mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
147 lines
6.2 KiB
Markdown
147 lines
6.2 KiB
Markdown
# Frigate - Realtime Object Detection for RTSP Cameras
|
|
Uses OpenCV and Tensorflow to perform realtime object detection locally for RTSP 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 motion/objects
|
|
- Motion detection runs in a separate process per region and signals to object detection to avoid wasting CPU cycles looking for objects when there is no motion
|
|
- Object detection with Tensorflow runs in a separate process per region
|
|
- Detected objects are placed on a shared mp.Queue and aggregated into a list of recently detected objects in a separate thread
|
|
- A person score is calculated as the sum of all scores/5
|
|
- Motion and object info is published over MQTT for integration into HomeAssistant or others
|
|
- An endpoint is available to view an MJPEG stream for debugging
|
|
|
|
|
|
|
|
## Example video
|
|
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 .
|
|
```
|
|
|
|
Download a model from the [zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md).
|
|
|
|
Download the cooresponding label map from [here](https://github.com/tensorflow/models/tree/master/research/object_detection/data).
|
|
|
|
Run the container with
|
|
```
|
|
docker run --rm \
|
|
-v <path_to_frozen_detection_graph.pb>:/frozen_inference_graph.pb:ro \
|
|
-v <path_to_labelmap.pbtext>:/label_map.pbtext:ro \
|
|
-v <path_to_config_dir>:/config:ro \
|
|
-p 5000:5000 \
|
|
-e RTSP_URL='<rtsp_url>' \
|
|
-e REGIONS='<box_size_1>,<x_offset_1>,<y_offset_1>,<min_person_size_1>,<min_motion_size_1>,<mask_file_1>:<box_size_2>,<x_offset_2>,<y_offset_2>,<min_person_size_2>,<min_motion_size_2>,<mask_file_2>' \
|
|
-e MQTT_HOST='your.mqtthost.com' \
|
|
-e MQTT_USER='username' \
|
|
-e MQTT_PASS='password' \
|
|
-e MQTT_TOPIC_PREFIX='cameras/1' \
|
|
-e DEBUG='0' \
|
|
frigate:latest
|
|
```
|
|
|
|
Example docker-compose:
|
|
```
|
|
frigate:
|
|
container_name: frigate
|
|
restart: unless-stopped
|
|
image: frigate:latest
|
|
volumes:
|
|
- <path_to_frozen_detection_graph.pb>:/frozen_inference_graph.pb:ro
|
|
- <path_to_labelmap.pbtext>:/label_map.pbtext:ro
|
|
- <path_to_config>:/config
|
|
ports:
|
|
- "127.0.0.1:5000:5000"
|
|
environment:
|
|
RTSP_URL: "<rtsp_url>"
|
|
REGIONS: "<box_size_1>,<x_offset_1>,<y_offset_1>,<min_person_size_1>,<min_motion_size_1>,<mask_file_1>:<box_size_2>,<x_offset_2>,<y_offset_2>,<min_person_size_2>,<min_motion_size_2>,<mask_file_2>"
|
|
MQTT_HOST: "your.mqtthost.com"
|
|
MQTT_USER: "username" #optional
|
|
MQTT_PASS: "password" #optional
|
|
MQTT_TOPIC_PREFIX: "cameras/1"
|
|
DEBUG: "0"
|
|
```
|
|
|
|
Here is an example `REGIONS` env variable:
|
|
`350,0,300,5000,200,mask-0-300.bmp:400,350,250,2000,200,mask-350-250.bmp:400,750,250,2000,200,mask-750-250.bmp`
|
|
|
|
First region broken down (all are required):
|
|
- `350` - size of the square (350px by 350px)
|
|
- `0` - x coordinate of upper left corner (top left of image is 0,0)
|
|
- `300` - y coordinate of upper left corner (top left of image is 0,0)
|
|
- `5000` - minimum person bounding box size (width*height for bounding box of identified person)
|
|
- `200` - minimum number of changed pixels to trigger motion
|
|
- `mask-0-300.bmp` - a bmp file with the masked regions as pure black, must be the same size as the region
|
|
|
|
Mask files go in the `/config` directory.
|
|
|
|
Access the mjpeg stream at http://localhost:5000
|
|
|
|
## Integration with HomeAssistant
|
|
```
|
|
camera:
|
|
- name: Camera Last Person
|
|
platform: generic
|
|
still_image_url: http://<ip>:5000/best_person.jpg
|
|
|
|
binary_sensor:
|
|
- name: Camera Motion
|
|
platform: mqtt
|
|
state_topic: "cameras/1/motion"
|
|
device_class: motion
|
|
availability_topic: "cameras/1/available"
|
|
|
|
sensor:
|
|
- name: Camera Person Score
|
|
platform: mqtt
|
|
state_topic: "cameras/1/objects"
|
|
value_template: '{{ value_json.person }}'
|
|
unit_of_measurement: '%'
|
|
availability_topic: "cameras/1/available"
|
|
```
|
|
|
|
## Tips
|
|
- Lower the framerate of the RTSP feed on the camera to reduce the CPU usage for capturing the feed
|
|
- Use SSDLite models to reduce CPU usage
|
|
|
|
## Future improvements
|
|
- [ ] Build tensorflow from source for CPU optimizations
|
|
- [ ] Add ability to turn detection on and off via MQTT
|
|
- [ ] MQTT motion occasionally gets stuck ON
|
|
- [ ] Output movie clips of people for notifications, etc.
|
|
- [ ] Integrate with homeassistant push camera
|
|
- [ ] Merge bounding boxes that span multiple regions
|
|
- [ ] Switch to a config file
|
|
- [ ] Allow motion regions to be different than object detection 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
|
|
- [ ] Look into neural compute stick
|
|
|
|
## Building Tensorflow from source for CPU optimizations
|
|
https://www.tensorflow.org/install/source#docker_linux_builds
|
|
used `tensorflow/tensorflow:1.12.0-devel-py3`
|
|
|
|
## Optimizing the graph (cant say I saw much difference in CPU usage)
|
|
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/graph_transforms/README.md#optimizing-for-deployment
|
|
```
|
|
docker run -it -v ${PWD}:/lab -v ${PWD}/../back_camera_model/models/ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb:/frozen_inference_graph.pb:ro tensorflow/tensorflow:1.12.0-devel-py3 bash
|
|
|
|
bazel build tensorflow/tools/graph_transforms:transform_graph
|
|
|
|
bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
|
|
--in_graph=/frozen_inference_graph.pb \
|
|
--out_graph=/lab/optimized_inception_graph.pb \
|
|
--inputs='image_tensor' \
|
|
--outputs='num_detections,detection_scores,detection_boxes,detection_classes' \
|
|
--transforms='
|
|
strip_unused_nodes(type=float, shape="1,300,300,3")
|
|
remove_nodes(op=Identity, op=CheckNumerics)
|
|
fold_constants(ignore_errors=true)
|
|
fold_batch_norms
|
|
fold_old_batch_norms'
|
|
``` |