Refactor processors and add LPR postprocessing (#16722)

* recordings data pub/sub

* function to process recording stream frames

* model runner

* lpr model runner

* refactor to mixin class and use model runner

* separate out realtime and post processors

* move model and mixin folders

* basic postprocessor

* clean up

* docs

* postprocessing logic

* clean up

* return none if recordings are disabled

* run postprocessor handle_requests too

* tweak expansion

* add put endpoint

* postprocessor tweaks with endpoint

frigate/data_processing/real_time/api.py

@@ -7,16 +7,22 @@ import numpy as np
 
 from frigate.config import FrigateConfig
 
-from ..types import DataProcessorMetrics
+from ..types import DataProcessorMetrics, DataProcessorModelRunner
 
 logger = logging.getLogger(__name__)
 
 
 class RealTimeProcessorApi(ABC):
     @abstractmethod
-    def __init__(self, config: FrigateConfig, metrics: DataProcessorMetrics) -> None:
+    def __init__(
+        self,
+        config: FrigateConfig,
+        metrics: DataProcessorMetrics,
+        model_runner: DataProcessorModelRunner,
+    ) -> None:
         self.config = config
         self.metrics = metrics
+        self.model_runner = model_runner
         pass
 
     @abstractmethod

frigate/data_processing/real_time/bird.py

@@ -22,7 +22,7 @@ except ModuleNotFoundError:
 logger = logging.getLogger(__name__)
 
 
-class BirdProcessor(RealTimeProcessorApi):
+class BirdRealTimeProcessor(RealTimeProcessorApi):
     def __init__(self, config: FrigateConfig, metrics: DataProcessorMetrics):
         super().__init__(config, metrics)
         self.interpreter: Interpreter = None

frigate/data_processing/real_time/face.py

@@ -27,7 +27,7 @@ logger = logging.getLogger(__name__)
 MIN_MATCHING_FACES = 2
 
 
-class FaceProcessor(RealTimeProcessorApi):
+class FaceRealTimeProcessor(RealTimeProcessorApi):
     def __init__(self, config: FrigateConfig, metrics: DataProcessorMetrics):
         super().__init__(config, metrics)
         self.face_config = config.face_recognition

frigate/data_processing/real_time/license_plate.py

@@ -0,0 +1,53 @@
+"""Handle processing images for face detection and recognition."""
+
+import datetime
+import logging
+
+import numpy as np
+
+from frigate.config import FrigateConfig
+from frigate.data_processing.common.license_plate.mixin import (
+    LicensePlateProcessingMixin,
+)
+from frigate.data_processing.common.license_plate.model import (
+    LicensePlateModelRunner,
+)
+
+from ..types import DataProcessorMetrics
+from .api import RealTimeProcessorApi
+
+logger = logging.getLogger(__name__)
+
+
+class LicensePlateRealTimeProcessor(LicensePlateProcessingMixin, RealTimeProcessorApi):
+    def __init__(
+        self,
+        config: FrigateConfig,
+        metrics: DataProcessorMetrics,
+        model_runner: LicensePlateModelRunner,
+        detected_license_plates: dict[str, dict[str, any]],
+    ):
+        self.detected_license_plates = detected_license_plates
+        self.model_runner = model_runner
+        self.lpr_config = config.lpr
+        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_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
+
+    def expire_object(self, object_id: str):
+        if object_id in self.detected_license_plates:
+            self.detected_license_plates.pop(object_id)