How to Use

This application applies real-time image filters to live webcam video, allowing for interactive adjustments and analysis. Start by clicking the "Start Webcam" button to activate your camera feed. Choose a filter from the available options, and fine-tune the results using the sliders for brightness, contrast, saturation, and filter-specific parameters.

Use the Mirror Video toggle to flip the video horizontally, and adjust the Frame Rate (FPS) slider to control how frequently the video is processed for smoother or faster performance.

Available Filters

• Normal: Displays the original video feed without modifications.
• Grayscale: Converts the image to shades of gray.
• Sepia: Adds a warm, brownish tint for a vintage look.
• Invert: Reverses all colors to create a negative effect.
• Pixelate: Reduces image resolution by grouping pixels. Adjust "Pixel Size" for blockiness.
• Blur: Softens the image by averaging neighboring pixels. Use "Blur Radius" to increase or decrease the effect.
• Sharpen: Enhances image edges and details. "Sharpen Amount" determines the intensity.
• Edge Detection: Highlights edges in the image using Sobel filters. "Edge Threshold" adjusts edge sensitivity.
• Emboss: Creates a 3D-like relief effect by detecting light and shadow variations.
• Posterize: Reduces the number of colors in the image. "Levels" controls the number of color shades.

Application Overview

This tool demonstrates real-time video filtering for various purposes. Beyond its interactive use, it can be extended for analyzing biomedical data, such as EEG spectrograms or neural imaging. For example:

• Edge Detection: Useful for detecting spatial patterns in brain images or identifying waveform boundaries in EEG data.
• Pixelate: Simplifies data visualizations to observe high-level patterns in complex datasets.
• Brightness/Contrast: Adjust image clarity for better analysis of biological or medical visuals.

This application demonstrates how real-time video processing can support biomedical research and neuroscience applications, particularly in visualizing and enhancing critical details within datasets.

Future Directions

• Improving performance by offloading computations to Web Workers for smoother real-time processing.
• Adding advanced tools to analyze and segment medical or biological images.
• Integrating machine learning for automatic pattern recognition and feature detection.
• Visualizing EEG spectrograms to detect patterns in brain activity.
• Enabling customizable filters for specialized biomedical applications.