implement deferred real-time processor with background task handling

frigate/data_processing/real_time/api.py

@@ -1,8 +1,12 @@
 """Local only processors for handling real time object processing."""
 
 import logging
+import threading
 from abc import ABC, abstractmethod
-from typing import Any
+from collections import deque
+from concurrent.futures import Future
+from queue import Empty, Full, Queue
+from typing import Any, Callable
 
 import numpy as np
 
@@ -74,3 +78,123 @@ class RealTimeProcessorApi(ABC):
             payload: The updated configuration object.
         """
         pass
+
+    def drain_results(self) -> list[dict[str, Any]]:
+        """Return pending results that need IPC side-effects.
+
+        Deferred processors accumulate results on a worker thread.
+        The maintainer calls this each loop iteration to collect them
+        and perform publishes on the main thread.
+
+        Synchronous processors return an empty list (default).
+        """
+        return []
+
+    def shutdown(self) -> None:
+        """Stop any background work and release resources.
+
+        Called when the processor is being removed or the maintainer
+        is shutting down. Default is a no-op for synchronous processors.
+        """
+        pass
+
+
+class DeferredRealtimeProcessorApi(RealTimeProcessorApi):
+    """Base class for processors that offload heavy work to a background thread.
+
+    Subclasses implement:
+      - process_frame(): do cheap gating + crop + copy, then call _enqueue_task()
+      - _process_task(task): heavy work (inference, consensus) on the worker thread
+      - handle_request(): optionally use _enqueue_request() for sync request/response
+      - expire_object(): call _enqueue_task() with a control message
+
+    The worker thread owns all processor state. No locks are needed because
+    only the worker mutates state. Results that need IPC are placed in
+    _pending_results via _emit_result(), and the maintainer drains them
+    each loop iteration.
+    """
+
+    def __init__(
+        self,
+        config: FrigateConfig,
+        metrics: DataProcessorMetrics,
+        max_queue: int = 8,
+    ) -> None:
+        super().__init__(config, metrics)
+        self._task_queue: Queue = Queue(maxsize=max_queue)
+        self._pending_results: deque[dict[str, Any]] = deque()
+        self._results_lock = threading.Lock()
+        self._stop_event = threading.Event()
+        self._worker = threading.Thread(
+            target=self._drain_loop,
+            daemon=True,
+            name=f"{type(self).__name__}_worker",
+        )
+        self._worker.start()
+
+    def _drain_loop(self) -> None:
+        """Worker thread main loop — drains the task queue until stopped."""
+        while not self._stop_event.is_set():
+            try:
+                task = self._task_queue.get(timeout=0.5)
+            except Empty:
+                continue
+
+            if (
+                isinstance(task, tuple)
+                and len(task) == 2
+                and isinstance(task[1], Future)
+            ):
+                # Request/response: (callable_and_args, future)
+                (func, args), future = task
+                try:
+                    result = func(args)
+                    future.set_result(result)
+                except Exception as e:
+                    future.set_exception(e)
+            else:
+                try:
+                    self._process_task(task)
+                except Exception:
+                    logger.exception("Error processing deferred task")
+
+    def _enqueue_task(self, task: Any) -> bool:
+        """Enqueue a task for the worker. Returns False if queue is full (dropped)."""
+        try:
+            self._task_queue.put_nowait(task)
+            return True
+        except Full:
+            logger.debug("Deferred processor queue full, dropping task")
+            return False
+
+    def _enqueue_request(self, func: Callable, args: Any, timeout: float = 10.0) -> Any:
+        """Enqueue a request and block until the worker returns a result."""
+        future: Future = Future()
+        self._task_queue.put(((func, args), future), timeout=timeout)
+        return future.result(timeout=timeout)
+
+    def _emit_result(self, result: dict[str, Any]) -> None:
+        """Called by the worker thread to stage a result for the maintainer."""
+        with self._results_lock:
+            self._pending_results.append(result)
+
+    def drain_results(self) -> list[dict[str, Any]]:
+        """Called by the maintainer on the main thread to collect pending results."""
+        with self._results_lock:
+            results = list(self._pending_results)
+            self._pending_results.clear()
+        return results
+
+    def shutdown(self) -> None:
+        """Signal the worker to stop and wait for it to finish."""
+        self._stop_event.set()
+        self._worker.join(timeout=5.0)
+
+    @abstractmethod
+    def _process_task(self, task: Any) -> None:
+        """Process a single task on the worker thread.
+
+        Subclasses implement inference, consensus, training image saves here.
+        Call _emit_result() to stage results for the maintainer to publish.
+        """
+        pass

frigate/data_processing/real_time/custom_classification.py

@@ -1,7 +1,6 @@
 """Real time processor that works with classification tflite models."""
 
