Portfolio

Overview:

The Automated Image Processing Application leverages Azure Functions to process images in real-time as they are uploaded to an Azure Blob Storage container. A Blob trigger initiates the function execution, which performs resizing images.

Key Features:

1. Real-time Image Processing
   • Whenever a user uploads an image to a designated Blob Storage container, an Azure Function is triggered automatically. This function processes the image in real time.
2. Scalability
   • By utilizing Azure’s serverless architecture, the image processing pipeline can handle thousands of images without any manual intervention.
3. Cost-Effective Serverless Solution
   • Since Azure Functions is a pay-per-use service, we only pay for the execution time of the function.
4. Seamless Integration with Azure Blob Storage
   • Azure Blob Storage acts as the storage backbone, holding both the source and processed images. By using Blob triggers, the solution seamlessly integrates with the storage service, eliminating the need for manual intervention.
5. Event-Driven Execution
   • The function is triggered automatically upon new image uploads, thanks to Azure Blob Triggers. This event-driven model ensures that image processing happens immediately and efficiently.

Tech Stack:

1. Azure Functions
   • Azure Functions automatically run the Python whenever a new image is uploaded to Blob Storage using Azure Blob Triggers.
2. Azure Blob Storage
   • Azure Blob Storage serves as both the source and destination for images. Images uploaded to a specific container trigger the Azure Function, which then processes the image and saves it back to another container.
3. Python (with Pillow Library)
   • Python’s Pillow library is used to resize, (crop, watermark) and manipulate images inside the Azure Function. It is easy to integrate with the Azure SDK and ideal for lightweight image processing tasks.

Category:

Tags:

Links: