Nms optimize for stationary cars (#9684)

* Use different nms values for different object types

* Add tests

* Format tests

frigate/const.py

@@ -26,6 +26,10 @@ LABEL_CONSOLIDATION_MAP = {
     "face": 0.5,
 }
 LABEL_CONSOLIDATION_DEFAULT = 0.9
+LABEL_NMS_MAP = {
+    "car": 0.6,
+}
+LABEL_NMS_DEFAULT = 0.4
 
 # Audio Consts
 

frigate/test/test_video.py

@@ -287,6 +287,15 @@ class TestObjectBoundingBoxes(unittest.TestCase):
         consolidated_detections = reduce_detections(frame_shape, detections)
         assert len(consolidated_detections) == len(detections)
 
+    def test_vert_stacked_cars_not_reduced(self):
+        detections = [
+            ("car", 0.8, (954, 312, 1247, 475), 498512, 1.48, (800, 200, 1400, 600)),
+            ("car", 0.85, (970, 380, 1273, 610), 698752, 1.56, (800, 200, 1400, 700)),
+        ]
+        frame_shape = (720, 1280)
+        consolidated_detections = reduce_detections(frame_shape, detections)
+        assert len(consolidated_detections) == len(detections)
+
 
 class TestRegionGrid(unittest.TestCase):
     def setUp(self) -> None:

frigate/util/object.py

@@ -10,7 +10,12 @@ import numpy as np
 from peewee import DoesNotExist
 
 from frigate.config import DetectConfig, ModelConfig
-from frigate.const import LABEL_CONSOLIDATION_DEFAULT, LABEL_CONSOLIDATION_MAP
+from frigate.const import (
+    LABEL_CONSOLIDATION_DEFAULT,
+    LABEL_CONSOLIDATION_MAP,
+    LABEL_NMS_DEFAULT,
+    LABEL_NMS_MAP,
+)
 from frigate.detectors.detector_config import PixelFormatEnum
 from frigate.models import Event, Regions, Timeline
 from frigate.util.image import (
@@ -466,6 +471,7 @@ def reduce_detections(
 
         selected_objects = []
         for group in detected_object_groups.values():
+            label = group[0][0]
             # o[2] is the box of the object: xmin, ymin, xmax, ymax
             # apply max/min to ensure values do not exceed the known frame size
             boxes = [
@@ -483,7 +489,9 @@ def reduce_detections(
             # due to min score requirement of NMSBoxes
             confidences = [0.6 if clipped(o, frame_shape) else o[1] for o in group]
 
-            idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
+            idxs = cv2.dnn.NMSBoxes(
+                boxes, confidences, 0.5, LABEL_NMS_MAP.get(label, LABEL_NMS_DEFAULT)
+            )
 
             # add objects
             for index in idxs: