Eye tracking on a Raspberry PI

Eye tracking technology has become increasingly popular in recent years for various applications, including virtual reality, human-computer interaction, and medical research. In this article, we will explore how to implement eye tracking on a Raspberry PI using TensorFlow, Python, AI, computer vision, and OpenCV.

Setting up the Raspberry PI

First, you will need to set up your Raspberry PI with the necessary software. Make sure you have TensorFlow, Python, and OpenCV installed on your device. You can easily install these packages using the following commands:

        
            sudo pip install tensorflow
            sudo pip install opencv-python
        
    

Implementing eye tracking

Now that your Raspberry PI is set up, you can start implementing the eye tracking functionality. You can use a webcam to capture the images of the user’s eyes and then use TensorFlow and AI algorithms to track the movement of the eyes.

Example code snippet:

        
            import cv2
            import tensorflow as tf

            # Load the pre-trained model for eye tracking
            model = tf.keras.models.load_model('eye_tracking_model.h5')

            # Capture video from webcam
            cap = cv2.VideoCapture(0)

            while(True):
                ret, frame = cap.read()
                # Preprocess the frame
                # Make predictions using the model
                # Display the results on the screen

            cap.release()
            cv2.destroyAllWindows()
        
    

Conclusion

Implementing eye tracking on a Raspberry PI can open up a world of possibilities for various applications. By combining TensorFlow, Python, AI, computer vision, and OpenCV, you can create a powerful eye tracking system that can be used in a variety of projects. So go ahead and start exploring the exciting world of eye tracking on your Raspberry PI!