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| import streamlit as st | |
| import cv2 | |
| import time | |
| import tensorflow as tf | |
| from tensorflow.keras.models import load_model | |
| import numpy as np | |
| from pygame import mixer | |
| import os | |
| os.environ["SDL_AUDIODRIVER"] = "dummy" | |
| from datetime import datetime | |
| model = load_model('Drowsiness_model_efficient.h5') | |
| html_temp= """ | |
| <div style="background-color:tomato;padding:10px"> | |
| <h2 style="color:white;text-align:centre;">Drowsiness Detection App </h2> | |
| </div> | |
| """ | |
| st.markdown(html_temp,unsafe_allow_html=True) | |
| st.markdown( | |
| """ | |
| This app is developed for drowsiness detection. This app will raise an alarm if the person is drowsy. | |
| """ | |
| ) | |
| Warning="By selecting the check box you are agree to use our app.\nDon't worry!! We will not save your any data." | |
| check=st.checkbox("I agree",help=Warning) | |
| if(check): | |
| st.write('Great!') | |
| btn=st.button("Start") | |
| st.write('Press (c) for ending the stream') | |
| if btn: | |
| #multiple cascades: https://github.com/Itseez/opencv/tree/master/data/haarcascades | |
| #https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml | |
| face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') | |
| #https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_eye.xml | |
| eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml') | |
| mixer.init() | |
| sound= mixer.Sound(r'mixkit-digital-clock-digital-alarm-buzzer-992.wav') | |
| # HTML template with embedded JavaScript for camera access | |
| camera_access_html = """ | |
| <html> | |
| <head> | |
| <script> | |
| let videoElement; | |
| function requestCameraAccess() { | |
| navigator.mediaDevices.getUserMedia({ video: true }) | |
| .then(function (stream) { | |
| videoElement = document.createElement('video'); | |
| document.body.appendChild(videoElement); | |
| videoElement.srcObject = stream; | |
| videoElement.play(); | |
| }) | |
| .catch(function (error) { | |
| alert('Camera access denied or an error occurred.'); | |
| }); | |
| } | |
| </script> | |
| </head> | |
| <body> | |
| <button onclick="requestCameraAccess()">Request Camera Access</button> | |
| </body> | |
| </html> | |
| """ | |
| # Display the HTML with camera access JavaScript | |
| st.markdown(camera_access_html, unsafe_allow_html=True) | |
| # Function to capture video frames | |
| def capture_video_frame(): | |
| if 'videoElement' in locals(): | |
| ret, frame = cap.read() | |
| if ret: | |
| return frame | |
| else: | |
| return None | |
| else: | |
| return None | |
| # Initialize video capture | |
| cap = None | |
| if 'videoElement' in locals(): | |
| cap = cv2.VideoCapture(0) | |
| Score = 0 | |
| openScore = 0 | |
| while 1: | |
| frame = capture_video_frame() | |
| if frame is None: | |
| st.warning("Please request camera access and start capturing frames.") | |
| break | |
| img = frame | |
| height,width = img.shape[0:2] | |
| frame = img | |
| gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| faces = face_cascade.detectMultiScale(gray, scaleFactor= 1.3, minNeighbors=2) | |
| for (x,y,w,h) in faces: | |
| cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2) | |
| roi_gray = gray[y:y+h, x:x+w] | |
| roi_color = img[y:y+h, x:x+w] | |
| eye= img[y:y+h,x:x+w] | |
| eye= cv2.resize(eye, (256 ,256)) | |
| im = tf.constant(eye, dtype = tf.float32) | |
| img_array = tf.expand_dims(im, axis = 0) | |
| prediction = model.predict(img_array) | |
| print(np.argmax(prediction[0])) | |
| # if eyes are closed | |
| if np.argmax(prediction[0])<0.50: | |
| cv2.putText(frame,'closed',(10,height-20),fontFace=cv2.FONT_HERSHEY_COMPLEX_SMALL,fontScale=1,color=(255,255,255), | |
| thickness=1,lineType=cv2.LINE_AA) | |
| cv2.putText(frame,'Score'+str(Score),(100,height-20),fontFace=cv2.FONT_HERSHEY_COMPLEX_SMALL,fontScale=1,color=(255,255,255), | |
| thickness=1,lineType=cv2.LINE_AA) | |
| Score=Score+1 | |
| if(Score>25): | |
| try: | |
| sound.play() | |
| except: | |
| pass | |
| # if eyes are open | |
| elif np.argmax(prediction[0])>0.60: | |
| cv2.putText(frame,'open',(10,height-20),fontFace=cv2.FONT_HERSHEY_COMPLEX_SMALL,fontScale=1,color=(255,255,255), | |
| thickness=1,lineType=cv2.LINE_AA) | |
| cv2.putText(frame,'Score'+str(Score),(100,height-20),fontFace=cv2.FONT_HERSHEY_COMPLEX_SMALL,fontScale=1,color=(255,255,255), | |
| thickness=1,lineType=cv2.LINE_AA) | |
| Score = Score-1 | |
| openScore = openScore +1 | |
| if (Score<0 or openScore >8): | |
| Score=0 | |
| cv2.imshow('frame',img) | |
| if cv2.waitKey(33) & 0xFF==ord('c'): | |
| break | |
| cap.release() | |
| cv2.destroyAllWindows() | |
| st.text("Thanks for using") | |
| if st.button("About"): | |
| st.text("Created by Surendra Kumar") | |
| ## footer | |
| from htbuilder import HtmlElement, div, ul, li, br, hr, a, p, img, styles, classes, fonts | |
| from htbuilder.units import percent, px | |
| from htbuilder.funcs import rgba, rgb | |
| def image(src_as_string, **style): | |
| return img(src=src_as_string, style=styles(**style)) | |
| def link(link, text, **style): | |
| return a(_href=link, _target="_blank", style=styles(**style))(text) | |
| def layout(*args): | |
| style = """ | |
| <style> | |
| # MainMenu {visibility: hidden;} | |
| footer {visibility: hidden;} | |
| .stApp { bottom: 105px; } | |
| </style> | |
| """ | |
| style_div = styles( | |
| position="fixed", | |
| left=0, | |
| bottom=0, | |
| margin=px(0, 0, 0, 0), | |
| width=percent(100), | |
| color="black", | |
| text_align="center", | |
| height="auto", | |
| opacity=1 | |
| ) | |
| style_hr = styles( | |
| display="block", | |
| margin=px(8, 8, "auto", "auto"), | |
| border_style="solid", | |
| border_width=px(0.5) | |
| ) | |
| body = p() | |
| foot = div( | |
| style=style_div | |
| )( | |
| hr( | |
| style=style_hr | |
| ), | |
| body | |
| ) | |
| st.markdown(style,unsafe_allow_html=True) | |
| for arg in args: | |
| if isinstance(arg, str): | |
| body(arg) | |
| elif isinstance(arg, HtmlElement): | |
| body(arg) | |
| st.markdown(str(foot), unsafe_allow_html=True) | |
| def footer(): | |
| myargs = [ | |
| "©️ surendraKumar", | |
| br(), | |
| link("https://www.linkedin.com/in/surendra-kumar-51802022b", image('https://icons.getbootstrap.com/assets/icons/linkedin.svg') ), | |
| br(), | |
| link("https://www.instagram.com/im_surendra_dhaka/",image('https://icons.getbootstrap.com/assets/icons/instagram.svg')), | |
| ] | |
| layout(*myargs) | |
| if __name__ == "__main__": | |
| footer() |