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Configuration error
Configuration error
| import streamlit as st | |
| import cv2 | |
| import tensorflow as tf | |
| from tensorflow.keras.models import load_model | |
| import numpy as np | |
| from pygame import mixer | |
| st.title('Driver Drowziness Detection') | |
| st.sidebar.subheader('About') | |
| st.sidebar.write('A computer vision system made with the help of opencv that can automatically detect driver drowsiness in a real-time video stream and then play an alarm if the driver appears to be drowsy.') | |
| dir_path= (r'Models') | |
| model = load_model(dir_path) | |
| face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') | |
| eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml') | |
| st.header("Webcam Live Feed") | |
| run = st.checkbox('Click to Run/Off the cam',value=True) | |
| FRAME_WINDOW = st.image([]) | |
| cap = cv2.VideoCapture(0) | |
| mixer.init() | |
| sound= mixer.Sound(r'alarm.wav') | |
| Score = 0 | |
| eye_cond = 1 | |
| st.subheader('Rules') | |
| st.write('The more focused you are on your ride, the lower your drowziness score') | |
| st.write('Alarm clock sounds when score reaches 25') | |
| st.markdown('To Stop the Alarm Just **Focus on Your Drive**') | |
| while run: | |
| col1,col2 = st.sidebar.columns(2) | |
| with col1: | |
| st.subheader('Score = ' + str(Score)) | |
| with col2: | |
| pass | |
| _, frame = cap.read() | |
| frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) | |
| height,width = frame.shape[0:2] | |
| gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) | |
| faces= face_cascade.detectMultiScale(gray, scaleFactor= 1.2, minNeighbors=3) | |
| eyes= eye_cascade.detectMultiScale(gray, scaleFactor= 1.1, minNeighbors=1) | |
| frame2 = cv2.rectangle(frame, (0,height-50),(200,height),(0,0,0),thickness=cv2.FILLED) | |
| sc = st.empty() | |
| def on_update(): | |
| data = getNewData() | |
| sc.text('Score :' + str(data)) | |
| for (x,y,w,h) in faces: | |
| cv2.rectangle(frame,pt1=(x,y),pt2=(x+w,y+h), color= (255,0,0), thickness=3 ) | |
| for (ex,ey,ew,eh) in eyes: | |
| # cv2.rectangle(frame,pt1=(ex,ey),pt2=(ex+ew,ey+eh), color= (255,0,0), thickness=5) | |
| # preprocessing steps | |
| eye= frame[ey:ey+eh,ex:ex+ew] | |
| eye= cv2.resize(eye,(80,80)) | |
| eye= eye/255 | |
| eye= eye.reshape(80,80,3) | |
| eye= np.expand_dims(eye,axis=0) | |
| # preprocessing is done now model prediction | |
| prediction = model.predict(eye) | |
| # if eyes are closed | |
| print(prediction) | |
| if prediction[0][0]>0.25: | |
| eye_cond=0 | |
| Score=Score+1 | |
| if(Score>25): | |
| try: | |
| sound.play() | |
| except: | |
| pass | |
| # if eyes are open | |
| elif prediction[0][1]>0.75: | |
| eye_cond=1 | |
| Score = Score-1 | |
| if (Score<0): | |
| Score=0 | |
| cv2.putText(frame,'Score'+str(Score),(10,height-20),fontFace=cv2.FONT_HERSHEY_COMPLEX_SMALL,fontScale=1,color=(255,255,255), | |
| thickness=1,lineType=cv2.LINE_AA) | |
| FRAME_WINDOW.image(frame) | |
| else: | |
| st.write('Stopped') | |