Refactor object genai to be a post-processor (#20331)

* Refactor object genai to be a post-processor

* Include function correctly

frigate/data_processing/post/object_descriptions.py

@@ -0,0 +1,349 @@
+"""Post processor for object descriptions using GenAI."""
+
+import datetime
+import logging
+import os
+import threading
+from pathlib import Path
+from typing import TYPE_CHECKING, Any
+
+import cv2
+import numpy as np
+from peewee import DoesNotExist
+
+from frigate.comms.inter_process import InterProcessRequestor
+from frigate.config import CameraConfig, FrigateConfig
+from frigate.const import CLIPS_DIR, UPDATE_EVENT_DESCRIPTION
+from frigate.data_processing.post.semantic_trigger import SemanticTriggerProcessor
+from frigate.data_processing.types import PostProcessDataEnum
+from frigate.genai import GenAIClient
+from frigate.models import Event
+from frigate.types import TrackedObjectUpdateTypesEnum
+from frigate.util.builtin import EventsPerSecond, InferenceSpeed
+from frigate.util.image import create_thumbnail, ensure_jpeg_bytes
+from frigate.util.path import get_event_thumbnail_bytes
+
+if TYPE_CHECKING:
+    from frigate.embeddings import Embeddings
+
+from ..post.api import PostProcessorApi
+from ..types import DataProcessorMetrics
+
+logger = logging.getLogger(__name__)
+
+MAX_THUMBNAILS = 10
+
+
+class ObjectDescriptionProcessor(PostProcessorApi):
+    def __init__(
+        self,
+        config: FrigateConfig,
+        embeddings: "Embeddings",
+        requestor: InterProcessRequestor,
+        metrics: DataProcessorMetrics,
+        client: GenAIClient,
+        semantic_trigger_processor: SemanticTriggerProcessor | None,
+    ):
+        super().__init__(config, metrics, None)
+        self.config = config
+        self.embeddings = embeddings
+        self.requestor = requestor
+        self.metrics = metrics
+        self.genai_client = client
+        self.semantic_trigger_processor = semantic_trigger_processor
+        self.tracked_events: dict[str, list[Any]] = {}
+        self.early_request_sent: dict[str, bool] = {}
+        self.object_desc_speed = InferenceSpeed(self.metrics.object_desc_speed)
+        self.object_desc_dps = EventsPerSecond()
+        self.object_desc_dps.start()
+
+    def __handle_frame_update(
+        self, camera: str, data: dict, yuv_frame: np.ndarray
+    ) -> None:
+        """Handle an update to a frame for an object."""
+        camera_config = self.config.cameras[camera]
+
+        # no need to save our own thumbnails if genai is not enabled
+        # or if the object has become stationary
+        if not data["stationary"]:
+            if data["id"] not in self.tracked_events:
+                self.tracked_events[data["id"]] = []
+
+            data["thumbnail"] = create_thumbnail(yuv_frame, data["box"])
+
+            # Limit the number of thumbnails saved
+            if len(self.tracked_events[data["id"]]) >= MAX_THUMBNAILS:
+                # Always keep the first thumbnail for the event
+                self.tracked_events[data["id"]].pop(1)
+
+            self.tracked_events[data["id"]].append(data)
+
+        # check if we're configured to send an early request after a minimum number of updates received
+        if camera_config.objects.genai.send_triggers.after_significant_updates:
+            if (
+                len(self.tracked_events.get(data["id"], []))
+                >= camera_config.objects.genai.send_triggers.after_significant_updates
+                and data["id"] not in self.early_request_sent
+            ):
+                if data["has_clip"] and data["has_snapshot"]:
+                    event: Event = Event.get(Event.id == data["id"])
+
+                    if (
+                        not camera_config.objects.genai.objects
+                        or event.label in camera_config.objects.genai.objects
+                    ) and (
+                        not camera_config.objects.genai.required_zones
+                        or set(data["entered_zones"])
+                        & set(camera_config.objects.genai.required_zones)
+                    ):
+                        logger.debug(f"{camera} sending early request to GenAI")
+
+                        self.early_request_sent[data["id"]] = True
+                        threading.Thread(
+                            target=self._genai_embed_description,
+                            name=f"_genai_embed_description_{event.id}",
+                            daemon=True,
+                            args=(
+                                event,
