diff --git a/model_server/base/roiset.py b/model_server/base/roiset.py
index 9f7de71687db8bdc913201116e2ffe3f4b3e47e3..ed8f44f873d059bb5039b1970558e398ce516e25 100644
--- a/model_server/base/roiset.py
+++ b/model_server/base/roiset.py
@@ -52,6 +52,7 @@ class RoiFilter(BaseModel):
class RoiSetMetaParams(BaseModel):
filters: Union[RoiFilter, None] = None
expand_box_by: List[int] = [128, 0]
+ deproject_channel: Union[int, None] = None
class RoiSetExportParams(BaseModel):
@@ -210,23 +211,33 @@ def filter_df_overlap_seg(df1: pd.DataFrame, df2: pd.DataFrame = None) -> pd.Dat
return dfbb
-def make_df_from_object_ids(acc_raw, acc_obj_ids, expand_box_by) -> pd.DataFrame:
+def make_df_from_object_ids(acc_raw, acc_obj_ids, expand_box_by, deproject_channel=None) -> pd.DataFrame:
"""
- Build dataframe associate object IDs with summary stats
+ Build dataframe that associate object IDs with summary stats;
:param acc_raw: accessor to raw image data
:param acc_obj_ids: accessor to map of object IDs
:param expand_box_by: number of pixels to expand bounding box in all directions (without exceeding image boundary)
-
+ :param deproject_channel: if objects' z-coordinates are not specified, compute them based on argmax of this channel
:return: pd.DataFrame
"""
# build dataframe of objects, assign z index to each object
- # TODO: don't assume that channel 0 is the basis of z-argmax
- # TODO: :param deproject: assign object's z-position based on argmax of raw data if True
- if acc_obj_ids.nz == 1: # deproject objects' z-coordinates from argmax of raw image
+ if acc_obj_ids.nz == 1 and acc_raw.nz > 1:
+
+ if deproject_channel is None or deproject_channel >= acc_raw.chroma or deproject_channel < 0:
+ if acc_raw.chroma == 1:
+ deproject_channel = 0
+ else:
+ raise NoDeprojectChannelSpecifiedError(
+ f'When labeling objects, either their z-coordinates or a valid deprojection channel are required.'
+ )
+ acc_raw.get_mono(deproject_channel)
+
+ zi_map = acc_raw.get_mono(deproject_channel).get_z_argmax().data_xy.astype('uint16')
+ assert len(zi_map.shape) == 2
df = pd.DataFrame(regionprops_table(
acc_obj_ids.data_xy,
- intensity_image=acc_raw.data.argmax(axis=3, keepdims=True)[:, :, 0, 0].astype('uint16'),
+ intensity_image=zi_map,
properties=('label', 'area', 'intensity_mean', 'bbox')
)).rename(columns={'bbox-0': 'y0', 'bbox-1': 'x0', 'bbox-2': 'y1', 'bbox-3': 'x1'})
df['zi'] = df['intensity_mean'].round().astype('int')
@@ -238,7 +249,11 @@ def make_df_from_object_ids(acc_raw, acc_obj_ids, expand_box_by) -> pd.DataFrame
)).rename(columns={
'bbox-0': 'y0', 'bbox-1': 'x0', 'bbox-2': 'z0', 'bbox-3': 'y1', 'bbox-4': 'x1', 'bbox-5': 'z1'
})
- df['zi'] = df['label'].apply(lambda x: (acc_obj_ids.data == x).sum(axis=(0, 1, 2)).argmax())
+
+ def _get_zi_from_label(la):
+ return acc_obj_ids.apply(lambda x: x == la).get_focus_vector().argmax()
+
+ df['zi'] = df['label'].apply(_get_zi_from_label)
df = df_insert_slices(df, acc_raw.shape_dict, expand_box_by)
@@ -374,7 +389,9 @@ class RoiSet(object):
df = filter_df(
make_df_from_object_ids(
- acc_raw, acc_obj_ids, expand_box_by=params.expand_box_by
+ acc_raw, acc_obj_ids,
+ expand_box_by=params.expand_box_by,
+ deproject_channel=params.deproject_channel,
),
params.filters,
)
@@ -404,6 +421,7 @@ class RoiSet(object):
r.y1 - r.y0,
r.x1 - r.x0
)
+ # TODO: use accessor.get_z_argmax
zmax = acc_raw.data.argmax(axis=3, keepdims=True)[:, :, 0, 0].astype('uint16')
else:
bbox_df['zi'] = bbox_zi
@@ -1022,6 +1040,9 @@ class BoundingBoxError(Error):
class DeserializeRoiSet(Error):
pass
+class NoDeprojectChannelSpecifiedError(Error):
+ pass
+
class DerivedChannelError(Error):
pass
diff --git a/tests/base/test_roiset.py b/tests/base/test_roiset.py
index ba553564dab20555b6f1691d1b71eceb8bc3dd0e..497bcb031b909af68cb5ea3fc11c043794000d84 100644
--- a/tests/base/test_roiset.py
+++ b/tests/base/test_roiset.py
@@ -187,18 +187,18 @@ class TestRoiSetMonoProducts(BaseTestRoiSetMonoProducts, unittest.TestCase):
return roiset
def test_classify_by_multiple_channels(self):
- roiset = RoiSet.from_binary_mask(self.stack, self.seg_mask)
+ roiset = RoiSet.from_binary_mask(self.stack, self.seg_mask, params=RoiSetMetaParams(deproject_channel=0))
roiset.classify_by('dummy_class', [0, 1], DummyInstanceSegmentationModel())
self.assertTrue(all(roiset.get_df()['classify_by_dummy_class'].unique() == [1]))
self.assertTrue(all(np.unique(roiset.get_object_class_map('dummy_class').data) == [0, 1]))
return roiset
def test_transfer_classification(self):
- roiset1 = RoiSet.from_binary_mask(self.stack, self.seg_mask)
+ roiset1 = RoiSet.from_binary_mask(self.stack, self.seg_mask, params=RoiSetMetaParams(deproject_channel=0))
# prepare alternative mask and compare
smoothed_mask = self.seg_mask.apply(lambda x: smooth(x, sig=1.5))
- roiset2 = RoiSet.from_binary_mask(self.stack, smoothed_mask)
+ roiset2 = RoiSet.from_binary_mask(self.stack, smoothed_mask, params=RoiSetMetaParams(deproject_channel=0))
dmask = (self.seg_mask.data / 255) + (smoothed_mask.data / 255)
self.assertTrue(np.all(np.unique(dmask) == [0, 1, 2]))
total_iou = (dmask == 2).sum() / ((dmask == 1).sum() + (dmask == 2).sum())
@@ -227,6 +227,7 @@ class TestRoiSetMonoProducts(BaseTestRoiSetMonoProducts, unittest.TestCase):
self.seg_mask,
params=RoiSetMetaParams(
filters={'area': {'min': 1e3, 'max': 1e4}},
+ deproject_channel=0,
)
)
roiset.classify_by(
@@ -295,6 +296,7 @@ class TestRoiSetMultichannelProducts(BaseTestRoiSetMonoProducts, unittest.TestCa
expand_box_by=(128, 2),
mask_type='boxes',
filters={'area': {'min': 1e3, 'max': 1e4}},
+ deproject_channel=0,
)
)