 import datetime
-import json
 import logging
 import os
 from typing import Any
@@ -10,25 +9,18 @@ import cv2
 import numpy as np
 
 from frigate.comms.embeddings_updater import EmbeddingsRequestEnum
-from frigate.comms.event_metadata_updater import (
-    EventMetadataPublisher,
-    EventMetadataTypeEnum,
-)
+from frigate.comms.event_metadata_updater import EventMetadataPublisher
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.config import FrigateConfig
-from frigate.config.classification import (
-    CustomClassificationConfig,
-    ObjectClassificationType,
-)
+from frigate.config.classification import CustomClassificationConfig
 from frigate.const import CLIPS_DIR, MODEL_CACHE_DIR
 from frigate.log import suppress_stderr_during
-from frigate.types import TrackedObjectUpdateTypesEnum
 from frigate.util.builtin import EventsPerSecond, InferenceSpeed, load_labels
 from frigate.util.image import calculate_region
 from frigate.util.object import box_overlaps
 
 from ..types import DataProcessorMetrics
-from .api import RealTimeProcessorApi
+from .api import DeferredRealtimeProcessorApi
 
 try:
     from tflite_runtime.interpreter import Interpreter
@@ -40,7 +32,7 @@ logger = logging.getLogger(__name__)
 MAX_OBJECT_CLASSIFICATIONS = 16
 
 
-class CustomStateClassificationProcessor(RealTimeProcessorApi):
+class CustomStateClassificationProcessor(DeferredRealtimeProcessorApi):
     def __init__(
         self,
         config: FrigateConfig,
@@ -48,7 +40,7 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         requestor: InterProcessRequestor,
         metrics: DataProcessorMetrics,
     ):
-        super().__init__(config, metrics)
+        super().__init__(config, metrics, max_queue=4)
         self.model_config = model_config
 
         if not self.model_config.name:
@@ -259,14 +251,34 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
             )
             return
 
-        frame = rgb[y1:y2, x1:x2]
+        cropped_frame = rgb[y1:y2, x1:x2]
 
         try:
-            resized_frame = cv2.resize(frame, (224, 224))
+            resized_frame = cv2.resize(cropped_frame, (224, 224))
         except Exception:
             logger.warning("Failed to resize image for state classification")
             return
 
+        # Copy for training image saves on worker thread
+        crop_bgr = cv2.cvtColor(cropped_frame, cv2.COLOR_RGB2BGR)
+
+        self._enqueue_task(("classify", camera, now, resized_frame, crop_bgr))
+
+    def _process_task(self, task: Any) -> None:
+        kind = task[0]
+        if kind == "classify":
+            _, camera, timestamp, resized_frame, crop_bgr = task
+            self._classify_state(camera, timestamp, resized_frame, crop_bgr)
+        elif kind == "reload":
+            self.__build_detector()
+
+    def _classify_state(
+        self,
+        camera: str,
+        timestamp: float,
+        resized_frame: np.ndarray,
+        crop_bgr: np.ndarray,
+    ) -> None:
         if self.interpreter is None:
             # When interpreter is None, always save (score is 0.0, which is < 1.0)
             if self._should_save_image(camera, "unknown", 0.0):
@@ -277,9 +289,9 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
                 )
                 write_classification_attempt(
                     self.train_dir,
-                    cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
+                    crop_bgr,
                     "none-none",
-                    now,
+                    timestamp,
                     "unknown",
                     0.0,
                     max_files=save_attempts,
@@ -298,7 +310,7 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         )
         best_id = int(np.argmax(probs))
         score = round(probs[best_id], 2)
-        self.__update_metrics(datetime.datetime.now().timestamp() - now)
+        self.__update_metrics(datetime.datetime.now().timestamp() - timestamp)
 
         detected_state = self.labelmap[best_id]
 
