Fix lpr metrics and add yolov9 plate detection metric (#16827)

frigate/data_processing/common/license_plate/mixin.py

@@ -816,6 +816,20 @@ class LicensePlateProcessingMixin:
         # 5. Return True if we should keep the previous plate (i.e., if it scores higher)
         return prev_score > curr_score
 
+    def __update_yolov9_metrics(self, duration: float) -> None:
+        """
+        Update inference metrics.
+        """
+        self.metrics.yolov9_lpr_fps.value = (
+            self.metrics.yolov9_lpr_fps.value * 9 + duration
+        ) / 10
+
+    def __update_lpr_metrics(self, duration: float) -> None:
+        """
+        Update inference metrics.
+        """
+        self.metrics.alpr_pps.value = (self.metrics.alpr_pps.value * 9 + duration) / 10
+
     def lpr_process(self, obj_data: dict[str, any], frame: np.ndarray):
         """Look for license plates in image."""
 
@@ -843,6 +857,7 @@ class LicensePlateProcessingMixin:
 
         if self.requires_license_plate_detection:
             logger.debug("Running manual license_plate detection.")
+
             car_box = obj_data.get("box")
 
             if not car_box:
@@ -867,6 +882,9 @@ class LicensePlateProcessingMixin:
             logger.debug(
                 f"YOLOv9 LPD inference time: {(datetime.datetime.now().timestamp() - yolov9_start) * 1000:.2f} ms"
             )
+            self.__update_yolov9_metrics(
+                datetime.datetime.now().timestamp() - yolov9_start
+            )
 
             if not license_plate:
                 logger.debug("Detected no license plates for car object.")
@@ -945,11 +963,15 @@ class LicensePlateProcessingMixin:
                 license_plate_frame,
             )
 
+        start = datetime.datetime.now().timestamp()
+
         # run detection, returns results sorted by confidence, best first
         license_plates, confidences, areas = self._process_license_plate(
             license_plate_frame
         )
 
+        self.__update_lpr_metrics(datetime.datetime.now().timestamp() - start)
+
         logger.debug(f"Text boxes: {license_plates}")
         logger.debug(f"Confidences: {confidences}")
         logger.debug(f"Areas: {areas}")

frigate/data_processing/post/license_plate.py

@@ -40,12 +40,6 @@ class LicensePlatePostProcessor(LicensePlateProcessingMixin, PostProcessorApi):
         self.config = config
         super().__init__(config, metrics, model_runner)
 
-    def __update_metrics(self, duration: float) -> None:
-        """
-        Update inference metrics.
-        """
-        self.metrics.alpr_pps.value = (self.metrics.alpr_pps.value * 9 + duration) / 10
-
     def process_data(
         self, data: dict[str, any], data_type: PostProcessDataEnum
     ) -> None:
@@ -57,8 +51,6 @@ class LicensePlatePostProcessor(LicensePlateProcessingMixin, PostProcessorApi):
         Returns:
             None.
         """
-        start = datetime.datetime.now().timestamp()
-
         event_id = data["event_id"]
         camera_name = data["camera"]
 
@@ -128,7 +120,10 @@ class LicensePlatePostProcessor(LicensePlateProcessingMixin, PostProcessorApi):
             return
 
         if WRITE_DEBUG_IMAGES:
-            cv2.imwrite(f"debug/frames/lpr_post_{start}.jpg", image)
+            cv2.imwrite(
+                f"debug/frames/lpr_post_{datetime.datetime.now().timestamp()}.jpg",
+                image,
+            )
 
         # convert to yuv for processing
         frame = cv2.cvtColor(image, cv2.COLOR_BGR2YUV_I420)
@@ -210,8 +205,6 @@ class LicensePlatePostProcessor(LicensePlateProcessingMixin, PostProcessorApi):
         logger.debug(f"Post processing plate: {event_id}, {frame_time}")
         self.lpr_process(keyframe_obj_data, frame)
 
-        self.__update_metrics(datetime.datetime.now().timestamp() - start)
-
     def handle_request(self, topic, request_data) -> dict[str, any] | None:
         if topic == EmbeddingsRequestEnum.reprocess_plate.value:
             event = request_data["event"]

frigate/data_processing/real_time/license_plate.py

@@ -1,6 +1,5 @@
 """Handle processing images for face detection and recognition."""
 
-import datetime
 import logging
 
 import numpy as np
@@ -33,17 +32,9 @@ class LicensePlateRealTimeProcessor(LicensePlateProcessingMixin, RealTimeProcess
         self.config = config
         super().__init__(config, metrics)
 
-    def __update_metrics(self, duration: float) -> None:
-        """
-        Update inference metrics.
-        """
-        self.metrics.alpr_pps.value = (self.metrics.alpr_pps.value * 9 + duration) / 10
-
     def process_frame(self, obj_data: dict[str, any], frame: np.ndarray):
         """Look for license plates in image."""
-        start = datetime.datetime.now().timestamp()
         self.lpr_process(obj_data, frame)
-        self.__update_metrics(datetime.datetime.now().timestamp() - start)
 
     def handle_request(self, topic, request_data) -> dict[str, any] | None:
         return

frigate/data_processing/types.py

@@ -10,12 +10,14 @@ class DataProcessorMetrics:
     text_embeddings_sps: Synchronized
     face_rec_fps: Synchronized
     alpr_pps: Synchronized
+    yolov9_lpr_fps: Synchronized
 
     def __init__(self):
         self.image_embeddings_fps = mp.Value("d", 0.01)
         self.text_embeddings_sps = mp.Value("d", 0.01)
         self.face_rec_fps = mp.Value("d", 0.01)
         self.alpr_pps = mp.Value("d", 0.01)
+        self.yolov9_lpr_fps = mp.Value("d", 0.01)
 
 
 class DataProcessorModelRunner:

frigate/stats/util.py

@@ -302,6 +302,10 @@ def stats_snapshot(
             stats["embeddings"]["plate_recognition_speed"] = round(
                 embeddings_metrics.alpr_pps.value * 1000, 2
             )
+            if "license_plate" not in config.objects.all_objects:
+                stats["embeddings"]["yolov9_plate_detection_speed"] = round(
+                    embeddings_metrics.yolov9_lpr_fps.value * 1000, 2
+                )
 
     get_processing_stats(config, stats, hwaccel_errors)