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|---|---|---|---|
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8f9ce32c79 |
2
LICENSE
2
LICENSE
@ -1,6 +1,6 @@
|
||||
The MIT License
|
||||
|
||||
Copyright (c) 2025 Frigate LLC (Frigate™)
|
||||
Copyright (c) 2020 Blake Blackshear
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
|
||||
19
README.md
19
README.md
@ -1,10 +1,8 @@
|
||||
<p align="center">
|
||||
<img align="center" alt="logo" src="docs/static/img/branding/frigate.png">
|
||||
<img align="center" alt="logo" src="docs/static/img/frigate.png">
|
||||
</p>
|
||||
|
||||
# Frigate NVR™ - Realtime Object Detection for IP Cameras
|
||||
|
||||
[](https://opensource.org/licenses/MIT)
|
||||
# Frigate - NVR With Realtime Object Detection for IP Cameras
|
||||
|
||||
<a href="https://hosted.weblate.org/engage/frigate-nvr/">
|
||||
<img src="https://hosted.weblate.org/widget/frigate-nvr/language-badge.svg" alt="Translation status" />
|
||||
@ -35,15 +33,6 @@ View the documentation at https://docs.frigate.video
|
||||
|
||||
If you would like to make a donation to support development, please use [Github Sponsors](https://github.com/sponsors/blakeblackshear).
|
||||
|
||||
## License
|
||||
|
||||
This project is licensed under the **MIT License**.
|
||||
|
||||
- **Code:** The source code, configuration files, and documentation in this repository are available under the [MIT License](LICENSE). You are free to use, modify, and distribute the code as long as you include the original copyright notice.
|
||||
- **Trademarks:** The "Frigate" name, the "Frigate NVR" brand, and the Frigate logo are **trademarks of Frigate LLC** and are **not** covered by the MIT License.
|
||||
|
||||
Please see our [Trademark Policy](TRADEMARK.md) for details on acceptable use of our brand assets.
|
||||
|
||||
## Screenshots
|
||||
|
||||
### Live dashboard
|
||||
@ -77,7 +66,3 @@ We use [Weblate](https://hosted.weblate.org/projects/frigate-nvr/) to support la
|
||||
<a href="https://hosted.weblate.org/engage/frigate-nvr/">
|
||||
<img src="https://hosted.weblate.org/widget/frigate-nvr/multi-auto.svg" alt="Translation status" />
|
||||
</a>
|
||||
|
||||
---
|
||||
|
||||
**Copyright © 2025 Frigate LLC.**
|
||||
|
||||
58
TRADEMARK.md
58
TRADEMARK.md
@ -1,58 +0,0 @@
|
||||
# Trademark Policy
|
||||
|
||||
**Last Updated:** November 2025
|
||||
|
||||
This document outlines the policy regarding the use of the trademarks associated with the Frigate NVR project.
|
||||
|
||||
## 1. Our Trademarks
|
||||
|
||||
The following terms and visual assets are trademarks (the "Marks") of **Frigate LLC**:
|
||||
|
||||
- **Frigate™**
|
||||
- **Frigate NVR™**
|
||||
- **Frigate+™**
|
||||
- **The Frigate Logo**
|
||||
|
||||
**Note on Common Law Rights:**
|
||||
Frigate LLC asserts all common law rights in these Marks. The absence of a federal registration symbol (®) does not constitute a waiver of our intellectual property rights.
|
||||
|
||||
## 2. Interaction with the MIT License
|
||||
|
||||
The software in this repository is licensed under the [MIT License](LICENSE).
|
||||
|
||||
**Crucial Distinction:**
|
||||
|
||||
- The **Code** is free to use, modify, and distribute under the MIT terms.
|
||||
- The **Brand (Trademarks)** is **NOT** licensed under MIT.
|
||||
|
||||
You may not use the Marks in any way that is not explicitly permitted by this policy or by written agreement with Frigate LLC.
|
||||
|
||||
## 3. Acceptable Use
|
||||
|
||||
You may use the Marks without prior written permission in the following specific contexts:
|
||||
|
||||
- **Referential Use:** To truthfully refer to the software (e.g., _"I use Frigate NVR for my home security"_).
|
||||
- **Compatibility:** To indicate that your product or project works with the software (e.g., _"MyPlugin for Frigate NVR"_ or _"Compatible with Frigate"_).
|
||||
- **Commentary:** In news articles, blog posts, or tutorials discussing the software.
|
||||
|
||||
## 4. Prohibited Use
|
||||
|
||||
You may **NOT** use the Marks in the following ways:
|
||||
|
||||
- **Commercial Products:** You may not use "Frigate" in the name of a commercial product, service, or app (e.g., selling an app named _"Frigate Viewer"_ is prohibited).
|
||||
- **Implying Affiliation:** You may not use the Marks in a way that suggests your project is official, sponsored by, or endorsed by Frigate LLC.
|
||||
- **Confusing Forks:** If you fork this repository to create a derivative work, you **must** remove the Frigate logo and rename your project to avoid user confusion. You cannot distribute a modified version of the software under the name "Frigate".
|
||||
- **Domain Names:** You may not register domain names containing "Frigate" that are likely to confuse users (e.g., `frigate-official-support.com`).
|
||||
|
||||
## 5. The Logo
|
||||
|
||||
The Frigate logo (the bird icon) is a visual trademark.
|
||||
|
||||
- You generally **cannot** use the logo on your own website or product packaging without permission.
|
||||
- If you are building a dashboard or integration that interfaces with Frigate, you may use the logo only to represent the Frigate node/service, provided it does not imply you _are_ Frigate.
|
||||
|
||||
## 6. Questions & Permissions
|
||||
|
||||
If you are unsure if your intended use violates this policy, or if you wish to request a specific license to use the Marks (e.g., for a partnership), please contact us at:
|
||||
|
||||
**help@frigate.video**
|
||||
@ -145,6 +145,6 @@ rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# Install yq, for frigate-prepare and go2rtc echo source
|
||||
curl -fsSL \
|
||||
"https://github.com/mikefarah/yq/releases/download/v4.48.2/yq_linux_$(dpkg --print-architecture)" \
|
||||
"https://github.com/mikefarah/yq/releases/download/v4.33.3/yq_linux_$(dpkg --print-architecture)" \
|
||||
--output /usr/local/bin/yq
|
||||
chmod +x /usr/local/bin/yq
|
||||
|
||||
@ -25,7 +25,7 @@ Examples of available modules are:
|
||||
|
||||
- `frigate.app`
|
||||
- `frigate.mqtt`
|
||||
- `frigate.object_detection.base`
|
||||
- `frigate.object_detection`
|
||||
- `detector.<detector_name>`
|
||||
- `watchdog.<camera_name>`
|
||||
- `ffmpeg.<camera_name>.<sorted_roles>` NOTE: All FFmpeg logs are sent as `error` level.
|
||||
@ -53,17 +53,6 @@ environment_vars:
|
||||
VARIABLE_NAME: variable_value
|
||||
```
|
||||
|
||||
#### TensorFlow Thread Configuration
|
||||
|
||||
If you encounter thread creation errors during classification model training, you can limit TensorFlow's thread usage:
|
||||
|
||||
```yaml
|
||||
environment_vars:
|
||||
TF_INTRA_OP_PARALLELISM_THREADS: "2" # Threads within operations (0 = use default)
|
||||
TF_INTER_OP_PARALLELISM_THREADS: "2" # Threads between operations (0 = use default)
|
||||
TF_DATASET_THREAD_POOL_SIZE: "2" # Data pipeline threads (0 = use default)
|
||||
```
|
||||
|
||||
### `database`
|
||||
|
||||
Tracked object and recording information is managed in a sqlite database at `/config/frigate.db`. If that database is deleted, recordings will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within Home Assistant.
|
||||
@ -258,7 +247,7 @@ curl -X POST http://frigate_host:5000/api/config/save -d @config.json
|
||||
if you'd like you can use your yaml config directly by using [`yq`](https://github.com/mikefarah/yq) to convert it to json:
|
||||
|
||||
```bash
|
||||
yq -o=json '.' config.yaml | curl -X POST 'http://frigate_host:5000/api/config/save?save_option=saveonly' --data-binary @-
|
||||
yq r -j config.yml | curl -X POST http://frigate_host:5000/api/config/save -d @-
|
||||
```
|
||||
|
||||
### Via Command Line
|
||||
|
||||
@ -35,15 +35,6 @@ For object classification:
|
||||
- Ideal when multiple attributes can coexist independently.
|
||||
- Example: Detecting if a `person` in a construction yard is wearing a helmet or not.
|
||||
|
||||
## Assignment Requirements
|
||||
|
||||
Sub labels and attributes are only assigned when both conditions are met:
|
||||
|
||||
1. **Threshold**: Each classification attempt must have a confidence score that meets or exceeds the configured `threshold` (default: `0.8`).
|
||||
2. **Class Consensus**: After at least 3 classification attempts, 60% of attempts must agree on the same class label. If the consensus class is `none`, no assignment is made.
|
||||
|
||||
This two-step verification prevents false positives by requiring consistent predictions across multiple frames before assigning a sub label or attribute.
|
||||
|
||||
## Example use cases
|
||||
|
||||
### Sub label
|
||||
@ -75,18 +66,14 @@ classification:
|
||||
|
||||
## Training the model
|
||||
|
||||
Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of two steps:
|
||||
Creating and training the model is done within the Frigate UI using the `Classification` page.
|
||||
|
||||
### Step 1: Name and Define
|
||||
|
||||
Enter a name for your model, select the object label to classify (e.g., `person`, `dog`, `car`), choose the classification type (sub label or attribute), and define your classes. Include a `none` class for objects that don't fit any specific category.
|
||||
|
||||
### Step 2: Assign Training Examples
|
||||
|
||||
The system will automatically generate example images from detected objects matching your selected label. You'll be guided through each class one at a time to select which images represent that class. Any images not assigned to a specific class will automatically be assigned to `none` when you complete the last class. Once all images are processed, training will begin automatically.
|
||||
### Getting Started
|
||||
|
||||
When choosing which objects to classify, start with a small number of visually distinct classes and ensure your training samples match camera viewpoints and distances typical for those objects.
|
||||
|
||||
// TODO add this section once UI is implemented. Explain process of selecting objects and curating training examples.
|
||||
|
||||
### Improving the Model
|
||||
|
||||
- **Problem framing**: Keep classes visually distinct and relevant to the chosen object types.
|
||||
|
||||
@ -48,23 +48,13 @@ classification:
|
||||
|
||||
## Training the model
|
||||
|
||||
Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of three steps:
|
||||
Creating and training the model is done within the Frigate UI using the `Classification` page.
|
||||
|
||||
### Step 1: Name and Define
|
||||
### Getting Started
|
||||
|
||||
Enter a name for your model and define at least 2 classes (states) that represent mutually exclusive states. For example, `open` and `closed` for a door, or `on` and `off` for lights.
|
||||
When choosing a portion of the camera frame for state classification, it is important to make the crop tight around the area of interest to avoid extra signals unrelated to what is being classified.
|
||||
|
||||
### Step 2: Select the Crop Area
|
||||
|
||||
Choose one or more cameras and draw a rectangle over the area of interest for each camera. The crop should be tight around the region you want to classify to avoid extra signals unrelated to what is being classified. You can drag and resize the rectangle to adjust the crop area.
|
||||
|
||||
### Step 3: Assign Training Examples
|
||||
|
||||
The system will automatically generate example images from your camera feeds. You'll be guided through each class one at a time to select which images represent that state.
|
||||
|
||||
**Important**: All images must be assigned to a state before training can begin. This includes images that may not be optimal, such as when people temporarily block the view, sun glare is present, or other distractions occur. Assign these images to the state that is actually present (based on what you know the state to be), not based on the distraction. This training helps the model correctly identify the state even when such conditions occur during inference.
|
||||
|
||||
Once all images are assigned, training will begin automatically.
|
||||
// TODO add this section once UI is implemented. Explain process of selecting a crop.
