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

@@ -216,12 +216,13 @@ def make_df_from_object_ids(acc_raw, acc_obj_ids, expand_box_by) -> pd.DataFrame
     :param acc_raw: accessor to raw image data
     :param acc_obj_ids: accessor to map of object IDs
     :param expand_box_by: number of pixels to expand bounding box in all directions (without exceeding image boundary)
-    # :param deproject: assign object's z-position based on argmax of raw data if True
+
     :return: pd.DataFrame
     """
     # build dataframe of objects, assign z index to each object
 
     # TODO: don't assume that channel 0 is the basis of z-argmax
+    # TODO: :param deproject: assign object's z-position based on argmax of raw data if True
     if acc_obj_ids.nz == 1:  # deproject objects' z-coordinates from argmax of raw image
         df = pd.DataFrame(regionprops_table(
             acc_obj_ids.data_xy,
@@ -246,6 +247,7 @@ def make_df_from_object_ids(acc_raw, acc_obj_ids, expand_box_by) -> pd.DataFrame
         cropped = acc.get_mono(0, mip=True).crop_hw((r.y0, r.x0, (r.y1 - r.y0), (r.x1 - r.x0))).data_xy
         return cropped
 
+
     df['binary_mask'] = df.apply(
         _make_binary_mask,
         axis=1,
@@ -314,6 +316,7 @@ def safe_add(a, g, b):
     ).astype(a.dtype)
 
 def make_object_ids_from_df(df: pd.DataFrame, sd: dict) -> InMemoryDataAccessor:
+    # TODO: generate rectangular masks if running without segmentation
     id_mask = np.zeros((sd['Y'], sd['X'], 1, sd['Z']), dtype='uint16')
     if 'binary_mask' not in df.columns:
         raise MissingSegmentationError('RoiSet dataframe does not contain segmentation')
@@ -405,17 +408,20 @@ class RoiSet(object):
             params.get('expand_box_by', 0)
         )
 
-        def _make_binary_mask(r):
-            # TODO: make square mask array
-            # 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_xy
-            return cropped
-
-        df['binary_mask'] = df.apply(
-            _make_binary_mask,
-            axis=1,
-            result_type='reduce',
-        )
+        # TODO: don't even make binary_mask column in obj det case
+
+        # 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_xy
+        #     return cropped
+        #
+        #
+        #
+        # df['binary_mask'] = df.apply(
+        #     _make_binary_mask,
+        #     axis=1,
+        #     result_type='reduce',
+        # )
 
         return RoiSet(acc_raw, df, params)
 
@@ -639,7 +645,10 @@ class RoiSet(object):
         return InMemoryDataAccessor(om)
 
     def export_dataframe(self, csv_path: Path) -> str:
+        # TODO: move this inside of .serialize()
         csv_path.parent.mkdir(parents=True, exist_ok=True)
+
+        # TODO: suppress errors or check if binary_mask doesn't exist
         self._df.drop(['expanded_slice', 'slice', 'relative_slice', 'binary_mask'], axis=1).to_csv(csv_path, index=False)
         return csv_path.name
 
@@ -961,6 +970,9 @@ class RoiSet(object):
 
         return self._df.apply(_poly_from_mask, axis=1)
 
+    @property
+    def contains_segmentation(self):
+        return 'binary_mask' in self._df.columns
 
     @property
     def acc_obj_ids(self):