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Merge remote-tracking branch 'origin/master' into dev

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Blake Blackshear
2025-09-24 06:57:50 -05:00
parent 7e2f5a3017 4914029a50
commit 652fdc6a38
21 changed files with 227 additions and 118 deletions
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docs/docs/configuration/camera_specific.md
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@@ -147,7 +147,7 @@ WEB Digest Algorithm - MD5
Reolink has many different camera models with inconsistently supported features and behavior. The below table shows a summary of various features and recommendations.
| Camera Resolution | Camera Generation | Recommended Stream Type | Additional Notes |
|-------------------|---------------------------|-----------------------------------|-------------------------------------------------------------------------|
| ----------------- | ------------------------- | --------------------------------- | ----------------------------------------------------------------------- |
| 5MP or lower | All | http-flv | Stream is h264 |
| 6MP or higher | Latest (ex: Duo3, CX-8##) | http-flv with ffmpeg 8.0, or rtsp | This uses the new http-flv-enhanced over H265 which requires ffmpeg 8.0 |
| 6MP or higher | Older (ex: RLC-8##) | rtsp | |
@@ -250,6 +250,7 @@ TP-Link VIGI cameras need some adjustments to the main stream settings on the ca
To use a USB camera (webcam) with Frigate, the recommendation is to use go2rtc's [FFmpeg Device](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg-device) support:
- Preparation outside of Frigate:
- Get USB camera path. Run `v4l2-ctl --list-devices` to get a listing of locally-connected cameras available. (You may need to install `v4l-utils` in a way appropriate for your Linux distribution). In the sample configuration below, we use `video=0` to correlate with a detected device path of `/dev/video0`
- Get USB camera formats & resolutions. Run `ffmpeg -f v4l2 -list_formats all -i /dev/video0` to get an idea of what formats and resolutions the USB Camera supports. In the sample configuration below, we use a width of 1024 and height of 576 in the stream and detection settings based on what was reported back.
- If using Frigate in a container (e.g. Docker on TrueNAS), ensure you have USB Passthrough support enabled, along with a specific Host Device (`/dev/video0`) + Container Device (`/dev/video0`) listed.
@@ -277,5 +278,3 @@ cameras:
width: 1024
height: 576
```
docs/docs/configuration/live.md
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@@ -272,3 +272,7 @@ Note that disabling a camera through the config file (`enabled: False`) removes
6. **I have unmuted some cameras on my dashboard, but I do not hear sound. Why?**
If your camera is streaming (as indicated by a red dot in the upper right, or if it has been set to continuous streaming mode), your browser may be blocking audio until you interact with the page. This is an intentional browser limitation. See [this article](https://developer.mozilla.org/en-US/docs/Web/Media/Autoplay_guide#autoplay_availability). Many browsers have a whitelist feature to change this behavior.
7. **My camera streams have lots of visual artifacts / distortion.**
Some cameras don't include the hardware to support multiple connections to the high resolution stream, and this can cause unexpected behavior. In this case it is recommended to [restream](./restream.md) the high resolution stream so that it can be used for live view and recordings.
docs/docs/configuration/object_detectors.md
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@@ -35,6 +35,7 @@ Frigate supports multiple different detectors that work on different types of ha
- [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt` Frigate image when a supported ONNX model is configured.
**Nvidia Jetson**
- [TensortRT](#nvidia-tensorrt-detector): TensorRT can run on Jetson devices, using one of many default models.
- [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt-jp6` Frigate image when a supported ONNX model is configured.
@@ -331,6 +332,12 @@ The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv
:::
:::warning
If you are using a Frigate+ YOLOv9 model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
:::
After placing the downloaded onnx model in your config folder, you can use the following configuration:
```yaml
@@ -592,6 +599,12 @@ There is no default model provided, the following formats are supported:
[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. See [the models section](#downloading-yolo-nas-model) for more information on downloading the YOLO-NAS model for use in Frigate.
:::warning
If you are using a Frigate+ YOLO-NAS model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
:::
After placing the downloaded onnx model in your config folder, you can use the following configuration:
```yaml
@@ -619,6 +632,12 @@ The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv
:::
:::warning
If you are using a Frigate+ YOLOv9 model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
:::
After placing the downloaded onnx model in your config folder, you can use the following configuration:
```yaml
@@ -1303,26 +1322,29 @@ Here are some tips for getting different model types
### Downloading D-FINE Model
To export as ONNX:
1. Clone: https://github.com/Peterande/D-FINE and install all dependencies.
2. Select and download a checkpoint from the [readme](https://github.com/Peterande/D-FINE).
3. Modify line 58 of `tools/deployment/export_onnx.py` and change batch size to 1: `data = torch.rand(1, 3, 640, 640)`
4. Run the export, making sure you select the right config, for your checkpoint.
Example:
D-FINE 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=s` in the first line to `s`, `m`, or `l` size.
```sh
docker build . --build-arg MODEL_SIZE=s --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 /dfine
RUN git clone https://github.com/Peterande/D-FINE.git .
RUN uv pip install --system -r requirements.txt
RUN uv pip install --system onnx onnxruntime onnxsim
# Create output directory and download checkpoint
RUN mkdir -p output
ARG MODEL_SIZE
RUN wget https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_${MODEL_SIZE}_obj2coco.pth -O output/dfine_${MODEL_SIZE}_obj2coco.pth
# Modify line 58 of export_onnx.py to change batch size to 1
RUN sed -i '58s/data = torch.rand(.*)/data = torch.rand(1, 3, 640, 640)/' tools/deployment/export_onnx.py
RUN python3 tools/deployment/export_onnx.py -c configs/dfine/objects365/dfine_hgnetv2_${MODEL_SIZE}_obj2coco.yml -r output/dfine_${MODEL_SIZE}_obj2coco.pth
FROM scratch
ARG MODEL_SIZE
COPY --from=build /dfine/output/dfine_${MODEL_SIZE}_obj2coco.onnx /dfine-${MODEL_SIZE}.onnx
EOF
```
python3 tools/deployment/export_onnx.py -c configs/dfine/objects365/dfine_hgnetv2_m_obj2coco.yml -r output/dfine_m_obj2coco.pth
```
:::tip
Model export has only been tested on Linux (or WSL2). Not all dependencies are in `requirements.txt`. Some live in the deployment folder, and some are still missing entirely and must be installed manually.
Make sure you change the batch size to 1 before exporting.
:::
### Download RF-DETR Model
@@ -1374,23 +1396,25 @@ python3 yolo_to_onnx.py -m yolov7-320
#### YOLOv9
YOLOv9 model can be exported as ONNX using the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=t` in the first line to the [model size](https://github.com/WongKinYiu/yolov9#performance) you would like to convert (available sizes are `t`, `s`, `m`, `c`, and `e`).
YOLOv9 model can be exported as ONNX using the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=t` and `IMG_SIZE=320` in the first line to the [model size](https://github.com/WongKinYiu/yolov9#performance) you would like to convert (available model sizes are `t`, `s`, `m`, `c`, and `e`, common image sizes are `320` and `640`).
```sh
docker build . --build-arg MODEL_SIZE=t --output . -f- <<'EOF'
docker build . --build-arg MODEL_SIZE=t --build-arg IMG_SIZE=320 --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 /yolov9
ADD https://github.com/WongKinYiu/yolov9.git .
RUN uv pip install --system -r requirements.txt
RUN uv pip install --system onnx onnxruntime onnx-simplifier>=0.4.1
RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier>=0.4.1
ARG MODEL_SIZE
ARG IMG_SIZE
ADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt
RUN sed -i "s/ckpt = torch.load(attempt_download(w), map_location='cpu')/ckpt = torch.load(attempt_download(w), map_location='cpu', weights_only=False)/g" models/experimental.py
RUN python3 export.py --weights ./yolov9-${MODEL_SIZE}.pt --imgsz 320 --simplify --include onnx
RUN python3 export.py --weights ./yolov9-${MODEL_SIZE}.pt --imgsz ${IMG_SIZE} --simplify --include onnx
FROM scratch
ARG MODEL_SIZE
COPY --from=build /yolov9/yolov9-${MODEL_SIZE}.onnx /
ARG IMG_SIZE
COPY --from=build /yolov9/yolov9-${MODEL_SIZE}.onnx /yolov9-${MODEL_SIZE}-${IMG_SIZE}.onnx
EOF
```