add maintainer test

when users don't explicitly set detect.width and detect.height, we probe for them. sometimes the probe hangs (camera doesn't support UDP, like some Reolinks), so this log message will make that clearer

.github/pull_request_template.md

@@ -26,7 +26,7 @@ _Please read the [contributing guidelines](https://github.com/blakeblackshear/fr
 
 - This PR fixes or closes issue: fixes #
 - This PR is related to issue:
-- Link to discussion with maintainers (**required** for any large or "planned" features):
+- Link to discussion with maintainers (**required** for large/pinned features):
 
 ## For new features
 

.github/workflows/pr_template_check.yml

@@ -13,7 +13,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Check PR description against template
-        uses: actions/github-script@v9
+        uses: actions/github-script@v7
         with:
           script: |
             const maintainers = ['blakeblackshear', 'NickM-27', 'hawkeye217', 'dependabot[bot]', 'weblate'];

.github/workflows/pull_request.yml

@@ -72,7 +72,7 @@ jobs:
         run: npm run e2e
         working-directory: ./web
       - name: Upload test artifacts
-        uses: actions/upload-artifact@v7
+        uses: actions/upload-artifact@v4
         if: failure()
         with:
           name: playwright-report

.github/workflows/stale.yml

@@ -18,9 +18,9 @@ jobs:
           close-issue-message: ""
           days-before-stale: 30
           days-before-close: 3
-          exempt-draft-pr: false
-          exempt-issue-labels: "planned,security"
-          exempt-pr-labels: "planned,security,dependencies"
+          exempt-draft-pr: true
+          exempt-issue-labels: "pinned,security"
+          exempt-pr-labels: "pinned,security,dependencies"
           operations-per-run: 120
       - name: Print outputs
         env:

.gitignore

@@ -22,8 +22,3 @@ core
 !/web/**/*.ts
 .idea/*
 .ipynb_checkpoints
-
-# Auto-generated Docker Compose Generator config files
-docs/src/components/DockerComposeGenerator/config/devices.ts
-docs/src/components/DockerComposeGenerator/config/hardware.ts
-docs/src/components/DockerComposeGenerator/config/ports.ts

CONTRIBUTING.md

@@ -10,14 +10,11 @@ If you've found a bug and want to fix it, go for it. Link to the relevant issue
 
 ### New features
 
-A pull request is more than just code — it's a request for the maintainers to review, integrate, and support the change long-term. We're selective about what we take on, and prioritize changes that align with the project's direction and can be responsibly maintained in the long term.
+Every new feature adds scope that the maintainers must test, maintain, and support long-term. Before writing code for a new feature:
 
-**Large or highly-requested features** raise the bar even higher. Popularity signals demand, but it doesn't pre-approve any particular implementation. The bigger the change, the higher the long-term cost, and the more important it is that we're aligned on scope and approach before any code is written. A large PR that lands without prior discussion is unlikely to be merged as-is, no matter how well it's implemented.
-
-Before writing code for a new feature:
-
-1. **Check for existing discussion.** Search [feature requests](https://github.com/blakeblackshear/frigate/issues) and [discussions](https://github.com/blakeblackshear/frigate/discussions) to see if it's been proposed or discussed. Feature requests tagged with "planned" are on our radar — we plan to get to them, but we don't maintain a public roadmap or timeline. Check in with us first if you have interest in contributing to one.
+1. **Check for existing discussion.** Search [feature requests](https://github.com/blakeblackshear/frigate/issues) and [discussions](https://github.com/blakeblackshear/frigate/discussions) to see if it's been proposed or discussed. Pinned feature requests are on our radar — we plan to get to them, but we don't maintain a public roadmap or timeline. Check in with us first if you have interest in contributing to one.
 2. **Start a discussion or feature request first.** This helps ensure your idea aligns with Frigate's direction before you invest time building it. Community interest in a feature request helps us gauge demand, though a great idea is a great idea even without a crowd behind it.
+3. **Be open to "no".** We try to be thoughtful about what we take on, and sometimes that means saying no to good code if the feature isn't the right fit for the project. These calls are sometimes subjective, and we won't always get them right. We're happy to discuss and reconsider.
 
 ## AI usage policy
 
@@ -42,8 +39,6 @@ We're not trying to gatekeep how you write code. Use whatever tools make you pro
 
 Some honest context: when we review a PR, we're not just evaluating whether the code works today. We're evaluating whether we can maintain it, debug it, and extend it long-term — often without the original author's involvement. Code that the author doesn't deeply understand is code that nobody understands, and that's a liability.
 
-One more thing worth saying directly: most maintainers already have access to the same AI tools you do. A PR that's entirely AI-generated — where the author can't explain the design, debug issues independently, or engage substantively in design discussions — doesn't offer something we couldn't produce ourselves. What makes a contribution genuinely valuable is the human judgment and domain understanding behind it, as well as the engagement during review that shapes it into something we can confidently take on long-term.
-
 ## Pull request guidelines
 
 ### Before submitting

docker/main/install_deps.sh

@@ -87,47 +87,40 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
     # intel packages use zst compression so we need to update dpkg
     apt-get install -y dpkg
 
-    # use intel apt repo for libmfx1 (legacy QSV, pre-Gen12)
+    # use intel apt intel packages
     wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg
     echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | tee /etc/apt/sources.list.d/intel-gpu-jammy.list
     apt-get -qq update
-
     # intel-media-va-driver-non-free is built from source in the
     # intel-media-driver Dockerfile stage for Battlemage (Xe2) support
     apt-get -qq install --no-install-recommends --no-install-suggests -y \
-        libmfx1
-    rm -f /usr/share/keyrings/intel-graphics.gpg
-    rm -f /etc/apt/sources.list.d/intel-gpu-jammy.list
+        libmfx1 libmfxgen1 libvpl2
 
-    # upgrade libva2, oneVPL runtime, and libvpl2 from trixie for Battlemage support
-    echo "deb http://deb.debian.org/debian trixie main" > /etc/apt/sources.list.d/trixie.list
-    apt-get -qq update
-    apt-get -qq install -y -t trixie libva2 libva-drm2 libzstd1
-    apt-get -qq install -y -t trixie libmfx-gen1.2 libvpl2
-    rm -f /etc/apt/sources.list.d/trixie.list
-    apt-get -qq update
     apt-get -qq install -y ocl-icd-libopencl1
 
     # install libtbb12 for NPU support
     apt-get -qq install -y libtbb12
 
-    # install legacy and standard intel compute packages
+    rm -f /usr/share/keyrings/intel-graphics.gpg
+    rm -f /etc/apt/sources.list.d/intel-gpu-jammy.list
+
+    # install legacy and standard intel icd and level-zero-gpu
     # see https://github.com/intel/compute-runtime/blob/master/LEGACY_PLATFORMS.md for more info
     # needed core package
-    wget https://github.com/intel/compute-runtime/releases/download/26.14.37833.4/libigdgmm12_22.9.0_amd64.deb
-    dpkg -i libigdgmm12_22.9.0_amd64.deb
-    rm libigdgmm12_22.9.0_amd64.deb
+    wget https://github.com/intel/compute-runtime/releases/download/25.13.33276.19/libigdgmm12_22.7.0_amd64.deb
+    dpkg -i libigdgmm12_22.7.0_amd64.deb
+    rm libigdgmm12_22.7.0_amd64.deb
 
-    # legacy compute-runtime packages
+    # legacy packages
     wget https://github.com/intel/compute-runtime/releases/download/24.35.30872.36/intel-opencl-icd-legacy1_24.35.30872.36_amd64.deb
     wget https://github.com/intel/compute-runtime/releases/download/24.35.30872.36/intel-level-zero-gpu-legacy1_1.5.30872.36_amd64.deb
     wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.17537.24/intel-igc-opencl_1.0.17537.24_amd64.deb
     wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.17537.24/intel-igc-core_1.0.17537.24_amd64.deb
-    # standard compute-runtime packages
-    wget https://github.com/intel/compute-runtime/releases/download/26.14.37833.4/intel-opencl-icd_26.14.37833.4-0_amd64.deb
-    wget https://github.com/intel/compute-runtime/releases/download/26.14.37833.4/libze-intel-gpu1_26.14.37833.4-0_amd64.deb
-    wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.32.7/intel-igc-opencl-2_2.32.7+21184_amd64.deb
-    wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.32.7/intel-igc-core-2_2.32.7+21184_amd64.deb
+    # standard packages
+    wget https://github.com/intel/compute-runtime/releases/download/25.13.33276.19/intel-opencl-icd_25.13.33276.19_amd64.deb
+    wget https://github.com/intel/compute-runtime/releases/download/25.13.33276.19/intel-level-zero-gpu_1.6.33276.19_amd64.deb
+    wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.10.10/intel-igc-opencl-2_2.10.10+18926_amd64.deb
+    wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.10.10/intel-igc-core-2_2.10.10+18926_amd64.deb
     # npu packages
     wget https://github.com/oneapi-src/level-zero/releases/download/v1.28.2/level-zero_1.28.2+u22.04_amd64.deb
     wget https://github.com/intel/linux-npu-driver/releases/download/v1.19.0/intel-driver-compiler-npu_1.19.0.20250707-16111289554_ubuntu22.04_amd64.deb
@@ -137,10 +130,6 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
     dpkg -i *.deb
     rm *.deb
     apt-get -qq install -f -y
-
-    # Battlemage uses the xe kernel driver, but the VA-API driver is still iHD.
-    # The oneVPL runtime may look for a driver named after the kernel module.
-    ln -sf /usr/lib/x86_64-linux-gnu/dri/iHD_drv_video.so /usr/lib/x86_64-linux-gnu/dri/xe_drv_video.so
 fi
 
 if [[ "${TARGETARCH}" == "arm64" ]]; then

docker/main/requirements-wheels.txt

@@ -11,7 +11,7 @@ joserfc == 1.2.*
 cryptography == 44.0.*
 pathvalidate == 3.3.*
 markupsafe == 3.0.*
-python-multipart == 0.0.26
+python-multipart == 0.0.20
 # Classification Model Training
 tensorflow == 2.19.* ; platform_machine == 'aarch64'
 tensorflow-cpu == 2.19.* ; platform_machine == 'x86_64'
@@ -42,7 +42,7 @@ opencv-python-headless == 4.11.0.*
 opencv-contrib-python == 4.11.0.*
 scipy == 1.16.*
 # OpenVino & ONNX
-openvino == 2025.4.*
+openvino == 2025.3.*
 onnxruntime == 1.22.*
 # Embeddings
 transformers == 4.45.*

docker/rocm/Dockerfile

@@ -32,14 +32,11 @@ RUN echo /opt/rocm/lib|tee /opt/rocm-dist/etc/ld.so.conf.d/rocm.conf
 FROM deps AS deps-prelim
 
 COPY docker/rocm/debian-backports.sources /etc/apt/sources.list.d/debian-backports.sources
-# install_deps.sh upgraded libstdc++6 from trixie for Battlemage; the matching
-# -dev package must also come from trixie or apt refuses to satisfy it.
-RUN echo "deb http://deb.debian.org/debian trixie main" > /etc/apt/sources.list.d/trixie.list && \
-    apt-get update && \
+RUN apt-get update && \
     apt-get install -y libnuma1 && \
     apt-get install -qq -y -t bookworm-backports mesa-va-drivers mesa-vulkan-drivers && \
-    apt-get install -qq -y -t trixie libstdc++-14-dev && \
-    rm -f /etc/apt/sources.list.d/trixie.list && \
+    # Install C++ standard library headers for HIPRTC kernel compilation fallback
+    apt-get install -qq -y libstdc++-12-dev && \
     rm -rf /var/lib/apt/lists/*
 
 WORKDIR /opt/frigate

docs/docs/configuration/face_recognition.md

@@ -19,7 +19,7 @@ Face recognition requires a one-time internet connection to download detection a
 
 ### Face Detection
 
-When running a Frigate+ model (or any custom model that natively detects faces) should ensure that `face` is added to the [list of objects to track](../plus/index.md#available-label-types) either globally or for a specific camera. This will allow face detection to run at the same time as object detection and be more efficient.
+When running a Frigate+ model (or any custom model that natively detects faces) should ensure that `face` is added to the [list of objects to track](../plus/#available-label-types) either globally or for a specific camera. This will allow face detection to run at the same time as object detection and be more efficient.
 
 When running a default COCO model or another model that does not include `face` as a detectable label, face detection will run via CV2 using a lightweight DNN model that runs on the CPU. In this case, you should _not_ define `face` in your list of objects to track.
 
@@ -171,7 +171,7 @@ When choosing images to include in the face training set it is recommended to al
 - If it is difficult to make out details in a persons face it will not be helpful in training.
 - Avoid images with extreme under/over-exposure.
 - Avoid blurry / pixelated images.
-- Avoid training on infrared (gray-scale). The models are trained on color images and will not be able to extract features from gray-scale images.
+- Avoid training on infrared (gray-scale). The models are trained on color images and will be able to extract features from gray-scale images.
 - Using images of people wearing hats / sunglasses may confuse the model.
 - Do not upload too many similar images at the same time, it is recommended to train no more than 4-6 similar images for each person to avoid over-fitting.
 

docs/docs/configuration/genai/config.md

@@ -201,7 +201,7 @@ Cloud Generative AI providers require an active internet connection to send imag
 
 ### Ollama Cloud
 
-Ollama also supports [cloud models](https://ollama.com/cloud), where model inference is performed in the cloud. You can connect directly to Ollama Cloud by setting `base_url` to `https://ollama.com` and providing an API key. Alternatively, you can run Ollama locally and use a cloud model name so your local instance forwards requests to the cloud. For more details, see the Ollama cloud model [docs](https://docs.ollama.com/cloud).
+Ollama also supports [cloud models](https://ollama.com/cloud), where your local Ollama instance handles requests from Frigate, but model inference is performed in the cloud. Set up Ollama locally, sign in with your Ollama account, and specify the cloud model name in your Frigate config. For more details, see the Ollama cloud model [docs](https://docs.ollama.com/cloud).
 
 #### Configuration
 
@@ -210,8 +210,7 @@ Ollama also supports [cloud models](https://ollama.com/cloud), where model infer
 
 1. Navigate to <NavPath path="Settings > Enrichments > Generative AI" />.
    - Set **Provider** to `ollama`
-   - Set **Base URL** to your local Ollama address (e.g., `http://localhost:11434`) or `https://ollama.com` for direct cloud inference
-   - Set **API key** if required by your endpoint (e.g., when using `https://ollama.com`)
+   - Set **Base URL** to your local Ollama address (e.g., `http://localhost:11434`)
    - Set **Model** to the cloud model name
 
 </TabItem>
@@ -224,16 +223,6 @@ genai:
   model: cloud-model-name
 ```
 
-or when using Ollama Cloud directly
-
-```yaml
-genai:
-  provider: ollama
-  base_url: https://ollama.com
-  model: cloud-model-name
-  api_key: your-api-key
-```
-
 </TabItem>
 </ConfigTabs>
 

docs/docs/configuration/object_detectors.md

@@ -494,7 +494,7 @@ detectors:
 | [YOLO-NAS](#yolo-nas)                 | ✅  | ✅  |                                                              |
 | [MobileNet v2](#ssdlite-mobilenet-v2) | ✅  | ✅  | Fast and lightweight model, less accurate than larger models |
 | [YOLOX](#yolox)                       | ✅  | ?   |                                                              |
-| [D-FINE / DEIMv2](#d-fine--deimv2)    | ❌  | ❌  |                                                              |
+| [D-FINE](#d-fine)                     | ❌  | ❌  |                                                              |
 
 #### SSDLite MobileNet v2
 
@@ -710,13 +710,13 @@ model:
 
 </details>
 
-#### D-FINE / DEIMv2
+#### D-FINE
 
-[D-FINE](https://github.com/Peterande/D-FINE) and [DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2) are DETR based models that share the same ONNX input/output format. The ONNX exported models are supported, but not included by default. See the models section for downloading [D-FINE](#downloading-d-fine-model) or [DEIMv2](#downloading-deimv2-model) for use in Frigate.
+[D-FINE](https://github.com/Peterande/D-FINE) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate.
 
 :::warning
 
-Currently D-FINE / DEIMv2 models only run on OpenVINO in CPU mode, GPUs currently fail to compile the model
+Currently D-FINE models only run on OpenVINO in CPU mode, GPUs currently fail to compile the model
 
 :::
 
@@ -766,31 +766,6 @@ Note that the labelmap uses a subset of the complete COCO label set that has onl
 
 </details>
 
-<details>
-  <summary>DEIMv2 Setup & Config</summary>
-
-After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
-
-```yaml
-detectors:
-  ov:
-    type: openvino
-    device: CPU
-
-model:
-  model_type: dfine
-  width: 640
-  height: 640
-  input_tensor: nchw
-  input_dtype: float
-  path: /config/model_cache/deimv2_hgnetv2_n.onnx
-  labelmap_path: /labelmap/coco-80.txt
-```
-
-Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
-
-</details>
-
 ## Apple Silicon detector
 
 The NPU in Apple Silicon can't be accessed from within a container, so the [Apple Silicon detector client](https://github.com/frigate-nvr/apple-silicon-detector) must first be setup. It is recommended to use the Frigate docker image with `-standard-arm64` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-standard-arm64`.
@@ -972,7 +947,7 @@ The AMD GPU kernel is known problematic especially when converting models to mxr
 
 See [ONNX supported models](#supported-models) for supported models, there are some caveats:
 
-- D-FINE / DEIMv2 models are not supported
+- D-FINE models are not supported
 - YOLO-NAS models are known to not run well on integrated GPUs
 
 ## ONNX
@@ -1028,7 +1003,7 @@ detectors:
 | [RF-DETR](#rf-detr)           | ✅         | ❌      | Supports CUDA Graphs for optimal Nvidia performance |
 | [YOLO-NAS](#yolo-nas-1)       | ⚠️         | ⚠️      | Not supported by CUDA Graphs                        |
 | [YOLOX](#yolox-1)             | ✅         | ✅      | Supports CUDA Graphs for optimal Nvidia performance |
-| [D-FINE / DEIMv2](#d-fine--deimv2-1) | ⚠️         | ❌      | Not supported by CUDA Graphs                        |
+| [D-FINE](#d-fine)             | ⚠️         | ❌      | Not supported by CUDA Graphs                        |
 
 There is no default model provided, the following formats are supported:
 
@@ -1240,9 +1215,9 @@ model:
 
 </details>
 
-#### D-FINE / DEIMv2
+#### D-FINE
 
-[D-FINE](https://github.com/Peterande/D-FINE) and [DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2) are DETR based models that share the same ONNX input/output format. The ONNX exported models are supported, but not included by default. See the models section for downloading [D-FINE](#downloading-d-fine-model) or [DEIMv2](#downloading-deimv2-model) for use in Frigate.
+[D-FINE](https://github.com/Peterande/D-FINE) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate.
 
 <details>
   <summary>D-FINE Setup & Config</summary>
@@ -1287,28 +1262,6 @@ model:
 
 </details>
 
-<details>
-  <summary>DEIMv2 Setup & Config</summary>
-
-After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
-
-```yaml
-detectors:
-  onnx:
-    type: onnx
-
-model:
-  model_type: dfine
-  width: 640
-  height: 640
-  input_tensor: nchw
-  input_dtype: float
-  path: /config/model_cache/deimv2_hgnetv2_n.onnx
-  labelmap_path: /labelmap/coco-80.txt
-```
-
-</details>
-
 Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
 ## CPU Detector (not recommended)
@@ -1452,7 +1405,7 @@ MemryX `.dfp` models are automatically downloaded at runtime, if enabled, to the
 
 #### YOLO-NAS
 
-The [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) model included in this detector is downloaded from the [Models Section](#downloading-yolo-nas-model) and compiled to DFP with [mx_nc](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage).
+The [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) model included in this detector is downloaded from the [Models Section](#downloading-yolo-nas-model) and compiled to DFP with [mx_nc](https://developer.memryx.com/tools/neural_compiler.html#usage).
 
 **Note:** The default model for the MemryX detector is YOLO-NAS 320x320.
 
@@ -1506,7 +1459,7 @@ model:
 
 #### YOLOv9
 
-The YOLOv9s model included in this detector is downloaded from [the original GitHub](https://github.com/WongKinYiu/yolov9) like in the [Models Section](#yolov9-1) and compiled to DFP with [mx_nc](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage).
+The YOLOv9s model included in this detector is downloaded from [the original GitHub](https://github.com/WongKinYiu/yolov9) like in the [Models Section](#yolov9-1) and compiled to DFP with [mx_nc](https://developer.memryx.com/tools/neural_compiler.html#usage).
 
 ##### Configuration
 
@@ -1648,39 +1601,19 @@ model:
 
 #### Using a Custom Model
 
-To use your own custom model, first compile it into a [.dfp](https://developer.memryx.com/2p1/specs/files.html#dataflow-program) file, which is the format used by MemryX.
+To use your own model:
 
-#### Compile the Model
+1.  Package your compiled model into a `.zip` file.
 
-Custom models must be compiled using **MemryX SDK 2.1**.
+2.  The `.zip` must contain the compiled `.dfp` file.
 
-Before compiling your model, install the MemryX Neural Compiler tools from the
-[Install Tools](https://developer.memryx.com/2p1/get_started/install_tools.html) page on the **host**.
+3.  Depending on the model, the compiler may also generate a cropped post-processing network. If present, it will be named with the suffix `_post.onnx`.
 
-> **Note:** It is recommended to compile the model on the host machine, or on another separate machine, rather than inside the Frigate Docker container. Installing the compiler inside Docker may conflict with container packages. It is recommended to create a Python virtual environment and install the compiler there.
+4.  Bind-mount the `.zip` file into the container and specify its path using `model.path` in your config.
 
-Once the SDK 2.1 environment is set up, follow the
-[MemryX Compiler](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage) documentation to compile your model.
+5.  Update the `labelmap_path` to match your custom model's labels.
 
-Example:
-
-```bash
-mx_nc -m yolonas.onnx -c 4 --autocrop -v --dfp_fname yolonas.dfp
-```
-
-For detailed instructions on compiling models, refer to the [MemryX Compiler](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage) docs and [Tutorials](https://developer.memryx.com/2p1/tutorials/tutorials.html).
-
-#### Package the Compiled Model
-
-1. Package your compiled model into a `.zip` file.
-
-2. The `.zip` file must contain the compiled `.dfp` file.
-
-3. Depending on the model, the compiler may also generate a cropped post-processing network. If present, it will be named with the suffix `_post.onnx`.
-
-4. Bind-mount the `.zip` file into the container and specify its path using `model.path` in your config.
-
-5. Update `labelmap_path` to match your custom model's labels.
+For detailed instructions on compiling models, refer to the [MemryX Compiler](https://developer.memryx.com/tools/neural_compiler.html#usage) docs and [Tutorials](https://developer.memryx.com/tutorials/tutorials.html).
 
 ```yaml
 # The detector automatically selects the default model if nothing is provided in the config.
@@ -2341,49 +2274,6 @@ COPY --from=build /dfine/output/dfine_${MODEL_SIZE}_obj2coco.onnx /dfine-${MODEL
 EOF
 ```
 
-### Downloading DEIMv2 Model
-
-[DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2) can be exported as ONNX by running the command below. Pretrained weights are available on Hugging Face for two backbone families:
-
-- **HGNetv2** (smaller/faster): `atto`, `femto`, `pico`, `n`
-- **DINOv3** (larger/more accurate): `s`, `m`, `l`, `x`
-
-Set `BACKBONE` and `MODEL_SIZE` in the first line to match your desired variant. Hugging Face model names use uppercase (e.g. `HGNetv2_N`, `DINOv3_S`), while config files use lowercase (e.g. `hgnetv2_n`, `dinov3_s`).
-
-```sh
-docker build . --rm --build-arg BACKBONE=hgnetv2 --build-arg MODEL_SIZE=n --output . -f- <<'EOF'
-FROM python:3.11-slim AS build
-RUN apt-get update && apt-get install --no-install-recommends -y git libgl1 libglib2.0-0 && rm -rf /var/lib/apt/lists/*
-COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
-WORKDIR /deimv2
-RUN git clone https://github.com/Intellindust-AI-Lab/DEIMv2.git .
-# Install CPU-only PyTorch first to avoid pulling CUDA variant
-RUN uv pip install --no-cache --system torch torchvision --index-url https://download.pytorch.org/whl/cpu
-RUN uv pip install --no-cache --system -r requirements.txt
-RUN uv pip install --no-cache --system onnx safetensors huggingface_hub
-RUN mkdir -p output
-ARG BACKBONE
-ARG MODEL_SIZE
-# Download from Hugging Face and convert safetensors to pth
-RUN python3 -c "\
-from huggingface_hub import hf_hub_download; \
-from safetensors.torch import load_file; \
-import torch; \
-backbone = '${BACKBONE}'.replace('hgnetv2','HGNetv2').replace('dinov3','DINOv3'); \
-size = '${MODEL_SIZE}'.upper(); \
-st = load_file(hf_hub_download('Intellindust/DEIMv2_' + backbone + '_' + size + '_COCO', 'model.safetensors')); \
-torch.save({'model': st}, 'output/deimv2.pth')"
-RUN sed -i "s/data = torch.rand(2/data = torch.rand(1/" tools/deployment/export_onnx.py
-# HuggingFace safetensors omits frozen constants that the model constructor initializes
-RUN sed -i "s/cfg.model.load_state_dict(state)/cfg.model.load_state_dict(state, strict=False)/" tools/deployment/export_onnx.py
-RUN python3 tools/deployment/export_onnx.py -c configs/deimv2/deimv2_${BACKBONE}_${MODEL_SIZE}_coco.yml -r output/deimv2.pth
-FROM scratch
-ARG BACKBONE
-ARG MODEL_SIZE
-COPY --from=build /deimv2/output/deimv2.onnx /deimv2_${BACKBONE}_${MODEL_SIZE}.onnx
-EOF
-```
-
 ### Downloading RF-DETR Model
 
 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.

docs/docs/configuration/record.md

@@ -195,7 +195,7 @@ Pre and post capture footage is included in the **recording timeline**, visible
 
 ## Will Frigate delete old recordings if my storage runs out?
 
-If there is less than an hour left of storage, the oldest hour of recordings will be deleted and a message will be printed in the Frigate logs. This emergency cleanup deletes the oldest recordings first regardless of retention settings to reclaim space as quickly as possible.
+As of Frigate 0.12 if there is less than an hour left of storage, the oldest 2 hours of recordings will be deleted.
 
 ## Configuring Recording Retention
 

docs/docs/configuration/restream.md

@@ -236,7 +236,7 @@ Enabling arbitrary exec sources allows execution of arbitrary commands through g
 
 ## Advanced Restream Configurations
 
-The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#source-exec) source in go2rtc can be used for custom ffmpeg commands and other applications. An example is below:
+The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#source-exec) source in go2rtc can be used for custom ffmpeg commands. An example is below:
 
 :::warning
 
@@ -244,11 +244,16 @@ The `exec:`, `echo:`, and `expr:` sources are disabled by default for security.
 
 :::
 
-NOTE: RTSP output will need to be passed with two curly braces `{{output}}`, whereas pipe output must be passed without curly braces.
+:::warning
+
+The `exec:`, `echo:`, and `expr:` sources are disabled by default for security. You must set `GO2RTC_ALLOW_ARBITRARY_EXEC=true` to use them. See [Security: Restricted Stream Sources](#security-restricted-stream-sources) for more information.
+
+:::
+
+NOTE: The output will need to be passed with two curly braces `{{output}}`
 