|
||||
|
||||
### Improving the Model
|
||||
|
||||
|
||||
@ -214,42 +214,6 @@ For restreamed cameras, go2rtc remains active but does not use system resources
|
||||
|
||||
Note that disabling a camera through the config file (`enabled: False`) removes all related UI elements, including historical footage access. To retain access while disabling the camera, keep it enabled in the config and use the UI or MQTT to disable it temporarily.
|
||||
|
||||
### Live player error messages
|
||||
|
||||
When your browser runs into problems playing back your camera streams, it will log short error messages to the browser console. They indicate playback, codec, or network issues on the client/browser side, not something server side with Frigate itself. Below are the common messages you may see and simple actions you can take to try to resolve them.
|
||||
|
||||
- **startup**
|
||||
|
||||
- What it means: The player failed to initialize or connect to the live stream (network or startup error).
|
||||
- What to try: Reload the Live view or click _Reset_. Verify `go2rtc` is running and the camera stream is reachable. Try switching to a different stream from the Live UI dropdown (if available) or use a different browser.
|
||||
|
||||
- Possible console messages from the player code:
|
||||
|
||||
- `Error opening MediaSource.`
|
||||
- `Browser reported a network error.`
|
||||
- `Max error count ${errorCount} exceeded.` (the numeric value will vary)
|
||||
|
||||
- **mse-decode**
|
||||
|
||||
- What it means: The browser reported a decoding error while trying to play the stream, which usually is a result of a codec incompatibility or corrupted frames.
|
||||
- What to try: Ensure your camera/restream is using H.264 video and AAC audio (these are the most compatible). If your camera uses a non-standard audio codec, configure `go2rtc` to transcode the stream to AAC. Try another browser (some browsers have stricter MSE/codec support) and, for iPhone, ensure you're on iOS 17.1 or newer.
|
||||
|
||||
- Possible console messages from the player code:
|
||||
|
||||
- `Safari cannot open MediaSource.`
|
||||
- `Safari reported InvalidStateError.`
|
||||
- `Safari reported decoding errors.`
|
||||
|
||||
- **stalled**
|
||||
|
||||
- What it means: Playback has stalled because the player has fallen too far behind live (extended buffering or no data arriving).
|
||||
- What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval — shorter intervals make playback start and recover faster. You can also try increasing the timeout value in the UI pane of Frigate's settings.
|
||||
|
||||
- Possible console messages from the player code:
|
||||
|
||||
- `Buffer time (10 seconds) exceeded, browser may not be playing media correctly.`
|
||||
- `Media playback has stalled after <n> seconds due to insufficient buffering or a network interruption.` (the seconds value will vary)
|
||||
|
||||
## Live view FAQ
|
||||
|
||||
1. **Why don't I have audio in my Live view?**
|
||||
@ -313,38 +277,3 @@ When your browser runs into problems playing back your camera streams, it will l
|
||||
7. **My camera streams have lots of visual artifacts / distortion.**
|
||||
|
||||
Some cameras don't include the hardware to support multiple connections to the high resolution stream, and this can cause unexpected behavior. In this case it is recommended to [restream](./restream.md) the high resolution stream so that it can be used for live view and recordings.
|
||||
|
||||
8. **Why does my camera stream switch aspect ratios on the Live dashboard?**
|
||||
|
||||
Your camera may change aspect ratios on the dashboard because Frigate uses different streams for different purposes. With go2rtc and Smart Streaming, Frigate shows a static image from the `detect` stream when no activity is present, and switches to the live stream when motion is detected. The camera image will change size if your streams use different aspect ratios.
|
||||
|
||||
To prevent this, make the `detect` stream match the go2rtc live stream's aspect ratio (resolution does not need to match, just the aspect ratio). You can either adjust the camera's output resolution or set the `width` and `height` values in your config's `detect` section to a resolution with an aspect ratio that matches.
|
||||
|
||||
Example: Resolutions from two streams
|
||||
|
||||
- Mismatched (may cause aspect ratio switching on the dashboard):
|
||||
|
||||
- Live/go2rtc stream: 1920x1080 (16:9)
|
||||
- Detect stream: 640x352 (~1.82:1, not 16:9)
|
||||
|
||||
- Matched (prevents switching):
|
||||
- Live/go2rtc stream: 1920x1080 (16:9)
|
||||
- Detect stream: 640x360 (16:9)
|
||||
|
||||
You can update the detect settings in your camera config to match the aspect ratio of your go2rtc live stream. For example:
|
||||
|
||||
```yaml
|
||||
cameras:
|
||||
front_door:
|
||||
detect:
|
||||
width: 640
|
||||
height: 360 # set this to 360 instead of 352
|
||||
ffmpeg:
|
||||
inputs:
|
||||
- path: rtsp://127.0.0.1:8554/front_door # main stream 1920x1080
|
||||
roles:
|
||||
- record
|
||||
- path: rtsp://127.0.0.1:8554/front_door_sub # sub stream 640x352
|
||||
roles:
|
||||
- detect
|
||||
```
|
||||
|
||||
@ -3,8 +3,6 @@ id: object_detectors
|
||||
title: Object Detectors
|
||||
---
|
||||
|
||||
import CommunityBadge from '@site/src/components/CommunityBadge';
|
||||
|
||||
# Supported Hardware
|
||||
|
||||
:::info
|
||||
@ -15,8 +13,8 @@ Frigate supports multiple different detectors that work on different types of ha
|
||||
|
||||
- [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB and m.2 format allowing for a wide range of compatibility with devices.
|
||||
- [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
|
||||
- <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
|
||||
- <CommunityBadge /> [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com).
|
||||
- [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
|
||||
- [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com).
|
||||
|
||||
**AMD**
|
||||
|
||||
@ -36,16 +34,16 @@ Frigate supports multiple different detectors that work on different types of ha
|
||||
|
||||
- [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt` Frigate image when a supported ONNX model is configured.
|
||||
|
||||
**Nvidia Jetson** <CommunityBadge />
|
||||
**Nvidia Jetson**
|
||||
|
||||
- [TensortRT](#nvidia-tensorrt-detector): TensorRT can run on Jetson devices, using one of many default models.
|
||||
- [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt-jp6` Frigate image when a supported ONNX model is configured.
|
||||
|
||||
**Rockchip** <CommunityBadge />
|
||||
**Rockchip**
|
||||
|
||||
- [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs.
|
||||
|
||||
**Synaptics** <CommunityBadge />
|
||||
**Synaptics**
|
||||
|
||||
- [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs.
|
||||
|
||||
@ -964,6 +962,7 @@ model:
|
||||
# path: /config/yolov9.zip
|
||||
# The .zip file must contain:
|
||||
# ├── yolov9.dfp (a file ending with .dfp)
|
||||
# └── yolov9_post.onnx (optional; only if the model includes a cropped post-processing network)
|
||||
```
|
||||
|
||||
#### YOLOX
|
||||
|
||||
@ -246,7 +246,7 @@ birdseye:
|
||||
# Optional: ffmpeg configuration
|
||||
# More information about presets at https://docs.frigate.video/configuration/ffmpeg_presets
|
||||
ffmpeg:
|
||||
# Optional: ffmpeg binary path (default: shown below)
|
||||
# Optional: ffmpeg binry path (default: shown below)
|
||||
# can also be set to `7.0` or `5.0` to specify one of the included versions
|
||||
# or can be set to any path that holds `bin/ffmpeg` & `bin/ffprobe`
|
||||
path: "default"
|
||||
|
||||
@ -3,8 +3,6 @@ id: hardware
|
||||
title: Recommended hardware
|
||||
---
|
||||
|
||||
import CommunityBadge from '@site/src/components/CommunityBadge';
|
||||
|
||||
## Cameras
|
||||
|
||||
Cameras that output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. It is also helpful if your camera supports multiple substreams to allow different resolutions to be used for detection, streaming, and recordings without re-encoding.
|
||||
@ -61,7 +59,7 @@ Frigate supports multiple different detectors that work on different types of ha
|
||||
|
||||
- [Supports primarily ssdlite and mobilenet model architectures](../../configuration/object_detectors#edge-tpu-detector)
|
||||
|
||||
- <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 M.2 accelerator module is available in m.2 format allowing for a wide range of compatibility with devices.
|
||||
- [MemryX](#memryx-mx3): The MX3 M.2 accelerator module is available in m.2 format allowing for a wide range of compatibility with devices.
|
||||
- [Supports many model architectures](../../configuration/object_detectors#memryx-mx3)
|
||||
- Runs best with tiny, small, or medium-size models
|
||||
|
||||
@ -86,26 +84,32 @@ Frigate supports multiple different detectors that work on different types of ha
|
||||
|
||||
**Nvidia**
|
||||
|
||||
- [TensortRT](#tensorrt---nvidia-gpu): TensorRT can run on Nvidia GPUs to provide efficient object detection.
|
||||
|
||||
- [TensortRT](#tensorrt---nvidia-gpu): TensorRT can run on Nvidia GPUs and Jetson devices.
|
||||
- [Supports majority of model architectures via ONNX](../../configuration/object_detectors#onnx-supported-models)
|
||||
- Runs well with any size models including large
|
||||
|
||||
- <CommunityBadge /> [Jetson](#nvidia-jetson): Jetson devices are supported via the TensorRT or ONNX detectors when running Jetpack 6.
|
||||
|
||||
**Rockchip** <CommunityBadge />
|
||||
**Rockchip**
|
||||
|
||||
- [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs to provide efficient object detection.
|
||||
- [Supports limited model architectures](../../configuration/object_detectors#choosing-a-model)
|
||||
- Runs best with tiny or small size models
|
||||
- Runs efficiently on low power hardware
|
||||
|
||||
**Synaptics** <CommunityBadge />
|
||||
**Synaptics**
|
||||
|
||||
- [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs to provide efficient object detection.
|
||||
|
||||
:::
|
||||
|
||||
### Synaptics
|
||||
|
||||
- **Synaptics** Default model is **mobilenet**
|
||||
|
||||
| Name | Synaptics SL1680 Inference Time |
|
||||
| ---------------- | ------------------------------- |
|
||||
| ssd mobilenet | ~ 25 ms |
|
||||
| yolov5m | ~ 118 ms |
|
||||
|
||||
### Hailo-8
|
||||
|
||||
Frigate supports both the Hailo-8 and Hailo-8L AI Acceleration Modules on compatible hardware platforms—including the Raspberry Pi 5 with the PCIe hat from the AI kit. The Hailo detector integration in Frigate automatically identifies your hardware type and selects the appropriate default model when a custom model isn’t provided.
|
||||
@ -257,7 +261,7 @@ Inference speeds may vary depending on the host platform. The above data was mea
|
||||
|
||||
### Nvidia Jetson
|
||||
|
||||
Jetson devices are supported via the TensorRT or ONNX detectors when running Jetpack 6. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration_video#nvidia-jetson-orin-agx-orin-nx-orin-nano-xavier-agx-xavier-nx-tx2-tx1-nano) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets), and will make use of the Jetson's GPU and DLA for object detection when configured with the [TensorRT detector](/configuration/object_detectors#nvidia-tensorrt-detector).
|
||||
Frigate supports all Jetson boards, from the inexpensive Jetson Nano to the powerful Jetson Orin AGX. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration_video#nvidia-jetson-orin-agx-orin-nx-orin-nano-xavier-agx-xavier-nx-tx2-tx1-nano) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets), and will make use of the Jetson's GPU and DLA for object detection when configured with the [TensorRT detector](/configuration/object_detectors#nvidia-tensorrt-detector).
|
||||
|
||||
Inference speed will vary depending on the YOLO model, jetson platform and jetson nvpmodel (GPU/DLA/EMC clock speed). It is typically 20-40 ms for most models. The DLA is more efficient than the GPU, but not faster, so using the DLA will reduce power consumption but will slightly increase inference time.
