Add application file

app.py

@@ -0,0 +1,83 @@
+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')