+                                [
+                                    data["thumbnail"]
+                                    for data in self.tracked_events[data["id"]]
+                                ],
+                            ),
+                        ).start()
+
+    def __handle_frame_finalize(
+        self, camera: str, event: Event, thumbnail: bytes
+    ) -> None:
+        """Handle the finalization of a frame."""
+        camera_config = self.config.cameras[camera]
+
+        if (
+            camera_config.objects.genai.enabled
+            and camera_config.objects.genai.send_triggers.tracked_object_end
+            and (
+                not camera_config.objects.genai.objects
+                or event.label in camera_config.objects.genai.objects
+            )
+            and (
+                not camera_config.objects.genai.required_zones
+                or set(event.zones) & set(camera_config.objects.genai.required_zones)
+            )
+        ):
+            self._process_genai_description(event, camera_config, thumbnail)
+
+    def __regenerate_description(self, event_id: str, source: str, force: bool) -> None:
+        """Regenerate the description for an event."""
+        try:
+            event: Event = Event.get(Event.id == event_id)
+        except DoesNotExist:
+            logger.error(f"Event {event_id} not found for description regeneration")
+            return
+
+        if self.genai_client is None:
+            logger.error("GenAI not enabled")
+            return
+
+        camera_config = self.config.cameras[event.camera]
+        if not camera_config.objects.genai.enabled and not force:
+            logger.error(f"GenAI not enabled for camera {event.camera}")
+            return
+
+        thumbnail = get_event_thumbnail_bytes(event)
+
+        # ensure we have a jpeg to pass to the model
+        thumbnail = ensure_jpeg_bytes(thumbnail)
+
+        logger.debug(
+            f"Trying {source} regeneration for {event}, has_snapshot: {event.has_snapshot}"
+        )
+
+        if event.has_snapshot and source == "snapshot":
+            snapshot_image = self._read_and_crop_snapshot(event)
+            if not snapshot_image:
+                return
+
+        embed_image = (
+            [snapshot_image]
+            if event.has_snapshot and source == "snapshot"
+            else (
+                [data["thumbnail"] for data in self.tracked_events[event_id]]
+                if len(self.tracked_events.get(event_id, [])) > 0
+                else [thumbnail]
+            )
+        )
+
+        self._genai_embed_description(event, embed_image)
+
+    def process_data(self, frame_data: dict, data_type: PostProcessDataEnum) -> None:
+        """Process a frame update."""
+        self.metrics.object_desc_dps.value = self.object_desc_dps.eps()
+
+        if data_type != PostProcessDataEnum.tracked_object:
+            return
+
+        state: str | None = frame_data.get("state", None)
+
+        if state is not None:
+            logger.debug(f"Processing {state} for {frame_data['camera']}")
+
+        if state == "update":
+            self.__handle_frame_update(
+                frame_data["camera"], frame_data["data"], frame_data["yuv_frame"]
+            )
+        elif state == "finalize":
+            self.__handle_frame_finalize(
+                frame_data["camera"], frame_data["event"], frame_data["thumbnail"]
+            )
+
+    def handle_request(self, topic: str, data: dict[str, Any]) -> str | None:
+        """Handle a request."""
+        if topic == "regenerate_description":
+            self.__regenerate_description(
+                data["event_id"], data["source"], data["force"]
+            )
+        return None
+
+    def _read_and_crop_snapshot(self, event: Event) -> bytes | None:
+        """Read, decode, and crop the snapshot image."""
+
+        snapshot_file = os.path.join(CLIPS_DIR, f"{event.camera}-{event.id}.jpg")
+
+        if not os.path.isfile(snapshot_file):
+            logger.error(
+                f"Cannot load snapshot for {event.id}, file not found: {snapshot_file}"
+            )
+            return None
+
+        try:
+            with open(snapshot_file, "rb") as image_file:
+                snapshot_image = image_file.read()
+
+                img = cv2.imdecode(
+                    np.frombuffer(snapshot_image, dtype=np.int8),
+                    cv2.IMREAD_COLOR,
+                )
+
+                # Crop snapshot based on region
+                # provide full image if region doesn't exist (manual events)