|
||||
|
||||
@ -278,15 +282,6 @@ Frigate supports hardware video processing on all Rockchip boards. However, hard
|
||||
|
||||
The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms for yolo-nas s.
|
||||
|
||||
### Synaptics
|
||||
|
||||
- **Synaptics** Default model is **mobilenet**
|
||||
|
||||
| Name | Synaptics SL1680 Inference Time |
|
||||
| ------------- | ------------------------------- |
|
||||
| ssd mobilenet | ~ 25 ms |
|
||||
| yolov5m | ~ 118 ms |
|
||||
|
||||
## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
|
||||
|
||||
This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.
|
||||
|
||||
@ -159,44 +159,11 @@ Message published for updates to tracked object metadata, for example:
|
||||
}
|
||||
```
|
||||
|
||||
#### Object Classification Update
|
||||
|
||||
Message published when [object classification](/configuration/custom_classification/object_classification) reaches consensus on a classification result.
|
||||
|
||||
**Sub label type:**
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "classification",
|
||||
"id": "1607123955.475377-mxklsc",
|
||||
"camera": "front_door_cam",
|
||||
"timestamp": 1607123958.748393,
|
||||
"model": "person_classifier",
|
||||
"sub_label": "delivery_person",
|
||||
"score": 0.87
|
||||
}
|
||||
```
|
||||
|
||||
**Attribute type:**
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "classification",
|
||||
"id": "1607123955.475377-mxklsc",
|
||||
"camera": "front_door_cam",
|
||||
"timestamp": 1607123958.748393,
|
||||
"model": "helmet_detector",
|
||||
"attribute": "yes",
|
||||
"score": 0.92
|
||||
}
|
||||
```
|
||||
|
||||
### `frigate/reviews`
|
||||
|
||||
Message published for each changed review item. The first message is published when the `detection` or `alert` is initiated.
|
||||
|
||||
An `update` with the same ID will be published when:
|
||||
|
||||
- The severity changes from `detection` to `alert`
|
||||
- Additional objects are detected
|
||||
- An object is recognized via face, lpr, etc.
|
||||
@ -341,11 +308,6 @@ Publishes transcribed text for audio detected on this camera.
|
||||
|
||||
**NOTE:** Requires audio detection and transcription to be enabled
|
||||
|
||||
### `frigate/<camera_name>/classification/<model_name>`
|
||||
|
||||
Publishes the current state detected by a state classification model for the camera. The topic name includes the model name as configured in your classification settings.
|
||||
The published value is the detected state class name (e.g., `open`, `closed`, `on`, `off`). The state is only published when it changes, helping to reduce unnecessary MQTT traffic.
|
||||
|
||||
### `frigate/<camera_name>/enabled/set`
|
||||
|
||||
Topic to turn Frigate's processing of a camera on and off. Expected values are `ON` and `OFF`.
|
||||
|
||||
@ -10,7 +10,7 @@ const config: Config = {
|
||||
baseUrl: "/",
|
||||
onBrokenLinks: "throw",
|
||||
onBrokenMarkdownLinks: "warn",
|
||||
favicon: "img/branding/favicon.ico",
|
||||
favicon: "img/favicon.ico",
|
||||
organizationName: "blakeblackshear",
|
||||
projectName: "frigate",
|
||||
themes: [
|
||||
@ -116,8 +116,8 @@ const config: Config = {
|
||||
title: "Frigate",
|
||||
logo: {
|
||||
alt: "Frigate",
|
||||
src: "img/branding/logo.svg",
|
||||
srcDark: "img/branding/logo-dark.svg",
|
||||
src: "img/logo.svg",
|
||||
srcDark: "img/logo-dark.svg",
|
||||
},
|
||||
items: [
|
||||
{
|
||||
@ -170,7 +170,7 @@ const config: Config = {
|
||||
],
|
||||
},
|
||||
],
|
||||
copyright: `Copyright © ${new Date().getFullYear()} Frigate LLC`,
|
||||
copyright: `Copyright © ${new Date().getFullYear()} Blake Blackshear`,
|
||||
},
|
||||
},
|
||||
plugins: [
|
||||
|
||||
@ -1,23 +0,0 @@
|
||||
import React from "react";
|
||||
|
||||
export default function CommunityBadge() {
|
||||
return (
|
||||
<span
|
||||
title="This detector is maintained by community members who provide code, maintenance, and support. See the contributing boards documentation for more information."
|
||||
style={{
|
||||
display: "inline-block",
|
||||
backgroundColor: "#f1f3f5",
|
||||
color: "#24292f",
|
||||
fontSize: "11px",
|
||||
fontWeight: 600,
|
||||
padding: "2px 6px",
|
||||
borderRadius: "3px",
|
||||
border: "1px solid #d1d9e0",
|
||||
marginLeft: "4px",
|
||||
cursor: "help",
|
||||
}}
|
||||
>
|
||||
Community Supported
|
||||
</span>
|
||||
);
|
||||
}
|
||||
30
docs/static/img/branding/LICENSE.md
vendored
30
docs/static/img/branding/LICENSE.md
vendored
@ -1,30 +0,0 @@
|
||||
# COPYRIGHT AND TRADEMARK NOTICE
|
||||
|
||||
The images, logos, and icons contained in this directory (the "Brand Assets") are
|
||||
proprietary to Frigate LLC and are NOT covered by the MIT License governing the
|
||||
rest of this repository.
|
||||
|
||||
1. TRADEMARK STATUS
|
||||
The "Frigate" name and the accompanying logo are common law trademarks™ of
|
||||
Frigate LLC. Frigate LLC reserves all rights to these marks.
|
||||
|
||||
2. LIMITED PERMISSION FOR USE
|
||||
Permission is hereby granted to display these Brand Assets strictly for the
|
||||
following purposes:
|
||||
a. To execute the software interface on a local machine.
|
||||
b. To identify the software in documentation or reviews (nominative use).
|
||||
|
||||
3. RESTRICTIONS
|
||||
You may NOT:
|
||||
a. Use these Brand Assets to represent a derivative work (fork) as an official
|
||||
product of Frigate LLC.
|
||||
b. Use these Brand Assets in a way that implies endorsement, sponsorship, or
|
||||
commercial affiliation with Frigate LLC.
|
||||
c. Modify or alter the Brand Assets.
|
||||
|
||||
If you fork this repository with the intent to distribute a modified or competing
|
||||
version of the software, you must replace these Brand Assets with your own
|
||||
original content.
|
||||
|
||||
ALL RIGHTS RESERVED.
|
||||
Copyright (c) 2025 Frigate LLC.
|
||||
|
Before Width: | Height: | Size: 15 KiB After Width: | Height: | Size: 15 KiB |
|
Before Width: | Height: | Size: 12 KiB After Width: | Height: | Size: 12 KiB |
|
Before Width: | Height: | Size: 936 B After Width: | Height: | Size: 936 B |
|
Before Width: | Height: | Size: 933 B After Width: | Height: | Size: 933 B |
@ -849,7 +849,6 @@ async def vod_ts(camera_name: str, start_ts: float, end_ts: float):
|
||||
|
||||
clips = []
|
||||
durations = []
|
||||
min_duration_ms = 100 # Minimum 100ms to ensure at least one video frame
|
||||
max_duration_ms = MAX_SEGMENT_DURATION * 1000
|
||||
|
||||
recording: Recordings
|
||||
@ -867,11 +866,11 @@ async def vod_ts(camera_name: str, start_ts: float, end_ts: float):
|
||||
if recording.end_time > end_ts:
|
||||
duration -= int((recording.end_time - end_ts) * 1000)
|
||||
|
||||
if duration < min_duration_ms:
|
||||
# skip if the clip has no valid duration (too short to contain frames)
|
||||
if duration <= 0:
|
||||
# skip if the clip has no valid duration
|
||||
continue
|
||||
|
||||
if min_duration_ms <= duration < max_duration_ms:
|
||||
if 0 < duration < max_duration_ms:
|
||||
clip["keyFrameDurations"] = [duration]
|
||||
clips.append(clip)
|
||||
durations.append(duration)
|
||||
|
||||
@ -792,10 +792,6 @@ class FrigateConfig(FrigateBaseModel):
|
||||
# copy over auth and proxy config in case auth needs to be enforced
|
||||
safe_config["auth"] = config.get("auth", {})
|
||||
safe_config["proxy"] = config.get("proxy", {})
|
||||
|
||||
# copy over database config for auth and so a new db is not created
|
||||
safe_config["database"] = config.get("database", {})
|
||||
|
||||
return cls.parse_object(safe_config, **context)
|
||||
|
||||
# Validate and return the config dict.
|
||||
|
||||
@ -1,7 +1,6 @@
|
||||
"""Real time processor that works with classification tflite models."""
|
||||
|
||||
import datetime
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
from typing import Any
|
||||
@ -22,7 +21,6 @@ from frigate.config.classification import (
|
||||
)
|
||||
from frigate.const import CLIPS_DIR, MODEL_CACHE_DIR
|
||||
from frigate.log import redirect_output_to_logger
|
||||
from frigate.types import TrackedObjectUpdateTypesEnum
|
||||
from frigate.util.builtin import EventsPerSecond, InferenceSpeed, load_labels
|
||||
from frigate.util.object import box_overlaps, calculate_region
|
||||
|
||||
@ -286,7 +284,6 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
|
||||
config: FrigateConfig,
|
||||
model_config: CustomClassificationConfig,
|
||||
sub_label_publisher: EventMetadataPublisher,
|
||||
requestor: InterProcessRequestor,
|
||||
metrics: DataProcessorMetrics,
|
||||
):
|
||||
super().__init__(config, metrics)
|
||||
@ -295,7 +292,6 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
|
||||
self.train_dir = os.path.join(CLIPS_DIR, self.model_config.name, "train")
|
||||
self.interpreter: Interpreter | None = None
|
||||
self.sub_label_publisher = sub_label_publisher
|
||||
self.requestor = requestor
|
||||
self.tensor_input_details: dict[str, Any] | None = None
|
||||
self.tensor_output_details: dict[str, Any] | None = None
|
||||
self.classification_history: dict[str, list[tuple[str, float, float]]] = {}
|
||||
@ -490,8 +486,6 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
|
||||
)
|
||||
|
||||
if consensus_label is not None:
|
||||
camera = obj_data["camera"]
|
||||
|
||||
if (
|
||||
self.model_config.object_config.classification_type
|
||||
== ObjectClassificationType.sub_label
|
||||
@ -500,20 +494,6 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
|
||||
(object_id, consensus_label, consensus_score),
|
||||
EventMetadataTypeEnum.sub_label,
|
||||
)
|
||||
self.requestor.send_data(
|
||||
"tracked_object_update",
|
||||
json.dumps(
|
||||
{
|
||||
"type": TrackedObjectUpdateTypesEnum.classification,
|
||||
"id": object_id,
|
||||
"camera": camera,
|
||||
"timestamp": now,
|
||||
"model": self.model_config.name,
|
||||
"sub_label": consensus_label,
|
||||
"score": consensus_score,
|
||||
}
|
||||
),
|
||||
)
|
||||
elif (
|
||||
self.model_config.object_config.classification_type
|
||||
== ObjectClassificationType.attribute
|
||||
@ -527,20 +507,6 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
|
||||
),
|
||||
EventMetadataTypeEnum.attribute.value,
|
||||
)
|
||||
self.requestor.send_data(
|
||||
"tracked_object_update",
|
||||
json.dumps(
|
||||
{
|
||||
"type": TrackedObjectUpdateTypesEnum.classification,
|
||||
"id": object_id,
|
||||
"camera": camera,
|
||||
"timestamp": now,
|
||||
"model": self.model_config.name,
|
||||
"attribute": consensus_label,
|
||||
"score": consensus_score,
|
||||
}
|
||||
),
|
||||
)
|
||||
|
||||
def handle_request(self, topic, request_data):
|
||||
if topic == EmbeddingsRequestEnum.reload_classification_model.value:
|
||||
|
||||
@ -18,6 +18,7 @@ from frigate.detectors.detector_config import (
|
||||
ModelTypeEnum,
|
||||
)
|
||||
from frigate.util.file import FileLock
|
||||
from frigate.util.model import post_process_yolo
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@ -177,6 +178,13 @@ class MemryXDetector(DetectionApi):
|
||||
logger.error(f"Failed to initialize MemryX model: {e}")
|
||||
raise
|
||||
|
||||
def load_yolo_constants(self):
|
||||
base = f"{self.cache_dir}/{self.model_folder}"
|
||||
# constants for yolov9 post-processing
|
||||
self.const_A = np.load(f"{base}/_model_22_Constant_9_output_0.npy")
|
||||
self.const_B = np.load(f"{base}/_model_22_Constant_10_output_0.npy")
|
||||
self.const_C = np.load(f"{base}/_model_22_Constant_12_output_0.npy")
|
||||
|
||||
def check_and_prepare_model(self):
|
||||
if not os.path.exists(self.cache_dir):
|
||||
os.makedirs(self.cache_dir, exist_ok=True)
|
||||
@ -228,6 +236,7 @@ class MemryXDetector(DetectionApi):
|
||||
|
||||
# Handle post model requirements by model type
|
||||
if self.memx_model_type in [
|
||||
ModelTypeEnum.yologeneric,
|
||||
ModelTypeEnum.yolonas,
|
||||
ModelTypeEnum.ssd,
|
||||
]:
|
||||
@ -236,10 +245,7 @@ class MemryXDetector(DetectionApi):
|
||||
f"No *_post.onnx file found in custom model zip for {self.memx_model_type.name}."