@@ -310,9 +322,9 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
             )
             write_classification_attempt(
                 self.train_dir,
-                cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
+                crop_bgr,
                 "none-none",
-                now,
+                timestamp,
                 detected_state,
                 score,
                 max_files=save_attempts,
@@ -327,9 +339,14 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         verified_state = self.verify_state_change(camera, detected_state)
 
         if verified_state is not None:
-            self.requestor.send_data(
-                f"{camera}/classification/{self.model_config.name}",
-                verified_state,
+            self._emit_result(
+                {
+                    "type": "classification",
+                    "processor": "state",
+                    "model_name": self.model_config.name,
+                    "camera": camera,
+                    "state": verified_state,
+                }
             )
 
     def handle_request(
@@ -337,14 +354,18 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
     ) -> dict[str, Any] | None:
         if topic == EmbeddingsRequestEnum.reload_classification_model.value:
             if request_data.get("model_name") == self.model_config.name:
-                self.__build_detector()
-                logger.info(
-                    f"Successfully loaded updated model for {self.model_config.name}"
-                )
-                return {
-                    "success": True,
-                    "message": f"Loaded {self.model_config.name} model.",
-                }
+
+                def _do_reload(data):
+                    self.__build_detector()
+                    logger.info(
+                        f"Successfully loaded updated model for {self.model_config.name}"
+                    )
+                    return {
+                        "success": True,
+                        "message": f"Loaded {self.model_config.name} model.",
+                    }
+
+                return self._enqueue_request(_do_reload, request_data)
             else:
                 return None
         else:
@@ -354,7 +375,7 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         pass
 
 
-class CustomObjectClassificationProcessor(RealTimeProcessorApi):
+class CustomObjectClassificationProcessor(DeferredRealtimeProcessorApi):
     def __init__(
         self,
         config: FrigateConfig,
@@ -363,7 +384,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         requestor: InterProcessRequestor,
         metrics: DataProcessorMetrics,
     ):
-        super().__init__(config, metrics)
+        super().__init__(config, metrics, max_queue=8)
         self.model_config = model_config
 
         if not self.model_config.name:
@@ -536,18 +557,41 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         )
 
         rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
-        crop = rgb[
-            y:y2,
-            x:x2,
-        ]
+        crop = rgb[y:y2, x:x2]
 
-        if crop.shape != (224, 224):
-            try:
-                resized_crop = cv2.resize(crop, (224, 224))
-            except Exception:
-                logger.warning("Failed to resize image for state classification")
-                return
+        try:
+            resized_crop = cv2.resize(crop, (224, 224))
+        except Exception:
+            logger.warning("Failed to resize image for object classification")
+            return
 
+        # Copy crop for training images (will be used on worker thread)
+        crop_bgr = cv2.cvtColor(crop, cv2.COLOR_RGB2BGR)
+
+        self._enqueue_task(
+            ("classify", object_id, obj_data["camera"], now, resized_crop, crop_bgr)
+        )
+
+    def _process_task(self, task: Any) -> None:
+        kind = task[0]
+        if kind == "classify":
+            _, object_id, camera, timestamp, resized_crop, crop_bgr = task
+            self._classify_object(object_id, camera, timestamp, resized_crop, crop_bgr)
+        elif kind == "expire":
+            _, object_id = task
+            if object_id in self.classification_history:
+                self.classification_history.pop(object_id)
+        elif kind == "reload":
+            self.__build_detector()
+
+    def _classify_object(
+        self,
+        object_id: str,
+        camera: str,
+        timestamp: float,
+        resized_crop: np.ndarray,
+        crop_bgr: np.ndarray,
+    ) -> None:
         if self.interpreter is None:
             save_attempts = (
                 self.model_config.save_attempts
@@ -556,9 +600,9 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             )
             write_classification_attempt(
                 self.train_dir,
-                cv2.cvtColor(crop, cv2.COLOR_RGB2BGR),
+                crop_bgr,
                 object_id,
-                now,
+                timestamp,
                 "unknown",
                 0.0,
                 max_files=save_attempts,
@@ -569,7 +613,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             if object_id not in self.classification_history:
                 self.classification_history[object_id] = []
 
