automatic-anomaly-detection-tool

Automatic Anomaly Detection Tool a.k.a Sushi Detection

This is a sample source code to create Sushi Detection demo.

Devices

This demo use following devices.

• Nvidia Jetson Nano
• Robot Hand
  • https://store.arduino.cc/usa/tinkerkit-braccio
• Servo Motor
  • https://servodatabase.com/servo/towerpro/sg90
• Arduino Uno Rev3
• USB Web camera

Message send between devices

Bread Board Circuit

1. Setup AWS resource

1.1. Create AWS IoT certificates

This demo have two devices connected to Greengrass. In this step you need to create three certificate for following devices.

• Greengrass Core
• Robot Controller
• Rail Controller

Follow this step to create certificates. Dont forget to activate and download the generated certificate, private key and public key.

Check each certificate ARN in the certificate detail page. This ARN is use in next step.

1.2. Upload files

Download Lambda Function Zip file and Machine Learning Model from following link and upload to your S3 bucket. The region must be same with Cloud Function region.

• Lambda Function
  • Demo Lambda zip
• ML Model
  • Demo ML Model

1.3. Create Cloud Formation stack

1. Sign in to the AWS Management Console and open the AWS CloudFormation console at https://console.aws.amazon.com/cloudformation.

2. If this is a new AWS CloudFormation account, click Create New Stack. Otherwise, click Create Stack. If you choose Create stact from drop down list choose With new resources (standard)

3. In the Template section, select Template is ready

4. In the Specify template section, select Upload a template file. Upload demo_CFn.yml

5. Click Next

6. Enter stack name

7. Fill Parameters

parameter	value
GreengrassCoreCertificateARN	certificate ARN which created at previous step
RailControllerCertificateARN	certificate ARN which created at previous step
RobotControllerCertificateARN	certificate ARN which created at previous step
GreengrassGroupName	your Greengrass name
InferenceLambdaSourceKey	your S3 object key(lambda funcion zip)
LambdaSourceBucket	S3Bucket(lambda funcion zip)
MLModelURI	ML Model S3 URL

8. Click Next. And click Next again.

9. Review the information for the stack. When you're satisfied with the settings, check I acknowledge that AWS CloudFormation might create IAM resources. and click Create.

10. It takes few minutes to complete creating Greengrass.

1.4. Download Greengrass software

This demo works for Greengrass version 1.9x.

1. Download Greengrass Core Software from here. Choose Armv8 (AArch64) Ubuntu 18.04 for Nvidia Jetson Nano.

2. Setup Jetson Nano

2.1. Create Jetson Nano SD Card

Follow the step on the original setup page.

https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit#write

Boot your Jetson Nano, after you install the image in SD card. And follow the Setup and First Boot. https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit#setup

2.2. Install software

Use terminal on Jetson Nano to install software.

Install required software

sudo apt-get install -y git build-essential libatlas-base-dev libopencv-dev graphviz vim curl

Install SageMaker Neo Runtime

Use terminal on Jetson Nano.

curl https://s3-us-west-2.amazonaws.com/neo-ai-dlr-release/v1.0/jetsonnano-aarch64-cu10-ubuntu18_04-glibc2_27-libstdcpp3_4/dlr-1.0-py2.py3-none-any.whl -o dlr-1.0-py2.py3-none-any.whl

pip install dlr-1.0-py2.py3-none-any.whl

2.3 Install Greengrass Core

Create user ggc_user and group ggc_group

sudo adduser --system ggc_user
sudo groupadd --system ggc_group

Copy files

1. Copy Greengrass Core software to Jetson Nano.
2. Copy certificate for Greengrass Core to Jetson Nano.
3. Extract Greengrass

sudo tar -zxvf greengrass-linux-x86-64-1.9.4.tar.gz -C /

4. Copy certificate to Greengrass directory

cp /path/to/your/certificate /greengrass/certs/

5. Download Root CA certificate

sudo curl https://www.amazontrust.com/repository/AmazonRootCA1.pem -o /greengrass/certs/root.ca.pem

6. Edit Greengrass config

Replace each part with your environoment.

