EyeSelect

EyeSelect is a Python module for detecting simple eye-based gestures (left, right, up, blink) from a webcam feed. It uses facial landmark detection to track eye pupil positions and determine directional movement or blinks.

Features

• Detects and reacts to:

  • Looking left
  • Looking right
  • Looking up
  • Blinking

• Works in real-time using a webcam.

• Callback system to trigger custom actions for each gesture.

Requirements

• Python 3.7+
• OpenCV
• NumPy
• MediaPipe

How It Works

1. Capture Video Feed using OpenCV.

2. Detect Face and Eyes using the custom FaceFinder and Face classes.

3. Track Pupil Movement over time and compute relative positions within the eye bounds.

4. Detect Movement Patterns based on standard deviations and directional thresholds:

   • Left/Right → Horizontal pupil displacement.
   • Up → Vertical pupil position vs. baseline.
   • Blink → High variation in vertical pupil position.

5. Trigger Callbacks when a gesture is confidently detected.

Example Usage

from eyeselect import EyeSelect
import cv2

# Define what to do on each gesture
ekeys = EyeSelect(
    left_cb=lambda: print("left"),
    right_cb=lambda: print("right"),
    blink_cb=lambda: print("blink"),
    up_cb=lambda: print("up")
)

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    ekeys.process(
        frame,
        left_th=-100,     # Threshold for left detection
        right_th=100,     # Threshold for right detection
        up_th=1.5,        # Threshold for up detection
        blink_th=40       # Threshold for blink detection
    )

Tuning Parameters

These thresholds can be tweaked in the process() function to better fit your lighting, camera, or user behavior:

• left_th, right_th: Horizontal offset from center for directional detection.
• up_th: Relative upward movement vs. a stable baseline.
• blink_th: Variance in vertical position to detect blinks.
• Internally uses standard deviation and position buffers for stability and debouncing.

Notes

• A visual debug window (Dot Display) shows tracked pupil positions.
• The system uses basic debouncing to avoid repeated detections.
• The precision depends heavily on lighting and face mesh quality.