Merge branch 'blakeblackshear:dev' into testing

Author: ivanjx

docs/docs/configuration/object_detectors.md

@@ -365,8 +365,8 @@ detectors:
 
 model:
   model_type: rfdetr
-  width: 560
-  height: 560
+  width: 320
+  height: 320
   input_tensor: nchw
   input_dtype: float
   path: /config/model_cache/rfdetr.onnx
@@ -616,8 +616,8 @@ detectors:
 
 model:
   model_type: rfdetr
-  width: 560
-  height: 560
+  width: 320
+  height: 320
   input_tensor: nchw
   input_dtype: float
   path: /config/model_cache/rfdetr.onnx
@@ -777,8 +777,8 @@ model:
   labelmap_path: /labelmap/coco-80.txt
   input_tensor: nchw
   input_pixel_format: rgb
-  width: 320
-  height: 320
+  width: 320 # MUST match the chosen model i.e yolov7-320 -> 320, yolov4-416 -> 416
+  height: 320 # MUST match the chosen model i.e yolov7-320 -> 320 yolov4-416 -> 416
 ```
 
 ## Rockchip platform
@@ -983,22 +983,21 @@ Make sure you change the batch size to 1 before exporting.
 
 ### Download RF-DETR Model
 
-To export as ONNX:
-
-1. `pip3 install rfdetr`
-2. `python3`
-3. `from rfdetr import RFDETRBase`
-4. `x = RFDETRBase()`
-5. `x.export()`
-
-#### Additional Configuration
+RF-DETR can be exported as ONNX by running the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=Nano` in the first line to `Nano`, `Small`, or `Medium` size.
 
-The input tensor resolution can be customized:
-
-```python
-from rfdetr import RFDETRBase
-x = RFDETRBase(resolution=560)  # resolution must be a multiple of 56
-x.export()
+```sh
+docker build . --build-arg MODEL_SIZE=Nano --output . -f- <<'EOF'
+FROM python:3.11 AS build
+RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
+COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
+WORKDIR /rfdetr
+RUN uv pip install --system rfdetr onnx onnxruntime onnxsim onnx-graphsurgeon
+ARG MODEL_SIZE
+RUN python3 -c "from rfdetr import RFDETR${MODEL_SIZE}; x = RFDETR${MODEL_SIZE}(resolution=320); x.export()"
+FROM scratch
+ARG MODEL_SIZE
+COPY --from=build /rfdetr/output/inference_model.onnx /rfdetr-${MODEL_SIZE}.onnx
+EOF
 ```
 
 ### Downloading YOLO-NAS Model

docs/docs/frigate/hardware.md

@@ -166,16 +166,12 @@ There are improved capabilities in newer GPU architectures that TensorRT can ben
 Inference speeds will vary greatly depending on the GPU and the model used.
 `tiny` variants are faster than the equivalent non-tiny model, some known examples are below:
 
-| Name            | YOLOv7 Inference Time | YOLO-NAS Inference Time   | RF-DETR Inference Time    |
-| --------------- | --------------------- | ------------------------- | ------------------------- |
-| GTX 1060 6GB    | ~ 7 ms                |                           |                           |
-| GTX 1070        | ~ 6 ms                |                           |                           |
-| GTX 1660 SUPER  | ~ 4 ms                |                           |                           |
-| RTX 3050        | 5 - 7 ms              | 320: ~ 10 ms 640: ~ 16 ms | 336: ~ 16 ms 560: ~ 40 ms |
-| RTX 3070 Mobile | ~ 5 ms                |                           |                           |
-| RTX 3070        | 4 - 6 ms              | 320: ~ 6 ms 640: ~ 12 ms  | 336: ~ 14 ms 560: ~ 36 ms |
-| Quadro P400 2GB | 20 - 25 ms            |                           |                           |
-| Quadro P2000    | ~ 12 ms               |                           |                           |
+| Name            | YOLOv9 Inference Time | YOLO-NAS Inference Time   | RF-DETR Inference Time |
+| --------------- | --------------------- | ------------------------- | ---------------------- |
+| RTX 3050        | t-320: 15 ms          | 320: ~ 10 ms 640: ~ 16 ms | Nano-320: ~ 12 ms      |
+| RTX 3070        | t-320: 11 ms          | 320: ~ 8 ms 640: ~ 14 ms  | Nano-320: ~ 9 ms       |
+| RTX A4000       |                       | 320: ~ 15 ms              |                        |
+| Tesla P40       |                       | 320: ~ 105 ms             |                        |
 
 ### ROCm - AMD GPU
 

frigate/embeddings/__init__.py

@@ -7,6 +7,7 @@
 import os
 import signal
 import threading
+from json.decoder import JSONDecodeError
 from types import FrameType
 from typing import Any, Optional, Union
 
@@ -73,13 +74,21 @@ def __init__(self, db: SqliteVecQueueDatabase):
         self.requestor = EmbeddingsRequestor()
 
         # load stats from disk
+        stats_file = os.path.join(CONFIG_DIR, ".search_stats.json")
         try:
-            with open(os.path.join(CONFIG_DIR, ".search_stats.json"), "r") as f:
+            with open(stats_file, "r") as f:
                 data = json.loads(f.read())
                 self.thumb_stats.from_dict(data["thumb_stats"])
                 self.desc_stats.from_dict(data["desc_stats"])
         except FileNotFoundError:
             pass
+        except JSONDecodeError:
+            logger.warning("Failed to decode semantic search stats, clearing file")
+            try:
+                with open(stats_file, "w") as f:
+                    f.write("")
+            except OSError as e:
+                logger.error(f"Failed to clear corrupted stats file: {e}")
 
     def stop(self):
         """Write the stats to disk as JSON on exit."""

frigate/events/cleanup.py

@@ -340,21 +340,22 @@ def run(self) -> None:
                 .where(Event.has_clip == False, Event.has_snapshot == False)
                 .iterator()
             )
-            events_to_delete = [e.id for e in events]
+            events_to_delete: list[Event] = [e for e in events]
 
-            for e in events:
+            for e in events_to_delete:
                 delete_event_thumbnail(e)
 
             logger.debug(f"Found {len(events_to_delete)} events that can be expired")
             if len(events_to_delete) > 0:
-                for i in range(0, len(events_to_delete), CHUNK_SIZE):
-                    chunk = events_to_delete[i : i + CHUNK_SIZE]
+                ids_to_delete = [e.id for e in events_to_delete]
+                for i in range(0, len(ids_to_delete), CHUNK_SIZE):
+                    chunk = ids_to_delete[i : i + CHUNK_SIZE]
                     logger.debug(f"Deleting {len(chunk)} events from the database")
                     Event.delete().where(Event.id << chunk).execute()
 
                     if self.config.semantic_search.enabled:
                         self.db.delete_embeddings_description(event_ids=chunk)
                         self.db.delete_embeddings_thumbnail(event_ids=chunk)
-                        logger.debug(f"Deleted {len(events_to_delete)} embeddings")
+                        logger.debug(f"Deleted {len(ids_to_delete)} embeddings")
 
         logger.info("Exiting event cleanup...")

web/public/locales/hu/views/settings.json

@@ -42,7 +42,7 @@
         "desc": "Automatikusan váltson át a kamera élő nézetére, amikor aktivitást észlel. Ha ez az opció ki van kapcsolva, akkor az Élő irányítópulton a statikus kameraképek csak percenként egyszer frissülnek."
       },
       "playAlertVideos": {
-        "label": "Riadó Videók Lejátszása",
+        "label": "Riasztási Videók Lejátszása",
         "desc": "Alapértelmezetten az Élő irányítópulton a legutóbbi riasztások kis, ismétlődő videóként jelennek meg. Kapcsolja ki ezt az opciót, ha csak állóképet szeretne megjeleníteni a legutóbbi riasztásokról ezen az eszközön/böngészőben."
       }
     },

web/public/locales/ko/audio.json

@@ -0,0 +1 @@
+{}

web/public/locales/ko/common.json

@@ -0,0 +1 @@
+{}

web/public/locales/ko/components/auth.json

@@ -0,0 +1 @@
+{}

web/public/locales/ko/components/camera.json

@@ -0,0 +1 @@
+{}

web/public/locales/ko/components/dialog.json

@@ -0,0 +1 @@
+{}