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drowsiness_detection.py
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import cv2
import os
import pygame
from keras.models import load_model
import numpy as np
from pygame import mixer
import time
import urllib
import pdb
##################################### ADDED HERE ###################################
pygame.mixer.pre_init(44100, 16, 2, 4096) #frequency, size, channels, buffersize
pygame.init() #turn all of pygame on.
sound = pygame.mixer.Sound("F:\\MAJOR 2\\Drowsiness_detection\\alarm.wav")
face = cv2.CascadeClassifier("F:\\MAJOR 2\\Drowsiness_detection\\haar cascade files\\haarcascade_frontalface_alt.xml")
leye = cv2.CascadeClassifier("F:\\MAJOR 2\\Drowsiness_detection\\haar cascade files\\haarcascade_lefteye_2splits.xml")
reye = cv2.CascadeClassifier("F:\\MAJOR 2\\Drowsiness_detection\\haar cascade files\\haarcascade_righteye_2splits.xml")
lbl=['Close','Open']
model = load_model("F:\MAJOR 2\\Drowsiness_detection\\models\\cnncat2.h5")
path = os.getcwd()
cap = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_COMPLEX_SMALL
count=0
score=0
thicc=2
rpred=[99]
lpred=[99]
"""
#Chnage the Quality of Frame
def make_1080p():
cap.set(3, 1920)
cap.set(4, 1080)
def make_720p():
cap.set(3, 1280)
cap.set(4, 720)
def make_480p():
cap.set(3, 640)
cap.set(4, 480)
def change_res(width, height):
cap.set(3, width)
cap.set(4, height)
make_720p()
change_res(1280, 720)
"""
while(True):
ret, frame = cap.read()
height,width = frame.shape[:2]
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face.detectMultiScale(gray,minNeighbors=5,scaleFactor=1.1,minSize=(25,25))
left_eye = leye.detectMultiScale(gray)
right_eye = reye.detectMultiScale(gray)
cv2.rectangle(frame, (0,height-50) , (200,height) , (0,0,0) , thickness=cv2.FILLED )
for (x,y,w,h) in faces:
cv2.rectangle(frame, (x,y) , (x+w,y+h) , (255,255,0) , 1 )
for (x,y,w,h) in right_eye:
r_eye=frame[y:y+h,x:x+w]
count=count+1
r_eye = cv2.cvtColor(r_eye,cv2.COLOR_BGR2GRAY)
r_eye = cv2.resize(r_eye,(24,24))
r_eye= r_eye/255
r_eye= r_eye.reshape(24,24,-1)
r_eye = np.expand_dims(r_eye,axis=0)
rpred = model.predict_classes(r_eye)
if(rpred[0]==1):
lbl='Open'
if(rpred[0]==0):
lbl='Closed'
break
for (x,y,w,h) in left_eye:
l_eye=frame[y:y+h,x:x+w]
count=count+1
l_eye = cv2.cvtColor(l_eye,cv2.COLOR_BGR2GRAY)
l_eye = cv2.resize(l_eye,(24,24))
l_eye= l_eye/255
l_eye=l_eye.reshape(24,24,-1)
l_eye = np.expand_dims(l_eye,axis=0)
lpred = model.predict_classes(l_eye)
if(lpred[0]==1):
lbl='Open'
if(lpred[0]==0):
lbl='Closed'
break
if(rpred[0]==0 and lpred[0]==0):
score=score+1
cv2.putText(frame,"Closed",(10,height-20), font, 1,(255,255,255),1,cv2.LINE_AA)
# if(rpred[0]==1 or lpred[0]==1):
else:
score=score-1
cv2.putText(frame,"Open",(10,height-20), font, 1,(255,255,255),1,cv2.LINE_AA)
if(score<0):
score=0
cv2.putText(frame,'Score:'+str(score),(100,height-20), font, 1,(255,255,255),1,cv2.LINE_AA)
if(score>15):
#person is feeling sleepy so we beep the alarm
cv2.imwrite(os.path.join(path,'image.jpg'),frame)
try:
sound.play()
except: # isplaying = False
pass
if(thicc<16):
thicc= thicc+2
else:
thicc=thicc-2
if(thicc<2):
thicc=2
cv2.rectangle(frame,(0,0),(width,height),(0,0,255),thicc)
cv2.imshow('MONITORING',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()