|
||||
)
|
||||
self.memx_post_model = post_candidates[0]
|
||||
elif self.memx_model_type in [
|
||||
ModelTypeEnum.yolox,
|
||||
ModelTypeEnum.yologeneric,
|
||||
]:
|
||||
elif self.memx_model_type == ModelTypeEnum.yolox:
|
||||
# Explicitly ignore any post model even if present
|
||||
self.memx_post_model = None
|
||||
else:
|
||||
@ -267,6 +273,8 @@ class MemryXDetector(DetectionApi):
|
||||
logger.info("Using cached models.")
|
||||
self.memx_model_path = dfp_path
|
||||
self.memx_post_model = post_path
|
||||
if self.memx_model_type == ModelTypeEnum.yologeneric:
|
||||
self.load_yolo_constants()
|
||||
return
|
||||
|
||||
# ---------- CASE 3: download MemryX model (no cache) ----------
|
||||
@ -295,6 +303,9 @@ class MemryXDetector(DetectionApi):
|
||||
else None
|
||||
)
|
||||
|
||||
if self.memx_model_type == ModelTypeEnum.yologeneric:
|
||||
self.load_yolo_constants()
|
||||
|
||||
finally:
|
||||
if os.path.exists(zip_path):
|
||||
try:
|
||||
@ -589,232 +600,127 @@ class MemryXDetector(DetectionApi):
|
||||
|
||||
self.output_queue.put(final_detections)
|
||||
|
||||
def _generate_anchors(self, sizes=[80, 40, 20]):
|
||||
"""Generate anchor points for YOLOv9 style processing"""
|
||||
yscales = []
|
||||
xscales = []
|
||||
for s in sizes:
|
||||
r = np.arange(s) + 0.5
|
||||
yscales.append(np.repeat(r, s))
|
||||
xscales.append(np.repeat(r[None, ...], s, axis=0).flatten())
|
||||
|
||||
yscales = np.concatenate(yscales)
|
||||
xscales = np.concatenate(xscales)
|
||||
anchors = np.stack([xscales, yscales], axis=1)
|
||||
return anchors
|
||||
|
||||
def _generate_scales(self, sizes=[80, 40, 20]):
|
||||
"""Generate scaling factors for each detection level"""
|
||||
factors = [8, 16, 32]
|
||||
s = np.concatenate([np.ones([int(s * s)]) * f for s, f in zip(sizes, factors)])
|
||||
return s[:, None]
|
||||
|
||||
@staticmethod
|
||||
def _softmax(x: np.ndarray, axis: int) -> np.ndarray:
|
||||
"""Efficient softmax implementation"""
|
||||
x = x - np.max(x, axis=axis, keepdims=True)
|
||||
np.exp(x, out=x)
|
||||
x /= np.sum(x, axis=axis, keepdims=True)
|
||||
return x
|
||||
|
||||
def dfl(self, x: np.ndarray) -> np.ndarray:
|
||||
"""Distribution Focal Loss decoding - YOLOv9 style"""
|
||||
x = x.reshape(-1, 4, 16)
|
||||
weights = np.arange(16, dtype=np.float32)
|
||||
p = self._softmax(x, axis=2)
|
||||
p = p * weights[None, None, :]
|
||||
out = np.sum(p, axis=2, keepdims=False)
|
||||
return out
|
||||
|
||||
def dist2bbox(
|
||||
self, x: np.ndarray, anchors: np.ndarray, scales: np.ndarray
|
||||
def onnx_reshape_with_allowzero(
|
||||
self, data: np.ndarray, shape: np.ndarray, allowzero: int = 0
|
||||
) -> np.ndarray:
|
||||
"""Convert distances to bounding boxes - YOLOv9 style"""
|
||||
lt = x[:, :2]
|
||||
rb = x[:, 2:]
|
||||
shape = shape.astype(int)
|
||||
input_shape = data.shape
|
||||
output_shape = []
|
||||
|
||||
x1y1 = anchors - lt
|
||||
x2y2 = anchors + rb
|
||||
for i, dim in enumerate(shape):
|
||||
if dim == 0 and allowzero == 0:
|
||||
output_shape.append(input_shape[i]) # Copy dimension from input
|
||||
else:
|
||||
output_shape.append(dim)
|
||||
|
||||
wh = x2y2 - x1y1
|
||||
c_xy = (x1y1 + x2y2) / 2
|
||||
# Now let NumPy infer any -1 if needed
|
||||
reshaped = np.reshape(data, output_shape)
|
||||
|
||||
out = np.concatenate([c_xy, wh], axis=1)
|
||||
out = out * scales
|
||||
return out
|
||||
|
||||
def post_process_yolo_optimized(self, outputs):
|
||||
"""
|
||||
Custom YOLOv9 post-processing optimized for MemryX ONNX outputs.
|
||||
Implements DFL decoding, confidence filtering, and NMS in pure NumPy.
|
||||
"""
|
||||
# YOLOv9 outputs: 6 outputs (lbox, lcls, mbox, mcls, sbox, scls)
|
||||
conv_out1, conv_out2, conv_out3, conv_out4, conv_out5, conv_out6 = outputs
|
||||
|
||||
# Determine grid sizes based on input resolution
|
||||
# YOLOv9 uses 3 detection heads with strides [8, 16, 32]
|
||||
# Grid sizes = input_size / stride
|
||||
sizes = [
|
||||
self.memx_model_height
|
||||
// 8, # Large objects (e.g., 80 for 640x640, 40 for 320x320)
|
||||
self.memx_model_height
|
||||
// 16, # Medium objects (e.g., 40 for 640x640, 20 for 320x320)
|
||||
self.memx_model_height
|
||||
// 32, # Small objects (e.g., 20 for 640x640, 10 for 320x320)
|
||||
]
|
||||
|
||||
# Generate anchors and scales if not already done
|
||||
if not hasattr(self, "anchors"):
|
||||
self.anchors = self._generate_anchors(sizes)
|
||||
self.scales = self._generate_scales(sizes)
|
||||
|
||||
# Process outputs in YOLOv9 format: reshape and moveaxis for ONNX format
|
||||
lbox = np.moveaxis(conv_out1, 1, -1) # Large boxes
|
||||
lcls = np.moveaxis(conv_out2, 1, -1) # Large classes
|
||||
mbox = np.moveaxis(conv_out3, 1, -1) # Medium boxes
|
||||
mcls = np.moveaxis(conv_out4, 1, -1) # Medium classes
|
||||
sbox = np.moveaxis(conv_out5, 1, -1) # Small boxes
|
||||
scls = np.moveaxis(conv_out6, 1, -1) # Small classes
|
||||
|
||||
# Determine number of classes dynamically from the class output shape
|
||||
# lcls shape should be (batch, height, width, num_classes)
|
||||
num_classes = lcls.shape[-1]
|
||||
|
||||
# Validate that all class outputs have the same number of classes
|
||||
if not (mcls.shape[-1] == num_classes and scls.shape[-1] == num_classes):
|
||||
raise ValueError(
|
||||
f"Class output shapes mismatch: lcls={lcls.shape}, mcls={mcls.shape}, scls={scls.shape}"
|
||||
)
|
||||
|
||||
# Concatenate boxes and classes
|
||||
boxes = np.concatenate(
|
||||
[
|
||||
lbox.reshape(-1, 64), # 64 is for 4 bbox coords * 16 DFL bins
|
||||
mbox.reshape(-1, 64),
|
||||
sbox.reshape(-1, 64),
|
||||
],
|
||||
axis=0,
|
||||
)
|
||||
|
||||
classes = np.concatenate(
|
||||
[
|
||||
lcls.reshape(-1, num_classes),
|
||||
mcls.reshape(-1, num_classes),
|
||||
scls.reshape(-1, num_classes),
|
||||
],
|
||||
axis=0,
|
||||
)
|
||||
|
||||
# Apply sigmoid to classes
|
||||
classes = self.sigmoid(classes)
|
||||
|
||||
# Apply DFL to box predictions
|
||||
boxes = self.dfl(boxes)
|
||||
|
||||
# YOLOv9 postprocessing with confidence filtering and NMS
|
||||
confidence_thres = 0.4
|
||||
iou_thres = 0.6
|
||||
|
||||
# Find the class with the highest score for each detection
|
||||
max_scores = np.max(classes, axis=1) # Maximum class score for each detection
|
||||
class_ids = np.argmax(classes, axis=1) # Index of the best class
|
||||
|
||||
# Filter out detections with scores below the confidence threshold
|
||||
valid_indices = np.where(max_scores >= confidence_thres)[0]
|
||||
if len(valid_indices) == 0:
|
||||
# Return empty detections array
|
||||
final_detections = np.zeros((20, 6), np.float32)
|
||||
return final_detections
|
||||
|
||||
# Select only valid detections
|
||||
valid_boxes = boxes[valid_indices]
|
||||
valid_class_ids = class_ids[valid_indices]
|
||||
valid_scores = max_scores[valid_indices]
|
||||
|
||||
# Convert distances to actual bounding boxes using anchors and scales
|
||||
valid_boxes = self.dist2bbox(
|
||||
valid_boxes, self.anchors[valid_indices], self.scales[valid_indices]
|
||||
)
|
||||
|
||||
# Convert bounding box coordinates from (x_center, y_center, w, h) to (x_min, y_min, x_max, y_max)
|
||||
x_center, y_center, width, height = (
|
||||
valid_boxes[:, 0],
|
||||
valid_boxes[:, 1],
|
||||
valid_boxes[:, 2],
|
||||
valid_boxes[:, 3],
|
||||
)
|
||||
x_min = x_center - width / 2
|
||||
y_min = y_center - height / 2
|
||||
x_max = x_center + width / 2
|
||||
y_max = y_center + height / 2
|
||||
|
||||
# Convert to format expected by cv2.dnn.NMSBoxes: [x, y, width, height]
|
||||
boxes_for_nms = []
|
||||
scores_for_nms = []
|
||||
|
||||
for i in range(len(valid_indices)):
|
||||
# Ensure coordinates are within bounds and positive
|
||||
x_min_clipped = max(0, x_min[i])
|
||||
y_min_clipped = max(0, y_min[i])
|
||||
x_max_clipped = min(self.memx_model_width, x_max[i])
|
||||
y_max_clipped = min(self.memx_model_height, y_max[i])
|
||||
|
||||
width_clipped = x_max_clipped - x_min_clipped
|
||||
height_clipped = y_max_clipped - y_min_clipped
|
||||
|
||||
if width_clipped > 0 and height_clipped > 0:
|
||||
boxes_for_nms.append(
|
||||
