RoiSet facilitates object detection models

• created distinct generator class methods for RoiSet
• RoiSet.from_bounding_boxes can create RoiSet with just YXWH coordinates and no segmentation, e.g. as output from object detection models
• can also inspect and quantify overlap between and within RoiSet(s), at both bounding box and segmentation level
• can generate RoiSet from polygon segmentation e.g. in COCO segmentation models

model_server/base/roiset.py

@@ -341,15 +341,12 @@ class RoiSet(object):
         :param df: dataframe containing at minimum bounding box and segmentation mask information
         :param params: optional arguments that influence the definition and representation of ROIs
         """
-        # assert acc_obj_ids.chroma == 1
-        # self.acc_obj_ids = acc_obj_ids
         self.acc_raw = acc_raw
         self.accs_derived = []
         self.params = params
 
         self._df = df
         self.count = len(self._df)
-        # self.object_class_maps = {}  # classification results
 
     def __iter__(self):
         """Expose ROI meta information via the Pandas.DataFrame API"""
@@ -555,7 +552,7 @@ class RoiSet(object):
             self.get_patch_masks_acc(expanded=False, pad_to=None)
         )
 
-        self._df['classify_by_' + name] = pd.Series(dtype='Int64')
+        se = pd.Series(dtype='Int64', index=self._df.index)
 
         for i, roi in enumerate(self):
             oc = np.unique(
@@ -563,7 +560,9 @@ class RoiSet(object):
                     obmap_patches.iat(i).data
                 )
             )[-1]
-            self._df.loc[roi.Index, 'classify_by_' + name] = oc
+            se[roi.Index] = oc
+        self.set_classification(name, se)
+
 
     def get_object_class_map(self, name: str) -> InMemoryDataAccessor:
         """
@@ -774,6 +773,23 @@ class RoiSet(object):
         dfe['patch'] = dfe.apply(lambda r: _make_patch(r), axis=1)
         return dfe
 
+    @property
+    def classification_columns(self):
+        """
+        Return list of columns that describe instance classification results
+        """
+        pr = 'classify_by_'
+        return [c.split(pr)[1] for c in self._df.columns if c.startswith(pr)]
+
+    def set_classification(self, cname: str, se: pd.Series):
+        """
+        Set instance classification result as a column addition on dataframe
+        :param cname: name of classification result
+        :param se: series containing class information
+        """
+        col = f'classify_by_{cname}'
+        self._df[col] = se
+
     def run_exports(self, where: Path, channel, prefix, params: RoiSetExportParams) -> dict:
         """
         Export various representations of ROIs, e.g. patches, annotated stacks, and object maps.
@@ -814,9 +830,7 @@ class RoiSet(object):
             if k == 'annotated_zstacks':
                 record[k] = str(Path(k) / self.export_annotated_zstack(subdir, prefix=pr, **kp))
             if k == 'object_classes':
-                pr = 'classify_by_'
-                cnames = [c.split(pr)[1] for c in self._df.columns if c.startswith(pr)]
-                for n in cnames:
+                for n in self.classification_columns:
                     fp = subdir / n / (pr + '.tif')
                     write_accessor_data_to_file(fp, self.get_object_class_map(n))
                     record[f'{k}_{n}'] = str(fp)