def test_foreground_position(self, arguments, input_data, _):
result = SpatialCropForegroundd(**arguments)(input_data)
np.testing.assert_allclose(result["image_meta_dict"]["foreground_start_coord"], np.array([0, 1, 1]))
np.testing.assert_allclose(result["image_meta_dict"]["foreground_end_coord"], np.array([1, 4, 4]))
arguments["start_coord_key"] = "test_start_coord"
arguments["end_coord_key"] = "test_end_coord"
result = SpatialCropForegroundd(**arguments)(input_data)
np.testing.assert_allclose(result["image_meta_dict"]["test_start_coord"], np.array([0, 1, 1]))
np.testing.assert_allclose(result["image_meta_dict"]["test_end_coord"], np.array([1, 4, 4]))
def train_pre_transforms(self, context: Context):
# Dataset preparation
t: List[Any] = [
LoadImaged(keys=("image", "label")),
AddChanneld(keys=("image", "label")),
SpatialCropForegroundd(keys=("image", "label"),
source_key="label",
spatial_size=self.roi_size),
Resized(keys=("image", "label"),
spatial_size=self.model_size,
mode=("area", "nearest")),
NormalizeIntensityd(keys="image", subtrahend=208.0,
divisor=388.0), # type: ignore
]
if self.dimension == 3:
t.append(FindAllValidSlicesd(label="label", sids="sids"))
t.extend([
AddInitialSeedPointd(label="label",
guidance="guidance",
sids="sids"),
AddGuidanceSignald(image="image", guidance="guidance"),
EnsureTyped(keys=("image", "label"), device=context.device),
SelectItemsd(keys=("image", "label", "guidance")),
])
return t
def get_pre_transforms(roi_size, model_size, dimensions):
t = [
LoadImaged(keys=('image', 'label')),
AddChanneld(keys=('image', 'label')),
SpatialCropForegroundd(keys=('image', 'label'),
source_key='label',
spatial_size=roi_size),
Resized(keys=('image', 'label'),
spatial_size=model_size,
mode=('area', 'nearest')),
NormalizeIntensityd(keys='image', subtrahend=208.0, divisor=388.0)
]
if dimensions == 3:
t.append(FindAllValidSlicesd(label='label', sids='sids'))
t.extend([
AddInitialSeedPointd(label='label', guidance='guidance', sids='sids'),
AddGuidanceSignald(image='image', guidance='guidance'),
ToTensord(keys=('image', 'label'))
])
return Compose(t)
def get_pre_transforms(roi_size, model_size, dimensions):
t = [
LoadImaged(keys=("image", "label")),
AddChanneld(keys=("image", "label")),
SpatialCropForegroundd(keys=("image", "label"),
source_key="label",
spatial_size=roi_size),
Resized(keys=("image", "label"),
spatial_size=model_size,
mode=("area", "nearest")),
NormalizeIntensityd(keys="image", subtrahend=208.0, divisor=388.0),
]
if dimensions == 3:
t.append(FindAllValidSlicesd(label="label", sids="sids"))
t.extend([
AddInitialSeedPointd(label="label", guidance="guidance", sids="sids"),
AddGuidanceSignald(image="image", guidance="guidance"),
EnsureTyped(keys=("image", "label")),
])
return Compose(t)
def test_correct_results(self, arguments, input_data, expected_result):
result = SpatialCropForegroundd(**arguments)(input_data)
np.testing.assert_allclose(result["image"], expected_result)
def test_correct_shape(self, arguments, input_data, expected_shape):
result = SpatialCropForegroundd(**arguments)(input_data)
np.testing.assert_equal(result["image"].shape, expected_shape)