Rekognition Demo

The purpose of this demo is to build a stack that uses Amazon Rekognition to recognize crowded places.

Overview

This repository is a demonstration of how to use the feature Detect Labels of Amazon Rekogntion to rekognize places where a crowd environment is detected and send a notification to an email address, in this demo we will simulate a satellite that will provide image of crowded places and no crowded places to our system.

I tested this demo on eu-west-1 region

Prerequisites

• Docker
• Docker Compose
• awscli
• Pre configured AWS credentials

Architecture Diagram

Setup instructions

Provision the infraestructure

You just need to run the cloudformation template to provision the infraestructure and all components

First you need to create a S3 bucket to store our application lambda code.

aws s3 mb s3://<MY_BUCKET_NAME>

ZIP lambda codes or use the ones provided.

zip ./lambda_package_crowd.zip lambda_recognize_crowd/lambda_function.py

zip ./lambda_package_notify.zip lambda-sns-notify/lambda_function.py

Upload the lambda packages to the S3 bucket that we created before.

aws s3 cp lambda_package_crowd.zip s3://<MY_BUCKET_NAME>/lambda_code/

aws s3 cp lambda_package_notify.zip s3://<MY_BUCKET_NAME>/lambda_code/

Now we need to create the stack using our Cloudformation template available in cloudformation/ folder

aws cloudformation create-stack --stack-name sattelite-rekognition-stack --template-body file://cloudformation/rekognitionstack.yaml --parameters ParameterKey=StackName,ParameterValue=sattelite-rekognition-stack ParameterKey=BucketName,ParameterValue=<NEW_BUCKET_NAME> ParameterKey=BucketLambdaCode,ParameterValue=<BUCKET_NAME_THAT_WE_PROVISIONED_BEFORE> ParameterKey=SnsSubscriptionEmail,ParameterValue=<YOUR_EMAIL_ADDRESS> --capabilities CAPABILITY_IAM

Wait for the stack to be created.

Local sattelite application.

Add some variables to our docker-compose.yaml file, go to satellite-app/ folder.

environment:
    BUCKET_NAME: "<NEW_BUCKET_NAME>"
    AWS_ACCESS_KEY_ID: "<YOUR_AWS_ACCESS_KEY_ID>"
    AWS_SECRET_ACCESS_KEY: "<YOUR_AWS_SECRET_ACCESS_KEY>"
    AWS_DEFAULT_REGION: "eu-west-1"

Add the variables in docker-compose.yaml file, remember <NEW_BUCKET_NAME> is the name of the bucket that Cloudformation provisioned not the one we created early.

Start docker daemon service if you haven't.

systemctl start docker

Browse to app directory, build into a docker and start our application.

cd satellite-app && docker-compose up --build

Access our application in http://localhost:5000

The result will be that:

Let's submit our image that will be analyzed, click in Submit Image

Our "Satellite" will send the image to Amazon S3 bucket and it will start the process of analyze our image.

If is a crowded place you will receive an notification in the e-mail that you choose above in the Cloudformation template.

Clean Up

Erase all objects in our Amazon S3 bucket that was provisioned by Cloudformation

aws s3 rm s3://<BUCKET_NAME_THAT_WAS_PROVISIONED_BY_CF> --recursive

Delete Cloudformation stack (It will delete all resources)

aws cloudformation delete-stack --stack-name sattelite-rekognition-stack

Delete bucket that we used to store our lambda code.

aws s3 rb s3://<MY_BUCKET_NAME> --force

Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.