DIY Eye Tracking with a Front Camera

Step 1: Gather the parts

You need a small eye tracking camera, a low cost board camera, USB extension cables if you need more cable length, a pair of cheap sunglasses with the lenses removed, soft wire cabling or another flexible material for mounting, and electrical tape for securing loose cable sections. A low-cost GC0308 eye tracking camera works for this build, and inexpensive dollar-store glasses are enough because the frame is only used to hold the camera near the eye.

Step 2: Attach the camera to the glasses

Wrap the soft wire around the camera and cable, then attach it to the glasses frame. Use electrical tape if you need to secure the camera cable or keep the mount from sliding. The goal is to make a firm but adjustable mount: the camera should stay in place after you reposition it, but you should still be able to bend the mount until the eye is fully visible in the camera feed.

Step 3: Install the Python dependencies

Download Orlosky3DEyeTracker.py and install the required Python packages. The tracker uses OpenCV and NumPy for image processing. Tkinter is used for the startup window and is included with many Python installations. The optional gl_sphere.py file enables the OpenGL sphere visualization, but the tracker will still run without it.

pip install opencv-python numpy

Step 4: Run the tracker

Open a terminal in the folder containing the script and run it with Python. On startup, the tracker opens a small window titled Select Input Source. Choose your camera from the dropdown and click Start Camera. You can also use Browse Video to test the tracker on an existing .mp4 or .avi file.

python Orlosky3DEyeTracker.py

Step 5: Position the eye in the camera view

After clicking Start Camera, adjust the camera until the eye is clearly visible in the OpenCV window. The script crops the frame, finds the darkest pupil region, thresholds the image at several levels, fits an ellipse to the pupil, and estimates the eye center from recent pupil rays. In normal use, the tracker self-calibrates to the observed eye position as you move.

Step 6: Read the gaze output

The OpenCV output window displays the current 3D gaze origin and gaze direction near the bottom of the frame. The script also writes these six values to gaze_vector.txt on each frame: the three origin coordinates followed by the three normalized gaze direction values. Use q to quit, the spacebar to pause, e to toggle ellipse capture, and c to clear captured ellipses.

x_origin,y_origin,z_origin,x_direction,y_direction,z_direction

Step 7: Use the vector in your own project

Once the tracker is running, you can read gaze_vector.txt from another program or modify the Python script to send the gaze vector directly to your application. This first setup gives you a raw 3D gaze direction. From there, you can calibrate it to a monitor, VR headset, or another display surface for interaction experiments.

For the VR installation process, see this video or this tutorial.

Step 8: Use AI to build your own gaze application

You can use an AI coding assistant or large language model to turn this tracker into your own application. Start by giving the model the Python script, explain what the gaze vector represents, and describe the interaction you want to build. Ask for small changes first, test each version, and paste any error messages or unexpected behavior back into the chat so the model can help you debug.

These sample prompts can help you get started:

I have a Python eye tracking script that writes gaze data to gaze_vector.txt as:
x_origin,y_origin,z_origin,x_direction,y_direction,z_direction

Explain how this data could be used to control a simple application.

Modify this Python eye tracking project so another Python program can read
gaze_vector.txt and move a cursor or marker on screen based on the gaze direction.
Keep the first version simple and explain where I should paste each code change.

Create a small demo app that reads gaze_vector.txt in real time and displays
a dot moving on a 2D window. Include smoothing so the dot does not jitter too much.

Help me add a calibration step to this gaze tracker. I want the user to look at
points on the screen, save the measured gaze vectors, and map future gaze vectors
to screen coordinates.

I want to use this eye tracker for a custom interaction project. Ask me the
minimum questions needed, then propose a simple first prototype using the existing
gaze_vector.txt output.

Here is the error message I get when running the eye tracking code. Explain what
is probably wrong and give me the smallest code or setup change to try first.