-            self.classification_history[object_id].append(("unknown", 0.0, now))
+            self.classification_history[object_id].append(("unknown", 0.0, timestamp))
             return
 
         input = np.expand_dims(resized_crop, axis=0)
@@ -584,7 +628,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         )
         best_id = int(np.argmax(probs))
         score = round(probs[best_id], 2)
-        self.__update_metrics(datetime.datetime.now().timestamp() - now)
+        self.__update_metrics(datetime.datetime.now().timestamp() - timestamp)
 
         save_attempts = (
             self.model_config.save_attempts
@@ -593,9 +637,9 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         )
         write_classification_attempt(
             self.train_dir,
-            cv2.cvtColor(crop, cv2.COLOR_RGB2BGR),
+            crop_bgr,
             object_id,
-            now,
+            timestamp,
             self.labelmap[best_id],
             score,
             max_files=save_attempts,
@@ -610,11 +654,11 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         sub_label = self.labelmap[best_id]
 
         logger.debug(
-            f"{self.model_config.name}: Object {object_id} (label={obj_data['label']}) passed threshold with sub_label={sub_label}, score={score}"
+            f"{self.model_config.name}: Object {object_id} passed threshold with sub_label={sub_label}, score={score}"
         )
 
         consensus_label, consensus_score = self.get_weighted_score(
-            object_id, sub_label, score, now
+            object_id, sub_label, score, timestamp
         )
 
         logger.debug(
@@ -622,80 +666,42 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         )
 
         if consensus_label is not None:
-            camera = obj_data["camera"]
-            logger.debug(
-                f"{self.model_config.name}: Publishing sub_label={consensus_label} for {obj_data['label']} object {object_id} on {camera}"
+            self._emit_result(
+                {
+                    "type": "classification",
+                    "processor": "object",
+                    "model_name": self.model_config.name,
+                    "classification_type": self.model_config.object_config.classification_type,
+                    "object_id": object_id,
+                    "camera": camera,
+                    "timestamp": timestamp,
+                    "label": consensus_label,
+                    "score": consensus_score,
+                }
             )
 
-            if (
-                self.model_config.object_config.classification_type
-                == ObjectClassificationType.sub_label
-            ):
-                self.sub_label_publisher.publish(
-                    (object_id, consensus_label, consensus_score),
-                    EventMetadataTypeEnum.sub_label,
-                )
-                self.requestor.send_data(
-                    "tracked_object_update",
-                    json.dumps(
-                        {
-                            "type": TrackedObjectUpdateTypesEnum.classification,
-                            "id": object_id,
-                            "camera": camera,
-                            "timestamp": now,
-                            "model": self.model_config.name,
-                            "sub_label": consensus_label,
-                            "score": consensus_score,
-                        }
-                    ),
-                )
-            elif (
-                self.model_config.object_config.classification_type
-                == ObjectClassificationType.attribute
-            ):
-                self.sub_label_publisher.publish(
-                    (
-                        object_id,
-                        self.model_config.name,
-                        consensus_label,
-                        consensus_score,
-                    ),
-                    EventMetadataTypeEnum.attribute.value,
-                )
-                self.requestor.send_data(
-                    "tracked_object_update",
-                    json.dumps(
-                        {
-                            "type": TrackedObjectUpdateTypesEnum.classification,
-                            "id": object_id,
-                            "camera": camera,
-                            "timestamp": now,
-                            "model": self.model_config.name,
-                            "attribute": consensus_label,
-                            "score": consensus_score,
-                        }
-                    ),
-                )
-
     def handle_request(self, topic: str, request_data: dict) -> dict | None:
         if topic == EmbeddingsRequestEnum.reload_classification_model.value:
             if request_data.get("model_name") == self.model_config.name:
-                self.__build_detector()
-                logger.info(
-                    f"Successfully loaded updated model for {self.model_config.name}"
-                )
-                return {
-                    "success": True,
-                    "message": f"Loaded {self.model_config.name} model.",
-                }
+
+                def _do_reload(data):
+                    self.__build_detector()
+                    logger.info(
+                        f"Successfully loaded updated model for {self.model_config.name}"
+                    )
+                    return {
+                        "success": True,
+                        "message": f"Loaded {self.model_config.name} model.",
+                    }
+
+                return self._enqueue_request(_do_reload, request_data)
             else:
                 return None
         else:
             return None
 
     def expire_object(self, object_id: str, camera: str) -> None:
-        if object_id in self.classification_history:
-            self.classification_history.pop(object_id)
+        self._enqueue_task(("expire", object_id))
 
