📩 Automated Image Resizing and Transfer System Using AWS Services

📌 Project Description

✅ Automated Image Processing System : The project develops an automated solution for image processing and management within AWS.
✅ Efficient Image Handling : It automatically resizes images and transfers them to a designated storage location.
✅ Real-Time Notifications : Stakeholders receive instant updates on image processing and transfers.
✅ AWS Service Integration : Utilizes AWS Lambda, S3, and SNS to orchestrate the workflow.

✨ Key Features

💡 Image Processing Automation – Automatically resize and optimize images upon upload.
🔒 Secure Storage – Store processed images in a secure and reliable S3 bucket.
📩 Real-time Notifications – Receive instant updates about image processing via SNS.
📈 Scalable Architecture – Designed to handle high image processing demands.
💰 Cost-efficient Solution – Leverages AWS serverless technologies to minimize operational costs.

📊 Overview

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🎥 Live Demo

1. Uploaded image to source-image-bucket.
2. Checked destination-resized-bucket for the resized image.
3. Verifed SNS notification for the processed image.

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🔍 Implementation Workflow

1️⃣ Automated Image Processing Workflow

1. User uploads an image to an S3 Source Bucket.
2. AWS S3 triggers Lambda upon new uploads.
3. Lambda processes and resizes the image using PIL (Python Imaging Library).
4. Resized images are stored in the Destination S3 Bucket.
5. CloudWatch Logs capture execution details.

2️⃣ AWS Lambda with Python Implementation

• Utilized boto3 for S3 interactions.
• Implemented image resizing (default: 300x300 pixels).
• Optimized image storage with BytesIO buffer.

3️⃣ Secure & Scalable AWS Infrastructure

• Configured IAM roles for Lambda execution.
• Enabled SNS notifications for error handling.
• Implemented CloudWatch logging & monitoring.

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🛠 Step-by-Step Setup

🔗 Step 1: Create Two S3 Buckets

1. Go to AWS S3 Console.
2. Create a Source Bucket (e.g., source-image-bucket).
3. Create a Destination Bucket (e.g., destination-resized-bucket).
4. Modify public access settings if required.

🔗 Step 2: Create an SNS Topic and Subscription

1. Navigate to the SNS Console.
2. Create a new SNS Topic (e.g., image-processing-topic).
3. Add a Subscription to the topic (choose email or Lambda as an endpoint).
4. Confirm the subscription status via email for notifications.

🔗 Step 3: Configure AWS Lambda Function

1. Go to AWS Lambda Console.
2. Click Create Function > Author from Scratch.
3. Name it resize-image-function.
4. Choose Python 3.x as the runtime.
5. Set up an execution role with S3, SNS, and CloudWatch permissions.

🔗 Step 4: Configure IAM Roles with Required Permissions

1. Go to the IAM Console.
2. Create a new IAM Role.
3. Attach the following permissions:
   • Full access to S3.
   • Invoke Lambda.
   • Publish to SNS.
   • Log execution in CloudWatch.
4. Result of Attached permissions:

🔗 Step 5: Attach IAM Policy to Lambda Function

1. Create an IAM policy with the required permissions.
2. Attach it to the Lambda function’s execution role.

🔗 Step 6: Configure Lambda Environment Variables

• SOURCE_BUCKET: source-image-bucket
• DESTINATION_BUCKET: destination-resized-bucket

🔗 Step 7: Add S3 Trigger to Lambda

1. Go to Lambda Console.
2. Navigate to Configuration > Triggers.
3. Add S3 Trigger for source-image-bucket.
4. Set event type to PUT (Object Created).

🔗 Step 8: Deploy and Test Python Code in Lambda

1. Open the Lambda Code Editor.
2. Paste the script from lambda_function.py.
3. Click Deploy.
4. Run a Test Event to verify execution.
5. Retrieve the updated code from lambda_function.py for execution.

🔗 Step 9: Add a Layer in Lambda

1. Go to the Lambda Console.
2. Select Layers under the Code section.
3. Click Add Layer.

🔗 Step 10: Enter the ARN for the required dependencies (e.g., PIL library layer ARN)

arn:aws:lambda:ap-south-1:770693421928:layer:Klayers-p39-pillow:1

🔗 Step 11: Upload an Image and Verify

1. Upload an image to source-image-bucket.
2. Check destination-resized-bucket for the resized image.
3. Verifed SNS notification for the processed image.

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🎯 Expected Outcomes

✅ Fully automated image resizing system in AWS.
✅ Hands-on experience in Lambda, S3, IAM, SNS, and CloudWatch.
✅ Secure and scalable AWS architecture.
✅ Efficient image processing with PIL and S3 storage.

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