening:
normalisation_layers.append(mean_var_normaliser)
if task_param.label_normalisation:
normalisation_layers.append(label_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 or \
self.action_param.rotation_angle_x or \
self.action_param.rotation_angle_y or \
self.action_param.rotation_angle_z:
rotation_layer = RandomRotationLayer()
if self.action_param.rotation_angle:
rotation_layer.init_uniform_angle(
self.action_param.rotation_angle)
else:
rotation_layer.init_non_uniform_angle(
self.action_param.rotation_angle_x,
self.action_param.rotation_angle_y,
self.action_param.rotation_angle_z)
augmentation_layers.append(rotation_layer)
for reader in self.readers:
reader.add_preprocessing_layers(
normalisation_layers +
augmentation_layers)
开发者ID:fepegar，项目名称:NiftyNet，代码行数:91，代码来源:classification_application.py
示例17: get_2d_reader
def get_2d_reader():
mod_2d_list = data_partitioner.initialise(MOD_2D_DATA).get_file_list()
reader = ImageReader(['image'])
reader.initialise(MOD_2D_DATA, MOD_2D_TASK, mod_2d_list)
return reader
开发者ID:nhu2000，项目名称:NiftyNet，代码行数:5，代码来源:test_sampler_selective.py
示例18: get_2d_reader
def get_2d_reader():
reader = ImageReader(['image', 'sampler'])
reader.initialise(MOD_2D_DATA, MOD_2D_TASK, mod_2d_list)
return reader
开发者ID:nhu2000，项目名称:NiftyNet，代码行数:4，代码来源:sampler_weighted_test.py
示例19: initialise_dataset_loader
def initialise_dataset_loader(
self, data_param=None, task_param=None, data_partitioner=None):
self.data_param = data_param
self.regression_param = task_param
file_lists = self.get_file_lists(data_partitioner)
# read each line of csv files into an instance of Subject
if self.is_training:
self.readers = []
for file_list in file_lists:
reader = ImageReader({'image', 'output', 'weight', 'sampler'})
reader.initialise(data_param, task_param, file_list)
self.readers.append(reader)
elif self.is_inference:
inference_reader = ImageReader(['image'])
file_list = data_partitioner.inference_files
inference_reader.initialise(data_param, task_param, file_lists[0])
self.readers = [inference_reader]
elif self.is_evaluation:
file_list = data_partitioner.inference_files
reader = ImageReader({'image', 'output', 'inferred'})
reader.initialise(data_param, task_param, file_lists[0])
self.readers = [reader]
else:
raise ValueError('Action `{}` not supported. Expected one of {}'
.format(self.action, self.SUPPORTED_ACTIONS))
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')
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)
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(volume_padding_layer +
normalisation_layers +
augmentation_layers)
开发者ID:fepegar，项目名称:NiftyNet，代码行数:68，代码来源:regression_application.py
示例20: test_images2d
def test_images2d(self):
reader = ImageReader(['image'])
# COLOR IMAGES
reader.initialise(IMAGE_2D_DATA, IMAGE_2D_TASK_COLOR,
image2d_data_list)
idx, data, interp_order = reader()
image = data['image']
# Check index
self.assertGreaterEqual(idx, 0)
self.assertLess(idx, 10)
# Check data type
self.assertGreaterEqual(image.min(), 0)
self.assertLessEqual(image.max(), 255)
self.assertEqual(image.dtype, np.float32)
# Check shape
self.assertEqual(image.ndim, 5)
self.assertAllEqual(image.shape, (100, 100, 1, 1, 3))
self.assertEqual(interp_order['image'], (1,))
# GRAY IMAGES
reader.initialise(IMAGE_2D_DATA, IMAGE_2D_TASK_GRAY,
image2d_data_list)
idx, data, interp_order = reader()
image = data['image']
# Check index
self.assertGreaterEqual(idx, 0)
self.assertLess(idx, 10)
# Check data type
self.assertGreaterEqual(image.min(), 0)
self.assertLessEqual(image.max(), 255)
self.assertEqual(image.dtype, np.float32)
# Check shape
self.assertEqual(image.ndim, 5)
self.assertAllEqual(image.shape, (100, 100, 1, 1, 1))
self.assertEqual(interp_order['image'], (1,))
gray_idx, gray_data, gray_order = reader(idx=5)
# SEGMENTATION MASKS
reader.initialise(IMAGE_2D_DATA, IMAGE_2D_TASK_MASK,
image2d_data_list)
idx, data, interp_order = reader()
image = data['image']
# Check index
self.assertGreaterEqual(idx, 0)
self.assertLess(idx, 10)
# Check data type
self.assertGreaterEqual(image.min(), 0)
self.assertLessEqual(image.max(), 255)
self.assertEqual(image.dtype, np.float32)
self.assertEqual(np.unique(image).size, 2)
# Check shape
self.assertEqual(image.ndim, 5)
self.assertAllEqual(image.shape, (100, 100, 1, 1, 1))
self.assertEqual(interp_order['image'], (0,))
# Compare segmentation masks to thresholding original image
mask_idx, mask_data, mask_order = reader(idx=5)
gray_data = gray_data['image']
mask_data = mask_data['image']
self.assertEqual(gray_idx, mask_idx)
self.assertEqual(gray_order['image'], (1,))
self.assertEqual(mask_order['image'], (0,))
self.assertAllEqual((gray_data > SEG_THRESHOLD) * 255, mask_data)
开发者ID:fepegar，项目名称:NiftyNet，代码行数:72，代码来源:image_reader_test.py
注：本文中的niftynet.io.image_reader.ImageReader类示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
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