Batch runner passes only JSON-normalized data to workflow, and saves this to CSV for each iteration

extensions/chaeo/examples/batch_run_patches.py

@@ -6,7 +6,6 @@ from time import localtime, strftime
 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__':
@@ -27,9 +26,6 @@ if __name__ == '__main__':
 
     csv_args = {'mode': 'w', 'header': True} # when creating file
     px_ilp = Path.home() / 'model-server' / 'ilastik' / 'AF405-bodies_boundaries.ilp'
-    px_model = IlastikPixelClassifierModel(
-        params={'project_file': px_ilp}
-    )
 
     for ff in where_czi.iterdir():
         pattern = 'Selection--W([\d]+)--P([\d]+)-T([\d]+)'
@@ -41,10 +37,9 @@ if __name__ == '__main__':
         if int(ma.groups()[1]) > 10: # skip second half of set
             continue
 
-
         export_kwargs = {
             'input_zstack_path': (where_czi / ff).__str__(),
-            'px_model': px_model.model_id,
+            'ilastik_project_file': px_ilp.__str__(),
             'pxmap_threshold': 0.25,
             'pixel_class': 0,
             'zmask_channel': 0,
@@ -55,19 +50,6 @@ if __name__ == '__main__':
             'zmask_expand_box_by': (128, 0),
         }
 
-        # result = export_patches_from_multichannel_zstack(
-        #     input_zstack_path=where_czi / ff,
-        #     px_model=px_model,
-        #     pxmap_threshold=0.25,
-        #     pixel_class=0,
-        #     zmask_channel=0,
-        #     patches_channel=4,
-        #     where_output=where_output,
-        #     mask_type='boxes',
-        #     zmask_filters={'area': (1e3, 1e8)},
-        #     zmask_expand_box_by=(128, 0),
-        # )
-
         result = export_patches_from_multichannel_zstack(**export_kwargs)
 
         # parse and record results
@@ -75,6 +57,7 @@ if __name__ == '__main__':
         df['filename'] = ff.name
         df.to_csv(where_output / 'df_objects.csv', **csv_args)
         pd.DataFrame(result['timer_results'], index=[0]).to_csv(where_output / 'timer_results.csv', **csv_args)
+        pd.json_normalize(export_kwargs).to_csv(where_output / 'workflow_params.csv', **csv_args)
         csv_args = {'mode': 'a', 'header': False} # append to CSV from here on
 
         # export intermediate data if flagged
@@ -82,5 +65,4 @@ if __name__ == '__main__':
             write_accessor_data_to_file(
                 where_output / k / (ff.stem + '.tif'),
                 InMemoryDataAccessor(result['interm'][k])
-                )
-
+                )
\ No newline at end of file

extensions/chaeo/workflows.py

 from pathlib import Path
 from typing import Dict
 
-from pydantic import BaseModel
-
-
 from extensions.ilastik.models import IlastikPixelClassifierModel
 from extensions.chaeo.annotators import draw_boxes_on_3d_image
 from extensions.chaeo.products import export_patches_from_zstack
@@ -13,7 +10,7 @@ from model_server.workflows import Timer
 
 def export_patches_from_multichannel_zstack(
         input_zstack_path: str,
-        px_model_id: str,
+        ilastik_project_file: str,
         pxmap_threshold: float,
         pixel_class: int,
         zmask_channel: int,
@@ -34,7 +31,9 @@ def export_patches_from_multichannel_zstack(
     mip = InMemoryDataAccessor(
         stack.get_one_channel_data(channel=0).data.max(axis=-1, keepdims=True)
     )
-    # px_model =
+    px_model = IlastikPixelClassifierModel(
+        params={'project_file': Path(ilastik_project_file)}
+    )
     pxmap, _ = px_model.infer(mip)
     ti.click('infer_pixel_probability')