mirror of
https://github.com/blakeblackshear/frigate.git
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123 lines
4.7 KiB
Markdown
123 lines
4.7 KiB
Markdown
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# Frigate - Realtime Object Detection for IP Cameras
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**Note:** This version requires the use of a [Google Coral USB Accelerator](https://coral.withgoogle.com/products/accelerator/)
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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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- Leverages multiprocessing and threads heavily with an emphasis on realtime over processing every frame
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- Allows you to define specific regions (squares) in the image to look for objects
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- No motion detection (for now)
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- Object detection with Tensorflow runs in a separate thread
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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
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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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Build the container with
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```
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docker build -t frigate .
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```
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The `mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite` model is included and used by default. You can use your own model and labels by mounting files in the container at `/frozen_inference_graph.pb` and `/label_map.pbtext`. Models must be compatible with the Coral according to [this](https://coral.withgoogle.com/models/).
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Run the container with
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```
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docker run --rm \
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--privileged \
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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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frigate:latest
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```
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Example docker-compose:
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```
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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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image: frigate:latest
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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.yml).
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Access the mjpeg stream at `http://localhost:5000/<camera_name>` and the best person snapshot at `http://localhost:5000/<camera_name>/best_person.jpg`
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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>/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>/objects"
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value_template: '{{ value_json.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>/best_person.jpg
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caption: A person was detected.
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```
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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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## Future improvements
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- [x] Remove motion detection for now
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- [x] Try running object detection in a thread rather than a process
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- [x] Implement min person size again
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- [x] Switch to a config file
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- [x] Handle multiple cameras in the same container
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- [ ] Attempt to figure out coral symlinking
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- [ ] Add object list to config with min scores for mqtt
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- [ ] Move mjpeg encoding to a separate process
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- [ ] Simplify motion detection (check entire image against mask, resize instead of gaussian blur)
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- [ ] See if motion detection is even worth running
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- [ ] Scan for people across entire image rather than specfic regions
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- [ ] Dynamically resize detection area and follow people
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- [ ] Add ability to turn detection on and off via MQTT
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- [ ] Output movie clips of people for notifications, etc.
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- [ ] Integrate with homeassistant push camera
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- [ ] Merge bounding boxes that span multiple regions
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- [ ] Implement mode to save labeled objects for training
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- [ ] Try and reduce CPU usage by simplifying the tensorflow model to just include the objects we care about
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- [ ] Look into GPU accelerated decoding of RTSP stream
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- [ ] Send video over a socket and use JSMPEG
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- [x] Look into neural compute stick
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