 ```yaml
 go2rtc:
   streams:
     stream1: exec:ffmpeg -hide_banner -re -stream_loop -1 -i /media/BigBuckBunny.mp4 -c copy -rtsp_transport tcp -f rtsp {{output}}
-    stream2: exec:rpicam-vid -t 0 --libav-format h264 -o -
 ```

docs/docs/frigate/installation.md

@@ -4,15 +4,12 @@ title: Installation
 ---
 
 import ShmCalculator from '@site/src/components/ShmCalculator'
-import DockerComposeGenerator from '@site/src/components/DockerComposeGenerator'
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
 
 Frigate is a Docker container that can be run on any Docker host including as a [Home Assistant App](https://www.home-assistant.io/apps/). Note that the Home Assistant App is **not** the same thing as the integration. The [integration](/integrations/home-assistant) is required to integrate Frigate into Home Assistant, whether you are running Frigate as a standalone Docker container or as a Home Assistant App.
 
 :::tip
 
-If you already have Frigate installed as a Home Assistant App, check out the [getting started guide](../guides/getting_started.md#configuring-frigate) to configure Frigate.
+If you already have Frigate installed as a Home Assistant App, check out the [getting started guide](../guides/getting_started#configuring-frigate) to configure Frigate.
 
 :::
 
@@ -289,7 +286,7 @@ The MemryX MX3 Accelerator is available in the M.2 2280 form factor (like an NVM
 
 #### Installation
 
-To get started with MX3 hardware setup for your system, refer to the [Hardware Setup Guide](https://developer.memryx.com/2p1/get_started/install_hardware.html).
+To get started with MX3 hardware setup for your system, refer to the [Hardware Setup Guide](https://developer.memryx.com/get_started/hardware_setup.html).
 
 Then follow these steps for installing the correct driver/runtime configuration:
 
@@ -298,12 +295,6 @@ Then follow these steps for installing the correct driver/runtime configuration:
 3. Run the script with `./user_installation.sh`
 4. **Restart your computer** to complete driver installation.
 
-:::warning
-
-For manual setup, use **MemryX SDK 2.1** only. Other SDK versions are not supported for this setup. See the [SDK 2.1 documentation](https://developer.memryx.com/2p1/index.html)
-
-:::
-
 #### Setup
 
 To set up Frigate, follow the default installation instructions, for example: `ghcr.io/blakeblackshear/frigate:stable`
@@ -477,16 +468,6 @@ Finally, configure [hardware object detection](/configuration/object_detectors#a
 
 Running through Docker with Docker Compose is the recommended install method.
 
-<Tabs>
-  <TabItem value="domestic" label="Docker Compose Generator" default>
-
-Generate a Frigate Docker Compose configuration based on your hardware and requirements.
-
-<DockerComposeGenerator/>
-
-
-  </TabItem>
-  <TabItem value="original" label="Example Docker Compose File">
 ```yaml
 services:
   frigate:
@@ -520,10 +501,6 @@ services:
     environment:
       FRIGATE_RTSP_PASSWORD: "password"
 ```
-  </TabItem>
-</Tabs>
-
-**Docker CLI**
 
 If you can't use Docker Compose, you can run the container with something similar to this:
 

docs/docs/integrations/third_party_extensions.md

@@ -17,10 +17,6 @@ Please use your own knowledge to assess and vet them before you install anything
 
 The [Advanced Camera Card](https://card.camera/#/README) is a Home Assistant dashboard card with deep Frigate integration.
 
-## [cctvQL](https://github.com/arunrajiah/cctvql)
-
-[cctvQL](https://github.com/arunrajiah/cctvql) is a natural language query layer for Frigate and other CCTV systems. It connects to Frigate's REST API and MQTT broker to let you ask conversational questions about cameras and events (e.g. "Was there motion at the front door last night?"), with support for real-time event streaming, anomaly detection, PTZ control, alert rules, and a Home Assistant custom component.
-
 ## [Double Take](https://github.com/skrashevich/double-take)
 
 [Double Take](https://github.com/skrashevich/double-take) provides an unified UI and API for processing and training images for facial recognition.
@@ -39,10 +35,6 @@ This is a fork (with fixed errors and new features) of [original Double Take](ht
 
 [Frigate telegram](https://github.com/OldTyT/frigate-telegram) makes it possible to send events from Frigate to Telegram. Events are sent as a message with a text description, video, and thumbnail.
 
-## [kiosk-monitor](https://github.com/extremeshok/kiosk-monitor)
-
-[kiosk-monitor](https://github.com/extremeshok/kiosk-monitor) is a Raspberry Pi watchdog that runs Chromium fullscreen on a Frigate dashboard (optionally with VLC on a second monitor for an RTSP camera stream), auto-restarts on frozen screens or unreachable URLs, and ships a Birdseye-aware Chromium helper that auto-sizes the grid to the display.
-
 ## [Periscope](https://github.com/maksz42/periscope)
 
 [Periscope](https://github.com/maksz42/periscope) is a lightweight Android app that turns old devices into live viewers for Frigate. It works on Android 2.2 and above, including Android TV. It supports authentication and HTTPS.

docs/docs/troubleshooting/faqs.md

@@ -111,16 +111,26 @@ TCP ensures that all data packets arrive in the correct order. This is crucial f
 
 You can still configure Frigate to use UDP by using ffmpeg input args or the preset `preset-rtsp-udp`. See the [ffmpeg presets](/configuration/ffmpeg_presets) documentation.
 
-### Frigate is slow to start up with a "probing detect stream" message in the logs
+### Frigate hangs on startup with a "probing detect stream" message in the logs
 
-When `detect.width` and `detect.height` are not set, Frigate probes each camera's detect stream on startup (and when saving the config) to auto-detect its resolution. For RTSP streams Frigate probes with ffprobe and automatically retries over TCP if UDP doesn't respond, with a 5 second timeout per attempt. A camera that cannot be reached over either transport will add up to ~10 seconds to startup before Frigate falls through with default dimensions, which may show up as width `0` and height `0` in Camera Probe Info under System Metrics.
+On startup, Frigate probes each camera's detect stream with OpenCV to auto-detect its resolution. OpenCV's FFmpeg backend may attempt RTSP over UDP during this probe regardless of the `-rtsp_transport tcp` in your `input_args` or preset. For cameras that do not respond to UDP (common on some Reolink models and others behind firewalls that block UDP), the probe can hang indefinitely and block Frigate from finishing startup, or it can return zeroed-out dimensions that show up as width `0` and height `0` in Camera Probe Info under System Metrics.
 
-To skip the probe entirely and make startup instant, set `detect.width` and `detect.height` explicitly in your camera config:
+There are two ways to avoid this:
 
-```yaml
-cameras:
-  my_camera:
-    detect:
-      width: 1280
-      height: 720
-```
+1. Set `detect.width` and `detect.height` explicitly in your camera config. When both are set, Frigate skips the auto-detect probe entirely:
+
+   ```yaml
+   cameras:
+     my_camera:
+       detect:
+         width: 1280
+         height: 720
+   ```
+
+2. Force OpenCV's FFmpeg backend to use TCP for RTSP by setting the environment variable on your Frigate container:
+
+   ```
+   OPENCV_FFMPEG_CAPTURE_OPTIONS=rtsp_transport;tcp
+   ```
+
+   This is a process-wide setting and applies to all cameras. If you have any cameras that require `preset-rtsp-udp`, use option 1 instead.

docs/package-lock.json

@@ -14,11 +14,9 @@
         "@docusaurus/theme-mermaid": "^3.7.0",
         "@inkeep/docusaurus": "^2.0.16",
         "@mdx-js/react": "^3.1.0",
-        "@types/js-yaml": "^4.0.9",
         "clsx": "^2.1.1",
         "docusaurus-plugin-openapi-docs": "^4.5.1",
         "docusaurus-theme-openapi-docs": "^4.5.1",
-        "js-yaml": "^4.1.1",
         "prism-react-renderer": "^2.4.1",
         "raw-loader": "^4.0.2",
         "react": "^18.3.1",
@@ -5749,11 +5747,6 @@
         "@types/istanbul-lib-report": "*"
       }
     },
-    "node_modules/@types/js-yaml": {
-      "version": "4.0.9",
-      "resolved": "https://mirrors.tencent.com/npm/@types/js-yaml/-/js-yaml-4.0.9.tgz",
-      "integrity": "sha512-k4MGaQl5TGo/iipqb2UDG2UwjXziSWkh0uysQelTlJpX1qGlpUZYm8PnO4DxG1qBomtJUdYJ6qR6xdIah10JLg=="
-    },
     "node_modules/@types/json-schema": {
       "version": "7.0.15",
       "resolved": "https://registry.npmjs.org/@types/json-schema/-/json-schema-7.0.15.tgz",
@@ -10904,9 +10897,9 @@
       "license": "MIT"
     },
     "node_modules/express/node_modules/path-to-regexp": {
-      "version": "0.1.13",
-      "resolved": "https://registry.npmjs.org/path-to-regexp/-/path-to-regexp-0.1.13.tgz",
-      "integrity": "sha512-A/AGNMFN3c8bOlvV9RreMdrv7jsmF9XIfDeCd87+I8RNg6s78BhJxMu69NEMHBSJFxKidViTEdruRwEk/WIKqA==",
+      "version": "0.1.12",
+      "resolved": "https://registry.npmjs.org/path-to-regexp/-/path-to-regexp-0.1.12.tgz",
+      "integrity": "sha512-RA1GjUVMnvYFxuqovrEqZoxxW5NUZqbwKtYz/Tt7nXerk0LbLblQmrsgdeOxV5SFHf0UDggjS/bSeOZwt1pmEQ==",
       "license": "MIT"
     },
     "node_modules/express/node_modules/range-parser": {
@@ -12890,7 +12883,7 @@
     },
     "node_modules/js-yaml": {
       "version": "4.1.1",
-      "resolved": "https://mirrors.tencent.com/npm/js-yaml/-/js-yaml-4.1.1.tgz",
+      "resolved": "https://registry.npmjs.org/js-yaml/-/js-yaml-4.1.1.tgz",
       "integrity": "sha512-qQKT4zQxXl8lLwBtHMWwaTcGfFOZviOJet3Oy/xmGk2gZH677CJM9EvtfdSkgWcATZhj/55JZ0rmy3myCT5lsA==",
       "license": "MIT",
       "dependencies": {
@@ -16815,9 +16808,9 @@
       }
     },
     "node_modules/postcss": {
-      "version": "8.5.13",
-      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.13.tgz",
-      "integrity": "sha512-qif0+jGGZoLWdHey3UFHHWP0H7Gbmsk8T5VEqyYFbWqPr1XqvLGBbk/sl8V5exGmcYJklJOhOQq1pV9IcsiFag==",
+      "version": "8.5.6",
+      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.6.tgz",
+      "integrity": "sha512-3Ybi1tAuwAP9s0r1UQ2J4n5Y0G05bJkpUIO0/bI9MhwmD70S5aTWbXGBwxHrelT+XM1k6dM0pk+SwNkpTRN7Pg==",
       "funding": [
         {
           "type": "opencollective",

docs/package.json

@@ -3,10 +3,9 @@
   "version": "0.0.0",
   "private": true,
   "scripts": {
-    "build:config": "node scripts/build-config.mjs",
     "docusaurus": "docusaurus",
-    "start": "npm run build:config && npm run regen-docs && docusaurus start --host 0.0.0.0",
-    "build": "npm run build:config && npm run regen-docs && docusaurus build",
+    "start": "npm run regen-docs && docusaurus start --host 0.0.0.0",
+    "build": "npm run regen-docs && docusaurus build",
     "swizzle": "docusaurus swizzle",
     "deploy": "docusaurus deploy",
     "clear": "docusaurus clear",
@@ -24,11 +23,9 @@
     "@docusaurus/theme-mermaid": "^3.7.0",
     "@inkeep/docusaurus": "^2.0.16",
     "@mdx-js/react": "^3.1.0",
-    "@types/js-yaml": "^4.0.9",
     "clsx": "^2.1.1",
     "docusaurus-plugin-openapi-docs": "^4.5.1",
     "docusaurus-theme-openapi-docs": "^4.5.1",
-    "js-yaml": "^4.1.1",
     "prism-react-renderer": "^2.4.1",
     "raw-loader": "^4.0.2",
     "react": "^18.3.1",

docs/scripts/build-config.mjs

@@ -1,64 +0,0 @@
-#!/usr/bin/env node
-
-/**
- * Build script: reads config.yaml and generates TypeScript files
- * for the Docker Compose Generator.
- *
- * Usage: node scripts/build-config.mjs
- */
-
-import fs from "node:fs";
-import path from "node:path";
-import { fileURLToPath } from "node:url";
-import yaml from "js-yaml";
-
-const __dirname = path.dirname(fileURLToPath(import.meta.url));
-const CONFIG_DIR = path.resolve(__dirname, "../src/components/DockerComposeGenerator/config");
-const YAML_PATH = path.join(CONFIG_DIR, "config.yaml");
-
-// Read & parse YAML
-const raw = fs.readFileSync(YAML_PATH, "utf8");
-const config = yaml.load(raw);
-
-if (!config.devices || !config.hardware || !config.ports) {
-  console.error("config.yaml must contain 'devices', 'hardware', and 'ports' sections.");
-  process.exit(1);
-}
-
-/**
- * Generate a .ts file from a section of the YAML config.
- */
-function generateTsFile(sectionName, items, typeName, varName, mapVarName, yamlFilename) {
-  const jsonItems = JSON.stringify(items, null, 2);
-  // Indent JSON to fit inside the array literal
-  const indented = jsonItems
-    .split("\n")
-    .map((line, i) => (i === 0 ? line : "  " + line))
-    .join("\n");
-
-  const content = `/**
- * AUTO-GENERATED FILE — do not edit directly.
- * Source: ${yamlFilename}
- * To update, edit the YAML file and run: npm run build:config
- */
-
-import type { ${typeName} } from "./types";
-
-export const ${varName}: ${typeName}[] = ${indented};
-
-/** Lookup map for quick access by ID */
-export const ${mapVarName}: Map<string, ${typeName}> = new Map(${varName}.map((item) => [item.id, item]));
-`;
-
-  const outPath = path.join(CONFIG_DIR, `${sectionName}.ts`);
-  fs.writeFileSync(outPath, content, "utf8");
-  console.log(`  ✓ Generated ${sectionName}.ts (${items.length} items)`);
-}
-
-console.log("Building config from config.yaml...");
-
-generateTsFile("devices", config.devices, "DeviceConfig", "devices", "deviceMap", "config.yaml");
-generateTsFile("hardware", config.hardware, "HardwareOption", "hardwareOptions", "hardwareMap", "config.yaml");
-generateTsFile("ports", config.ports, "PortConfig", "ports", "portMap", "config.yaml");
-
-console.log("Done!");

docs/src/components/DockerComposeGenerator/DockerComposeGenerator.tsx

@@ -1,108 +0,0 @@
-import React from "react";
-import Admonition from "@theme/Admonition";
-import DeviceSelector from "./components/DeviceSelector";
-import HardwareOptions from "./components/HardwareOptions";
-import PortConfigSection from "./components/PortConfig";
-import StoragePaths from "./components/StoragePaths";
-import NvidiaGpuConfig from "./components/NvidiaGpuConfig";
-import OtherOptions from "./components/OtherOptions";
-import GeneratedOutput from "./components/GeneratedOutput";
-import { useConfigGenerator } from "./hooks/useConfigGenerator";
-import styles from "./styles.module.css";
-
-/**
- * Simple markdown-link-to-React renderer for help text.
- * Only supports [text](url) syntax — no nested brackets.
- */
-function renderHelpText(text: string): React.ReactNode {
-  const parts = text.split(/(\[[^\]]+\]\([^)]+\))/g);
-  return parts.map((part, i) => {
-    const match = part.match(/^\[([^\]]+)\]\(([^)]+)\)$/);
-    if (match) {
-      return (
-        <a key={i} href={match[2]}>
-          {match[1]}
-        </a>
-      );
-    }
-    return <React.Fragment key={i}>{part}</React.Fragment>;
-  });
-}
-
-export default function DockerComposeGenerator() {
-  const {
-    deviceId, device, hardwareEnabled,
-    portEnabled,
-    nvidiaGpuCount, nvidiaGpuDeviceId,
-    configPath, mediaPath, rtspPassword, timezone, shmSize,
-    shmSizeError, gpuDeviceIdError, configPathError, mediaPathError,
-    hasAnyHardware, generatedYaml,
-    selectDevice, toggleHardware, togglePort,
-    handleShmSizeChange, handleConfigPathChange, handleMediaPathChange,
-    handleNvidiaGpuCountChange, handleNvidiaGpuDeviceIdChange,
-    setRtspPassword, setTimezone, isHardwareDisabled,
-  } = useConfigGenerator();
-
-  return (
-    <div className={styles.generator}>
-      <div className={styles.card}>
-        <DeviceSelector selectedId={deviceId} onSelect={selectDevice} />
-
-        {device.helpText && (
-          <Admonition type={device.helpType || "info"}>
-            {renderHelpText(device.helpText)}
-          </Admonition>
-        )}
-
-        {device.needsNvidiaConfig && (
-          <NvidiaGpuConfig
-            gpuCount={nvidiaGpuCount}
-            gpuDeviceId={nvidiaGpuDeviceId}
-            gpuDeviceIdError={gpuDeviceIdError}
-            onGpuCountChange={handleNvidiaGpuCountChange}
-            onGpuDeviceIdChange={handleNvidiaGpuDeviceIdChange}
-          />
-        )}
-
-        <HardwareOptions
-          deviceId={deviceId}
-          hardwareEnabled={hardwareEnabled}
-          onToggle={toggleHardware}
-          isDisabled={isHardwareDisabled}
-        />
-
-        <StoragePaths
-          configPath={configPath}
-          mediaPath={mediaPath}
-          configPathError={configPathError}
-          mediaPathError={mediaPathError}
-          onConfigPathChange={handleConfigPathChange}
-          onMediaPathChange={handleMediaPathChange}
-        />
-
-        <PortConfigSection
-          portEnabled={portEnabled}
-          onTogglePort={togglePort}
-        />
-
-        <OtherOptions
-          rtspPassword={rtspPassword}
-          timezone={timezone}
-          shmSize={shmSize}
-          shmSizeError={shmSizeError}
-          onRtspPasswordChange={setRtspPassword}
-          onTimezoneChange={setTimezone}
-          onShmSizeChange={handleShmSizeChange}
-        />
-
-        <GeneratedOutput
-          yaml={generatedYaml}
-          configPath={configPath}
-          mediaPath={mediaPath}
-          hasAnyHardware={hasAnyHardware}
-          deviceId={deviceId}
-        />
-      </div>
-    </div>
-  );
-}

docs/src/components/DockerComposeGenerator/components/DeviceSelector.tsx

@@ -1,147 +0,0 @@
-import React from "react";
-import { useColorMode } from "@docusaurus/theme-common";
-import { devices } from "../config";
-import type { DeviceConfig } from "../config";
-import styles from "../styles.module.css";
-
-interface Props {
-  selectedId: string;
-  onSelect: (id: string) => void;
-}
-
-/**
- * Determine the icon type from the icon string:
- * - Starts with "<svg" → inline SVG
- * - Starts with "/" or "http" → image URL/path
- * - Otherwise → emoji text
- */
-function getIconType(icon: string): "svg" | "image" | "emoji" {
-  const trimmed = icon.trim();
-  if (trimmed.startsWith("<svg")) return "svg";
-  if (trimmed.startsWith("/") || trimmed.startsWith("http://") || trimmed.startsWith("https://")) return "image";
-  return "emoji";
-}
-
-/**
- * Check if the style object contains background-* properties,
- * indicating the image should be rendered as a CSS background-image
- * rather than an <img> tag.
- */
-function hasBackgroundProps(style: React.CSSProperties | undefined): boolean {
-  if (!style) return false;
-  return Object.keys(style).some((key) => {
-    const k = key.toLowerCase().replace(/-/g, "");
-    return k === "backgroundsize" || k === "backgroundposition" || k === "backgroundrepeat" || k === "backgroundimage";
-  });
-}
-
-/**
- * Convert a style object to CSS custom properties (e.g. { width: "24px" } → { "--svg-width": "24px" })
- * so they can be consumed by CSS rules targeting child elements like <svg>.
- */
-function toCssVars(style: React.CSSProperties | undefined, prefix: string): React.CSSProperties {
-  if (!style) return {};
-  const vars: Record<string, string> = {};
-  for (const [key, value] of Object.entries(style)) {
-    const cssKey = key.replace(/([A-Z])/g, "-$1").toLowerCase();
-    vars[`--${prefix}-${cssKey}`] = value;
-  }
-  return vars as React.CSSProperties;
-}
-
-function DeviceIcon({ device }: { device: DeviceConfig }) {
-  const { isDarkTheme } = useColorMode();
-  const iconStr = isDarkTheme && device.iconDark ? device.iconDark : device.icon;
-  const iconStyle = (isDarkTheme && device.iconDarkStyle
-    ? device.iconDarkStyle
-    : device.iconStyle) as React.CSSProperties | undefined;
-  const svgStyle = (isDarkTheme && device.svgDarkStyle
-    ? device.svgDarkStyle
-    : device.svgStyle) as React.CSSProperties | undefined;
-
-  const iconType = getIconType(iconStr);
-
-  if (iconType === "svg") {
-    return (
-      <div
-        className={styles.deviceIconSvg}
-        style={{ ...iconStyle, ...toCssVars(svgStyle, "svg") }}
-        dangerouslySetInnerHTML={{ __html: iconStr }}
-      />
-    );
-  }
-
-  if (iconType === "image") {
-    // When iconStyle contains background-* properties, render as background-image
-    // on the container div instead of an <img> tag, enabling background-size/position control.
-    if (hasBackgroundProps(iconStyle)) {
-      return (
-        <div
-          className={styles.deviceIconImage}
-          style={{
-            backgroundImage: `url(${iconStr})`,
-            backgroundRepeat: "no-repeat",
-            backgroundPosition: "center",
-            backgroundSize: "contain",
-            ...iconStyle,
-          }}
-        />
-      );
-    }
-    return (
-      <div className={styles.deviceIconImage}>
-        <img src={iconStr} alt={device.name} style={iconStyle} />
-      </div>
-    );
-  }
-
-  return (
-    <div className={styles.deviceIcon} style={iconStyle}>
-      {iconStr}
-    </div>
-  );
-}
-
-function DeviceCard({
-  device,
-  active,
-  onClick,
-}: {
-  device: DeviceConfig;
-  active: boolean;
-  onClick: () => void;
-}) {
-  return (
-    <div
-      className={`${styles.deviceCard} ${active ? styles.deviceCardActive : ""}`}
-      onClick={onClick}
-      role="button"
-      tabIndex={0}
-      onKeyDown={(e) => {
-        if (e.key === "Enter" || e.key === " ") onClick();
-      }}
-    >
-      <DeviceIcon device={device} />
-      <div className={styles.deviceName}>{device.name}</div>
-      <div className={styles.deviceDesc}>{device.description}</div>
-    </div>
-  );
-}
-
-export default function DeviceSelector({ selectedId, onSelect }: Props) {
-  return (
-    <div className={styles.formSection}>
-      <h4>Device Type</h4>
-      <div className={styles.deviceGrid}>
-        {devices.map((d) => (
-          <DeviceCard
-            key={d.id}
-            device={d}
-            active={selectedId === d.id}
-            onClick={() => onSelect(d.id)}
-          />
-        ))}
-      </div>
-    </div>
-  );
-}

docs/src/components/DockerComposeGenerator/components/GeneratedOutput.tsx

@@ -1,60 +0,0 @@
-import React, { useState, useCallback } from "react";
-import CodeBlock from "@theme/CodeBlock";
-import Admonition from "@theme/Admonition";
-import styles from "../styles.module.css";
-
-interface Props {
-  yaml: string;
-  configPath: string;
-  mediaPath: string;
-  hasAnyHardware: boolean;
-  deviceId: string;
-}
-
-export default function GeneratedOutput({
-  yaml,
-  configPath,
-  mediaPath,
-  hasAnyHardware,
-  deviceId,
-}: Props) {
-  const [copied, setCopied] = useState(false);
-
-  const handleCopy = useCallback(() => {
-    navigator.clipboard.writeText(yaml).then(() => {
-      setCopied(true);
-      setTimeout(() => setCopied(false), 2000);
-    });
-  }, [yaml]);
-
-  return (
-    <div className={styles.resultSection}>
-      <div className={styles.resultHeader}>
-        <h4>Generated Configuration</h4>
-        <button className="button button--primary button--sm" onClick={handleCopy}>
-          {copied ? "Copied!" : "Copy"}
-        </button>
-      </div>
-
-      {!configPath && (
-        <Admonition type="tip">
-          <p>You haven&apos;t specified a config file directory. You may want to modify the default path.</p>
-        </Admonition>
-      )}
-      {!mediaPath && (
-        <Admonition type="tip">
-          <p>You haven&apos;t specified a recording storage directory. You may want to modify the default path.</p>
-        </Admonition>
-      )}
-      {deviceId === "stable" && !hasAnyHardware && (
-        <Admonition type="warning">
-          <p>You haven&apos;t selected any hardware acceleration. Please check if you have supported hardware available.</p>
-        </Admonition>
-      )}
-
-      <CodeBlock language="yaml" title="docker-compose.yml">
-        {yaml}
-      </CodeBlock>
-    </div>
-  );
-}

docs/src/components/DockerComposeGenerator/components/HardwareOptions.tsx

@@ -1,62 +0,0 @@
-import React from "react";
-import { hardwareOptions } from "../config";
-import type { HardwareOption } from "../config";
-import styles from "../styles.module.css";
-
-interface Props {
-  deviceId: string;
-  hardwareEnabled: Record<string, boolean>;
-  onToggle: (hwId: string) => void;
-  isDisabled: (hwId: string) => boolean;
-}
-
-function renderDescription(text: string): React.ReactNode {
-  const parts = text.split(/(\[[^\]]+\]\([^)]+\))/g);
-  return parts.map((part, i) => {
-    const match = part.match(/^\[([^\]]+)\]\(([^)]+)\)$/);
-    if (match) {
-      return <a key={i} href={match[2]}>{match[1]}</a>;
-    }
-    return <React.Fragment key={i}>{part}</React.Fragment>;
-  });
-}
-
-function HardwareCheckbox({
-  hw, disabled, checked, onToggle,
-}: {
-  hw: HardwareOption; disabled: boolean; checked: boolean; onToggle: () => void;
-}) {
-  return (
-    <div className={styles.hardwareItem}>
-      <label className={`${styles.checkboxLabel} ${disabled ? styles.checkboxDisabled : ""}`}>
-        <input type="checkbox" checked={checked} onChange={onToggle} disabled={disabled} />
-        <span>{hw.label}</span>
-      </label>
-      {checked && hw.description && (
-        <div className={styles.hardwareDescription}>{renderDescription(hw.description)}</div>
-      )}
-    </div>
-  );
-}
-
-export default function HardwareOptions({ deviceId, hardwareEnabled, onToggle, isDisabled }: Props) {
-  return (
-    <div className={styles.formSection}>
-      <h4>Generic Hardware Devices</h4>
-      {deviceId !== "stable" && (
-        <p className={styles.helpText}>
-          Some options have been auto-configured based on your device type.
-        </p>
-      )}
-      <div className={styles.checkboxGrid}>
-        {hardwareOptions.map((hw) => {
-          const disabled = isDisabled(hw.id);
-          const checked = disabled ? false : !!hardwareEnabled[hw.id];
-          return (
-            <HardwareCheckbox key={hw.id} hw={hw} disabled={disabled} checked={checked} onToggle={() => onToggle(hw.id)} />
-          );
-        })}
-      </div>
-    </div>
-  );
-}

docs/src/components/DockerComposeGenerator/components/NvidiaGpuConfig.tsx

@@ -1,64 +0,0 @@
-import React from "react";
-import styles from "../styles.module.css";
-
-interface Props {
-  gpuCount: string;
-  gpuDeviceId: string;
-  gpuDeviceIdError: boolean;
-  onGpuCountChange: (value: string) => void;
-  onGpuDeviceIdChange: (value: string) => void;
-}
-
-export default function NvidiaGpuConfig({
-  gpuCount,
-  gpuDeviceId,
-  gpuDeviceIdError,
-  onGpuCountChange,
-  onGpuDeviceIdChange,
-}: Props) {
-  const showDeviceId = gpuCount !== "";
-
-  return (
-    <div className={styles.nvidiaConfig}>
-      <div className={styles.formGroup}>
-        <label htmlFor="dcg-gpu-count" className={styles.label}>
-          GPU count:
-        </label>
-        <input
-          id="dcg-gpu-count"
-          type="text"
-          inputMode="numeric"
-          pattern="[0-9]*"
-          className={styles.input}
-          value={gpuCount}
-          placeholder="all"
-          onChange={(e) => onGpuCountChange(e.target.value.replace(/\D/g, ""))}
-        />
-      </div>
-      {showDeviceId && (
-        <div className={styles.formGroup}>
-          <label htmlFor="dcg-gpu-device-id" className={styles.label}>
-            GPU device IDs (required, comma-separated):
-          </label>
-          <input
-            id="dcg-gpu-device-id"
-            type="text"
-            className={`${styles.input} ${gpuDeviceIdError ? styles.inputError : ""}`}
-            value={gpuDeviceId}
-            placeholder="0"
-            onChange={(e) => onGpuDeviceIdChange(e.target.value)}
-          />
-          {gpuDeviceIdError ? (
-            <p className={styles.helpText}>
-              ⚠️ GPU device IDs are required when GPU count is a number
-            </p>
-          ) : (
-            <p className={styles.helpText}>
-              Single GPU: 0 &nbsp;|&nbsp; Multiple GPUs: 0,1,2
-            </p>
-          )}
-        </div>
-      )}
-    </div>
-  );
-}

docs/src/components/DockerComposeGenerator/components/OtherOptions.tsx

@@ -1,122 +0,0 @@
-import React, { useMemo } from "react";
-import CodeInline from "@theme/CodeInline";
-import styles from "../styles.module.css";
-
-const AUTO_TIMEZONE_VALUE = "__auto__";
-
-function getTimezoneList(): string[] {
-  if (typeof Intl !== "undefined") {
-    const intl = Intl as typeof Intl & {
-      supportedValuesOf?: (key: string) => string[];
-    };
-    const supported = intl.supportedValuesOf?.("timeZone");
-    if (supported && supported.length > 0) {
-      return [...supported].sort();
-    }
-  }
-
-  const fallback = Intl.DateTimeFormat().resolvedOptions().timeZone;
-  return fallback ? [fallback] : ["UTC"];
-}
-
-interface Props {
-  rtspPassword: string;
-  timezone: string;
-  shmSize: string;
-  shmSizeError: boolean;
-  onRtspPasswordChange: (value: string) => void;
-  onTimezoneChange: (value: string) => void;
-  onShmSizeChange: (value: string) => void;
-}
-
-export default function OtherOptions({
-  rtspPassword,
-  timezone,
-  shmSize,
-  shmSizeError,
-  onRtspPasswordChange,
-  onTimezoneChange,
-  onShmSizeChange,
-}: Props) {
-  const timezones = useMemo(() => getTimezoneList(), []);
-  const systemTimezone =
-    Intl.DateTimeFormat().resolvedOptions().timeZone || "Etc/UTC";
-  const selectedValue = timezone || AUTO_TIMEZONE_VALUE;
-
-  return (
-    <div className={styles.formSection}>
-      <h4>Other Options</h4>
-      <div className={styles.formGrid}>
-        <div className={styles.formGroup}>
-          <label htmlFor="dcg-timezone" className={styles.label}>
-            Timezone:
-          </label>
-          <select
-            id="dcg-timezone"
-            className={`${styles.input} ${styles.select}`}
-            value={selectedValue}
-            onChange={(e) =>
-              onTimezoneChange(
-                e.target.value === AUTO_TIMEZONE_VALUE ? "" : e.target.value
-              )
-            }
-          >
-            <option value={AUTO_TIMEZONE_VALUE}>
-              Use browser timezone ({systemTimezone})
-            </option>
-            {timezones.map((tz) => (
-              <option key={tz} value={tz}>
-                {tz}
-              </option>
-            ))}
-          </select>
-        </div>
-        <div className={styles.formGroup}>
-          <label htmlFor="dcg-shm-size" className={styles.label}>
-            Shared memory (SHM):
-          </label>
-          <input
-            id="dcg-shm-size"
-            type="text"
-            className={`${styles.input} ${shmSizeError ? styles.inputError : ""}`}
-            value={shmSize}
-            placeholder="512mb"
-            onChange={(e) => onShmSizeChange(e.target.value)}
-          />
-          {shmSizeError ? (
-            <p className={styles.helpText}>
-              ⚠️ Invalid format. Use a number followed by a unit (e.g. 512mb, 1gb)
-            </p>
-          ) : (
-            <p className={styles.helpText}>
-              See{" "}
-              <a href="/frigate/installation#calculating-required-shm-size">
-                calculating required SHM size
-              </a>{" "}
-              for the correct value.
-            </p>
-          )}
-        </div>
-        <div className={styles.formGroup}>
-          <label htmlFor="dcg-rtsp-password" className={styles.label}>
-            RTSP password:
-          </label>
-          <input
-            id="dcg-rtsp-password"
-            type="text"
-            className={styles.input}
-            value={rtspPassword}
-            placeholder="password"
-            onChange={(e) => onRtspPasswordChange(e.target.value)}
-          />
-          <p className={styles.helpText}>
-            Optional. You can specify{" "}
-            <CodeInline>{"{FRIGATE_RTSP_PASSWORD}"}</CodeInline>{" "}
-            in the config file to reference camera stream passwords. This is NOT
-            the Frigate login password.
-          </p>
-        </div>
-      </div>
-    </div>
-  );
-}

docs/src/components/DockerComposeGenerator/components/PortConfig.tsx

@@ -1,71 +0,0 @@
-import React from "react";
-import Admonition from "@theme/Admonition";
-import { ports } from "../config";
-import styles from "../styles.module.css";
-
-interface Props {
-  portEnabled: Record<string, boolean>;
-  onTogglePort: (portId: string) => void;
-}
-
-function PortItem({
-  port,
-  enabled,
-  onToggle,
-}: {
-  port: typeof ports[number];
-  enabled: boolean;
-  onToggle: () => void;
-}) {
-  const showWarning = port.warningContent && (
-    port.warningWhen === "checked" ? enabled :
-    port.warningWhen === "unchecked" ? !enabled : enabled
-  );
-
-  return (
-    <div className={styles.hardwareItem}>
-      <label className={`${styles.checkboxLabel} ${port.locked ? styles.checkboxDisabled : ""}`}>
-        <input
-          type="checkbox"
-          checked={enabled}
-          onChange={onToggle}
-          disabled={port.locked}
-        />
-        <span>
-          {port.locked && "🔒 "}
-          Port {port.host}
-          {port.protocol !== "tcp" && `/${port.protocol}`}
-        </span>
-      </label>
-      {port.description && (
-        <div className={styles.hardwareDescription}>{port.description}</div>
-      )}
-      {showWarning && (
-        <Admonition type={port.warningType || "warning"}>
-          {port.warningContent}
-        </Admonition>
-      )}
-    </div>
-  );
-}
-
-export default function PortConfigSection({
-  portEnabled,
-  onTogglePort,
-}: Props) {
-  return (
-    <div className={styles.formSection}>
-      <h4>Port Configuration</h4>
-      <div className={styles.checkboxGrid}>
-        {ports.map((port) => (
-          <PortItem
-            key={port.id}
-            port={port}
-            enabled={!!portEnabled[port.id]}
-            onToggle={() => onTogglePort(port.id)}
-          />
-        ))}
-      </div>
-    </div>
-  );
-}

docs/src/components/DockerComposeGenerator/components/StoragePaths.tsx

@@ -1,66 +0,0 @@
-import React from "react";
-import styles from "../styles.module.css";
-
-interface Props {
-  configPath: string;
-  mediaPath: string;
-  configPathError: boolean;
-  mediaPathError: boolean;
-  onConfigPathChange: (value: string) => void;
-  onMediaPathChange: (value: string) => void;
-}
-
-export default function StoragePaths({
-  configPath,
-  mediaPath,
-  configPathError,
-  mediaPathError,
-  onConfigPathChange,
-  onMediaPathChange,
-}: Props) {
-  return (
-    <div className={styles.formSection}>
-      <h4>Storage Paths</h4>
-      <div className={styles.formGrid}>
-        <div className={styles.formGroup}>
-          <label htmlFor="dcg-config-path" className={styles.label}>
-            Config / DB / model cache directory (on your host):
-          </label>
-          <input
-            id="dcg-config-path"
-            type="text"
-            className={`${styles.input} ${configPathError ? styles.inputError : ""}`}
-            value={configPath}
-            placeholder="/path/to/your/config"
-            onChange={(e) => onConfigPathChange(e.target.value)}
-          />
-          {configPathError && (
-            <p className={styles.helpText}>
-              ⚠️ Path contains invalid characters. Only letters, numbers,
-              underscores, hyphens, slashes, and dots are allowed.
-            </p>
-          )}
-        </div>
-        <div className={styles.formGroup}>
-          <label htmlFor="dcg-media-path" className={styles.label}>
-            Recording storage directory (on your host):
-          </label>
-          <input
-            id="dcg-media-path"
-            type="text"
-            className={`${styles.input} ${mediaPathError ? styles.inputError : ""}`}
-            value={mediaPath}
-            placeholder="/path/to/your/storage"
-            onChange={(e) => onMediaPathChange(e.target.value)}
-          />
-          {mediaPathError && (
-            <p className={styles.helpText}>
-              ⚠️ Path contains invalid characters. Only letters, numbers,
-              underscores, hyphens, slashes, and dots are allowed.
-            </p>
-          )}
-        </div>
-      </div>
-    </div>
-  );
-}

docs/src/components/DockerComposeGenerator/config/index.ts

@@ -1,12 +0,0 @@
-export { devices, deviceMap } from "./devices";
-export { hardwareOptions, hardwareMap } from "./hardware";
-export { ports, portMap } from "./ports";
-
-export type {
-  DeviceConfig,
-  DeviceMapping,
-  VolumeMapping,
-  HardwareOption,
-  PortConfig,
-  NvidiaDeployConfig,
-} from "./types";

docs/src/components/DockerComposeGenerator/config/types.ts

@@ -1,154 +0,0 @@
-/**
- * Type definitions for the Docker Compose Generator configuration.
- * All device, hardware, and port options are declaratively defined
- * so that adding a new device only requires editing config files.
- */
-
-/** A single device mapping entry (e.g. /dev/dri:/dev/dri) */
-export interface DeviceMapping {
-  /** Host device path */
-  host: string;
-  /** Container device path (defaults to host if omitted) */
-  container?: string;
-  /** Inline comment for this device line */
-  comment?: string;
-}
-
-/** A single volume mapping entry */
-export interface VolumeMapping {
-  /** Host path */
-  host: string;
-  /** Container path */
-  container: string;
-  /** Whether the mount is read-only */
-  readOnly?: boolean;
-  /** Inline comment */
-  comment?: string;
-}
-
-/** NVIDIA deploy configuration for docker-compose */
-export interface NvidiaDeployConfig {
-  /** "all" or a specific number */
-  count: string;
-  /** Specific GPU device IDs (when count is a number) */
-  deviceIds?: string[];
-}
-
-/** Full device type definition */
-export interface DeviceConfig {
-  /** Unique identifier, e.g. "intel" */
-  id: string;
-  /** Display name, e.g. "Intel GPU" */
-  name: string;
-  /** Short description */
-  description: string;
-  /**
-   * Icon for the device card. Supports:
-   * - Emoji string (e.g. "🖥️")
-   * - Image URL or static path (e.g. "/img/intel.svg", "https://example.com/icon.png")
-   * - Inline SVG markup (e.g. "<svg>...</svg>")
-   */
-  icon: string;
-  /**
-   * Additional CSS properties applied to the icon element.
-   * - For image-type icons: if any `background-*` property (e.g. `background-size`,
-   *   `background-position`) is present, the image is rendered as a CSS `background-image`
-   *   on the container div, enabling full background positioning control.
-   *   Otherwise the image is rendered as an `<img>` tag and styles apply to it.
-   * - For emoji/SVG icons: styles apply to the container div.
-   */
-  iconStyle?: Record<string, string>;
-  /**
-   * Additional CSS properties applied directly to the inner `<svg>` element
-   * when the icon is an inline SVG. Use this to override the default
-   * `width: 100%; height: 100%` or set `fill`, `transform`, etc.
-   * Ignored for emoji and image-type icons.
-   */
-  svgStyle?: Record<string, string>;
-  /**
-   * Icon for dark mode. Same format as `icon`. When provided, this icon
-   * replaces `icon` when the user is in dark mode.
-   */
-  iconDark?: string;
-  /** Additional CSS properties for the dark mode icon container */
-  iconDarkStyle?: Record<string, string>;
-  /**
-   * SVG-specific styles for dark mode. Same as `svgStyle` but applied
-   * when dark mode is active. Merged over `svgStyle` in dark mode.
-   */
-  svgDarkStyle?: Record<string, string>;
-  /** Docker image tag, e.g. "stable" */
-  imageTag: string;
-  /**
-   * Image tag suffix appended to the base tag.
-   * e.g. "-standard-arm64" produces "stable-standard-arm64"
-   */
-  imageTagSuffix?: string;
-  /** Hardware option IDs to auto-enable when this device is selected */
-  autoHardware: string[];
-  /** Help text shown as an admonition when this device is selected */
-  helpText?: string;
-  /** Admonition type for help text */
-  helpType?: "info" | "warning" | "danger";
-  /** Device mappings always added for this device type */
-  devices?: DeviceMapping[];
-  /** Volume mappings always added for this device type */
-  volumes?: VolumeMapping[];
-  /** Extra environment variables for this device type */
-  env?: Record<string, string>;
-  /** NVIDIA deploy config (only for tensorrt) */
-  nvidiaDeploy?: NvidiaDeployConfig;
-  /** Runtime setting, e.g. "nvidia" for Jetson */
-  runtime?: string;
-  /** Extra hosts entries, e.g. "host.docker.internal:host-gateway" */
-  extraHosts?: string[];
-  /** Security options, e.g. ["apparmor=unconfined"] */
-  securityOpt?: string[];
-  /** Whether this device type needs the NVIDIA GPU config UI */
-  needsNvidiaConfig?: boolean;
-}
-
-/** Generic hardware acceleration option definition */
-export interface HardwareOption {
-  /** Unique identifier, e.g. "usbCoral" */
-  id: string;
-  /** Display label */
-  label: string;
-  /**
-   * Description shown below the checkbox when this option is enabled.
-   * Supports markdown link syntax: [text](url)
-   */
-  description?: string;
-  /** Device IDs that disable this option */
-  disabledWhen?: string[];
-  /** Device mappings added when this option is enabled */
-  devices?: DeviceMapping[];
-  /** Volume mappings added when this option is enabled */
-  volumes?: VolumeMapping[];
-  /** Extra environment variables */
-  env?: Record<string, string>;
-}
-
-/** Port definition */
-export interface PortConfig {
-  /** Unique identifier (also the default host port as string) */
-  id: string;
-  /** Host port number */
-  host: number;
-  /** Container port number */
-  container: number;
-  /** Protocol */
-  protocol?: "tcp" | "udp";
-  /** Description of the port's purpose */
-  description: string;
-  /** Whether enabled by default */
-  defaultEnabled: boolean;
-  /** Whether this port is locked (always enabled, cannot be toggled off) */
-  locked?: boolean;
-  /** Admonition type for the warning */
-  warningType?: "warning" | "danger";
-  /** Warning content (markdown) */
-  warningContent?: string;
-  /** When to show the warning: when the port is checked or unchecked */
-  warningWhen?: "checked" | "unchecked";
-}

docs/src/components/DockerComposeGenerator/generator/index.ts

@@ -1,250 +0,0 @@
-import type {
-  DeviceConfig,
-  DeviceMapping,
-  VolumeMapping,
-} from "../config/types";
-import { hardwareMap } from "../config";
-
-// ---------------------------------------------------------------------------
-// Input type
-// ---------------------------------------------------------------------------
-
-export interface GeneratorInput {
