Business Implications
The project demonstrates a scalable and cost-effective image recognition solution powered by AWS. It enables organizations to automate image tagging, streamline digital asset management, and integrate visual recognition capabilities into enterprise workflows — improving efficiency in media, retail, and surveillance applications.
Steps Performed
Built a Python-based system using Amazon Rekognition and S3 for image label detection, enabling automated object recognition and visualization with confidence scores.
1.
Create S3 Bucket And Upload Images
Created a secure S3 bucket in AWS to store and organize sample images for processing. Uploaded multiple images with diverse objects to enhance Rekognition’s accuracy during labeling and detection.
2.
Install And Configure AWS CLI
Installed the AWS Command Line Interface (CLI) to interact with AWS services programmatically. Configured access keys, regions, and permissions for authenticated Rekognition and S3 operations.
3.
Implement Rekognition Detection Logic
Used Boto3 to initialize the Rekognition client and implement the detect_labels function. The model analyzed each image, detected up to 10 objects, and returned their confidence scores.
4.
Visualize Image Labels
Loaded image data from S3 using PIL and visualized results using Matplotlib, displaying bounding boxes and labels for identified objects directly over the image.
5.
Run Main Function And Test Model
Executed the main Python script to test the end-to-end workflow. Verified Rekognition’s labeling accuracy and ensured consistent detection across images with varying complexity.
AWS Services Used
Amazon Rekognition
Amazon S3
AWS CLI
AWS IAM
AWS Lambda
Amazon CloudWatch
Python
Boto3
Matplotlib
PIL (Python Imaging Library)
Technical Tools Used
Computer Vision Modeling
Image Recognition Automation
Cloud Architecture Deployment
Python & AWS SDK Integration
Skills Demonstrated
AWS Image Labels Generator - AWS Rekognition
AI-Based Object Recognition Using Rekognition
Developed an AI-powered image label generator using Amazon Rekognition to automatically detect and tag objects in uploaded images stored in Amazon S3. The system identifies multiple labels per image with confidence scores, enabling intelligent image categorization and visual recognition.
