MagicLens is an application designed to serve as a personal photo classifier and organizer with both frontend and backend components. The project's primary objective is to facilitate users in efficiently managing and sorting their local photos based on personalized criteria and preferences. The application uses advanced face and eye detection to leverage the power of the OpenCV library. Machine learning algorithms, including SVM, logistic regression, and random forest, enable personalized photo sorting based on user-defined criteria. At the same time, deep learning plays a major role in commercial applications. The project further emphasizes modularity by exporting the model to a file and implementing a Python Flask server, ensuring seamless communication with the user interface. The UI, developed using HTML, CSS, JavaScript, and JQuery, offers an intuitive and visually appealing platform for users to organize their images effortlessly according to their preferences. MagicLens is a comprehensive solution combining cutting-edge technologies to provide a user-centric and efficient.
The primary objectives of the 'Magic Lens' project are as follows:
- To develop a facial recognition system capable of accurately identifying individuals in photos.
- To implement machine learning algorithms, specifically Support Vector Machines (SVM), for facial recognition tasks.
- To create a user-friendly interface for seamless interaction with the facial recognition system.
The scope of the 'Magic Lens' project includes:
- Research and development of facial recognition algorithms, focusing on SVM models.
- Integration of OpenCV, PyWavelets, and scikit-learn for image processing and machine learning.
- Development of a web-based front-end interface using HTML, JavaScript, and jQuery.
- Implementation of Flask for backend server logic and interaction with machine learning models.
DEPLOYED URL - https://magiclens.onrender.com