+                height, width = img.shape[:2]
+                x1_rel, y1_rel, width_rel, height_rel = event.data.get(
+                    "region", [0, 0, 1, 1]
+                )
+                x1, y1 = int(x1_rel * width), int(y1_rel * height)
+
+                cropped_image = img[
+                    y1 : y1 + int(height_rel * height),
+                    x1 : x1 + int(width_rel * width),
+                ]
+
+                _, buffer = cv2.imencode(".jpg", cropped_image)
+
+                return buffer.tobytes()
+        except Exception:
+            return None
+
+    def _process_genai_description(
+        self, event: Event, camera_config: CameraConfig, thumbnail
+    ) -> None:
+        if event.has_snapshot and camera_config.objects.genai.use_snapshot:
+            snapshot_image = self._read_and_crop_snapshot(event)
+            if not snapshot_image:
+                return
+
+        num_thumbnails = len(self.tracked_events.get(event.id, []))
+
+        # ensure we have a jpeg to pass to the model
+        thumbnail = ensure_jpeg_bytes(thumbnail)
+
+        embed_image = (
+            [snapshot_image]
+            if event.has_snapshot and camera_config.objects.genai.use_snapshot
+            else (
+                [data["thumbnail"] for data in self.tracked_events[event.id]]
+                if num_thumbnails > 0
+                else [thumbnail]
+            )
+        )
+
+        if camera_config.objects.genai.debug_save_thumbnails and num_thumbnails > 0:
+            logger.debug(f"Saving {num_thumbnails} thumbnails for event {event.id}")
+
+            Path(os.path.join(CLIPS_DIR, f"genai-requests/{event.id}")).mkdir(
+                parents=True, exist_ok=True
+            )
+
+            for idx, data in enumerate(self.tracked_events[event.id], 1):
+                jpg_bytes: bytes | None = data["thumbnail"]
+
+                if jpg_bytes is None:
+                    logger.warning(f"Unable to save thumbnail {idx} for {event.id}.")
+                else:
+                    with open(
+                        os.path.join(
+                            CLIPS_DIR,
+                            f"genai-requests/{event.id}/{idx}.jpg",
+                        ),
+                        "wb",
+                    ) as j:
+                        j.write(jpg_bytes)
+
+        # Generate the description. Call happens in a thread since it is network bound.
+        threading.Thread(
+            target=self._genai_embed_description,
+            name=f"_genai_embed_description_{event.id}",
+            daemon=True,
+            args=(
+                event,
+                embed_image,
+            ),
+        ).start()
+
+        # Delete tracked events based on the event_id
+        if event.id in self.tracked_events:
+            del self.tracked_events[event.id]
+
+    def _genai_embed_description(self, event: Event, thumbnails: list[bytes]) -> None:
+        """Embed the description for an event."""
+        start = datetime.datetime.now().timestamp()
+        camera_config = self.config.cameras[event.camera]
+        description = self.genai_client.generate_object_description(
+            camera_config, thumbnails, event
+        )
+
+        if not description:
+            logger.debug("Failed to generate description for %s", event.id)
+            return
+
+        # fire and forget description update
+        self.requestor.send_data(
+            UPDATE_EVENT_DESCRIPTION,
+            {
+                "type": TrackedObjectUpdateTypesEnum.description,
+                "id": event.id,
+                "description": description,
+                "camera": event.camera,
+            },
+        )
+
+        # Embed the description
+        if self.config.semantic_search.enabled:
+            self.embeddings.embed_description(event.id, description)
+
+            # Check semantic trigger for this description
+            if self.semantic_trigger_processor is not None:
+                self.semantic_trigger_processor.process_data(
+                    {"event_id": event.id, "camera": event.camera, "type": "text"},
+                    PostProcessDataEnum.tracked_object,
+                )
+
+        # Update inference timing metrics
+        self.object_desc_speed.update(datetime.datetime.now().timestamp() - start)
+        self.object_desc_dps.update()
+
+        logger.debug(
+            "Generated description for %s (%d images): %s",
+            event.id,
+            len(thumbnails),
+            description,
+        )