-  device: DeviceConfig;
-  selectedHardware: string[];
-  enabledPorts: string[];
-  configPath: string;
-  mediaPath: string;
-  rtspPassword?: string;
-  timezone: string;
-  shmSize: string;
-  nvidiaGpuCount?: string;
-  nvidiaGpuDeviceId?: string;
-}
-
-// ---------------------------------------------------------------------------
-// Helpers
-// ---------------------------------------------------------------------------
-
-function deviceLine(dm: DeviceMapping): string {
-  const host = dm.host;
-  const container = dm.container ?? dm.host;
-  const mapping = host === container ? host : `${host}:${container}`;
-  const comment = dm.comment ? ` # ${dm.comment}` : "";
-  return `      - ${mapping}${comment}`;
-}
-
-function volumeLine(vm: VolumeMapping): string {
-  const ro = vm.readOnly ? ":ro" : "";
-  const comment = vm.comment ? ` # ${vm.comment}` : "";
-  return `      - ${vm.host}:${vm.container}${ro}${comment}`;
-}
-
-// ---------------------------------------------------------------------------
-// YAML builder — each section returns an array of lines
-// ---------------------------------------------------------------------------
-
-function buildImage(device: DeviceConfig): string[] {
-  const tag = device.imageTagSuffix
-    ? `${device.imageTag}${device.imageTagSuffix}`
-    : device.imageTag;
-  return [`    image: ghcr.io/blakeblackshear/frigate:${tag}`];
-}
-
-function buildDevices(
-  device: DeviceConfig,
-  hwDevices: DeviceMapping[]
-): string[] {
-  const all: DeviceMapping[] = [
-    ...(device.devices ?? []),
-    ...hwDevices,
-  ];
-  if (all.length === 0) return [];
-  return [
-    "    devices:",
-    ...all.map(deviceLine),
-  ];
-}
-
-function buildVolumes(
-  device: DeviceConfig,
-  hwVolumes: VolumeMapping[],
-  configPath: string,
-  mediaPath: string
-): string[] {
-  const all: VolumeMapping[] = [
-    ...(device.volumes ?? []),
-    ...hwVolumes,
-  ];
-  return [
-    "    volumes:",
-    "      - /etc/localtime:/etc/localtime:ro # Sync host time",
-    `      - ${configPath}:/config # Config file directory`,
-    `      - ${mediaPath}:/media/frigate # Recording storage directory`,
-    "      - type: tmpfs # 1GB in-memory filesystem for recording segment storage",
-    "        target: /tmp/cache",
-    "        tmpfs:",
-    "          size: 1000000000",
-    ...all.map(volumeLine),
-  ];
-}
-
-function buildPorts(enabledPorts: string[]): string[] {
-  return [
-    "    ports:",
-    ...enabledPorts,
-  ];
-}
-
-function buildEnvironment(
-  device: DeviceConfig,
-  hwEnv: Record<string, string>,
-  rtspPassword: string | undefined,
-  timezone: string
-): string[] {
-  const allEnv: Record<string, string> = {
-    ...hwEnv,
-    ...(device.env ?? {}),
-  };
-
-  const lines: string[] = ["    environment:"];
-
-  if (rtspPassword) {
-    lines.push(
-      `      FRIGATE_RTSP_PASSWORD: "${rtspPassword}" # RTSP password — change to your own`
-    );
-  }
-
-  lines.push(`      TZ: "${timezone}" # Timezone`);
-
-  for (const [key, value] of Object.entries(allEnv)) {
-    lines.push(`      ${key}: "${value}"`);
-  }
-
-  return lines;
-}
-
-function buildDeploy(device: DeviceConfig, input: GeneratorInput): string[] {
-  if (device.id === "stable-tensorrt") {
-    const count = input.nvidiaGpuCount || "all";
-    const isAll = count === "all";
-    const deviceId = input.nvidiaGpuDeviceId?.trim();
-
-    if (isAll) {
-      return [
-        "    deploy:",
-        "      resources:",
-        "        reservations:",
-        "          devices:",
-        "            - driver: nvidia",
-        "              count: all # Use all GPUs",
-        "              capabilities: [gpu]",
-      ];
-    }
-
-    if (deviceId) {
-      const ids = deviceId
-        .split(",")
-        .map((s) => s.trim())
-        .filter(Boolean)
-        .map((s) => `'${s}'`)
-        .join(", ");
-      return [
-        "    deploy:",
-        "      resources:",
-        "        reservations:",
-        "          devices:",
-        "            - driver: nvidia",
-        `              device_ids: [${ids}] # GPU device IDs`,
-        `              count: ${count} # GPU count`,
-        "              capabilities: [gpu]",
-      ];
-    }
-
-    return [
-      "    deploy:",
-      "      resources:",
-      "        reservations:",
-      "          devices:",
-      "            - driver: nvidia",
-      `              count: ${count} # GPU count`,
-      "              capabilities: [gpu]",
-    ];
-  }
-
-  return [];
-}
-
-function buildRuntime(device: DeviceConfig): string[] {
-  if (device.runtime) {
-    return [`    runtime: ${device.runtime}`];
-  }
-  return [];
-}
-
-function buildExtraHosts(device: DeviceConfig): string[] {
-  if (!device.extraHosts?.length) return [];
-  return [
-    "    extra_hosts:",
-    ...device.extraHosts.map(
-      (h, i) =>
-        `      - "${h}"${i === 0 ? " # Required to talk to the NPU detector" : ""}`
-    ),
-  ];
-}
-
-function buildSecurityOpt(device: DeviceConfig): string[] {
-  if (!device.securityOpt?.length) return [];
-  return [
-    "    security_opt:",
-    ...device.securityOpt.map((s) => `      - ${s}`),
-  ];
-}
-
-// ---------------------------------------------------------------------------
-// Public API
-// ---------------------------------------------------------------------------
-
-/**
- * Generate a docker-compose YAML string from the given input.
- * The output is pure YAML with inline comments (no Shiki annotations).
- */
-export function generateDockerCompose(input: GeneratorInput): string {
-  const { device } = input;
-
-  // Collect hardware-level devices, volumes, and env
-  const hwDevices: DeviceMapping[] = [];
-  const hwVolumes: VolumeMapping[] = [];
-  const hwEnv: Record<string, string> = {};
-
-  for (const hwId of input.selectedHardware) {
-    const hw = hardwareMap.get(hwId);
-    if (!hw) continue;
-    // Skip GPU device mapping for tensorrt images (it uses deploy instead)
-    if (hw.id === "gpu" && device.imageTag === "stable-tensorrt") continue;
-    hwDevices.push(...(hw.devices ?? []));
-    hwVolumes.push(...(hw.volumes ?? []));
-    Object.assign(hwEnv, hw.env ?? {});
-  }
-
-  const lines: string[] = [
-    "services:",
-    "  frigate:",
-    "    container_name: frigate",
-    "    privileged: true # This may not be necessary for all setups",
-    "    restart: unless-stopped",
-    "    stop_grace_period: 30s # Allow enough time to shut down the various services",
-    ...buildImage(device),
-    `    shm_size: "${input.shmSize || "512mb"}" # Update for your cameras based on SHM calculation`,
-    ...buildRuntime(device),
-    ...buildDeploy(device, input),
-    ...buildExtraHosts(device),
-    ...buildSecurityOpt(device),
-    ...buildDevices(device, hwDevices),
-    ...buildVolumes(device, hwVolumes, input.configPath, input.mediaPath),
-    ...buildPorts(input.enabledPorts),
-    ...buildEnvironment(device, hwEnv, input.rtspPassword, input.timezone),
-  ];
-
-  return lines.join("\n");
-}

docs/src/components/DockerComposeGenerator/hooks/useConfigGenerator.ts

@@ -1,195 +0,0 @@
-import { useState, useCallback, useMemo } from "react";
-import { deviceMap, hardwareMap, portMap } from "../config";
-import { generateDockerCompose } from "../generator";
-import type { GeneratorInput } from "../generator";
-
-/**
- * Main hook that holds all form state and generates the Docker Compose output.
- * Configuration is loaded synchronously from build-time generated .ts files.
- */
-export function useConfigGenerator() {
-  const [deviceId, setDeviceId] = useState("stable");
-
-  const [hardwareEnabled, setHardwareEnabled] = useState<Record<string, boolean>>(() => {
-    const defaultDevice = deviceMap.get("stable");
-    const initial: Record<string, boolean> = {};
-    if (defaultDevice) {
-      for (const hwId of defaultDevice.autoHardware) {
-        initial[hwId] = true;
-      }
-    }
-    return initial;
-  });
-
-  const [portEnabled, setPortEnabled] = useState<Record<string, boolean>>(() => {
-    const initial: Record<string, boolean> = {};
-    for (const p of portMap.values()) {
-      initial[p.id] = p.defaultEnabled;
-    }
-    return initial;
-  });
-
-  const [nvidiaGpuCount, setNvidiaGpuCount] = useState("");
-  const [nvidiaGpuDeviceId, setNvidiaGpuDeviceId] = useState("");
-  const [configPath, setConfigPath] = useState("");
-  const [mediaPath, setMediaPath] = useState("");
-  const [rtspPassword, setRtspPassword] = useState("");
-  const [timezone, setTimezone] = useState("");
-  const [shmSize, setShmSize] = useState("512mb");
-  const [shmSizeError, setShmSizeError] = useState(false);
-  const [gpuDeviceIdError, setGpuDeviceIdError] = useState(false);
-  const [configPathError, setConfigPathError] = useState(false);
-  const [mediaPathError, setMediaPathError] = useState(false);
-
-  const device = useMemo(() => deviceMap.get(deviceId)!, [deviceId]);
-
-  const selectDevice = useCallback((id: string) => {
-    const newDevice = deviceMap.get(id);
-    if (!newDevice) return;
-    setDeviceId(id);
-    setHardwareEnabled(() => {
-      const next: Record<string, boolean> = {};
-      for (const hwId of newDevice.autoHardware) {
-        next[hwId] = true;
-      }
-      return next;
-    });
-    setNvidiaGpuCount("");
-    setNvidiaGpuDeviceId("");
-    setGpuDeviceIdError(false);
-  }, []);
-
-  const toggleHardware = useCallback((hwId: string) => {
-    setHardwareEnabled((prev) => ({ ...prev, [hwId]: !prev[hwId] }));
-  }, []);
-
-  const togglePort = useCallback((portId: string) => {
-    const port = portMap.get(portId);
-    if (port?.locked) return;
-    setPortEnabled((prev) => ({ ...prev, [portId]: !prev[portId] }));
-  }, []);
-
-  const isHardwareDisabled = useCallback(
-    (hwId: string): boolean => {
-      const hw = hardwareMap.get(hwId);
-      if (!hw) return false;
-      return hw.disabledWhen?.includes(deviceId) ?? false;
-    },
-    [deviceId]
-  );
-
-  const validateShmSize = useCallback((value: string): boolean => {
-    if (!value) return true;
-    return /^\d+(\.\d+)?[bkmgBKMG]{1,2}$/.test(value);
-  }, []);
-
-  const validatePath = useCallback((value: string): boolean => {
-    if (!value) return true;
-    return /^[a-zA-Z0-9_\-/./]+$/.test(value);
-  }, []);
-
-  const handleShmSizeChange = useCallback(
-    (value: string) => {
-      const filtered = value.replace(/[^0-9.bkmgBKMG]/g, "");
-      const valid = validateShmSize(filtered);
-      setShmSize(filtered);
-      setShmSizeError(!valid && filtered !== "");
-    },
-    [validateShmSize]
-  );
-
-  const handleConfigPathChange = useCallback(
-    (value: string) => {
-      const filtered = value.replace(/[^a-zA-Z0-9_\-/./]/g, "");
-      const valid = validatePath(filtered);
-      setConfigPath(filtered);
-      setConfigPathError(!valid && filtered !== "");
-    },
-    [validatePath]
-  );
-
-  const handleMediaPathChange = useCallback(
-    (value: string) => {
-      const filtered = value.replace(/[^a-zA-Z0-9_\-/./]/g, "");
-      const valid = validatePath(filtered);
-      setMediaPath(filtered);
-      setMediaPathError(!valid && filtered !== "");
-    },
-    [validatePath]
-  );
-
-  const handleNvidiaGpuCountChange = useCallback((value: string) => {
-    // Only allow digits
-    setNvidiaGpuCount(value);
-    if (value === "") {
-      setNvidiaGpuDeviceId("");
-      setGpuDeviceIdError(false);
-    } else {
-      setGpuDeviceIdError(false);
-    }
-  }, []);
-
-  const handleNvidiaGpuDeviceIdChange = useCallback((value: string) => {
-    setNvidiaGpuDeviceId(value.trim());
-    setGpuDeviceIdError(false);
-  }, []);
-
-  const enabledPortLines = useMemo(() => {
-    const lines: string[] = [];
-    for (const [id, enabled] of Object.entries(portEnabled)) {
-      if (!enabled) continue;
-      const p = portMap.get(id);
-      if (!p) continue;
-      const proto = p.protocol && p.protocol !== "tcp" ? `/${p.protocol}` : "";
-      const comment = p.description ? ` # ${p.description}` : "";
-      lines.push(`      - "${p.host}:${p.container}${proto}"${comment}`);
-    }
-    return lines;
-  }, [portEnabled]);
-
-  const selectedHardwareIds = useMemo(() => {
-    return Object.entries(hardwareEnabled)
-      .filter(([id, enabled]) => {
-        if (!enabled) return false;
-        const hw = hardwareMap.get(id);
-        if (!hw) return false;
-        if (hw.disabledWhen?.includes(deviceId)) return false;
-        return true;
-      })
-      .map(([id]) => id);
-  }, [hardwareEnabled, deviceId]);
-
-  const generatedYaml = useMemo(() => {
-    const input: GeneratorInput = {
-      device,
-      selectedHardware: selectedHardwareIds,
-      enabledPorts: enabledPortLines,
-      configPath: configPath || "/path/to/your/config",
-      mediaPath: mediaPath || "/path/to/your/storage",
-      rtspPassword,
-      timezone: timezone || Intl.DateTimeFormat().resolvedOptions().timeZone || "Etc/UTC",
-      shmSize: shmSize || "512mb",
-      nvidiaGpuCount,
-      nvidiaGpuDeviceId,
-    };
-    return generateDockerCompose(input);
-  }, [
-    device, selectedHardwareIds, enabledPortLines,
-    configPath, mediaPath, rtspPassword, timezone, shmSize,
-    nvidiaGpuCount, nvidiaGpuDeviceId,
-  ]);
-
-  const hasAnyHardware = selectedHardwareIds.length > 0 || !!device?.devices?.length;
-
-  return {
-    deviceId, device, hardwareEnabled, portEnabled,
-    nvidiaGpuCount, nvidiaGpuDeviceId,
-    configPath, mediaPath, rtspPassword, timezone, shmSize,
-    shmSizeError, gpuDeviceIdError, configPathError, mediaPathError,
-    hasAnyHardware, generatedYaml,
-    selectDevice, toggleHardware, togglePort,
-    handleShmSizeChange, handleConfigPathChange, handleMediaPathChange,
-    handleNvidiaGpuCountChange, handleNvidiaGpuDeviceIdChange,
-    setRtspPassword, setTimezone, isHardwareDisabled,
-  };
-}

docs/src/components/DockerComposeGenerator/index.ts

@@ -1 +0,0 @@
-export { default } from "./DockerComposeGenerator";

docs/src/components/DockerComposeGenerator/styles.module.css

@@ -1,381 +0,0 @@
-/* ===================================================================
-   Docker Compose Generator — styles
-   Uses Docusaurus / Infima CSS variables for theme compatibility.
-   =================================================================== */
-
-.generator {
-  margin: 2rem 0;
-}
-
-.card {
-  background: var(--ifm-background-surface-color);
-  border: 1px solid var(--ifm-color-emphasis-400);
-  border-radius: 12px;
-  padding: 2rem;
-  box-shadow: var(--ifm-global-shadow-lw);
-}
-
-[data-theme="light"] .card {
-  background: var(--ifm-color-emphasis-100);
-  border: 1px solid var(--ifm-color-emphasis-300);
-}
-
-/* --- Form sections --- */
-
-.formSection {
-  margin-bottom: 1.5rem;
-  padding-bottom: 1.5rem;
-  border-bottom: 1px solid var(--ifm-color-emphasis-400);
-}
-
-.formSection:last-child {
-  border-bottom: none;
-  margin-bottom: 0;
-  padding-bottom: 0;
-}
-
-.formSection h4 {
-  margin: 0 0 1rem 0;
-  color: var(--ifm-font-color-base);
-  font-size: 1.1rem;
-  font-weight: var(--ifm-font-weight-semibold);
-}
-
-/* --- Form controls --- */
-
-.formGroup {
-  margin-bottom: 1rem;
-}
-
-.formGroup:last-child {
-  margin-bottom: 0;
-}
-
-.label {
-  display: block;
-  margin-bottom: 0.25rem;
-  color: var(--ifm-font-color-base);
-  font-weight: var(--ifm-font-weight-semibold);
-  font-size: 0.9rem;
-}
-
-.input {
-  width: 100%;
-  padding: 0.5rem 0.75rem;
-  border: 1px solid var(--ifm-color-emphasis-400);
-  border-radius: 6px;
-  background: var(--ifm-background-color);
-  color: var(--ifm-font-color-base);
-  font-size: 0.95rem;
-  transition: border-color 0.2s, box-shadow 0.2s;
-}
-
-[data-theme="light"] .input {
-  background: #fff;
-  border: 1px solid #d0d7de;
-}
-
-.input:focus {
-  outline: none;
-  border-color: var(--ifm-color-primary);
-  box-shadow: 0 0 0 3px var(--ifm-color-primary-lightest);
-}
-
-[data-theme="dark"] .input {
-  border-color: var(--ifm-color-emphasis-300);
-}
-
-.inputError {
-  border-color: #e74c3c;
-  animation: shake 0.3s ease-in-out;
-}
-
-@keyframes shake {
-  0%,
-  100% {
-    transform: translateX(0);
-  }
-  25% {
-    transform: translateX(-5px);
-  }
-  75% {
-    transform: translateX(5px);
-  }
-}
-
-/* --- Select dropdown --- */
-
-.select {
-  cursor: pointer;
-  appearance: none;
-  -moz-appearance: none;
-  -webkit-appearance: none;
-  background: var(--ifm-background-color)
-    url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='12' height='12' viewBox='0 0 12 12'%3E%3Cpath fill='%23666' d='M6 8L1 3h10z'/%3E%3C/svg%3E")
-    no-repeat right 0.75rem center / 12px 12px;
-  padding-right: 2rem;
-}
-
-[data-theme="light"] .select {
-  background: #fff
-    url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='12' height='12' viewBox='0 0 12 12'%3E%3Cpath fill='%23555' d='M6 8L1 3h10z'/%3E%3C/svg%3E")
-    no-repeat right 0.75rem center / 12px 12px;
-}
-
-.helpText {
-  margin: 0.5rem 0 0 0;
-  font-size: 0.85rem;
-  color: var(--ifm-font-color-secondary);
-  line-height: 1.5;
-}
-
-.helpText a {
-  color: var(--ifm-color-primary);
-}
-
-/* --- Device grid --- */
-
-.deviceGrid {
-  display: grid;
-  grid-template-columns: repeat(auto-fill, minmax(130px, 1fr));
-  gap: 0.75rem;
-  margin-top: 0.5rem;
-}
-
-.deviceCard {
-  padding: 0.75rem;
-  border: 2px solid var(--ifm-color-emphasis-400);
-  border-radius: 12px;
-  cursor: pointer;
-  transition: all 0.2s;
-  text-align: center;
-  background: var(--ifm-background-color);
-  display: flex;
-  flex-direction: column;
-  align-items: center;
-}
-
-[data-theme="light"] .deviceCard {
-  border: 2px solid #d0d7de;
-  background: #fff;
-}
-
-.deviceCard:hover {
-  border-color: var(--ifm-color-primary);
-  background: var(--ifm-color-emphasis-100);
-  transform: translateY(-2px);
-}
-
-.deviceCardActive {
-  border-color: var(--ifm-color-primary);
-  background: var(--ifm-color-primary-lightest);
-  box-shadow: 0 0 0 1px var(--ifm-color-primary);
-}
-
-[data-theme="light"] .deviceCardActive {
-  background: color-mix(in srgb, var(--ifm-color-primary) 12%, #fff);
-}
-
-[data-theme="dark"] .deviceCardActive {
-  background: color-mix(in srgb, var(--ifm-color-primary) 25%, #1b1b1b);
-}
-
-[data-theme="dark"] .deviceCardActive .deviceName {
-  color: var(--ifm-color-primary-light);
-}
-
-[data-theme="dark"] .deviceCardActive .deviceDesc {
-  color: var(--ifm-color-primary-light);
-  opacity: 0.85;
-}
-
-.deviceIcon {
-  font-size: 2rem;
-  margin-bottom: 0.25rem;
-  height: 40px;
-  width: 50px;
-  display: flex;
-  align-items: center;
-  justify-content: center;
-}
-
-.deviceIconSvg {
-  margin-bottom: 0.25rem;
-  height: 40px;
-  width: 50px;
-  display: flex;
-  align-items: center;
-  justify-content: center;
-  overflow: visible;
-  /* Allow iconStyle width/height to override */
-  flex-shrink: 0;
-}
-
-.deviceIconSvg svg {
-  width: var(--svg-width, 100%);
-  height: var(--svg-height, 100%);
-  fill: var(--svg-fill, currentColor);
-  transform: var(--svg-transform, none);
-}
-
-.deviceIconImage {
-  margin-bottom: 0.25rem;
-  height: 40px;
-  width: 50px;
-  display: flex;
-  align-items: center;
-  justify-content: center;
-}
-
-.deviceIconImage img {
-  max-width: 100%;
-  max-height: 100%;
-  object-fit: contain;
-}
-
-.deviceName {
-  font-weight: var(--ifm-font-weight-semibold);
-  color: var(--ifm-font-color-base);
-  margin-bottom: 0.15rem;
-  font-size: 0.9rem;
-}
-
-.deviceDesc {
-  font-size: 0.75rem;
-  color: var(--ifm-font-color-secondary);
-  line-height: 1.3;
-}
-
-/* --- Checkbox grid --- */
-
-.checkboxGrid {
-  display: grid;
-  grid-template-columns: repeat(2, 1fr);
-  gap: 0.5rem;
-}
-
-@media (max-width: 576px) {
-  .checkboxGrid {
-    grid-template-columns: 1fr;
-  }
-}
-
-.hardwareItem {
-  margin-bottom: 0;
-}
-
-.hardwareDescription {
-  margin: 0.15rem 0 0.4rem 1.6rem;
-  font-size: 0.8rem;
-  color: var(--ifm-font-color-secondary);
-  line-height: 1.5;
-}
-
-.hardwareDescription a {
-  color: var(--ifm-color-primary);
-  text-decoration: underline;
-  text-underline-offset: 2px;
-}
-
-.checkboxLabel {
-  display: flex;
-  align-items: center;
-  gap: 0.5rem;
-  cursor: pointer;
-  padding: 0.4rem 0.5rem;
-  border-radius: 6px;
-  transition: background-color 0.2s;
-  font-size: 0.9rem;
-}
-
-.checkboxLabel:hover {
-  background: var(--ifm-color-emphasis-100);
-}
-
-.checkboxLabel input[type="checkbox"] {
-  width: 1.1rem;
-  height: 1.1rem;
-  cursor: pointer;
-  flex-shrink: 0;
-}
-
-.checkboxLabel span {
-  color: var(--ifm-font-color-base);
-}
-
-.checkboxDisabled {
-  cursor: not-allowed;
-}
-
-.checkboxDisabled:hover {
-  background: transparent;
-}
-
-.checkboxDisabled input[type="checkbox"] {
-  cursor: not-allowed;
-  opacity: 0.5;
-}
-
-/* --- Form grid (side-by-side) --- */
-
-.formGrid {
-  display: grid;
-  grid-template-columns: repeat(2, 1fr);
-  gap: 1rem;
-}
-
-@media (max-width: 576px) {
-  .formGrid {
-    grid-template-columns: 1fr;
-  }
-}
-
-.formGrid .formGroup {
-  margin-bottom: 0;
-}
-
-/* --- Port section --- */
-
-.portSection {
-  margin-bottom: 0.75rem;
-}
-
-.warningBadge {
-  margin-left: auto;
-  color: #e67e22;
-  font-size: 0.85rem;
-}
-
-/* --- NVIDIA config --- */
-
-.nvidiaConfig {
-  margin-top: 1rem;
-  margin-bottom: 1.5rem;
-  padding: 1rem;
-  background: var(--ifm-background-color);
-  border-radius: 8px;
-  border-left: 3px solid var(--ifm-color-primary);
-}
-
-[data-theme="light"] .nvidiaConfig {
-  background: #f6f8fa;
-  border-left: 3px solid var(--ifm-color-primary);
-}
-
-/* --- Result section --- */
-
-.resultSection {
-  margin-top: 2rem;
-}
-
-.resultHeader {
-  display: flex;
-  justify-content: space-between;
-  align-items: center;
-  margin-bottom: 1rem;
-}
-
-.resultHeader h4 {
-  margin: 0;
-  color: var(--ifm-font-color-base);
-}

frigate/api/app.py

@@ -146,13 +146,8 @@ def config(request: Request):
         for name, detector in config_obj.detectors.items()
     }
 
-    # remove environment_vars for non-admin users
-    if request.headers.get("remote-role") != "admin":
-        config.pop("environment_vars", None)
-
-    # remove mqtt credentials
+    # remove the mqtt password
     config["mqtt"].pop("password", None)
-    config["mqtt"].pop("user", None)
 
     # remove the proxy secret
     config["proxy"].pop("auth_secret", None)

frigate/api/chat.py

@@ -36,7 +36,6 @@ from frigate.api.defs.response.chat_response import (
 )
 from frigate.api.defs.tags import Tags
 from frigate.api.event import events
-from frigate.config import FrigateConfig
 from frigate.genai.utils import build_assistant_message_for_conversation
 from frigate.jobs.vlm_watch import (
     get_vlm_watch_job,
@@ -402,38 +401,9 @@ def get_tools() -> JSONResponse:
     return JSONResponse(content={"tools": tools})
 
 
-def _resolve_zones(
-    zones: List[str],
-    config: FrigateConfig,
-    target_cameras: List[str],
-) -> List[str]:
-    """Map zone names to their canonical config keys, case-insensitively.
-
-    LLMs frequently echo a user's casing ("Front Yard") instead of the
-    configured key ("front_yard"). The downstream zone filter is a SQLite GLOB
-    over the JSON-encoded zones column, which is case-sensitive — so an
-    unnormalized name silently returns zero matches. Build a lookup over the
-    relevant cameras' configured zones and substitute when we find a match;
-    unknown names pass through so behavior matches what the model asked for.
-    """
-    if not zones:
-        return zones
-
-    lookup: Dict[str, str] = {}
-    for camera_id in target_cameras:
-        camera_config = config.cameras.get(camera_id)
-        if camera_config is None:
-            continue
-        for zone_name in camera_config.zones.keys():
-            lookup.setdefault(zone_name.lower(), zone_name)
-
-    return [lookup.get(z.lower(), z) for z in zones]
-
-
 async def _execute_search_objects(
     arguments: Dict[str, Any],
     allowed_cameras: List[str],
-    config: FrigateConfig,
 ) -> JSONResponse:
     """
     Execute the search_objects tool.
@@ -467,11 +437,6 @@ async def _execute_search_objects(
     # Convert zones array to comma-separated string if provided
     zones = arguments.get("zones")
     if isinstance(zones, list):
-        camera_arg = arguments.get("camera")
-        target_cameras = (
-            [camera_arg] if camera_arg and camera_arg != "all" else allowed_cameras
-        )
-        zones = _resolve_zones(zones, config, target_cameras)
         zones = ",".join(zones)
     elif zones is None:
         zones = "all"
@@ -563,11 +528,6 @@ async def _execute_find_similar_objects(
     sub_labels = arguments.get("sub_labels")
     zones = arguments.get("zones")
 
-    if zones:
-        zones = _resolve_zones(
-            zones, request.app.frigate_config, cameras or list(allowed_cameras)
-        )
-
     similarity_mode = arguments.get("similarity_mode", "fused")
     if similarity_mode not in ("visual", "semantic", "fused"):
         similarity_mode = "fused"
@@ -695,9 +655,7 @@ async def execute_tool(
     logger.debug(f"Executing tool: {tool_name} with arguments: {arguments}")
 
     if tool_name == "search_objects":
-        return await _execute_search_objects(
-            arguments, allowed_cameras, request.app.frigate_config
-        )
+        return await _execute_search_objects(arguments, allowed_cameras)
 
     if tool_name == "find_similar_objects":
         result = await _execute_find_similar_objects(
@@ -877,9 +835,7 @@ async def _execute_tool_internal(
     This is used by the chat completion endpoint to execute tools.
     """
     if tool_name == "search_objects":
-        response = await _execute_search_objects(
-            arguments, allowed_cameras, request.app.frigate_config
-        )
+        response = await _execute_search_objects(arguments, allowed_cameras)
         try:
             if hasattr(response, "body"):
                 body_str = response.body.decode("utf-8")
@@ -943,9 +899,6 @@ async def _execute_start_camera_watch(
 
     await require_camera_access(camera, request=request)
 
-    if zones:
-        zones = _resolve_zones(zones, config, [camera])
-
     genai_manager = request.app.genai_manager
     chat_client = genai_manager.chat_client
     if chat_client is None or not chat_client.supports_vision:

frigate/api/defs/response/export_response.py

@@ -107,14 +107,6 @@ class ExportJobModel(BaseModel):
         default=None,
         description="Result metadata for completed jobs",
     )
-    current_step: str = Field(
-        default="queued",
-        description="Current execution step (queued, preparing, encoding, encoding_retry, finalizing)",
-    )
-    progress_percent: float = Field(
-        default=0.0,
-        description="Progress percentage of the current step (0.0 - 100.0)",
-    )
 
 
 ExportJobsResponse = List[ExportJobModel]

frigate/api/event.py

@@ -754,15 +754,6 @@ def events_search(
                 status_code=404,
             )
 
-        if search_event.camera not in allowed_cameras:
-            return JSONResponse(
-                content={
-                    "success": False,
-                    "message": "Event not found",
-                },
-                status_code=404,
-            )
-
         thumb_result = context.search_thumbnail(search_event)
         thumb_ids = {result[0]: result[1] for result in thumb_result}
         search_results = {

frigate/api/export.py

@@ -5,15 +5,13 @@ import logging
 import random
 import string
 import time
-import zipfile
-from collections import deque
 from pathlib import Path
-from typing import Iterator, List, Optional
+from typing import List, Optional
 
 import psutil
 from fastapi import APIRouter, Depends, Query, Request
-from fastapi.responses import JSONResponse, StreamingResponse
-from pathvalidate import sanitize_filename, sanitize_filepath
+from fastapi.responses import JSONResponse
+from pathvalidate import sanitize_filepath
 from peewee import DoesNotExist
 from playhouse.shortcuts import model_to_dict
 
@@ -363,136 +361,6 @@ def get_export_case(case_id: str):
         )
 
 
-_ZIP_STREAM_CHUNK_SIZE = 1024 * 1024  # 1 MiB
-
-
-class _StreamingZipBuffer:
-    """File-like sink for ZipFile that exposes written bytes via drain().
-
-    ZipFile writes synchronously into this buffer; the generator drains the
-    queue between writes so StreamingResponse can yield bytes without
-    materializing the whole archive in memory.
-    """
-
-    def __init__(self) -> None:
-        self._queue: deque[bytes] = deque()
-        self._offset = 0
-
-    def write(self, data: bytes) -> int:
-        if data:
-            self._queue.append(bytes(data))
-            self._offset += len(data)
-        return len(data)
-
-    def tell(self) -> int:
-        return self._offset
-
-    def flush(self) -> None:
-        pass
-
-    def drain(self) -> Iterator[bytes]:
-        while self._queue:
-            yield self._queue.popleft()
-
-
-def _unique_archive_name(export: Export, used: set[str]) -> str:
-    base = sanitize_filename(export.name) if export.name else None
-    if not base:
-        base = f"{export.camera}_{int(datetime.datetime.timestamp(export.date))}"
-
-    candidate = f"{base}.mp4"
-    counter = 1
-    while candidate in used:
-        candidate = f"{base}_{counter}.mp4"
-        counter += 1
-
-    used.add(candidate)
-    return candidate
-
-
-def _stream_case_archive(exports: List[Export]) -> Iterator[bytes]:
-    """Yield bytes of a zip archive built from the given exports' mp4 files."""
-    buffer = _StreamingZipBuffer()
-    used_names: set[str] = set()
-
-    # ZIP_STORED: mp4 is already compressed, recompressing wastes CPU for ~0% size win.
-    with zipfile.ZipFile(
-        buffer,
-        mode="w",
-        compression=zipfile.ZIP_STORED,
-        allowZip64=True,
-    ) as archive:
-        for export in exports:
-            source = Path(export.video_path)
-            if not source.exists():
-                continue
-
-            arcname = _unique_archive_name(export, used_names)
-
-            with (
-                archive.open(arcname, mode="w", force_zip64=True) as entry,
-                source.open("rb") as src,
-            ):
-                while True:
-                    chunk = src.read(_ZIP_STREAM_CHUNK_SIZE)
-                    if not chunk:
-                        break
-
-                    entry.write(chunk)
-                    yield from buffer.drain()
-
-            yield from buffer.drain()
-
-    yield from buffer.drain()
-
-
-@router.get(
-    "/cases/{case_id}/download",
-    dependencies=[Depends(allow_any_authenticated())],
-    summary="Download export case as zip",
-    description="Streams a zip archive containing every completed export's mp4 for the given case.",
-)
-def download_export_case(
-    case_id: str,
-    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
-):
-    try:
-        case = ExportCase.get(ExportCase.id == case_id)
-    except DoesNotExist:
-        return JSONResponse(
-            content={"success": False, "message": "Export case not found"},
-            status_code=404,
-        )
-
-    exports = list(
-        Export.select()
-        .where(
-            Export.export_case == case_id,
-            ~Export.in_progress,
-            Export.camera << allowed_cameras,
-        )
-        .order_by(Export.date.asc())
-    )
-
-    if not exports:
-        return JSONResponse(
-            content={"success": False, "message": "No exports available to download."},
-            status_code=404,
-        )
-
-    archive_base = sanitize_filename(case.name) if case.name else ""
-    if not archive_base:
-        archive_base = case_id
-
-    return StreamingResponse(
-        _stream_case_archive(exports),
-        media_type="application/zip",
-        headers={
-            "Content-Disposition": f'attachment; filename="{archive_base}.zip"',
-        },
-    )
-
-
 @router.patch(
     "/cases/{case_id}",
     response_model=GenericResponse,

frigate/api/media.py

@@ -1368,17 +1368,12 @@ def preview_gif(
         file_start = f"preview_{camera_name}-"
         start_file = f"{file_start}{start_ts}.{PREVIEW_FRAME_TYPE}"
         end_file = f"{file_start}{end_ts}.{PREVIEW_FRAME_TYPE}"
-
-        camera_files = [
-            entry.name
-            for entry in os.scandir(preview_dir)
-            if entry.name.startswith(file_start)
-        ]
-        camera_files.sort()
-
         selected_previews = []
 
-        for file in camera_files:
+        for file in sorted(os.listdir(preview_dir)):
+            if not file.startswith(file_start):
+                continue
+
             if file < start_file:
                 continue
 
@@ -1555,17 +1550,12 @@ def preview_mp4(
         file_start = f"preview_{camera_name}-"
         start_file = f"{file_start}{start_ts}.{PREVIEW_FRAME_TYPE}"
         end_file = f"{file_start}{end_ts}.{PREVIEW_FRAME_TYPE}"
-
-        camera_files = [
-            entry.name
-            for entry in os.scandir(preview_dir)
-            if entry.name.startswith(file_start)
-        ]
-        camera_files.sort()
-
         selected_previews = []
 
-        for file in camera_files:
+        for file in sorted(os.listdir(preview_dir)):
+            if not file.startswith(file_start):
+                continue
+
             if file < start_file:
                 continue
 

frigate/api/preview.py

@@ -148,17 +148,12 @@ def get_preview_frames_from_cache(camera_name: str, start_ts: float, end_ts: flo
     file_start = f"preview_{camera_name}-"
     start_file = f"{file_start}{start_ts}.{PREVIEW_FRAME_TYPE}"
     end_file = f"{file_start}{end_ts}.{PREVIEW_FRAME_TYPE}"
-
-    camera_files = [
-        entry.name
-        for entry in os.scandir(preview_dir)
-        if entry.name.startswith(file_start)
-    ]
-    camera_files.sort()
-
     selected_previews = []
 
-    for file in camera_files:
+    for file in sorted(os.listdir(preview_dir)):
+        if not file.startswith(file_start):
+            continue
+
         if file < start_file:
             continue
 

frigate/api/record.py

@@ -35,7 +35,7 @@ logger = logging.getLogger(__name__)
 router = APIRouter(tags=[Tags.recordings])
 
 
-@router.get("/recordings/storage", dependencies=[Depends(require_role(["admin"]))])
+@router.get("/recordings/storage", dependencies=[Depends(allow_any_authenticated())])
 def get_recordings_storage_usage(request: Request):
     recording_stats = request.app.stats_emitter.get_latest_stats()["service"][
         "storage"

frigate/app.py

@@ -189,6 +189,17 @@ class FrigateApp:
             except PermissionError:
                 logger.error("Unable to write to /config to save DB state")
 
+        def cleanup_timeline_db(db: SqliteExtDatabase) -> None:
+            db.execute_sql(
+                "DELETE FROM timeline WHERE source_id NOT IN (SELECT id FROM event);"
+            )
+
+            try:
+                with open(f"{CONFIG_DIR}/.timeline", "w") as f:
+                    f.write(str(datetime.datetime.now().timestamp()))
+            except PermissionError:
+                logger.error("Unable to write to /config to save DB state")
+
         # Migrate DB schema
         migrate_db = SqliteExtDatabase(self.config.database.path)
 
@@ -205,6 +216,11 @@ class FrigateApp:
 
         router.run()
 
+        # this is a temporary check to clean up user DB from beta
+        # will be removed before final release
+        if not os.path.exists(f"{CONFIG_DIR}/.timeline"):
+            cleanup_timeline_db(migrate_db)
+
         # check if vacuum needs to be run
         if os.path.exists(f"{CONFIG_DIR}/.vacuum"):
             with open(f"{CONFIG_DIR}/.vacuum") as f:

frigate/comms/webpush.py

@@ -429,10 +429,7 @@ class WebPushClient(Communicator):
             else:
                 title = base_title
 
-            if payload["after"]["data"]["metadata"].get("shortSummary"):
-                message = payload["after"]["data"]["metadata"]["shortSummary"]
-            else:
-                message = f"Detected on {camera_name}"
+            message = payload["after"]["data"]["metadata"]["shortSummary"]
         else:
             zone_names = payload["after"]["data"]["zones"]
             formatted_zone_names = []
@@ -552,14 +549,6 @@ class WebPushClient(Communicator):
         logger.debug(f"Sending camera monitoring push notification for {camera_name}")
 
         for user in self.web_pushers:
-            if not self._user_has_camera_access(user, camera):
-                logger.debug(
-                    "Skipping notification for user %s - no access to camera %s",
-                    user,
-                    camera,
-                )
-                continue
-
             self.send_push_notification(
                 user=user,
                 payload=payload,

frigate/comms/ws.py

@@ -17,90 +17,9 @@ from ws4py.websocket import WebSocket as WebSocket_
 
 from frigate.comms.base_communicator import Communicator
 from frigate.config import FrigateConfig
-from frigate.const import (
-    CLEAR_ONGOING_REVIEW_SEGMENTS,
-    EXPIRE_AUDIO_ACTIVITY,
-    INSERT_MANY_RECORDINGS,
-    INSERT_PREVIEW,
-    NOTIFICATION_TEST,
-    REQUEST_REGION_GRID,
-    UPDATE_AUDIO_ACTIVITY,
-    UPDATE_AUDIO_TRANSCRIPTION_STATE,
-    UPDATE_BIRDSEYE_LAYOUT,
-    UPDATE_CAMERA_ACTIVITY,
-    UPDATE_EMBEDDINGS_REINDEX_PROGRESS,
-    UPDATE_EVENT_DESCRIPTION,
-    UPDATE_MODEL_STATE,
-    UPDATE_REVIEW_DESCRIPTION,
-    UPSERT_REVIEW_SEGMENT,
-)
 
 logger = logging.getLogger(__name__)
 
-# Internal IPC topics — NEVER allowed from WebSocket, regardless of role
-_WS_BLOCKED_TOPICS = frozenset(
-    {
-        INSERT_MANY_RECORDINGS,
-        INSERT_PREVIEW,
-        REQUEST_REGION_GRID,
-        UPSERT_REVIEW_SEGMENT,
-        CLEAR_ONGOING_REVIEW_SEGMENTS,
-        UPDATE_CAMERA_ACTIVITY,
-        UPDATE_AUDIO_ACTIVITY,
-        EXPIRE_AUDIO_ACTIVITY,
-        UPDATE_EVENT_DESCRIPTION,
-        UPDATE_REVIEW_DESCRIPTION,
-        UPDATE_MODEL_STATE,
-        UPDATE_EMBEDDINGS_REINDEX_PROGRESS,
-        UPDATE_BIRDSEYE_LAYOUT,
-        UPDATE_AUDIO_TRANSCRIPTION_STATE,
-        NOTIFICATION_TEST,
-    }
-)
-
-# Read-only topics any authenticated user (including viewer) can send
-_WS_VIEWER_TOPICS = frozenset(
-    {
-        "onConnect",
-        "modelState",
-        "audioTranscriptionState",
-        "birdseyeLayout",
-        "embeddingsReindexProgress",
-    }
-)
-
-
-def _check_ws_authorization(
-    topic: str,
-    role_header: str | None,
-    separator: str,
-) -> bool:
-    """Check if a WebSocket message is authorized.
-
-    Args:
-        topic: The message topic.
-        role_header: The HTTP_REMOTE_ROLE header value, or None.
-        separator: The role separator character from proxy config.
-
-    Returns:
-        True if authorized, False if blocked.
-    """
-    # Block IPC-only topics unconditionally
-    if topic in _WS_BLOCKED_TOPICS:
-        return False
-
-    # No role header: default to viewer (fail-closed)
-    if role_header is None:
-        return topic in _WS_VIEWER_TOPICS
-
-    # Check if any role is admin
-    roles = [r.strip() for r in role_header.split(separator)]
-    if "admin" in roles:
-        return True
-
-    # Non-admin: only viewer topics allowed
-    return topic in _WS_VIEWER_TOPICS
-
 
 class WebSocket(WebSocket_):  # type: ignore[misc]
     def unhandled_error(self, error: Any) -> None:
@@ -130,7 +49,6 @@ class WebSocketClient(Communicator):
 
         class _WebSocketHandler(WebSocket):
             receiver = self._dispatcher
-            role_separator = self.config.proxy.separator or ","
 
             def received_message(self, message: WebSocket.received_message) -> None:  # type: ignore[name-defined]
                 try:
@@ -145,25 +63,11 @@ class WebSocketClient(Communicator):
                     )
                     return
 
-                topic = json_message["topic"]
-
-                # Authorization check (skip when environ is None — direct internal connection)
-                role_header = (
-                    self.environ.get("HTTP_REMOTE_ROLE") if self.environ else None
+                logger.debug(
+                    f"Publishing mqtt message from websockets at {json_message['topic']}."
                 )
-                if self.environ is not None and not _check_ws_authorization(
-                    topic, role_header, self.role_separator
-                ):
-                    logger.warning(
-                        "Blocked unauthorized WebSocket message: topic=%s, role=%s",
-                        topic,
-                        role_header,
-                    )
-                    return
-
-                logger.debug(f"Publishing mqtt message from websockets at {topic}.")
                 self.receiver(
-                    topic,
+                    json_message["topic"],
                     json_message["payload"],
                 )
 

frigate/config/camera/updater.py

@@ -20,7 +20,6 @@ class CameraConfigUpdateEnum(str, Enum):
     ffmpeg = "ffmpeg"
     live = "live"
     motion = "motion"  # includes motion and motion masks
-    mqtt = "mqtt"
     notifications = "notifications"
     objects = "objects"
     object_genai = "object_genai"
@@ -34,7 +33,6 @@ class CameraConfigUpdateEnum(str, Enum):
     lpr = "lpr"
     snapshots = "snapshots"
     timestamp_style = "timestamp_style"
-    ui = "ui"
     zones = "zones"
 
 

frigate/const.py

@@ -15,7 +15,7 @@ TRIGGER_DIR = f"{CLIPS_DIR}/triggers"
 BIRDSEYE_PIPE = "/tmp/cache/birdseye"
 CACHE_DIR = "/tmp/cache"
 REPLAY_CAMERA_PREFIX = "_replay_"
-REPLAY_DIR = os.path.join(CLIPS_DIR, "replay")
+REPLAY_DIR = os.path.join(CACHE_DIR, "replay")
 PLUS_ENV_VAR = "PLUS_API_KEY"
 PLUS_API_HOST = "https://api.frigate.video"
 

frigate/data_processing/common/face/model.py

@@ -133,61 +133,6 @@ class FaceRecognizer(ABC):
             return 0.0
 
 
-def build_class_mean(
-    embs: list[np.ndarray],
-    trim: float = 0.15,
-    outlier_threshold: float = 0.30,
-    min_keep_frac: float = 0.7,
-    max_iters: int = 3,
-) -> np.ndarray:
-    """Build a class-mean embedding with two-layer outlier protection.
-
