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

@@ -48,6 +48,7 @@ class RoiFilterRange(BaseModel):
 
 class RoiFilter(BaseModel):
     area: Union[RoiFilterRange, None] = None
+    diag: Union[RoiFilterRange, None] = None
 
 
 class RoiSetMetaParams(BaseModel):
@@ -115,7 +116,7 @@ def filter_df(df: pd.DataFrame, filters: RoiFilter = None) -> pd.DataFrame:
     query_str = 'label > 0'  # always true
     if filters is not None:  # parse filters
         for k, val in filters.dict(exclude_unset=True).items():
-            assert k in ('area')
+            assert k in ('area', 'diag')
             vmin = val['min']
             vmax = val['max']
             assert vmin >= 0
@@ -280,6 +281,7 @@ def df_insert_slices(df: pd.DataFrame, sd: dict, expand_box_by) -> pd.DataFrame:
 
     df['h'] = df['y1'] - df['y0']
     df['w'] = df['x1'] - df['x0']
+    df['diag'] = (df['w']**2 + df['h']**2).apply(sqrt)
     ebxy, ebz = expand_box_by
     df['ebb_y0'] = (df.y0 - ebxy).apply(lambda x: max(x, 0))
     df['ebb_y1'] = (df.y1 + ebxy).apply(lambda x: min(x, h))