diff --git a/extensions/chaeo/accessors.py b/extensions/chaeo/accessors.py
index f606470e971f6c354827f732e0186c3fdadab967..82f726ba86fddf817583e349027a83b134d483e2 100644
--- a/extensions/chaeo/accessors.py
+++ b/extensions/chaeo/accessors.py
@@ -16,15 +16,25 @@ class MonoPatchStack(InMemoryDataAccessor):
assert data.ndim == 3
self._data = np.expand_dims(data, 2)
elif isinstance(data, list): # list of YX patches
- nda = np.array(data).squeeze()
- assert nda.ndim == 3
- self._data = np.expand_dims(
- np.moveaxis(
- nda,
- [1, 2, 0],
- [0, 1, 2]),
- 2
- )
+ if len(data) == 0:
+ self._data = np.ndarray([0, 0, 0, 0], dtype='uin9')
+ elif len(data) == 1:
+ self._data = np.expand_dims(
+ np.array(
+ data[0].squeeze()
+ ),
+ (2, 3)
+ )
+ else:
+ nda = np.array(data).squeeze()
+ assert nda.ndim == 3
+ self._data = np.expand_dims(
+ np.moveaxis(
+ nda,
+ [1, 2, 0],
+ [0, 1, 2]),
+ 2
+ )
else:
raise InvalidDataForPatchStackError(f'Cannot create accessor from {type(data)}')
diff --git a/extensions/chaeo/batch_jobs/20231028_Porto_PA.py b/extensions/chaeo/batch_jobs/20231028_Porto_PA.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4afbcdd4ecfc1c9c795c01360c00e0361f4a7f1
--- /dev/null
+++ b/extensions/chaeo/batch_jobs/20231028_Porto_PA.py
@@ -0,0 +1,56 @@
+from pathlib import Path
+
+from model_server.util import autonumber_new_directory, get_matching_files, loop_workflow
+from extensions.chaeo.ecotaxa import write_ecotaxa_tsv
+from extensions.chaeo.workflows import export_patches_from_multichannel_zstack
+from extensions.ilastik.models import IlastikPixelClassifierModel
+
+
+if __name__ == '__main__':
+ sample_id = '20231028-porto-PA'
+ root = Path('c:/Users/rhodes/projects/proj0012-trec-handoff/owncloud-sync/TREC-HD/Images/')
+ where_czi = (root / 'TREC_STOP_26_Porto/Selection').__str__()
+ where_output = autonumber_new_directory(
+ 'c:/Users/rhodes/projects/proj0011-plankton-seg/exp0023/output',
+ 'batch-output'
+ )
+
+ px_ilp = Path('c:/Users/rhodes/projects/proj0011-plankton-seg/exp0017/pxAF405_dim8bit.ilp').__str__()
+
+ params = {
+ 'pxmap_threshold': 0.25,
+ 'pxmap_foreground_channel': 0,
+ 'segmentation_channel': 0,
+ 'zmask_zindex': None,
+ 'patches_channel': 2,
+ 'zmask_type': 'boxes',
+ 'zmask_filters': {'area': (1e3, 1e8)},
+ 'zmask_expand_box_by': (128, 3),
+ 'export_pixel_probabilities': True,
+ 'export_2d_patches_for_training': True,
+ 'draw_bounding_box_on_2d_patch': True,
+ 'export_2d_patches_for_annotation': True,
+ 'export_3d_patches': False,
+ 'export_annotated_zstack': True,
+ 'export_patch_masks': True,
+ 'zmask_clip': 0.01,
+ 'rgb_overlay_channels': (1, None, None),
+ 'rgb_overlay_weights': (0.2, 1.0, 1.0),
+ 'draw_label_on_zstack': True,
+ }
+
+ input_files = get_matching_files(where_czi, 'czi', coord_filter={})
+
+ loop_workflow(
+ input_files,
+ where_output,
+ export_patches_from_multichannel_zstack,
+ [IlastikPixelClassifierModel(params={'project_file': Path(px_ilp)})],
+ params,