-    Layer 1 (iterative, vector-wise): drop whole embeddings whose cosine
-    similarity to the current class mean is below ``outlier_threshold``.
-    Catches mislabeled or corrupted training samples (wrong face in the
-    folder, full-frame screenshots, extreme crops) that per-dimension
-    trimming cannot detect.
-
-    Layer 2 (per-dimension): ``scipy.stats.trim_mean`` on the retained set
-    to smooth per-component noise (lighting, expression, alignment jitter).
-
-    Collections with fewer than 5 images bypass outlier rejection — too few
-    samples to establish a reliable class center.
-    """
-    arr = np.stack(embs, axis=0)
-
-    if len(arr) < 5:
-        return np.asarray(stats.trim_mean(arr, trim, axis=0))
-
-    keep = np.ones(len(arr), dtype=bool)
-    floor = max(5, int(np.ceil(min_keep_frac * len(arr))))
-
-    for _ in range(max_iters):
-        mean = stats.trim_mean(arr[keep], trim, axis=0)
-        m_norm = mean / (np.linalg.norm(mean) + 1e-9)
-        e_norms = arr / (np.linalg.norm(arr, axis=1, keepdims=True) + 1e-9)
-        cos = e_norms @ m_norm
-        new_keep = cos >= outlier_threshold
-
-        if new_keep.sum() < floor:
-            top = np.argsort(-cos)[:floor]
-            new_keep = np.zeros(len(arr), dtype=bool)
-            new_keep[top] = True
-
-        if np.array_equal(new_keep, keep):
-            break
-        keep = new_keep
-
-    dropped = int((~keep).sum())
-
-    if dropped:
-        logger.debug(
-            f"Vector-wise outlier filter dropped {dropped}/{len(arr)} embeddings"
-        )
-
-    return np.asarray(stats.trim_mean(arr[keep], trim, axis=0))
-
-
 def similarity_to_confidence(
     cosine_similarity: float,
     median: float = 0.3,
@@ -284,7 +229,7 @@ class FaceNetRecognizer(FaceRecognizer):
 
         for name, embs in face_embeddings_map.items():
             if embs:
-                self.mean_embs[name] = build_class_mean(embs)
+                self.mean_embs[name] = stats.trim_mean(embs, 0.15)
 
         logger.debug("Finished building ArcFace model")
 
@@ -395,7 +340,7 @@ class ArcFaceRecognizer(FaceRecognizer):
 
         for name, embs in face_embeddings_map.items():
             if embs:
-                self.mean_embs[name] = build_class_mean(embs)
+                self.mean_embs[name] = stats.trim_mean(embs, 0.15)
 
         logger.debug("Finished building ArcFace model")
 

frigate/data_processing/common/license_plate/mixin.py

@@ -1073,6 +1073,10 @@ class LicensePlateProcessingMixin:
                     top_score = score
                     top_box = bbox
 
+                if score > top_score:
+                    top_score = score
+                    top_box = bbox
+
         # Return the top scoring bounding box if found
         if top_box is not None:
             # expand box by 5% to help with OCR
@@ -1088,6 +1092,9 @@ class LicensePlateProcessingMixin:
                 ]
             ).clip(0, [input.shape[1], input.shape[0]] * 2)
 
+            logger.debug(
+                f"{camera}: Found license plate. Bounding box: {expanded_box.astype(int)}"
+            )
             return tuple(int(x) for x in expanded_box)  # type: ignore[return-value]
         else:
             return None  # No detection above the threshold
@@ -1353,8 +1360,8 @@ class LicensePlateProcessingMixin:
                 )
 
                 # check that license plate is valid
-                # quadruple the value because we've doubled both dimensions of the car
-                if license_plate_area < self.config.cameras[camera].lpr.min_area * 4:
+                # double the value because we've doubled the size of the car
+                if license_plate_area < self.config.cameras[camera].lpr.min_area * 2:
                     logger.debug(f"{camera}: License plate is less than min_area")
                     return
 
@@ -1458,7 +1465,6 @@ class LicensePlateProcessingMixin:
                     license_plate_frame,
                 )
 
-        logger.debug(f"{camera}: Found license plate. Bounding box: {list(plate_box)}")
         logger.debug(f"{camera}: Running plate recognition for id: {id}.")
 
         # run detection, returns results sorted by confidence, best first

frigate/data_processing/post/review_descriptions.py

@@ -39,8 +39,6 @@ logger = logging.getLogger(__name__)
 
 RECORDING_BUFFER_EXTENSION_PERCENT = 0.10
 MIN_RECORDING_DURATION = 10
-MAX_IMAGE_TOKENS = 24000
-MAX_FRAMES_PER_SECOND = 1
 
 
 class ReviewDescriptionProcessor(PostProcessorApi):
@@ -62,22 +60,14 @@ class ReviewDescriptionProcessor(PostProcessorApi):
     def calculate_frame_count(
         self,
         camera: str,
-        duration: float,
         image_source: ImageSourceEnum = ImageSourceEnum.preview,
         height: int = 480,
     ) -> int:
-        """Calculate optimal number of frames based on event duration, context size,
-        image source, and resolution.
+        """Calculate optimal number of frames based on context size, image source, and resolution.
 
-        Per-image token cost is asked of the GenAI provider so providers that know
-        their model's true cost (e.g. llama.cpp can probe the loaded mmproj) can
-        diverge from the default ~1-token-per-1250-pixels heuristic. The frame
-        budget is bounded by:
-          - remaining context window after prompt + response reservations
-          - a fixed MAX_IMAGE_TOKENS ceiling
-          - MAX_FRAMES_PER_SECOND x duration, to avoid drowning short events in
-            near-duplicate frames where the model latches onto the redundant middle
-            and skips the start/end action
+        Token usage varies by resolution: larger images (ultra-wide aspect ratios) use more tokens.
+        Estimates ~1 token per 1250 pixels. Targets 98% context utilization with safety margin.
+        Capped at 20 frames.
         """
         client = self.genai_manager.description_client
 
@@ -115,15 +105,14 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                 width = target_width
                 height = int(target_width / aspect_ratio)
 
-        tokens_per_image = client.estimate_image_tokens(width, height)
+        pixels_per_image = width * height
+        tokens_per_image = pixels_per_image / 1250
         prompt_tokens = 3800
         response_tokens = 300
-        context_budget = context_size - prompt_tokens - response_tokens
-        image_token_budget = min(context_budget, MAX_IMAGE_TOKENS)
-        max_frames_by_tokens = int(image_token_budget / tokens_per_image)
-        max_frames_by_duration = int(duration * MAX_FRAMES_PER_SECOND)
-        max_frames = min(max_frames_by_tokens, max_frames_by_duration)
-        return max(max_frames, 3)
+        available_tokens = context_size - prompt_tokens - response_tokens
+        max_frames = int(available_tokens / tokens_per_image)
+
+        return min(max(max_frames, 3), 20)
 
     def process_data(
         self, data: dict[str, Any], data_type: PostProcessDataEnum
@@ -366,17 +355,12 @@ class ReviewDescriptionProcessor(PostProcessorApi):
         file_start = f"preview_{camera}-"
         start_file = f"{file_start}{start_time}.webp"
         end_file = f"{file_start}{end_time}.webp"
-
-        camera_files = [
-            entry.name
-            for entry in os.scandir(preview_dir)
-            if entry.name.startswith(file_start)
-        ]
-        camera_files.sort()
-
         all_frames: list[str] = []
 
-        for file in camera_files:
+        for file in sorted(os.listdir(preview_dir)):
+            if not file.startswith(file_start):
+                continue
+
             if file < start_file:
                 if len(all_frames):
                     all_frames[0] = os.path.join(preview_dir, file)
@@ -392,9 +376,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
             all_frames.append(os.path.join(preview_dir, file))
 
         frame_count = len(all_frames)
-        desired_frame_count = self.calculate_frame_count(
-            camera, duration=end_time - start_time
-        )
+        desired_frame_count = self.calculate_frame_count(camera)
 
         if frame_count <= desired_frame_count:
             return all_frames
@@ -418,7 +400,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
         """Get frames from recordings at specified timestamps."""
         duration = end_time - start_time
         desired_frame_count = self.calculate_frame_count(
-            camera, duration, ImageSourceEnum.recordings, height
+            camera, ImageSourceEnum.recordings, height
         )
 
         # Calculate evenly spaced timestamps throughout the duration

frigate/data_processing/post/types.py

@@ -1,48 +1,21 @@
-from typing import Annotated
-
-from pydantic import BaseModel, ConfigDict, Field, StringConstraints
-
-ObservationItem = Annotated[str, StringConstraints(min_length=20, max_length=160)]
+from pydantic import BaseModel, ConfigDict, Field
 
 
 class ReviewMetadata(BaseModel):
     model_config = ConfigDict(extra="ignore", protected_namespaces=())
 
-    observations: list[ObservationItem] = Field(
-        ...,
-        min_length=3,
-        max_length=15,
-        description=(
-            "Enumerate the significant observations across all frames, in "
-            "chronological order, BEFORE composing the scene narrative. "
-            "Include the very start of the activity — for example, a vehicle "
-            "entering the frame or pulling into the driveway — even if it "
-            "lasts only a few frames and the rest of the clip is dominated "
-            "by a longer activity. Include each arrival, departure, motion "
-            "event, object handled, and notable change in position or state. "
-            "Each item is a single concrete fact written as a complete "
-            "sentence. Do not summarize, interpret, or assign meaning here — "
-            "that belongs in the scene field."
-        ),
-    )
     title: str = Field(
-        max_length=80,
-        description="Under 10 words. Name the apparent purpose or outcome of the activity together with the location involved. Do not narrate or list the sequence of actions step by step.",
+        description="A short title characterizing what took place and where, under 10 words."
     )
     scene: str = Field(
-        min_length=150,
-        max_length=600,
-        description="A chronological narrative of what happens from start to finish, drawing directly from the items in observations.",
+        description="A chronological narrative of what happens from start to finish."
     )
     shortSummary: str = Field(
-        min_length=70,
-        max_length=120,
-        description="A brief 2-sentence summary of the scene, suitable for notifications.",
+        description="A brief 2-sentence summary of the scene, suitable for notifications."
     )
     confidence: float = Field(
         ge=0.0,
-        le=1.0,
-        description="Confidence in the analysis as a decimal between 0.0 and 1.0, where 0.0 means no confidence and 1.0 means complete confidence. Express ONLY as a decimal.",
+        description="Confidence in the analysis, from 0 to 1.",
     )
     potential_threat_level: int = Field(
         ge=0,

frigate/detectors/plugins/openvino.py

@@ -52,12 +52,6 @@ class OvDetector(DetectionApi):
         self.h = detector_config.model.height
         self.w = detector_config.model.width
 
-        logger.info(
-            "Loading OpenVINO model %s on device %s",
-            detector_config.model.path,
-            detector_config.device,
-        )
-
         self.runner = OpenVINOModelRunner(
             model_path=detector_config.model.path,
             device=detector_config.device,

frigate/embeddings/__init__.py

@@ -4,7 +4,6 @@ import base64
 import json
 import logging
 import os
-import sys
 import threading
 from json.decoder import JSONDecodeError
 from multiprocessing.synchronize import Event as MpEvent
@@ -53,14 +52,6 @@ class EmbeddingProcess(FrigateProcess):
             self.stop_event,
         )
         maintainer.start()
-        maintainer.join()
-
-        # If the maintainer thread exited but no shutdown was requested, it
-        # crashed. Surface as a non-zero exit so the watchdog restarts us
-        # instead of treating the silent thread death as a clean shutdown.
-        if not self.stop_event.is_set():
-            logger.error("Embeddings maintainer thread exited unexpectedly")
-            sys.exit(1)
 
 
 class EmbeddingsContext:

frigate/embeddings/maintainer.py

@@ -310,10 +310,6 @@ class EmbeddingMaintainer(threading.Thread):
             self._handle_custom_classification_update(topic, payload)
             return
 
-        if topic == "config/genai":
-            self.config.genai = payload
-            self.genai_manager.update_config(self.config)
-
         # Broadcast to all processors — each decides if the topic is relevant
         for processor in self.realtime_processors:
             processor.update_config(topic, payload)
@@ -517,16 +513,10 @@ class EmbeddingMaintainer(threading.Thread):
                 try:
                     event: Event = Event.get(Event.id == event_id)
                 except DoesNotExist:
-                    for processor in self.post_processors:
-                        if isinstance(processor, ObjectDescriptionProcessor):
-                            processor.cleanup_event(event_id)
                     continue
 
                 # Skip the event if not an object
                 if event.data.get("type") != "object":
-                    for processor in self.post_processors:
-                        if isinstance(processor, ObjectDescriptionProcessor):
-                            processor.cleanup_event(event_id)
                     continue
 
                 # Extract valid thumbnail

frigate/events/audio.py

@@ -205,7 +205,6 @@ class AudioEventMaintainer(threading.Thread):
             self.transcription_thread.start()
 
         self.was_enabled = camera.enabled
-        self.was_audio_enabled = camera.audio.enabled
 
     def detect_audio(self, audio: np.ndarray) -> None:
         if not self.camera_config.audio.enabled or self.stop_event.is_set():
@@ -364,17 +363,6 @@ class AudioEventMaintainer(threading.Thread):
                 time.sleep(0.1)
                 continue
 
-            audio_enabled = self.camera_config.audio.enabled
-            if audio_enabled != self.was_audio_enabled:
-                if not audio_enabled:
-                    self.logger.debug(
-                        f"Disabling audio detections for {self.camera_config.name}, ending events"
-                    )
-                    self.requestor.send_data(
-                        EXPIRE_AUDIO_ACTIVITY, self.camera_config.name
-                    )
-                self.was_audio_enabled = audio_enabled
-
             self.read_audio()
 
         if self.audio_listener:

frigate/genai/__init__.py

@@ -2,7 +2,6 @@
 
 import datetime
 import importlib
-import json
 import logging
 import os
 import re
@@ -10,7 +9,6 @@ from typing import Any, Callable, Optional
 
 import numpy as np
 from playhouse.shortcuts import model_to_dict
-from pydantic import ValidationError
 
 from frigate.config import CameraConfig, GenAIConfig, GenAIProviderEnum
 from frigate.const import CLIPS_DIR
@@ -153,6 +151,9 @@ Each line represents a detection state, not necessarily unique individuals. The
             if "other_concerns" in schema.get("required", []):
                 schema["required"].remove("other_concerns")
 
+        # OpenAI strict mode requires additionalProperties: false on all objects
+        schema["additionalProperties"] = False
+
         response_format = {
             "type": "json_schema",
             "json_schema": {
@@ -180,36 +181,7 @@ Each line represents a detection state, not necessarily unique individuals. The
 
             try:
                 metadata = ReviewMetadata.model_validate_json(clean_json)
-            except ValidationError as ve:
-                # Constraint violations (length, item count, ranges) are logged
-                # at debug and the response is kept anyway — a slightly
-                # off-spec answer is still usable, and dropping the whole
-                # response loses the narrative content the model produced.
-                for err in ve.errors():
-                    loc = ".".join(str(p) for p in err["loc"]) or "<root>"
-                    logger.debug(
-                        "Review metadata soft validation: %s — %s (input: %r)",
-                        loc,
-                        err["msg"],
-                        err.get("input"),
-                    )
-                try:
-                    raw = json.loads(clean_json)
-                except json.JSONDecodeError as je:
-                    logger.error("Failed to parse review description JSON: %s", je)
-                    return None
-                # observations and confidence are required on the model; fill an empty default
-                # if the response omitted it so attribute access stays safe.
-                raw.setdefault("observations", [])
-                raw.setdefault("confidence", 0.0)
-                metadata = ReviewMetadata.model_construct(**raw)
-            except Exception as e:
-                logger.error(
-                    f"Failed to parse review description as the response did not match expected format. {e}"
-                )
-                return None
 
-            try:
                 # Normalize confidence if model returned a percentage (e.g. 85 instead of 0.85)
                 if metadata.confidence > 1.0:
                     metadata.confidence = min(metadata.confidence / 100.0, 1.0)
@@ -222,7 +194,10 @@ Each line represents a detection state, not necessarily unique individuals. The
                 metadata.time = review_data["start"]
                 return metadata
             except Exception as e:
-                logger.error(f"Failed to post-process review metadata: {e}")
+                # rarely LLMs can fail to follow directions on output format
+                logger.warning(
+                    f"Failed to parse review description as the response did not match expected format. {e}"
+                )
                 return None
         else:
             logger.debug(
@@ -369,14 +344,6 @@ Guidelines:
         """Get the context window size for this provider in tokens."""
         return 4096
 
-    def estimate_image_tokens(self, width: int, height: int) -> float:
-        """Estimate prompt tokens consumed by a single image of the given dimensions.
-
-        Default heuristic: ~1 token per 1250 pixels. Providers that can measure or
-        know their model's exact image-token cost should override.
-        """
-        return (width * height) / 1250
-
     def embed(
         self,
         texts: list[str] | None = None,

frigate/genai/gemini.py

@@ -136,44 +136,22 @@ class GeminiClient(GenAIClient):
                             )
                         )
                 elif role == "assistant":
-                    parts: list[types.Part] = []
-                    if content:
-                        parts.append(types.Part.from_text(text=content))
-                    for tc in msg.get("tool_calls") or []:
-                        func = tc.get("function") or {}
-                        tc_name = func.get("name") or ""
-                        tc_args: Any = func.get("arguments")
-                        if isinstance(tc_args, str):
-                            try:
-                                tc_args = json.loads(tc_args)
-                            except (json.JSONDecodeError, TypeError):
-                                tc_args = {}
-                        if not isinstance(tc_args, dict):
-                            tc_args = {}
-                        if tc_name:
-                            parts.append(
-                                types.Part.from_function_call(
-                                    name=tc_name, args=tc_args
-                                )
-                            )
-                    if not parts:
-                        parts.append(types.Part.from_text(text=" "))
-                    gemini_messages.append(types.Content(role="model", parts=parts))
+                    gemini_messages.append(
+                        types.Content(
+                            role="model", parts=[types.Part.from_text(text=content)]
+                        )
+                    )
                 elif role == "tool":
                     # Handle tool response
-                    response_payload = (
-                        content if isinstance(content, dict) else {"result": content}
-                    )
+                    function_response = {
+                        "name": msg.get("name", ""),
+                        "response": content,
+                    }
                     gemini_messages.append(
                         types.Content(
                             role="function",
                             parts=[
-                                types.Part.from_function_response(
-                                    name=msg.get("name")
-                                    or msg.get("tool_call_id")
-                                    or "",
-                                    response=response_payload,
-                                )
+                                types.Part.from_function_response(function_response)  # type: ignore[misc,call-arg,arg-type]
                             ],
                         )
                     )
@@ -365,44 +343,22 @@ class GeminiClient(GenAIClient):
                             )
                         )
                 elif role == "assistant":
-                    parts: list[types.Part] = []
-                    if content:
-                        parts.append(types.Part.from_text(text=content))
-                    for tc in msg.get("tool_calls") or []:
-                        func = tc.get("function") or {}
-                        tc_name = func.get("name") or ""
-                        tc_args: Any = func.get("arguments")
-                        if isinstance(tc_args, str):
-                            try:
-                                tc_args = json.loads(tc_args)
-                            except (json.JSONDecodeError, TypeError):
-                                tc_args = {}
-                        if not isinstance(tc_args, dict):
-                            tc_args = {}
-                        if tc_name:
-                            parts.append(
-                                types.Part.from_function_call(
-                                    name=tc_name, args=tc_args
-                                )
-                            )
-                    if not parts:
-                        parts.append(types.Part.from_text(text=" "))
-                    gemini_messages.append(types.Content(role="model", parts=parts))
+                    gemini_messages.append(
+                        types.Content(
+                            role="model", parts=[types.Part.from_text(text=content)]
+                        )
+                    )
                 elif role == "tool":
                     # Handle tool response
-                    response_payload = (
-                        content if isinstance(content, dict) else {"result": content}
-                    )
+                    function_response = {
+                        "name": msg.get("name", ""),
+                        "response": content,
+                    }
                     gemini_messages.append(
                         types.Content(
                             role="function",
                             parts=[
-                                types.Part.from_function_response(
-                                    name=msg.get("name")
-                                    or msg.get("tool_call_id")
-                                    or "",
-                                    response=response_payload,
-                                )
+                                types.Part.from_function_response(function_response)  # type: ignore[misc,call-arg,arg-type]
                             ],
                         )
                     )

frigate/genai/llama_cpp.py

@@ -42,9 +42,6 @@ class LlamaCppClient(GenAIClient):
     _supports_vision: bool
     _supports_audio: bool
     _supports_tools: bool
-    _image_token_cache: dict[tuple[int, int], int]
-    _text_baseline_tokens: int | None
-    _media_marker: str
 
     def _init_provider(self) -> str | None:
         """Initialize the client and query model metadata from the server."""
@@ -55,9 +52,6 @@ class LlamaCppClient(GenAIClient):
         self._supports_vision = False
         self._supports_audio = False
         self._supports_tools = False
-        self._image_token_cache = {}
-        self._text_baseline_tokens = None
-        self._media_marker = "<__media__>"
 
         base_url = (
             self.genai_config.base_url.rstrip("/")
@@ -143,13 +137,6 @@ class LlamaCppClient(GenAIClient):
             chat_caps = props.get("chat_template_caps", {})
             self._supports_tools = chat_caps.get("supports_tools", False)
 
-            # Media marker for multimodal embeddings; the server randomizes this
-            # per startup unless LLAMA_MEDIA_MARKER is set, so we must read it
-            # from /props rather than hardcoding "<__media__>".
-            media_marker = props.get("media_marker")
-            if isinstance(media_marker, str) and media_marker:
-                self._media_marker = media_marker
-
             logger.info(
                 "llama.cpp model '%s' initialized — context: %s, vision: %s, audio: %s, tools: %s",
                 configured_model,
@@ -285,91 +272,6 @@ class LlamaCppClient(GenAIClient):
             return self._context_size
         return 4096
 
-    def estimate_image_tokens(self, width: int, height: int) -> float:
-        """Probe the llama.cpp server to learn the model's image-token cost at the
-        requested dimensions.
-
-        llama.cpp's image tokenization is a deterministic function of dimensions and
-        the loaded mmproj, so the result is cached per (width, height) for the
-        lifetime of the process. Falls back to the base pixel heuristic if the
-        server is unreachable or the response is malformed.
-        """
-        if self.provider is None:
-            return super().estimate_image_tokens(width, height)
-
-        cached = self._image_token_cache.get((width, height))
-
-        if cached is not None:
-            return cached
-
-        try:
-            baseline = self._probe_baseline_tokens()
-            with_image = self._probe_image_prompt_tokens(width, height)
-            tokens = max(1, with_image - baseline)
-        except Exception as e:
-            logger.debug(
-                "llama.cpp image-token probe failed for %dx%d (%s); using heuristic",
-                width,
-                height,
-                e,
-            )
-            return super().estimate_image_tokens(width, height)
-
-        self._image_token_cache[(width, height)] = tokens
-        logger.debug(
-            "llama.cpp model '%s' uses ~%d tokens for %dx%d images",
-            self.genai_config.model,
-            tokens,
-            width,
-            height,
-        )
-        return tokens
-
-    def _probe_baseline_tokens(self) -> int:
-        """Return prompt_tokens for a minimal text-only request. Cached after first call."""
-        if self._text_baseline_tokens is not None:
-            return self._text_baseline_tokens
-
-        self._text_baseline_tokens = self._probe_prompt_tokens(
-            [{"type": "text", "text": "."}]
-        )
-        return self._text_baseline_tokens
-
-    def _probe_image_prompt_tokens(self, width: int, height: int) -> int:
-        """Return prompt_tokens for a single synthetic image plus minimal text."""
-        img = Image.new("RGB", (width, height), (128, 128, 128))
-        buf = io.BytesIO()
-        img.save(buf, format="JPEG", quality=60)
-        encoded = base64.b64encode(buf.getvalue()).decode("utf-8")
-        return self._probe_prompt_tokens(
-            [
-                {"type": "text", "text": "."},
-                {
-                    "type": "image_url",
-                    "image_url": {"url": f"data:image/jpeg;base64,{encoded}"},
-                },
-            ]
-        )
-
-    def _probe_prompt_tokens(self, content: list[dict[str, Any]]) -> int:
-        """POST a 1-token chat completion and return reported prompt_tokens.
-
-        Uses a generous timeout to absorb a cold model load on the first probe
-        when the server lazily loads models on demand (e.g. llama-swap).
-        """
-        payload = {
-            "model": self.genai_config.model,
-            "messages": [{"role": "user", "content": content}],
-            "max_tokens": 1,
-        }
-        response = requests.post(
-            f"{self.provider}/v1/chat/completions",
-            json=payload,
-            timeout=60,
-        )
-        response.raise_for_status()
-        return int(response.json()["usage"]["prompt_tokens"])
-
     def _build_payload(
         self,
         messages: list[dict[str, Any]],
@@ -474,11 +376,10 @@ class LlamaCppClient(GenAIClient):
             jpeg_bytes = _to_jpeg(img)
             to_encode = jpeg_bytes if jpeg_bytes is not None else img
             encoded = base64.b64encode(to_encode).decode("utf-8")
-            # prompt_string must contain the server's media marker placeholder.
-            # The marker is randomized per server startup (read from /props).
+            # prompt_string must contain <__media__> placeholder for image tokenization
             content.append(
                 {
-                    "prompt_string": f"{self._media_marker}\n",
+                    "prompt_string": "<__media__>\n",
                     "multimodal_data": [encoded],  # type: ignore[dict-item]
                 }
             )

frigate/genai/ollama.py

@@ -31,12 +31,6 @@ class OllamaClient(GenAIClient):
     provider: ApiClient | None
     provider_options: dict[str, Any]
 
-    def _auth_headers(self) -> dict | None:
-        if self.genai_config.api_key:
-            return {"Authorization": "Bearer " + self.genai_config.api_key}
-
-        return None
-
     def _init_provider(self) -> ApiClient | None:
         """Initialize the client."""
         self.provider_options = {
@@ -45,11 +39,7 @@ class OllamaClient(GenAIClient):
         }
 
         try:
-            client = ApiClient(
-                host=self.genai_config.base_url,
-                timeout=self.timeout,
-                headers=self._auth_headers(),
-            )
+            client = ApiClient(host=self.genai_config.base_url, timeout=self.timeout)
             # ensure the model is available locally
             response = client.show(self.genai_config.model)
             if response.get("error"):
@@ -123,15 +113,6 @@ class OllamaClient(GenAIClient):
                 schema = response_format.get("json_schema", {}).get("schema")
                 if schema:
                     ollama_options["format"] = self._clean_schema_for_ollama(schema)
-            logger.debug(
-                "Ollama generate request: model=%s, prompt_len=%s, image_count=%s, "
-                "has_format=%s, options=%s",
-                self.genai_config.model,
-                len(prompt),
-                len(images) if images else 0,
-                "format" in ollama_options,
-                {k: v for k, v in ollama_options.items() if k != "format"},
-            )
             result = self.provider.generate(
                 self.genai_config.model,
                 prompt,
@@ -139,24 +120,9 @@ class OllamaClient(GenAIClient):
                 **ollama_options,
             )
             logger.debug(
-                "Ollama generate response: done=%s, done_reason=%s, eval_count=%s, "
-                "prompt_eval_count=%s, response_len=%s",
-                result.get("done"),
-                result.get("done_reason"),
-                result.get("eval_count"),
-                result.get("prompt_eval_count"),
-                len(result.get("response", "") or ""),
+                f"Ollama tokens used: eval_count={result.get('eval_count')}, prompt_eval_count={result.get('prompt_eval_count')}"
             )
-            response_text = str(result["response"]).strip()
-            if not response_text:
-                logger.warning(
-                    "Ollama returned a blank response for model %s (done_reason=%s, "
-                    "eval_count=%s). Check model output, ensure thinking is disabled.",
-                    self.genai_config.model,
-                    result.get("done_reason"),
-                    result.get("eval_count"),
-                )
-            return response_text
+            return str(result["response"]).strip()
         except (
             TimeoutException,
             ResponseError,
@@ -176,9 +142,7 @@ class OllamaClient(GenAIClient):
                 return []
             try:
                 client = ApiClient(
-                    host=self.genai_config.base_url,
-                    timeout=self.timeout,
-                    headers=self._auth_headers(),
+                    host=self.genai_config.base_url, timeout=self.timeout
                 )
             except Exception:
                 return []
@@ -356,7 +320,6 @@ class OllamaClient(GenAIClient):
                 async_client = OllamaAsyncClient(
                     host=self.genai_config.base_url,
                     timeout=self.timeout,
-                    headers=self._auth_headers(),
                 )
                 response = await async_client.chat(**request_params)
                 result = self._message_from_response(response)
@@ -372,7 +335,6 @@ class OllamaClient(GenAIClient):
             async_client = OllamaAsyncClient(
                 host=self.genai_config.base_url,
                 timeout=self.timeout,
-                headers=self._auth_headers(),
             )
             content_parts: list[str] = []
             final_message: dict[str, Any] | None = None

frigate/genai/openai.py

@@ -73,39 +73,14 @@ class OpenAIClient(GenAIClient):
                 **self.genai_config.runtime_options,
             }
             if response_format:
-                # OpenAI strict mode requires additionalProperties: false on the schema
-                if response_format.get("type") == "json_schema" and response_format.get(
-                    "json_schema", {}
-                ).get("strict"):
-                    schema = response_format.get("json_schema", {}).get("schema")
-                    if isinstance(schema, dict):
-                        schema["additionalProperties"] = False
                 request_params["response_format"] = response_format
-
             result = self.provider.chat.completions.create(**request_params)
-
             if (
                 result is not None
                 and hasattr(result, "choices")
                 and len(result.choices) > 0
             ):
-                message = result.choices[0].message
-                content = message.content
-
-                if not content:
-                    # When reasoning is enabled for some OpenAI backends the actual response
-                    # is incorrectly placed in reasoning_content instead of content.
-                    # This is buggy/incorrect behavior — reasoning should not be
-                    # enabled for these models.
-                    reasoning_content = getattr(message, "reasoning_content", None)
-                    if reasoning_content:
-                        logger.warning(
-                            "Response content was empty but reasoning_content was provided; "
-                            "reasoning appears to be enabled and should be disabled for this model."
-                        )
-                        content = reasoning_content
-
-                return str(content.strip()) if content else None
+                return str(result.choices[0].message.content.strip())
             return None
         except (TimeoutException, Exception) as e:
             logger.warning("OpenAI returned an error: %s", str(e))

frigate/jobs/export.py

@@ -7,13 +7,11 @@ import time
 from dataclasses import dataclass
 from pathlib import Path
 from queue import Full, Queue
-from typing import Any, Callable, Optional
+from typing import Any, Optional
 
 from peewee import DoesNotExist
 
-from frigate.comms.inter_process import InterProcessRequestor
 from frigate.config import FrigateConfig
-from frigate.const import UPDATE_JOB_STATE
 from frigate.jobs.job import Job
 from frigate.models import Export
 from frigate.record.export import PlaybackSourceEnum, RecordingExporter
@@ -25,16 +23,6 @@ logger = logging.getLogger(__name__)
 # Prevents a runaway client from unbounded memory growth.
 MAX_QUEUED_EXPORT_JOBS = 100
 
-# Minimum interval between progress broadcasts. FFmpeg can emit progress
-# events many times per second; we coalesce them so the WebSocket isn't
-# flooded with redundant updates.
-PROGRESS_BROADCAST_MIN_INTERVAL = 1.0
-
-# Delay before removing a completed job from the in-memory map. Gives the
-# frontend a chance to receive the final state via WebSocket before SWR
-# polling takes over.
-COMPLETED_JOB_CLEANUP_DELAY = 5.0
-
 
 class ExportQueueFullError(RuntimeError):
     """Raised when the export queue is at capacity."""
@@ -55,8 +43,6 @@ class ExportJob(Job):
     ffmpeg_input_args: Optional[str] = None
     ffmpeg_output_args: Optional[str] = None
     cpu_fallback: bool = False
-    current_step: str = "queued"
-    progress_percent: float = 0.0
 
     def to_dict(self) -> dict[str, Any]:
         """Convert to dictionary for API responses.
@@ -78,8 +64,6 @@ class ExportJob(Job):
             "end_time": self.end_time,
             "error_message": self.error_message,
             "results": self.results,
-            "current_step": self.current_step,
-            "progress_percent": self.progress_percent,
         }
 
 
@@ -107,38 +91,6 @@ class ExportQueueWorker(threading.Thread):
                 self.manager.queue.task_done()
 
 
-class JobStatePublisher:
-    """Publishes a single job state payload to the dispatcher.
-
-    Each call opens a short-lived :py:class:`InterProcessRequestor`, sends
-    the payload, and closes the socket. The short-lived design avoids
-    REQ/REP state corruption that would arise from sharing a single REQ
-    socket across the API thread and worker threads (REQ sockets must
-    strictly alternate send/recv).
-
-    With the 1s broadcast throttle in place, socket creation overhead is
-    negligible. The class also exists so tests can substitute a no-op
-    instance instead of stubbing ZMQ — see ``BaseTestHttp.setUp``.
-    """
-
-    def publish(self, payload: dict[str, Any]) -> None:
-        try:
-            requestor = InterProcessRequestor()
-        except Exception as err:
-            logger.warning("Failed to open job state requestor: %s", err)
-            return
-
-        try:
-            requestor.send_data(UPDATE_JOB_STATE, payload)
-        except Exception as err:
-            logger.debug("Job state broadcast failed: %s", err)
-        finally:
-            try:
-                requestor.stop()
-            except Exception:
-                pass
-
-
 class ExportJobManager:
     """Concurrency-limited manager for queued export jobs."""
 
@@ -147,7 +99,6 @@ class ExportJobManager:
         config: FrigateConfig,
         max_concurrent: int,
         max_queued: int = MAX_QUEUED_EXPORT_JOBS,
-        publisher: Optional[JobStatePublisher] = None,
     ) -> None:
         self.config = config
         self.max_concurrent = max(1, max_concurrent)
@@ -156,68 +107,6 @@ class ExportJobManager:
         self.lock = threading.Lock()
         self.workers: list[ExportQueueWorker] = []
         self.started = False
-        self.publisher = publisher if publisher is not None else JobStatePublisher()
-        self._last_broadcast_monotonic: float = 0.0
-        self._broadcast_throttle_lock = threading.Lock()
-
-    def _broadcast_all_jobs(self, force: bool = False) -> None:
-        """Publish aggregate export job state via the job_state WS topic.
-
-        When ``force`` is False, broadcasts within
-        ``PROGRESS_BROADCAST_MIN_INTERVAL`` of the previous one are skipped
-        to avoid flooding the WebSocket with rapid progress updates.
-        ``force`` bypasses the throttle and is used for status transitions
-        (enqueue/start/finish) where the frontend needs the latest state.
-        """
-        now = time.monotonic()
-        with self._broadcast_throttle_lock:
-            if (
-                not force
-                and now - self._last_broadcast_monotonic
-                < PROGRESS_BROADCAST_MIN_INTERVAL
-            ):
-                return
-            self._last_broadcast_monotonic = now
-
-        with self.lock:
-            active = [
-                j
-                for j in self.jobs.values()
-                if j.status in (JobStatusTypesEnum.queued, JobStatusTypesEnum.running)
-            ]
-
-        any_running = any(j.status == JobStatusTypesEnum.running for j in active)
-        payload: dict[str, Any] = {
-            "job_type": "export",
-            "status": "running" if any_running else "queued",
-            "results": {"jobs": [j.to_dict() for j in active]},
-        }
-
-        try:
-            self.publisher.publish(payload)
-        except Exception as err:
-            logger.warning("Publisher raised during job state broadcast: %s", err)
-
-    def _make_progress_callback(self, job: ExportJob) -> Callable[[str, float], None]:
-        """Build a callback the exporter can invoke during execution."""
-
-        def on_progress(step: str, percent: float) -> None:
-            job.current_step = step
-            job.progress_percent = percent
-            self._broadcast_all_jobs()
-
-        return on_progress
-
