This repository contains a Python script that processes a video to detect and highlight the region of interest (ROI) around the eye. The script performs real-time video analysis, applying various image processing techniques to isolate and track eye movement.

• Grayscale Conversion: Converts the region of interest (ROI) to grayscale for easier processing.
• Gaussian Blur: Applies a Gaussian blur to reduce noise and smooth the image.
• Thresholding: Uses thresholding to create a binary image, isolating the eye for contour detection.
• Contour Detection: Identifies and highlights the largest contour (presumed to be the eye) with bounding boxes and crosshairs.
• Real-time Visualization: Displays the processed video frames in real-time.

To run this script, you need to have Python installed along with the following dependencies:

pip install opencv-python numpy

1. Clone the repository:

git clone https://github.com/akhilaprabodha/Eye-Motion-Tracking.git
cd Eye-Motion-Tracking

2. Run the script:

python eye_motion_tracking.py

3. Script Explanation:

• The script opens a video file (eye_recording.flv) or captures video from a camera.
• It defines the region of interest (ROI) within the video frame to focus on the eye.
• The ROI is processed through grayscale conversion, Gaussian blur, and thresholding.
• Contours are detected and the largest one is highlighted with a bounding box and crosshairs.
• The processed frames are displayed and saved as three separate output videos.

1. Thresholded Output 🔲

• This video shows the thresholded version of the ROI, where the eye is isolated in a binary image.

2. Grayscale ROI Output 🌑

• This video displays the grayscale version of the ROI, which is used as the base for further processing.

3. Contour Detection ROI Output 🎯

• This video highlights the ROI with the detected contour, bounding box, and crosshairs.

Contributions are welcome! If you have any improvements or additional features in mind, feel free to open a pull request.