3D VIRTUAL TRAIL ROOM

The rapid evolution of e-commerce has amplified the demand for innovative, immersive user experiences, especially in the fashion retail sector. This paper presents the design and development of a Virtual Trial Room, a web-based application that utilizes real-time computer vision techniques to allow users to virtually try on clothes and face accessories. Leveraging the capabilities of a standard laptop camera, the system detects human faces and body features using the Haar cascade classifier, and overlays selected products onto the user’s live video stream. This interactive platform is built using Python and integrates several powerful libraries including OpenCV for computer vision, Pillow for image manipulation, Flask for web framework development, and Flask-SocketIO for real-time bi-directional communication. The application supports user authentication, product selection, cart management, and a "Try On" feature that functions as a virtual mirror.The system architecture is optimized for scalability, maintainability, and portability, ensuring ease of deployment across different environments. Furthermore, a MySQL database is integrated for managing user and product data, while PyOTP adds an extra layer of security through One-Time Password verification. The project adheres to a modular design approach, promoting maintainability through well-documented code, consistent naming conventions, and reusable components. Experimental deployment confirms that the application maintains high availability and performance under varying loads. The proposed system showcases the potential of combining machine learning, computer vision, and web technologies to transform the digital retail experience, offering customers a practical and engaging alternative to physical trial rooms