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/accessors.py

@@ -525,7 +525,7 @@ class PatchStack(InMemoryDataAccessor):
         assuming the each patch in the patch stack represents a single object.
         :param mask of the same dimensions
         """
-        if self.shape != mask.shape or not mask.is_mask():
+        if not mask.can_mask(self):
             raise DataShapeError(f'Patch stack object dataframe expects a mask of the same dimensions')
         df = pd.DataFrame([
             {