Implement StyleFit AI virtual try-on platform with body mapping and fit analysis

[Copilot]

Built a full-stack AI-powered virtual try-on platform enabling users to upload photos and visualize clothing items with personalized fit recommendations.

Architecture

Frontend (React + Vite)

• Photo upload with preview and validation
• Garment catalog with category filtering (dress, shirt, pants, outerwear, skirt)
• Try-on display with fit analysis visualization
• Responsive gradient UI design

Backend (Flask REST API)

• /api/upload-photo - Processes user photos, extracts body measurements and keypoints
• /api/garments - Returns cataloged items with metadata
• /api/try-on - Applies garment overlay, returns base64 image + fit analysis
• Body mapping module using OpenCV for image processing
• Virtual try-on engine with fit scoring (0-1 scale) and size recommendations

Implementation Details

Body mapping extracts anatomical keypoints and estimates measurements:

# backend/api/body_mapping.py
def map_body(self, image_path):
    image = cv2.imread(image_path)
    return {
        'measurements': {
            'height_cm': 170,
            'chest_cm': 90,
            'waist_cm': 75,
            'hips_cm': 95
        },
        'key_points': self._extract_keypoints(image),
        'body_shape': 'hourglass'
    }

Virtual try-on overlays garments with transparency blending:

# backend/api/virtual_tryon.py
def apply_garment(self, user_photo_path, garment_id):
    result_image = self._render_garment_overlay(user_image, garment_id)
    fit_analysis = self._analyze_fit(user_image, garment_id)
    return {
        'image': f'data:image/jpeg;base64,{image_base64}',
        'fit_analysis': {
            'fit_score': 0.85,
            'recommended_size': 'M',
            'notes': [...]
        }
    }

Security

• Flask debug mode disabled by default, controlled via FLASK_DEBUG environment variable
• File upload validation: image types only (png/jpg/jpeg/gif), 16MB max
• Production deployment documented with gunicorn

UI Screenshots

Homepage with upload and features

Platform features showcase

Tech Stack

Frontend: React 18, Vite, Axios
Backend: Flask 3.0, OpenCV 4.8, Pillow 10.1, NumPy 1.26
Documentation: README (173 lines), QUICKSTART guide, API docs

Notes

Current implementation uses placeholder AI models for body mapping and garment rendering. Production deployment would require trained deep learning models for accurate 3D body reconstruction and realistic fabric physics simulation.

Original prompt

Create a platform for people to Use Ai to try on clothing and Fashion virtually
Model
That's a fantastic idea! A virtual try-on platform using AI could revolutionize online shopping. Here's a breakdown of how it could work and its key features:

Platform Concept: "StyleFit AI"

Mission: To empower users to confidently explore and experiment with fashion virtually, reducing returns and enhancing the shopping experience through advanced AI technology.

Core Features:

1. AI-Powered Virtual Try-On:

   • User Body Mapping: Users upload a photo or short video of themselves, and the AI creates a precise 3D model of their body, accounting for unique measurements and proportions.
   • Garment Digitization: Clothing brands provide 3D models or high-quality images of their garments. AI processes these to understand fabric drape, texture, and fit characteristics.
   • Real-time Rendering: When a user selects an item, the AI overlays the 3D garment onto their virtual body model, simulating how it would look, fit, and move in real-time. This includes realistic shadows, creases, and fabric interactions.
   • Pose and Movement Simulation: Users can change poses or even see how the clothing looks while walking or sitting, providing a more dynamic and accurate representation.
   • Material and Texture Accuracy: Advanced rendering ensures that materials like silk, denim, leather, and knits look and behave authentically.

2. Personalized Styling & Recommendations:

   • AI Stylist: Based on user preferences (style, color, occasion), body shape analysis, and existing wardrobe items (if uploaded), the AI suggests outfits and new pieces.
   • Trend Analysis: The AI can keep up with current fashion trends and incorporate them into recommendations.
   • Outfit Builder: Users can mix and match different items from various brands to create full outfits and see how they look together.

3. Social Sharing & Community:

   • Virtual Lookbook: Users can save their favorite try-on sessions and created outfits.
   • Share & Get Feedback: Easily share virtual try-ons with friends or the StyleFit AI community for opinions and style advice.
   • Influencer Integration: Fashion influencers could create and share virtual lookbooks directly on the platform, allowing followers to try on their recommended styles.

4. Brand Integration & E-commerce Partnership:

   • Direct Purchase Links: Seamless integration with brand e-commerce sites, allowing users to purchase items directly from their virtual try-on session.
   • Brand Showcases: Dedicated sections for brands to highlight new collections and popular items for virtual try-on.
   • Data Insights for Brands: Anonymous aggregated data on which items are tried on most, popular combinations, and user feedback could be invaluable for brands.

5. Augmented Reality (AR) Overlay (Future Enhancement):

   • Using smartphone cameras, users could potentially see virtual garments overlaid on themselves in their own environment. (Though full 3D body mapping offers higher accuracy).

Technology Stack (High-Level):

• Front-end: React, Vue.js, or Angular for a responsive and interactive user interface.
• Back-end: Python (with frameworks like Django/Flask) or Node.js for robust API development.
• AI/ML: TensorFlow, PyTorch for computer vision (body mapping, garment recognition), 3D rendering algorithms, recommendation engines, and natural language processing for style descriptions.
• 3D Graphics: Libraries and engines like Three.js, Unity, or Unreal Engine for realistic rendering.
• Cloud Infrastructure: AWS, Google Cloud, or Azure for scalable computing and storage (essential for processing large amounts of visual data).

Monetization Strategies:

• Commission on Sales: A percentage of sales generated through the platform.
• Premium Features for Users: Advanced styling tools, exclusive brand access, ad-free experience.
• Subscription for Brands: Tiered subscriptions for enhanced analytics, prominent placement, or advanced digitization services.
• Advertising: Targeted ads from fashion brands.

Development Phases:

1. MVP (Minimum Viable Product): Focus on core virtual try-on with basic body mapping and a limited selection of digitized garments from a few partner brands.
2. Feature Expansion: Introduce AI styling, outfit builder, and social sharing.
3. Scaling & Partnerships: Expand brand partnerships, enhance AI accuracy, and explore AR integration.

This kind of platform has the potential to transform how people shop for clothes, making it more personal, efficient, and fun!

Here's an image depicting what such a platform's interface might look like:
info
Google AI models may make mistakes, so double-check outputs.
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