Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT.
Use of a [Google Coral Accelerator](https://coral.ai/products/) is optional, but highly recommended. The Coral will outperform even the best CPUs and can process 100+ FPS with very little overhead.
If you can't use docker compose, you can run the container with:
```bash
docker run --rm \
--name frigate \
--privileged \
-v /dev/bus/usb:/dev/bus/usb \
-v <path_to_config_dir>:/config:ro \
-v /etc/localtime:/etc/localtime:ro \
-p 5000:5000 \
-e FRIGATE_RTSP_PASSWORD='password' \
blakeblackshear/frigate:stable-amd64
```
### Kubernetes
Use the [helm chart](https://github.com/k8s-at-home/charts/tree/master/charts/frigate).
### Virtualization
For ideal performance, Frigate needs access to underlying hardware for the Coral and GPU devices for ffmpeg decoding. Running Frigate in a VM on top of Proxmox, ESXi, Virtualbox, etc. is not recommended. The virtualization layer typically introduces a sizable amount of overhead for communication with Coral devices.
#### Proxmox
Some people have had success running Frigate in LXC directly with the following config:
The default shm-size of 64m is fine for setups with 3 or less 1080p cameras. If frigate is exiting with "Bus error" messages, it could be because you have too many high resolution cameras and you need to specify a higher shm size.
HassOS users can manage their configuration directly in the addon Configuration tab. For other installations, the default location for the config file is `/config/config.yml`. This can be overridden with the `CONFIG_FILE` environment variable. Camera specific ffmpeg parameters are documented [here](docs/cameras.md).
```yaml
# Optional: port for http server (default: shown below)
web_port: 5000
# Optional: detectors configuration
# USB Coral devices will be auto detected with CPU fallback
detectors:
# Required: name of the detector
coral:
# Required: type of the detector
# Valid values are 'edgetpu' (requires device property below) and 'cpu'.
type: edgetpu
# Optional: device name as defined here: https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api
device: usb
# Required: mqtt configuration
mqtt:
# Required: host name
host: mqtt.server.com
# Optional: port (default: shown below)
port: 1883
# Optional: topic prefix (default: shown below)
# WARNING: must be unique if you are running multiple instances
topic_prefix: frigate
# Optional: client id (default: shown below)
# WARNING: must be unique if you are running multiple instances
client_id: frigate
# Optional: user
user: mqtt_user
# Optional: password
# NOTE: Environment variables that begin with 'FRIGATE_' may be referenced in {}.
# eg. password: '{FRIGATE_MQTT_PASSWORD}'
password: password
# Optional: Global configuration for saving clips
save_clips:
# Optional: Maximum length of time to retain video during long events. (default: shown below)
# NOTE: If an object is being tracked for longer than this amount of time, the cache
# will begin to expire and the resulting clip will be the last x seconds of the event.
max_seconds: 300
# Optional: Location to save event clips. (default: shown below)
clips_dir: /clips
# Optional: Location to save cache files for creating clips. (default: shown below)
# NOTE: To reduce wear on SSDs and SD cards, use a tmpfs volume.
- **Google Coral**: It is strongly recommended to use a Google Coral, but Frigate will fall back to CPU in the event one is not found. Offloading TensorFlow to the Google Coral is an order of magnitude faster and will reduce your CPU load dramatically. A $60 device will outperform $2000 CPU.
- **Resolution**: Choose a camera resolution where the smallest object you want to detect barely fits inside a 300x300px square. The model used by Frigate is trained on 300x300px images, so you will get worse performance and no improvement in accuracy by using a larger resolution since Frigate resizes the area where it is looking for objects to 300x300 anyway.
- **FPS**: 5 frames per second should be adequate. Higher frame rates will require more CPU usage without improving detections or accuracy. Reducing the frame rate on your camera will have the greatest improvement on system resources.
- **Hardware Acceleration**: Make sure you configure the `hwaccel_args` for your hardware. They provide a significant reduction in CPU usage if they are available.
- **Masks**: Masks can be used to ignore motion and reduce your idle CPU load. If you have areas with regular motion such as timestamps or trees blowing in the wind, frigate will constantly try to determine if that motion is from a person or other object you are tracking. Those detections not only increase your average CPU usage, but also clog the pipeline for detecting objects elsewhere. If you are experiencing high values for `detection_fps` when no objects of interest are in the cameras, you should use masks to tell frigate to ignore movement from trees, bushes, timestamps, or any part of the image where detections should not be wasted looking for objects.
Frigate works on Raspberry Pi 3b/4 and x86 machines. It is recommended to update your configuration to enable hardware accelerated decoding in ffmpeg. Depending on your system, these parameters may not be compatible.
Intel-based CPUs via Quicksync (https://trac.ffmpeg.org/wiki/Hardware/QuickSync)
```yaml
ffmpeg:
hwaccel_args:
- -hwaccel
- vaapi
- -hwaccel_device
- /dev/dri/renderD128
- -hwaccel_output_format
- yuv420p
```
[Back to top](#documentation)
## Detectors
By default Frigate will look for a USB Coral device and fall back to the CPU if it cannot be found. If you have PCI or multiple Coral devices, you need to configure your detector devices in the config file. When using multiple detectors, they run in dedicated processes, but pull from a common queue of requested detections across all cameras.
Frigate supports `edgetpu` and `cpu` as detector types. The device value should be specified according to the [Documentation for the TensorFlow Lite Python API](https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api).
