Originally developed by gerrito333 on Github Keep your cat’s prey out of your home with a machine learning trained pet door. Turn your Sureflap cat flap into a smart flap that locks when you cat tries to take prey into your home. Leverage Google Coral Edge TPU to run millisecond inference on infrared images captured on your Raspberry Pi. Object detection is based on TFLite and a MobileNet v1 SSD.

Background story on Medium

Warning! The code in this repository is still in an early stage of development and will not work as it is. If you are looking for something that works I recommend the original repository from gerrito333 combined with the comments from Tronje-the-Falconer.

The goal of this repository is to operate without motion detectors.

Required hardware:

• Sureflap or Sureflap Connect
• Raspberry Pi 4B
• Raspberry USB Power supply
• Case for Raspberry
• MicroSD Card
• Google Coral
• Infrared camera
• Camera cable
• Jumper cables
• Relay

Setup Mausjaeger Service:

update your Pi-OS

sudo apt update && sudo apt upgrade -y && sudo apt dist-upgrade -y

Enable Systemrequirements (Camera...

sudo raspi-config

Install Google Coral TFLite Runtime and other dependencies

echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list
curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -
sudo apt update && sudo apt install libedgetpu1-std python3-tflite-runtime git python3-picamera libtiff5 python3-pip

Install Dependencies an compile OpenCV

The pip3-Package will not work on the RPi4, so you have do this yourself. Grab a coffee, this will take some time...

Install Dependencies

sudo apt install cmake build-essential pkg-config git libjpeg-dev libtiff-dev libjasper-dev libpng-dev libwebp-dev libopenexr-dev libavcodec-dev libavformat-dev libswscale-dev libv4l-dev libxvidcore-dev libx264-dev libdc1394-22-dev libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev libgtk-3-dev python3-pyqt5 libatlas-base-dev liblapacke-dev gfortran libhdf5-dev libhdf5-103-1 python3-dev python3-pip python3-numpy

enlarge swap before compiling

sudo nano /etc/dphys-swapfile

Change

CONF_SWAPSIZE=100

to

CONF_SWAPSIZE=2048

Restart swap-Service

sudo systemctl restart dphys-swapfile

Get the latest version of OpenCV from GitHub.

cd ~
git clone https://github.com/opencv/opencv.git
git clone https://github.com/opencv/opencv_contrib.git

Create build dir and move into

mkdir ~/opencv/build
cd ~/opencv/build

Generate makefile and compile

cmake -D CMAKE_BUILD_TYPE=RELEASE \
    -D CMAKE_INSTALL_PREFIX=/usr/local \
    -D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \
    -D ENABLE_NEON=ON \
    -D ENABLE_VFPV3=ON \
    -D BUILD_TESTS=OFF \
    -D INSTALL_PYTHON_EXAMPLES=OFF \
    -D OPENCV_ENABLE_NONFREE=ON \
    -D CMAKE_SHARED_LINKER_FLAGS=-latomic \
    -D BUILD_EXAMPLES=OFF ..
	
make -j$(nproc)
sudo make install
sudo ldconfig

change swap back to original

sudo nano /etc/dphys-swapfile

Change

CONF_SWAPSIZE=2048

to

CONF_SWAPSIZE=100

Restart swap-Service

sudo systemctl restart dphys-swapfile

Install Python Dependencies

sudo pip3 install argparse

Clone this repo on your raspberry

git clone https://github.com/de-wax/mousehunter-edge.git

create image-folder

cd ~/mousehunter-edge/mausjaeger/
mkdir images

Enable, start and check Mausjaeger-Service

sudo systemctl enable ~/mousehunter-edge/mausjaeger/mausjaeger.service
sudo systemctl start mausjaeger.service

Check if service is running

systemctl status mausjaeger.service

also look at the log-File at: /var/log/mausjaeger.log

Optional: Enable Log-Rotate for mausjaeger.log

sudo cp /home/pi/mousehunter-edge/mausjaeger/mausjaeger /etc/logrotate.d/mausjaeger
sudo systemctl restart logrotate

