Upstream 0.17.1

Makefile

@@ -1,7 +1,7 @@
 default_target: local
 
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.17.0
+VERSION = 0.17.1
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
 BOARDS= #Initialized empty

docker/main/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/run

@@ -55,7 +55,7 @@ function setup_homekit_config() {
 
     if [[ ! -f "${config_path}" ]]; then
         echo "[INFO] Creating empty config file for HomeKit..."
-        echo '{}' > "${config_path}"
+        : > "${config_path}"
     fi
 
     # Convert YAML to JSON for jq processing
@@ -65,23 +65,25 @@ function setup_homekit_config() {
         return 0
     }
 
-    # Use jq to filter and keep only the homekit section
-    local cleaned_json="/tmp/cache/homekit_cleaned.json"
-    jq '
-        # Keep only the homekit section if it exists, otherwise empty object
-        if has("homekit") then {homekit: .homekit} else {} end
-    ' "${temp_json}" > "${cleaned_json}" 2>/dev/null || {
-        echo '{}' > "${cleaned_json}"
-    }
+    # Use jq to extract the homekit section, if it exists
+    local homekit_json
+    homekit_json=$(jq '
+        if has("homekit") then {homekit: .homekit} else null end
+    ' "${temp_json}" 2>/dev/null) || homekit_json="null"
 
-    # Convert back to YAML and write to the config file
-    yq eval -P "${cleaned_json}" > "${config_path}" 2>/dev/null || {
-        echo "[WARNING] Failed to convert cleaned config to YAML, creating minimal config"
-        echo '{}' > "${config_path}"
-    }
+    # If no homekit section, write an empty config file
+    if [[ "${homekit_json}" == "null" ]]; then
+        : > "${config_path}"
+    else
+        # Convert homekit JSON back to YAML and write to the config file
+        echo "${homekit_json}" | yq eval -P - > "${config_path}" 2>/dev/null || {
+            echo "[WARNING] Failed to convert cleaned config to YAML, creating minimal config"
+            : > "${config_path}"
+        }
+    fi
 
     # Clean up temp files
-    rm -f "${temp_json}" "${cleaned_json}"
+    rm -f "${temp_json}"
 }
 
 set_libva_version

docs/docs/configuration/advanced.md

@@ -44,13 +44,21 @@ go2rtc:
 
 ### `environment_vars`
 
-This section can be used to set environment variables for those unable to modify the environment of the container, like within Home Assistant OS.
+This section can be used to set environment variables for those unable to modify the environment of the container, like within Home Assistant OS. Docker users should set environment variables in their `docker run` command (`-e FRIGATE_MQTT_PASSWORD=secret`) or `docker-compose.yml` file (`environment:` section) instead. Note that values set here are stored in plain text in your config file, so if the goal is to keep credentials out of your configuration, use Docker environment variables or Docker secrets instead.
+
+Variables prefixed with `FRIGATE_` can be referenced in config fields that support environment variable substitution (such as MQTT host and credentials, camera stream URLs, and ONVIF host and credentials) using the `{FRIGATE_VARIABLE_NAME}` syntax.
 
 Example:
 
 ```yaml
 environment_vars:
-  VARIABLE_NAME: variable_value
+  FRIGATE_MQTT_USER: my_mqtt_user
+  FRIGATE_MQTT_PASSWORD: my_mqtt_password
+
+mqtt:
+  host: "{FRIGATE_MQTT_HOST}"
+  user: "{FRIGATE_MQTT_USER}"
+  password: "{FRIGATE_MQTT_PASSWORD}"
 ```
 
 #### TensorFlow Thread Configuration

docs/docs/configuration/authentication.md

@@ -86,7 +86,7 @@ Frigate looks for a JWT token secret in the following order:
 
 1. An environment variable named `FRIGATE_JWT_SECRET`
 2. A file named `FRIGATE_JWT_SECRET` in the directory specified by the `CREDENTIALS_DIRECTORY` environment variable (defaults to the Docker Secrets directory: `/run/secrets/`)
-3. A `jwt_secret` option from the Home Assistant Add-on options
+3. A `jwt_secret` option from the Home Assistant App options
 4. A `.jwt_secret` file in the config directory
 
 If no secret is found on startup, Frigate generates one and stores it in a `.jwt_secret` file in the config directory.
@@ -232,7 +232,7 @@ The viewer role provides read-only access to all cameras in the UI and API. Cust
 
 ### Role Configuration Example
 
-```yaml
+```yaml {11-16}
 cameras:
   front_door:
     # ... camera config

docs/docs/configuration/birdseye.md

@@ -24,7 +24,7 @@ A custom icon can be added to the birdseye background by providing a 180x180 ima
 
 If you want to include a camera in Birdseye view only for specific circumstances, or just don't include it at all, the Birdseye setting can be set at the camera level.
 
-```yaml
+```yaml {8-10,12-14}
 # Include all cameras by default in Birdseye view
 birdseye:
   enabled: True
@@ -48,6 +48,7 @@ By default birdseye shows all cameras that have had the configured activity in t
 ```yaml
 birdseye:
   enabled: True
+  # highlight-next-line
   inactivity_threshold: 15
 ```
 
@@ -78,9 +79,11 @@ birdseye:
 cameras:
   front:
     birdseye:
+      # highlight-next-line
       order: 1
   back:
     birdseye:
+      # highlight-next-line
       order: 2
 ```
 
@@ -92,7 +95,7 @@ It is possible to limit the number of cameras shown on birdseye at one time. Whe
 
 For example, this can be configured to only show the most recently active camera.
 
-```yaml
+```yaml {3-4}
 birdseye:
   enabled: True
   layout:
@@ -103,7 +106,7 @@ birdseye:
 
 By default birdseye tries to fit 2 cameras in each row and then double in size until a suitable layout is found. The scaling can be configured with a value between 1.0 and 5.0 depending on use case.
 
-```yaml
+```yaml {3-4}
 birdseye:
   enabled: True
   layout:

docs/docs/configuration/camera_specific.md

@@ -23,14 +23,15 @@ Some cameras support h265 with different formats, but Safari only supports the a
 cameras:
   h265_cam: # <------ Doesn't matter what the camera is called
     ffmpeg:
+      # highlight-next-line
       apple_compatibility: true # <- Adds compatibility with MacOS and iPhone
 ```
 
 ## MJPEG Cameras
 
 Note that mjpeg cameras require encoding the video into h264 for recording, and restream roles. This will use significantly more CPU than if the cameras supported h264 feeds directly. It is recommended to use the restream role to create an h264 restream and then use that as the source for ffmpeg.
 
-```yaml
+```yaml {3,10}
 go2rtc:
   streams:
     mjpeg_cam: "ffmpeg:http://your_mjpeg_stream_url#video=h264#hardware" # <- use hardware acceleration to create an h264 stream usable for other components.
@@ -96,6 +97,7 @@ This camera is H.265 only. To be able to play clips on some devices (like MacOs
 cameras:
   annkec800: # <------ Name the camera
     ffmpeg:
+      # highlight-next-line
       apple_compatibility: true # <- Adds compatibility with MacOS and iPhone
       output_args:
         record: preset-record-generic-audio-aac
@@ -274,7 +276,7 @@ To use a USB camera (webcam) with Frigate, the recommendation is to use go2rtc's
 
 - In your Frigate Configuration File, add the go2rtc stream and roles as appropriate:
 
-```
+```yaml {4,11-12}
 go2rtc:
   streams:
     usb_camera:

docs/docs/configuration/cameras.md

@@ -66,7 +66,7 @@ Not every PTZ supports ONVIF, which is the standard protocol Frigate uses to com
 
 Add the onvif section to your camera in your configuration file:
 
-```yaml
+```yaml {4-8}
 cameras:
   back:
     ffmpeg: ...

docs/docs/configuration/custom_classification/object_classification.md

@@ -7,11 +7,11 @@ Object classification allows you to train a custom MobileNetV2 classification mo
 
 ## Minimum System Requirements
 
-Object classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate.
+Object classification models are lightweight and run very fast on CPU.
 
 Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
 
-A CPU with AVX instructions is required for training and inference.
+A CPU with AVX + AVX2 instructions is required for training and inference.
 
 ## Classes
 
@@ -27,7 +27,6 @@ For object classification:
 ### Classification Type
 
 - **Sub label**:
-
   - Applied to the object’s `sub_label` field.
   - Ideal for a single, more specific identity or type.
   - Example: `cat` → `Leo`, `Charlie`, `None`.
@@ -119,6 +118,7 @@ Enable debug logs for classification models by adding `frigate.data_processing.r
 logger:
   default: info
   logs:
+    # highlight-next-line
     frigate.data_processing.real_time.custom_classification: debug
 ```
 

docs/docs/configuration/custom_classification/state_classification.md

@@ -7,11 +7,11 @@ State classification allows you to train a custom MobileNetV2 classification mod
 
 ## Minimum System Requirements
 
-State classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate.
+State classification models are lightweight and run very fast on CPU.
 
 Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
 
-A CPU with AVX instructions is required for training and inference.
+A CPU with AVX + AVX2 instructions is required for training and inference.
 
 ## Classes
 
@@ -85,6 +85,7 @@ Enable debug logs for classification models by adding `frigate.data_processing.r
 logger:
   default: info
   logs:
+    # highlight-next-line
     frigate.data_processing.real_time.custom_classification: debug
 ```
 

docs/docs/configuration/face_recognition.md

@@ -32,6 +32,8 @@ All of these features run locally on your system.
 
 ## Minimum System Requirements
 
+A CPU with AVX + AVX2 instructions is required to run Face Recognition.
+
 The `small` model is optimized for efficiency and runs on the CPU, most CPUs should run the model efficiently.
 
 The `large` model is optimized for accuracy, an integrated or discrete GPU / NPU is required. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation.
@@ -143,17 +145,14 @@ Start with the [Usage](#usage) section and re-read the [Model Requirements](#mod
 1. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Recent Recognitions tab in the Frigate UI's Face Library.
 
    If you are using a Frigate+ or `face` detecting model:
-
    - Watch the debug view (Settings --> Debug) to ensure that `face` is being detected along with `person`.
    - You may need to adjust the `min_score` for the `face` object if faces are not being detected.
 
    If you are **not** using a Frigate+ or `face` detecting model:
-
    - Check your `detect` stream resolution and ensure it is sufficiently high enough to capture face details on `person` objects.
    - You may need to lower your `detection_threshold` if faces are not being detected.
 
 2. Any detected faces will then be _recognized_.
-
    - Make sure you have trained at least one face per the recommendations above.
    - Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
 

docs/docs/configuration/genai/config.md

@@ -109,7 +109,7 @@ genai:
 
 To use a different Gemini-compatible API endpoint, set the `provider_options` with the `base_url` key to your provider's API URL. For example:
 
-```
+```yaml {4,5}
 genai:
   provider: gemini
   ...
@@ -152,7 +152,7 @@ To use a different OpenAI-compatible API endpoint, set the `OPENAI_BASE_URL` env
 
 For OpenAI-compatible servers (such as llama.cpp) that don't expose the configured context size in the API response, you can manually specify the context size in `provider_options`:
 
-```yaml
+```yaml {5,6}
 genai:
   provider: openai
   base_url: http://your-llama-server

docs/docs/configuration/genai/review_summaries.md

@@ -80,6 +80,7 @@ By default, review summaries use preview images (cached preview frames) which ha
 review:
   genai:
     enabled: true
+    # highlight-next-line
     image_source: recordings # Options: "preview" (default) or "recordings"
 ```
 
@@ -104,7 +105,7 @@ If recordings are not available for a given time period, the system will automat
 
 Along with the concern of suspicious activity or immediate threat, you may have concerns such as animals in your garden or a gate being left open. These concerns can be configured so that the review summaries will make note of them if the activity requires additional review. For example:
 
-```yaml
+```yaml {4,5}
 review:
   genai:
     enabled: true
@@ -116,7 +117,7 @@ review:
 
 By default, review summaries are generated in English. You can configure Frigate to generate summaries in your preferred language by setting the `preferred_language` option:
 
-```yaml
+```yaml {4}
 review:
   genai:
     enabled: true

docs/docs/configuration/hardware_acceleration_enrichments.md

@@ -12,23 +12,20 @@ Some of Frigate's enrichments can use a discrete GPU or integrated GPU for accel
 Object detection and enrichments (like Semantic Search, Face Recognition, and License Plate Recognition) are independent features. To use a GPU / NPU for object detection, see the [Object Detectors](/configuration/object_detectors.md) documentation. If you want to use your GPU for any supported enrichments, you must choose the appropriate Frigate Docker image for your GPU / NPU and configure the enrichment according to its specific documentation.
 
 - **AMD**
-
   - ROCm support in the `-rocm` Frigate image is automatically detected for enrichments, but only some enrichment models are available due to ROCm's focus on LLMs and limited stability with certain neural network models. Frigate disables models that perform poorly or are unstable to ensure reliable operation, so only compatible enrichments may be active.
 
 - **Intel**
-
   - OpenVINO will automatically be detected and used for enrichments in the default Frigate image.
   - **Note:** Intel NPUs have limited model support for enrichments. GPU is recommended for enrichments when available.
 
 - **Nvidia**
-
   - Nvidia GPUs will automatically be detected and used for enrichments in the `-tensorrt` Frigate image.
   - Jetson devices will automatically be detected and used for enrichments in the `-tensorrt-jp6` Frigate image.
 
 - **RockChip**
   - RockChip NPU will automatically be detected and used for semantic search v1 and face recognition in the `-rk` Frigate image.
 
-Utilizing a GPU for enrichments does not require you to use the same GPU for object detection. For example, you can run the `tensorrt` Docker image for enrichments and still use other dedicated hardware like a Coral or Hailo for object detection. However, one combination that is not supported is TensorRT for object detection and OpenVINO for enrichments.
+Utilizing a GPU for enrichments does not require you to use the same GPU for object detection. For example, you can run the `tensorrt` Docker image to run enrichments on an Nvidia GPU and still use other dedicated hardware like a Coral or Hailo for object detection. However, one combination that is not supported is the `tensorrt` image for object detection on an Nvidia GPU and Intel iGPU for enrichments.
 
 :::note