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

@@ -212,7 +212,7 @@ def filter_df_overlap_seg(df1: pd.DataFrame, df2: pd.DataFrame = None) -> pd.Dat
     return dfbb
 
 
-def make_df_from_object_ids(acc_raw, acc_obj_ids, expand_box_by, deproject_channel=None) -> pd.DataFrame:
+def make_df_from_object_ids(acc_raw, acc_obj_ids, expand_box_by, deproject_channel=None, filters=None) -> pd.DataFrame:
     """
     Build dataframe that associate object IDs with summary stats;
     :param acc_raw: accessor to raw image data
@@ -258,17 +258,19 @@ def make_df_from_object_ids(acc_raw, acc_obj_ids, expand_box_by, deproject_chann
 
     df = df_insert_slices(df, acc_raw.shape_dict, expand_box_by)
 
+    df_fil = filter_df(df, filters)
+
     def _make_binary_mask(r):
         acc = InMemoryDataAccessor(acc_obj_ids.data == r.label)
         cropped = acc.get_mono(0, mip=True).crop_hw((r.y0, r.x0, (r.y1 - r.y0), (r.x1 - r.x0))).data_yx
         return cropped
 
-    df['binary_mask'] = df.apply(
+    df_fil['binary_mask'] = df_fil.apply(
         _make_binary_mask,
         axis=1,
         result_type='reduce',
     )
-    return df
+    return df_fil
 
 
 def df_insert_slices(df: pd.DataFrame, sd: dict, expand_box_by) -> pd.DataFrame:
@@ -387,13 +389,11 @@ class RoiSet(object):
         """
         assert acc_obj_ids.chroma == 1
 
-        df = filter_df(
-            make_df_from_object_ids(
-                acc_raw, acc_obj_ids,
-                expand_box_by=params.expand_box_by,
-                deproject_channel=params.deproject_channel,
-            ),
-            params.filters,
+        df = make_df_from_object_ids(
+            acc_raw, acc_obj_ids,
+            expand_box_by=params.expand_box_by,
+            deproject_channel=params.deproject_channel,
+            filters=params.filters,
         )
 
         return cls(acc_raw, df, params)