From cb1bba3e40f1063a37813a1f53dd5fd732ccc7af Mon Sep 17 00:00:00 2001
From: Christopher Rhodes <christopher.rhodes@embl.de>
Date: Wed, 7 Feb 2024 15:10:04 +0100
Subject: [PATCH] Removed zmask_meta references
---
.../extensions/chaeo/tests/test_zstack.py | 17 ++--
model_server/extensions/chaeo/zmask.py | 77 +++++--------------
2 files changed, 24 insertions(+), 70 deletions(-)
diff --git a/model_server/extensions/chaeo/tests/test_zstack.py b/model_server/extensions/chaeo/tests/test_zstack.py
index dfc548db..41571203 100644
--- a/model_server/extensions/chaeo/tests/test_zstack.py
+++ b/model_server/extensions/chaeo/tests/test_zstack.py
@@ -54,11 +54,9 @@ class TestRoiSetMonoProducts(BaseTestRoiSetMonoProducts, unittest.TestCase):
)
return roiset
- def test_zmask_makes_correct_boxes(self, **kwargs):
+ def test_roi_mask_shape(self, **kwargs):
roiset = self._make_roi_set(**kwargs)
zmask = roiset.get_zmask()
- meta = roiset.zmask_meta
- interm = roiset.interm
zmask_acc = InMemoryDataAccessor(zmask)
self.assertTrue(zmask_acc.is_mask())
@@ -73,16 +71,10 @@ class TestRoiSetMonoProducts(BaseTestRoiSetMonoProducts, unittest.TestCase):
# assert non-trivial meta info in boxes
self.assertGreater(roiset.count, 1)
- sh = meta[1]['mask'].shape
- ar = meta[1]['info'].area
+ sh = roiset.get_df().iloc[1]['mask'].shape
+ ar = roiset.get_df().iloc[1]['area']
self.assertGreaterEqual(sh[0] * sh[1], ar)
- # assert dimensionality of intermediate data products
- self.assertEqual(interm['label_map'].shape, zmask.shape[0:2])
- self.assertEqual(interm['argmax'].shape, zmask.shape[0:2])
-
- return roiset
-
def test_zmask_works_on_non_zstacks(self, **kwargs):
acc_zstack_slice = InMemoryDataAccessor(self.stack_ch_pa.data[:, :, :, 0])
self.assertEqual(acc_zstack_slice.nz, 1)
@@ -176,7 +168,8 @@ class TestRoiSetMonoProducts(BaseTestRoiSetMonoProducts, unittest.TestCase):
def test_classify_by(self):
roiset = self._make_roi_set()
roiset.classify_by('dummy_class', 0, DummyInstanceSegmentationModel())
- self.assertTrue(all(roiset.get_df()['classify_by_dummy_class'].unique() == [1.]))
+ self.assertTrue(all(roiset.get_df()['classify_by_dummy_class'].unique() == [1]))
+ self.assertTrue(all(np.unique(roiset.object_class_maps['dummy_class'].data) == [0, 1]))
def test_object_map_workflow(self):
pp = pipeline_params
diff --git a/model_server/extensions/chaeo/zmask.py b/model_server/extensions/chaeo/zmask.py
index 4ed84a6b..55030731 100644
--- a/model_server/extensions/chaeo/zmask.py
+++ b/model_server/extensions/chaeo/zmask.py
@@ -21,17 +21,19 @@ from model_server.extensions.chaeo.process import mask_largest_object
from model_server.extensions.chaeo.products import get_roiset_patches, get_patch_masks, export_patch_masks
-def get_label_ids(acc_seg_mask):
- return label(acc_seg_mask.data[:, :, 0, 0]).astype('uint16')
+def get_label_ids(acc_seg_mask: GenericImageDataAccessor) -> InMemoryDataAccessor:
+ return InMemoryDataAccessor(label(acc_seg_mask.data[:, :, 0, 0]).astype('uint16'))
class RoiSet(object):
def __init__(
self,
- acc_obj_ids: GenericImageDataAccessor, # TODO: enforce subtype of binary or label ID mask
+ acc_obj_ids: GenericImageDataAccessor,
acc_raw: GenericImageDataAccessor,
params: RoiSetMetaParams = RoiSetMetaParams(),
):
+ assert acc_obj_ids.chroma == 1
+ assert acc_obj_ids.nz == 1
self.acc_obj_ids = acc_obj_ids
self.acc_raw = acc_raw
self.params = params
@@ -43,41 +45,7 @@ class RoiSet(object):
params.filters,
)
- # temporarily build zmask meta here
- meta = []