frigate/data_processing/types.py

@@ -22,6 +22,8 @@ class DataProcessorMetrics:
     yolov9_lpr_pps: Synchronized
     review_desc_speed: Synchronized
     review_desc_dps: Synchronized
+    object_desc_speed: Synchronized
+    object_desc_dps: Synchronized
     classification_speeds: dict[str, Synchronized]
     classification_cps: dict[str, Synchronized]
 
@@ -38,6 +40,8 @@ class DataProcessorMetrics:
         self.yolov9_lpr_pps = manager.Value("d", 0.0)
         self.review_desc_speed = manager.Value("d", 0.0)
         self.review_desc_dps = manager.Value("d", 0.0)
+        self.object_desc_speed = manager.Value("d", 0.0)
+        self.object_desc_dps = manager.Value("d", 0.0)
         self.classification_speeds = manager.dict()
         self.classification_cps = manager.dict()
 

frigate/embeddings/maintainer.py

@@ -3,14 +3,10 @@
 import base64
 import datetime
 import logging
-import os
 import threading
 from multiprocessing.synchronize import Event as MpEvent
-from pathlib import Path
-from typing import Any, Optional
+from typing import Any
 
-import cv2
-import numpy as np
 from peewee import DoesNotExist
 
 from frigate.comms.detections_updater import DetectionSubscriber, DetectionTypeEnum
@@ -30,16 +26,12 @@ from frigate.comms.recordings_updater import (
     RecordingsDataTypeEnum,
 )
 from frigate.comms.review_updater import ReviewDataSubscriber
-from frigate.config import CameraConfig, FrigateConfig
+from frigate.config import FrigateConfig
 from frigate.config.camera.camera import CameraTypeEnum
 from frigate.config.camera.updater import (
     CameraConfigUpdateEnum,
     CameraConfigUpdateSubscriber,
 )
-from frigate.const import (
-    CLIPS_DIR,
-    UPDATE_EVENT_DESCRIPTION,
-)
 from frigate.data_processing.common.license_plate.model import (
     LicensePlateModelRunner,
 )
@@ -50,6 +42,7 @@ from frigate.data_processing.post.audio_transcription import (
 from frigate.data_processing.post.license_plate import (
     LicensePlatePostProcessor,
 )
+from frigate.data_processing.post.object_descriptions import ObjectDescriptionProcessor
 from frigate.data_processing.post.review_descriptions import ReviewDescriptionProcessor
 from frigate.data_processing.post.semantic_trigger import SemanticTriggerProcessor
 from frigate.data_processing.real_time.api import RealTimeProcessorApi
@@ -67,13 +60,8 @@ from frigate.db.sqlitevecq import SqliteVecQueueDatabase
 from frigate.events.types import EventTypeEnum, RegenerateDescriptionEnum
 from frigate.genai import get_genai_client
 from frigate.models import Event, Recordings, ReviewSegment, Trigger
-from frigate.types import TrackedObjectUpdateTypesEnum
 from frigate.util.builtin import serialize
-from frigate.util.image import (
-    SharedMemoryFrameManager,
-    calculate_region,
-    ensure_jpeg_bytes,
-)
+from frigate.util.image import SharedMemoryFrameManager
 from frigate.util.path import get_event_thumbnail_bytes
 
 from .embeddings import Embeddings
@@ -235,20 +223,30 @@ class EmbeddingMaintainer(threading.Thread):
                 AudioTranscriptionPostProcessor(self.config, self.requestor, metrics)
             )
 
