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

tests/base/test_roiset.py

@@ -5,12 +5,12 @@ import numpy as np
 from pathlib import Path
 
 import pandas as pd
-from skimage import draw
 
 from model_server.base.roiset import filter_df_overlap_bbox, filter_df_overlap_seg, RoiSetExportParams, RoiSetMetaParams
 from model_server.base.roiset import RoiSet
 from model_server.base.accessors import generate_file_accessor, InMemoryDataAccessor, write_accessor_data_to_file, PatchStack
 from model_server.base.models import DummyInstanceSegmentationModel
+from model_server.base.process import smooth
 import model_server.conf.testing as conf
 
 data = conf.meta['image_files']
@@ -193,6 +193,12 @@ class TestRoiSetMonoProducts(BaseTestRoiSetMonoProducts, unittest.TestCase):
         self.assertTrue(all(np.unique(roiset.get_object_class_map('dummy_class').data) == [0, 1]))
         return roiset
 
+    def test_transfer_classification(self):
+        roiset1 = RoiSet.from_binary_mask(self.stack, self.seg_mask)
+        smoothed_mask = self.seg_mask.apply(lambda x: smooth(x, sig=3.0))
+        roiset2 = RoiSet.from_binary_mask(self.stack, smoothed_mask)
+        self.assertTrue(False)
+
     def test_classify_by_with_derived_channel(self):
         class ModelWithDerivedInputs(DummyInstanceSegmentationModel):
             def infer(self, img, mask):