Detecting Driver Fatigue with Python and OpenCV

Utilizing eye aspect ratio analysis and facial landmarks, the sleep detection tool is intended to detect indicators of sleepiness or drowsiness in people. By leveraging the face recognition library, the program detects and extracts facial landmarks, with a particular focus on locating the positions of the eyes. The software analyzes the eye aspect ratio (EAR), which takes into account the relative distances between particular eye landmarks, to ascertain if an individual has fully or partially closed their eyes.Continuous video frame capture is performed from a chosen video source, typically a webcam, allowing real-time monitoring of the user. Each frame is processed to detect and analyze the eye regions, computing the EAR values for both the left and right eyes. If the EAR falls below a predefined threshold for a consecutive number of frames, it indicates that the person’s eyes are closed for an extended duration, suggesting potential drowsiness or sleepiness.To enhance user safety, an alarm sound is played when the program detects drowsiness, providing an immediate alert to the individual. The alarm sound serves as an auditory cue, helping the person regain alertness and avoid potential accidents or undesired consequences. Additionally, the program displays the computed EAR on the video feed, allowing for visual feedback and monitoring of the eye aspect ratio values for debugging purposes and fine-tuning the system’s sensitivity.By employing this sleep detection program, various applications can benefit from its capabilities. For instance, it can be utilized in the context of drowsy driving detection systems, alerting drivers when they exhibit signs of fatigue and helping prevent accidents due to impaired driving. Moreover, the program holds potential for use in other scenarios where monitoring individuals’ alertness and detecting sleep-related patterns is essential, such as workplace safety or medical monitoring.In summary, the sleep detection program employs facial landmarks and eye aspect ratio analysis to detect and alert individuals about drowsiness or sleepiness. With its real-time video processing capabilities, audio alarms, and visual feedback, the program contributes to promoting safety and attentiveness in various contexts, mitigating risks associated with fatigue-induced impairment.