[x_min_clipped, y_min_clipped, width_clipped, height_clipped]
|
||||
)
|
||||
scores_for_nms.append(float(valid_scores[i]))
|
||||
|
||||
final_detections = np.zeros((20, 6), np.float32)
|
||||
|
||||
if len(boxes_for_nms) == 0:
|
||||
return final_detections
|
||||
|
||||
# Apply NMS using OpenCV
|
||||
indices = cv2.dnn.NMSBoxes(
|
||||
boxes_for_nms, scores_for_nms, confidence_thres, iou_thres
|
||||
)
|
||||
|
||||
if len(indices) > 0:
|
||||
# Flatten indices if they are returned as a list of arrays
|
||||
if isinstance(indices[0], list) or isinstance(indices[0], np.ndarray):
|
||||
indices = [i[0] for i in indices]
|
||||
|
||||
# Limit to top 20 detections
|
||||
indices = indices[:20]
|
||||
|
||||
# Convert to Frigate format: [class_id, confidence, y_min, x_min, y_max, x_max] (normalized)
|
||||
for i, idx in enumerate(indices):
|
||||
class_id = valid_class_ids[idx]
|
||||
confidence = valid_scores[idx]
|
||||
|
||||
# Get the box coordinates
|
||||
box = boxes_for_nms[idx]
|
||||
x_min_norm = box[0] / self.memx_model_width
|
||||
y_min_norm = box[1] / self.memx_model_height
|
||||
x_max_norm = (box[0] + box[2]) / self.memx_model_width
|
||||
y_max_norm = (box[1] + box[3]) / self.memx_model_height
|
||||
|
||||
final_detections[i] = [
|
||||
class_id,
|
||||
confidence,
|
||||
y_min_norm, # Frigate expects y_min first
|
||||
x_min_norm,
|
||||
y_max_norm,
|
||||
x_max_norm,
|
||||
]
|
||||
|
||||
return final_detections
|
||||
return reshaped
|
||||
|
||||
def process_output(self, *outputs):
|
||||
"""Output callback function -- receives frames from the MX3 and triggers post-processing"""
|
||||
if self.memx_model_type == ModelTypeEnum.yologeneric:
|
||||
# Use complete YOLOv9-style postprocessing (includes NMS)
|
||||
final_detections = self.post_process_yolo_optimized(outputs)
|
||||
if not self.memx_post_model:
|
||||
conv_out1 = outputs[0]
|
||||
conv_out2 = outputs[1]
|
||||
conv_out3 = outputs[2]
|
||||
conv_out4 = outputs[3]
|
||||
conv_out5 = outputs[4]
|
||||
conv_out6 = outputs[5]
|
||||
|
||||
concat_1 = self.onnx_concat([conv_out1, conv_out2], axis=1)
|
||||
concat_2 = self.onnx_concat([conv_out3, conv_out4], axis=1)
|
||||
concat_3 = self.onnx_concat([conv_out5, conv_out6], axis=1)
|
||||
|
||||
shape = np.array([1, 144, -1], dtype=np.int64)
|
||||
|
||||
reshaped_1 = self.onnx_reshape_with_allowzero(
|
||||
concat_1, shape, allowzero=0
|
||||
)
|
||||
reshaped_2 = self.onnx_reshape_with_allowzero(
|
||||
concat_2, shape, allowzero=0
|
||||
)
|
||||
reshaped_3 = self.onnx_reshape_with_allowzero(
|
||||
concat_3, shape, allowzero=0
|
||||
)
|
||||
|
||||
concat_4 = self.onnx_concat([reshaped_1, reshaped_2, reshaped_3], 2)
|
||||
|
||||
axis = 1
|
||||
split_sizes = [64, 80]
|
||||
|
||||
# Calculate indices at which to split
|
||||
indices = np.cumsum(split_sizes)[
|
||||
:-1
|
||||
] # [64] — split before the second chunk
|
||||
|
||||
# Perform split along axis 1
|
||||
split_0, split_1 = np.split(concat_4, indices, axis=axis)
|
||||
|
||||
num_boxes = 2100 if self.memx_model_height == 320 else 8400
|
||||
shape1 = np.array([1, 4, 16, num_boxes])
|
||||
reshape_4 = self.onnx_reshape_with_allowzero(
|
||||
split_0, shape1, allowzero=0
|
||||
)
|
||||
|
||||
transpose_1 = reshape_4.transpose(0, 2, 1, 3)
|
||||
|
||||
axis = 1 # As per ONNX softmax node
|
||||
|
||||
# Subtract max for numerical stability
|
||||
x_max = np.max(transpose_1, axis=axis, keepdims=True)
|
||||
x_exp = np.exp(transpose_1 - x_max)
|
||||
x_sum = np.sum(x_exp, axis=axis, keepdims=True)
|
||||
softmax_output = x_exp / x_sum
|
||||
|
||||
# Weight W from the ONNX initializer (1, 16, 1, 1) with values 0 to 15
|
||||
W = np.arange(16, dtype=np.float32).reshape(
|
||||
1, 16, 1, 1
|
||||
) # (1, 16, 1, 1)
|
||||
|
||||
# Apply 1x1 convolution: this is a weighted sum over channels
|
||||
conv_output = np.sum(
|
||||
softmax_output * W, axis=1, keepdims=True
|
||||
) # shape: (1, 1, 4, 8400)
|
||||
|
||||
shape2 = np.array([1, 4, num_boxes])
|
||||
reshape_5 = self.onnx_reshape_with_allowzero(
|
||||
conv_output, shape2, allowzero=0
|
||||
)
|
||||
|
||||
# ONNX Slice — get first 2 channels: [0:2] along axis 1
|
||||
slice_output1 = reshape_5[:, 0:2, :] # Result: (1, 2, 8400)
|
||||
|
||||
# Slice channels 2 to 4 → axis = 1
|
||||
slice_output2 = reshape_5[:, 2:4, :]
|
||||
|
||||
# Perform Subtraction
|
||||
sub_output = self.const_A - slice_output1 # Equivalent to ONNX Sub
|
||||
|
||||
# Perform the ONNX-style Add
|
||||
add_output = self.const_B + slice_output2
|
||||
|
||||
sub1 = add_output - sub_output
|
||||
|
||||
add1 = sub_output + add_output
|
||||
|
||||
div_output = add1 / 2.0
|
||||
|
||||
concat_5 = self.onnx_concat([div_output, sub1], axis=1)
|
||||
|
||||
# Expand B to (1, 1, 8400) so it can broadcast across axis=1 (4 channels)
|
||||
const_C_expanded = self.const_C[:, np.newaxis, :] # Shape: (1, 1, 8400)
|
||||
|
||||
# Perform ONNX-style element-wise multiplication
|
||||
mul_output = concat_5 * const_C_expanded # Result: (1, 4, 8400)
|
||||
|
||||
sigmoid_output = self.sigmoid(split_1)
|
||||
outputs = self.onnx_concat([mul_output, sigmoid_output], axis=1)
|
||||
|
||||
final_detections = post_process_yolo(
|
||||
outputs, self.memx_model_width, self.memx_model_height
|
||||
)
|
||||
self.output_queue.put(final_detections)
|
||||
|
||||
elif self.memx_model_type == ModelTypeEnum.yolonas:
|
||||
|
||||
@ -195,7 +195,6 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
self.config,
|
||||
model_config,
|
||||
self.event_metadata_publisher,
|
||||
self.requestor,
|
||||
self.metrics,
|
||||
)
|
||||
)
|
||||
@ -340,7 +339,6 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
self.config,
|
||||
model_config,
|
||||
self.event_metadata_publisher,
|
||||
self.requestor,
|
||||
self.metrics,
|
||||
)
|
||||
|
||||
|
||||
@ -30,4 +30,3 @@ class TrackedObjectUpdateTypesEnum(str, Enum):
|
||||
description = "description"
|
||||
face = "face"
|
||||
lpr = "lpr"
|
||||
classification = "classification"
|
||||
|
||||
@ -130,13 +130,8 @@ def get_soc_type() -> Optional[str]:
|
||||
"""Get the SoC type from device tree."""
|
||||
try:
|
||||
with open("/proc/device-tree/compatible") as file:
|
||||
content = file.read()
|
||||
|
||||
# Check for Jetson devices
|
||||
if "nvidia" in content:
|
||||
return None
|
||||
|
||||
return content.split(",")[-1].strip("\x00")
|
||||
soc = file.read().split(",")[-1].strip("\x00")
|
||||
return soc
|
||||
except FileNotFoundError:
|
||||
logger.debug("Could not determine SoC type from device tree")
|
||||
return None
|
||||
|
||||
|
Before Width: | Height: | Size: 3.9 KiB After Width: | Height: | Size: 3.9 KiB |
@ -1,33 +0,0 @@
|
||||
# COPYRIGHT AND TRADEMARK NOTICE
|
||||
|
||||
The images, logos, and icons contained in this directory (the "Brand Assets") are
|
||||
proprietary to Frigate LLC and are NOT covered by the MIT License governing the
|
||||
rest of this repository.
|
||||
|
||||
1. TRADEMARK STATUS
|
||||
The "Frigate" name and the accompanying logo are common law trademarks™ of
|
||||
Frigate LLC. Frigate LLC reserves all rights to these marks.
|
||||
|
||||
2. LIMITED PERMISSION FOR USE
|
||||
Permission is hereby granted to display these Brand Assets strictly for the
|
||||
following purposes:
|
||||
a. To execute the software interface on a local machine.
|
||||
b. To identify the software in documentation or reviews (nominative use).
|
||||
|
||||
3. RESTRICTIONS
|
||||
You may NOT:
|
||||
a. Use these Brand Assets to represent a derivative work (fork) as an official
|
||||
product of Frigate LLC.
|
||||
b. Use these Brand Assets in a way that implies endorsement, sponsorship, or
|
||||
commercial affiliation with Frigate LLC.
|
||||
c. Modify or alter the Brand Assets.
|
||||
|
||||
If you fork this repository with the intent to distribute a modified or competing
|
||||
version of the software, you must replace these Brand Assets with your own
|
||||
original content.
|
||||
|
||||
For full usage guidelines, strictly see the TRADEMARK.md file in the
|
||||
repository root.
|
||||
|
||||
ALL RIGHTS RESERVED.
|
||||
Copyright (c) 2025 Frigate LLC.