 
 def write_classification_attempt(

frigate/embeddings/maintainer.py

@@ -2,6 +2,7 @@
 
 import base64
 import datetime
+import json
 import logging
 import threading
 from multiprocessing.synchronize import Event as MpEvent
@@ -33,6 +34,7 @@ from frigate.config.camera.updater import (
     CameraConfigUpdateEnum,
     CameraConfigUpdateSubscriber,
 )
+from frigate.config.classification import ObjectClassificationType
 from frigate.data_processing.common.license_plate.model import (
     LicensePlateModelRunner,
 )
@@ -61,6 +63,7 @@ from frigate.db.sqlitevecq import SqliteVecQueueDatabase
 from frigate.events.types import EventTypeEnum, RegenerateDescriptionEnum
 from frigate.genai import GenAIClientManager
 from frigate.models import Event, Recordings, ReviewSegment, Trigger
+from frigate.types import TrackedObjectUpdateTypesEnum
 from frigate.util.builtin import serialize
 from frigate.util.file import get_event_thumbnail_bytes
 from frigate.util.image import SharedMemoryFrameManager
@@ -274,10 +277,15 @@ class EmbeddingMaintainer(threading.Thread):
             self._process_recordings_updates()
             self._process_review_updates()
             self._process_frame_updates()
+            self._process_deferred_results()
             self._expire_dedicated_lpr()
             self._process_finalized()
             self._process_event_metadata()
 
+        # Shutdown deferred processors
+        for processor in self.realtime_processors:
+            processor.shutdown()
+
         self.config_updater.stop()
         self.enrichment_config_subscriber.stop()
         self.event_subscriber.stop()
@@ -316,10 +324,9 @@ class EmbeddingMaintainer(threading.Thread):
         model_name = topic.split("/")[-1]
 
         if model_config is None:
-            self.realtime_processors = [
-                processor
-                for processor in self.realtime_processors
-                if not (
+            remaining = []
+            for processor in self.realtime_processors:
+                if (
                     isinstance(
                         processor,
                         (
@@ -328,8 +335,11 @@ class EmbeddingMaintainer(threading.Thread):
                         ),
                     )
                     and processor.model_config.name == model_name
-                )
-            ]
+                ):
+                    processor.shutdown()
+                else:
+                    remaining.append(processor)
+            self.realtime_processors = remaining
 
             logger.info(
                 f"Successfully removed classification processor for model: {model_name}"
@@ -697,6 +707,68 @@ class EmbeddingMaintainer(threading.Thread):
 
         self.frame_manager.close(frame_name)
 
+    def _process_deferred_results(self) -> None:
+        """Drain results from deferred processors and perform IPC side-effects."""
+        for processor in self.realtime_processors:
+            results = processor.drain_results()
+
+            for result in results:
+                if result.get("type") != "classification":
+                    continue
+
+                if result["processor"] == "state":
+                    self.requestor.send_data(
+                        f"{result['camera']}/classification/{result['model_name']}",
+                        result["state"],
+                    )
+                elif result["processor"] == "object":
+                    object_id = result["object_id"]
+                    camera = result["camera"]
+                    timestamp = result["timestamp"]
+                    model_name = result["model_name"]
+                    label = result["label"]
+                    score = result["score"]
+                    classification_type = result["classification_type"]
+
+                    if classification_type == ObjectClassificationType.sub_label:
+                        self.event_metadata_publisher.publish(
+                            (object_id, label, score),
+                            EventMetadataTypeEnum.sub_label,
+                        )
+                        self.requestor.send_data(
+                            "tracked_object_update",
+                            json.dumps(
+                                {
+                                    "type": TrackedObjectUpdateTypesEnum.classification,
+                                    "id": object_id,
+                                    "camera": camera,
+                                    "timestamp": timestamp,
+                                    "model": model_name,
+                                    "sub_label": label,
+                                    "score": score,
+                                }
+                            ),
+                        )
+                    elif classification_type == ObjectClassificationType.attribute:
+                        self.event_metadata_publisher.publish(
+                            (object_id, model_name, label, score),
+                            EventMetadataTypeEnum.attribute.value,
+                        )
+                        self.requestor.send_data(
+                            "tracked_object_update",
+                            json.dumps(
+                                {
+                                    "type": TrackedObjectUpdateTypesEnum.classification,
+                                    "id": object_id,
+                                    "camera": camera,
+                                    "timestamp": timestamp,
+                                    "model": model_name,
+                                    "attribute": label,
+                                    "score": score,
+                                }
+                            ),
+                        )
+
     def _embed_thumbnail(self, event_id: str, thumbnail: bytes) -> None:
         """Embed the thumbnail for an event."""
         if not self.config.semantic_search.enabled: