INTELLOCK

INNOVATIVE DRUNK PREVENTATION PROJECT

Overview

This project is a real-time pupil detection system that calculates the circumference of a person's pupils using OpenCV and Dlib. The system captures live video, detects facial landmarks, and measures the pupil circumference in millimeters. The data is stored in a CSV file for analysis.

Features

• Real-time pupil detection using OpenCV and Dlib.
• Accurate pupil circumference measurement in millimeters.
• Live visualization of detected pupils and their measurements.
• Data logging in a CSV file for further analysis.
• Automatic frame capturing at a set frequency.

System Requirements

• Python 3.x
• OpenCV (cv2)
• Dlib (dlib)
• NumPy (numpy)

Installation

1. Install dependencies:

   pip install opencv-python dlib numpy

2. Download Dlib’s facial landmark model:

   • Download shape_predictor_68_face_landmarks.dat from Dlib Model.
   • Extract it and place it in the same folder as the script.

3. Run the program:

   python pupil_detection.py

How It Works

1. Captures live video from the webcam.
2. Detects a face and extracts eye landmarks.
3. Calculates pupil diameter and converts it to circumference using a fixed pixel-to-mm ratio.
4. Displays the measurements in real-time.
5. Saves data periodically in pupil_data.csv.

CSV Data Format

The system logs the following information in pupil_data.csv:

Left_Pupil_Circumference_mm	Right_Pupil_Circumference_mm
1.45 mm	1.67 mm
2.23 mm	2.45 mm

Customization

• Adjust the frame capture frequency:

  write_frequency = 10  # Captures data every 10 frames

• Change the pixel-to-mm ratio (based on camera calibration):

  pixels_per_mm = 15

Potential Applications

• Medical Analysis: Detect neurological issues based on pupil size.
• Driver Monitoring: Measure pupil dilation in drowsiness or impairment detection.
• Cognitive Research: Track focus and cognitive load based on pupil dilation.