Build a Digital Wardrobe with Amazon Rekognition
In this blog, I’ll walk through how to build a digital wardrobe where users can upload images of their clothing, allowing for virtual storage and enhanced shopping experiences. This feature boosts customer engagement and customer retention by offering personalized outfit suggestions and styling ideas based on new trends.
The video tutorial of this article is available here.
Tech Stack:
The tech stack I used to build the digital wardrobe are:
Amazon S3 for storing images.
EventBridge to trigger Lambda functions when new images are uploaded.
AWS Lambda for the core functionality.
Amazon Rekognition to detect and label clothing items in images using Machine Learning label detection technique.
DynamoDB to store image metadata like detected labels.
IAM Roles to manage permissions.
CloudWatch for monitoring purposes.
How it Works:
The following steps describe how this functionality works.
User Image Upload: Users upload their clothing images via a mobile or web app to an S3 bucket, where images are stored.
Triggering Event: When an image is uploaded, EventBridge triggers a Lambda function to analyze it.
Label Detection: The Lambda function reads the image from S3 and sends it to Amazon Rekognition. Rekognition then detects labels (e.g., dress, shoes) with confidence scores.
Data Storage: Labels with confidence scores above 90% are saved in DynamoDB. The image metadata is now accessible for styling recommendations and personalized notifications.
Step-by-Step AWS Setup:
Create S3 Bucket: Start by creating an S3 bucket, e.g., “digital-wardrobe-bucket” to store images.
Create DynamoDB Table: Set up a DynamoDB table (e.g., “virtual-wardrobe”) to store image labels and metadata.
Set Up Lambda Function: Develop the Lambda function that integrates Amazon Rekognition. The core functionality includes reading images from S3, analyzing them, and storing detected labels in DynamoDB.
Configure IAM Roles: Assign appropriate permissions for S3, DynamoDB, and Rekognition. Follow AWS best practices by using least privilege principles.
Test the Workflow: Upload images, trigger the Lambda function, and review the results in DynamoDB.
Label Detection using Amazon Rekognition:
Here’s a high-level look at the core functionality:
Get Image Labels: Calls the Rekognition API to detect clothing items. It filters labels with confidence scores greater than 90%.
Store Clothing Data: Saves the detected labels into DynamoDB.
Main Lambda Handler: Orchestrates the flow, processing images from S3, detecting clothing labels, and storing the results.
# Detect labels in the image
labels = rekognition.detect_labels(Image={'Bytes': image_bytes}, MaxLabels=3)
# Extract the top labels and their confidence levels
top_labels = []
for l in labels['Labels']:
if l['Confidence'] > 90:
top_labels.append(l['Name'])
return top_labels
Personalization and Customer Engagement:
Once set up, this digital wardrobe allows users to store, organize, and receive styling suggestions for their clothes. E-commerce platforms can also leverage this to offer personalized notifications for new arrivals that match a user’s wardrobe.
Use Cases:
E-Commerce Fashion Sites: Personalize the shopping experience.
Retail: Offer virtual wardrobes that integrate with physical stores.
Styling Apps: Suggest outfits based on current trends and user preferences.
Understanding Amazon Rekognition:
Amazon Rekognition is a powerful machine learning service that identifies objects, scenes, and activities in images. Here’s how it works:
Image Processing: The uploaded image is analyzed using pre-trained machine learning models to detect patterns and recognize objects.
Labeling: Rekognition assigns labels to the detected objects, such as “shirt”, “dress”, or “handbag”, each with a confidence score. The higher the score, the more confident the system is about its accuracy. In this case, I filter for confidence scores above 90% to ensure precise labeling.
Scenes and Objects: Apart from clothing items, Rekognition can also detect broader scenes like “outdoors” or “office environment”, making it versatile for various use cases.
Amazon Rekognition can be enhanced with custom models, allowing you to train domain-specific recognition for items not covered in the general label set. For example, in healthcare applications, custom models can be trained to recognize specific objects like medical instruments.
Conclusion:
By leveraging AWS services like Lambda, Rekognition, and DynamoDB, you can build a scalable digital wardrobe that enhances user engagement through personalized experiences. This solution is perfect for e-commerce fashion sites looking to innovate and retain customers.