diff --git a/model_server/base/roiset.py b/model_server/base/roiset.py
index 96162c95b6a069be79ab6771c63ed27d3ee52f00..8b7642774a4e84c9f476b5aa744ec62652ed551e 100644
--- a/model_server/base/roiset.py
+++ b/model_server/base/roiset.py
@@ -370,8 +370,9 @@ class RoiSet(object):
"""Expose ROI meta information via the Pandas.DataFrame API"""
return self._df.itertuples(name='Roi')
- @staticmethod
+ @classmethod
def from_object_ids(
+ cls,
acc_raw: GenericImageDataAccessor,
acc_obj_ids: GenericImageDataAccessor,
params: RoiSetMetaParams = RoiSetMetaParams(),
@@ -395,10 +396,11 @@ class RoiSet(object):
params.filters,
)
- return RoiSet(acc_raw, df, params)
+ return cls(acc_raw, df, params)
- @staticmethod
+ @classmethod
def from_bounding_boxes(
+ cls,
acc_raw: GenericImageDataAccessor,
bbox_yxhw: List[Dict],
bbox_zi: Union[List[int], int] = None,
@@ -451,11 +453,12 @@ class RoiSet(object):
axis=1,
result_type='reduce',
)
- return RoiSet(acc_raw, df, params)
+ return cls(acc_raw, df, params)
- @staticmethod
+ @classmethod
def from_binary_mask(
+ cls,
acc_raw: GenericImageDataAccessor,
acc_seg: GenericImageDataAccessor,
allow_3d=False,
@@ -470,7 +473,7 @@ class RoiSet(object):
:param connect_3d: objects can span multiple z-positions if True; objects are unique to a single z if False
:param params: optional arguments that influence the definition and representation of ROIs
"""
- return RoiSet.from_object_ids(
+ return cls.from_object_ids(
acc_raw,
get_label_ids(
acc_seg,
@@ -480,8 +483,9 @@ class RoiSet(object):
params
)
- @staticmethod
+ @classmethod
def from_polygons_2d(
+ cls,
acc_raw,
polygons: List[np.ndarray],
params: RoiSetMetaParams = RoiSetMetaParams()
@@ -496,7 +500,7 @@ class RoiSet(object):
for p in polygons:
sl = draw.polygon(p[:, 1], p[:, 0])
mask[sl] = True
- return RoiSet.from_binary_mask(
+ return cls.from_binary_mask(
acc_raw,
InMemoryDataAccessor(mask),
allow_3d=False,
@@ -566,35 +570,21 @@ class RoiSet(object):
def classify_by(
self, name: str, channels: list[int],
object_classification_model: InstanceSegmentationModel,
- derived_channel_functions: list[callable] = None
):
"""
Insert a column in RoiSet data table that associates each ROI with an integer class, determined by passing
- specified inputs through an instance segmentation classifier. Optionally derive additional inputs for object
- classification by passing a raw input channel through one or more functions.
+ specified inputs through an instance segmentation classifier.
:param name: name of column to insert
:param channels: list of nc raw input channels to send to classifier
:param object_classification_model: InstanceSegmentation model object
- :param derived_channel_functions: list of functions that each receive a PatchStack accessor with nc channels and
- that return a single-channel PatchStack accessor of the same shape
:return: None
"""
if self.count == 0:
self._df['classify_by_' + name] = None
return True
- # combine channels
- data_derived = [acc.data for acc in self.accs_derived]
- input_acc = PatchStack(
- np.concatenate(
- [*mono_data, *data_derived],
- axis=raw_acc._ga('C')
- )
- )
-
- else:
- input_acc = raw_acc
+ input_acc = self.get_patches_acc(channels=channels, expanded=False, pad_to=None) # all channels
# do this on a patch basis, i.e. only one object per frame
obmap_patches = object_classification_model.label_patch_stack(
@@ -1035,8 +1025,8 @@ class RoiSet(object):
def acc_obj_ids(self):
return make_object_ids_from_df(self._df, self.acc_raw.shape_dict)
- @staticmethod
- def deserialize(acc_raw: GenericImageDataAccessor, where: Path, prefix='roiset') -> Self:
+ @classmethod
+ def deserialize(cls, acc_raw: GenericImageDataAccessor, where: Path, prefix='roiset') -> Self:
"""
Create an RoiSet object from saved files and an image accessor
:param acc_raw: accessor to image that contains ROIs
@@ -1062,14 +1052,14 @@ class RoiSet(object):
df['binary_mask'] = df.apply(_read_binary_mask, axis=1)
id_mask = make_object_ids_from_df(df, acc_raw.shape_dict)
- return RoiSet.from_object_ids(acc_raw, id_mask)
+ return cls.from_object_ids(acc_raw, id_mask)
else: # assume bounding boxes only
df['y'] = df['y0']
df['x'] = df['x0']
df['h'] = df['y1'] - df['y0']
df['w'] = df['x1'] - df['x0']
- return RoiSet.from_bounding_boxes(
+ return cls.from_bounding_boxes(
acc_raw,
df[['y', 'x', 'h', 'w']].to_dict(orient='records'),
list(df['zi'])
diff --git a/tests/base/test_roiset.py b/tests/base/test_roiset.py
index de18e6908c185081b4b49288914fd417bbabd8b7..0a03973e78ca824083f2821157c70bb9adc77265 100644
--- a/tests/base/test_roiset.py
+++ b/tests/base/test_roiset.py
@@ -219,42 +219,6 @@ class TestRoiSetMonoProducts(BaseTestRoiSetMonoProducts, unittest.TestCase):
)
- def test_classify_by_with_derived_channel(self):
- class ModelWithDerivedInputs(DummyInstanceSegmentationModel):
- def infer(self, img, mask):
- return PatchStack(super().infer(img, mask).data * img.chroma)
-
- roiset = RoiSet.from_binary_mask(
- self.stack,
- self.seg_mask,
- params=RoiSetMetaParams(
- filters={'area': {'min': 1e3, 'max': 1e4}},
- deproject_channel=0,
- )
- )
- roiset.classify_by(
- 'multiple_input_model',
- [0, 1],
- ModelWithDerivedInputs(),
- derived_channel_functions=[
- lambda acc: PatchStack(2 * acc.get_channels([0]).data),
- lambda acc: PatchStack((0.5 * acc.get_channels([1]).data).astype('uint8'))
- ]
- )
- self.assertTrue(all(roiset.get_df()['classify_by_multiple_input_model'].unique() == [4]))
- self.assertTrue(all(np.unique(roiset.get_object_class_map('multiple_input_model').data) == [0, 4]))
-
- self.assertEqual(len(roiset.accs_derived), 2)
- for di in roiset.accs_derived:
- self.assertEqual(roiset.get_patches_acc().hw, di.hw)
- self.assertEqual(roiset.get_patches_acc().nz, di.nz)
- self.assertEqual(roiset.get_patches_acc().count, di.count)
-
- dpas = roiset.run_exports(output_path / 'derived_channels', 0, 'der', RoiSetExportParams(derived_channels=True))
- for fp in dpas['derived_channels']:
- assert Path(fp).exists()
- return roiset
-
def test_export_object_classes(self):
record = self.test_classify_by().run_exports(
output_path / 'object_class_maps',