part	value
[ROOT_CA_PEM_HERE]	root.ca.pem
[CLOUD_PEM_CRT_HERE]	your certificate file name. ex) abcd1234-certificate.pem.crt
[CLOUD_PEM_KEY_HERE]	your private key file name. ex) afcf52c6b2-private.pem.key
[THING_ARN_HERE]	Greengrass Core ARN ex) arn:aws:iot:us-east-w:1234567890:thing/sushi_Core
coreThing.iotHost	check your AWS IoT Endpoint from AWS IoT console -> Settings -> Custom endpoint
coreThing.ggHost	replace [AWS_REGION_HERE] with your region.
runtime.cgroup.useSystemd	`[yes

sudo vim /greengrass/config/config.json 

{
    "coreThing": {
        "caPath": "[ROOT_CA_PEM_HERE]",
        "certPath": "[CLOUD_PEM_CRT_HERE]",
        "keyPath": "[CLOUD_PEM_KEY_HERE]",
        "thingArn": "[THING_ARN_HERE]",
        "iotHost": "[HOST_PREFIX_HERE]-ats.iot.[AWS_REGION_HERE].amazonaws.com",
        "ggHost": "greengrass-ats.iot.[AWS_REGION_HERE].amazonaws.com"
    },
    "runtime": {
        "cgroup": {
            "useSystemd": "[yes|no]"
        }
    },
    "managedRespawn": false,
    "crypto": {
        "caPath" : "file://certs/[ROOT_CA_PEM_HERE]",
        "principals": {
            "IoTCertificate": {
                "privateKeyPath": "file://certs/[CLOUD_PEM_KEY_HERE]",
                "certificatePath": "file://certs/[CLOUD_PEM_CRT_HERE]"
            },
            "SecretsManager": {
                "privateKeyPath": "file://certs/[CLOUD_PEM_KEY_HERE]"
            }
        }
    }
}

2.4. Start Greengrass

Connect USB camera before starting Greengrass daemon.

Start Greengrass

sudo /greengrass/ggc/core/greengrassd start

check Greengrass log.

sudo tail -F /greengrass/ggc/var/log/system/runtime.log

Auto start Greengrass

1. Copy Greengrass/greengrass.service.txt to /etc/systemd/system/greengrass.service

2. Enable service

sudo systemctl enable greengrass.service

3. Start service

sudo systemctl start greengrass.service

Deploy Greengrass

See here to deploy Greengrass to your device.

The deployed Lambda Function source code can find in this repo LambdaFunction directory.

3. Setup Rail Controller and Robot Controller

This demo use Raspberry Pi 3+ to control servo motor and comunicate with Arduino Uno.

3.1. Install Raspberry Pi required software

1. Install software

sudo pip install AWSIoTPythonSDK

3.2. Setup Rail Controller

1. rename appconfig_template.json to appconfig.json
2. edit appconfig.json to your environoment

attribute	value
IOT_ENDPOINT	your AWS IoT endpoint
CERT_FILE	your certificate file name. ex) abcd1234-certificate.pem.crt
PRIVATE_KEY_FILE	your private key file name. ex) afcf52c6b2-private.pem.key
THING_NAME	your RailController thing name

3. Copy all demo source in this repo /RailController to Raspberry Pi /home/pi/RailController

4. SSH into Raspberry pi

5. Correct WorkingDirectory and ExecStart path in railcontroller.service.txt

6. Copy railcontroller.service.txt to /etc/systemd/system/railcontroller.service

7. Enable service

sudo systemctl enable railcontroller.service

8. Start service

sudo systemctl start railcontroller.service

3.3. Setup Arduino Uno

Use arduino IDE to compile and install to Arduino Uno.

1. Open arduino/arduino_robot.ino with Arduino IDE
2. compile and upload to Arduino Uno

3.4. Setup Robot Controller

1. rename appconfig_template.json to appconfig.json
2. edit appconfig.json to your environoment

attribute	value
IOT_ENDPOINT	your AWS IoT endpoint
CERT_FILE	your certificate file name. ex) abcd1234-certificate.pem.crt
PRIVATE_KEY_FILE	your private key file name. ex) afcf52c6b2-private.pem.key
THING_NAME	your RobotController thing name

3. Copy all demo source in this repo /RobotController to Raspberry Pi /home/pi/RobotController

4. SSH into Raspberry pi and move directory to /home/pi/RobotController

5. Correct WorkingDirectory and ExecStart path in robotcontroller.service.txt

6. Copy robotcontroller.service.txt to /etc/systemd/system/robotcontroller.service

7. Enable service

sudo systemctl enable robotcontroller.service

8. Start service

sudo systemctl start robotcontroller.service

Train your own machine learning model

This demo use SageMaker built-in Image Classification template to create model. The created model use SageMaker Neo to compile.

• About built-in Image Classification Algorithm
  • https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html
• About compiling with SageMaker Neo
  • https://docs.aws.amazon.com/sagemaker/latest/dg/neo.html

Use Object Detection with Image Classification

This demo use OpenCV to detect sushi saucer. But you can use Object Detection to detect sushi saucer. SageMaker built-in Object Detection Algorithm is not supported at SageMaker Neo(2019/11). If you want to use Object Detection with Image Classification use MXNet.

Authors

• Jun Ichikawa [email protected]
• Tatsuhiro Iida [email protected]

License

This library is licensed under the Apache 2.0 License.