Updates for TREC pipelines

Changes supporting use of pipelines in trec-adaptive-feedback project:

• generally, accessors' and models' IDs can be set with respective argument
• test client methods now handle response success check and variable parsing
• implemented functional threshold segmentation and (in base.roiset) classification models; threshold is relative to dataset full range (0.0 to 1.0)
• consequently, more tests cover base package without dependency on ilastik
• RoiSet returned by pipelines get IDs and are registered in server session, accesible at respective endpoints
• Pydantic models only used by FastAPI now; underlying model, accessor, and pipeline functions should interact with python objects and parameter dictionaries only
• RoiSet filters also include minimal and diagonal height, width dimension; are applied before time-intensity pixel array operations to increase performance
• performance improvement when passing patch stacks to ilastik pixel classifier
• RoiSet.classify_by can optionally filter by additional classification columns, e.g. to implement intensity gating

model_server/extensions/ilastik/router.py

@@ -14,42 +14,45 @@ import model_server.extensions.ilastik.pipelines.px_then_ob
 router.include_router(model_server.extensions.ilastik.pipelines.px_then_ob.router)
 
 @router.put('/seg/load/')
-def load_px_model(p: ilm.IlastikPixelClassifierParams) -> dict:
+def load_px_model(p: ilm.IlastikPixelClassifierParams, model_id=None) -> dict:
     """
     Load an ilastik pixel classifier model from its project file
     """
     return load_ilastik_model(
         ilm.IlastikPixelClassifierModel,
         p,
+        model_id=model_id,
     )
 
 @router.put('/pxmap_to_obj/load/')
-def load_pxmap_to_obj_model(p: ilm.IlastikParams) -> dict:
+def load_pxmap_to_obj_model(p: ilm.IlastikParams, model_id=None) -> dict:
     """
     Load an ilastik object classifier from pixel predictions model from its project file
     """
     return load_ilastik_model(
         ilm.IlastikObjectClassifierFromPixelPredictionsModel,
         p,
+        model_id=model_id,
     )
 
 @router.put('/seg_to_obj/load/')
-def load_seg_to_obj_model(p: ilm.IlastikParams) -> dict:
+def load_seg_to_obj_model(p: ilm.IlastikParams, model_id=None) -> dict:
     """
     Load an ilastik object classifier from segmentation model from its project file
     """
     return load_ilastik_model(
         ilm.IlastikObjectClassifierFromSegmentationModel,
         p,
+        model_id=model_id,
     )
 
-def load_ilastik_model(model_class: ilm.IlastikModel, p: ilm.IlastikParams) -> dict:
+def load_ilastik_model(model_class: ilm.IlastikModel, p: ilm.IlastikParams, model_id=None) -> dict:
     project_file = p.project_file
     if not p.duplicate:
         existing_model_id = session.find_param_in_loaded_models('project_file', project_file, is_path=True)
         if existing_model_id is not None:
             session.log_info(f'An ilastik model from {project_file} already existing exists; did not load a duplicate')
             return {'model_id': existing_model_id}
-    result = session.load_model(model_class, key=p.model_id, params=p)
+    result = session.load_model(model_class, key=model_id, params=p)
     session.log_info(f'Loaded ilastik model {result} from {project_file}')
     return {'model_id': result}
\ No newline at end of file