AWS Vision Insights

Image analysis and emotions classification with Amazon Rekognition

📖 Summary

• 1 - AWS Architecture
• 2 - How to use
• 3 - File structure

1 - AWS Architecture

2 - How to use:

Clone this repository:

git clone https://github.com/LuizScarsi/aws-vision-insights.git

Inside the src folder:

• Create a .env file to store the BUCKET_NAME and AWS_REGION_NAME:

  touch .env
  

• Follow the example from example.env file:

  BUCKET_NAME=bucket-name
  AWS_REGION_NAME=region-name
  

Install serverless framework. More information here

npm install -g serverless

Create your credentials (AWS Acess Key and AWS Secret Access Key) with IAM. More information here

Run the following command inserting your credentials:

serverless config credentials \
  --provider aws \
  --key AKIAIOSFODNN7EXAMPLE \
  --secret wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY

It is also possible to configure with aws-cli running the following command:

$ aws configure
AWS Access Key ID [None]: AKIAIOSFODNN7EXAMPLE
AWS Secret Access Key [None]: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
Default region name [None]: us-east-1
Default output format [None]: ENTER

To deploy in your AWS account access the src folder and run:

serverless deploy

3 - File structure:

• src
  • controllers
    • v1_controller.py
    • v2_controller.py
  • routes
    • v1
      • v1_description.py
      • v1_vision.py
    • v2
      • v2_description.py
      • v2_vision.py
    • health.py
  • services
    • rekognition.py
    • s3.py
  • utils
    • util.py
  • .example.env
  • serveless.yaml
• README.md