-    def _schedule_job_cleanup(self, job_id: str) -> None:
-        """Drop a completed job from ``self.jobs`` after a short delay."""
-
-        def cleanup() -> None:
-            with self.lock:
-                self.jobs.pop(job_id, None)
-
-        timer = threading.Timer(COMPLETED_JOB_CLEANUP_DELAY, cleanup)
-        timer.daemon = True
-        timer.start()
 
     def ensure_started(self) -> None:
         """Ensure worker threads are started exactly once."""
@@ -262,8 +151,6 @@ class ExportJobManager:
         with self.lock:
             self.jobs[job.id] = job
 
-        self._broadcast_all_jobs(force=True)
-
         return job.id
 
     def get_job(self, job_id: str) -> Optional[ExportJob]:
@@ -328,7 +215,6 @@ class ExportJobManager:
         """Execute a queued export job."""
         job.status = JobStatusTypesEnum.running
         job.start_time = time.time()
-        self._broadcast_all_jobs(force=True)
 
         exporter = RecordingExporter(
             self.config,
@@ -343,7 +229,6 @@ class ExportJobManager:
             job.ffmpeg_input_args,
             job.ffmpeg_output_args,
             job.cpu_fallback,
-            on_progress=self._make_progress_callback(job),
         )
 
         try:
@@ -372,8 +257,6 @@ class ExportJobManager:
             job.error_message = str(err)
         finally:
             job.end_time = time.time()
-            self._broadcast_all_jobs(force=True)
-            self._schedule_job_cleanup(job.id)
 
 
 _job_manager: Optional[ExportJobManager] = None

frigate/output/birdseye.py

@@ -8,6 +8,7 @@ import os
 import queue
 import subprocess as sp
 import threading
+import time
 import traceback
 from multiprocessing.synchronize import Event as MpEvent
 from typing import Any, Optional
@@ -18,7 +19,6 @@ import numpy as np
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.config import BirdseyeModeEnum, FfmpegConfig, FrigateConfig
 from frigate.const import BASE_DIR, BIRDSEYE_PIPE, INSTALL_DIR, UPDATE_BIRDSEYE_LAYOUT
-from frigate.output.ws_auth import ws_has_camera_access
 from frigate.util.image import (
     SharedMemoryFrameManager,
     copy_yuv_to_position,
@@ -236,14 +236,12 @@ class BroadcastThread(threading.Thread):
         converter: FFMpegConverter,
         websocket_server: Any,
         stop_event: MpEvent,
-        config: FrigateConfig,
     ):
         super().__init__()
         self.camera = camera
         self.converter = converter
         self.websocket_server = websocket_server
         self.stop_event = stop_event
-        self.config = config
 
     def run(self) -> None:
         while not self.stop_event.is_set():
@@ -258,7 +256,6 @@ class BroadcastThread(threading.Thread):
                     if (
                         not ws.terminated
                         and ws.environ["PATH_INFO"] == f"/{self.camera}"
-                        and ws_has_camera_access(ws, self.camera, self.config)
                     ):
                         try:
                             ws.send(buf, binary=True)
@@ -809,11 +806,7 @@ class Birdseye:
             config.birdseye.restream,
         )
         self.broadcaster = BroadcastThread(
-            "birdseye",
-            self.converter,
-            websocket_server,
-            stop_event,
-            config,
+            "birdseye", self.converter, websocket_server, stop_event
         )
         self.birdseye_manager = BirdsEyeFrameManager(self.config, stop_event)
         self.frame_manager = SharedMemoryFrameManager()
@@ -881,7 +874,7 @@ class Birdseye:
                 coordinates = self.birdseye_manager.get_camera_coordinates()
                 self.requestor.send_data(UPDATE_BIRDSEYE_LAYOUT, coordinates)
         if self._idle_interval:
-            now = datetime.datetime.now().timestamp()
+            now = time.monotonic()
             is_idle = len(self.birdseye_manager.camera_layout) == 0
             if (
                 is_idle

frigate/output/camera.py

@@ -7,8 +7,7 @@ import threading
 from multiprocessing.synchronize import Event as MpEvent
 from typing import Any
 
-from frigate.config import CameraConfig, FfmpegConfig, FrigateConfig
-from frigate.output.ws_auth import ws_has_camera_access
+from frigate.config import CameraConfig, FfmpegConfig
 
 logger = logging.getLogger(__name__)
 
@@ -103,14 +102,12 @@ class BroadcastThread(threading.Thread):
         converter: FFMpegConverter,
         websocket_server: Any,
         stop_event: MpEvent,
-        config: FrigateConfig,
     ):
         super().__init__()
         self.camera = camera
         self.converter = converter
         self.websocket_server = websocket_server
         self.stop_event = stop_event
-        self.config = config
 
     def run(self) -> None:
         while not self.stop_event.is_set():
@@ -125,7 +122,6 @@ class BroadcastThread(threading.Thread):
                     if (
                         not ws.terminated
                         and ws.environ["PATH_INFO"] == f"/{self.camera}"
-                        and ws_has_camera_access(ws, self.camera, self.config)
                     ):
                         try:
                             ws.send(buf, binary=True)
@@ -139,11 +135,7 @@ class BroadcastThread(threading.Thread):
 
 class JsmpegCamera:
     def __init__(
-        self,
-        config: CameraConfig,
-        frigate_config: FrigateConfig,
-        stop_event: MpEvent,
-        websocket_server: Any,
+        self, config: CameraConfig, stop_event: MpEvent, websocket_server: Any
     ) -> None:
         self.config = config
         self.input: queue.Queue[bytes] = queue.Queue(maxsize=config.detect.fps)
@@ -162,11 +154,7 @@ class JsmpegCamera:
             config.live.quality,
         )
         self.broadcaster = BroadcastThread(
-            config.name or "",
-            self.converter,
-            websocket_server,
-            stop_event,
-            frigate_config,
+            config.name or "", self.converter, websocket_server, stop_event
         )
 
         self.converter.start()

frigate/output/output.py

@@ -32,7 +32,6 @@ from frigate.const import (
 from frigate.output.birdseye import Birdseye
 from frigate.output.camera import JsmpegCamera
 from frigate.output.preview import PreviewRecorder
-from frigate.output.ws_auth import ws_has_camera_access
 from frigate.util.image import SharedMemoryFrameManager, get_blank_yuv_frame
 from frigate.util.process import FrigateProcess
 
@@ -103,7 +102,7 @@ class OutputProcess(FrigateProcess):
     ) -> None:
         camera_config = self.config.cameras[camera]
         jsmpeg_cameras[camera] = JsmpegCamera(
-            camera_config, self.config, self.stop_event, websocket_server
+            camera_config, self.stop_event, websocket_server
         )
         preview_recorders[camera] = PreviewRecorder(camera_config)
         preview_write_times[camera] = 0
@@ -263,7 +262,6 @@ class OutputProcess(FrigateProcess):
             # send camera frame to ffmpeg process if websockets are connected
             if any(
                 ws.environ["PATH_INFO"].endswith(camera)
-                and ws_has_camera_access(ws, camera, self.config)
                 for ws in websocket_server.manager
             ):
                 # write to the converter for the camera if clients are listening to the specific camera
@@ -277,7 +275,6 @@ class OutputProcess(FrigateProcess):
                     self.config.birdseye.restream
                     or any(
                         ws.environ["PATH_INFO"].endswith("birdseye")
-                        and ws_has_camera_access(ws, "birdseye", self.config)
                         for ws in websocket_server.manager
                     )
                 )
@@ -349,13 +346,6 @@ def move_preview_frames(loc: str) -> None:
             if not os.path.exists(preview_holdover):
                 return
 
-            if not os.access(preview_holdover, os.R_OK | os.W_OK):
-                logger.error(
-                    "Insufficient permissions on preview restart cache at %s",
-                    preview_holdover,
-                )
-                return
-
             shutil.move(preview_holdover, preview_cache)
     except shutil.Error:
         logger.error("Failed to restore preview cache.")

frigate/output/preview.py

@@ -361,17 +361,14 @@ class PreviewRecorder:
             small_frame,
             cv2.COLOR_YUV2BGR_I420,
         )
-        cache_path = get_cache_image_name(self.camera_name, frame_time)
-
-        if not cv2.imwrite(
-            cache_path,
+        cv2.imwrite(
+            get_cache_image_name(self.camera_name, frame_time),
             small_frame,
             [
                 int(cv2.IMWRITE_WEBP_QUALITY),
                 PREVIEW_QUALITY_WEBP[self.config.record.preview.quality],
             ],
-        ):
-            logger.error("Failed to write preview frame to %s", cache_path)
+        )
 
     def write_data(
         self,

frigate/output/ws_auth.py

@@ -1,43 +0,0 @@
-"""Authorization helpers for JSMPEG websocket clients."""
-
-from typing import Any
-
-from frigate.config import FrigateConfig
-from frigate.models import User
-
-
-def _get_valid_ws_roles(ws: Any, config: FrigateConfig) -> list[str]:
-    role_header = ws.environ.get("HTTP_REMOTE_ROLE", "")
-    roles = [
-        role.strip()
-        for role in role_header.split(config.proxy.separator)
-        if role.strip()
-    ]
-    return [role for role in roles if role in config.auth.roles]
-
-
-def ws_has_camera_access(ws: Any, camera_name: str, config: FrigateConfig) -> bool:
-    """Return True when a websocket client is authorized for the camera path."""
-    roles = _get_valid_ws_roles(ws, config)
-
-    if not roles:
-        return False
-
-    roles_dict = config.auth.roles
-
-    # Birdseye is a composite stream, so only users with unrestricted access
-    # should receive it.
-    if camera_name == "birdseye":
-        return any(role == "admin" or not roles_dict.get(role) for role in roles)
-
-    all_camera_names = set(config.cameras.keys())
-
-    for role in roles:
-        if role == "admin" or not roles_dict.get(role):
-            return True
-
-        allowed_cameras = User.get_allowed_cameras(role, roles_dict, all_camera_names)
-        if camera_name in allowed_cameras:
-            return True
-
-    return False

frigate/record/export.py

@@ -4,16 +4,14 @@ import datetime
 import logging
 import os
 import random
-import re
 import shutil
 import string
 import subprocess as sp
 import threading
 from enum import Enum
 from pathlib import Path
-from typing import Callable, Optional
+from typing import Optional
 
-import pytz  # type: ignore[import-untyped]
 from peewee import DoesNotExist
 
 from frigate.config import FfmpegConfig, FrigateConfig
@@ -29,7 +27,7 @@ from frigate.ffmpeg_presets import (
     EncodeTypeEnum,
     parse_preset_hardware_acceleration_encode,
 )
-from frigate.models import Export, Previews, Recordings, ReviewSegment
+from frigate.models import Export, Previews, Recordings
 from frigate.util.time import is_current_hour
 
 logger = logging.getLogger(__name__)
@@ -38,10 +36,6 @@ logger = logging.getLogger(__name__)
 DEFAULT_TIME_LAPSE_FFMPEG_ARGS = "-vf setpts=0.04*PTS -r 30"
 TIMELAPSE_DATA_INPUT_ARGS = "-an -skip_frame nokey"
 
-# Matches the setpts factor used in timelapse exports (e.g. setpts=0.04*PTS).
-# Captures the floating-point factor so we can scale expected duration.
-SETPTS_FACTOR_RE = re.compile(r"setpts=([0-9]*\.?[0-9]+)\*PTS")
-
 # ffmpeg flags that can read from or write to arbitrary files
 BLOCKED_FFMPEG_ARGS = frozenset(
     {
@@ -122,7 +116,6 @@ class RecordingExporter(threading.Thread):
         ffmpeg_input_args: Optional[str] = None,
         ffmpeg_output_args: Optional[str] = None,
         cpu_fallback: bool = False,
-        on_progress: Optional[Callable[[str, float], None]] = None,
     ) -> None:
         super().__init__()
         self.config = config
@@ -137,345 +130,14 @@ class RecordingExporter(threading.Thread):
         self.ffmpeg_input_args = ffmpeg_input_args
         self.ffmpeg_output_args = ffmpeg_output_args
         self.cpu_fallback = cpu_fallback
-        self.on_progress = on_progress
 
         # ensure export thumb dir
         Path(os.path.join(CLIPS_DIR, "export")).mkdir(exist_ok=True)
 
-    def _emit_progress(self, step: str, percent: float) -> None:
-        """Invoke the progress callback if one was supplied."""
-        if self.on_progress is None:
-            return
-        try:
-            self.on_progress(step, max(0.0, min(100.0, percent)))
-        except Exception:
-            logger.exception("Export progress callback failed")
-
-    def _expected_output_duration_seconds(self) -> float:
-        """Compute the expected duration of the output video in seconds.
-
-        Users often request a wide time range (e.g. a full hour) when only
-        a few minutes of recordings actually live on disk for that span,
-        so the requested range overstates the work and progress would
-        plateau very early. We sum the actual saved seconds from the
-        Recordings/Previews tables and use that as the input duration.
-        Timelapse exports then scale this by the setpts factor.
-        """
-        requested_duration = max(0.0, float(self.end_time - self.start_time))
-
-        recorded = self._sum_source_duration_seconds()
-        input_duration = (
-            recorded if recorded is not None and recorded > 0 else requested_duration
-        )
-
-        if not self.ffmpeg_output_args:
-            return input_duration
-
-        match = SETPTS_FACTOR_RE.search(self.ffmpeg_output_args)
-        if match is None:
-            return input_duration
-
-        try:
-            factor = float(match.group(1))
-        except ValueError:
-            return input_duration
-
-        if factor <= 0:
-            return input_duration
-
-        return input_duration * factor
-
-    def _sum_source_duration_seconds(self) -> Optional[float]:
-        """Sum saved-video seconds inside [start_time, end_time].
-
-        Queries Recordings or Previews depending on the playback source,
-        clamps each segment to the requested range, and returns the total.
-        Returns ``None`` on any error so the caller can fall back to the
-        requested range duration without losing progress reporting.
-        """
-        try:
-            if self.playback_source == PlaybackSourceEnum.recordings:
-                rows = (
-                    Recordings.select(Recordings.start_time, Recordings.end_time)
-                    .where(
-                        Recordings.start_time.between(self.start_time, self.end_time)
-                        | Recordings.end_time.between(self.start_time, self.end_time)
-                        | (
-                            (self.start_time > Recordings.start_time)
-                            & (self.end_time < Recordings.end_time)
-                        )
-                    )
-                    .where(Recordings.camera == self.camera)
-                    .iterator()
-                )
-            else:
-                rows = (
-                    Previews.select(Previews.start_time, Previews.end_time)
-                    .where(
-                        Previews.start_time.between(self.start_time, self.end_time)
-                        | Previews.end_time.between(self.start_time, self.end_time)
-                        | (
-                            (self.start_time > Previews.start_time)
-                            & (self.end_time < Previews.end_time)
-                        )
-                    )
-                    .where(Previews.camera == self.camera)
-                    .iterator()
-                )
-        except Exception:
-            logger.exception(
-                "Failed to sum source duration for export %s", self.export_id
-            )
-            return None
-
-        total = 0.0
-        try:
-            for row in rows:
-                clipped_start = max(float(row.start_time), float(self.start_time))
-                clipped_end = min(float(row.end_time), float(self.end_time))
-                if clipped_end > clipped_start:
-                    total += clipped_end - clipped_start
-        except Exception:
-            logger.exception(
-                "Failed to read recording rows for export %s", self.export_id
-            )
-            return None
-
-        return total
-
-    def _inject_progress_flags(self, ffmpeg_cmd: list[str]) -> list[str]:
-        """Insert FFmpeg progress reporting flags before the output path.
-
-        ``-progress pipe:2`` writes structured key=value lines to stderr,
-        ``-nostats`` suppresses the noisy default stats output.
-        """
-        if not ffmpeg_cmd:
-            return ffmpeg_cmd
-        return ffmpeg_cmd[:-1] + ["-progress", "pipe:2", "-nostats", ffmpeg_cmd[-1]]
-
-    def _run_ffmpeg_with_progress(
-        self,
-        ffmpeg_cmd: list[str],
-        playlist_lines: str | list[str],
-        step: str = "encoding",
-    ) -> tuple[int, str]:
-        """Run an FFmpeg export command, parsing progress events from stderr.
-
-        Returns ``(returncode, captured_stderr)``. Stdout is left attached to
-        the parent process so we don't have to drain it (and risk a deadlock
-        if the buffer fills). Progress percent is computed against the
-        expected output duration; values are clamped to [0, 100] inside
-        :py:meth:`_emit_progress`.
-        """
-        cmd = ["nice", "-n", str(PROCESS_PRIORITY_LOW)] + self._inject_progress_flags(
-            ffmpeg_cmd
-        )
-
-        if isinstance(playlist_lines, list):
-            stdin_payload = "\n".join(playlist_lines)
-        else:
-            stdin_payload = playlist_lines
-
-        expected_duration = self._expected_output_duration_seconds()
-
-        self._emit_progress(step, 0.0)
-
-        proc = sp.Popen(
-            cmd,
-            stdin=sp.PIPE,
-            stderr=sp.PIPE,
-            text=True,
-            encoding="ascii",
-            errors="replace",
-        )
-
-        assert proc.stdin is not None
-        assert proc.stderr is not None
-
-        try:
-            proc.stdin.write(stdin_payload)
-        except (BrokenPipeError, OSError):
-            # FFmpeg may have rejected the input early; still wait for it
-            # to terminate so the returncode is meaningful.
-            pass
-        finally:
-            try:
-                proc.stdin.close()
-            except (BrokenPipeError, OSError):
-                pass
-
-        captured: list[str] = []
-
-        try:
-            for raw_line in proc.stderr:
-                captured.append(raw_line)
-                line = raw_line.strip()
-
-                if not line:
-                    continue
-
-                if line.startswith("out_time_us="):
-                    if expected_duration <= 0:
-                        continue
-                    try:
-                        out_time_us = int(line.split("=", 1)[1])
-                    except (ValueError, IndexError):
-                        continue
-                    if out_time_us < 0:
-                        continue
-                    out_seconds = out_time_us / 1_000_000.0
-                    percent = (out_seconds / expected_duration) * 100.0
-                    self._emit_progress(step, percent)
-                elif line == "progress=end":
-                    self._emit_progress(step, 100.0)
-                    break
-        except Exception:
-            logger.exception("Failed reading FFmpeg progress for %s", self.export_id)
-
-        proc.wait()
-
-        # Drain any remaining stderr so callers can log it on failure.
-        try:
-            remaining = proc.stderr.read()
-            if remaining:
-                captured.append(remaining)
-        except Exception:
-            pass
-
-        return proc.returncode, "".join(captured)
-
     def get_datetime_from_timestamp(self, timestamp: int) -> str:
-        # return in iso format using the configured ui.timezone when set,
-        # so the auto-generated export name reflects local time rather
-        # than the container's UTC clock
-        tz_name = self.config.ui.timezone
-        if tz_name:
-            try:
-                tz = pytz.timezone(tz_name)
-            except pytz.UnknownTimeZoneError:
-                tz = None
-            if tz is not None:
-                return datetime.datetime.fromtimestamp(timestamp, tz=tz).strftime(
-                    "%Y-%m-%d %H:%M:%S"
-                )
+        # return in iso format
         return datetime.datetime.fromtimestamp(timestamp).strftime("%Y-%m-%d %H:%M:%S")
 
-    def _chapter_metadata_path(self) -> str:
-        return os.path.join(CACHE_DIR, f"export_chapters_{self.export_id}.txt")
-
-    def _build_chapter_metadata_file(self, recordings: list) -> Optional[str]:
-        """Write an FFmpeg metadata file with chapters for review items in range.
-
-        Chapter offsets are computed in *output time*: the VOD endpoint
-        concatenates recording clips back-to-back, so wall-clock gaps
-        between recordings collapse in the produced video. We walk the
-        same recording rows that feed the playlist and convert each
-        review item's wall-clock boundaries into output-time offsets.
-        Returns ``None`` when there are no recordings, no review items,
-        or any chapter would have zero output duration.
-        """
-        if not recordings:
-            return None
-
-        windows: list[tuple[float, float, float]] = []
-        output_offset = 0.0
-        for rec in recordings:
-            clipped_start = max(float(rec.start_time), float(self.start_time))
-            clipped_end = min(float(rec.end_time), float(self.end_time))
-            if clipped_end <= clipped_start:
-                continue
-            windows.append((clipped_start, clipped_end, output_offset))
-            output_offset += clipped_end - clipped_start
-
-        if not windows:
-            return None
-
-        try:
-            review_rows = list(
-                ReviewSegment.select(
-                    ReviewSegment.start_time,
-                    ReviewSegment.end_time,
-                    ReviewSegment.severity,
-                    ReviewSegment.data,
-                )
-                .where(
-                    ReviewSegment.start_time.between(self.start_time, self.end_time)
-                    | ReviewSegment.end_time.between(self.start_time, self.end_time)
-                    | (
-                        (self.start_time > ReviewSegment.start_time)
-                        & (self.end_time < ReviewSegment.end_time)
-                    )
-                )
-                .where(ReviewSegment.camera == self.camera)
-                .order_by(ReviewSegment.start_time.asc())
-                .iterator()
-            )
-        except Exception:
-            logger.exception(
-                "Failed to query review segments for export %s", self.export_id
-            )
-            return None
-
-        if not review_rows:
-            return None
-
-        total_output = windows[-1][2] + (windows[-1][1] - windows[-1][0])
-
-        def wall_to_output(t: float) -> float:
-            t = max(float(self.start_time), min(float(self.end_time), t))
-            for w_start, w_end, w_offset in windows:
-                if t < w_start:
-                    return w_offset
-                if t <= w_end:
-                    return w_offset + (t - w_start)
-            return total_output
-
-        chapter_blocks: list[str] = []
-        for review in review_rows:
-            start_out = wall_to_output(float(review.start_time))
-            end_out = wall_to_output(float(review.end_time))
-
-            # Drop chapters that fall entirely in a recording gap, or are
-            # too short to be navigable in a player.
-            if end_out - start_out < 1.0:
-                continue
-
-            data = review.data or {}
-            labels: list[str] = []
-            for obj in data.get("objects") or []:
-                label = str(obj).split("-")[0]
-                if label and label not in labels:
-                    labels.append(label)
-
-            title = str(review.severity).capitalize()
-            if labels:
-                title = f"{title}: {', '.join(labels)}"
-
-            chapter_blocks.append(
-                "[CHAPTER]\n"
-                "TIMEBASE=1/1000\n"
-                f"START={int(start_out * 1000)}\n"
-                f"END={int(end_out * 1000)}\n"
-                f"title={title}"
-            )
-
-        if not chapter_blocks:
-            return None
-
-        meta_path = self._chapter_metadata_path()
-        try:
-            with open(meta_path, "w", encoding="utf-8") as f:
-                f.write(";FFMETADATA1\n")
-                f.write("\n".join(chapter_blocks))
-                f.write("\n")
-        except OSError:
-            logger.exception(
-                "Failed to write chapter metadata file for export %s", self.export_id
-            )
-            return None
-
-        return meta_path
-
     def save_thumbnail(self, id: str) -> str:
         thumb_path = os.path.join(CLIPS_DIR, f"export/{id}.webp")
 
@@ -551,18 +213,12 @@ class RecordingExporter(threading.Thread):
             start_file = f"{file_start}{self.start_time}.{PREVIEW_FRAME_TYPE}"
             end_file = f"{file_start}{self.end_time}.{PREVIEW_FRAME_TYPE}"
             selected_preview = None
-            # Preview frames are written at most 1-2 fps during activity
-            # and as little as one every 30s during quiet periods, so a
-            # short export window can contain zero frames. Track the most
-            # recent frame before the window as a fallback.
-            fallback_preview = None
 
             for file in sorted(os.listdir(preview_dir)):
                 if not file.startswith(file_start):
                     continue
 
                 if file < start_file:
-                    fallback_preview = os.path.join(preview_dir, file)
                     continue
 
                 if file > end_file:
@@ -571,9 +227,6 @@ class RecordingExporter(threading.Thread):
                 selected_preview = os.path.join(preview_dir, file)
                 break
 
-            if not selected_preview:
-                selected_preview = fallback_preview
-
             if not selected_preview:
                 return ""
 
@@ -589,24 +242,6 @@ class RecordingExporter(threading.Thread):
         if type(internal_port) is str:
             internal_port = int(internal_port.split(":")[-1])
 
-        recordings = list(
-            Recordings.select(
-                Recordings.start_time,
-                Recordings.end_time,
-            )
-            .where(
-                Recordings.start_time.between(self.start_time, self.end_time)
-                | Recordings.end_time.between(self.start_time, self.end_time)
-                | (
-                    (self.start_time > Recordings.start_time)
-                    & (self.end_time < Recordings.end_time)
-                )
-            )
-            .where(Recordings.camera == self.camera)
-            .order_by(Recordings.start_time.asc())
-            .iterator()
-        )
-
         playlist_lines: list[str] = []
         if (self.end_time - self.start_time) <= MAX_PLAYLIST_SECONDS:
             playlist_url = f"http://127.0.0.1:{internal_port}/vod/{self.camera}/start/{self.start_time}/end/{self.end_time}/index.m3u8"
@@ -614,13 +249,32 @@ class RecordingExporter(threading.Thread):
                 f"-y -protocol_whitelist pipe,file,http,tcp -i {playlist_url}"
             )
         else:
-            # Chunk the recording rows into pages so each playlist line
-            # references a bounded sub-range rather than the full export.
+            # get full set of recordings
+            export_recordings = (
+                Recordings.select(
+                    Recordings.start_time,
+                    Recordings.end_time,
+                )
+                .where(
+                    Recordings.start_time.between(self.start_time, self.end_time)
+                    | Recordings.end_time.between(self.start_time, self.end_time)
+                    | (
+                        (self.start_time > Recordings.start_time)
+                        & (self.end_time < Recordings.end_time)
+                    )
+                )
+                .where(Recordings.camera == self.camera)
+                .order_by(Recordings.start_time.asc())
+            )
+
+            # Use pagination to process records in chunks
             page_size = 1000
-            for i in range(0, len(recordings), page_size):
-                chunk = recordings[i : i + page_size]
+            num_pages = (export_recordings.count() + page_size - 1) // page_size
+
+            for page in range(1, num_pages + 1):
+                playlist = export_recordings.paginate(page, page_size)
                 playlist_lines.append(
-                    f"file 'http://127.0.0.1:{internal_port}/vod/{self.camera}/start/{float(chunk[0].start_time)}/end/{float(chunk[-1].end_time)}/index.m3u8'"
+                    f"file 'http://127.0.0.1:{internal_port}/vod/{self.camera}/start/{float(playlist[0].start_time)}/end/{float(playlist[-1].end_time)}/index.m3u8'"
                 )
 
             ffmpeg_input = "-y -protocol_whitelist pipe,file,http,tcp -f concat -safe 0 -i /dev/stdin"
@@ -641,12 +295,8 @@ class RecordingExporter(threading.Thread):
                 )
             ).split(" ")
         else:
-            chapters_path = self._build_chapter_metadata_file(recordings)
-            chapter_args = (
-                f" -i {chapters_path} -map 0 -map_metadata 1" if chapters_path else ""
-            )
             ffmpeg_cmd = (
-                f"{self.config.ffmpeg.ffmpeg_path} -hide_banner {ffmpeg_input}{chapter_args} -c copy -movflags +faststart"
+                f"{self.config.ffmpeg.ffmpeg_path} -hide_banner {ffmpeg_input} -c copy -movflags +faststart"
             ).split(" ")
 
         # add metadata
@@ -756,7 +406,6 @@ class RecordingExporter(threading.Thread):
         logger.debug(
             f"Beginning export for {self.camera} from {self.start_time} to {self.end_time}"
         )
-        self._emit_progress("preparing", 0.0)
         export_name = (
             self.user_provided_name
             or f"{self.camera.replace('_', ' ')} {self.get_datetime_from_timestamp(self.start_time)} {self.get_datetime_from_timestamp(self.end_time)}"
@@ -794,23 +443,16 @@ class RecordingExporter(threading.Thread):
         except DoesNotExist:
             return
 
-        # When neither custom ffmpeg arg is set the default path uses
-        # `-c copy` (stream copy — no re-encoding). Report that as a
-        # distinct step so the UI doesn't mislabel a remux as encoding.
-        # The retry branch below always re-encodes because cpu_fallback
-        # requires custom args; it stays "encoding_retry".
-        is_stream_copy = (
-            self.ffmpeg_input_args is None and self.ffmpeg_output_args is None
-        )
-        initial_step = "copying" if is_stream_copy else "encoding"
-
-        returncode, stderr = self._run_ffmpeg_with_progress(
-            ffmpeg_cmd, playlist_lines, step=initial_step
+        p = sp.run(
+            ["nice", "-n", str(PROCESS_PRIORITY_LOW)] + ffmpeg_cmd,
+            input="\n".join(playlist_lines),
+            encoding="ascii",
+            capture_output=True,
         )
 
         # If export failed and cpu_fallback is enabled, retry without hwaccel
         if (
-            returncode != 0
+            p.returncode != 0
             and self.cpu_fallback
             and self.ffmpeg_input_args is not None
             and self.ffmpeg_output_args is not None
@@ -828,23 +470,23 @@ class RecordingExporter(threading.Thread):
                     video_path, use_hwaccel=False
                 )
 
-            returncode, stderr = self._run_ffmpeg_with_progress(
-                ffmpeg_cmd, playlist_lines, step="encoding_retry"
+            p = sp.run(
+                ["nice", "-n", str(PROCESS_PRIORITY_LOW)] + ffmpeg_cmd,
+                input="\n".join(playlist_lines),
+                encoding="ascii",
+                capture_output=True,
             )
 
-        Path(self._chapter_metadata_path()).unlink(missing_ok=True)
-
-        if returncode != 0:
+        if p.returncode != 0:
             logger.error(
                 f"Failed to export {self.playback_source.value} for command {' '.join(ffmpeg_cmd)}"
             )
-            logger.error(stderr)
+            logger.error(p.stderr)
             Path(video_path).unlink(missing_ok=True)
             Export.delete().where(Export.id == self.export_id).execute()
             Path(thumb_path).unlink(missing_ok=True)
             return
         else:
-            self._emit_progress("finalizing", 100.0)
             Export.update({Export.in_progress: False}).where(
                 Export.id == self.export_id
             ).execute()

frigate/storage.py

@@ -197,7 +197,7 @@ class StorageMaintainer(threading.Thread):
         # check if need to delete retained segments
         if deleted_segments_size < hourly_bandwidth:
             logger.error(
-                f"Could not clear {hourly_bandwidth} MB, currently {deleted_segments_size:.2f} MB have been cleared. Retained recordings must be deleted."
+                f"Could not clear {hourly_bandwidth} MB, currently {deleted_segments_size} MB have been cleared. Retained recordings must be deleted."
             )
             recordings = (
                 Recordings.select(
@@ -225,7 +225,7 @@ class StorageMaintainer(threading.Thread):
                     # this file was not found so we must assume no space was cleaned up
                     pass
         else:
-            logger.info(f"Cleaned up {deleted_segments_size:.2f} MB of recordings")
+            logger.info(f"Cleaned up {deleted_segments_size} MB of recordings")
 
         logger.debug(f"Expiring {len(deleted_recordings)} recordings")
         # delete up to 100,000 at a time

frigate/test/http_api/base_http_test.py

@@ -2,7 +2,6 @@ import datetime
 import logging
 import os
 import unittest
-from unittest.mock import patch
 
 from fastapi import Request
 from fastapi.testclient import TestClient
@@ -15,7 +14,6 @@ from frigate.api.fastapi_app import create_fastapi_app
 from frigate.config import FrigateConfig
 from frigate.const import BASE_DIR, CACHE_DIR
 from frigate.debug_replay import DebugReplayManager
-from frigate.jobs.export import JobStatePublisher
 from frigate.models import Event, Recordings, ReviewSegment
 from frigate.review.types import SeverityEnum
 from frigate.test.const import TEST_DB, TEST_DB_CLEANUPS
@@ -46,19 +44,6 @@ class BaseTestHttp(unittest.TestCase):
         self.db = SqliteQueueDatabase(TEST_DB)
         self.db.bind(models)
 
-        # The export job manager broadcasts via JobStatePublisher on
-        # enqueue/start/finish. There is no dispatcher process bound to
-        # the IPC socket in tests, so a real publish() would block on
-        # recv_json forever. Replace publish with a no-op for the
-        # lifetime of this test; the lookup goes through the class so any
-        # already-instantiated publisher (the singleton manager's) picks
-        # up the no-op too.
-        publisher_patch = patch.object(
-            JobStatePublisher, "publish", lambda self, payload: None
-        )
-        publisher_patch.start()
-        self.addCleanup(publisher_patch.stop)
-
         self.minimal_config = {
             "mqtt": {"host": "mqtt"},
             "cameras": {

frigate/test/http_api/test_http_app.py

@@ -23,26 +23,6 @@ class TestHttpApp(BaseTestHttp):
             response_json = response.json()
             assert response_json == self.test_stats
 
-    def test_recordings_storage_requires_admin(self):
-        stats = Mock(spec=StatsEmitter)
-        stats.get_latest_stats.return_value = self.test_stats
-        app = super().create_app(stats)
-        app.storage_maintainer = Mock()
-        app.storage_maintainer.calculate_camera_usages.return_value = {
-            "front_door": {"usage": 2.0},
-        }
-
-        with AuthTestClient(app) as client:
-            response = client.get(
-                "/recordings/storage",
-                headers={"remote-user": "viewer", "remote-role": "viewer"},
-            )
-            assert response.status_code == 403
-
-            response = client.get("/recordings/storage")
-            assert response.status_code == 200
-            assert response.json()["front_door"]["usage_percent"] == 25.0
-
     def test_config_set_in_memory_replaces_objects_track_list(self):
         self.minimal_config["cameras"]["front_door"]["objects"] = {
             "track": ["person", "car"],

frigate/test/http_api/test_http_event.py

@@ -219,25 +219,6 @@ class TestHttpApp(BaseTestHttp):
             assert len(events) == 1
             assert events[0]["id"] == event_id
 
-    def test_similarity_search_hides_unauthorized_anchor_event(self):
-        mock_embeddings = Mock()
-        self.app.frigate_config.semantic_search.enabled = True
-        self.app.embeddings = mock_embeddings
-
-        with AuthTestClient(self.app) as client:
-            super().insert_mock_event("hidden.anchor", camera="back_door")
-            response = client.get(
-                "/events/search",
-                params={
-                    "search_type": "similarity",
-                    "event_id": "hidden.anchor",
-                },
-            )
-
-        assert response.status_code == 404
-        assert response.json()["message"] == "Event not found"
-        mock_embeddings.search_thumbnail.assert_not_called()
-
     def test_get_good_event(self):
         id = "123456.random"
 

frigate/test/test_chat_find_similar_objects.py

@@ -145,12 +145,9 @@ class TestExecuteFindSimilarObjects(unittest.TestCase):
             embeddings=embeddings,
             frigate_config=SimpleNamespace(
                 semantic_search=SimpleNamespace(enabled=semantic_enabled),
-                cameras={"driveway": object()},
-                auth=SimpleNamespace(roles={"admin": [], "viewer": ["driveway"]}),
-                proxy=SimpleNamespace(separator=","),
             ),
         )
-        return SimpleNamespace(app=app, headers={})
+        return SimpleNamespace(app=app)
 
     def test_semantic_search_disabled_returns_error(self):
         req = self._make_request(semantic_enabled=False)
@@ -183,7 +180,7 @@ class TestExecuteFindSimilarObjects(unittest.TestCase):
             _execute_find_similar_objects(
                 req,
                 {"event_id": "anchor", "cameras": ["nonexistent_cam"]},
-                allowed_cameras=["driveway"],
+                allowed_cameras=["nonexistent_cam"],
             )
         )
         self.assertEqual(result["results"], [])

frigate/test/test_export_progress.py

@@ -1,503 +0,0 @@
-"""Tests for export progress tracking, broadcast, and FFmpeg parsing."""
-
-import io
-import os
-import shutil
-import tempfile
-import unittest
-from unittest.mock import MagicMock, patch
-
-from frigate.jobs.export import (
-    PROGRESS_BROADCAST_MIN_INTERVAL,
-    ExportJob,
-    ExportJobManager,
-)
-from frigate.record.export import PlaybackSourceEnum, RecordingExporter
-from frigate.types import JobStatusTypesEnum
-
-
-def _make_exporter(
-    end_minus_start: int = 100,
-    ffmpeg_input_args=None,
-    ffmpeg_output_args=None,
-    on_progress=None,
-) -> RecordingExporter:
-    """Build a RecordingExporter without invoking its real __init__ side
-    effects (which create directories and require a full FrigateConfig)."""
-    exporter = RecordingExporter.__new__(RecordingExporter)
-    exporter.config = MagicMock()
-    exporter.export_id = "test_export"
-    exporter.camera = "front"
-    exporter.user_provided_name = None
-    exporter.user_provided_image = None
-    exporter.start_time = 1_000
-    exporter.end_time = 1_000 + end_minus_start
-    exporter.playback_source = PlaybackSourceEnum.recordings
-    exporter.export_case_id = None
-    exporter.ffmpeg_input_args = ffmpeg_input_args
-    exporter.ffmpeg_output_args = ffmpeg_output_args
-    exporter.cpu_fallback = False
-    exporter.on_progress = on_progress
-    return exporter
-
-
-class TestExportJobToDict(unittest.TestCase):
-    def test_to_dict_includes_progress_fields(self) -> None:
-        job = ExportJob(camera="front", request_start_time=0, request_end_time=10)
-        result = job.to_dict()
-
-        assert "current_step" in result
-        assert "progress_percent" in result
-        assert result["current_step"] == "queued"
-        assert result["progress_percent"] == 0.0
-
-    def test_to_dict_reflects_updated_progress(self) -> None:
-        job = ExportJob(camera="front", request_start_time=0, request_end_time=10)
-        job.current_step = "encoding"
-        job.progress_percent = 42.5
-
-        result = job.to_dict()
-
-        assert result["current_step"] == "encoding"
-        assert result["progress_percent"] == 42.5
-
-
-class TestExpectedOutputDuration(unittest.TestCase):
-    def test_normal_export_uses_input_duration(self) -> None:
-        exporter = _make_exporter(end_minus_start=600)
-        assert exporter._expected_output_duration_seconds() == 600.0
-
-    def test_timelapse_uses_setpts_factor(self) -> None:
-        exporter = _make_exporter(
-            end_minus_start=1000,
-            ffmpeg_input_args="-y",
-            ffmpeg_output_args="-vf setpts=0.04*PTS -r 30",
-        )
-        # 1000s input * 0.04 = 40s of output
-        assert exporter._expected_output_duration_seconds() == 40.0
-
-    def test_unknown_factor_falls_back_to_input_duration(self) -> None:
-        exporter = _make_exporter(
-            end_minus_start=300,
-            ffmpeg_input_args="-y",
-            ffmpeg_output_args="-c:v libx264 -preset veryfast",
-        )
-        assert exporter._expected_output_duration_seconds() == 300.0
-
-    def test_zero_factor_falls_back_to_input_duration(self) -> None:
-        exporter = _make_exporter(
-            end_minus_start=300,
-            ffmpeg_input_args="-y",
-            ffmpeg_output_args="-vf setpts=0*PTS",
-        )
-        assert exporter._expected_output_duration_seconds() == 300.0
-
-    def test_uses_actual_recorded_seconds_when_available(self) -> None:
-        """If the DB shows only 120s of saved recordings inside a 1h
-        requested range, progress should be computed against 120s."""
-        exporter = _make_exporter(end_minus_start=3600)
-        exporter._sum_source_duration_seconds = lambda: 120.0  # type: ignore[method-assign]
-        assert exporter._expected_output_duration_seconds() == 120.0
-
-    def test_actual_recorded_seconds_scaled_by_setpts(self) -> None:
-        """Recorded duration must still be scaled by the timelapse factor."""
-        exporter = _make_exporter(
-            end_minus_start=3600,
-            ffmpeg_input_args="-y",
-            ffmpeg_output_args="-vf setpts=0.04*PTS -r 30",
-        )
-        exporter._sum_source_duration_seconds = lambda: 600.0  # type: ignore[method-assign]
-        # 600s * 0.04 = 24s of output
-        assert exporter._expected_output_duration_seconds() == 24.0
-
-    def test_db_failure_falls_back_to_requested_range(self) -> None:
-        exporter = _make_exporter(end_minus_start=300)
-        exporter._sum_source_duration_seconds = lambda: None  # type: ignore[method-assign]
-        assert exporter._expected_output_duration_seconds() == 300.0
-
-
-class TestProgressFlagInjection(unittest.TestCase):
-    def test_inserts_before_output_path(self) -> None:
-        exporter = _make_exporter()
-        cmd = ["ffmpeg", "-i", "input.m3u8", "-c", "copy", "/tmp/output.mp4"]
-
-        result = exporter._inject_progress_flags(cmd)
-
-        assert result == [
-            "ffmpeg",
-            "-i",
-            "input.m3u8",
-            "-c",
-            "copy",
-            "-progress",
-            "pipe:2",
-            "-nostats",
-            "/tmp/output.mp4",
-        ]
-
-    def test_handles_empty_cmd(self) -> None:
-        exporter = _make_exporter()
-        assert exporter._inject_progress_flags([]) == []
-
-
-class TestFfmpegProgressParsing(unittest.TestCase):
-    """Verify percentage calculation from FFmpeg ``-progress`` output."""
-
-    def _run_with_stderr(
-        self,
-        stderr_text: str,
-        expected_duration_seconds: int = 90,
-    ) -> list[tuple[str, float]]:
-        """Helper: run _run_ffmpeg_with_progress against a mocked Popen
-        whose stderr emits the supplied text. Returns the list of
-        (step, percent) tuples that the on_progress callback received."""
-        captured: list[tuple[str, float]] = []
-
-        def on_progress(step: str, percent: float) -> None:
-            captured.append((step, percent))
-
-        exporter = _make_exporter(
-            end_minus_start=expected_duration_seconds,
-            on_progress=on_progress,
-        )
-
-        fake_proc = MagicMock()
-        fake_proc.stdin = io.StringIO()
-        fake_proc.stderr = io.StringIO(stderr_text)
-        fake_proc.returncode = 0
-        fake_proc.wait = MagicMock(return_value=0)
-
-        with patch("frigate.record.export.sp.Popen", return_value=fake_proc):
-            returncode, _stderr = exporter._run_ffmpeg_with_progress(
-                ["ffmpeg", "-i", "x.m3u8", "/tmp/out.mp4"], "playlist", step="encoding"
-            )
-
-        assert returncode == 0
-        return captured
-
-    def test_parses_out_time_us_into_percent(self) -> None:
-        # 90s duration; 45s out_time => 50%
-        stderr = "out_time_us=45000000\nprogress=continue\n"
-        captured = self._run_with_stderr(stderr, expected_duration_seconds=90)
-
-        # The first call is the synchronous 0.0 emit before Popen runs.
-        assert captured[0] == ("encoding", 0.0)
-        assert any(percent == 50.0 for step, percent in captured if step == "encoding")
-
-    def test_progress_end_emits_100_percent(self) -> None:
-        stderr = "out_time_us=10000000\nprogress=end\n"
-        captured = self._run_with_stderr(stderr, expected_duration_seconds=90)
-
-        assert captured[-1] == ("encoding", 100.0)
-
-    def test_clamps_overshoot_at_100(self) -> None:
-        # 150s of output reported against 90s expected duration.
-        stderr = "out_time_us=150000000\nprogress=continue\n"
-        captured = self._run_with_stderr(stderr, expected_duration_seconds=90)
-
-        encoding_values = [p for s, p in captured if s == "encoding" and p > 0]
-        assert all(p <= 100.0 for p in encoding_values)
-        assert encoding_values[-1] == 100.0
-
-    def test_ignores_garbage_lines(self) -> None:
-        stderr = (
-            "frame=  120 fps= 30 q=23.0 size=    512kB\n"
-            "out_time_us=not-a-number\n"
-            "out_time_us=30000000\n"
-            "progress=continue\n"
-        )
-        captured = self._run_with_stderr(stderr, expected_duration_seconds=90)
-
-        # We expect 0.0 (from initial emit) plus the 30s/90s = 33.33...% step
-        encoding_percents = sorted({round(p, 2) for s, p in captured})
-        assert 0.0 in encoding_percents
-        assert any(abs(p - (30 / 90 * 100)) < 0.01 for p in encoding_percents)
-
-
-class TestBroadcastAggregation(unittest.TestCase):
-    """Verify ExportJobManager broadcast payload shape and throttling."""
-
-    def _make_manager(self) -> tuple[ExportJobManager, MagicMock]:
-        """Build a manager with an injected mock publisher. Returns
-        ``(manager, publisher)`` so tests can assert on broadcast payloads
-        without touching ZMQ at all."""
-        config = MagicMock()
-        publisher = MagicMock()
-        manager = ExportJobManager(
-            config, max_concurrent=2, max_queued=10, publisher=publisher
-        )
-        return manager, publisher
-
-    @staticmethod
-    def _last_payload(publisher: MagicMock) -> dict:
-        return publisher.publish.call_args.args[0]
-
-    def test_empty_jobs_broadcasts_empty_list(self) -> None:
-        manager, publisher = self._make_manager()
-        manager._broadcast_all_jobs(force=True)
-
-        publisher.publish.assert_called_once()
-        payload = self._last_payload(publisher)
-        assert payload["job_type"] == "export"
-        assert payload["status"] == "queued"
-        assert payload["results"]["jobs"] == []
-
-    def test_single_running_job_payload(self) -> None:
-        manager, publisher = self._make_manager()
-        job = ExportJob(camera="front", request_start_time=0, request_end_time=10)
-        job.status = JobStatusTypesEnum.running
-        job.current_step = "encoding"
-        job.progress_percent = 75.0
-        manager.jobs[job.id] = job
-
-        manager._broadcast_all_jobs(force=True)
-
-        payload = self._last_payload(publisher)
-        assert payload["status"] == "running"
-        assert len(payload["results"]["jobs"]) == 1
-        broadcast_job = payload["results"]["jobs"][0]
-        assert broadcast_job["current_step"] == "encoding"
-        assert broadcast_job["progress_percent"] == 75.0
-
-    def test_multiple_jobs_broadcast(self) -> None:
-        manager, publisher = self._make_manager()
-        for i, status in enumerate(
-            (JobStatusTypesEnum.queued, JobStatusTypesEnum.running)
-        ):
-            job = ExportJob(
-                id=f"job_{i}",
-                camera="front",
-                request_start_time=0,
-                request_end_time=10,
-            )
-            job.status = status
-            manager.jobs[job.id] = job
-
-        manager._broadcast_all_jobs(force=True)
-
-        payload = self._last_payload(publisher)
-        assert payload["status"] == "running"
-        assert len(payload["results"]["jobs"]) == 2
-
-    def test_completed_jobs_are_excluded(self) -> None:
-        manager, publisher = self._make_manager()
-        active = ExportJob(id="active", camera="front")
-        active.status = JobStatusTypesEnum.running
-        finished = ExportJob(id="done", camera="front")
-        finished.status = JobStatusTypesEnum.success
-        manager.jobs[active.id] = active
-        manager.jobs[finished.id] = finished
-
-        manager._broadcast_all_jobs(force=True)
-
-        payload = self._last_payload(publisher)
-        ids = [j["id"] for j in payload["results"]["jobs"]]
-        assert ids == ["active"]
-
-    def test_throttle_skips_rapid_unforced_broadcasts(self) -> None:
-        manager, publisher = self._make_manager()
-        job = ExportJob(camera="front")
-        job.status = JobStatusTypesEnum.running
-        manager.jobs[job.id] = job
-
-        manager._broadcast_all_jobs(force=True)
-        # Immediately following non-forced broadcasts should be skipped.
-        for _ in range(5):
-            manager._broadcast_all_jobs(force=False)
-
-        assert publisher.publish.call_count == 1
-
-    def test_throttle_allows_broadcast_after_interval(self) -> None:
-        manager, publisher = self._make_manager()
-        job = ExportJob(camera="front")
-        job.status = JobStatusTypesEnum.running
-        manager.jobs[job.id] = job
-
-        with patch("frigate.jobs.export.time.monotonic") as mock_mono:
-            mock_mono.return_value = 100.0
-            manager._broadcast_all_jobs(force=True)
-
-            mock_mono.return_value = 100.0 + PROGRESS_BROADCAST_MIN_INTERVAL + 0.01
-            manager._broadcast_all_jobs(force=False)
-
-        assert publisher.publish.call_count == 2
-
-    def test_force_bypasses_throttle(self) -> None:
-        manager, publisher = self._make_manager()
-        job = ExportJob(camera="front")
-        job.status = JobStatusTypesEnum.running
-        manager.jobs[job.id] = job
-
-        manager._broadcast_all_jobs(force=True)
-        manager._broadcast_all_jobs(force=True)
-
-        assert publisher.publish.call_count == 2
-
-    def test_publisher_exceptions_do_not_propagate(self) -> None:
-        """A failing publisher must not break the manager: broadcasts are
-        best-effort since the dispatcher may not be available (tests,
-        startup races)."""
-        manager, publisher = self._make_manager()
-        publisher.publish.side_effect = RuntimeError("comms down")
-
-        job = ExportJob(camera="front")
-        job.status = JobStatusTypesEnum.running
-        manager.jobs[job.id] = job
-
-        # Swallow our own RuntimeError if the manager doesn't; the real
-        # JobStatePublisher handles its own exceptions internally, so the
-        # manager can stay naive. But if something bubbles up it should
-        # not escape _broadcast_all_jobs — enforce that contract here.
-        try:
-            manager._broadcast_all_jobs(force=True)
-        except RuntimeError:
-            self.fail("_broadcast_all_jobs must tolerate publisher failures")
-
-    def test_progress_callback_updates_job_and_broadcasts(self) -> None:
-        manager, _publisher = self._make_manager()
-        job = ExportJob(camera="front")
-        job.status = JobStatusTypesEnum.running
-        manager.jobs[job.id] = job
-
-        callback = manager._make_progress_callback(job)
-        callback("encoding", 33.0)
-
-        assert job.current_step == "encoding"
-        assert job.progress_percent == 33.0
-
-
-class TestGetDatetimeFromTimestamp(unittest.TestCase):
-    """Auto-generated export name should honor config.ui.timezone, not
-    fall back to the container's UTC clock when a timezone is configured.
-    """
-
-    def test_uses_configured_ui_timezone(self) -> None:
-        exporter = _make_exporter()
-        exporter.config.ui.timezone = "America/New_York"
-        # 2025-01-15 12:00:00 UTC is 07:00:00 EST
-        assert exporter.get_datetime_from_timestamp(1736942400) == "2025-01-15 07:00:00"
-
-    def test_falls_back_to_local_when_timezone_unset(self) -> None:
-        exporter = _make_exporter()
-        exporter.config.ui.timezone = None
-        # No assertion on the exact wall-clock value — just confirm no
-        # exception and that pytz isn't required when the field is unset.
-        assert isinstance(exporter.get_datetime_from_timestamp(1736942400), str)
-
-    def test_invalid_timezone_falls_back_to_local(self) -> None:
-        exporter = _make_exporter()
-        exporter.config.ui.timezone = "Not/A_Real_Zone"
-        assert isinstance(exporter.get_datetime_from_timestamp(1736942400), str)
-
-
-class TestSaveThumbnailFromPreviewFrames(unittest.TestCase):
-    """Short exports in the current hour can fall between preview frame
-    writes (1-2 fps during activity, every 30s otherwise). When no frame
-    falls inside the export window, save_thumbnail should fall back to
-    the most recent prior frame instead of returning no thumbnail."""
-
-    def setUp(self) -> None:
-        self.tmp_root = tempfile.mkdtemp(prefix="frigate_thumb_test_")
-        self.preview_dir = os.path.join(self.tmp_root, "cache", "preview_frames")
-        self.export_clips = os.path.join(self.tmp_root, "clips", "export")
-        os.makedirs(self.preview_dir, exist_ok=True)
-        os.makedirs(self.export_clips, exist_ok=True)
-
-    def tearDown(self) -> None:
-        shutil.rmtree(self.tmp_root, ignore_errors=True)
-
-    def _write_frame(self, camera: str, frame_time: float) -> str:
-        path = os.path.join(self.preview_dir, f"preview_{camera}-{frame_time}.webp")
-        with open(path, "wb") as f:
-            f.write(b"fake-webp-bytes")
-        return path
-
-    def _make_short_current_hour_exporter(self) -> RecordingExporter:
-        # Use a "now-ish" timestamp so save_thumbnail's start-of-hour
-        # comparison takes the current-hour branch (preview frames).
-        import datetime
-
-        now = datetime.datetime.now(datetime.timezone.utc).timestamp()
-        exporter = _make_exporter()
-        exporter.export_id = "thumb_short"
-        exporter.start_time = now
-        exporter.end_time = now + 3
-        return exporter
-
-    def test_short_export_falls_back_to_prior_preview_frame(self) -> None:
-        exporter = self._make_short_current_hour_exporter()
-        # Most recent preview frame is 10s before the export window
-        prior = self._write_frame(exporter.camera, exporter.start_time - 10.0)
-        thumb_target = os.path.join(self.export_clips, f"{exporter.export_id}.webp")
-
-        with (
-            patch(
-                "frigate.record.export.CACHE_DIR", os.path.join(self.tmp_root, "cache")
-            ),
-            patch(
-                "frigate.record.export.CLIPS_DIR", os.path.join(self.tmp_root, "clips")
-            ),
-        ):
-            result = exporter.save_thumbnail(exporter.export_id)
-
-        assert result == thumb_target
-        assert os.path.isfile(thumb_target)
-        with open(thumb_target, "rb") as f, open(prior, "rb") as src:
-            assert f.read() == src.read()
-
-    def test_returns_empty_when_no_preview_frames_exist(self) -> None:
-        exporter = self._make_short_current_hour_exporter()
-
-        with (
-            patch(
-                "frigate.record.export.CACHE_DIR", os.path.join(self.tmp_root, "cache")
-            ),
-            patch(
-                "frigate.record.export.CLIPS_DIR", os.path.join(self.tmp_root, "clips")
-            ),
-        ):
-            result = exporter.save_thumbnail(exporter.export_id)
-
-        assert result == ""
-
-    def test_prefers_in_window_frame_over_prior_frame(self) -> None:
-        exporter = self._make_short_current_hour_exporter()
-        self._write_frame(exporter.camera, exporter.start_time - 10.0)
-        in_window = self._write_frame(exporter.camera, exporter.start_time + 1.0)
-        thumb_target = os.path.join(self.export_clips, f"{exporter.export_id}.webp")
-
-        with (
-            patch(
-                "frigate.record.export.CACHE_DIR", os.path.join(self.tmp_root, "cache")
-            ),
-            patch(
-                "frigate.record.export.CLIPS_DIR", os.path.join(self.tmp_root, "clips")
-            ),
-        ):
-            result = exporter.save_thumbnail(exporter.export_id)
-
-        assert result == thumb_target
-        with open(thumb_target, "rb") as f, open(in_window, "rb") as src:
-            assert f.read() == src.read()
-
-
-class TestSchedulesCleanup(unittest.TestCase):
-    def test_schedule_job_cleanup_removes_after_delay(self) -> None:
-        config = MagicMock()
-        manager = ExportJobManager(config, max_concurrent=1, max_queued=1)
-        job = ExportJob(id="cleanup_me", camera="front")
-        manager.jobs[job.id] = job
-
-        with patch("frigate.jobs.export.threading.Timer") as mock_timer:
-            manager._schedule_job_cleanup(job.id)
-            mock_timer.assert_called_once()
-            delay, fn = mock_timer.call_args.args
-            assert delay > 0
-
-            # Invoke the callback directly to confirm it removes the job.
-            fn()
-            assert job.id not in manager.jobs
-
-
-if __name__ == "__main__":
-    unittest.main()

frigate/test/test_output_ws_auth.py

@@ -1,57 +0,0 @@
-"""Tests for JSMPEG websocket authorization."""
-
-import unittest
-from types import SimpleNamespace
-
-from frigate.config import FrigateConfig
-from frigate.output.ws_auth import ws_has_camera_access
-
-
-class TestWsHasCameraAccess(unittest.TestCase):
-    def setUp(self):
-        self.config = FrigateConfig(
-            mqtt={"host": "mqtt"},
-            auth={"roles": {"limited_user": ["front_door"]}},
-            cameras={
-                "front_door": {
-                    "ffmpeg": {
-                        "inputs": [
-                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
-                        ]
-                    },
-                    "detect": {"height": 1080, "width": 1920, "fps": 5},
-                },
-                "back_door": {
-                    "ffmpeg": {
-                        "inputs": [
-                            {"path": "rtsp://10.0.0.2:554/video", "roles": ["detect"]}
-                        ]
-                    },
-                    "detect": {"height": 1080, "width": 1920, "fps": 5},
-                },
-            },
-        )
-
-    def _make_ws(self, role: str):
-        return SimpleNamespace(environ={"HTTP_REMOTE_ROLE": role})
-
-    def test_restricted_role_only_gets_allowed_camera(self):
-        ws = self._make_ws("limited_user")
-        self.assertTrue(ws_has_camera_access(ws, "front_door", self.config))
-        self.assertFalse(ws_has_camera_access(ws, "back_door", self.config))
-
-    def test_unrestricted_role_can_access_any_camera(self):
-        ws = self._make_ws("viewer")
-        self.assertTrue(ws_has_camera_access(ws, "front_door", self.config))
-        self.assertTrue(ws_has_camera_access(ws, "back_door", self.config))
-
-    def test_birdseye_requires_unrestricted_access(self):
-        self.assertTrue(
-            ws_has_camera_access(self._make_ws("admin"), "birdseye", self.config)
-        )
-        self.assertTrue(
-            ws_has_camera_access(self._make_ws("viewer"), "birdseye", self.config)
-        )
-        self.assertFalse(
-            ws_has_camera_access(self._make_ws("limited_user"), "birdseye", self.config)
-        )

frigate/test/test_webpush_camera_monitoring.py

@@ -1,29 +0,0 @@
-"""Tests for camera monitoring notification authorization."""
-
-import unittest
-from types import SimpleNamespace
-from unittest.mock import MagicMock
-
-from frigate.comms.webpush import WebPushClient
-
-
-class TestCameraMonitoringNotifications(unittest.TestCase):
-    def test_send_camera_monitoring_filters_by_camera_access(self):
-        client = WebPushClient.__new__(WebPushClient)
-        client.config = SimpleNamespace(
-            cameras={"front_door": SimpleNamespace(friendly_name=None)}
-        )
-        client.web_pushers = {"allowed": [], "denied": []}
-        client.user_cameras = {"allowed": {"front_door"}, "denied": set()}
-        client.check_registrations = MagicMock()
-        client.cleanup_registrations = MagicMock()
-        client.send_push_notification = MagicMock()
-
-        client.send_camera_monitoring(
-            {"camera": "front_door", "message": "Monitoring condition met"}
-        )
-
-        self.assertEqual(client.send_push_notification.call_count, 1)
-        self.assertEqual(
-            client.send_push_notification.call_args.kwargs["user"], "allowed"
-        )

frigate/test/test_ws_auth.py

@@ -1,166 +0,0 @@
-"""Tests for WebSocket authorization checks."""
-
-import unittest
-
-from frigate.comms.ws import _check_ws_authorization
-from frigate.const import INSERT_MANY_RECORDINGS, UPDATE_CAMERA_ACTIVITY
-
-
-class TestCheckWsAuthorization(unittest.TestCase):
-    """Tests for the _check_ws_authorization pure function."""
-
-    DEFAULT_SEPARATOR = ","
-
-    # --- IPC topic blocking (unconditional, regardless of role) ---
-
-    def test_ipc_topic_blocked_for_admin(self):
-        self.assertFalse(
-            _check_ws_authorization(
-                INSERT_MANY_RECORDINGS, "admin", self.DEFAULT_SEPARATOR
-            )
-        )
-
-    def test_ipc_topic_blocked_for_viewer(self):
-        self.assertFalse(
-            _check_ws_authorization(
-                UPDATE_CAMERA_ACTIVITY, "viewer", self.DEFAULT_SEPARATOR
-            )
-        )
-
-    def test_ipc_topic_blocked_when_no_role(self):
-        self.assertFalse(
-            _check_ws_authorization(
-                INSERT_MANY_RECORDINGS, None, self.DEFAULT_SEPARATOR
-            )
-        )
-
-    # --- Viewer allowed topics ---
-
-    def test_viewer_can_send_on_connect(self):
-        self.assertTrue(
-            _check_ws_authorization("onConnect", "viewer", self.DEFAULT_SEPARATOR)
-        )
-
-    def test_viewer_can_send_model_state(self):
-        self.assertTrue(
-            _check_ws_authorization("modelState", "viewer", self.DEFAULT_SEPARATOR)
-        )
-
-    def test_viewer_can_send_audio_transcription_state(self):
-        self.assertTrue(
-            _check_ws_authorization(
-                "audioTranscriptionState", "viewer", self.DEFAULT_SEPARATOR
-            )
-        )
-
-    def test_viewer_can_send_birdseye_layout(self):
-        self.assertTrue(
-            _check_ws_authorization("birdseyeLayout", "viewer", self.DEFAULT_SEPARATOR)
-        )
-
-    def test_viewer_can_send_embeddings_reindex_progress(self):
-        self.assertTrue(
-            _check_ws_authorization(
-                "embeddingsReindexProgress", "viewer", self.DEFAULT_SEPARATOR
-            )
-        )
-
-    # --- Viewer blocked from admin topics ---
-
-    def test_viewer_blocked_from_restart(self):
-        self.assertFalse(
-            _check_ws_authorization("restart", "viewer", self.DEFAULT_SEPARATOR)
-        )
-
-    def test_viewer_blocked_from_camera_detect_set(self):
-        self.assertFalse(
-            _check_ws_authorization(
-                "front_door/detect/set", "viewer", self.DEFAULT_SEPARATOR
-            )
-        )
-
-    def test_viewer_blocked_from_camera_ptz(self):
-        self.assertFalse(
-            _check_ws_authorization("front_door/ptz", "viewer", self.DEFAULT_SEPARATOR)
-        )
-
-    def test_viewer_blocked_from_global_notifications_set(self):
-        self.assertFalse(
-            _check_ws_authorization(
-                "notifications/set", "viewer", self.DEFAULT_SEPARATOR
-            )
-        )
-
-    def test_viewer_blocked_from_camera_notifications_suspend(self):
-        self.assertFalse(
-            _check_ws_authorization(
-                "front_door/notifications/suspend", "viewer", self.DEFAULT_SEPARATOR
-            )
-        )
-
-    def test_viewer_blocked_from_arbitrary_unknown_topic(self):
-        self.assertFalse(
-            _check_ws_authorization(
-                "some_random_topic", "viewer", self.DEFAULT_SEPARATOR
-            )
-        )
-
-    # --- Admin access ---
-
-    def test_admin_can_send_restart(self):
-        self.assertTrue(
-            _check_ws_authorization("restart", "admin", self.DEFAULT_SEPARATOR)
-        )
-
-    def test_admin_can_send_camera_detect_set(self):
-        self.assertTrue(
-            _check_ws_authorization(
-                "front_door/detect/set", "admin", self.DEFAULT_SEPARATOR
-            )
-        )
-
-    def test_admin_can_send_camera_ptz(self):
-        self.assertTrue(
-            _check_ws_authorization("front_door/ptz", "admin", self.DEFAULT_SEPARATOR)
-        )
-
-    # --- Comma-separated roles ---
-
-    def test_comma_separated_admin_viewer_grants_admin(self):
-        self.assertTrue(
-            _check_ws_authorization("restart", "admin,viewer", self.DEFAULT_SEPARATOR)
-        )
-
-    def test_comma_separated_viewer_admin_grants_admin(self):
-        self.assertTrue(
-            _check_ws_authorization("restart", "viewer,admin", self.DEFAULT_SEPARATOR)
-        )
-
-    def test_comma_separated_with_spaces(self):
-        self.assertTrue(
-            _check_ws_authorization("restart", "viewer, admin", self.DEFAULT_SEPARATOR)
-        )
-
-    # --- Custom separator ---
-
-    def test_pipe_separator(self):
-        self.assertTrue(_check_ws_authorization("restart", "viewer|admin", "|"))
-
-    def test_pipe_separator_no_admin(self):
-        self.assertFalse(_check_ws_authorization("restart", "viewer|editor", "|"))
-
-    # --- No role header (fail-closed) ---
-
-    def test_no_role_header_blocks_admin_topics(self):
-        self.assertFalse(
-            _check_ws_authorization("restart", None, self.DEFAULT_SEPARATOR)
-        )
-
-    def test_no_role_header_allows_viewer_topics(self):
-        self.assertTrue(
-            _check_ws_authorization("onConnect", None, self.DEFAULT_SEPARATOR)
-        )
-
-
-if __name__ == "__main__":
-    unittest.main()

frigate/util/classification.py

@@ -24,12 +24,8 @@ from frigate.log import redirect_output_to_logger, suppress_stderr_during
 from frigate.models import Event, Recordings, ReviewSegment
 from frigate.types import ModelStatusTypesEnum
 from frigate.util.downloader import ModelDownloader
-from frigate.util.file import get_event_thumbnail_bytes, load_event_snapshot_image
-from frigate.util.image import (
-    calculate_region,
-    get_image_from_recording,
-    relative_box_to_absolute,
-)
+from frigate.util.file import get_event_thumbnail_bytes
+from frigate.util.image import get_image_from_recording
 from frigate.util.process import FrigateProcess
 
 BATCH_SIZE = 16
@@ -717,7 +713,7 @@ def collect_object_classification_examples(
     This function:
     1. Queries events for the specified label
     2. Selects 100 balanced events across different cameras and times
-    3. Crops each event's clean snapshot around the object bounding box
+    3. Retrieves thumbnails for selected events (with 33% center crop applied)
     4. Selects 24 most visually distinct thumbnails
     5. Saves to dataset directory
 
@@ -836,106 +832,66 @@ def _select_balanced_events(
 
 def _extract_event_thumbnails(events: list[Event], output_dir: str) -> list[str]:
     """
-    Extract a training image for each event.
-
-    Preferred path: load the full-frame clean snapshot and crop around the
-    stored bounding box with the same calculate_region(..., max(w, h), 1.0)
-    call the live ObjectClassificationProcessor uses, so wizard examples
-    are framed like inference-time inputs.
-
-    Fallback: if no clean snapshot exists (snapshots disabled, or only a
-    legacy annotated JPG is on disk), center-crop the stored thumbnail
-    using a step ladder sized from the box/region area ratio.
+    Extract thumbnails from events and save to disk.
 
     Args:
         events: List of Event objects
-        output_dir: Directory to save crops
+        output_dir: Directory to save thumbnails
 
     Returns:
-        List of paths to successfully extracted images
+        List of paths to successfully extracted thumbnail images
     """
-    image_paths = []
+    thumbnail_paths = []
 
     for idx, event in enumerate(events):
         try:
-            img = _load_event_classification_crop(event)
-            if img is None:
-                continue
+            thumbnail_bytes = get_event_thumbnail_bytes(event)
 
-            resized = cv2.resize(img, (224, 224))
-            output_path = os.path.join(output_dir, f"thumbnail_{idx:04d}.jpg")
-            cv2.imwrite(output_path, resized)
-            image_paths.append(output_path)
+            if thumbnail_bytes:
+                nparr = np.frombuffer(thumbnail_bytes, np.uint8)
+                img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+                if img is not None:
+                    height, width = img.shape[:2]
+
+                    crop_size = 1.0
+                    if event.data and "box" in event.data and "region" in event.data:
+                        box = event.data["box"]
+                        region = event.data["region"]
+
+                        if len(box) == 4 and len(region) == 4:
+                            box_w, box_h = box[2], box[3]
+                            region_w, region_h = region[2], region[3]
+
+                            box_area = (box_w * box_h) / (region_w * region_h)
+
+                            if box_area < 0.05:
+                                crop_size = 0.4
+                            elif box_area < 0.10:
+                                crop_size = 0.5
+                            elif box_area < 0.20:
+                                crop_size = 0.65
+                            elif box_area < 0.35:
+                                crop_size = 0.80
+                            else:
+                                crop_size = 0.95
+
+                    crop_width = int(width * crop_size)
+                    crop_height = int(height * crop_size)
+
+                    x1 = (width - crop_width) // 2
+                    y1 = (height - crop_height) // 2
+                    x2 = x1 + crop_width
+                    y2 = y1 + crop_height
+
+                    cropped = img[y1:y2, x1:x2]
+                    resized = cv2.resize(cropped, (224, 224))
+                    output_path = os.path.join(output_dir, f"thumbnail_{idx:04d}.jpg")
+                    cv2.imwrite(output_path, resized)
+                    thumbnail_paths.append(output_path)
 
         except Exception as e:
-            logger.debug(f"Failed to extract image for event {event.id}: {e}")
+            logger.debug(f"Failed to extract thumbnail for event {event.id}: {e}")
             continue
 
-    return image_paths
-
-
-def _load_event_classification_crop(event: Event) -> np.ndarray | None:
-    """Prefer a snapshot-based object crop; fall back to a center-cropped thumbnail."""
-    if event.data and "box" in event.data:
-        snapshot, _ = load_event_snapshot_image(event, clean_only=True)
-        if snapshot is not None:
-            abs_box = relative_box_to_absolute(snapshot.shape, event.data["box"])
-            if abs_box is not None:
-                xmin, ymin, xmax, ymax = abs_box
-                box_w = xmax - xmin
-                box_h = ymax - ymin
-                if box_w > 0 and box_h > 0:
-                    x1, y1, x2, y2 = calculate_region(
-                        snapshot.shape,
-                        xmin,
-                        ymin,
-                        xmax,
-                        ymax,
-                        max(box_w, box_h),
-                        1.0,
-                    )
-                    cropped = snapshot[y1:y2, x1:x2]
-                    if cropped.size > 0:
-                        return cropped
-
-    thumbnail_bytes = get_event_thumbnail_bytes(event)
-    if not thumbnail_bytes:
-        return None
-
-    nparr = np.frombuffer(thumbnail_bytes, np.uint8)
-    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-    if img is None or img.size == 0:
-        return None
-
-    height, width = img.shape[:2]
-    crop_size = 1.0
-
-    if event.data and "box" in event.data and "region" in event.data:
-        box = event.data["box"]
-        region = event.data["region"]
-
-        if len(box) == 4 and len(region) == 4:
-            box_w, box_h = box[2], box[3]
-            region_w, region_h = region[2], region[3]
-            box_area = (box_w * box_h) / (region_w * region_h)
-
-            if box_area < 0.05:
-                crop_size = 0.4
-            elif box_area < 0.10:
-                crop_size = 0.5
-            elif box_area < 0.20:
-                crop_size = 0.65
-            elif box_area < 0.35:
-                crop_size = 0.80
-            else:
-                crop_size = 0.95
-
-    crop_width = int(width * crop_size)
-    crop_height = int(height * crop_size)
-    x1 = (width - crop_width) // 2
-    y1 = (height - crop_height) // 2
-    cropped = img[y1 : y1 + crop_height, x1 : x1 + crop_width]
-    if cropped.size == 0:
-        return None
-
-    return cropped
+    return thumbnail_paths

frigate/util/services.py

@@ -711,44 +711,23 @@ def ffprobe_stream(ffmpeg, path: str, detailed: bool = False) -> sp.CompletedPro
     else:
         format_entries = None
 
-    def run(rtsp_transport: Optional[str] = None) -> sp.CompletedProcess:
-        cmd = [ffmpeg.ffprobe_path]
-        if rtsp_transport:
-            cmd += ["-rtsp_transport", rtsp_transport]
-        cmd += [
-            "-timeout",
-            "1000000",
-            "-print_format",
-            "json",
-            "-show_entries",
-            f"stream={stream_entries}",
-        ]
-        if detailed and format_entries:
-            cmd.extend(["-show_entries", f"format={format_entries}"])
-        cmd.extend(["-loglevel", "error", clean_path])
-        try:
-            return sp.run(cmd, capture_output=True, timeout=6)
-        except sp.TimeoutExpired as e:
-            logger.info(
-                "ffprobe timed out while probing %s (transport=%s)",
-                clean_camera_user_pass(path),
-                rtsp_transport or "default",
-            )
-            return sp.CompletedProcess(
-                args=cmd,
-                returncode=1,
-                stdout=e.stdout or b"",
-                stderr=(e.stderr or b"") + b"\nffprobe timed out",
-            )
+    ffprobe_cmd = [
+        ffmpeg.ffprobe_path,
+        "-timeout",
+        "1000000",
+        "-print_format",
+        "json",
+        "-show_entries",
+        f"stream={stream_entries}",
+    ]
 
-    result = run()
+    # Add format entries for detailed mode
+    if detailed and format_entries:
+        ffprobe_cmd.extend(["-show_entries", f"format={format_entries}"])
 
-    # For RTSP: retry with explicit TCP transport if the first attempt failed
-    # (default UDP may be blocked)
-    if result.returncode != 0 and clean_path.startswith("rtsp://"):
-        result = run(rtsp_transport="tcp")
+    ffprobe_cmd.extend(["-loglevel", "error", clean_path])
 
-    return result
+    return sp.run(ffprobe_cmd, capture_output=True)
 
 
 def vainfo_hwaccel(device_name: Optional[str] = None) -> sp.CompletedProcess:
@@ -828,15 +807,10 @@ async def get_video_properties(
 ) -> dict[str, Any]:
     async def probe_with_ffprobe(
         url: str,
-        rtsp_transport: Optional[str] = None,
     ) -> tuple[bool, int, int, Optional[str], float]:
         """Fallback using ffprobe: returns (valid, width, height, codec, duration)."""
-        cmd = [ffmpeg.ffprobe_path]
-        if rtsp_transport:
-            cmd += ["-rtsp_transport", rtsp_transport]
-        cmd += [
-            "-rw_timeout",
-            "5000000",
+        cmd = [
+            ffmpeg.ffprobe_path,
             "-v",
             "quiet",
             "-print_format",
@@ -845,23 +819,11 @@ async def get_video_properties(
             "-show_streams",
             url,
         ]
-        proc = None
         try:
             proc = await asyncio.create_subprocess_exec(
                 *cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE
             )
-            try:
-                stdout, _ = await asyncio.wait_for(proc.communicate(), timeout=6)
-            except asyncio.TimeoutError:
-                logger.info(
-                    "ffprobe timed out while probing %s (transport=%s)",
-                    clean_camera_user_pass(url),
-                    rtsp_transport or "default",
-                )
-                proc.kill()
-                await proc.wait()
-                return False, 0, 0, None, -1
-
+            stdout, _ = await proc.communicate()
             if proc.returncode != 0:
                 return False, 0, 0, None, -1
 
@@ -910,26 +872,12 @@ async def get_video_properties(
         cap.release()
         return valid, width, height, fourcc, duration
 
-    is_rtsp = url.startswith("rtsp://")
+    # try cv2 first
+    has_video, width, height, fourcc, duration = probe_with_cv2(url)
 
-    if is_rtsp:
-        # skip cv2 for RTSP: its FFmpeg backend has a hardcoded ~30s internal
-        # timeout that cannot be shortened per-call, and ffprobe bounded by
-        # -rw_timeout handles RTSP probing reliably
+    # fallback to ffprobe if needed
+    if not has_video or (get_duration and duration < 0):
         has_video, width, height, fourcc, duration = await probe_with_ffprobe(url)
-    else:
-        # try cv2 first for local files, HTTP, RTMP
-        has_video, width, height, fourcc, duration = probe_with_cv2(url)
-
-        # fallback to ffprobe if needed
-        if not has_video or (get_duration and duration < 0):
-            has_video, width, height, fourcc, duration = await probe_with_ffprobe(url)
-
-    # last resort for RTSP: try TCP transport, since default UDP may be blocked
-    if (not has_video or (get_duration and duration < 0)) and is_rtsp:
-        has_video, width, height, fourcc, duration = await probe_with_ffprobe(
-            url, rtsp_transport="tcp"
-        )
 
     result: dict[str, Any] = {"has_valid_video": has_video}
     if has_video:

frigate/video/ffmpeg.py

@@ -24,7 +24,7 @@ from frigate.config.camera.updater import (
 )
 from frigate.const import PROCESS_PRIORITY_HIGH
 from frigate.log import LogPipe
-from frigate.util.builtin import EventsPerSecond, get_ffmpeg_arg_list
+from frigate.util.builtin import EventsPerSecond
 from frigate.util.ffmpeg import start_or_restart_ffmpeg, stop_ffmpeg
 from frigate.util.image import (
     FrameManager,
@@ -34,23 +34,6 @@ from frigate.util.process import FrigateProcess
 
 logger = logging.getLogger(__name__)
 
-# all built-in record presets use this segment_time
-DEFAULT_RECORD_SEGMENT_TIME = 10
-
-
-def _get_record_segment_time(config: CameraConfig) -> int:
-    """Extract -segment_time from the camera's record output args."""
-    record_args = get_ffmpeg_arg_list(config.ffmpeg.output_args.record)
-
-    if record_args and record_args[0].startswith("preset"):
-        return DEFAULT_RECORD_SEGMENT_TIME
-
-    try:
-        idx = record_args.index("-segment_time")
-        return int(record_args[idx + 1])
-    except (ValueError, IndexError):
-        return DEFAULT_RECORD_SEGMENT_TIME
-
 
 def capture_frames(
     ffmpeg_process: sp.Popen[Any],
@@ -181,12 +164,6 @@ class CameraWatchdog(threading.Thread):
         self.latest_cache_segment_time: float = 0
         self.record_enable_time: datetime | None = None
 
-        # `valid` segments are published with the segment's start time, so the
-        # gap between consecutive publishes can reach 2 * segment_time. Pad the
-        # staleness threshold so it's never tighter than that worst case.
-        segment_time = _get_record_segment_time(self.config)
-        self.record_stale_threshold = max(120, 2 * segment_time + 30)
-
         # Stall tracking (based on last processed frame)
         self._stall_timestamps: deque[float] = deque()
         self._stall_active: bool = False
@@ -340,16 +317,16 @@ class CameraWatchdog(threading.Thread):
                     if camera != self.config.name:
                         continue
 
-                    if topic.endswith(RecordingsDataTypeEnum.invalid.value):
-                        self.logger.warning(
-                            f"Invalid recording segment detected for {camera} at {segment_time}"
-                        )
-                        self.latest_invalid_segment_time = segment_time
-                    elif topic.endswith(RecordingsDataTypeEnum.valid.value):
+                    if topic.endswith(RecordingsDataTypeEnum.valid.value):
                         self.logger.debug(
                             f"Latest valid recording segment time on {camera}: {segment_time}"
                         )
                         self.latest_valid_segment_time = segment_time
+                    elif topic.endswith(RecordingsDataTypeEnum.invalid.value):
+                        self.logger.warning(
+                            f"Invalid recording segment detected for {camera} at {segment_time}"
+                        )
+                        self.latest_invalid_segment_time = segment_time
                     elif topic.endswith(RecordingsDataTypeEnum.latest.value):
                         if segment_time is not None:
                             self.latest_cache_segment_time = segment_time
@@ -436,17 +413,16 @@ class CameraWatchdog(threading.Thread):
 
                     # ensure segments are still being created and that they have valid video data
                     # Skip checks during grace period to allow segments to start being created
-                    stale_window = timedelta(seconds=self.record_stale_threshold)
                     cache_stale = not in_grace_period and now_utc > (
-                        latest_cache_dt + stale_window
+                        latest_cache_dt + timedelta(seconds=120)
                     )
                     valid_stale = not in_grace_period and now_utc > (
-                        latest_valid_dt + stale_window
+                        latest_valid_dt + timedelta(seconds=120)
                     )
                     invalid_stale_condition = (
                         self.latest_invalid_segment_time > 0
                         and not in_grace_period
-                        and now_utc > (latest_invalid_dt + stale_window)
+                        and now_utc > (latest_invalid_dt + timedelta(seconds=120))
                         and self.latest_valid_segment_time
                         <= self.latest_invalid_segment_time
                     )
@@ -463,7 +439,7 @@ class CameraWatchdog(threading.Thread):
                             )
 
                         self.logger.error(
-                            f"{reason} for {self.config.name} in the last {self.record_stale_threshold}s. Restarting the ffmpeg record process..."
+                            f"{reason} for {self.config.name} in the last 120s. Restarting the ffmpeg record process..."
                         )
                         p["process"] = start_or_restart_ffmpeg(
                             p["cmd"],

frigate/watchdog.py

@@ -28,7 +28,6 @@ class MonitoredProcess:
     restart_timestamps: deque[float] = field(
         default_factory=lambda: deque(maxlen=MAX_RESTARTS)
     )
-    clean_exit_logged: bool = False
 
     def is_restarting_too_fast(self, now: float) -> bool:
         while (
@@ -73,9 +72,7 @@ class FrigateWatchdog(threading.Thread):
 
         exitcode = entry.process.exitcode
         if exitcode == 0:
-            if not entry.clean_exit_logged:
-                logger.info("Process %s exited cleanly, not restarting", entry.name)
-                entry.clean_exit_logged = True
+            logger.info("Process %s exited cleanly, not restarting", entry.name)
             return
 
         logger.warning(

notebooks/YOLO_NAS_Pretrained_Export.ipynb

@@ -1,95 +1,88 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "runtime-notice"
-   },
-   "source": [
-    "**Before running:** go to **Runtime → Change runtime type → Fallback runtime version: 2025.07** (Python 3.11). The current Colab default (Python 3.12+) is incompatible with `super-gradients`."
-   ]
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rmuF9iKWTbdk"
+      },
+      "outputs": [],
+      "source": [
+        "! pip install -q git+https://github.com/Deci-AI/super-gradients.git"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NiRCt917KKcL"
+      },
+      "outputs": [],
+      "source": [
+        "! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.12/dist-packages/super_gradients/training/pretrained_models.py\n",
+        "! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.12/dist-packages/super_gradients/training/utils/checkpoint_utils.py"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dTB0jy_NNSFz"
+      },
+      "outputs": [],
+      "source": [
+        "from super_gradients.common.object_names import Models\n",
+        "from super_gradients.conversion import DetectionOutputFormatMode\n",
+        "from super_gradients.training import models\n",
+        "\n",
+        "model = models.get(Models.YOLO_NAS_S, pretrained_weights=\"coco\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GymUghyCNXem"
+      },
+      "outputs": [],
+      "source": [
+        "# export the model for compatibility with Frigate\n",
+        "\n",
+        "model.export(\"yolo_nas_s.onnx\",\n",
+        "             output_predictions_format=DetectionOutputFormatMode.FLAT_FORMAT,\n",
+        "             max_predictions_per_image=20,\n",
+        "             num_pre_nms_predictions=300,\n",
+        "             confidence_threshold=0.4,\n",
+        "             input_image_shape=(320,320),\n",
+        "            )"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uBhXV5g4Nh42"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import files\n",
+        "\n",
+        "files.download('yolo_nas_s.onnx')"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "rmuF9iKWTbdk"
-   },
-   "outputs": [],
-   "source": [
-    "! pip install -q \"jedi>=0.16\"\n",
-    "! pip install -q git+https://github.com/Deci-AI/super-gradients.git"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "NiRCt917KKcL"
-   },
-   "outputs": [],
-   "source": "! sed -i 's/sghub\\.deci\\.ai/d2gjn4b69gu75n.cloudfront.net/g; s/sg-hub-nv\\.s3\\.amazonaws\\.com/d2gjn4b69gu75n.cloudfront.net/g' /usr/local/lib/python*/dist-packages/super_gradients/training/pretrained_models.py\n! sed -i 's/sghub\\.deci\\.ai/d2gjn4b69gu75n.cloudfront.net/g; s/sg-hub-nv\\.s3\\.amazonaws\\.com/d2gjn4b69gu75n.cloudfront.net/g' /usr/local/lib/python*/dist-packages/super_gradients/training/utils/checkpoint_utils.py"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "dTB0jy_NNSFz"
-   },
-   "outputs": [],
-   "source": [
-    "from super_gradients.common.object_names import Models\n",
-    "from super_gradients.conversion import DetectionOutputFormatMode\n",
-    "from super_gradients.training import models\n",
-    "\n",
-    "model = models.get(Models.YOLO_NAS_S, pretrained_weights=\"coco\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "GymUghyCNXem"
-   },
-   "outputs": [],
-   "source": [
-    "# export the model for compatibility with Frigate\n",
-    "\n",
-    "model.export(\"yolo_nas_s.onnx\",\n",
-    "             output_predictions_format=DetectionOutputFormatMode.FLAT_FORMAT,\n",
-    "             max_predictions_per_image=20,\n",
-    "             num_pre_nms_predictions=300,\n",
-    "             confidence_threshold=0.4,\n",
-    "             input_image_shape=(320,320),\n",
-    "            )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "uBhXV5g4Nh42"
-   },
-   "outputs": [],
-   "source": [
-    "from google.colab import files\n",
-    "\n",
-    "files.download('yolo_nas_s.onnx')"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

testing-scripts/analyze_recording_keyframes.py

@@ -1,376 +0,0 @@
-#!/usr/bin/env python3
-"""Analyze keyframe and timestamp structure of Frigate recording segments.
-
-This is a diagnostic tool for investigating seek precision / GOP behavior on
-recorded segments. It does not modify anything.
-
-ffprobe is only available inside the Frigate container, at
-    /usr/lib/ffmpeg/$DEFAULT_FFMPEG_VERSION/bin/ffprobe
-This script auto-resolves that path from the DEFAULT_FFMPEG_VERSION env var
-(or falls back to scanning /usr/lib/ffmpeg/*/bin/ffprobe). Pass --ffprobe to
-override if needed.
-
-All recording segments on the filesystem are in UTC. The --timestamp flag
-expects a UTC Unix timestamp.
-
-Typical use:
-    # Inside the Frigate container (or wherever recordings are mounted)
-    python3 analyze_recording_keyframes.py <camera_name>
-
-    # Analyze 10 most recent segments
-    python3 analyze_recording_keyframes.py <camera_name> --count 10
-
-    # Locate the segment that contains a specific UTC Unix timestamp and
-    # show it plus surrounding segments
-    python3 analyze_recording_keyframes.py <camera> --timestamp 1713471234.567
-
-    # Custom recordings directory
-    python3 analyze_recording_keyframes.py <camera> --recordings-dir /media/frigate/recordings
-
-    # Override the ffprobe path explicitly
-    python3 analyze_recording_keyframes.py <camera> --ffprobe /usr/lib/ffmpeg/7.0/bin/ffprobe
-"""
-
-import argparse
-import datetime
-import json
-import os
-import subprocess
-import sys
-from pathlib import Path
-from statistics import mean, median, stdev
-
-
-def resolve_ffprobe_path(override: str | None) -> str:
-    """Resolve the ffprobe binary path.
-
-    Inside the Frigate container, ffprobe lives at
-    /usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffprobe — the exact version
-    depends on the image build and is exposed as an env var.
-    """
-    if override:
-        return override
-    version = os.environ.get("DEFAULT_FFMPEG_VERSION", "")
-    if version:
-        path = f"/usr/lib/ffmpeg/{version}/bin/ffprobe"
-        if Path(path).is_file():
-            return path
-    # Fall back to scanning the Frigate ffmpeg install root.
-    for candidate in sorted(Path("/usr/lib/ffmpeg").glob("*/bin/ffprobe")):
-        if candidate.is_file():
-            return str(candidate)
-    print(
-        "Could not locate ffprobe. Pass --ffprobe <path> or set "
-        "DEFAULT_FFMPEG_VERSION.",
-        file=sys.stderr,
-    )
-    sys.exit(1)
-
-
-def find_recent_segments(recordings_dir: Path, camera: str, count: int) -> list[Path]:
-    """Return the N most recent .mp4 segments for the given camera.
-
-    Expected layout: <recordings_dir>/<YYYY-MM-DD>/<HH>/<camera>/<MM>.<SS>.mp4
-    """
-    pattern = f"*/*/{camera}/*.mp4"
-    segments = sorted(recordings_dir.glob(pattern))
-    return segments[-count:]
-
-
-def find_segments_near_timestamp(
-    recordings_dir: Path, camera: str, target_ts: float, count: int
-) -> tuple[list[Path], Path | None]:
-    """Return `count` segments centered on the one containing `target_ts`.
-
-    Also returns the specific segment that should contain the timestamp, so
-    callers can highlight it in output.
-    """
-    pattern = f"*/*/{camera}/*.mp4"
-    with_ts: list[tuple[float, Path]] = []
-    for seg in sorted(recordings_dir.glob(pattern)):
-        ts = filename_to_timestamp(seg)
-        if ts is not None:
-            with_ts.append((ts, seg))
-
-    if not with_ts:
-        return [], None
-
-    # Largest filename_ts that is <= target_ts — that's the segment that
-    # should contain the timestamp (Frigate catalogs segments by filename).
-    target_idx = -1
-    for i, (ts, _) in enumerate(with_ts):
-        if ts <= target_ts:
-            target_idx = i
-        else:
-            break
-
-    if target_idx < 0:
-        # target_ts is before the earliest segment we have — just return the
-        # first `count` segments so the user can see what's available.
-        window = with_ts[:count]
-        return [seg for _, seg in window], None
-
-    half = count // 2
-    start = max(0, target_idx - half)
-    end = min(len(with_ts), start + count)
-    start = max(0, end - count)
-
-    window = with_ts[start:end]
-    return [seg for _, seg in window], with_ts[target_idx][1]
-
-
-def filename_to_timestamp(segment: Path) -> float | None:
-    """Parse the wall-clock time from Frigate's segment path layout."""
-    try:
-        date = segment.parent.parent.parent.name  # YYYY-MM-DD
-        hour = segment.parent.parent.name  # HH
-        mm_ss = segment.stem  # MM.SS
-        minute, second = mm_ss.split(".")
-        dt = datetime.datetime.strptime(
-            f"{date} {hour}:{minute}:{second}",
-            "%Y-%m-%d %H:%M:%S",
-        ).replace(tzinfo=datetime.timezone.utc)
-        return dt.timestamp()
-    except (ValueError, IndexError):
-        return None
-
-
-def run_ffprobe(ffprobe: str, args: list[str]) -> dict:
-    """Run ffprobe and return parsed JSON, or empty dict on failure."""
-    result = subprocess.run(
-        [ffprobe, "-v", "error", *args, "-of", "json"],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    if result.returncode != 0:
-        print(f"  ffprobe error: {result.stderr.strip()}", file=sys.stderr)
-        return {}
-    try:
-        return json.loads(result.stdout)
-    except json.JSONDecodeError:
-        return {}
-
-
-def get_format_info(ffprobe: str, segment: Path) -> tuple[dict, dict]:
-    """Return (format_dict, stream_dict) for the first video stream."""
-    data = run_ffprobe(
-        ffprobe,
-        [
-            "-show_entries",
-            "format=duration,start_time",
-            "-show_entries",
-            "stream=codec_name,profile,r_frame_rate,width,height",
-            "-select_streams",
-            "v:0",
-            str(segment),
-        ],
-    )
-    fmt = data.get("format", {})
-    streams = data.get("streams") or [{}]
-    return fmt, streams[0]
-
-
-def get_video_packets(ffprobe: str, segment: Path) -> list[dict]:
-    """Return video packets with pts_time and flags."""
-    data = run_ffprobe(
-        ffprobe,
-        [
-            "-select_streams",
-            "v",
-            "-show_entries",
-            "packet=pts_time,dts_time,flags",
-            str(segment),
-        ],
-    )
-    return data.get("packets", [])
-
-
-def analyze(ffprobe: str, segment: Path, highlight: bool = False) -> None:
-    marker = "  <-- contains target timestamp" if highlight else ""
-    print(f"\n=== {segment} ==={marker}")
-
-    fmt, stream = get_format_info(ffprobe, segment)
-    duration = float(fmt.get("duration", 0) or 0)
-    start_time = float(fmt.get("start_time", 0) or 0)
-    codec = stream.get("codec_name", "?")
-    profile = stream.get("profile", "?")
-    width = stream.get("width", "?")
-    height = stream.get("height", "?")
-    fps = stream.get("r_frame_rate", "?/1")
-
-    filename_ts = filename_to_timestamp(segment)
-    filename_iso = (
-        datetime.datetime.fromtimestamp(
-            filename_ts, tz=datetime.timezone.utc
-        ).isoformat()
-        if filename_ts is not None
-        else "?"
-    )
-
-    print(f"  Codec:           {codec} ({profile})  {width}x{height}  {fps}")
-    print(f"  Filename time:   {filename_ts}  ({filename_iso})")
-    print(f"  Format duration: {duration:.3f}s")
-    print(f"  Format start:    {start_time:.3f}s  (PTS offset of first packet)")
-
-    packets = get_video_packets(ffprobe, segment)
-    if not packets:
-        print("  (no video packets)")
-        return
-
-    keyframe_times: list[float] = []
-    first_pts: float | None = None
-    last_pts: float | None = None
-
-    for pkt in packets:
-        pts_str = pkt.get("pts_time")
-        if pts_str is None or pts_str == "N/A":
-            continue
-        pts = float(pts_str)
-        if first_pts is None:
-            first_pts = pts
-        last_pts = pts
-        if "K" in pkt.get("flags", ""):
-            keyframe_times.append(pts)
-
-    total_packets = len(packets)
-    kf_count = len(keyframe_times)
-
-    print(f"  Video packets:   {total_packets}")
-    print(f"  Keyframes:       {kf_count}")
-    if first_pts is not None and last_pts is not None:
-        print(
-            f"  Packet PTS:      first={first_pts:.3f}s  last={last_pts:.3f}s  "
-            f"span={last_pts - first_pts:.3f}s"
-        )
-
-    if keyframe_times:
-        print(
-            f"  Keyframe PTS:    first={keyframe_times[0]:.3f}s  "
-            f"last={keyframe_times[-1]:.3f}s"
-        )
-        formatted = ", ".join(f"{t:.3f}" for t in keyframe_times)
-        print(f"  Keyframe times:  [{formatted}]")
-
-    if len(keyframe_times) >= 2:
-        gaps = [b - a for a, b in zip(keyframe_times, keyframe_times[1:])]
-        avg_fps_estimate = (
-            total_packets / (last_pts - first_pts)
-            if last_pts and first_pts is not None and last_pts > first_pts
-            else 0
-        )
-        print(
-            f"  GOP gaps (s):    min={min(gaps):.3f}  max={max(gaps):.3f}  "
-            f"mean={mean(gaps):.3f}  median={median(gaps):.3f}"
-        )
-        if len(gaps) > 1:
-            print(f"                   stdev={stdev(gaps):.3f}")
-        print(
-            f"  Est. mean GOP:   ~{mean(gaps) * avg_fps_estimate:.1f} frames"
-            if avg_fps_estimate
-            else ""
-        )
-        if max(gaps) > 5:
-            print(
-                "  !! Max GOP > 5s — consistent with adaptive/smart codec "
-                "(even if 'Smart Codec' is off in the UI, some cameras still "
-                "produce irregular GOPs under specific encoder profiles)"
-            )
-    elif kf_count == 1:
-        print("  !! Only one keyframe in segment — very long GOP")
-
-    # Report how well filename time aligns with first-packet PTS.
-    # (Filename time is what Frigate uses as recording.start_time in the DB.)
-    if filename_ts is not None and first_pts is not None:
-        print(
-            f"  Notes: first packet PTS is {first_pts:.3f}s into the file; "
-            f"Frigate treats filename time as PTS=0 for seek math."
-        )
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(
-        description=__doc__,
-        formatter_class=argparse.RawDescriptionHelpFormatter,
-    )
-    parser.add_argument("camera", help="Camera name (matches the recordings subfolder)")
-    parser.add_argument(
-        "--count",
-        type=int,
-        default=5,
-        help="Number of most recent segments to analyze (default: 5)",
-    )
-    parser.add_argument(
-        "--recordings-dir",
-        default="/media/frigate/recordings",
-        help="Path to the recordings directory (default: /media/frigate/recordings)",
-    )
-    parser.add_argument(
-        "--ffprobe",
-        default=None,
-        help=(
-            "Full path to the ffprobe binary. Defaults to the Frigate-bundled "
-            "binary at /usr/lib/ffmpeg/$DEFAULT_FFMPEG_VERSION/bin/ffprobe."
-        ),
-    )
-    parser.add_argument(
-        "--timestamp",
-        type=float,
-        default=None,
-        help=(
-            "Unix timestamp (UTC seconds, decimals allowed) to locate. The "
-            "script finds the segment that should contain this time and "
-            "analyzes it plus surrounding segments (count controls the "
-            "window). All on-disk segments are stored in UTC, so pass a UTC "
-            "Unix timestamp."
-        ),
-    )
-    args = parser.parse_args()
-
-    ffprobe = resolve_ffprobe_path(args.ffprobe)
-
-    recordings_dir = Path(args.recordings_dir)
-    if not recordings_dir.is_dir():
-        print(
-            f"Recordings directory not found: {recordings_dir}",
-            file=sys.stderr,
-        )
-        sys.exit(1)
-
-    target_segment: Path | None = None
-    if args.timestamp is not None:
-        segments, target_segment = find_segments_near_timestamp(
-            recordings_dir, args.camera, args.timestamp, args.count
-        )
-        target_iso = datetime.datetime.fromtimestamp(
-            args.timestamp, tz=datetime.timezone.utc
-        ).isoformat()
-        mode = f"around timestamp {args.timestamp} ({target_iso})"
-    else:
-        segments = find_recent_segments(recordings_dir, args.camera, args.count)
-        mode = "most recent"
-
-    if not segments:
-        print(
-            f"No segments found for camera '{args.camera}' under {recordings_dir}",
-            file=sys.stderr,
-        )
-        sys.exit(1)
-
-    if args.timestamp is not None and target_segment is None:
-        print(
-            f"!! Target timestamp {args.timestamp} is before the earliest "
-            f"segment on disk; showing the earliest available segments instead.",
-            file=sys.stderr,
-        )
-
-    print(
-        f"Analyzing {len(segments)} {mode} segment(s) for camera "
-        f"'{args.camera}' under {recordings_dir} (ffprobe: {ffprobe})"
-    )
-    for segment in segments:
-        analyze(ffprobe, segment, highlight=(segment == target_segment))
-
-
-if __name__ == "__main__":
-    main()

testing-scripts/face_dataset.py

@@ -1,783 +0,0 @@
-"""
-Face recognition investigation script.
-
-Standalone replica of Frigate's ArcFace pipeline (see
-frigate/data_processing/common/face/model.py and
-frigate/embeddings/onnx/face_embedding.py) for analyzing a face collection
-outside the running service. Useful for:
-
-  - Diagnosing why a person's collection produces false positives
-  - Finding outlier/contaminating training images
-  - Inspecting the effect of the shipped vector-wise outlier filter
-
-Layout:
-  - Core pipeline: LandmarkAligner, ArcFaceEmbedder, arcface_preprocess,
-    similarity_to_confidence, blur_reduction — all mirroring the production
-    code exactly
-  - Default run: summarize positive and negative sets against a baseline
-    trim_mean class representation
-  - Optional diagnostics (flags): vector-outlier filter behavior, degenerate
-    "tiny crop" embedding clustering, and multi-identity contamination
-
-Usage:
-    python3 face_investigate.py \\
-        --positive <positive_folder> \\
-        --negative <negative_folder> \\
-        [--model-cache /path/to/model_cache] \\
-        [--vector-outlier] [--degenerate] [--contamination]
-
-The positive folder should contain training images for a single identity
-(same layout as FACE_DIR/<name>/*.webp). The negative folder should contain
-runtime crops to test against — a mix of true matches and misfires.
-"""
-
-from __future__ import annotations
-
-import argparse
-import os
-import sys
-from dataclasses import dataclass
-from typing import Iterable
-
-import cv2
-import numpy as np
-import onnxruntime as ort
-from PIL import Image
-from scipy import stats
-
-ARCFACE_INPUT_SIZE = 112
-
-
-# ---------------------------------------------------------------------------
-# Replicated Frigate pipeline
-# ---------------------------------------------------------------------------
-
-
-def _process_image_frigate(image: np.ndarray) -> Image.Image:
-    """Mirror BaseEmbedding._process_image for an ndarray input.
-
-    NOTE: Frigate passes the output of `cv2.imread` (BGR) directly in. PIL's
-    `Image.fromarray` does NOT reorder channels, so the embedder effectively
-    receives a BGR-ordered tensor. We replicate that faithfully here. (Tested
-    — swapping to RGB produces near-identical embeddings; this model is
-    robust to channel order.)
-    """
-    return Image.fromarray(image)
-
-
-def arcface_preprocess(image_bgr: np.ndarray) -> np.ndarray:
-    """Mirror ArcfaceEmbedding._preprocess_inputs."""
-    pil = _process_image_frigate(image_bgr)
-
-    width, height = pil.size
-    if width != ARCFACE_INPUT_SIZE or height != ARCFACE_INPUT_SIZE:
-        if width > height:
-            new_height = int(((height / width) * ARCFACE_INPUT_SIZE) // 4 * 4)
-            pil = pil.resize((ARCFACE_INPUT_SIZE, new_height))
-        else:
-            new_width = int(((width / height) * ARCFACE_INPUT_SIZE) // 4 * 4)
-            pil = pil.resize((new_width, ARCFACE_INPUT_SIZE))
-
-    og = np.array(pil).astype(np.float32)
-    og_h, og_w, channels = og.shape
-
-    frame = np.zeros(
-        (ARCFACE_INPUT_SIZE, ARCFACE_INPUT_SIZE, channels), dtype=np.float32
-    )
-    x_center = (ARCFACE_INPUT_SIZE - og_w) // 2
-    y_center = (ARCFACE_INPUT_SIZE - og_h) // 2
-    frame[y_center : y_center + og_h, x_center : x_center + og_w] = og
-
-    frame = (frame / 127.5) - 1.0
-    frame = np.transpose(frame, (2, 0, 1))
-    frame = np.expand_dims(frame, axis=0)
-    return frame
-
-
-class LandmarkAligner:
-    """Mirror FaceRecognizer.align_face."""
-
-    def __init__(self, landmark_model_path: str):
-        if not os.path.exists(landmark_model_path):
-            raise FileNotFoundError(landmark_model_path)
-        self.detector = cv2.face.createFacemarkLBF()
-        self.detector.loadModel(landmark_model_path)
-
-    def align(
-        self, image: np.ndarray, out_w: int, out_h: int
-    ) -> tuple[np.ndarray, dict]:
-        land_image = (
-            cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if image.ndim == 3 else image
-        )
-        _, lands = self.detector.fit(
-            land_image, np.array([(0, 0, land_image.shape[1], land_image.shape[0])])
-        )
-        landmarks = lands[0][0]
-
-        leftEyePts = landmarks[42:48]
-        rightEyePts = landmarks[36:42]
-        leftEyeCenter = leftEyePts.mean(axis=0).astype("int")
-        rightEyeCenter = rightEyePts.mean(axis=0).astype("int")
-
-        dY = rightEyeCenter[1] - leftEyeCenter[1]
-        dX = rightEyeCenter[0] - leftEyeCenter[0]
-        angle = np.degrees(np.arctan2(dY, dX)) - 180
-        dist = float(np.sqrt((dX**2) + (dY**2)))
-
-        desiredRightEyeX = 1.0 - 0.35
-        desiredDist = (desiredRightEyeX - 0.35) * out_w
-        scale = desiredDist / dist if dist > 0 else 1.0
-
-        eyesCenter = (
-            int((leftEyeCenter[0] + rightEyeCenter[0]) // 2),
-            int((leftEyeCenter[1] + rightEyeCenter[1]) // 2),
-        )
-        M = cv2.getRotationMatrix2D(eyesCenter, angle, scale)
-        tX = out_w * 0.5
-        tY = out_h * 0.35
-        M[0, 2] += tX - eyesCenter[0]
-        M[1, 2] += tY - eyesCenter[1]
-
-        aligned = cv2.warpAffine(
-            image, M, (out_w, out_h), flags=cv2.INTER_CUBIC
-        )
-        info = dict(
-            angle=float(angle),
-            eye_dist_px=dist,
-            scale=float(scale),
-            landmarks=landmarks,
-        )
-        return aligned, info
-
-
-class ArcFaceEmbedder:
-    def __init__(self, model_path: str):
-        self.session = ort.InferenceSession(
-            model_path, providers=["CPUExecutionProvider"]
-        )
-        self.input_name = self.session.get_inputs()[0].name
-
-    def embed(self, image_bgr: np.ndarray) -> np.ndarray:
-        tensor = arcface_preprocess(image_bgr)
-        out = self.session.run(None, {self.input_name: tensor})[0]
-        return out.squeeze()
-
-
-def similarity_to_confidence(
-    cos_sim: float,
-    median: float = 0.3,
-    range_width: float = 0.6,
-    slope_factor: float = 12,
-) -> float:
-    slope = slope_factor / range_width
-    return float(1.0 / (1.0 + np.exp(-slope * (cos_sim - median))))
-
-
-def laplacian_variance(image: np.ndarray) -> float:
-    return float(cv2.Laplacian(image, cv2.CV_64F).var())
-
-
-def blur_reduction(variance: float) -> float:
-    if variance < 120:
-        return 0.06
-    elif variance < 160:
-        return 0.04
-    elif variance < 200:
-        return 0.02
-    elif variance < 250:
-        return 0.01
-    return 0.0
-
-
-def cosine(a: np.ndarray, b: np.ndarray) -> float:
-    denom = np.linalg.norm(a) * np.linalg.norm(b)
-    if denom == 0:
-        return 0.0
-    return float(np.dot(a, b) / denom)
-
-
-def l2(v: np.ndarray) -> np.ndarray:
-    return v / (np.linalg.norm(v) + 1e-9)
-
-
-# ---------------------------------------------------------------------------
-# Sample loading
-# ---------------------------------------------------------------------------
-
-
-@dataclass
-class FaceSample:
-    path: str
-    shape: tuple[int, int]
-    embedding: np.ndarray
-    blur_var: float
-    align_info: dict
-
-
-def load_folder(
-    folder: str, aligner: LandmarkAligner, embedder: ArcFaceEmbedder
-) -> list[FaceSample]:
-    samples: list[FaceSample] = []
-    names = sorted(os.listdir(folder))
-    for name in names:
-        if name.startswith("."):
-            continue
-        path = os.path.join(folder, name)
-        if not os.path.isfile(path):
-            continue
-        img = cv2.imread(path)
-        if img is None:
-            print(f"  [skip unreadable] {name}")
-            continue
-        aligned, info = aligner.align(img, img.shape[1], img.shape[0])
-        emb = embedder.embed(aligned)
-        samples.append(
-            FaceSample(
-                path=path,
-                shape=(img.shape[1], img.shape[0]),
-                embedding=emb,
-                blur_var=laplacian_variance(img),
-                align_info=info,
-            )
-        )
-    return samples
-
-
-def trimmed_mean(embs: Iterable[np.ndarray], trim: float = 0.15) -> np.ndarray:
-    arr = np.stack(list(embs), axis=0)
-    return stats.trim_mean(arr, trim, axis=0)
-
-
-# ---------------------------------------------------------------------------
-# Baseline analyses (always run)
-# ---------------------------------------------------------------------------
-
-
-def summarize_positive(samples: list[FaceSample], mean_emb: np.ndarray) -> None:
-    """Summary of training set: per-sample cos to class mean, intra-class stats.
-
-    Outliers with cos far below the rest are likely degrading the mean —
-    they'd be the first candidates the shipped vector-outlier filter drops.
-    """
-    print("\n" + "=" * 78)
-    print(f"POSITIVE SET ANALYSIS  ({len(samples)} images)")
-    print("=" * 78)
-
-    rows = []
-    for s in samples:
-        cs = cosine(s.embedding, mean_emb)
-        conf = similarity_to_confidence(cs)
-        red = blur_reduction(s.blur_var)
-        rows.append(
-            dict(
-                name=os.path.basename(s.path),
-                shape=f"{s.shape[0]}x{s.shape[1]}",
-                eye_px=s.align_info["eye_dist_px"],
-                angle=s.align_info["angle"] + 180,
-                blur=s.blur_var,
-                cos=cs,
-                conf=conf,
-                red=red,
-                adj_conf=max(0.0, conf - red),
-            )
-        )
-
-    rows.sort(key=lambda r: r["cos"])
-    sims = np.array([r["cos"] for r in rows])
-    print(
-        f"\nCosine-to-trimmed-mean: mean={sims.mean():.3f} std={sims.std():.3f} "
-        f"min={sims.min():.3f} max={sims.max():.3f}"
-    )
-
-    print("\n-- Worst matches (bottom 10, most likely hurting the mean) --")
-    print(
-        f"{'cos':>6}  {'conf':>6}  {'blur':>7}  {'eyes':>6}  "
-        f"{'angle':>6}  {'shape':>9}  name"
-    )
-    for r in rows[:10]:
-        print(
-            f"{r['cos']:6.3f}  {r['conf']:6.3f}  {r['blur']:7.1f}  "
-            f"{r['eye_px']:6.1f}  {r['angle']:6.1f}  {r['shape']:>9}  {r['name']}"
-        )
-
-    print("\n-- Best matches (top 5) --")
-    for r in rows[-5:][::-1]:
-        print(
-            f"{r['cos']:6.3f}  {r['conf']:6.3f}  {r['blur']:7.1f}  "
-            f"{r['eye_px']:6.1f}  {r['angle']:6.1f}  {r['shape']:>9}  {r['name']}"
-        )
-
-    # Pairwise analysis — flags embeddings poorly correlated with the rest
-    print("\n-- Pairwise intra-class similarity (mean cos vs. other positives) --")
-    embs = np.stack([s.embedding for s in samples], axis=0)
-    norms = embs / (np.linalg.norm(embs, axis=1, keepdims=True) + 1e-9)
-    sim_matrix = norms @ norms.T
-    np.fill_diagonal(sim_matrix, np.nan)
-    mean_pairwise = np.nanmean(sim_matrix, axis=1)
-    names = [os.path.basename(s.path) for s in samples]
-    ordered = sorted(zip(names, mean_pairwise), key=lambda t: t[1])
-    print(f"{'mean_cos':>9}  name")
-    for nm, mp in ordered[:10]:
-        print(f"{mp:9.3f}  {nm}")
-    print(f"\n  overall mean pairwise cos: {np.nanmean(sim_matrix):.3f}")
-    print(f"  median pairwise cos:       {np.nanmedian(sim_matrix):.3f}")
-
-
-def summarize_negative(
-    neg_samples: list[FaceSample],
-    mean_emb: np.ndarray,
-    pos_samples: list[FaceSample],
-) -> None:
-    """Score each negative against the class mean, then show its top-3
-    nearest positives. High-scoring negatives that match specific outlier
-    positives hint at training-set contamination.
-    """
-    print("\n" + "=" * 78)
-    print(f"NEGATIVE SET ANALYSIS  ({len(neg_samples)} images)")
-    print("=" * 78)
-    print(
-        f"\n{'cos':>6}  {'conf':>6}  {'red':>5}  {'adj':>5}  "
-        f"{'blur':>7}  {'eyes':>6}  {'shape':>9}  name"
-    )
-    for s in neg_samples:
-        cs = cosine(s.embedding, mean_emb)
-        conf = similarity_to_confidence(cs)
-        red = blur_reduction(s.blur_var)
-        print(
-            f"{cs:6.3f}  {conf:6.3f}  {red:5.2f}  {max(0, conf - red):5.2f}  "
-            f"{s.blur_var:7.1f}  {s.align_info['eye_dist_px']:6.1f}  "
-            f"{s.shape[0]}x{s.shape[1]:<5}  {os.path.basename(s.path)}"
-        )
-
-    print("\n-- For each negative, top-3 most similar positives --")
-    pos_embs = np.stack([p.embedding for p in pos_samples])
-    pos_norm = pos_embs / (np.linalg.norm(pos_embs, axis=1, keepdims=True) + 1e-9)
-    for s in neg_samples:
-        v = s.embedding / (np.linalg.norm(s.embedding) + 1e-9)
-        sims = pos_norm @ v
-        idx = np.argsort(-sims)[:3]
-        print(f"\n  {os.path.basename(s.path)}:")
-        for i in idx:
-            print(
-                f"    {sims[i]:6.3f}  {os.path.basename(pos_samples[i].path)}  "
-                f"blur={pos_samples[i].blur_var:.1f} "
-                f"eyes={pos_samples[i].align_info['eye_dist_px']:.1f}"
-            )
-
-
-# ---------------------------------------------------------------------------
-# Optional diagnostics
-# ---------------------------------------------------------------------------
-
-
-def vector_outlier_test(
-    pos: list[FaceSample], neg: list[FaceSample], base_trim: float = 0.15
-) -> None:
-    """Measure the shipped vector-wise outlier filter at various thresholds.
-
-    The production filter at `build_class_mean` in
-    frigate/data_processing/common/face/model.py uses T=0.30. This test
-    sweeps T so you can see which images would be dropped on a new collection
-    and how that affects the negative scores.
-
-    Algorithm: iteratively recompute trim_mean on the kept set, drop any
-    embedding with cos < T to that mean, repeat until converged. Floor at
-    50% of the collection to avoid collapse.
-    """
-    print("\n" + "=" * 78)
-    print("VECTOR-WISE OUTLIER PRE-FILTER — layered on trim_mean(0.15)")
-    print("=" * 78)
-
-    all_embs = np.stack([s.embedding for s in pos])
-
-    def iterative_mean(
-        embs: np.ndarray,
-        threshold: float,
-        iters: int = 3,
-        min_keep_frac: float = 0.5,
-    ) -> tuple[np.ndarray, np.ndarray]:
-        keep = np.ones(len(embs), dtype=bool)
-        floor = max(5, int(np.ceil(min_keep_frac * len(embs))))
-        for _ in range(iters):
-            m = stats.trim_mean(embs[keep], base_trim, axis=0)
-            m_norm = m / (np.linalg.norm(m) + 1e-9)
-            e_norms = embs / (np.linalg.norm(embs, axis=1, keepdims=True) + 1e-9)
-            cos_to_mean = e_norms @ m_norm
-            new_keep = cos_to_mean >= threshold
-            if new_keep.sum() < floor:
-                top_idx = np.argsort(-cos_to_mean)[:floor]
-                new_keep = np.zeros_like(new_keep)
-                new_keep[top_idx] = True
-            if np.array_equal(new_keep, keep):
-                break
-            keep = new_keep
-        final = stats.trim_mean(embs[keep], base_trim, axis=0)
-        return final, keep
-
-    provisional = stats.trim_mean(all_embs, base_trim, axis=0)
-    p_norm = provisional / (np.linalg.norm(provisional) + 1e-9)
-    e_norms_all = all_embs / (np.linalg.norm(all_embs, axis=1, keepdims=True) + 1e-9)
-    cos_to_prov = e_norms_all @ p_norm
-    print("\nDistribution of cos(positive, provisional trim_mean):")
-    print(
-        f"  min={cos_to_prov.min():.3f}  p10={np.percentile(cos_to_prov, 10):.3f}  "
-        f"p25={np.percentile(cos_to_prov, 25):.3f}  "
-        f"median={np.median(cos_to_prov):.3f}  "
-        f"p75={np.percentile(cos_to_prov, 75):.3f}  max={cos_to_prov.max():.3f}"
-    )
-
-    baseline_mean = stats.trim_mean(all_embs, base_trim, axis=0)
-    baseline_pos = np.array([cosine(p.embedding, baseline_mean) for p in pos])
-    baseline_neg = (
-        np.array([cosine(n.embedding, baseline_mean) for n in neg])
-        if neg
-        else np.array([])
-    )
-    baseline_conf_neg = np.array(
-        [similarity_to_confidence(c) for c in baseline_neg]
-    )
-
-    print(
-        f"\nBaseline (trim_mean only, {len(pos)} images):"
-        f"\n  pos cos  min={baseline_pos.min():.3f} "
-        f"mean={baseline_pos.mean():.3f} max={baseline_pos.max():.3f}"
-    )
-    if len(neg):
-        print(
-            f"  neg cos  min={baseline_neg.min():.3f} "
-            f"mean={baseline_neg.mean():.3f} max={baseline_neg.max():.3f}"
-        )
-        print(
-            f"  neg conf min={baseline_conf_neg.min():.3f} "
-            f"mean={baseline_conf_neg.mean():.3f} max={baseline_conf_neg.max():.3f}"
-        )
-        print(
-            f"  margin (pos.min - neg.max): "
-            f"{baseline_pos.min() - baseline_neg.max():+.3f}"
-        )
-
-    print("\nIterative (refine mean → drop vectors with cos<T → repeat):")
-    print(
-        f"\n{'T':>5}  {'kept':>6}  {'pos min':>7}  {'pos mean':>8}  "
-        f"{'neg max':>7}  {'neg mean':>8}  {'neg conf.max':>12}  {'margin':>7}"
-    )
-    for T in [0.15, 0.20, 0.25, 0.28, 0.30, 0.33, 0.36, 0.40]:
-        mean, keep = iterative_mean(all_embs, T)
-        pos_sims = np.array([cosine(p.embedding, mean) for p in pos])
-        neg_sims = (
-            np.array([cosine(n.embedding, mean) for n in neg])
-            if neg
-            else np.array([])
-        )
-        neg_conf = np.array([similarity_to_confidence(c) for c in neg_sims])
-        margin = pos_sims.min() - (neg_sims.max() if len(neg_sims) else 0)
-        print(
-            f"{T:5.2f}  {int(keep.sum()):>3}/{len(pos):<2}  "
-            f"{pos_sims.min():7.3f}  {pos_sims.mean():8.3f}  "
-            f"{neg_sims.max() if len(neg_sims) else float('nan'):7.3f}  "
-            f"{neg_sims.mean() if len(neg_sims) else float('nan'):8.3f}  "
-            f"{neg_conf.max() if len(neg_conf) else float('nan'):12.3f}  "
-            f"{margin:+7.3f}"
-        )
-
-    # Show which images get dropped at the shipped threshold + neighbors
-    for T_show in (0.25, 0.30, 0.33):
-        _, keep = iterative_mean(all_embs, T_show)
-        print(
-            f"\nAt T={T_show}, the {int((~keep).sum())} dropped positives are:"
-        )
-        final_mean = stats.trim_mean(all_embs[keep], base_trim, axis=0)
-        m_n = final_mean / (np.linalg.norm(final_mean) + 1e-9)
-        for i, (p, k) in enumerate(zip(pos, keep)):
-            if not k:
-                e_n = p.embedding / (np.linalg.norm(p.embedding) + 1e-9)
-                cos_final = float(e_n @ m_n)
-                print(
-                    f"  cos_to_clean_mean={cos_final:6.3f}  "
-                    f"shape={p.shape[0]}x{p.shape[1]}  "
-                    f"eyes={p.align_info['eye_dist_px']:6.1f}  "
-                    f"blur={p.blur_var:7.1f}  "
-                    f"{os.path.basename(p.path)}"
-                )
-
-
-def degenerate_embedding_test(
-    pos: list[FaceSample], neg: list[FaceSample]
-) -> None:
-    """Detect whether negatives and low-quality positives share a degenerate
-    'tiny/noisy face' region of the embedding space.
-
-    Signal: if neg-to-neg cos is higher than pos-to-pos cos, the negatives
-    aren't really per-identity embeddings — they're dominated by upsample /
-    low-resolution artifacts that all map to a similar corner of embedding
-    space regardless of who the face belongs to.
-
-    Also rebuilds the mean using only high-intra-similarity positives to
-    show whether a cleaner training set separates the negatives.
-    """
-    print("\n" + "=" * 78)
-    print("DEGENERATE-EMBEDDING TEST")
-    print("=" * 78)
-
-    pos_embs = np.stack([l2(s.embedding) for s in pos])
-    neg_embs = np.stack([l2(s.embedding) for s in neg])
-
-    nn = neg_embs @ neg_embs.T
-    np.fill_diagonal(nn, np.nan)
-    pp = pos_embs @ pos_embs.T
-    np.fill_diagonal(pp, np.nan)
-    pn = pos_embs @ neg_embs.T
-
-    print(
-        f"\n  neg<->neg mean cos : {np.nanmean(nn):.3f}   "
-        f"(how tightly negatives cluster together)"
-    )
-    print(
-        f"  pos<->pos mean cos : {np.nanmean(pp):.3f}   "
-        f"(how tightly positives cluster)"
-    )
-    print(
-        f"  pos<->neg mean cos : {pn.mean():.3f}   "
-        f"(cross-class — should be low for a clean class)"
-    )
-    if np.nanmean(nn) > np.nanmean(pp):
-        print(
-            "\n  >> neg<->neg > pos<->pos: negatives cluster more tightly than\n"
-            "     positives. This is the degenerate-embedding signature —\n"
-            "     upsampled tiny crops share a common 'face-like blob' region\n"
-            "     regardless of identity."
-        )
-
-    mean_intra = np.nanmean(pp, axis=1)
-    for thresh in (0.30, 0.33, 0.36):
-        keep = mean_intra >= thresh
-        if keep.sum() < 5:
-            continue
-        clean_embs = [pos[i].embedding for i in range(len(pos)) if keep[i]]
-        clean_mean = stats.trim_mean(np.stack(clean_embs), 0.15, axis=0)
-        neg_scores = np.array([cosine(n.embedding, clean_mean) for n in neg])
-        neg_confs = np.array([similarity_to_confidence(c) for c in neg_scores])
-        pos_scores = np.array(
-            [
-                cosine(pos[i].embedding, clean_mean)
-                for i in range(len(pos))
-                if keep[i]
-            ]
-        )
-        print(
-            f"\n  mean_intra >= {thresh}: keeping {int(keep.sum())}/{len(pos)} positives"
-        )
-        print(
-            f"    pos cos vs mean   : min={pos_scores.min():.3f} "
-            f"mean={pos_scores.mean():.3f} max={pos_scores.max():.3f}"
-        )
-        print(
-            f"    neg cos vs mean   : min={neg_scores.min():.3f} "
-            f"mean={neg_scores.mean():.3f} max={neg_scores.max():.3f}"
-        )
-        print(
-            f"    neg conf          : min={neg_confs.min():.3f} "
-            f"mean={neg_confs.mean():.3f} max={neg_confs.max():.3f}"
-        )
-        print(
-            f"    margin (pos.min - neg.max): "
-            f"{pos_scores.min() - neg_scores.max():+.3f}"
-        )
-
-
-def contamination_analysis(
-    pos: list[FaceSample], neg: list[FaceSample]
-) -> None:
-    """Check whether the positive collection contains a second identity.
-
-    Two signals:
-      (a) Per-positive: if an image is closer to at least one negative than
-          to the rest of the positive class, it's likely a mislabeled face.
-      (b) 2-means split of the positive embeddings: if one cluster center
-          lands close to the negative mean, that cluster is a contaminating
-          sub-identity that's pulling the class mean toward the negatives.
-    """
-    print("\n" + "=" * 78)
-    print("CONTAMINATION ANALYSIS")
-    print("=" * 78)
-
-    pos_embs = np.stack([l2(s.embedding) for s in pos])
-    neg_embs = np.stack([l2(s.embedding) for s in neg])
-    pos_names = [os.path.basename(s.path) for s in pos]
-
-    pos_pos = pos_embs @ pos_embs.T
-    np.fill_diagonal(pos_pos, np.nan)
-    pos_neg = pos_embs @ neg_embs.T
-
-    mean_intra = np.nanmean(pos_pos, axis=1)
-    max_to_neg = pos_neg.max(axis=1)
-    mean_to_neg = pos_neg.mean(axis=1)
-
-    print(
-        "\nPositives closer to a negative than to their own class avg"
-        "\n(these are candidates for mislabeled images):"
-    )
-    print(
-        f"\n{'max_neg':>7}  {'mean_neg':>8}  {'mean_intra':>10}  "
-        f"{'delta':>6}  name"
-    )
-    rows = list(zip(pos_names, max_to_neg, mean_to_neg, mean_intra))
-    rows.sort(key=lambda r: -(r[1] - r[3]))
-    for nm, mxn, mnn, mi in rows[:15]:
-        delta = mxn - mi
-        marker = "  <<" if delta > 0 else ""
-        print(f"{mxn:7.3f}  {mnn:8.3f}  {mi:10.3f}  {delta:6.3f}  {nm}{marker}")
-
-    # 2-means in cosine space (no sklearn dependency).
-    print("\n2-means split of positive embeddings (cosine space):")
-    rng = np.random.default_rng(0)
-    best = None
-    for _ in range(5):
-        idx = rng.choice(len(pos_embs), 2, replace=False)
-        centers = pos_embs[idx].copy()
-        for _ in range(50):
-            sims = pos_embs @ centers.T
-            labels = np.argmax(sims, axis=1)
-            new_centers = np.stack(
-                [
-                    l2(pos_embs[labels == k].mean(axis=0))
-                    if np.any(labels == k)
-                    else centers[k]
-                    for k in range(2)
-                ]
-            )
-            if np.allclose(new_centers, centers):
-                break
-            centers = new_centers
-        tight = float(np.mean([sims[i, labels[i]] for i in range(len(labels))]))
-        if best is None or tight > best[0]:
-            best = (tight, labels.copy(), centers.copy())
-
-    _, labels, centers = best
-    sizes = [int((labels == k).sum()) for k in range(2)]
-    neg_mean = l2(neg_embs.mean(axis=0))
-    print(
-        f"  cluster 0: size={sizes[0]:>2}  "
-        f"center<->other_center_cos={float(centers[0] @ centers[1]):.3f}  "
-        f"center<->neg_mean_cos={float(centers[0] @ neg_mean):.3f}"
-    )
-    print(
-        f"  cluster 1: size={sizes[1]:>2}  "
-        f"center<->neg_mean_cos={float(centers[1] @ neg_mean):.3f}"
-    )
-
-    neg_aligned = 0 if centers[0] @ neg_mean > centers[1] @ neg_mean else 1
-    print(
-        f"\n  cluster {neg_aligned} is more similar to the negatives — "
-        f"its members are the contamination candidates:"
-    )
-    for i, lbl in enumerate(labels):
-        if lbl == neg_aligned:
-            print(
-                f"    max_to_neg={max_to_neg[i]:.3f}  "
-                f"mean_intra={mean_intra[i]:.3f}  {pos_names[i]}"
-            )
-
-    keep_mask = labels != neg_aligned
-    if keep_mask.sum() >= 3:
-        clean_embs = [pos[i].embedding for i in range(len(pos)) if keep_mask[i]]
-        clean_mean = stats.trim_mean(np.stack(clean_embs), 0.15, axis=0)
-        print(
-            f"\n  Rebuilding class mean from the OTHER cluster "
-            f"({keep_mask.sum()} images):"
-        )
-        print(f"  {'cos':>6}  {'conf':>6}  name")
-        for n in neg:
-            cs = cosine(n.embedding, clean_mean)
-            cf = similarity_to_confidence(cs)
-            print(f"  {cs:6.3f}  {cf:6.3f}  {os.path.basename(n.path)}")
-
-
-# ---------------------------------------------------------------------------
-# main
-# ---------------------------------------------------------------------------
-
-
-def main() -> int:
-    ap = argparse.ArgumentParser(
-        description="Analyze a face recognition collection outside Frigate.",
-        formatter_class=argparse.RawDescriptionHelpFormatter,
-        epilog=__doc__,
-    )
-    ap.add_argument("--positive", required=True, help="Training folder for one identity")
-    ap.add_argument(
-        "--negative",
-        default=None,
-        help="Runtime-crop folder to score against (optional)",
-    )
-    ap.add_argument(
-        "--model-cache",
-        default="/config/model_cache",
-        help="Directory containing facedet/arcface.onnx and facedet/landmarkdet.yaml",
-    )
-    ap.add_argument(
-        "--trim",
-        type=float,
-        default=0.15,
-        help="trim_mean proportion (Frigate uses 0.15)",
-    )
-    ap.add_argument(
-        "--vector-outlier",
-        action="store_true",
-        help="Sweep the vector-wise outlier filter threshold",
-    )
-    ap.add_argument(
-        "--degenerate",
-        action="store_true",
-        help="Test whether negatives share a degenerate embedding region",
-    )
-    ap.add_argument(
-        "--contamination",
-        action="store_true",
-        help="Check whether the positive folder contains a second identity",
-    )
-    args = ap.parse_args()
-
-    arcface_path = os.path.join(args.model_cache, "facedet", "arcface.onnx")
-    landmark_path = os.path.join(args.model_cache, "facedet", "landmarkdet.yaml")
-    for p in (arcface_path, landmark_path):
-        if not os.path.exists(p):
-            print(f"ERROR: model file not found: {p}")
-            return 1
-
-    print(f"Loading ArcFace from {arcface_path}")
-    embedder = ArcFaceEmbedder(arcface_path)
-    print(f"Loading landmark model from {landmark_path}")
-    aligner = LandmarkAligner(landmark_path)
-
-    print(f"\nLoading positives from {args.positive} ...")
-    pos = load_folder(args.positive, aligner, embedder)
-    print(f"  {len(pos)} positives loaded")
-
-    neg: list[FaceSample] = []
-    if args.negative:
-        print(f"\nLoading negatives from {args.negative} ...")
-        neg = load_folder(args.negative, aligner, embedder)
-        print(f"  {len(neg)} negatives loaded")
-
-    if not pos:
-        print("no positive samples — aborting")
-        return 1
-
-    mean_emb = trimmed_mean([s.embedding for s in pos], trim=args.trim)
-    summarize_positive(pos, mean_emb)
-    if neg:
-        summarize_negative(neg, mean_emb, pos)
-
-    if args.vector_outlier:
-        vector_outlier_test(pos, neg, args.trim)
-    if args.degenerate and neg:
-        degenerate_embedding_test(pos, neg)
-    if args.contamination and neg:
-        contamination_analysis(pos, neg)
-
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())

web/e2e/fixtures/mock-data/debug-replay.ts

@@ -1,54 +0,0 @@
-/**
- * Debug replay status factory.
- *
- * The Replay page polls /api/debug_replay/status every 1s via SWR.
- * The no-session state shows an empty state; the active state
- * renders the live camera image + debug toggles + objects/messages
- * tabs. Used by replay.spec.ts.
- */
-
-export type DebugReplayStatus = {
-  active: boolean;
-  replay_camera: string | null;
-  source_camera: string | null;
-  start_time: number | null;
-  end_time: number | null;
-  live_ready: boolean;
-};
-
-export function noSessionStatus(): DebugReplayStatus {
-  return {
-    active: false,
-    replay_camera: null,
-    source_camera: null,
-    start_time: null,
-    end_time: null,
-    live_ready: false,
-  };
-}
-
-export function activeSessionStatus(
-  opts: {
-    camera?: string;
-    sourceCamera?: string;
-    startTime?: number;
-    endTime?: number;
-    liveReady?: boolean;
-  } = {},
-): DebugReplayStatus {
-  const {
-    camera = "front_door",
-    sourceCamera = "front_door",
-    startTime = Date.now() / 1000 - 3600,
-    endTime = Date.now() / 1000 - 1800,
-    liveReady = true,
-  } = opts;
-  return {
-    active: true,
-    replay_camera: camera,
-    source_camera: sourceCamera,
-    start_time: startTime,
-    end_time: endTime,
-    live_ready: liveReady,
-  };
-}

web/e2e/fixtures/mock-data/faces.ts

@@ -1,45 +0,0 @@
-/**
- * Face library factories.
- *
- * The /api/faces endpoint returns a record keyed by collection name
- * with the list of face image filenames. Grouped training attempts
- * live under the "train" key with filenames of the form
- * `${event_id}-${timestamp}-${label}-${score}.webp`.
- *
- * Used by face-library.spec.ts and chat.spec.ts (attachment chip).
- */
-
-export type FacesMock = Record<string, string[]>;
-
-export function basicFacesMock(): FacesMock {
-  return {
-    alice: ["alice-1.webp", "alice-2.webp"],
-    bob: ["bob-1.webp"],
-    charlie: ["charlie-1.webp"],
-  };
-}
-
-export function emptyFacesMock(): FacesMock {
-  return {};
-}
-
-/**
- * Adds a grouped recent-recognition training attempt to an existing
- * faces mock. The grouping key on the backend is the event id — so
- * images with the same event-id prefix render as one dialog-able card.
- */
-export function withGroupedTrainingAttempt(
-  base: FacesMock,
-  opts: {
-    eventId: string;
-    attempts: Array<{ timestamp: number; label: string; score: number }>;
-  },
-): FacesMock {
-  const trainImages = opts.attempts.map(
-    (a) => `${opts.eventId}-${a.timestamp}-${a.label}-${a.score}.webp`,
-  );
-  return {
-    ...base,
-    train: [...(base.train ?? []), ...trainImages],
-  };
-}

web/e2e/helpers/api-mocker.ts

@@ -113,12 +113,11 @@ export class ApiMocker {
       route.fulfill({ json: [] }),
     );
 
-    // Sub-labels and attributes (for explore filters).
-    // Use trailing ** so query-string variants (e.g. ?split_joined=1) match.
-    await this.page.route("**/api/sub_labels**", (route) =>
+    // Sub-labels and attributes (for explore filters)
+    await this.page.route("**/api/sub_labels", (route) =>
       route.fulfill({ json: [] }),
     );
-    await this.page.route("**/api/labels**", (route) =>
+    await this.page.route("**/api/labels", (route) =>
       route.fulfill({ json: ["person", "car"] }),
     );
     await this.page.route("**/api/*/attributes", (route) =>

web/e2e/helpers/clipboard.ts

@@ -1,25 +0,0 @@
-/**
- * Clipboard read helper for e2e tests.
- *
- * Clipboard API requires a browser permission in headless mode.
- * grantClipboardPermissions() must be called before any readClipboard()
- * attempt. Used by logs.spec.ts (Copy button) and config-editor.spec.ts
- * (Copy button).
- */
-
-import type { BrowserContext, Page } from "@playwright/test";
-
-/**
- * Grant clipboard-read + clipboard-write permissions on the context.
- * Call in beforeEach or at the top of a test before the Copy action.
- */
-export async function grantClipboardPermissions(
-  context: BrowserContext,
-): Promise<void> {
-  await context.grantPermissions(["clipboard-read", "clipboard-write"]);
-}
-
-/** Read the current clipboard contents via the page's navigator.clipboard. */
-export async function readClipboard(page: Page): Promise<string> {
-  return page.evaluate(async () => await navigator.clipboard.readText());
-}

web/e2e/helpers/monaco.ts

@@ -1,58 +0,0 @@
-/**
- * Monaco editor DOM helpers for e2e tests.
- *
- * Monaco is imported as a module-local object in the app and is NOT
- * exposed on window; we drive + read through the rendered DOM and
- * keyboard instead. Used by config-editor.spec.ts only.
- */
-
-import { expect, type Page } from "@playwright/test";
-
-/**
- * Returns the current visible text of the first Monaco editor on the
- * page. Monaco virtualizes long files — this reads only the rendered
- * lines. For short configs (our mocks) that's the full content.
- */
-export async function getMonacoVisibleText(page: Page): Promise<string> {
-  return page.locator(".monaco-editor .view-lines").first().innerText();
-}
-
-/**
- * Focus the editor and replace its full content with `value` via
- * keyboard. Uses Ctrl+A (Cmd+A on macOS Playwright is equivalent)
- * + Delete + type. Works cross-platform because Playwright normalizes.
- */
-export async function replaceMonacoValue(
-  page: Page,
-  value: string,
-): Promise<void> {
-  const editor = page.locator(".monaco-editor").first();
-  await editor.click();
-  await page.keyboard.press("ControlOrMeta+A");
-  await page.keyboard.press("Delete");
-  // Use `type` with zero delay — Monaco handles each key.
-  await page.keyboard.type(value, { delay: 0 });
-}
-
-/**
- * Returns true when the editor shows at least one error-severity
- * marker. Monaco renders error underlines as `.squiggly-error` in
- * the `.view-overlays` layer.
- */
-export async function hasErrorMarkers(page: Page): Promise<boolean> {
-  const count = await page.locator(".monaco-editor .squiggly-error").count();
-  return count > 0;
-}
-
-/**
- * Poll until an error marker appears. Monaco schedules marker updates
- * asynchronously after content changes (debounce + schema validation).
- */
-export async function waitForErrorMarker(
-  page: Page,
-  timeoutMs: number = 10_000,
-): Promise<void> {
-  await expect
-    .poll(() => hasErrorMarkers(page), { timeout: timeoutMs })
-    .toBe(true);
-}

web/e2e/helpers/overlay-interaction.ts

@@ -1,41 +0,0 @@
-/**
- * Overlay interaction helpers for Radix-based UI tests.
- *
- * These helpers exist to guard the class of bugs fixed by de-duping
- * `@radix-ui/react-dismissable-layer` across the tree: body pointer-events
- * getting stuck, dropdown typeahead breaking, tooltips re-popping after a
- * dropdown closes, and related nested-overlay regressions.
- */
-
-import { expect, type Page } from "@playwright/test";
-
-/**
- * Assert that `<body>` is interactive (no stuck `pointer-events: none`).
- *
- * Call after closing any overlay. This is the fast secondary assertion —
- * test specs should also assert a user-visible behavior like "a button
- * responded to a click" so the test fails on meaningful breakage rather
- * than just a CSS invariant.
- */
-export async function expectBodyInteractive(page: Page) {
-  const stuck = await page.evaluate(
-    () => document.body.style.pointerEvents === "none",
-  );
-  expect(stuck, "body.style.pointer-events stuck after overlay close").toBe(
-    false,
-  );
-}
-
-/**
- * Wait until the `<body>` is no longer marked with `pointer-events: none`.
- *
- * Useful right after closing an overlay when Radix's cleanup runs in the
- * next frame. Throws if the style does not clear within `timeoutMs`.
- */
-export async function waitForBodyInteractive(page: Page, timeoutMs = 2000) {
-  await page.waitForFunction(
-    () => document.body.style.pointerEvents !== "none",
-    null,
-    { timeout: timeoutMs },
-  );
-}

web/e2e/helpers/ws-frames.ts

@@ -1,65 +0,0 @@
-/**
- * WebSocket frame capture helper.
- *
- * The ws-mocker intercepts the /ws route, so Playwright's page-level
- * `websocket` event never fires. This helper patches client-side
- * WebSocket.prototype.send before any app code runs and mirrors every
- * sent frame into a window-level array the test can read back.
- *
- * Used by live.spec.ts (feature toggles, PTZ preset commands) and
- * config-editor.spec.ts (restart command via useRestart).
- */
-
-import { expect, type Page } from "@playwright/test";
-
-export type CapturedFrame = string;
-
-declare global {
-  interface Window {
-    __sentWsFrames: CapturedFrame[];
-  }
-}
-
-/**
- * Patch WebSocket.prototype.send to capture every outbound frame into
- * window.__sentWsFrames. Must be called BEFORE page.goto().
- */
-export async function installWsFrameCapture(page: Page): Promise<void> {
-  await page.addInitScript(() => {
-    window.__sentWsFrames = [];
-    const origSend = WebSocket.prototype.send;
-    WebSocket.prototype.send = function (data) {
-      try {
-        window.__sentWsFrames.push(
-          typeof data === "string" ? data : "(binary)",
-        );
-      } catch {
-        // ignore — best-effort tracing
-      }
-      return origSend.call(this, data);
-    };
-  });
-}
-
-/** Read all captured frames at call time. */
-export async function readWsFrames(page: Page): Promise<CapturedFrame[]> {
-  return page.evaluate(() => window.__sentWsFrames ?? []);
-}
-
-/**
- * Poll until at least one captured frame matches the predicate.
- * Throws via expect if the frame never arrives within timeout.
- */
-export async function waitForWsFrame(
-  page: Page,
-  matcher: (frame: CapturedFrame) => boolean,
-  opts: { timeout?: number; message?: string } = {},
-): Promise<void> {
-  const { timeout = 2_000, message } = opts;
-  await expect
-    .poll(async () => (await readWsFrames(page)).some(matcher), {
-      timeout,
-      message,
-    })
-    .toBe(true);
-}

web/e2e/helpers/ws-mocker.ts

@@ -79,20 +79,7 @@ export class WsMocker {
       this.send("model_state", JSON.stringify({}));
     }
     if (data.topic === "embeddingsReindexProgress") {
-      // Send a completed reindex state so Explore renders when
-      // semantic_search.enabled is true. A null payload leaves the page
-      // in a permanent loading spinner because !reindexState is truthy.
-      this.send(
-        "embeddings_reindex_progress",
-        JSON.stringify({
-          status: "completed",
-          processed_objects: 0,
-          total_objects: 0,
-          thumbnails: 0,
-          descriptions: 0,
-          time_remaining: null,
-        }),
-      );
+      this.send("embeddings_reindex_progress", JSON.stringify(null));
     }
     if (data.topic === "birdseyeLayout") {
       this.send("birdseye_layout", JSON.stringify(null));

web/e2e/pages/live.page.ts

@@ -1,55 +0,0 @@
-/**
- * Live dashboard + single-camera page object.
- *
- * Encapsulates selectors and viewport-conditional openers for the
- * Live route. Does NOT own assertions — specs call expect on the
- * locators returned from these getters.
- */
-
-import type { Locator, Page } from "@playwright/test";
-import { BasePage } from "./base.page";
-
-export class LivePage extends BasePage {
-  constructor(page: Page, isDesktop: boolean) {
-    super(page, isDesktop);
-  }
-
-  /** The camera card wrapper on the dashboard, keyed by camera name. */
-  cameraCard(name: string): Locator {
-    return this.page.locator(`[data-camera='${name}']`);
-  }
-
-  /** Back button on the single-camera view header (desktop text). */
-  get backButton(): Locator {
-    return this.page.getByText("Back", { exact: true });
-  }
-
-  /** History button on the single-camera view header (desktop text). */
-  get historyButton(): Locator {
-    return this.page.getByText("History", { exact: true });
-  }
-
-  /** All CameraFeatureToggle elements (active + inactive). */
-  get featureToggles(): Locator {
-    // Use div selector to exclude NavItem anchor elements that share the same classes.
-    return this.page.locator(
-      "div.flex.flex-col.items-center.justify-center.bg-selected, div.flex.flex-col.items-center.justify-center.bg-secondary",
-    );
-  }
-
-  /** Only the active (bg-selected) feature toggles. */
-  get activeFeatureToggles(): Locator {
-    // Use div selector to exclude NavItem anchor elements that share the same classes.
-    return this.page.locator(
-      "div.flex.flex-col.items-center.justify-center.bg-selected",
-    );
-  }
-
-  /** Open the right-click context menu on a camera card (desktop only). */
-  async openContextMenuOn(cameraName: string): Promise<Locator> {
-    await this.cameraCard(cameraName).first().click({ button: "right" });
-    return this.page
-      .locator('[role="menu"], [data-radix-menu-content]')
-      .first();
-  }
-}

web/e2e/pages/review.page.ts

@@ -1,52 +0,0 @@
-/**
- * Review/events page object.
- *
- * Encapsulates severity tab, filter bar, calendar, and mobile filter
- * drawer selectors. Does NOT own assertions.
- */
-
-import type { Locator, Page } from "@playwright/test";
-import { BasePage } from "./base.page";
-
-export class ReviewPage extends BasePage {
-  constructor(page: Page, isDesktop: boolean) {
-    super(page, isDesktop);
-  }
-
-  get alertsTab(): Locator {
-    return this.page.getByLabel("Alerts");
-  }
-
-  get detectionsTab(): Locator {
-    return this.page.getByLabel("Detections");
-  }
-
-  get motionTab(): Locator {
-    return this.page.getByRole("radio", { name: "Motion" });
-  }
-
-  get camerasFilterTrigger(): Locator {
-    return this.page.getByRole("button", { name: /cameras/i }).first();
-  }
-
-  get calendarTrigger(): Locator {
-    return this.page.getByRole("button", { name: /24 hours|calendar|date/i });
-  }
-
-  get showReviewedToggle(): Locator {
-    return this.page.getByRole("button", { name: /reviewed/i });
-  }
-
-  get reviewItems(): Locator {
-    return this.page.locator(".review-item");
-  }
-
-  /** The filter popover content (desktop) or drawer (mobile). */
-  get filterOverlay(): Locator {
-    return this.page
-      .locator(
-        '[data-radix-popper-content-wrapper], [role="dialog"], [data-vaul-drawer]',
-      )
-      .first();
-  }
-}

web/e2e/scripts/lint-specs.mjs

@@ -14,6 +14,10 @@
  *
  * @mobile rule: every .spec.ts under specs/ (not specs/_meta/) must
  * contain at least one test title or describe with the substring "@mobile".
+ *
+ * Specs in PENDING_REWRITE are exempt from all rules until they are
+ * rewritten with proper assertions and mobile coverage. Remove each
+ * entry when its spec is updated.
  */
 
 import { readFileSync, readdirSync, statSync } from "node:fs";
@@ -24,6 +28,24 @@ const __dirname = dirname(fileURLToPath(import.meta.url));
 const SPECS_DIR = resolve(__dirname, "..", "specs");
 const META_PREFIX = resolve(SPECS_DIR, "_meta");
 
+// Specs exempt from lint rules until they are rewritten with proper
+// assertions and mobile coverage. Remove each entry when its spec is updated.
+const PENDING_REWRITE = new Set([
+  "auth.spec.ts",
+  "chat.spec.ts",
+  "classification.spec.ts",
+  "config-editor.spec.ts",
+  "explore.spec.ts",
+  "export.spec.ts",
+  "face-library.spec.ts",
+  "live.spec.ts",
+  "logs.spec.ts",
+  "navigation.spec.ts",
+  "replay.spec.ts",
+  "review.spec.ts",
+  "system.spec.ts",
+]);
+
 const BANNED_PATTERNS = [
   {
     name: "page.waitForTimeout",
@@ -40,12 +62,14 @@ const BANNED_PATTERNS = [
   {
     name: "conditional count() assertion",
     regex: /\bif\s*\(\s*\(?\s*await\s+[^)]*\.count\s*\(\s*\)\s*\)?\s*[><=!]/,
-    advice: "Assertions must be unconditional. Use expect(...).toHaveCount(n).",
+    advice:
+      "Assertions must be unconditional. Use expect(...).toHaveCount(n).",
   },
   {
     name: "vacuous textContent length assertion",
     regex: /expect\([^)]*\.length\)\.toBeGreaterThan\(0\)/,
-    advice: "Assert specific content, not that some text exists.",
+    advice:
+      "Assert specific content, not that some text exists.",
   },
 ];
 
@@ -65,6 +89,8 @@ function walk(dir) {
 }
 
 function lintFile(file) {
+  const basename = file.split("/").pop();
+  if (PENDING_REWRITE.has(basename)) return [];
   if (file.includes("/specs/settings/")) return [];
 
   const errors = [];

web/e2e/specs/auth.spec.ts

@@ -1,110 +1,147 @@
 /**
- * Auth and role tests -- HIGH tier.
+ * Auth and cross-cutting tests -- HIGH tier.
  *
- * Admin access to /system, /config, /logs; viewer access denied
- * markers (via i18n heading, not a data-testid we don't own);
- * viewer nav restrictions; all-routes smoke.
+ * Tests protected route access for admin/viewer roles,
+ * access denied page rendering, viewer nav restrictions,
+ * and all routes smoke test.
  */
 
 import { test, expect } from "../fixtures/frigate-test";
 import { viewerProfile } from "../fixtures/mock-data/profile";
 
-test.describe("Auth — admin access @high", () => {
-  test("admin /system renders general tab", async ({ frigateApp }) => {
+test.describe("Auth - Admin Access @high", () => {
+  test("admin can access /system and sees system tabs", async ({
+    frigateApp,
+  }) => {
     await frigateApp.goto("/system");
+    await expect(frigateApp.page.locator("#pageRoot")).toBeVisible();
+    await frigateApp.page.waitForTimeout(3000);
+    // System page should have named tab buttons
     await expect(frigateApp.page.getByLabel("Select general")).toBeVisible({
-      timeout: 15_000,
+      timeout: 5_000,
     });
   });
 
-  test("admin /config renders Monaco editor", async ({ frigateApp }) => {
+  test("admin can access /config and Monaco editor loads", async ({
+    frigateApp,
+  }) => {
     await frigateApp.goto("/config");
-    await expect(
-      frigateApp.page
-        .locator(".monaco-editor, [data-keybinding-context]")
-        .first(),
-    ).toBeVisible({ timeout: 15_000 });
+    await frigateApp.page.waitForTimeout(5000);
+    const editor = frigateApp.page.locator(
+      ".monaco-editor, [data-keybinding-context]",
+    );
+    await expect(editor.first()).toBeVisible({ timeout: 10_000 });
   });
 
-  test("admin /logs renders frigate tab", async ({ frigateApp }) => {
+  test("admin can access /logs and sees service tabs", async ({
+    frigateApp,
+  }) => {
     await frigateApp.goto("/logs");
+    await expect(frigateApp.page.locator("#pageRoot")).toBeVisible();
     await expect(frigateApp.page.getByLabel("Select frigate")).toBeVisible({
       timeout: 5_000,
     });
   });
+
+  test("admin sees Classification nav on desktop", async ({ frigateApp }) => {
+    if (frigateApp.isMobile) {
+      test.skip();
+      return;
+    }
+    await frigateApp.goto("/");
+    await expect(
+      frigateApp.page.locator('a[href="/classification"]'),
+    ).toBeVisible();
+  });
 });
 
-test.describe("Auth — viewer restrictions @high", () => {
-  for (const path of ["/system", "/config", "/logs"]) {
-    test(`viewer on ${path} sees AccessDenied`, async ({ frigateApp }) => {
-      await frigateApp.installDefaults({ profile: viewerProfile() });
-      await frigateApp.page.goto(path);
-      await frigateApp.page.waitForSelector("#pageRoot", { timeout: 10_000 });
-      await expect(
-        frigateApp.page.getByRole("heading", {
-          level: 2,
-          name: /access denied/i,
-        }),
-      ).toBeVisible({ timeout: 10_000 });
-    });
-  }
-
-  test("viewer sees cameras on /", async ({ frigateApp }) => {
+test.describe("Auth - Viewer Restrictions @high", () => {
+  test("viewer sees Access Denied on /system", async ({ frigateApp, page }) => {
     await frigateApp.installDefaults({ profile: viewerProfile() });
-    await frigateApp.page.goto("/");
-    await expect(
-      frigateApp.page.locator("[data-camera='front_door']"),
-    ).toBeVisible({ timeout: 10_000 });
+    await page.goto("/system");
+    await page.waitForTimeout(2000);
+    // Should show "Access Denied" text
+    await expect(page.getByText("Access Denied")).toBeVisible({
+      timeout: 5_000,
+    });
   });
 
-  test("viewer sees severity tabs on /review", async ({ frigateApp }) => {
+  test("viewer sees Access Denied on /config", async ({ frigateApp, page }) => {
     await frigateApp.installDefaults({ profile: viewerProfile() });
-    await frigateApp.page.goto("/review");
-    await expect(frigateApp.page.getByLabel("Alerts")).toBeVisible({
+    await page.goto("/config");
+    await page.waitForTimeout(2000);
+    await expect(page.getByText("Access Denied")).toBeVisible({
+      timeout: 5_000,
+    });
+  });
+
+  test("viewer sees Access Denied on /logs", async ({ frigateApp, page }) => {
+    await frigateApp.installDefaults({ profile: viewerProfile() });
+    await page.goto("/logs");
+    await page.waitForTimeout(2000);
+    await expect(page.getByText("Access Denied")).toBeVisible({
+      timeout: 5_000,
+    });
+  });
+
+  test("viewer can access Live page and sees cameras", async ({
+    frigateApp,
+    page,
+  }) => {
+    await frigateApp.installDefaults({ profile: viewerProfile() });
+    await page.goto("/");
+    await page.waitForSelector("#pageRoot", { timeout: 10_000 });
+    await expect(page.locator("[data-camera='front_door']")).toBeVisible({
       timeout: 10_000,
     });
   });
 
-  test("viewer can access all non-admin routes without AccessDenied", async ({
+  test("viewer can access Review page and sees severity tabs", async ({
     frigateApp,
+    page,
+  }) => {
+    await frigateApp.installDefaults({ profile: viewerProfile() });
+    await page.goto("/review");
+    await page.waitForSelector("#pageRoot", { timeout: 10_000 });
+    await expect(page.getByLabel("Alerts")).toBeVisible({ timeout: 5_000 });
+  });
+
+  test("viewer can access all main user routes without crash", async ({
+    frigateApp,
+    page,
   }) => {
     await frigateApp.installDefaults({ profile: viewerProfile() });
     const routes = ["/", "/review", "/explore", "/export", "/settings"];
     for (const route of routes) {
-      await frigateApp.page.goto(route);
-      await frigateApp.page.waitForSelector("#pageRoot", { timeout: 10_000 });
-      await expect(
-        frigateApp.page.getByRole("heading", {
-          level: 2,
-          name: /access denied/i,
-        }),
-      ).toHaveCount(0);
+      await page.goto(route);
+      await page.waitForSelector("#pageRoot", { timeout: 10_000 });
     }
   });
 });
 
-test.describe("Auth — viewer nav restrictions (desktop) @high", () => {
-  test.skip(({ frigateApp }) => frigateApp.isMobile, "Sidebar only on desktop");
-
-  test("viewer sidebar hides admin routes", async ({ frigateApp }) => {
-    await frigateApp.installDefaults({ profile: viewerProfile() });
-    await frigateApp.page.goto("/");
-    await frigateApp.page.waitForSelector("#pageRoot", { timeout: 10_000 });
-    for (const href of ["/system", "/config", "/logs"]) {
-      await expect(
-        frigateApp.page.locator(`aside a[href='${href}']`),
-      ).toHaveCount(0);
-    }
-  });
-});
-
-test.describe("Auth — all routes smoke @high @mobile", () => {
-  test("every common route renders #pageRoot", async ({ frigateApp }) => {
-    for (const route of ["/", "/review", "/explore", "/export", "/settings"]) {
+test.describe("Auth - All Routes Smoke @high", () => {
+  test("all user routes render without crash", async ({ frigateApp }) => {
+    const routes = ["/", "/review", "/explore", "/export", "/settings"];
+    for (const route of routes) {
       await frigateApp.goto(route);
       await expect(frigateApp.page.locator("#pageRoot")).toBeVisible({
         timeout: 10_000,
       });
     }
   });
+
+  test("admin routes render with specific content", async ({ frigateApp }) => {
+    // System page should have tab controls
+    await frigateApp.goto("/system");
+    await frigateApp.page.waitForTimeout(3000);
+    await expect(frigateApp.page.getByLabel("Select general")).toBeVisible({
+      timeout: 5_000,
+    });
+
+    // Logs page should have service tabs
+    await frigateApp.goto("/logs");
+    await expect(frigateApp.page.getByLabel("Select frigate")).toBeVisible({
+      timeout: 5_000,
+    });
+  });
 });

web/e2e/specs/chat.spec.ts

@@ -1,311 +1,34 @@
 /**
  * Chat page tests -- MEDIUM tier.
  *
- * Starting state, NDJSON streaming contract (not SSE), assistant
- * bubble grows as chunks arrive, error path, and mobile viewport.
+ * Tests chat interface rendering, input area, and example prompt buttons.
  */
 
-import { test, expect, type FrigateApp } from "../fixtures/frigate-test";
+import { test, expect } from "../fixtures/frigate-test";
 
-/**
- * Install a window.fetch override on the page so that POSTs to
- * chat/completion resolve with a real ReadableStream that emits the
- * given chunks over time. This is the only way to validate
- * chunk-by-chunk rendering through Playwright — page.route() does not
- * support streaming responses.
- *
- * Must be called BEFORE frigateApp.goto(). The override also exposes
- * `__chatRequests` on window so tests can assert the outgoing body.
- */
-async function installChatStreamOverride(
-  app: FrigateApp,
-  chunks: Array<Record<string, unknown>>,
-  opts: { chunkDelayMs?: number; status?: number } = {},
-) {
-  const { chunkDelayMs = 40, status = 200 } = opts;
-  await app.page.addInitScript(
-    ({ chunks, chunkDelayMs, status }) => {
-      (window as unknown as { __chatRequests: unknown[] }).__chatRequests = [];
-      const origFetch = window.fetch;
-      window.fetch = async (input, init) => {
-        const url =
-          typeof input === "string"
-            ? input
-            : input instanceof URL
-              ? input.toString()
-              : (input as Request).url;
-        if (url.includes("chat/completion")) {
-          const body =
-            init?.body instanceof String || typeof init?.body === "string"
-              ? JSON.parse(init!.body as string)
-              : null;
-          (
-            window as unknown as { __chatRequests: unknown[] }
-          ).__chatRequests.push({ url, body });
-          if (status !== 200) {
-            return new Response(JSON.stringify({ error: "boom" }), {
-              status,
-            });
-          }
-          const encoder = new TextEncoder();
-          const stream = new ReadableStream({
-            async start(controller) {
-              for (const chunk of chunks) {
-                await new Promise((r) => setTimeout(r, chunkDelayMs));
-                controller.enqueue(
-                  encoder.encode(JSON.stringify(chunk) + "\n"),
-                );
-              }
-              controller.close();
-            },
-          });
-          return new Response(stream, { status: 200 });
-        }
-        return origFetch.call(window, input as RequestInfo, init);
-      };
-    },
-    { chunks, chunkDelayMs, status },
-  );
-}
-
-test.describe("Chat — starting state @medium", () => {
-  test("empty message list renders ChatStartingState with title and input", async ({
-    frigateApp,
-  }) => {
+test.describe("Chat Page @medium", () => {
+  test("chat page renders without crash", async ({ frigateApp }) => {
     await frigateApp.goto("/chat");
-    await expect(
-      frigateApp.page.getByRole("heading", { level: 1 }),
-    ).toBeVisible({ timeout: 10_000 });
-    await expect(frigateApp.page.getByPlaceholder(/ask/i)).toBeVisible();
-    // Four quick-reply buttons from starting_requests.*
-    const quickReplies = frigateApp.page.locator(
-      "button:has-text('Show recent events'), button:has-text('Show camera status'), button:has-text('What happened'), button:has-text('Watch')",
-    );
-    await expect(quickReplies.first()).toBeVisible({ timeout: 5_000 });
+    await frigateApp.page.waitForTimeout(2000);
+    await expect(frigateApp.page.locator("body")).toBeVisible();
   });
-});
 
-test.describe("Chat — streaming @medium", () => {
-  test("submission POSTs to chat/completion with stream: true", async ({
-    frigateApp,
-  }) => {
-    await installChatStreamOverride(frigateApp, [
-      { type: "content", delta: "Hel" },
-      { type: "content", delta: "lo" },
-    ]);
+  test("chat page has interactive input or buttons", async ({ frigateApp }) => {
     await frigateApp.goto("/chat");
-    const input = frigateApp.page.getByPlaceholder(/ask/i);
-    await expect(input).toBeVisible({ timeout: 10_000 });
-    await input.fill("hello chat");
-    await input.press("Enter");
-
-    await expect
-      .poll(
-        async () =>
-          frigateApp.page.evaluate(
-            () =>
-              (window as unknown as { __chatRequests: unknown[] })
-                .__chatRequests?.length ?? 0,
-          ),
-        { timeout: 5_000 },
-      )
-      .toBeGreaterThan(0);
-
-    const request = await frigateApp.page.evaluate(
-      () =>
-        (
-          window as unknown as {
-            __chatRequests: Array<{
-              url: string;
-              body: { stream: boolean; messages: Array<{ content: string }> };
-            }>;
-          }
-        ).__chatRequests[0],
-    );
-    expect(request.body.stream).toBe(true);
-    expect(
-      request.body.messages[request.body.messages.length - 1].content,
-    ).toBe("hello chat");
-  });
-
-  test("NDJSON content chunks accumulate in the assistant bubble", async ({
-    frigateApp,
-  }) => {
-    await installChatStreamOverride(
-      frigateApp,
-      [
-        { type: "content", delta: "Hel" },
-        { type: "content", delta: "lo, " },
-        { type: "content", delta: "world!" },
-      ],
-      { chunkDelayMs: 50 },
-    );
-    await frigateApp.goto("/chat");
-    const input = frigateApp.page.getByPlaceholder(/ask/i);
-    await expect(input).toBeVisible({ timeout: 10_000 });
-    await input.fill("greet me");
-    await input.press("Enter");
-
-    await expect(frigateApp.page.getByText(/Hello, world!/i)).toBeVisible({
-      timeout: 10_000,
-    });
-  });
-
-  test("tool_calls chunks render a ToolCallsGroup", async ({ frigateApp }) => {
-    await installChatStreamOverride(frigateApp, [
-      {
-        type: "tool_calls",
-        tool_calls: [
-          {
-            id: "call_1",
-            name: "search_objects",
-            arguments: { label: "person" },
-          },
-        ],
-      },
-      { type: "content", delta: "Searching for people." },
-    ]);
-    await frigateApp.goto("/chat");
-    const input = frigateApp.page.getByPlaceholder(/ask/i);
-    await expect(input).toBeVisible({ timeout: 10_000 });
-    await input.fill("find people");
-    await input.press("Enter");
-
-    // ToolCallsGroup normalizes "search_objects" → "Search Objects" via
-    // normalizeName(). Match the rendered display label instead.
-    await expect(frigateApp.page.getByText(/search objects/i)).toBeVisible({
-      timeout: 10_000,
-    });
-    await expect(
-      frigateApp.page.getByText(/searching for people/i),
-    ).toBeVisible({ timeout: 5_000 });
+    await frigateApp.page.waitForTimeout(2000);
+    const interactive = frigateApp.page.locator("input, textarea, button");
+    const count = await interactive.count();
+    expect(count).toBeGreaterThan(0);
   });
-});
-
-test.describe("Chat — stop @medium", () => {
-  test("Stop button aborts an in-flight stream and freezes the partial message", async ({
-    frigateApp,
-  }) => {
-    // A long chunk sequence with big delays gives us time to hit Stop.
-    await installChatStreamOverride(
-      frigateApp,
-      [
-        { type: "content", delta: "First chunk. " },
-        { type: "content", delta: "Second chunk. " },
-        { type: "content", delta: "Third chunk. " },
-      ],
-      { chunkDelayMs: 300 },
-    );
-    await frigateApp.goto("/chat");
-    const input = frigateApp.page.getByPlaceholder(/ask/i);
-    await expect(input).toBeVisible({ timeout: 10_000 });
-    await input.fill("slow response please");
-    await input.press("Enter");
-
-    // Wait for the first chunk to render
-    await expect(frigateApp.page.getByText(/First chunk\./)).toBeVisible({
-      timeout: 10_000,
-    });
-
-    // The Stop button is a destructive rounded button shown while isLoading.
-    // It contains only an FaStop SVG icon (no visible text). Find it by the
-    // destructive variant class or fall back to aria-label.
-    const stopBtn = frigateApp.page
-      .locator("button.bg-destructive, button[class*='destructive']")
-      .first();
-    await stopBtn.click({ timeout: 3_000 }).catch(async () => {
-      await frigateApp.page
-        .getByRole("button", { name: /stop|cancel/i })
-        .first()
-        .click();
-    });
-
-    // Third chunk should never appear.
-    await expect(frigateApp.page.getByText(/Third chunk\./)).toHaveCount(0);
-  });
-});
-
-test.describe("Chat — error @medium", () => {
-  test("non-OK response renders an error banner", async ({ frigateApp }) => {
-    await installChatStreamOverride(frigateApp, [], { status: 500 });
-    await frigateApp.goto("/chat");
-    const input = frigateApp.page.getByPlaceholder(/ask/i);
-    await expect(input).toBeVisible({ timeout: 10_000 });
-    await input.fill("trigger error");
-    await input.press("Enter");
-    // The error banner is a role="alert" paragraph; target by role so we
-    // don't collide with the user-message bubble that contains "trigger
-    // error" (which would match /error/ in strict mode).
-    await expect(
-      frigateApp.page.getByRole("alert").filter({
-        hasText: /boom|something went wrong/i,
-      }),
-    ).toBeVisible({ timeout: 5_000 });
-  });
-});
-
-test.describe("Chat — attachment chip @medium", () => {
-  test("attaching an event renders a ChatAttachmentChip", async ({
-    frigateApp,
-  }) => {
-    // The chat starts with an empty message list (ChatStartingState).
-    // After sending a message, ChatEntry with the paperclip button appears.
-    // We use the stream override so the first message completes quickly.
-    await installChatStreamOverride(frigateApp, [
-      { type: "content", delta: "Done." },
-    ]);
-    await frigateApp.goto("/chat");
-
-    // Send a first message to transition out of ChatStartingState so the
-    // full ChatEntry (with the paperclip) is visible.
-    const input = frigateApp.page.getByPlaceholder(/ask/i);
-    await expect(input).toBeVisible({ timeout: 10_000 });
-    await input.fill("hello");
-    await input.press("Enter");
-    // Wait for the assistant response to complete so isLoading becomes false
-    // and the paperclip button is re-enabled.
-    await expect(frigateApp.page.getByText(/Done\./i)).toBeVisible({
-      timeout: 10_000,
-    });
-
-    // The paperclip button has aria-label from t("attachment_picker_placeholder")
-    // = "Attach an event".
-    const paperclip = frigateApp.page
-      .getByRole("button", { name: /attach an event/i })
-      .first();
-    await expect(paperclip).toBeVisible({ timeout: 5_000 });
-    await paperclip.click();
-
-    // The popover shows a paste input with placeholder "Or paste event ID".
-    const idInput = frigateApp.page
-      .locator('input[placeholder*="event" i], input[aria-label*="attach" i]')
-      .first();
-    await expect(idInput).toBeVisible({ timeout: 3_000 });
-    await idInput.fill("test-event-1");
-    await frigateApp.page
-      .getByRole("button", { name: /^attach$/i })
-      .first()
-      .click();
-
-    // The ChatAttachmentChip renders in the composer area. It shows an
-    // activity indicator while loading event data (event_ids API not mocked),
-    // so assert on the chip container being present in the composer.
-    await expect(
-      frigateApp.page.locator(
-        "[class*='inline-flex'][class*='rounded-lg'][class*='border']",
-      ),
-    ).toBeVisible({ timeout: 5_000 });
-  });
-});
-
-test.describe("Chat — mobile @medium @mobile", () => {
-  test.skip(({ frigateApp }) => !frigateApp.isMobile, "Mobile-only");
 
-  test("chat input is focusable at mobile viewport", async ({ frigateApp }) => {
+  test("chat input accepts text", async ({ frigateApp }) => {
     await frigateApp.goto("/chat");
-    const input = frigateApp.page.getByPlaceholder(/ask/i);
-    await expect(input).toBeVisible({ timeout: 10_000 });
-    await input.focus();
-    await expect(input).toBeFocused();
+    await frigateApp.page.waitForTimeout(2000);
+    const input = frigateApp.page.locator("input, textarea").first();
+    if (await input.isVisible().catch(() => false)) {
+      await input.fill("What cameras detected a person today?");
+      const value = await input.inputValue();
+      expect(value.length).toBeGreaterThan(0);
+    }
   });
 });