Scarecrow-Cam

Maintenance & Project Status

This project is in what I would describe as “Limited maintenance only” status. I’ll continue to listen to CVE notifications and make sure you have some way of running it, but I don’t intend on working on any major releases.

This project, at the time of writing, was a fun idea that used a somewhat novel approach to solve a real-life problem - a showcase, not a real, long-term project. But like so many tech showcases, that’s all it is: Designed well enough to work and make a point, but not important enough to keep updated.

Since writing this in 2019, things have moved on: This project depends on a very specific, old, and hence not necessarily well-performing Tensorflow model and was never designed to be containerized from the ground up, which makes the build and deployment process exceptionally brittle and, frankly, tedious to maintain. About half the dependencies called out in the INSTALL.md file are probably not actually needed.

There are also some (given the nature of a showcase, somewhat intentional) design oversights that could be mitigated, namely the weak abstraction at many points of the project, such as in the plugin model; while re-working those is certainly possible, it’s not something I’ll be spending any time on soon.

Last but not least, there simply are better ways of deploying an object detection model on a Raspberry these days, such as TensorFlow Lite that allow you to run a model directly on the Pi. Local detection can still react accordingly to a detection threshold (which could be centrally managed, e.g. in a database) in the same way it does here, or even directly via GPIO. Nothing stops you from still maintaining a central point of accessing alerts from multiple cameras, but running the model locally will always outperform a network video stream.

Introduction

A Raspberry Pi powered, distributed (edge) computing camera setups that runs a Tensorflow object detection model to determine whether a person is on the camera. The Raspberry Pi is used for video streaming and triggering actions (such as playing audio, turning on lights, or triggering an Arduino), whereas a server or laptop runs the object detection. With a suitable TFLite installation, this can happen locally on the Raspberry as well.

Based on the detection criteria, a plugin model allows to trigger downstream actions.

Based on my blog.

Architecture

Side note: The setup shown here only fits the use-case of edge to a degree, as we run local detection on a separate machine; technically, the Raspberry Pi is capable of running Tensorflow on board, e.g. through TFLite or esp32cam.

You can change this behavior by relying on a local tensorflor instance and having the ZMQ communication run over localhost.

Updates

Please see CHANGELOG.md for details.

Configuration and data

Copy config/config.ini.sample to conf/config.ini with the settings for your Raspberry and server.

For playing audio, please adjust conf/plugins.d/audio.ini.

[Audio]
Path=../audio_files

For an appropriate path.

If you want to change the model, please check the Model Zoo. The blog article used the outdated ssd_mobilenet_v1_coco_2017_11_17.

[Tensorflow]
ModelUrl=ssd_mobilenet_v3_large_coco_2019_08_14
LabelMapPath=./models/research/object_detection/data/mscoco_label_map.pbtxt

Installing

Requirements

This project requires:

• A Raspberry Pi + the camera module v2 (the client)
• Any Linux machine on the same network (the server)

Using Docker (recommended)

Using Docker is the preferred way of running scarecrow, as it handles dependencies internally and operates within a controllable environment.

Build Image

git submodule update --init --recursive && git submodule update --remote
docker build . -t scarecrow

Run separately

Both client and server containers should be ran separately.

Server

This runs tensorflow and opens the zmq listener.

docker run -it \
    --name scarecrow_server \
    -p 5455:5454 \
    --ipc=host \
    -v $(pwd)/conf:/config \
    -v $(pwd)/models/research/object_detection/data:/models \
    scarecrow \
    /usr/local/bin/scarecrow_server --config /config

Client

Check cameras in /dev with ffplay /dev/video$x

docker run -it \
    --name scarecrow_client \
    -p 5454:5454 \
    -p 5558:5558 \
    --ipc=host \
    -v $(pwd)/conf:/config \
    -v $(pwd)/resources/audio_files:/data \
    --device /dev/snd:/dev/snd \
    --device=/dev/video2:/dev/video0 \
    scarecrow \
    /usr/local/bin/scarecrow_client --config /config --input 0

Docker Compose

Experimental

docker-compose up

Manual install (advanced)

scarecrow-cam can be installed using pip:

pip3 install . --upgrade

Please see INSTALL.md for details and troubleshooting.

Run (RasPi)

scarecrow_client --config ./conf --input 0 # for picam
scarecrow_client --config ./conf --input ./resources/tests/walking_test_5s.mp4 # for local video 

Run (Server)

scarecrow_server --config ./conf

Plugins

A plugin model allows to trigger downstream actions. These actions are triggered based on the configuration.

Plugins can be enabled by setting the following in config.ini:

[Plugins]
Enabled=audio
Disabled=

Currently, the following plugins are avaibale:

Plugin	Description	Requirements	Configuration	Base
audio	Plays audio files once a person is detected	Either playsound, pygame, or omxplayer	conf/plugins.d/audio.ini	ZMQ
store_video	Stores video files on the server, with a defined buffer or length	Path and Encoding settings	conf/plugins.d/store_video.ini	ZServerMQ

License

This project is licensed under the GNU GPLv3 License - see the LICENSE file for details.