From 912086138b8d9877b46d46cba40c7bd9c42be29d Mon Sep 17 00:00:00 2001
From: Swathi A <61613945+swathi-469@users.noreply.github.com>
Date: Sun, 29 Aug 2021 19:41:54 +0530
Subject: [PATCH] Drowsiness Detection

---
 Drowsiness detection/alarm.wav                |   Bin 0 -> 1019502 bytes
 Drowsiness detection/drowsiness detection.py  |   106 +
 .../haarcascade_frontalface_alt.xml           | 24350 ++++++++++++++++
 .../haarcascade_lefteye_2splits.xml           |  7390 +++++
 .../haarcascade_righteye_2splits.xml          |  7407 +++++
 Drowsiness detection/model.py                 |    57 +
 6 files changed, 39310 insertions(+)
 create mode 100644 Drowsiness detection/alarm.wav
 create mode 100644 Drowsiness detection/drowsiness detection.py
 create mode 100644 Drowsiness detection/haar cascade files/haarcascade_frontalface_alt.xml
 create mode 100644 Drowsiness detection/haar cascade files/haarcascade_lefteye_2splits.xml
 create mode 100644 Drowsiness detection/haar cascade files/haarcascade_righteye_2splits.xml
 create mode 100644 Drowsiness detection/model.py

diff --git a/Drowsiness detection/alarm.wav b/Drowsiness detection/alarm.wav
new file mode 100644
index 0000000000000000000000000000000000000000..f7bf38c95b25b004a7710d8aa5e6c5ae1b6e1709
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diff --git a/Drowsiness detection/drowsiness detection.py b/Drowsiness detection/drowsiness detection.py
new file mode 100644
index 000000000..36f1f2209
--- /dev/null
+++ b/Drowsiness detection/drowsiness detection.py	
@@ -0,0 +1,106 @@
+import cv2
+import os
+from keras.models import load_model
+import numpy as np
+from pygame import mixer
+import time
+
+
+mixer.init()
+sound = mixer.Sound('alarm.wav')
+
+face = cv2.CascadeClassifier('haar cascade files\haarcascade_frontalface_alt.xml')
+leye = cv2.CascadeClassifier('haar cascade files\haarcascade_lefteye_2splits.xml')
+reye = cv2.CascadeClassifier('haar cascade files\haarcascade_righteye_2splits.xml')
+
+
+
+lbl=['Close','Open']
+
+model = load_model('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]
+
+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) , (100,100,100) , 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('frame',frame)
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+cap.release()
+cv2.destroyAllWindows()
diff --git a/Drowsiness detection/haar cascade files/haarcascade_frontalface_alt.xml b/Drowsiness detection/haar cascade files/haarcascade_frontalface_alt.xml
new file mode 100644
index 000000000..ade4b2121
--- /dev/null
+++ b/Drowsiness detection/haar cascade files/haarcascade_frontalface_alt.xml	
@@ -0,0 +1,24350 @@
+<?xml version="1.0"?>
+<!--
+    Stump-based 20x20 gentle adaboost frontal face detector.
+    Created by Rainer Lienhart.
+
+////////////////////////////////////////////////////////////////////////////////////////
+
+  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+
+  By downloading, copying, installing or using the software you agree to this license.
+  If you do not agree to this license, do not download, install,
+  copy or use the software.
+
+
+                        Intel License Agreement
+                For Open Source Computer Vision Library
+
+ Copyright (C) 2000, Intel Corporation, all rights reserved.
+ Third party copyrights are property of their respective owners.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+   * Redistribution's of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+
+   * Redistribution's in binary form must reproduce the above copyright notice,
+     this list of conditions and the following disclaimer in the documentation
+     and/or other materials provided with the distribution.
+
+   * The name of Intel Corporation may not be used to endorse or promote products
+     derived from this software without specific prior written permission.
+
+ This software is provided by the copyright holders and contributors "as is" and
+ any express or implied warranties, including, but not limited to, the implied
+ warranties of merchantability and fitness for a particular purpose are disclaimed.
+ In no event shall the Intel Corporation or contributors be liable for any direct,
+ indirect, incidental, special, exemplary, or consequential damages
+ (including, but not limited to, procurement of substitute goods or services;
+ loss of use, data, or profits; or business interruption) however caused
+ and on any theory of liability, whether in contract, strict liability,
+ or tort (including negligence or otherwise) arising in any way out of
+ the use of this software, even if advised of the possibility of such damage.
+-->
+<opencv_storage>
+<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
+  <stageParams>
+    <maxWeakCount>213</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount></featureParams>
+  <stageNum>22</stageNum>
+  <stages>
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>8.2268941402435303e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 0 4.0141958743333817e-03</internalNodes>
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+          <leafValues>
+            9.0049281716346741e-02 6.3748198747634888e-01</leafValues></_></weakClassifiers></_>
+    <_>
+      <maxWeakCount>16</maxWeakCount>
+      <stageThreshold>6.9566087722778320e+00</stageThreshold>
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diff --git a/Drowsiness detection/haar cascade files/haarcascade_lefteye_2splits.xml b/Drowsiness detection/haar cascade files/haarcascade_lefteye_2splits.xml
new file mode 100644
index 000000000..9a9ef58fb
--- /dev/null
+++ b/Drowsiness detection/haar cascade files/haarcascade_lefteye_2splits.xml	
@@ -0,0 +1,7390 @@
+<?xml version="1.0"?>
+<!--
+    Tree-based 20x20 left eye detector.
+    The detector is trained by 6665 positive samples from FERET, VALID and BioID face databases. 
+    Created by Shiqi Yu (http://yushiqi.cn/research/eyedetection).
+
+////////////////////////////////////////////////////////////////////////////////////////
+
+  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+
+  By downloading, copying, installing or using the software you agree to this license.
+  If you do not agree to this license, do not download, install,
+  copy or use the software.
+
+
+                        Intel License Agreement
+                For Open Source Computer Vision Library
+
+ Copyright (C) 2000, Intel Corporation, all rights reserved.
+ Third party copyrights are property of their respective owners.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+   * Redistribution's of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+
+   * Redistribution's in binary form must reproduce the above copyright notice,
+     this list of conditions and the following disclaimer in the documentation
+     and/or other materials provided with the distribution.
+
+   * The name of Intel Corporation may not be used to endorse or promote products
+     derived from this software without specific prior written permission.
+
+ This software is provided by the copyright holders and contributors "as is" and
+ any express or implied warranties, including, but not limited to, the implied
+ warranties of merchantability and fitness for a particular purpose are disclaimed.
+ In no event shall the Intel Corporation or contributors be liable for any direct,
+ indirect, incidental, special, exemplary, or consequential damages
+ (including, but not limited to, procurement of substitute goods or services;
+ loss of use, data, or profits; or business interruption) however caused
+ and on any theory of liability, whether in contract, strict liability,
+ or tort (including negligence or otherwise) arising in any way out of
+ the use of this software, even if advised of the possibility of such damage.
+-->
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+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
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diff --git a/Drowsiness detection/haar cascade files/haarcascade_righteye_2splits.xml b/Drowsiness detection/haar cascade files/haarcascade_righteye_2splits.xml
new file mode 100644
index 000000000..db4571cde
--- /dev/null
+++ b/Drowsiness detection/haar cascade files/haarcascade_righteye_2splits.xml	
@@ -0,0 +1,7407 @@
+<?xml version="1.0"?>
+<!--
+    Tree-based 20x20 right eye detector.
+    The detector is trained by 6665 positive samples from FERET, VALID and BioID face databases. 
+    Created by Shiqi Yu (http://yushiqi.cn/research/eyedetection).
