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/base/pipelines/roiset_obmap.py

 from typing import Dict, Union
 
-from pydantic import BaseModel, Field, validator
+from pydantic import BaseModel, Field
 
 from ..accessors import GenericImageDataAccessor
 from .router import router
 from .segment_zproj import segment_zproj_pipeline
 from .shared import call_pipeline
 from ..roiset import get_label_ids, RoiSet, RoiSetMetaParams, RoiSetExportParams
-from ..session import session
 
 from ..pipelines.shared import PipelineTrace, PipelineParams, PipelineRecord
 
-from ..models import Model, InstanceSegmentationModel
+from ..models import Model, InstanceMaskSegmentationModel
 
 
 class RoiSetObjectMapParams(PipelineParams):
@@ -35,10 +34,6 @@ class RoiSetObjectMapParams(PipelineParams):
         None,
         description='Object classifier used to classify segmented objectss'
     )
-    pixel_classifier_derived_model_id: Union[str, None] = Field(
-        None,
-        description='Pixel classifier used to derive channel(s) as additional inputs to object classification'
-    )
     patches_channel: int = Field(
         description='Channel of input image used in patches sent to object classifier'
     )
@@ -55,35 +50,19 @@ class RoiSetObjectMapParams(PipelineParams):
         'deproject_channel': None,
     })
     export_params: RoiSetExportParams = RoiSetExportParams()
-    derived_channels_input_channel: Union[int, None] = Field(
-        None,
-        description='Channel of input image from which to compute derived channels; use all if empty'
-    )
-    derived_channels_output_channels: Union[int, list] = Field(
-        None,
-        description='Derived channels to send to object classifier; use all if empty'
-    )
+
     export_label_interm: bool = False
 
 
 class RoiSetToObjectMapRecord(PipelineRecord):
-    roiset_table: dict
+    pass
 
 @router.put('/roiset_to_obmap/infer')
 def roiset_object_map(p: RoiSetObjectMapParams) -> RoiSetToObjectMapRecord:
     """
     Compute a RoiSet from 2d segmentation, apply to z-stack, and optionally apply object classification.
     """
-    record, rois = call_pipeline(roiset_object_map_pipeline, p)
-
-    table = rois.get_serializable_dataframe()
-
-    session.write_to_table('RoiSet', {'input_filename': p.accessor_id}, table)
-    ret = RoiSetToObjectMapRecord(
-        roiset_table=table.to_dict(),
-        **record.dict()
-    )
-    return ret
+    return call_pipeline(roiset_object_map_pipeline, p)
 
 
 def roiset_object_map_pipeline(
@@ -91,11 +70,11 @@ def roiset_object_map_pipeline(
         models: Dict[str, Model],
         **k
 ) -> (PipelineTrace, RoiSet):
-    d = PipelineTrace(accessors['accessor'])
+    d = PipelineTrace(accessors[''])
 
     d['mask'] = segment_zproj_pipeline(
         accessors,
-        {'model': models['pixel_classifier_segmentation_model']},
+        {'': models['pixel_classifier_segmentation_']},
         **k['segmentation'],
     ).last
 
@@ -107,9 +86,9 @@ def roiset_object_map_pipeline(
         d[ki] = vi
 
     # optionally run an object classifier if specified
-    if obmod := models.get('object_classifier_model'):
+    if obmod := models.get('object_classifier_'):
         obmod_name = k['object_classifier_model_id']
-        assert isinstance(obmod, InstanceSegmentationModel)
+        assert isinstance(obmod, InstanceMaskSegmentationModel)
         rois.classify_by(
             obmod_name,
             [k['patches_channel']],