+        semantic_trigger_processor: SemanticTriggerProcessor | None = None
         if self.config.semantic_search.enabled:
+            semantic_trigger_processor = SemanticTriggerProcessor(
+                db,
+                self.config,
+                self.requestor,
+                metrics,
+                self.embeddings,
+            )
+            self.post_processors.append(semantic_trigger_processor)
+
+        if any(c.objects.genai.enabled_in_config for c in self.config.cameras.values()):
             self.post_processors.append(
-                SemanticTriggerProcessor(
-                    db,
+                ObjectDescriptionProcessor(
                     self.config,
-                    self.requestor,
-                    metrics,
                     self.embeddings,
+                    self.requestor,
+                    self.metrics,
+                    self.genai_client,
+                    semantic_trigger_processor,
                 )
             )
 
         self.stop_event = stop_event
-        self.tracked_events: dict[str, list[Any]] = {}
-        self.early_request_sent: dict[str, bool] = {}
 
         # recordings data
         self.recordings_available_through: dict[str, float] = {}
@@ -337,11 +335,8 @@ class EmbeddingMaintainer(threading.Thread):
 
         camera_config = self.config.cameras[camera]
 
-        # no need to process updated objects if face recognition, lpr, genai are disabled
-        if (
-            not camera_config.objects.genai.enabled
-            and len(self.realtime_processors) == 0
-        ):
+        # no need to process updated objects if no processors are active
+        if len(self.realtime_processors) == 0 and len(self.post_processors) == 0:
             return
 
         # Create our own thumbnail based on the bounding box and the frame time
@@ -361,57 +356,17 @@ class EmbeddingMaintainer(threading.Thread):
         for processor in self.realtime_processors:
             processor.process_frame(data, yuv_frame)
 
-        # no need to save our own thumbnails if genai is not enabled
-        # or if the object has become stationary
-        if self.genai_client is not None and not data["stationary"]:
-            if data["id"] not in self.tracked_events:
-                self.tracked_events[data["id"]] = []
-
-            data["thumbnail"] = self._create_thumbnail(yuv_frame, data["box"])
-
-            # Limit the number of thumbnails saved
-            if len(self.tracked_events[data["id"]]) >= MAX_THUMBNAILS:
-                # Always keep the first thumbnail for the event
-                self.tracked_events[data["id"]].pop(1)
-
-            self.tracked_events[data["id"]].append(data)
-
-        # check if we're configured to send an early request after a minimum number of updates received
-        if (
-            self.genai_client is not None
-            and camera_config.objects.genai.send_triggers.after_significant_updates
-        ):
-            if (
-                len(self.tracked_events.get(data["id"], []))
-                >= camera_config.objects.genai.send_triggers.after_significant_updates
-                and data["id"] not in self.early_request_sent
-            ):
-                if data["has_clip"] and data["has_snapshot"]:
-                    event: Event = Event.get(Event.id == data["id"])
-
-                    if (
-                        not camera_config.objects.genai.objects
-                        or event.label in camera_config.objects.genai.objects
-                    ) and (
-                        not camera_config.objects.genai.required_zones
-                        or set(data["entered_zones"])
-                        & set(camera_config.objects.genai.required_zones)
-                    ):
-                        logger.debug(f"{camera} sending early request to GenAI")
-
-                        self.early_request_sent[data["id"]] = True
-                        threading.Thread(
-                            target=self._genai_embed_description,
-                            name=f"_genai_embed_description_{event.id}",
-                            daemon=True,
-                            args=(
-                                event,
-                                [
-                                    data["thumbnail"]
-                                    for data in self.tracked_events[data["id"]]
-                                ],
-                            ),
-                        ).start()
+        for processor in self.post_processors:
+            if isinstance(processor, ObjectDescriptionProcessor):
+                processor.process_data(
+                    {
+                        "camera": camera,
+                        "data": data,
+                        "state": "update",
+                        "yuv_frame": yuv_frame,
+                    },
+                    PostProcessDataEnum.tracked_object,
+                )
 
         self.frame_manager.close(frame_name)
 
@@ -424,12 +379,13 @@ class EmbeddingMaintainer(threading.Thread):
                 break
 
             event_id, camera, updated_db = ended
-            camera_config = self.config.cameras[camera]
 
             # expire in realtime processors
             for processor in self.realtime_processors:
                 processor.expire_object(event_id, camera)
 
