diff --git a/model_server/extensions/ilastik/models.py b/model_server/extensions/ilastik/models.py
index 90edcec53164158d6c3fe9f4e4be3e74e489600e..2dea13329513d0256baf7e9f2c698d4731301893 100644
--- a/model_server/extensions/ilastik/models.py
+++ b/model_server/extensions/ilastik/models.py
@@ -162,8 +162,15 @@ class IlastikObjectClassifierFromSegmentationModel(IlastikModel, InstanceSegment
return ObjectClassificationWorkflowBinary
def infer(self, input_img: GenericImageDataAccessor, segmentation_img: GenericImageDataAccessor) -> (np.ndarray, dict):
- if self.model_chroma != input_img.chroma or self.model_3d != input_img.is_3d():
- raise IlastikInputShapeError()
+ if self.model_chroma != input_img.chroma:
+ raise IlastikInputShapeError(
+ f'Model {self} expects {self.model_chroma} input channels but received only {input_img.chroma}'
+ )
+ if self.model_3d != input_img.is_3d():
+ if self.model_3d:
+ raise IlastikInputShapeError(f'Model is 3D but input image is 2D')
+ else:
+ raise IlastikInputShapeError(f'Model is 2D but input image is 3D')
assert segmentation_img.is_mask()
if isinstance(input_img, PatchStack):
@@ -225,8 +232,13 @@ class IlastikObjectClassifierFromPixelPredictionsModel(IlastikModel, ImageToImag
if self.model_chroma != input_img.chroma or self.model_3d != input_img.is_3d():
raise IlastikInputShapeError()
- tagged_input_data = vigra.taggedView(input_img.data, 'yxcz')
- tagged_pxmap_data = vigra.taggedView(pxmap_img.data, 'yxcz')
+ if isinstance(input_img, PatchStack):
+ assert isinstance(pxmap_img, PatchStack)
+ tagged_input_data = vigra.taggedView(input_img.pczyx, 'tczyx')
+ tagged_pxmap_data = vigra.taggedView(pxmap_img.pczyx, 'tczyx')
+ else:
+ tagged_input_data = vigra.taggedView(input_img.data, 'yxcz')
+ tagged_pxmap_data = vigra.taggedView(pxmap_img.data, 'yxcz')
dsi = [
{
@@ -239,12 +251,20 @@ class IlastikObjectClassifierFromPixelPredictionsModel(IlastikModel, ImageToImag
assert len(obmaps) == 1, 'ilastik generated more than one object map'
- yxcz = np.moveaxis(
- obmaps[0],
- [1, 2, 3, 0],
- [0, 1, 2, 3]
- )
- return InMemoryDataAccessor(data=yxcz), {'success': True}
+ if isinstance(input_img, PatchStack):
+ pyxcz = np.moveaxis(
+ obmaps[0],
+ [0, 1, 2, 3, 4],
+ [0, 4, 1, 2, 3]
+ )
+ return PatchStack(data=pyxcz), {'success': True}
+ else:
+ yxcz = np.moveaxis(
+ obmaps[0],
+ [1, 2, 3, 0],
+ [0, 1, 2, 3]
+ )
+ return InMemoryDataAccessor(data=yxcz), {'success': True}
def label_instance_class(self, img: GenericImageDataAccessor, pxmap: GenericImageDataAccessor, **kwargs):
@@ -262,10 +282,30 @@ class IlastikObjectClassifierFromPixelPredictionsModel(IlastikModel, ImageToImag
raise InvalidInputImageError('Expecting input image and pixel probabilities to be the same shape')
pxch = kwargs.get('pixel_classification_channel', 0)
pxtr = kwargs.get('pixel_classification_threshold', 0.5)
- mask = InMemoryDataAccessor(pxmap.get_one_channel_data(pxch).data > pxtr)
+ mask = img._derived_accessor(pxmap.get_one_channel_data(pxch).data > pxtr)
obmap, _ = self.infer(img, mask)
return obmap
+ def make_instance_segmentation_model(self, px_ch: int):
+ """
+ Generate an instance segmentation model, i.e. one that takes binary masks instead of pixel probabilities as a
+ second input.
+ :param px_ch: channel of pixel probability map to use
+ :return:
+ InstanceSegmentationModel object
+ """
+ class _Mod(self.__class__, InstanceSegmentationModel):
+ def label_instance_class(
+ self, img: GenericImageDataAccessor, mask: GenericImageDataAccessor, **kwargs
+ ) -> GenericImageDataAccessor:
+ if mask.dtype == 'bool':
+ norm_mask = 1.0 * mask.data
+ else:
+ norm_mask = mask.data / np.iinfo(mask.dtype).max
+ norm_mask_acc = mask._derived_accessor(norm_mask.astype('float32'))
+ return super().label_instance_class(img, norm_mask_acc, pixel_classification_channel=px_ch)
+ return _Mod(params={'project_file': self.project_file})
+
class Error(Exception):
diff --git a/model_server/extensions/ilastik/router.py b/model_server/extensions/ilastik/router.py
index 151e37915c16dd8a3943dc0b07e1e188d45606a3..c40679c02a95bb386aaad613c26cc29e28b48055 100644
--- a/model_server/extensions/ilastik/router.py
+++ b/model_server/extensions/ilastik/router.py
@@ -27,14 +27,8 @@ def load_ilastik_model(model_class: ilm.IlastikModel, project_file: str, duplica
if existing_model_id is not None:
session.log_info(f'An ilastik model from {project_file} already existing exists; did not load a duplicate')
return {'model_id': existing_model_id}
- try:
- result = session.load_model(model_class, {'project_file': project_file})
- session.log_info(f'Loaded ilastik model {result} from {project_file}')
- except (FileNotFoundError, ParameterExpectedError):
- raise HTTPException(
- status_code=404,
- detail=f'Could not load project file {project_file}',
- )
+ result = session.load_model(model_class, {'project_file': project_file})
+ session.log_info(f'Loaded ilastik model {result} from {project_file}')
return {'model_id': result}
@router.put('/seg/load/')
diff --git a/model_server/extensions/ilastik/tests/test_ilastik.py b/model_server/extensions/ilastik/tests/test_ilastik.py
index 7a3a9c38bd495af6f146d63e6adb96f4215767c2..a1a4983efbded47e54804521c04394c55795439c 100644
--- a/model_server/extensions/ilastik/tests/test_ilastik.py
+++ b/model_server/extensions/ilastik/tests/test_ilastik.py
@@ -199,7 +199,7 @@ class TestIlastikOverApi(TestServerBaseClass):
'ilastik/seg/load/',
query={'project_file': 'improper.ilp'},
)
- self.assertEqual(resp_load.status_code, 404)
+ self.assertEqual(resp_load.status_code, 500)
def test_load_ilastik_pixel_model(self):
@@ -319,6 +319,7 @@ class TestIlastikOverApi(TestServerBaseClass):
)
self.assertEqual(resp_infer.status_code, 200, resp_infer.content.decode())
+
class TestIlastikObjectClassification(unittest.TestCase):
def setUp(self):
stack = generate_file_accessor(roiset_test_data['multichannel_zstack']['path'])