+ catch_and_continue=False,
+ )
+
+ csv_path = (Path(where_output) / 'workflow_data.csv').__str__()
+ write_ecotaxa_tsv(csv_path, where_output, sample_id=sample_id, scope_id='EMBL-MS-Zeiss-LSM900')
+
+ print('Finished')
\ No newline at end of file
diff --git a/extensions/chaeo/products.py b/extensions/chaeo/products.py
index a0291874c0ffd2f68c012ffa8f3753debb561549..0f1ac9765728c182914d59b17d87ae6598a5271c 100644
--- a/extensions/chaeo/products.py
+++ b/extensions/chaeo/products.py
@@ -120,6 +120,7 @@ def get_patches_from_zmask_meta(
**kwargs
) -> MonoPatchStack:
patches = []
+
for mi in zmask_meta:
sl = mi['slice']
@@ -197,6 +198,7 @@ def get_patches_from_zmask_meta(
patch = pad(patch, pad_to)
patches.append(patch)
+
if not make_3d and pc == 1:
return MonoPatchStack(patches)
else:
diff --git a/extensions/chaeo/workflows.py b/extensions/chaeo/workflows.py
index 57072bb835d46bf6b83dc5521a12b9693fd04018..119ee1ff2922f038ee335ca995e5ebc52f46c539 100644
--- a/extensions/chaeo/workflows.py
+++ b/extensions/chaeo/workflows.py
@@ -122,7 +122,7 @@ def export_patches_from_multichannel_zstack(
)
ti.click('export_pixel_probability')
- if export_3d_patches:
+ if export_3d_patches and len(zmask_meta) > 0:
files = export_patches_from_zstack(
Path(output_folder_path) / '3d_patches',
stack.get_one_channel_data(patches_channel),
@@ -134,7 +134,7 @@ def export_patches_from_multichannel_zstack(
)
ti.click('export_3d_patches')
- if export_2d_patches_for_annotation:
+ if export_2d_patches_for_annotation and len(zmask_meta) > 0:
files = export_multichannel_patches_from_zstack(
Path(output_folder_path) / '2d_patches_annotation',
stack,
@@ -159,7 +159,7 @@ def export_patches_from_multichannel_zstack(
# prepopulate patch UUID
df['patch_id'] = df.apply(lambda _: uuid4(), axis=1)
- if export_2d_patches_for_training:
+ if export_2d_patches_for_training and len(zmask_meta) > 0:
files = export_multichannel_patches_from_zstack(
Path(output_folder_path) / '2d_patches_training',
stack.get_one_channel_data(patches_channel),
@@ -171,7 +171,7 @@ def export_patches_from_multichannel_zstack(
)
ti.click('export_2d_patches')
- if export_patch_masks:
+ if export_patch_masks and len(zmask_meta) > 0:
files = export_patch_masks_from_zstack(
Path(output_folder_path) / 'patch_masks',
stack.get_one_channel_data(patches_channel),
@@ -195,13 +195,16 @@ def export_patches_from_multichannel_zstack(
# generate multichannel projection from label centroids
dff = df[df['keeper']]
- interm['projected'] = project_stack_from_focal_points(
- dff['centroid-0'].to_numpy(),
- dff['centroid-1'].to_numpy(),
- dff['zi'].to_numpy(),
- stack,
- degree=4,
- )
+ if len(zmask_meta) > 0:
+ interm['projected'] = project_stack_from_focal_points(
+ dff['centroid-0'].to_numpy(),
+ dff['centroid-1'].to_numpy(),
+ dff['zi'].to_numpy(),
+ stack,
+ degree=4,
+ )
+ else: # else just return MIP
+ interm['projected'] = stack.data.max(axis=-1)
return {
'pixel_model_id': pixel_classifier.model_id,