fix: feature selection error for zero area features

src/aws/osml/model_runner/inference/feature_selection.py

@@ -82,6 +82,22 @@ def _get_lists_from_features(self, feature_list: List[Feature]) -> Tuple[List, L
         for feature in feature_list:
             # [min_x, min_y, max_x, max_y]
             bounds_imcoords = get_feature_image_bounds(feature)
+
+            # This is a workaround for assumptions made by the NMS library and normalization code in this class.
+            # All of that code assumes that features have bounding boxes with a non-zero area. That assumption
+            # does not hold for features reported as a single point geometry or others that might simply be
+            # erroneously reported with a zero width or height bbox. No matter the cause, we would like those
+            # features to pass through our feature selection processing without triggering errors. Here we
+            # add 0.1 of a pixel to the width or height of any bbox if it is currently zero. This does not change
+            # the actual reported geometry of the feature in any way it just ensures the assumption of a non-zero
+            # area is true.
+            bounds_imcoords = (
+                bounds_imcoords[0],
+                bounds_imcoords[1],
+                bounds_imcoords[2] + 0.1 if bounds_imcoords[0] == bounds_imcoords[2] else bounds_imcoords[2],
+                bounds_imcoords[3] + 0.1 if bounds_imcoords[1] == bounds_imcoords[3] else bounds_imcoords[3],
+            )
+
             category, score = self._get_category_and_score_from_feature(feature)
             boxes.append(bounds_imcoords)
             categories.append(category)

test/aws/osml/model_runner/inference/test_feature_selection.py

@@ -192,6 +192,26 @@ def test_feature_selection_nms_overlaps_multiple_categories(self):
         processed_features = feature_selector.select_features(original_features)
         assert len(processed_features) == 3
 
+    def test_feature_selection_nms_point_feature(self):
+        from aws.osml.model_runner.common import FeatureDistillationNMS
+        from aws.osml.model_runner.inference import FeatureSelector
+
+        feature_selection_option = FeatureDistillationNMS()
+        feature_selector = FeatureSelector(options=feature_selection_option)
+
+        test_feature = [
+            Feature(
+                geometry=Point((85.000111, 32.983222, 0.0)),
+                id="point-feature",
+                properties={
+                    "bounds_imcoords": [409.6, 409.6, 409.6, 409.6],
+                    "featureClasses": [{"iri": "boat", "score": 0.85}],
+                },
+            )
+        ]
+        processed_features = feature_selector.select_features(test_feature)
+        assert len(processed_features) == 1
+
     def test_feature_selection_soft_nms_overlaps(self):
         from aws.osml.model_runner.common import FeatureDistillationSoftNMS
         from aws.osml.model_runner.inference import FeatureSelector