|
||||
|
Before Width: | Height: | Size: 558 B After Width: | Height: | Size: 558 B |
|
Before Width: | Height: | Size: 800 B After Width: | Height: | Size: 800 B |
|
Before Width: | Height: | Size: 15 KiB After Width: | Height: | Size: 15 KiB |
|
Before Width: | Height: | Size: 12 KiB After Width: | Height: | Size: 12 KiB |
|
Before Width: | Height: | Size: 2.9 KiB After Width: | Height: | Size: 2.9 KiB |
|
Before Width: | Height: | Size: 2.6 KiB After Width: | Height: | Size: 2.6 KiB |
@ -2,29 +2,29 @@
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8" />
|
||||
<link rel="icon" href="/images/branding/favicon.ico" />
|
||||
<link rel="icon" href="/images/favicon.ico" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Frigate</title>
|
||||
<link
|
||||
rel="apple-touch-icon"
|
||||
sizes="180x180"
|
||||
href="/images/branding/apple-touch-icon.png"
|
||||
href="/images/apple-touch-icon.png"
|
||||
/>
|
||||
<link
|
||||
rel="icon"
|
||||
type="image/png"
|
||||
sizes="32x32"
|
||||
href="/images/branding/favicon-32x32.png"
|
||||
href="/images/favicon-32x32.png"
|
||||
/>
|
||||
<link
|
||||
rel="icon"
|
||||
type="image/png"
|
||||
sizes="16x16"
|
||||
href="/images/branding/favicon-16x16.png"
|
||||
href="/images/favicon-16x16.png"
|
||||
/>
|
||||
<link rel="icon" type="image/svg+xml" href="/images/branding/favicon.svg" />
|
||||
<link rel="icon" type="image/svg+xml" href="/images/favicon.svg" />
|
||||
<link rel="manifest" href="/site.webmanifest" crossorigin="use-credentials" />
|
||||
<link rel="mask-icon" href="/images/branding/favicon.svg" color="#3b82f7" />
|
||||
<link rel="mask-icon" href="/images/favicon.svg" color="#3b82f7" />
|
||||
<meta name="theme-color" content="#ffffff" media="(prefers-color-scheme: light)" />
|
||||
<meta name="theme-color" content="#000000" media="(prefers-color-scheme: dark)" />
|
||||
</head>
|
||||
|
||||
@ -2,29 +2,29 @@
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8" />
|
||||
<link rel="icon" href="/images/branding/favicon.ico" />
|
||||
<link rel="icon" href="/images/favicon.ico" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Frigate</title>
|
||||
<link
|
||||
rel="apple-touch-icon"
|
||||
sizes="180x180"
|
||||
href="/images/branding/apple-touch-icon.png"
|
||||
href="/images/apple-touch-icon.png"
|
||||
/>
|
||||
<link
|
||||
rel="icon"
|
||||
type="image/png"
|
||||
sizes="32x32"
|
||||
href="/images/branding/favicon-32x32.png"
|
||||
href="/images/favicon-32x32.png"
|
||||
/>
|
||||
<link
|
||||
rel="icon"
|
||||
type="image/png"
|
||||
sizes="16x16"
|
||||
href="/images/branding/favicon-16x16.png"
|
||||
href="/images/favicon-16x16.png"
|
||||
/>
|
||||
<link rel="icon" type="image/svg+xml" href="/images/branding/favicon.svg" />
|
||||
<link rel="icon" type="image/svg+xml" href="/images/favicon.svg" />
|
||||
<link rel="manifest" href="/site.webmanifest" crossorigin="use-credentials" />
|
||||
<link rel="mask-icon" href="/images/branding/favicon.svg" color="#3b82f7" />
|
||||
<link rel="mask-icon" href="/images/favicon.svg" color="#3b82f7" />
|
||||
<meta name="theme-color" content="#ffffff" media="(prefers-color-scheme: light)" />
|
||||
<meta name="theme-color" content="#000000" media="(prefers-color-scheme: dark)" />
|
||||
</head>
|
||||
|
||||
@ -103,7 +103,7 @@
|
||||
"regenerate": "A new description has been requested from {{provider}}. Depending on the speed of your provider, the new description may take some time to regenerate.",
|
||||
"updatedSublabel": "Successfully updated sub label.",
|
||||
"updatedLPR": "Successfully updated license plate.",
|
||||
"audioTranscription": "Successfully requested audio transcription. Depending on the speed of your Frigate server, the transcription may take some time to complete."
|
||||
"audioTranscription": "Successfully requested audio transcription."
|
||||
},
|
||||
"error": {
|
||||
"regenerate": "Failed to call {{provider}} for a new description: {{errorMessage}}",
|
||||
|
||||
@ -76,12 +76,7 @@
|
||||
}
|
||||
},
|
||||
"npuUsage": "NPU Usage",
|
||||
"npuMemory": "NPU Memory",
|
||||
"intelGpuWarning": {
|
||||
"title": "Intel GPU Stats Warning",
|
||||
"message": "GPU stats unavailable",
|
||||
"description": "This is a known bug in Intel's GPU stats reporting tools (intel_gpu_top) where it will break and repeatedly return a GPU usage of 0% even in cases where hardware acceleration and object detection are correctly running on the (i)GPU. This is not a Frigate bug. You can restart the host to temporarily fix the issue and confirm that the GPU is working correctly. This does not affect performance."
|
||||
}
|
||||
"npuMemory": "NPU Memory"
|
||||
},
|
||||
"otherProcesses": {
|
||||
"title": "Other Processes",
|
||||
|
||||
@ -572,8 +572,9 @@ export function SortTypeContent({
|
||||
className="w-full space-y-1"
|
||||
>
|
||||
{availableSortTypes.map((value) => (
|
||||
<div key={value} className="flex flex-row gap-2">
|
||||
<div className="flex flex-row gap-2">
|
||||
<RadioGroupItem
|
||||
key={value}
|
||||
value={value}
|
||||
id={`sort-${value}`}
|
||||
className={
|
||||
|
||||
@ -42,10 +42,9 @@ export default function DetailActionsMenu({
|
||||
return `start/${startTime}/end/${endTime}`;
|
||||
}, [search]);
|
||||
|
||||
// currently, audio event ids are not saved in review items
|
||||
const { data: reviewItem } = useSWR<ReviewSegment>(
|
||||
search.data?.type === "audio" ? null : [`review/event/${search.id}`],
|
||||
);
|
||||
const { data: reviewItem } = useSWR<ReviewSegment>([
|
||||
`review/event/${search.id}`,
|
||||
]);
|
||||
|
||||
return (
|
||||
<DropdownMenu open={isOpen} onOpenChange={setIsOpen}>
|
||||
|
||||
@ -1295,7 +1295,6 @@ function ObjectDetailsTab({
|
||||
|
||||
{search.data.type === "object" &&
|
||||
config?.plus?.enabled &&
|
||||
search.end_time != undefined &&
|
||||
search.has_snapshot && (
|
||||
<div
|
||||
className={cn(
|
||||
|
||||
@ -56,7 +56,6 @@ export function TrackingDetails({
|
||||
const apiHost = useApiHost();
|
||||
const imgRef = useRef<HTMLImageElement | null>(null);
|
||||
const [imgLoaded, setImgLoaded] = useState(false);
|
||||
const [isVideoLoading, setIsVideoLoading] = useState(true);
|
||||
const [displaySource, _setDisplaySource] = useState<"video" | "image">(
|
||||
"video",
|
||||
);
|
||||
@ -71,10 +70,6 @@ export function TrackingDetails({
|
||||
(event.start_time ?? 0) + annotationOffset / 1000 - REVIEW_PADDING,
|
||||
);
|
||||
|
||||
useEffect(() => {
|
||||
setIsVideoLoading(true);
|
||||
}, [event.id]);
|
||||
|
||||
const { data: eventSequence } = useSWR<TrackingDetailsSequence[]>([
|
||||
"timeline",
|
||||
{
|
||||
@ -532,28 +527,22 @@ export function TrackingDetails({
|
||||
)}
|
||||
>
|
||||
{displaySource == "video" && (
|
||||
<>
|
||||
<HlsVideoPlayer
|
||||
videoRef={videoRef}
|
||||
containerRef={containerRef}
|
||||
visible={true}
|
||||
currentSource={videoSource}
|
||||
hotKeys={false}
|
||||
supportsFullscreen={false}
|
||||
fullscreen={false}
|
||||
frigateControls={true}
|
||||
onTimeUpdate={handleTimeUpdate}
|
||||
onSeekToTime={handleSeekToTime}
|
||||
onUploadFrame={onUploadFrameToPlus}
|
||||
onPlaying={() => setIsVideoLoading(false)}
|
||||
isDetailMode={true}
|
||||
camera={event.camera}
|
||||
currentTimeOverride={currentTime}
|
||||
/>
|
||||
{isVideoLoading && (
|
||||
<ActivityIndicator className="absolute left-1/2 top-1/2 -translate-x-1/2 -translate-y-1/2" />
|
||||
)}
|
||||
</>
|
||||
<HlsVideoPlayer
|
||||
videoRef={videoRef}
|
||||
containerRef={containerRef}
|
||||
visible={true}
|
||||
currentSource={videoSource}
|
||||
hotKeys={false}
|
||||
supportsFullscreen={false}
|
||||
fullscreen={false}
|
||||
frigateControls={true}
|
||||
onTimeUpdate={handleTimeUpdate}
|
||||
onSeekToTime={handleSeekToTime}
|
||||
onUploadFrame={onUploadFrameToPlus}
|
||||
isDetailMode={true}
|
||||
camera={event.camera}
|
||||
currentTimeOverride={currentTime}
|
||||
/>
|
||||
)}
|
||||
{displaySource == "image" && (
|
||||
<>
|
||||
|
||||
@ -94,52 +94,24 @@ export default function HlsVideoPlayer({
|
||||
const [loadedMetadata, setLoadedMetadata] = useState(false);
|
||||
const [bufferTimeout, setBufferTimeout] = useState<NodeJS.Timeout>();
|
||||
|
||||
const applyVideoDimensions = useCallback(
|
||||
(width: number, height: number) => {
|
||||
if (setFullResolution) {
|
||||
setFullResolution({ width, height });
|
||||
}
|
||||
setVideoDimensions({ width, height });
|
||||
if (height > 0) {
|
||||
setTallCamera(width / height < ASPECT_VERTICAL_LAYOUT);
|
||||
}
|
||||
},
|
||||
[setFullResolution],
|
||||
);
|
||||
|
||||
const handleLoadedMetadata = useCallback(() => {
|
||||
setLoadedMetadata(true);
|
||||
if (!videoRef.current) {
|
||||
return;
|
||||
}
|
||||
if (videoRef.current) {
|
||||
const width = videoRef.current.videoWidth;
|
||||
const height = videoRef.current.videoHeight;
|
||||
|
||||
const width = videoRef.current.videoWidth;
|
||||
const height = videoRef.current.videoHeight;
|
||||
|
||||
// iOS Safari occasionally reports 0x0 for videoWidth/videoHeight
|
||||
// Poll with requestAnimationFrame until dimensions become available (or timeout).
