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/roiset.py

@@ -618,25 +618,6 @@ class RoiSet(object):
             se[roi.Index] = oc
         self.set_classification(f'classify_by_{name}', se)
 
-    def aggregate_classifications(self, query: str, name: str = 'aggregate'):
-        """
-        Run query on DataFrame and put the results in a new boolean column
-        """
-        cname = 'classify_by_' + name
-        if cname in self._df.columns:
-            raise DataFrameQueryError(f'Name {cname} is already used in RoiSet dataframe')
-
-        if self.count == 0:
-            self._df[cname] = None
-            return True
-
-        try:
-            self.set_classification(
-                cname,
-                self._df.eval(query)
-            )
-        except Exception as e:
-            raise DataFrameQueryError(e)
 
     def get_instance_classification(self, roiset_from: Self, iou_min: float = 0.5) -> pd.DataFrame:
         """
@@ -671,10 +652,11 @@ class RoiSet(object):
 
         return df_overlaps
 
-    def get_object_class_map(self, name: str) -> InMemoryDataAccessor:
+    def get_object_class_map(self, name: str, filter_by: Union[List, None] = None) -> InMemoryDataAccessor:
         """
         For a given classification result, return a map where object IDs are replaced by each object's class
         :param name: name of the classification result, same as passed to RoiSet.classify_by()
+        :param filter_by: only include ROIs if the intersection of all specified classifications is True
         :return: accessor of object class map
         """
         colname = ('classify_by_' + name)
@@ -684,8 +666,11 @@ class RoiSet(object):
 
         def _label_object_class(roi):
             om[self.acc_obj_ids.data == roi.label] = roi[colname]
-        self._df.apply(_label_object_class, axis=1)
-
+        if filter_by is None:
+            self._df.apply(_label_object_class, axis=1)
+        else:
+            pd_fil = self._df[[f'classify_by_{fb}' for fb in filter_by]]
+            self._df.loc[pd_fil.all(axis=1), :].apply(_label_object_class, axis=1)
         return InMemoryDataAccessor(om)
 
     def get_serializable_dataframe(self) -> pd.DataFrame: