Merge branch 'dev' of github.com:blakeblackshear/frigate into testing

Author: ivanjx

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

@@ -4,6 +4,11 @@
 
 set -o errexit -o nounset -o pipefail
 
+# opt out of openvino telemetry
+if [ -e /usr/local/bin/opt_in_out ]; then
+  /usr/local/bin/opt_in_out --opt_out
+fi
+
 # Logs should be sent to stdout so that s6 can collect them
 
 # Tell S6-Overlay not to restart this service

docker/main/rootfs/usr/local/go2rtc/create_config.py

@@ -53,7 +53,7 @@
 
 # Need to set default location for HA config
 if go2rtc_config.get("hass") is None:
-    go2rtc_config["hass"] = {"config": "/config"}
+    go2rtc_config["hass"] = {"config": "/homeassistant"}
 
 # we want to ensure that logs are easy to read
 if go2rtc_config.get("log") is None:

docs/docs/configuration/camera_specific.md

@@ -15,6 +15,17 @@ Many cameras support encoding options which greatly affect the live view experie
 
 :::
 
+## H.265 Cameras via Safari
+
+Some cameras support h265 with different formats, but Safari only supports the annexb format. When using h265 camera streams for recording with devices that use the Safari browser, the `apple_compatibility` option should be used.
+
+```yaml
+cameras:
+  h265_cam: # <------ Doesn't matter what the camera is called
+    ffmpeg:
+      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.

docs/docs/configuration/face_recognition.md

@@ -3,7 +3,7 @@ id: face_recognition
 title: Face Recognition
 ---
 
-Face recognition identifies known individuals by matching detected faces with previously learned facial data. When a known person is recognized, their name will be added as a `sub_label`. This information is included in the UI, filters, as well as in notifications.
+Face recognition identifies known individuals by matching detected faces with previously learned facial data. When a known `person` is recognized, their name will be added as a `sub_label`. This information is included in the UI, filters, as well as in notifications.
 
 ## Model Requirements
 
@@ -13,6 +13,12 @@ When running a Frigate+ model (or any custom model that natively detects faces)
 
 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.
 
+:::note
+
+Frigate needs to first detect a `person` before it can detect and recognize a face.
+
+:::
+
 ### Face Recognition
 
 Frigate has support for two face recognition model types:
@@ -22,11 +28,13 @@ Frigate has support for two face recognition model types:
 
 In both cases, a lightweight face landmark detection model is also used to align faces before running recognition.
 
+All of these features run locally on your system.
+
 ## Minimum System Requirements
 
 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 is highly recommended.
+The `large` model is optimized for accuracy, an integrated or discrete GPU is highly recommended. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation.
 
 ## Configuration
 
@@ -62,6 +70,13 @@ Fine-tune face recognition with these optional parameters:
 - `blur_confidence_filter`: Enables a filter that calculates how blurry the face is and adjusts the confidence based on this.
   - Default: `True`.
 
+## Usage
+
+1. **Enable face recognition** in your configuration file and restart Frigate.
+2. **Upload your face** using the **Add Face** button's wizard in the Face Library section of the Frigate UI.
+3. When Frigate detects and attempts to recognize a face, it will appear in the **Train** tab of the Face Library, along with its associated recognition confidence.
+4. From the **Train** tab, you can **assign the face** to a new or existing person to improve recognition accuracy for the future.
+
 ## Creating a Robust Training Set
 
 The number of images needed for a sufficient training set for face recognition varies depending on several factors:
@@ -133,6 +148,7 @@ No, using another face recognition service will interfere with Frigate's built i
 ### Does face recognition run on the recording stream?
 
 Face recognition does not run on the recording stream, this would be suboptimal for many reasons:
+
 1. The latency of accessing the recordings means the notifications would not include the names of recognized people because recognition would not complete until after.
 2. The embedding models used run on a set image size, so larger images will be scaled down to match this anyway.
 3. Motion clarity is much more important than extra pixels, over-compression and motion blur are much more detrimental to results than resolution.

docs/docs/configuration/ffmpeg_presets.md

@@ -9,7 +9,7 @@ Some presets of FFmpeg args are provided by default to make the configuration ea
 
 It is highly recommended to use hwaccel presets in the config. These presets not only replace the longer args, but they also give Frigate hints of what hardware is available and allows Frigate to make other optimizations using the GPU such as when encoding the birdseye restream or when scaling a stream that has a size different than the native stream size.
 