Single USB Coral:
```yaml
detectors:
coral:
type: edgetpu
device: usb
```
Multiple USB Corals:
```yaml
detectors:
coral1:
type: edgetpu
device: usb:0
coral2:
type: edgetpu
device: usb:1
```
Mixing Corals:
```yaml
detectors:
coral_usb:
type: edgetpu
device: usb
coral_pci:
type: edgetpu
device: pci
```
CPU Detectors (not recommended):
```yaml
detectors:
cpu1:
type: cpu
cpu2:
type: cpu
```
[Back to top](#documentation)
## Reducing False Positives
Tune your object filters to adjust false positives: `min_area`, `max_area`, `min_score`, `threshold`.
For object filters in your configuration, any single detection below `min_score` will be ignored as a false positive. `threshold` is based on the median of the history of scores (padded to 3 values) for a tracked object. Consider the following frames when `min_score` is set to 0.6 and threshold is set to 0.85:
| Frame | Current Score | Score History | Computed Score | Detected Object |
In frame 2, the score is below the `min_score` value, so frigate ignores it and it becomes a 0.0. The computed score is the median of the score history (padding to at least 3 values), and only when that computed score crosses the `threshold` is the object marked as a true positive. That happens in frame 4 in the example.
[Back to top](#documentation)
## Masks
The following types of masks are supported:
-`poly`: (Recommended) List of x,y points like zone configuration
-`base64`: Base64 encoded image file
-`image`: Image file in the `/config` directory
`base64` and `image` masks must be the same aspect ratio and resolution as your camera.
1. Download a camera snapshot image with the same resolution as the camera feed (`/<camera_name>/latest.jpg`).
1. Upload the image to https://www.image-map.net/
1. Select "shape" poly - start in the lowest left corner and place the first marker (point) and continue upwards and then to the right until the polygon shape covers the area that you want to mask out (ignore).
1. When you are finished with the polygon click "Show me the code!" and copy all coordinates (point), ie. `"0,461,3,0,1919,0,1919,843,1699,492,1344,458,1346,336,973,317,869,375,866,432"`
1. Adjust any -1 values to 0 and then add it all to the configuration (see the example configuration for correct indentation and placement)
Example of a finished row corresponding to the below example image:
Zones allow you to define a specific area of the frame and apply additional filters for object types so you can determine whether or not an object is within a particular area. Zones cannot have the same name as a camera. If desired, a single zone can include multiple cameras if you have multiple cameras covering the same area by configuring zones with the same name for each camera.
During testing, `draw_zones` should be set in the config to draw the zone on the frames so you can adjust as needed. The zone line will increase in thickness when any object enters the zone.
[Back to top](#documentation)
## Recording Clips
**Note**: Previous versions of frigate included `-vsync drop` in input parameters. This is not compatible with FFmpeg's segment feature and must be removed from your input parameters if you have overrides set.
Frigate can save video clips without any CPU overhead for encoding by simply copying the stream directly with FFmpeg. It leverages FFmpeg's segment functionality to maintain a cache of video for each camera. The cache files are written to disk at `cache_dir` and do not introduce memory overhead. When an object is being tracked, it will extend the cache to ensure it can assemble a clip when the event ends. Once the event ends, it again uses FFmpeg to assemble a clip by combining the video clips without any encoding by the CPU. Assembled clips are are saved to the `clips_dir` directory along with a json file containing the current information about the tracked object.
### Global Configuration Options
-`max_seconds`: This limits the size of the cache when an object is being tracked. If an object is stationary and being tracked for a long time, the cache files will expire and this value will be the maximum clip length for the *end* of the event. For example, if this is set to 300 seconds and an object is being tracked for 600 seconds, the clip will end up being the last 300 seconds. Defaults to 300 seconds.
### Per-camera Configuration Options
-`pre_capture`: Defines how much time should be included in the clip prior to the beginning of the event. Defaults to 30 seconds.
-`objects`: List of object types to save clips for. Object types here must be listed for tracking at the camera or global configuration. Defaults to all tracked objects.
[Back to top](#documentation)
## Integration with HomeAssistant
Setup a camera, binary_sensor, sensor and optionally automation as shown for each camera you define in frigate. Replace <camera_name> with the camera name as defined in the frigate `config.yml` (The `frigate_coral_fps` and `frigate_coral_inference` sensors only need to be defined once)
You can access a higher resolution mjpeg stream by appending `h=height-in-pixels` to the endpoint. For example `http://localhost:5000/back?h=1080`. You can also increase the FPS by appending `fps=frame-rate` to the URL such as `http://localhost:5000/back?fps=10` or both with `?fps=10&h=1000`
Publishes a jpeg encoded frame of the detected object type. When the object is no longer detected, the highest confidence image is published or the original image
Publishes `ON` when the object enters the zone and `OFF` when the object disappears or exits the zone. Designed to be used a as a binary sensor in HomeAssistant for whether or not that object type is detected in the zone.
Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use your own models with volume mounts:
- CPU Model: `/cpu_model.tflite`
- EdgeTPU Model: `/edgetpu_model.tflite`
- Labels: `/labelmap.txt`
### Customizing the Labelmap
The labelmap can be customized to your needs. A common reason to do this is to combine multiple object types that are easily confused when you don't need to be as granular such as car/truck. You must retain the same number of labels, but you can change the names. To change:
- Download the [COCO labelmap](https://dl.google.com/coral/canned_models/coco_labels.txt)
- Modify the label names as desired. For example, change `7 truck` to `7 car`
- Mount the new file at `/labelmap.txt` in the container with an additional volume
If you see repeated "On connect called" messages in your config, check for another instance of frigate. This happens when multiple frigate containers are trying to connect to mqtt with the same client_id.
