mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-23 09:26:32 -06:00
123 lines
4.7 KiB
Markdown
123 lines
4.7 KiB
Markdown
# Frigate - Realtime Object Detection for IP Cameras
|
|
**Note:** This version requires the use of a [Google Coral USB Accelerator](https://coral.withgoogle.com/products/accelerator/)
|
|
|
|
Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT.
|
|
|
|
- Leverages multiprocessing and threads heavily with an emphasis on realtime over processing every frame
|
|
- Allows you to define specific regions (squares) in the image to look for objects
|
|
- No motion detection (for now)
|
|
- Object detection with Tensorflow runs in a separate thread
|
|
- Object info is published over MQTT for integration into HomeAssistant as a binary sensor
|
|
- An endpoint is available to view an MJPEG stream for debugging
|
|
|
|
|
|
|
|
## Example video (from older version)
|
|
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.
|
|
[](http://www.youtube.com/watch?v=nqHbCtyo4dY "Frigate")
|
|
|
|
## Getting Started
|
|
Build the container with
|
|
```
|
|
docker build -t frigate .
|
|
```
|
|
|
|
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/).
|
|
|
|
Run the container with
|
|
```
|
|
docker run --rm \
|
|
--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' \
|
|
frigate:latest
|
|
```
|
|
|
|
Example docker-compose:
|
|
```
|
|
frigate:
|
|
container_name: frigate
|
|
restart: unless-stopped
|
|
privileged: true
|
|
image: frigate:latest
|
|
volumes:
|
|
- /dev/bus/usb:/dev/bus/usb
|
|
- /etc/localtime:/etc/localtime:ro
|
|
- <path_to_config>:/config
|
|
ports:
|
|
- "5000:5000"
|
|
environment:
|
|
FRIGATE_RTSP_PASSWORD: "password"
|
|
```
|
|
|
|
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).
|
|
|
|
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`
|
|
|
|
## Integration with HomeAssistant
|
|
```
|
|
camera:
|
|
- name: Camera Last Person
|
|
platform: mqtt
|
|
topic: frigate/<camera_name>/person/snapshot
|
|
- name: Camera Last Car
|
|
platform: mqtt
|
|
topic: frigate/<camera_name>/car/snapshot
|
|
|
|
binary_sensor:
|
|
- name: Camera Person
|
|
platform: mqtt
|
|
state_topic: "frigate/<camera_name>/person"
|
|
device_class: motion
|
|
availability_topic: "frigate/available"
|
|
|
|
automation:
|
|
- alias: Alert me if a person is detected while armed away
|
|
trigger:
|
|
platform: state
|
|
entity_id: binary_sensor.camera_person
|
|
from: 'off'
|
|
to: 'on'
|
|
condition:
|
|
- condition: state
|
|
entity_id: alarm_control_panel.home_alarm
|
|
state: armed_away
|
|
action:
|
|
- service: notify.user_telegram
|
|
data:
|
|
message: "A person was detected."
|
|
data:
|
|
photo:
|
|
- url: http://<ip>:5000/<camera_name>/person/best.jpg
|
|
caption: A person was detected.
|
|
```
|
|
|
|
## Tips
|
|
- Lower the framerate of the video feed on the camera to reduce the CPU usage for capturing the feed
|
|
|
|
## Future improvements
|
|
- [x] Remove motion detection for now
|
|
- [x] Try running object detection in a thread rather than a process
|
|
- [x] Implement min person size again
|
|
- [x] Switch to a config file
|
|
- [x] Handle multiple cameras in the same container
|
|
- [ ] Attempt to figure out coral symlinking
|
|
- [ ] Add object list to config with min scores for mqtt
|
|
- [ ] Move mjpeg encoding to a separate process
|
|
- [ ] Simplify motion detection (check entire image against mask, resize instead of gaussian blur)
|
|
- [ ] See if motion detection is even worth running
|
|
- [ ] Scan for people across entire image rather than specfic regions
|
|
- [ ] Dynamically resize detection area and follow people
|
|
- [ ] Add ability to turn detection on and off via MQTT
|
|
- [ ] Output movie clips of people for notifications, etc.
|
|
- [ ] Integrate with homeassistant push camera
|
|
- [ ] Merge bounding boxes that span multiple regions
|
|
- [ ] Implement mode to save labeled objects for training
|
|
- [ ] Try and reduce CPU usage by simplifying the tensorflow model to just include the objects we care about
|
|
- [ ] Look into GPU accelerated decoding of RTSP stream
|
|
- [ ] Send video over a socket and use JSMPEG
|
|
- [x] Look into neural compute stick
|