|
||||
if (width > 0 && height > 0) {
|
||||
applyVideoDimensions(width, height);
|
||||
return;
|
||||
}
|
||||
|
||||
let attempts = 0;
|
||||
const maxAttempts = 120; // ~2 seconds at 60fps
|
||||
const tryGetDims = () => {
|
||||
if (!videoRef.current) return;
|
||||
const w = videoRef.current.videoWidth;
|
||||
const h = videoRef.current.videoHeight;
|
||||
if (w > 0 && h > 0) {
|
||||
applyVideoDimensions(w, h);
|
||||
return;
|
||||
if (setFullResolution) {
|
||||
setFullResolution({
|
||||
width,
|
||||
height,
|
||||
});
|
||||
}
|
||||
if (attempts < maxAttempts) {
|
||||
attempts += 1;
|
||||
requestAnimationFrame(tryGetDims);
|
||||
}
|
||||
};
|
||||
requestAnimationFrame(tryGetDims);
|
||||
}, [videoRef, applyVideoDimensions]);
|
||||
|
||||
setVideoDimensions({ width, height });
|
||||
|
||||
setTallCamera(width / height < ASPECT_VERTICAL_LAYOUT);
|
||||
}
|
||||
}, [videoRef, setFullResolution]);
|
||||
|
||||
useEffect(() => {
|
||||
if (!videoRef.current) {
|
||||
@ -158,8 +130,6 @@ export default function HlsVideoPlayer({
|
||||
return;
|
||||
}
|
||||
|
||||
setLoadedMetadata(false);
|
||||
|
||||
const currentPlaybackRate = videoRef.current.playbackRate;
|
||||
|
||||
if (!useHlsCompat) {
|
||||
|
||||
@ -91,7 +91,7 @@ function MSEPlayer({
|
||||
(error: LivePlayerError, description: string = "Unknown error") => {
|
||||
// eslint-disable-next-line no-console
|
||||
console.error(
|
||||
`${camera} - MSE error '${error}': ${description} See the documentation: https://docs.frigate.video/configuration/live/#live-player-error-messages`,
|
||||
`${camera} - MSE error '${error}': ${description} See the documentation: https://docs.frigate.video/configuration/live/#live-view-faq`,
|
||||
);
|
||||
onError?.(error);
|
||||
},
|
||||
|
||||
@ -309,7 +309,6 @@ function PreviewVideoPlayer({
|
||||
playsInline
|
||||
muted
|
||||
disableRemotePlayback
|
||||
disablePictureInPicture
|
||||
onSeeked={onPreviewSeeked}
|
||||
onLoadedData={() => {
|
||||
if (firstLoad) {
|
||||
|
||||
@ -42,7 +42,7 @@ export default function WebRtcPlayer({
|
||||
(error: LivePlayerError, description: string = "Unknown error") => {
|
||||
// eslint-disable-next-line no-console
|
||||
console.error(
|
||||
`${camera} - WebRTC error '${error}': ${description} See the documentation: https://docs.frigate.video/configuration/live/#live-player-error-messages`,
|
||||
`${camera} - WebRTC error '${error}': ${description} See the documentation: https://docs.frigate.video/configuration/live/#live-view-faq`,
|
||||
);
|
||||
onError?.(error);
|
||||
},
|
||||
|
||||
@ -2,10 +2,7 @@ import { Recording } from "@/types/record";
|
||||
import { DynamicPlayback } from "@/types/playback";
|
||||
import { PreviewController } from "../PreviewPlayer";
|
||||
import { TimeRange, TrackingDetailsSequence } from "@/types/timeline";
|
||||
import {
|
||||
calculateInpointOffset,
|
||||
calculateSeekPosition,
|
||||
} from "@/utils/videoUtil";
|
||||
import { calculateInpointOffset } from "@/utils/videoUtil";
|
||||
|
||||
type PlayerMode = "playback" | "scrubbing";
|
||||
|
||||
@ -75,20 +72,38 @@ export class DynamicVideoController {
|
||||
return;
|
||||
}
|
||||
|
||||
if (
|
||||
this.recordings.length == 0 ||
|
||||
time < this.recordings[0].start_time ||
|
||||
time > this.recordings[this.recordings.length - 1].end_time
|
||||
) {
|
||||
this.setNoRecording(true);
|
||||
return;
|
||||
}
|
||||
|
||||
if (this.playerMode != "playback") {
|
||||
this.playerMode = "playback";
|
||||
}
|
||||
|
||||
const seekSeconds = calculateSeekPosition(
|
||||
time,
|
||||
this.recordings,
|
||||
this.inpointOffset,
|
||||
);
|
||||
let seekSeconds = 0;
|
||||
(this.recordings || []).every((segment) => {
|
||||
// if the next segment is past the desired time, stop calculating
|
||||
if (segment.start_time > time) {
|
||||
return false;
|
||||
}
|
||||
|
||||
if (seekSeconds === undefined) {
|
||||
this.setNoRecording(true);
|
||||
return;
|
||||
}
|
||||
if (segment.end_time < time) {
|
||||
seekSeconds += segment.end_time - segment.start_time;
|
||||
return true;
|
||||
}
|
||||
|
||||
seekSeconds +=
|
||||
segment.end_time - segment.start_time - (segment.end_time - time);
|
||||
return true;
|
||||
});
|
||||
|
||||
// adjust for HLS inpoint offset
|
||||
seekSeconds -= this.inpointOffset;
|
||||
|
||||
if (seekSeconds != 0) {
|
||||
this.playerController.currentTime = seekSeconds;
|
||||
|
||||
@ -14,10 +14,7 @@ import { VideoResolutionType } from "@/types/live";
|
||||
import axios from "axios";
|
||||
import { cn } from "@/lib/utils";
|
||||
import { useTranslation } from "react-i18next";
|
||||
import {
|
||||
calculateInpointOffset,
|
||||
calculateSeekPosition,
|
||||
} from "@/utils/videoUtil";
|
||||
import { calculateInpointOffset } from "@/utils/videoUtil";
|
||||
import { isFirefox } from "react-device-detect";
|
||||
|
||||
/**
|
||||
@ -112,10 +109,10 @@ export default function DynamicVideoPlayer({
|
||||
const [isLoading, setIsLoading] = useState(false);
|
||||
const [isBuffering, setIsBuffering] = useState(false);
|
||||
const [loadingTimeout, setLoadingTimeout] = useState<NodeJS.Timeout>();
|
||||
|
||||
// Don't set source until recordings load - we need accurate startPosition
|
||||
// to avoid hls.js clamping to video end when startPosition exceeds duration
|
||||
const [source, setSource] = useState<HlsSource | undefined>(undefined);
|
||||
const [source, setSource] = useState<HlsSource>({
|
||||
playlist: `${apiHost}vod/${camera}/start/${timeRange.after}/end/${timeRange.before}/master.m3u8`,
|
||||
startPosition: startTimestamp ? startTimestamp - timeRange.after : 0,
|
||||
});
|
||||
|
||||
// start at correct time
|
||||
|
||||
@ -187,7 +184,7 @@ export default function DynamicVideoPlayer({
|
||||
);
|
||||
|
||||
useEffect(() => {
|
||||
if (!recordings?.length) {
|
||||
if (!controller || !recordings?.length) {
|
||||
if (recordings?.length == 0) {
|
||||
setNoRecording(true);
|
||||
}
|
||||
@ -195,6 +192,10 @@ export default function DynamicVideoPlayer({
|
||||
return;
|
||||
}
|
||||
|
||||
if (playerRef.current) {
|
||||
playerRef.current.autoplay = !isScrubbing;
|
||||
}
|
||||
|
||||
let startPosition = undefined;
|
||||
|
||||
if (startTimestamp) {
|
||||
@ -202,12 +203,14 @@ export default function DynamicVideoPlayer({
|
||||
recordingParams.after,
|
||||
(recordings || [])[0],
|
||||
);
|
||||
|
||||
startPosition = calculateSeekPosition(
|
||||
startTimestamp,
|
||||
recordings,
|
||||
inpointOffset,
|
||||
const idealStartPosition = Math.max(
|
||||
0,
|
||||
startTimestamp - timeRange.after - inpointOffset,
|
||||
);
|
||||
|
||||
if (idealStartPosition >= recordings[0].start_time - timeRange.after) {
|
||||
startPosition = idealStartPosition;
|
||||
}
|
||||
}
|
||||
|
||||
setSource({
|
||||
@ -215,18 +218,6 @@ export default function DynamicVideoPlayer({
|
||||
startPosition,
|
||||
});
|
||||
|
||||
// eslint-disable-next-line react-hooks/exhaustive-deps
|
||||
}, [recordings]);
|
||||
|
||||
useEffect(() => {
|
||||
if (!controller || !recordings?.length) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (playerRef.current) {
|
||||
playerRef.current.autoplay = !isScrubbing;
|
||||
}
|
||||
|
||||
setLoadingTimeout(setTimeout(() => setIsLoading(true), 1000));
|
||||
|
||||
controller.newPlayback({
|
||||
@ -234,7 +225,7 @@ export default function DynamicVideoPlayer({
|
||||
timeRange,
|
||||
});
|
||||
|
||||
// we only want this to change when controller or recordings update
|
||||
// we only want this to change when recordings update
|
||||
// eslint-disable-next-line react-hooks/exhaustive-deps
|
||||
}, [controller, recordings]);
|
||||
|
||||
@ -272,48 +263,46 @@ export default function DynamicVideoPlayer({
|
||||
|
||||
return (
|
||||
<>
|
||||
{source && (
|
||||
<HlsVideoPlayer
|
||||
videoRef={playerRef}
|
||||
containerRef={containerRef}
|
||||
visible={!(isScrubbing || isLoading)}
|
||||
currentSource={source}
|
||||
hotKeys={hotKeys}
|
||||
supportsFullscreen={supportsFullscreen}
|
||||
fullscreen={fullscreen}
|
||||
inpointOffset={inpointOffset}
|
||||
onTimeUpdate={onTimeUpdate}
|
||||
onPlayerLoaded={onPlayerLoaded}
|
||||
onClipEnded={onValidateClipEnd}
|
||||
onSeekToTime={(timestamp, play) => {
|
||||
if (onSeekToTime) {
|
||||
onSeekToTime(timestamp, play);
|
||||
}
|
||||
}}
|
||||
onPlaying={() => {
|
||||
if (isScrubbing) {
|
||||
playerRef.current?.pause();
|
||||
}
|
||||
<HlsVideoPlayer
|
||||
videoRef={playerRef}
|
||||
containerRef={containerRef}
|
||||
visible={!(isScrubbing || isLoading)}
|
||||
currentSource={source}
|
||||
hotKeys={hotKeys}
|
||||
supportsFullscreen={supportsFullscreen}
|
||||
fullscreen={fullscreen}
|
||||
inpointOffset={inpointOffset}
|
||||
onTimeUpdate={onTimeUpdate}
|
||||
onPlayerLoaded={onPlayerLoaded}
|
||||
onClipEnded={onValidateClipEnd}
|
||||
onSeekToTime={(timestamp, play) => {
|
||||
if (onSeekToTime) {
|
||||
onSeekToTime(timestamp, play);
|
||||
}
|
||||
}}
|
||||
onPlaying={() => {
|
||||
if (isScrubbing) {
|
||||
playerRef.current?.pause();
|
||||
}
|
||||
|
||||
if (loadingTimeout) {
|
||||
clearTimeout(loadingTimeout);
|
||||
}
|
||||
if (loadingTimeout) {
|
||||
clearTimeout(loadingTimeout);
|
||||
}
|
||||
|
||||
setNoRecording(false);
|
||||
}}
|
||||
setFullResolution={setFullResolution}
|
||||
onUploadFrame={onUploadFrameToPlus}
|
||||
toggleFullscreen={toggleFullscreen}
|
||||
onError={(error) => {
|
||||
if (error == "stalled" && !isScrubbing) {
|
||||
setIsBuffering(true);
|
||||
}
|
||||
}}
|
||||
isDetailMode={isDetailMode}
|
||||
camera={contextCamera || camera}
|
||||
currentTimeOverride={currentTime}
|
||||
/>
|
||||
)}
|
||||
setNoRecording(false);
|
||||
}}
|
||||
setFullResolution={setFullResolution}
|
||||
onUploadFrame={onUploadFrameToPlus}
|
||||
toggleFullscreen={toggleFullscreen}
|
||||
onError={(error) => {
|
||||
if (error == "stalled" && !isScrubbing) {
|
||||
setIsBuffering(true);
|
||||
}
|
||||
}}
|
||||
isDetailMode={isDetailMode}
|
||||
camera={contextCamera || camera}
|
||||
currentTimeOverride={currentTime}
|
||||
/>
|
||||
<PreviewPlayer
|
||||
className={cn(
|
||||
className,
|
||||
|
||||
@ -24,57 +24,3 @@ export function calculateInpointOffset(
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
/**
|
||||
* Calculates the video player time (in seconds) for a given timestamp
|
||||
* by iterating through recording segments and summing their durations.
|
||||
* This accounts for the fact that the video is a concatenation of segments,
|
||||
* not a single continuous stream.