+            thumbnail: bytes | None = None
+
             if updated_db:
                 try:
                     event: Event = Event.get(Event.id == event_id)
@@ -446,23 +402,6 @@ class EmbeddingMaintainer(threading.Thread):
                 # Embed the thumbnail
                 self._embed_thumbnail(event_id, thumbnail)
 
-                # Run GenAI
-                if (
-                    camera_config.objects.genai.enabled
-                    and camera_config.objects.genai.send_triggers.tracked_object_end
-                    and self.genai_client is not None
-                    and (
-                        not camera_config.objects.genai.objects
-                        or event.label in camera_config.objects.genai.objects
-                    )
-                    and (
-                        not camera_config.objects.genai.required_zones
-                        or set(event.zones)
-                        & set(camera_config.objects.genai.required_zones)
-                    )
-                ):
-                    self._process_genai_description(event, camera_config, thumbnail)
-
             # call any defined post processors
             for processor in self.post_processors:
                 if isinstance(processor, LicensePlatePostProcessor):
@@ -492,16 +431,25 @@ class EmbeddingMaintainer(threading.Thread):
                         {"event_id": event_id, "camera": camera, "type": "image"},
                         PostProcessDataEnum.tracked_object,
                     )
+                elif isinstance(processor, ObjectDescriptionProcessor):
+                    if not updated_db:
+                        continue
+
+                    processor.process_data(
+                        {
+                            "event": event,
+                            "camera": camera,
+                            "state": "finalize",
+                            "thumbnail": thumbnail,
+                        },
+                        PostProcessDataEnum.tracked_object,
+                    )
                 else:
                     processor.process_data(
                         {"event_id": event_id, "camera": camera},
                         PostProcessDataEnum.tracked_object,
                     )
 
-            # Delete tracked events based on the event_id
-            if event_id in self.tracked_events:
-                del self.tracked_events[event_id]
-
     def _expire_dedicated_lpr(self) -> None:
         """Remove plates not seen for longer than expiration timeout for dedicated lpr cameras."""
         now = datetime.datetime.now().timestamp()
@@ -570,9 +518,16 @@ class EmbeddingMaintainer(threading.Thread):
         event_id, source, force = payload
 
         if event_id:
-            self.handle_regenerate_description(
-                event_id, RegenerateDescriptionEnum(source), force
-            )
+            for processor in self.post_processors:
+                if isinstance(processor, ObjectDescriptionProcessor):
+                    processor.handle_request(
+                        "regenerate_description",
+                        {
+                            "event_id": event_id,
+                            "source": RegenerateDescriptionEnum(source),
+                            "force": force,
+                        },
+                    )
 
     def _process_frame_updates(self) -> None:
         """Process event updates"""
@@ -622,208 +577,9 @@ class EmbeddingMaintainer(threading.Thread):
 
         self.frame_manager.close(frame_name)
 
-    def _create_thumbnail(self, yuv_frame, box, height=500) -> Optional[bytes]:
-        """Return jpg thumbnail of a region of the frame."""
-        frame = cv2.cvtColor(yuv_frame, cv2.COLOR_YUV2BGR_I420)
-        region = calculate_region(
-            frame.shape, box[0], box[1], box[2], box[3], height, multiplier=1.4
-        )
-        frame = frame[region[1] : region[3], region[0] : region[2]]
-        width = int(height * frame.shape[1] / frame.shape[0])
-        frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
-        ret, jpg = cv2.imencode(".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), 100])
-
-        if ret:
-            return jpg.tobytes()
-
-        return None
-
     def _embed_thumbnail(self, event_id: str, thumbnail: bytes) -> None:
         """Embed the thumbnail for an event."""
         if not self.config.semantic_search.enabled:
             return
 
