Virtual Try-On Project

This project uses OpenCV, Mediapipe, and NumPy to create an interactive virtual try-on experience. The application overlays a virtual accessory (e.g., glasses) onto the user's face using webcam input and allows users to control the brightness of the camera feed and capture screenshots using hand gestures.

Features

• Virtual Accessory Placement: Automatically places a virtual accessory (such as glasses) on the user's face, aligned with their eyes.
• Brightness Control: Use hand gestures to adjust the brightness of the live video feed.
• Screenshot Capture: Capture a screenshot by performing a thumbs-up gesture with the left hand.
• Real-Time Interaction: Live webcam feed with real-time adjustments and accessory placement.
• Accessory Customization: Load and apply custom accessories (PNG images with an alpha channel) from a folder.

Requirements

• Python 3.x (Tested on Python 3.7 and above)
• OpenCV: For webcam feed and image processing
• Mediapipe: For face and hand landmark detection
• NumPy: For numerical operations and image manipulation
• Tkinter: For the user interface to apply accessories

To install the required dependencies, you can use the following pip command:

pip install opencv-python mediapipe numpy

Assets Folder

The project relies on an accessory image (e.g., glasses or mask) that should be placed inside the assets folder.

• assets/mask.png: This is the virtual accessory image used for the try-on. You can replace it with any image (e.g., glasses, hats) that has a transparent background (alpha channel).

Running the Application

To run the application, simply execute the main.py file:

python main.py

The webcam will open, and the system will start processing the video feed. The virtual accessory will be placed on the user's face in real-time, and hand gestures will control brightness and allow for screenshot capture.

• Right Hand: Adjusts brightness by controlling the distance between the thumb and index finger.
• Left Hand: Captures a screenshot when a thumbs-up gesture is detected.

Press 'q' to exit the program.

How It Works

• Face Landmark Detection: The program uses Mediapipe’s FaceMesh solution to detect face landmarks, particularly the eyes, to position the virtual accessory.
• Hand Landmark Detection: The program uses Mediapipe’s Hands solution to track the user's hand movements. The right hand controls brightness, and the left hand triggers screenshots with a thumbs-up gesture.
• Accessory Placement: The accessory image is resized based on the distance between the user's eyes and overlaid on the webcam feed. Transparency is respected using the alpha channel of the accessory image.
• Brightness Control:The right hand’s thumb and index finger distance is used to control the brightness of the video feed in real time.
• Screenshot Functionality: When the left hand forms a thumbs-up gesture, a screenshot is taken and saved with a timestamped filename.

Working Demo:

License

This project is licensed under the MIT License. See the LICENSE file for more details.