Efficient-Online-Cloth-Size-Prediction-and-Virtual-Cloth-Trial-Model

Overview

This project introduces a Virtual Fitting Room (VFR) system designed to predict apparel sizes and enable virtual garment try-on experiences using webcams or smartphones, eliminating the need for physical changing rooms. The VFR employs image processing techniques, including pose estimation and superimposition, to provide real-time, accurate size predictions and visualizations for users. It addresses several issues in traditional apparel shopping, such as long queues, size discrepancies among brands, and the challenge of buying clothes for others.

Motivation

Traditional clothing shopping experiences often involve time-consuming trials, long queues at changing rooms, and challenges in purchasing for others or people with disabilities. Additionally, sizing variations across brands contribute to high return rates and inventory management difficulties. The motivation behind this project is to improve and modernize the apparel shopping process by providing an efficient and convenient solution.

Key Features

1. Size Prediction: The VFR estimates the user's apparel size based on body characteristics extracted from webcam or smartphone images. This enables users to select garments with confidence, reducing the need for trying them on physically.

2. Virtual Try-On: Users can visualize selected garments superimposed on their own images, helping them make informed decisions about fit, style, and overall look.

3. Queue Reduction: By eliminating the need for physical trials, the VFR reduces queues at changing rooms, resulting in a more pleasant shopping experience, particularly during peak times.

4. Brand-Agnostic Sizing: The system's size predictions are brand-agnostic, meaning users can confidently select sizes across different clothing brands.

5. Accessibility: The VFR benefits differently-abled individuals by providing them with an independent and hassle-free shopping experience.

System Workflow

1. The user stands in front of a camera at a predefined distance, and their image is captured.

2. Pose estimation techniques identify important features, such as the user's physique.

3. The VFR calculates the garment size based on the distance between reference points (e.g., elbow joints).

4. The user selects desired garments, and the VFR superimposes these virtual garments onto their image.

5. Users can visualize the fit and style of the selected garments without physically trying them on.

Algorithm for Size Prediction

The size prediction algorithm follows these steps:

1. Capture the user's image using the camera.

2. Use pose estimation to detect and locate key body points, such as elbow joints.

3. Calculate the distance between the left and right elbow joints.

4. Based on the calculated distance, categorize the garment size as Small, Medium, or Large.

5. Provide the predicted size to the user.

Applications

• Retail Stores: Enhance the in-store shopping experience, reduce queues, and decrease return rates.
• Online Retail: Provide virtual try-on capabilities for e-commerce platforms.
• Fashion Designers: Assist fashion designers in look tests and outfit selection.
• Differently-abled Individuals: Enable independent apparel shopping.
• Inventory Management: Track inventory and customer preferences more efficiently.

Constraints

• The user must stand at a predefined distance from the camera for accurate size prediction.
• Continuous power supply is required for the system to operate.
• The background should be plain and constant to avoid interference in image processing.

Hardware Requirements

1. Camera
2. LCD monitor (for virtual try-on)
3. Storage device (for image data)
4. Image Processing Unit

Conclusion

The Virtual Fitting Room (VFR) project aims to revolutionize the apparel shopping experience by providing accurate size predictions, virtual try-on capabilities, and accessibility benefits. By addressing common shopping challenges and improving efficiency, this system benefits both customers and retailers. Further developments could include expanding the database of available garments and providing personalized clothing recommendations based on user preferences and physique.

Please note that this document provides an overview of the project. For detailed technical documentation and implementation instructions, please refer to the project's Instructions