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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+mixkit-digital-clock-digital-alarm-buzzer-992.wav filter=lfs diff=lfs merge=lfs -text

Drowsiness_detection_app.py

@@ -0,0 +1,176 @@
+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
+
+
+
+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')
+            cap = cv2.VideoCapture(0)
+            Score = 0
+            openScore = 0
+            while 1:
+
+                ret, img = cap.read()
+                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()

Drowsiness_model_efficient.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d1b14a94533977bf0c6b70f7628ff69f136705912961619133478631fbeb746b
+size 118088992

mixkit-digital-clock-digital-alarm-buzzer-992.wav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:577768b0c1c2dfe5511579d8226d74781d501a723a08f61e8d83842a2b6858c0
+size 1552734