         self.embeddings.embed_thumbnail(event_id, thumbnail)
-
-    def _process_genai_description(
-        self, event: Event, camera_config: CameraConfig, thumbnail
-    ) -> None:
-        if event.has_snapshot and camera_config.objects.genai.use_snapshot:
-            snapshot_image = self._read_and_crop_snapshot(event, camera_config)
-            if not snapshot_image:
-                return
-
-        num_thumbnails = len(self.tracked_events.get(event.id, []))
-
-        # ensure we have a jpeg to pass to the model
-        thumbnail = ensure_jpeg_bytes(thumbnail)
-
-        embed_image = (
-            [snapshot_image]
-            if event.has_snapshot and camera_config.objects.genai.use_snapshot
-            else (
-                [data["thumbnail"] for data in self.tracked_events[event.id]]
-                if num_thumbnails > 0
-                else [thumbnail]
-            )
-        )
-
-        if camera_config.objects.genai.debug_save_thumbnails and num_thumbnails > 0:
-            logger.debug(f"Saving {num_thumbnails} thumbnails for event {event.id}")
-
-            Path(os.path.join(CLIPS_DIR, f"genai-requests/{event.id}")).mkdir(
-                parents=True, exist_ok=True
-            )
-
-            for idx, data in enumerate(self.tracked_events[event.id], 1):
-                jpg_bytes: bytes = data["thumbnail"]
-
-                if jpg_bytes is None:
-                    logger.warning(f"Unable to save thumbnail {idx} for {event.id}.")
-                else:
-                    with open(
-                        os.path.join(
-                            CLIPS_DIR,
-                            f"genai-requests/{event.id}/{idx}.jpg",
-                        ),
-                        "wb",
-                    ) as j:
-                        j.write(jpg_bytes)
-
-        # Generate the description. Call happens in a thread since it is network bound.
-        threading.Thread(
-            target=self._genai_embed_description,
-            name=f"_genai_embed_description_{event.id}",
-            daemon=True,
-            args=(
-                event,
-                embed_image,
-            ),
-        ).start()
-
-    def _genai_embed_description(self, event: Event, thumbnails: list[bytes]) -> None:
-        """Embed the description for an event."""
-        camera_config = self.config.cameras[event.camera]
-
-        description = self.genai_client.generate_object_description(
-            camera_config, thumbnails, event
-        )
-
-        if not description:
-            logger.debug("Failed to generate description for %s", event.id)
-            return
-
-        # fire and forget description update
-        self.requestor.send_data(
-            UPDATE_EVENT_DESCRIPTION,
-            {
-                "type": TrackedObjectUpdateTypesEnum.description,
-                "id": event.id,
-                "description": description,
-                "camera": event.camera,
-            },
-        )
-
-        # Embed the description
-        if self.config.semantic_search.enabled:
-            self.embeddings.embed_description(event.id, description)
-
-        # Check semantic trigger for this description
-        for processor in self.post_processors:
-            if isinstance(processor, SemanticTriggerProcessor):
-                processor.process_data(
-                    {"event_id": event.id, "camera": event.camera, "type": "text"},
-                    PostProcessDataEnum.tracked_object,
-                )
-            else:
-                continue
-
-        logger.debug(
-            "Generated description for %s (%d images): %s",
-            event.id,
-            len(thumbnails),
-            description,
-        )
-
-    def _read_and_crop_snapshot(self, event: Event, camera_config) -> bytes | None:
-        """Read, decode, and crop the snapshot image."""
-
-        snapshot_file = os.path.join(CLIPS_DIR, f"{event.camera}-{event.id}.jpg")
-
-        if not os.path.isfile(snapshot_file):
-            logger.error(
-                f"Cannot load snapshot for {event.id}, file not found: {snapshot_file}"
-            )
-            return None
-
-        try:
-            with open(snapshot_file, "rb") as image_file:
-                snapshot_image = image_file.read()
-
-                img = cv2.imdecode(
-                    np.frombuffer(snapshot_image, dtype=np.int8),
-                    cv2.IMREAD_COLOR,
-                )
-
-                # Crop snapshot based on region
-                # provide full image if region doesn't exist (manual events)
-                height, width = img.shape[:2]
-                x1_rel, y1_rel, width_rel, height_rel = event.data.get(
-                    "region", [0, 0, 1, 1]
-                )
-                x1, y1 = int(x1_rel * width), int(y1_rel * height)
-
-                cropped_image = img[
-                    y1 : y1 + int(height_rel * height),
-                    x1 : x1 + int(width_rel * width),
-                ]
-
-                _, buffer = cv2.imencode(".jpg", cropped_image)
-
-                return buffer.tobytes()
-        except Exception:
-            return None
-
-    def handle_regenerate_description(
-        self, event_id: str, source: str, force: bool
-    ) -> None:
-        try:
-            event: Event = Event.get(Event.id == event_id)
-        except DoesNotExist:
-            logger.error(f"Event {event_id} not found for description regeneration")
-            return
-
-        if self.genai_client is None:
-            logger.error("GenAI not enabled")
-            return
-
-        camera_config = self.config.cameras[event.camera]
-        if not camera_config.objects.genai.enabled and not force:
-            logger.error(f"GenAI not enabled for camera {event.camera}")
-            return
-
-        thumbnail = get_event_thumbnail_bytes(event)
-
-        # ensure we have a jpeg to pass to the model
-        thumbnail = ensure_jpeg_bytes(thumbnail)
-
-        logger.debug(
-            f"Trying {source} regeneration for {event}, has_snapshot: {event.has_snapshot}"
-        )
-
-        if event.has_snapshot and source == "snapshot":
-            snapshot_image = self._read_and_crop_snapshot(event, camera_config)
-            if not snapshot_image:
-                return
-
-        embed_image = (
-            [snapshot_image]
-            if event.has_snapshot and source == "snapshot"
-            else (
-                [data["thumbnail"] for data in self.tracked_events[event_id]]
-                if len(self.tracked_events.get(event_id, [])) > 0
-                else [thumbnail]
-            )
-        )
-
-        self._genai_embed_description(event, embed_image)

