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128 lines
4.6 KiB
Markdown
128 lines
4.6 KiB
Markdown
# Frigate - Realtime Object Detection for IP Cameras
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Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT.
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Use of a [Google Coral USB Accelerator](https://coral.withgoogle.com/products/accelerator/) is optional, but highly recommended. On my Intel i7 processor, I can process 2-3 FPS with the CPU. The Coral can process 100+ FPS with very low CPU load.
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- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
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- Uses a very low overhead motion detection to determine where to run object detection
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- Object detection with Tensorflow runs in a separate process
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- Object info is published over MQTT for integration into HomeAssistant as a binary sensor
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- An endpoint is available to view an MJPEG stream for debugging, but should not be used continuously
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## Example video (from older version)
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You see multiple bounding boxes because it draws bounding boxes from all frames in the past 1 second where a person was detected. Not all of the bounding boxes were from the current frame.
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[](http://www.youtube.com/watch?v=nqHbCtyo4dY "Frigate")
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## Getting Started
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Run the container with
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```bash
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docker run --rm \
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--privileged \
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--shm-size=512m \ # should work for a 2-3 cameras
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-v /dev/bus/usb:/dev/bus/usb \
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-v <path_to_config_dir>:/config:ro \
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-v /etc/localtime:/etc/localtime:ro \
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-p 5000:5000 \
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-e FRIGATE_RTSP_PASSWORD='password' \
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blakeblackshear/frigate:stable
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```
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Example docker-compose:
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```yaml
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frigate:
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container_name: frigate
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restart: unless-stopped
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privileged: true
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shm_size: '1g' # should work for 5-7 cameras
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image: blakeblackshear/frigate:stable
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volumes:
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- /dev/bus/usb:/dev/bus/usb
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- /etc/localtime:/etc/localtime:ro
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- <path_to_config>:/config
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ports:
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- "5000:5000"
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environment:
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FRIGATE_RTSP_PASSWORD: "password"
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```
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A `config.yml` file must exist in the `config` directory. See example [here](config/config.example.yml) and device specific info can be found [here](docs/DEVICES.md).
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Access the mjpeg stream at `http://localhost:5000/<camera_name>` and the best snapshot for any object type with at `http://localhost:5000/<camera_name>/<object_name>/best.jpg`
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Debug info is available at `http://localhost:5000/debug/stats`
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## Integration with HomeAssistant
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```
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camera:
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- name: Camera Last Person
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platform: mqtt
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topic: frigate/<camera_name>/person/snapshot
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- name: Camera Last Car
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platform: mqtt
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topic: frigate/<camera_name>/car/snapshot
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binary_sensor:
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- name: Camera Person
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platform: mqtt
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state_topic: "frigate/<camera_name>/person"
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device_class: motion
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availability_topic: "frigate/available"
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automation:
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- alias: Alert me if a person is detected while armed away
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trigger:
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platform: state
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entity_id: binary_sensor.camera_person
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from: 'off'
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to: 'on'
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condition:
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- condition: state
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entity_id: alarm_control_panel.home_alarm
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state: armed_away
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action:
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- service: notify.user_telegram
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data:
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message: "A person was detected."
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data:
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photo:
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- url: http://<ip>:5000/<camera_name>/person/best.jpg
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caption: A person was detected.
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sensor:
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- platform: rest
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name: Frigate Debug
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resource: http://localhost:5000/debug/stats
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scan_interval: 5
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json_attributes:
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- back
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- coral
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value_template: 'OK'
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- platform: template
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sensors:
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back_fps:
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value_template: '{{ states.sensor.frigate_debug.attributes["back"]["fps"] }}'
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unit_of_measurement: 'FPS'
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back_skipped_fps:
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value_template: '{{ states.sensor.frigate_debug.attributes["back"]["skipped_fps"] }}'
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unit_of_measurement: 'FPS'
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back_detection_fps:
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value_template: '{{ states.sensor.frigate_debug.attributes["back"]["detection_fps"] }}'
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unit_of_measurement: 'FPS'
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frigate_coral_fps:
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value_template: '{{ states.sensor.frigate_debug.attributes["coral"]["fps"] }}'
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unit_of_measurement: 'FPS'
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frigate_coral_inference:
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value_template: '{{ states.sensor.frigate_debug.attributes["coral"]["inference_speed"] }}'
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unit_of_measurement: 'ms'
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```
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## Using a custom model
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Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use your own models with volume mounts:
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- CPU Model: `/cpu_model.tflite`
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- EdgeTPU Model: `/edgetpu_model.tflite`
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- Labels: `/labelmap.txt`
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## Tips
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- Lower the framerate of the video feed on the camera to reduce the CPU usage for capturing the feed
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