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

@@ -10,6 +10,7 @@ from pydantic import BaseModel
 from scipy.stats import moment
 from skimage.filters import sobel
 
+from skimage import draw
 from skimage.measure import approximate_polygon, find_contours, label, points_in_poly, regionprops, regionprops_table, shannon_entropy
 from skimage.morphology import binary_dilation, disk
 
@@ -181,10 +182,6 @@ def make_df_from_object_ids(acc_raw, acc_obj_ids, expand_box_by) -> pd.DataFrame
     )
     return df
 
-# TODO: implement
-def make_df_from_polygons(acc_raw, polygons:np.ndarray) -> pd.DataFrame:
-    pass
-
 
 def df_insert_slices(df: pd.DataFrame, sd: dict, expand_box_by) -> pd.DataFrame:
     h = sd['Y']
@@ -345,20 +342,43 @@ class RoiSet(object):
         Create a RoiSet from a binary segmentation mask (either 2D or 3D)
         :param acc_raw: accessor to a generally a multichannel z-stack
         :param acc_seg: accessor of a binary segmentation mask (mono) of either two or three dimensions
-        :param allow_3d: return a 3D map if True; return a 2D map of the mask's maximum intensity project if False
+        :param allow_3d: use a 3D map if True; use a 2D map of the mask's maximum intensity project if False
         :param connect_3d: objects can span multiple z-positions if True; objects are unique to a single z if False
         :param params: optional arguments that influence the definition and representation of ROIs
-        :return: object identities map
         """
-        return RoiSet.from_object_ids(acc_raw, get_label_ids(acc_seg, allow_3d=allow_3d, connect_3d=connect_3d), params)
+        return RoiSet.from_object_ids(
+            acc_raw,
+            get_label_ids(
+                acc_seg,
+                allow_3d=allow_3d,
+                connect_3d=connect_3d
+            ),
+            params
+        )
 
     @staticmethod
-    #TODO: implement
-    def from_polygons(acc_raw, polygons: np.ndarray):
-        binary_poly = np.zeros(acc_raw.mono(0).shape, dtype=bool)
-        for p in poly:
-            pidcs = draw.polygon(p[:, 1], p[:, 0])
-            binary_poly[pidcs] = True
+    def from_polygons_2d(
+            acc_raw,
+            polygons: List[np.ndarray],
+            params: RoiSetMetaParams = RoiSetMetaParams()
+    ):
+        """
+        Create a RoiSet where objects are defined from a list of polygon coordinates
+        :param acc_raw: accessor to a generally a multichannel z-stack
+        :param polygons: list of (variable x 2) np.ndarrays describing (x, y) polymer coordinates
+        :param params: optional arguments that influence the definition and representation of ROIs
+        """
+        mask = np.zeros(acc_raw.get_mono(0, mip=True).shape, dtype=bool)
+        for p in polygons:
+            sl = draw.polygon(p[:, 1], p[:, 0])
+            mask[sl] = True
+        return RoiSet.from_binary_mask(
+            acc_raw,
+            InMemoryDataAccessor(mask),
+            allow_3d=False,
+            connect_3d=False,
+            params=params,
+        )
 
 
     # TODO: get overlapping segments
@@ -394,7 +414,7 @@ class RoiSet(object):
         write_accessor_data_to_file(fp, annotated)
         return (prefix + '.tif')
 
-    def get_zmask(self, mask_type='boxes'):
+    def get_zmask(self, mask_type='boxes') -> np.ndarray:
         """
         Return a mask of same dimensionality as raw data
 
@@ -780,7 +800,7 @@ class RoiSet(object):
         return record
 
     def get_polygons(self, poly_threshold=0, dilation_radius=1) -> pd.DataFrame:
-        """
+        self.coordinates_ = """
         Fit polygons to all object boundaries in the RoiSet
         :param poly_threshold: threshold distance for polygon fit; a smaller number follows sharp features more closely
         :param dilation_radius: radius of binary dilation to apply before fitting polygon