def test_segmentation(self):
"""Test if batch size makes a difference on the segmentation output"""
scan = self.SCAN_TYPE(dicom_path=self.dicom_dirpath)
tissue = FemoralCartilage()
tissue.find_weights(SEGMENTATION_WEIGHTS_FOLDER)
dims = scan.get_dimensions()
input_shape = (dims[0], dims[1], 1)
model = get_model(SEGMENTATION_MODEL,
input_shape=input_shape,
weights_path=tissue.weights_file_path)
scan.segment(model, tissue)
def handle_segmentation(vargin, scan: ScanSequence, tissue: Tissue):
segment_weights_path = vargin[SEGMENTATION_WEIGHTS_DIR_KEY][0]
tissue.find_weights(segment_weights_path)
# Load model
dims = scan.get_dimensions()
input_shape = (dims[0], dims[1], 1)
model = get_model(vargin[SEGMENTATION_MODEL_KEY],
input_shape=input_shape,
weights_path=tissue.weights_file_path)
model.batch_size = vargin[SEGMENTATION_BATCH_SIZE_KEY]
return model
def test_segmentation(self):
"""Test automatic segmentation
Expected: NotImplementedError
"""
scan = self.SCAN_TYPE(dicom_path=self.dicom_dirpath)
tissue = FemoralCartilage()
tissue.find_weights(SEGMENTATION_WEIGHTS_FOLDER)
dims = scan.get_dimensions()
input_shape = (dims[0], dims[1], 1)
model = get_model(SEGMENTATION_MODEL,
input_shape=input_shape,
weights_path=tissue.weights_file_path)
# automatic segmentation currently not implemented
with self.assertRaises(NotImplementedError):
scan.segment(model, tissue)
def test_segmentation_multiclass(self):
"""Test support for multiclass segmentation."""
scan = self.SCAN_TYPE.from_dicom(self.dicom_dirpath,
num_workers=util.num_workers())
tissue = FemoralCartilage()
tissue.find_weights(SEGMENTATION_WEIGHTS_FOLDER),
dims = scan.get_dimensions()
input_shape = (dims[0], dims[1], 1)
model = get_model(SEGMENTATION_MODEL,
input_shape=input_shape,
weights_path=tissue.weights_file_path)
scan.segment(model, tissue, use_rss=True)
# This should call __del__ in KerasSegModel
model = None
K.clear_session()
def handle_segmentation(vargin, scan: ScanSequence, tissue: Tissue):
if not vargin[SEGMENTATION_MODEL_KEY] and not vargin[SEGMENTATION_CONFIG_KEY]:
raise ValueError(
"Either `--{}` or `--{}` must be specified".format(
SEGMENTATION_MODEL_KEY, SEGMENTATION_CONFIG_KEY
)
)
segment_weights_path = vargin[SEGMENTATION_WEIGHTS_DIR_KEY][0]
if isinstance(tissue, Sequence):
weights = [t.find_weights(segment_weights_path) for t in tissue]
assert all(weights_file == weights[0] for weights_file in weights)
weights_path = weights[0]
else:
weights_path = tissue.find_weights(segment_weights_path)
# Load model
dims = scan.get_dimensions()
# TODO: Input shape should be determined by combination of model + scan.
# Currently fixed in 2D plane
input_shape = (dims[0], dims[1], 1)
if vargin[SEGMENTATION_MODEL_KEY]:
# Use built-in model
model = get_model(
vargin[SEGMENTATION_MODEL_KEY], input_shape=input_shape, weights_path=weights_path
)
else:
# Use config
model = model_from_config(
vargin[SEGMENTATION_CONFIG_KEY],
weights_dir=segment_weights_path,
input_shape=input_shape,
)
model.batch_size = vargin[SEGMENTATION_BATCH_SIZE_KEY]
return model