adapt analytics to free-form model scenes, split notice counts, and peewee 4

frigate/analytics/collectors/detection.py

@@ -7,7 +7,7 @@ from frigate.analytics.context import ReportContext
 from frigate.analytics.schema import DetectionModel, DetectionSection, ModelSource
 from frigate.config.config import DEFAULT_MODEL
 from frigate.const import MODEL_CACHE_DIR
-from frigate.detectors.detector_config import SceneEnum
+from frigate.detectors.detector_config import DEFAULT_SCENE
 from frigate.detectors.detector_types import DetectorTypeEnum
 from frigate.detectors.device import runner_names
 
@@ -38,7 +38,7 @@ def collect(ctx: ReportContext) -> DetectionSection:
 
     # FrigateApp.start_detectors names the processes in this same order
     names = iter(runner_names([spec for _, specs in model_specs for spec in specs]))
-    models: dict[SceneEnum, DetectionModel] = {}
+    models: list[DetectionModel] = []
 
     for model, specs in model_specs:
         speeds: list[float] = []
@@ -49,13 +49,16 @@ def collect(ctx: ReportContext) -> DetectionSection:
             if isinstance(speed, int | float) and speed > 0:
                 speeds.append(float(speed))
 
-        models[model.scene] = DetectionModel(
-            detector=DetectorTypeEnum(specs[0].detector),
-            devices=len(specs),
-            model_type=model.model_type,
-            input=f"{model.width}x{model.height}",
-            source=model_source(model.path),
-            inference_ms=rate(sum(speeds) / len(speeds)) if speeds else None,
+        models.append(
+            DetectionModel(
+                default=model.scene == DEFAULT_SCENE,
+                detector=DetectorTypeEnum(specs[0].detector),
+                devices=len(specs),
+                model_type=model.model_type,
+                input=f"{model.width}x{model.height}",
+                source=model_source(model.path),
+                inference_ms=rate(sum(speeds) / len(speeds)) if speeds else None,
+            )
         )
 
     return DetectionSection(

frigate/analytics/collectors/health.py

@@ -32,11 +32,14 @@ def notice_deltas(notice_stats: list[dict[str, Any]]) -> dict[Any, NoticeCounts]
             continue
 
         occurrences = max(row["occurrences"] - row["reported_occurrences"], 0)
-        dismissals = max(row["dismissals"] - row["reported_dismissals"], 0)
+        acknowledgements = max(
+            row["acknowledgements"] - row["reported_acknowledgements"], 0
+        )
+        mutes = max(row["mutes"] - row["reported_mutes"], 0)
 
-        if occurrences or dismissals:
+        if occurrences or acknowledgements or mutes:
             deltas[NoticeKindKey(row["kind"])] = NoticeCounts(
-                occurrences=occurrences, dismissals=dismissals
+                occurrences=occurrences, acknowledgements=acknowledgements, mutes=mutes
             )
 
     return deltas

frigate/analytics/schema.py

@@ -13,7 +13,7 @@ from frigate.config.camera.birdseye import BirdseyeModeEnum
 from frigate.config.camera.camera import CameraTypeEnum
 from frigate.config.camera.genai import GenAIProviderEnum, GenAIRoleEnum
 from frigate.config.classification import ModelSizeEnum
-from frigate.detectors.detector_config import ModelTypeEnum, SceneEnum
+from frigate.detectors.detector_config import ModelTypeEnum
 from frigate.detectors.detector_types import DetectorTypeEnum
 from frigate.ffmpeg_presets import PRESETS_HW_ACCEL_DECODE, PRESETS_INPUT
 from frigate.notices.types import NOTICE_KINDS
@@ -53,6 +53,7 @@ class GpuVendor(StrEnum):
     nvidia = "nvidia"
     rockchip = "rockchip"
     rpi = "rpi"
+    apple_silicon = "apple-silicon"
     other = "other"
 
 
@@ -63,6 +64,7 @@ class DecodeFamily(StrEnum):
     intel_qsv = "intel-qsv"
     jetson = "jetson"
     rpi = "rpi"
+    apple_silicon = "apple-silicon"
     other = "other"
 
 
@@ -73,12 +75,14 @@ class HardwareKey(StrEnum):
     openvino_npu = "openvino:NPU"
     onnx_amd = "onnx:amd"
     onnx_nvidia = "onnx:nvidia"
+    onnx_lighter = "onnx:lighter"
     tensorrt = "tensorrt"
     hailo = "hailo"
     memryx = "memryx"
     deepx = "deepx"
     rknn = "rknn"
     axengine = "axengine"
+    axelera = "axelera"
     synaptics = "synaptics"
     cpu = "cpu"
     other = "other"
@@ -228,6 +232,7 @@ class HardwareSection(AnalyticsModel):
 
 
 class DetectionModel(AnalyticsModel):
+    default: bool = metric("Whether the model serves cameras that set no scene")
     detector: DetectorTypeEnum = metric("Detector type the model runs on")
     devices: int = count("Devices the model runs on")
     model_type: ModelTypeEnum = metric("Model architecture")
@@ -240,7 +245,7 @@ class DetectionModel(AnalyticsModel):
 
 
 class DetectionSection(AnalyticsModel):
-    models: dict[SceneEnum, DetectionModel] = metric("Detection models keyed by scene")
+    models: list[DetectionModel] = metric("Detection models")
     detection_fps: float = metric("Detections per second across cameras", ge=0)
     skipped_fps: float = metric("Frames per second skipped across cameras", ge=0)
 
@@ -369,7 +374,8 @@ class FeaturesSection(AnalyticsModel):
 
 class NoticeCounts(AnalyticsModel):
     occurrences: int = count("Occurrences since the last accepted report")
-    dismissals: int = count("Dismissals since the last accepted report")
+    acknowledgements: int = count("Acknowledgements since the last accepted report")
+    mutes: int = count("Mutes since the last accepted report")
 
 
 class HealthSection(AnalyticsModel):