- for ob in self.get_df().itertuples(name='LabeledObject'):
- sl = np.s_[ob.ebb_y0: ob.ebb_y1, ob.ebb_x0: ob.ebb_x1, :, ob.ebb_z0: ob.ebb_z1 + 1] # TODO: on-the-fly in RoiSet, given DF
-
- # compute contours
- obmask = (acc_obj_ids == ob.label) # TODO: on-the-fly
- contour = find_contours(obmask) # TODO: on-the-fly
- mask = obmask[ob.y0: ob.y1, ob.x0: ob.x1]
-
- rbb = { # TODO: just put in the DF
- 'y0': ob.rel_y0,
- 'y1': ob.rel_y1,
- 'x0': ob.rel_x0,
- 'x1': ob.rel_x1,
- }
-
- meta.append({
- 'df_index': ob.Index,
- 'info': ob,
- 'slice': sl,
- 'relative_bounding_box': rbb, # TODO: put in DF
- 'contour': contour, # TODO: delegate to getter
- 'mask': mask # TODO: delegate to getter
- })
- self.zmask_meta = meta
-
- # return intermediate image arrays # TODO: make on-the-fly
- self.interm = {
- 'label_map': acc_obj_ids,
- 'argmax': acc_raw.data.argmax(axis=3, keepdims=True)[:, :, 0, 0].astype('uint16'),
- }
-
self.count = len(self._df)
- self.object_id_labels = self.interm['label_map']
self.object_class_maps = {} # classification results
def __iter__(self):
@@ -98,7 +66,7 @@ class RoiSet(object):
df = (
pd.DataFrame(
regionprops_table(
- acc_obj_ids,
+ acc_obj_ids.data[:, :, 0, 0],
intensity_image=argmax,
properties=('label', 'area', 'intensity_mean', 'solidity', 'bbox', 'centroid')
)
@@ -142,7 +110,7 @@ class RoiSet(object):
axis=1
)
df['mask'] = df.apply(
- lambda r: (acc_obj_ids == r.label)[r.y0: r.y1, r.x0: r.x1],
+ lambda r: (acc_obj_ids.data == r.label)[r.y0: r.y1, r.x0: r.x1, 0, 0],
axis=1
)
return df
@@ -182,9 +150,6 @@ class RoiSet(object):
projected = self.acc_raw.data.max(axis=-1)
return projected
- def get_object_mask_by_class(self, class_id):
- return self.object_id_labels == class_id
-
def get_patch_masks(self, **kwargs):
return get_patch_masks(self, **kwargs)
@@ -217,7 +182,7 @@ class RoiSet(object):
assert mask_type in ('contours', 'boxes')
zi_st = np.zeros(self.acc_raw.shape, dtype='bool')
- lamap = self.acc_obj_ids
+ lamap = self.acc_obj_ids.data
# make an object map where label is replaced by focus position in stack and background is -1
lut = np.zeros(lamap.max() + 1) - 1
@@ -248,27 +213,23 @@ class RoiSet(object):
# do this on a patch basis, i.e. only one object per frame
obmap_patches = object_classification_model.label_instance_class(
- self.get_raw_patches(channel=channel), # TODO: enforce df index
+ self.get_raw_patches(channel=channel),
self.get_patch_masks()
)
- lamap = self.object_id_labels
- om = np.zeros(lamap.shape, dtype=lamap.dtype)
+ om = np.zeros(self.acc_obj_ids.shape, self.acc_obj_ids.dtype)
- df = self.get_df()
- idx = df.index
- se = pd.Series(data=np.nan, index=idx)
+ self._df['classify_by_' + name] = pd.Series(dtype='Int64')
# assign labels to object map:
- for i in range(0, len(idx)):
- # object_id = self.zmask_meta[i]['info'].label
- object_id = df.loc[idx[i], 'label']
- result_patch = mask_largest_object(obmap_patches.iat(i))
- object_class = np.unique(result_patch)[1]
- om[self.object_id_labels == object_id] = object_class
- se.loc[idx[i]] = object_class
-
- self.add_df_col('classify_by_' + name, se)
+ for i, roi in enumerate(self):
+ oc = np.unique(
+ mask_largest_object(
+ obmap_patches.iat(i)
+ )
+ )[1]
+ self._df.loc[roi.Index, 'classify_by_' + name] = oc
+ om[self.acc_obj_ids.data == roi.label] = oc
self.object_class_maps[name] = InMemoryDataAccessor(om)
def run_exports(self, where, channel, prefix, params: RoiSetExportParams):
--
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