Draw data flow diagram for " The project uses computer vision techniques using OpenCV and a ResNet101 deep learning model to construct a real time Driver Drowsiness detection model. Video frames are first taken from the webcam, faces and eyes are identified within the frames, and regions of interest corresponding to the right and left eyes are extracted. The region of interest (ROI) for each eye is calculated using the coordinates obtained from the eye detection process. Specifically, face and eye detection are performed using Haar cascades provided by OpenCV (face, leye, and reye). Following the detection of faces and eyes within the video frame (frame), the bounding box coordinates (x, y, w, h) of each eye are determined. Before being given into the model for classification, these eye regions are preprocessed and scaled to establish whether the eyes are open or closed. A score is updated to track the level of drowsiness based on the predictions. An alarm is set off if the score rises beyond a predetermined threshold, indicating potential drowsiness. To give feedback, visual indicators like bounding boxes and score boxes are also shown on the video feed. " | Fantasy