This project demonstrates a simple, event-driven image processing pipeline using AWS services. The pipeline is designed to automatically pixelate images uploaded to an S3 bucket and store the processed images in a separate bucket. This demonstrates the integration of AWS Lambda with S3 for serverless computing and automated workflows.
The pipeline uses two Amazon S3 buckets:
- Source Bucket: Where original images are uploaded.
- Processed Bucket: Where pixelated images are stored.
When an image is added to the source bucket, an AWS Lambda function is triggered via a PUT event. The Lambda function is responsible for processing the image.
The core of the pipeline is an AWS Lambda function that:
- Is triggered by image uploads (PUT events) in the source S3 bucket.
- Extracts bucket and object information from the event.
- Uses the Python Imaging Library (PIL) to create pixelated versions of the image.
The Lambda function creates 5 different pixelated variations of each image:
- 8x8 pixels
- 16x16 pixels
- 32x32 pixels
- 48x48 pixels
- 64x64 pixels
These variations are then uploaded to the processed bucket.
- AWS S3: For storing original and processed images.
- AWS Lambda: For executing the image processing logic.
- Python Imaging Library (PIL): For manipulating image pixels.
- Upload an image to the source S3 bucket.
- The Lambda function will automatically process the image and upload the pixelated versions to the processed bucket.
This project showcases the power of AWS services in building an automated, serverless image processing pipeline. It demonstrates the practical use of AWS Lambda for event-driven tasks and provides a foundation for more complex image processing and other automated workflows.