|
||||
*
|
||||
* @param timestamp - The target timestamp to seek to
|
||||
* @param recordings - Array of recording segments
|
||||
* @param inpointOffset - HLS inpoint offset to subtract from the result
|
||||
* @returns The calculated seek position in seconds, or undefined if timestamp is out of range
|
||||
*/
|
||||
export function calculateSeekPosition(
|
||||
timestamp: number,
|
||||
recordings: Recording[],
|
||||
inpointOffset: number = 0,
|
||||
): number | undefined {
|
||||
if (!recordings || recordings.length === 0) {
|
||||
return undefined;
|
||||
}
|
||||
|
||||
// Check if timestamp is within the recordings range
|
||||
if (
|
||||
timestamp < recordings[0].start_time ||
|
||||
timestamp > recordings[recordings.length - 1].end_time
|
||||
) {
|
||||
return undefined;
|
||||
}
|
||||
|
||||
let seekSeconds = 0;
|
||||
|
||||
(recordings || []).every((segment) => {
|
||||
// if the next segment is past the desired time, stop calculating
|
||||
if (segment.start_time > timestamp) {
|
||||
return false;
|
||||
}
|
||||
|
||||
if (segment.end_time < timestamp) {
|
||||
// Add the full duration of this segment
|
||||
seekSeconds += segment.end_time - segment.start_time;
|
||||
return true;
|
||||
}
|
||||
|
||||
// We're in this segment - calculate position within it
|
||||
seekSeconds +=
|
||||
segment.end_time - segment.start_time - (segment.end_time - timestamp);
|
||||
return true;
|
||||
});
|
||||
|
||||
// Adjust for HLS inpoint offset
|
||||
seekSeconds -= inpointOffset;
|
||||
|
||||
return seekSeconds >= 0 ? seekSeconds : undefined;
|
||||
}
|
||||
|
||||
@ -16,6 +16,7 @@ import ImageLoadingIndicator from "@/components/indicators/ImageLoadingIndicator
|
||||
import useImageLoaded from "@/hooks/use-image-loaded";
|
||||
import ActivityIndicator from "@/components/indicators/activity-indicator";
|
||||
import { useTrackedObjectUpdate } from "@/api/ws";
|
||||
import { isEqual } from "lodash";
|
||||
import TimeAgo from "@/components/dynamic/TimeAgo";
|
||||
import SearchResultActions from "@/components/menu/SearchResultActions";
|
||||
import { SearchTab } from "@/components/overlay/detail/SearchDetailDialog";
|
||||
@ -24,12 +25,14 @@ import { useTranslation } from "react-i18next";
|
||||
import { getTranslatedLabel } from "@/utils/i18n";
|
||||
|
||||
type ExploreViewProps = {
|
||||
searchDetail: SearchResult | undefined;
|
||||
setSearchDetail: (search: SearchResult | undefined) => void;
|
||||
setSimilaritySearch: (search: SearchResult) => void;
|
||||
onSelectSearch: (item: SearchResult, ctrl: boolean, page?: SearchTab) => void;
|
||||
};
|
||||
|
||||
export default function ExploreView({
|
||||
searchDetail,
|
||||
setSearchDetail,
|
||||
setSimilaritySearch,
|
||||
onSelectSearch,
|
||||
@ -80,6 +83,20 @@ export default function ExploreView({
|
||||
}
|
||||
}, [wsUpdate, mutate]);
|
||||
|
||||
// update search detail when results change
|
||||
|
||||
useEffect(() => {
|
||||
if (searchDetail && events) {
|
||||
const updatedSearchDetail = events.find(
|
||||
(result) => result.id === searchDetail.id,
|
||||
);
|
||||
|
||||
if (updatedSearchDetail && !isEqual(updatedSearchDetail, searchDetail)) {
|
||||
setSearchDetail(updatedSearchDetail);
|
||||
}
|
||||
}
|
||||
}, [events, searchDetail, setSearchDetail]);
|
||||
|
||||
if (isLoading) {
|
||||
return (
|
||||
<ActivityIndicator className="absolute left-1/2 top-1/2 -translate-x-1/2 -translate-y-1/2" />
|
||||
|
||||
@ -19,6 +19,7 @@ import useKeyboardListener, {
|
||||
import scrollIntoView from "scroll-into-view-if-needed";
|
||||
import InputWithTags from "@/components/input/InputWithTags";
|
||||
import { ScrollArea, ScrollBar } from "@/components/ui/scroll-area";
|
||||
import { isEqual } from "lodash";
|
||||
import { formatDateToLocaleString } from "@/utils/dateUtil";
|
||||
import SearchThumbnailFooter from "@/components/card/SearchThumbnailFooter";
|
||||
import ExploreSettings from "@/components/settings/SearchSettings";
|
||||
@ -212,7 +213,7 @@ export default function SearchView({
|
||||
|
||||
// detail
|
||||
|
||||
const [selectedId, setSelectedId] = useState<string>();
|
||||
const [searchDetail, setSearchDetail] = useState<SearchResult>();
|
||||
const [page, setPage] = useState<SearchTab>("snapshot");
|
||||
|
||||
// remove duplicate event ids
|
||||
@ -228,16 +229,6 @@ export default function SearchView({
|
||||
return results;
|
||||
}, [searchResults]);
|
||||
|
||||
const searchDetail = useMemo(() => {
|
||||
if (!selectedId) return undefined;
|
||||
// summary view
|
||||
if (defaultView === "summary" && exploreEvents) {
|
||||
return exploreEvents.find((r) => r.id === selectedId);
|
||||
}
|
||||
// grid view
|
||||
return uniqueResults.find((r) => r.id === selectedId);
|
||||
}, [selectedId, uniqueResults, exploreEvents, defaultView]);
|
||||
|
||||
// search interaction
|
||||
|
||||
const [selectedObjects, setSelectedObjects] = useState<string[]>([]);
|
||||
@ -265,7 +256,7 @@ export default function SearchView({
|
||||
}
|
||||
} else {
|
||||
setPage(page);
|
||||
setSelectedId(item.id);
|
||||
setSearchDetail(item);
|
||||
}
|
||||
},
|
||||
[selectedObjects],
|
||||
@ -304,12 +295,26 @@ export default function SearchView({
|
||||
}
|
||||
};
|
||||
|
||||
// clear selected item when search results clear
|
||||
// update search detail when results change
|
||||
|
||||
useEffect(() => {
|
||||
if (!searchResults && !exploreEvents) {
|
||||
setSelectedId(undefined);
|
||||
if (searchDetail) {
|
||||
const results =
|
||||
defaultView === "summary" ? exploreEvents : searchResults?.flat();
|
||||
if (results) {
|
||||
const updatedSearchDetail = results.find(
|
||||
(result) => result.id === searchDetail.id,
|
||||
);
|
||||
|
||||
if (
|
||||
updatedSearchDetail &&
|
||||
!isEqual(updatedSearchDetail, searchDetail)
|
||||
) {
|
||||
setSearchDetail(updatedSearchDetail);
|
||||
}
|
||||
}
|
||||
}
|
||||
}, [searchResults, exploreEvents]);
|
||||
}, [searchResults, exploreEvents, searchDetail, defaultView]);
|
||||
|
||||
const hasExistingSearch = useMemo(
|
||||
() => searchResults != undefined || searchFilter != undefined,
|
||||
@ -335,7 +340,7 @@ export default function SearchView({
|
||||
? results.length - 1
|
||||
: (currentIndex - 1 + results.length) % results.length;
|
||||
|
||||
setSelectedId(results[newIndex].id);
|
||||
setSearchDetail(results[newIndex]);
|
||||
}
|
||||
}, [uniqueResults, exploreEvents, searchDetail, defaultView]);
|
||||
|
||||
@ -352,7 +357,7 @@ export default function SearchView({
|
||||
const newIndex =
|
||||
currentIndex === -1 ? 0 : (currentIndex + 1) % results.length;
|
||||
|
||||
setSelectedId(results[newIndex].id);
|
||||
setSearchDetail(results[newIndex]);
|
||||
}
|
||||
}, [uniqueResults, exploreEvents, searchDetail, defaultView]);
|
||||
|
||||
@ -504,7 +509,7 @@ export default function SearchView({
|
||||
<SearchDetailDialog
|
||||
search={searchDetail}
|
||||
page={page}
|
||||
setSearch={(item) => setSelectedId(item?.id)}
|
||||
setSearch={setSearchDetail}
|
||||
setSearchPage={setPage}
|
||||
setSimilarity={
|
||||
searchDetail && (() => setSimilaritySearch(searchDetail))
|
||||
@ -624,7 +629,7 @@ export default function SearchView({
|
||||
detail: boolean,
|
||||
) => {
|
||||
if (detail && selectedObjects.length == 0) {
|
||||
setSelectedId(value.id);
|
||||
setSearchDetail(value);
|
||||
} else {
|
||||
onSelectSearch(
|
||||
value,
|
||||
@ -719,7 +724,8 @@ export default function SearchView({
|
||||
defaultView == "summary" && (
|
||||
<div className="scrollbar-container flex size-full flex-col overflow-y-auto">
|
||||
<ExploreView
|
||||
setSearchDetail={(item) => setSelectedId(item?.id)}
|
||||
searchDetail={searchDetail}
|
||||
setSearchDetail={setSearchDetail}
|
||||
setSimilaritySearch={setSimilaritySearch}
|
||||
onSelectSearch={onSelectSearch}
|
||||
/>
|
||||
|
||||
@ -375,50 +375,6 @@ export default function GeneralMetrics({
|
||||
return Object.keys(series).length > 0 ? Object.values(series) : undefined;
|
||||
}, [statsHistory]);
|
||||
|
||||
// Check if Intel GPU has all 0% usage values (known bug)
|
||||
const showIntelGpuWarning = useMemo(() => {
|
||||
if (!statsHistory || statsHistory.length < 3) {
|
||||
return false;
|
||||
}
|
||||
|
||||
const gpuKeys = Object.keys(statsHistory[0]?.gpu_usages ?? {});
|
||||
const hasIntelGpu = gpuKeys.some(
|
||||
(key) => key === "intel-vaapi" || key === "intel-qsv",
|
||||
);
|
||||
|
||||
if (!hasIntelGpu) {
|
||||
return false;
|
||||
}
|
||||
|
||||
// Check if all GPU usage values are 0% across all stats
|
||||
let allZero = true;
|
||||
let hasDataPoints = false;
|
||||
|
||||
for (const stats of statsHistory) {
|
||||
if (!stats) {
|
||||
continue;
|
||||
}
|
||||
|
||||
Object.entries(stats.gpu_usages || {}).forEach(([key, gpuStats]) => {
|
||||
if (key === "intel-vaapi" || key === "intel-qsv") {
|
||||
if (gpuStats.gpu) {
|
||||
hasDataPoints = true;
|
||||
const gpuValue = parseFloat(gpuStats.gpu.slice(0, -1));
|
||||
if (!isNaN(gpuValue) && gpuValue > 0) {
|
||||
allZero = false;
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
if (!allZero) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
return hasDataPoints && allZero;
|
||||
}, [statsHistory]);
|
||||
|
||||
// npu stats
|
||||
|
||||
const npuSeries = useMemo(() => {
|
||||
@ -683,46 +639,8 @@ export default function GeneralMetrics({
|
||||
<>
|
||||
{statsHistory.length != 0 ? (
|
||||
<div className="rounded-lg bg-background_alt p-2.5 md:rounded-2xl">
|
||||
<div className="mb-5 flex flex-row items-center justify-between">
|
||||
<div className="mb-5">
|
||||
{t("general.hardwareInfo.gpuUsage")}
|
||||
{showIntelGpuWarning && (
|
||||
<Popover>
|
||||
<PopoverTrigger asChild>
|
||||
<button
|
||||
className="flex flex-row items-center gap-1.5 text-yellow-600 focus:outline-none dark:text-yellow-500"
|
||||
aria-label={t(
|
||||
"general.hardwareInfo.intelGpuWarning.title",
|
||||
)}
|
||||
>
|
||||
<CiCircleAlert
|
||||
className="size-5"
|
||||
aria-label={t(
|
||||
"general.hardwareInfo.intelGpuWarning.title",
|
||||
)}
|
||||
/>
|
||||
<span className="text-sm">
|
||||
{t(
|
||||
"general.hardwareInfo.intelGpuWarning.message",
|
||||
)}
|
||||
</span>
|
||||
</button>
|
||||
</PopoverTrigger>
|
||||
<PopoverContent className="w-80">
|
||||
<div className="space-y-2">
|
||||
<div className="font-semibold">
|
||||
{t(
|
||||
"general.hardwareInfo.intelGpuWarning.title",
|
||||
)}
|
||||
</div>
|
||||
<div>
|
||||
{t(
|
||||
"general.hardwareInfo.intelGpuWarning.description",
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
</PopoverContent>
|
||||
</Popover>
|
||||
)}
|
||||
</div>
|
||||
{gpuSeries.map((series) => (
|
||||
<ThresholdBarGraph
|
||||
|
||||
Loading…
Reference in New Issue
Block a user