frigate/genai/__init__.py

@@ -32,7 +32,7 @@ def register_genai_provider(key: GenAIProviderEnum):
 class GenAIClient:
     """Generative AI client for Frigate."""
 
-    def __init__(self, genai_config: GenAIConfig, timeout: int = 60) -> None:
+    def __init__(self, genai_config: GenAIConfig, timeout: int = 120) -> None:
         self.genai_config: GenAIConfig = genai_config
         self.timeout = timeout
         self.provider = self._init_provider()

frigate/stats/util.py

@@ -361,6 +361,14 @@ def stats_snapshot(
                 embeddings_metrics.review_desc_dps.value, 2
             )
 
+        if embeddings_metrics.object_desc_speed.value > 0.0:
+            stats["embeddings"]["object_description_speed"] = round(
+                embeddings_metrics.object_desc_speed.value * 1000, 2
+            )
+            stats["embeddings"]["object_descriptions"] = round(
+                embeddings_metrics.object_desc_dps.value, 2
+            )
+
         for key in embeddings_metrics.classification_speeds.keys():
             stats["embeddings"][f"{key}_classification_speed"] = round(
                 embeddings_metrics.classification_speeds[key].value * 1000, 2

frigate/util/image.py

@@ -995,7 +995,26 @@ def get_histogram(image, x_min, y_min, x_max, y_max):
     return cv2.normalize(hist, hist).flatten()
 
 
-def ensure_jpeg_bytes(image_data):
+def create_thumbnail(
+    yuv_frame: np.ndarray, box: tuple[int, int, int, int], height=500
+) -> Optional[bytes]:
+    """Return jpg thumbnail of a region of the frame."""
+    frame = cv2.cvtColor(yuv_frame, cv2.COLOR_YUV2BGR_I420)
+    region = calculate_region(
+        frame.shape, box[0], box[1], box[2], box[3], height, multiplier=1.4
+    )
+    frame = frame[region[1] : region[3], region[0] : region[2]]
+    width = int(height * frame.shape[1] / frame.shape[0])
+    frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
+    ret, jpg = cv2.imencode(".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), 100])
+
+    if ret:
+        return jpg.tobytes()
+
+    return None
+
+
+def ensure_jpeg_bytes(image_data: bytes) -> bytes:
     """Ensure image data is jpeg bytes for genai"""
     try:
         img_array = np.frombuffer(image_data, dtype=np.uint8)