Add face recognitions per second

frigate/data_processing/common/license_plate/mixin.py

@@ -955,7 +955,7 @@ class LicensePlateProcessingMixin:
         """
         Update inference metrics.
         """
-        self.metrics.alpr_pps.value = (self.metrics.alpr_pps.value * 9 + duration) / 10
+        self.metrics.alpr_speed.value = (self.metrics.alpr_speed.value * 9 + duration) / 10
 
     def _generate_plate_event(self, camera: str, plate: str, plate_score: float) -> str:
         """Generate a unique ID for a plate event based on camera and text."""

frigate/data_processing/real_time/face.py

@@ -24,6 +24,7 @@ from frigate.data_processing.common.face.model import (
     FaceNetRecognizer,
     FaceRecognizer,
 )
+from frigate.util.builtin import EventsPerSecond
 from frigate.util.image import area
 
 from ..types import DataProcessorMetrics
@@ -51,6 +52,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
         self.requires_face_detection = "face" not in self.config.objects.all_objects
         self.person_face_history: dict[str, list[tuple[str, float, int]]] = {}
         self.recognizer: FaceRecognizer | None = None
+        self.faces_per_second = EventsPerSecond()
 
         download_path = os.path.join(MODEL_CACHE_DIR, "facedet")
         self.model_files = {
@@ -103,6 +105,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
             score_threshold=0.5,
             nms_threshold=0.3,
         )
+        self.faces_per_second.start()
 
     def __detect_face(
         self, input: np.ndarray, threshold: float
@@ -152,6 +155,8 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
 
     def process_frame(self, obj_data: dict[str, any], frame: np.ndarray):
         """Look for faces in image."""
+        self.metrics.face_rec_fps.value = self.faces_per_second.eps()
+
         if not self.config.cameras[obj_data["camera"]].face_recognition.enabled:
             return
 
@@ -251,6 +256,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
                 max(0, face_box[0]) : min(frame.shape[1], face_box[2]),
             ]
 
+        self.faces_per_second.update()
         res = self.recognizer.classify(face_frame)
 
         if not res:

frigate/data_processing/types.py

@@ -9,6 +9,7 @@ class DataProcessorMetrics:
     image_embeddings_speed: Synchronized
     text_embeddings_speed: Synchronized
     face_rec_speed: Synchronized
+    face_rec_fps: Synchronized
     alpr_speed: Synchronized
     yolov9_lpr_speed: Synchronized
 
@@ -16,6 +17,7 @@ class DataProcessorMetrics:
         self.image_embeddings_speed = mp.Value("d", 0.01)
         self.text_embeddings_speed = mp.Value("d", 0.01)
         self.face_rec_speed = mp.Value("d", 0.01)
+        self.face_rec_fps = mp.Value("d", 0.0)
         self.alpr_speed = mp.Value("d", 0.01)
         self.yolov9_lpr_speed = mp.Value("d", 0.01)
 

frigate/stats/util.py

@@ -305,8 +305,8 @@ def stats_snapshot(
             stats["embeddings"]["face_recognition_speed"] = round(
                 embeddings_metrics.face_rec_speed.value * 1000, 2
             )
-            stats["embeddings"]["face_recognition_rps"] = round(
-                embeddings_metrics.face_rec_rps, 2
+            stats["embeddings"]["face_recognition_fps"] = round(
+                embeddings_metrics.face_rec_speed.value, 2
             )
 
         if config.lpr.enabled:

web/src/views/system/FeatureMetrics.tsx

@@ -7,6 +7,7 @@ import { Skeleton } from "@/components/ui/skeleton";
 import { ThresholdBarGraph } from "@/components/graph/SystemGraph";
 import { cn } from "@/lib/utils";
 import { useTranslation } from "react-i18next";
+import { CameraLineGraph } from "@/components/graph/CameraGraph";
 
 type FeatureMetricsProps = {
   lastUpdated: number;
@@ -102,15 +103,26 @@ export default function FeatureMetrics({
               {embeddingInferenceTimeSeries.map((series) => (
                 <div className="rounded-lg bg-background_alt p-2.5 md:rounded-2xl">
                   <div className="mb-5 capitalize">{series.name}</div>
-                  <ThresholdBarGraph
-                    key={series.name}
-                    graphId={`${series.name}-inference`}
-                    name={series.name}
-                    unit="ms"
-                    threshold={EmbeddingThreshold}
-                    updateTimes={updateTimes}
-                    data={[series]}
-                  />
+                  {series.name.endsWith("Speed") ? (
+                    <ThresholdBarGraph
+                      key={series.name}
+                      graphId={`${series.name}-inference`}
+                      name={series.name}
+                      unit="ms"
+                      threshold={EmbeddingThreshold}
+                      updateTimes={updateTimes}
+                      data={[series]}
+                    />
+                  ) : (
+                    <CameraLineGraph
+                      key={series.name}
+                      graphId={`${series.name}-fps`}
+                      dataLabels={["Recognitions Per Second"]}
+                      unit=""
+                      updateTimes={updateTimes}
+                      data={[series]}
+                    />
+                  )}
                 </div>
               ))}
             </>