Setup Imagewatcher Service

Install pypi for gpio support

pip3 install RPi.GPIO

install pinotify

pip3 install pyinotify

Install watchdog

pip3 install watchdog

Install boto3

pip3 install boto3

Install Pillow

pip3 install Pillow==2.2.2

Install APScheduler

pip3 install APScheduler

install confuse

pip3 install confuse

Amazon support

install the AWS Command Line Interface

pip3 install awscli --upgrade --user

Add AWS CLI executable to your Command Line Path

export PATH=/home/pi/.localbin:$PATH

Configure the default AWS CLI

aws configure

AWS Access Key ID [None]: <<<YOUR_ACCESS_KEY_ID>>>
AWS Secret Access Key [None]: <<<YOUR_SECRET_ACCESS_KEY>>>
Default region name [None]: <<<YOUR_REGION_NAME_EXAMPLE_eu-central-1>>>
Default output format [None]: json

Coral support

install the compiler

echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list
curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -
sudo apt-get update

install Edge TPU runtime library

sudo apt-get install libedgetpu1-std

Mousehunter

Clone this repo on your raspberry

git clone https://github.com/gerrito333/mousehunter-edge.git

create image- and logfolder with logfile

cd mousehunter-edge/mausjaeger/
mkdir images
mkdir logs
cd logs
touch mausjaeger.log

config mousehunter

copy config yaml to ~/.config/mousehunter-edge/

mkdir .config
cd .config
mkdir mousehunter-edge
cd ~/mousehunter-edge/imagewatcher
cp config.yaml ~/.config/mousehunter-edge/config.yaml

Connect motion detector OUT to GPIO8, check pinout to see your layout.

                                3V3  (1) (2)  5V         <--- motion detector VCC
                              GPIO2  (3) (4)  5V         <--- + port of relay
                              GPIO3  (5) (6)  GND        <--- motion detector GND
                              GPIO4  (7) (8)  GPIO14
                                GND  (9) (10) GPIO15
                              GPIO17 (11) (12) GPIO18
                              GPIO27 (13) (14) GND
                              GPIO22 (15) (16) GPIO23
                                 3V3 (17) (18) GPIO24
                              GPIO10 (19) (20) GND
                               GPIO9 (21) (22) GPIO25
                              GPIO11 (23) (24) GPIO8      <--- motion detector OUT
                                 GND (25) (26) GPIO7
                               GPIO0 (27) (28) GPIO1
                               GPIO5 (29) (30) GND
                               GPIO6 (31) (32) GPIO12
                              GPIO13 (33) (34) GND
                              GPIO19 (35) (36) GPIO16
     s-port of relay --->     GPIO26 (37) (38) GPIO20
     - port of relay --->        GND (39) (40) GPIO21

Configure ~/.config/mousehunter-edge/config.yaml with your AWS bucket name and ensure that aws credentials can read the objects in it. Keep APNToken and certfile empty if you do not have it.

sudo nano ~/.config/mousehunter-edge/config.yaml

bucket: <<<YOUR_AWS_S3_BUCKET>>>
curfewTime: 15
APNToken: <<<YOUR_APPLE_PUSH_NOTIFICATION_SERVICE_APNs_TOKEN>>>
alertThreshold: 2.0
certfile: <<<YOUR_APPLE_CERTIFICATE_FOR_APN_FOR_APN_USAGE>>>

Update WorkingDirectory values for both .service files if not '/home/pi/mousehunter-edge/'.

sudo nano ~/mousehunter-edge/mausjaeger/mausjaeger.service
sudo nano ~/mousehunter-edge/imagewatcher/imagewatcher.service

sudo systemctl enable ~/mousehunter-edge/imagewatcher/imagewatcher.service
sudo systemctl start imagewatcher.service

Proove if services are running

systemctl status imagewatcher.service

Retrain your own object detection model:

label you data with labelImg: https://github.com/tzutalin/labelImg

Convert your labeled data to the TFRecord file format, e.g. with https://roboflow.com

Follow the coral.ai tuturials part "Set up the Docker container" on a machine with strong CPU. No GPU is required. https://coral.ai/docs/edgetpu/retrain-detection/#set-up-the-docker-container

Copy your TFRecords to docker/object_detection/data

Configure train_input_reader, eval_input_reader and model.ssd.num_classes the training pipeline:

docker/object_detection/out/ckpt/pipeline.config

Remove all lines following "PREPARING dataset" and execute the script:

docker/object_detection/scripts/prepare_checkpoint_and_dataset.sh

Contine to follow the coral.ai tutorial part "Start training" and "Compile the model for the Edge TPU" Copy the created model to the imagewatcher/model/ folder. ..