diff --git a/extensions/chaeo/examples/batch_run_patches.py b/extensions/chaeo/examples/batch_run_patches.py
index 291b5c52a5f1900d19f57711446f14427753b20e..09d2ee8b2a9eeac137936a603da281a9b91d39dd 100644
--- a/extensions/chaeo/examples/batch_run_patches.py
+++ b/extensions/chaeo/examples/batch_run_patches.py
@@ -4,6 +4,7 @@ import pandas as pd
from extensions.chaeo.workflows import export_patches_from_multichannel_zstack
from extensions.ilastik.models import IlastikPixelClassifierModel
+from model_server.accessors import InMemoryDataAccessor, write_accessor_data_to_file
if __name__ == '__main__':
where_czi = Path(
@@ -43,6 +44,9 @@ if __name__ == '__main__':
pd.DataFrame(result['timer_results'], index=[0]).to_csv(where_output / 'timer_results.csv', **csv_args)
csv_args = {'mode': 'a', 'header': False} # append to CSV from here on
-
-
-
+ # export intermediate data if flagged
+ for k in result['interm'].keys():
+ write_accessor_data_to_file(
+ where_output / k / (ff.stem + '.tif'),
+ InMemoryDataAccessor(result['interm'][k])
+ )
diff --git a/extensions/chaeo/tests/test_zstack.py b/extensions/chaeo/tests/test_zstack.py
index 3ce9596945ad844c7f8841e0c9ee5808485f35cb..f2394889336d9c3216dc8714b1fa56ffa9c9638a 100644
--- a/extensions/chaeo/tests/test_zstack.py
+++ b/extensions/chaeo/tests/test_zstack.py
@@ -27,7 +27,7 @@ class TestZStackDerivedDataProducts(unittest.TestCase):
# write_accessor_data_to_file(output_path / 'obmap.tif', self.obmap)
def test_zmask_makes_correct_boxes(self, mask_type='boxes', **kwargs):
- zmask, meta, df = build_zmask_from_object_mask(
+ zmask, meta, df, interm = build_zmask_from_object_mask(
self.obmap.get_one_channel_data(0),
self.stack.get_one_channel_data(0),
mask_type=mask_type,
@@ -51,6 +51,10 @@ class TestZStackDerivedDataProducts(unittest.TestCase):
ar = meta[1]['info'].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 zmask, meta
def test_zmask_makes_correct_contours(self):
diff --git a/extensions/chaeo/workflows.py b/extensions/chaeo/workflows.py
index 997826eee276f2e8c66895903a023a0ba0ef9754..3afc7d2604d435c20bfcacd0ab9b0a13d1cde26e 100644
--- a/extensions/chaeo/workflows.py
+++ b/extensions/chaeo/workflows.py
@@ -49,7 +49,7 @@ def export_patches_from_multichannel_zstack(
ti.click('threshold_pixel_mask')
# make zmask
- zmask, zmask_meta, df = build_zmask_from_object_mask(
+ zmask, zmask_meta, df, interm = build_zmask_from_object_mask(
obmask.get_one_channel_data(pixel_class),
stack.get_one_channel_data(zmask_channel),
mask_type=mask_type,
@@ -89,4 +89,5 @@ def export_patches_from_multichannel_zstack(
'success': True,
'timer_results': ti.events,
'dataframe': df,
+ 'interm': interm,
}
\ No newline at end of file
diff --git a/extensions/chaeo/zmask.py b/extensions/chaeo/zmask.py
index 8304b836f9ce57c6bc75545f72a35f8d24bbc795..1996a641a154f8d1c4dda1a8a8d838ba8095de03 100644
--- a/extensions/chaeo/zmask.py
+++ b/extensions/chaeo/zmask.py
@@ -30,6 +30,9 @@ def build_zmask_from_object_mask(
contour: object's contour returned by skimage.measure.find_contours
mask: mask of object in relative frame of (optionally) expanded bounding box
pd.DataFrame: objects, including bounding, box information after filtering
+ Dict of intermediate image products:
+ label_map: np.ndarray (h x w) where each unique object has an integer label
+ argmax: np.ndarray (h x w x 1 x 1) z-index of highest intensity in zstack
"""
# validate inputs
@@ -42,7 +45,7 @@ def build_zmask_from_object_mask(
assert zstack.hw == obmask.hw
# assign object labels and build object query
- lamap = label(obmask.data[:, :, 0, 0])
+ lamap = label(obmask.data[:, :, 0, 0]).astype('uint16')
query_str = 'label > 0' # always true
if filters is not None:
for k in filters.keys():
@@ -52,7 +55,7 @@ def build_zmask_from_object_mask(
query_str = query_str + f' & {k} > {vmin} & {k} < {vmax}'
# build dataframe of objects, assign z index to each object
- argmax = zstack.data.argmax(axis=3, keepdims=True)[:, :, 0, 0]
+ argmax = zstack.data.argmax(axis=3, keepdims=True)[:, :, 0, 0].astype('uint16')
df = (
pd.DataFrame(
regionprops_table(
@@ -61,7 +64,6 @@ def build_zmask_from_object_mask(
properties=('label', 'area', 'intensity_mean', 'solidity', 'bbox')
)
)
- .query(query_str)
.rename(
columns={
'bbox-0': 'y0',
@@ -72,6 +74,8 @@ def build_zmask_from_object_mask(
)
)
df['zi'] = df['intensity_mean'].round().astype('int')
+ df['keeper'] = False
+ df.loc[df.query(query_str).index, 'keeper'] = True
# make an object map where label is replaced by focus position in stack and background is -1
lut = np.zeros(lamap.max() + 1) - 1
@@ -82,7 +86,7 @@ def build_zmask_from_object_mask(
h, w, c, nz = zstack.shape
meta = []
- for ob in df.itertuples(name='LabeledObject'):
+ for ob in df[df['keeper']].itertuples(name='LabeledObject'):
y0 = max(ob.y0 - ebxy, 0)
y1 = min(ob.y1 + ebxy, h - 1)
x0 = max(ob.x0 - ebxy, 0)
@@ -133,4 +137,10 @@ def build_zmask_from_object_mask(
sl = bb['slice']
zi_st[sl] = 1
- return zi_st, meta, df
\ No newline at end of file
+ # return intermediate image arrays
+ interm = {
+ 'label_map': lamap,
+ 'argmax': argmax,
+ }
+
+ return zi_st, meta, df, interm
\ No newline at end of file