-See [the hwaccel docs](/configuration/hardware_acceleration.md) for more info on how to setup hwaccel for your GPU / iGPU.
+See [the hwaccel docs](/configuration/hardware_acceleration_video.md) for more info on how to setup hwaccel for your GPU / iGPU.
 
 | Preset                | Usage                          | Other Notes                                           |
 | --------------------- | ------------------------------ | ----------------------------------------------------- |

docs/docs/configuration/hardware_acceleration_enrichments.md

@@ -0,0 +1,32 @@
+---
+id: hardware_acceleration_enrichments
+title: Enrichments
+---
+
+# Enrichments
+
+Some of Frigate's enrichments can use a discrete GPU for accelerated processing.
+
+## Requirements
+
+Object detection and enrichments (like Semantic Search, Face Recognition, and License Plate Recognition) are independent features. To use a GPU 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 and configure the enrichment according to its specific documentation.
+
+- **AMD**
+
+  - ROCm will automatically be detected and used for enrichments in the `-rocm` Frigate image.
+
+- **Intel**
+
+  - OpenVINO will automatically be detected and used for enrichments in the default Frigate image.
+
+- **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.
+
+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 for object detection.
+
+:::note
+
+A Google Coral is a TPU (Tensor Processing Unit), not a dedicated GPU (Graphics Processing Unit) and therefore does not provide any kind of acceleration for Frigate's enrichments.
+
+:::

…s/configuration/hardware_acceleration.md …iguration/hardware_acceleration_video.mddocs/docs/configuration/hardware_acceleration.md renamed to docs/docs/configuration/hardware_acceleration_video.md docs/docs/configuration/hardware_acceleration.md renamed to docs/docs/configuration/hardware_acceleration_video.md

@@ -1,15 +1,15 @@
 ---
-id: hardware_acceleration
-title: Hardware Acceleration
+id: hardware_acceleration_video
+title: Video Decoding
 ---
 
-# Hardware Acceleration
+# Video Decoding
 
-It is highly recommended to use a GPU for hardware acceleration in Frigate. Some types of hardware acceleration are detected and used automatically, but you may need to update your configuration to enable hardware accelerated decoding in ffmpeg.
+It is highly recommended to use a GPU for hardware acceleration video decoding in Frigate. Some types of hardware acceleration are detected and used automatically, but you may need to update your configuration to enable hardware accelerated decoding in ffmpeg.
 
 Depending on your system, these parameters may not be compatible. More information on hardware accelerated decoding for ffmpeg can be found here: https://trac.ffmpeg.org/wiki/HWAccelIntro
 
-# Officially Supported
+# Object Detection
 
 ## Raspberry Pi 3/4
 
@@ -69,12 +69,12 @@ Or map in all the `/dev/video*` devices.
 
 **Recommended hwaccel Preset**
 
-| CPU Generation | Intel Driver | Recommended Preset | Notes                               |
-| -------------- | ------------ | ------------------ | ----------------------------------- |
-| gen1 - gen7    | i965         | preset-vaapi       | qsv is not supported                |
-| gen8 - gen12   | iHD          | preset-vaapi       | preset-intel-qsv-* can also be used |
-| gen13+         | iHD / Xe     | preset-intel-qsv-* |                                     |
-| Intel Arc GPU  | iHD / Xe     | preset-intel-qsv-* |                                     |
+| CPU Generation | Intel Driver | Recommended Preset  | Notes                                |
+| -------------- | ------------ | ------------------- | ------------------------------------ |
+| gen1 - gen7    | i965         | preset-vaapi        | qsv is not supported                 |
+| gen8 - gen12   | iHD          | preset-vaapi        | preset-intel-qsv-\* can also be used |
+| gen13+         | iHD / Xe     | preset-intel-qsv-\* |                                      |
+| Intel Arc GPU  | iHD / Xe     | preset-intel-qsv-\* |                                      |
 
 :::