+
+////////////////////////////////////////////////////////////////////////////////////////
+
+  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+
+  By downloading, copying, installing or using the software you agree to this license.
+  If you do not agree to this license, do not download, install,
+  copy or use the software.
+
+
+                        Intel License Agreement
+                For Open Source Computer Vision Library
+
+ Copyright (C) 2000, Intel Corporation, all rights reserved.
+ Third party copyrights are property of their respective owners.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+   * Redistribution's of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+
+   * Redistribution's in binary form must reproduce the above copyright notice,
+     this list of conditions and the following disclaimer in the documentation
+     and/or other materials provided with the distribution.
+
+   * The name of Intel Corporation may not be used to endorse or promote products
+     derived from this software without specific prior written permission.
+
+ This software is provided by the copyright holders and contributors "as is" and
+ any express or implied warranties, including, but not limited to, the implied
+ warranties of merchantability and fitness for a particular purpose are disclaimed.
+ In no event shall the Intel Corporation or contributors be liable for any direct,
+ indirect, incidental, special, exemplary, or consequential damages
+ (including, but not limited to, procurement of substitute goods or services;
+ loss of use, data, or profits; or business interruption) however caused
+ and on any theory of liability, whether in contract, strict liability,
+ or tort (including negligence or otherwise) arising in any way out of
+ the use of this software, even if advised of the possibility of such damage.
+-->
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+  <featureType>HAAR</featureType>
+  <height>20</height>
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diff --git a/Drowsiness detection/model.py b/Drowsiness detection/model.py
new file mode 100644
index 000000000..614cec4d6
--- /dev/null
+++ b/Drowsiness detection/model.py	
@@ -0,0 +1,57 @@
+import os
+from keras.preprocessing import image
+import matplotlib.pyplot as plt 
+import numpy as np
+from keras.utils.np_utils import to_categorical
+import random,shutil
+from keras.models import Sequential
+from keras.layers import Dropout,Conv2D,Flatten,Dense, MaxPooling2D, BatchNormalization
+from keras.models import load_model
+
+
+def generator(dir, gen=image.ImageDataGenerator(rescale=1./255), shuffle=True,batch_size=1,target_size=(24,24),class_mode='categorical' ):
+
+    return gen.flow_from_directory(dir,batch_size=batch_size,shuffle=shuffle,color_mode='grayscale',class_mode=class_mode,target_size=target_size)
+
+BS= 32
+TS=(24,24)
+train_batch= generator('data/train',shuffle=True, batch_size=BS,target_size=TS)
+valid_batch= generator('data/valid',shuffle=True, batch_size=BS,target_size=TS)
+SPE= len(train_batch.classes)//BS
+VS = len(valid_batch.classes)//BS
+print(SPE,VS)
+
+
+# img,labels= next(train_batch)
+# print(img.shape)
+
+model = Sequential([
+    Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(24,24,1)),
+    MaxPooling2D(pool_size=(1,1)),
+    Conv2D(32,(3,3),activation='relu'),
+    MaxPooling2D(pool_size=(1,1)),
+#32 convolution filters used each of size 3x3
+#again
+    Conv2D(64, (3, 3), activation='relu'),
+    MaxPooling2D(pool_size=(1,1)),
+
+#64 convolution filters used each of size 3x3
+#choose the best features via pooling
+    
+#randomly turn neurons on and off to improve convergence
+    Dropout(0.25),
+#flatten since too many dimensions, we only want a classification output
+    Flatten(),
+#fully connected to get all relevant data
+    Dense(128, activation='relu'),
+#one more dropout for convergence' sake :) 
+    Dropout(0.5),
+#output a softmax to squash the matrix into output probabilities
+    Dense(2, activation='softmax')
+])
+
+model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])
+
+model.fit_generator(train_batch, validation_data=valid_batch,epochs=15,steps_per_epoch=SPE ,validation_steps=VS)
+
+model.save('models/cnnCat2.h5', overwrite=True)
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