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+.DS_Store
diff --git a/MachineLearning Projects/Driver-Drowsiness-Detection/README.md b/MachineLearning Projects/Driver-Drowsiness-Detection/README.md
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+
+# Driver Drowsiness Detection
+
+A system that alarms the driver as soon as it detects that the driver is becoming drowsy to prevent any accidents.
+
+
+
+## Quick Start 🚀
+
+### Clone the Repository
+
+```sh
+git clone https://github.com/adityajai25/driver-drowsiness-detection.git
+```
+Then 
+
+```sh
+cd driver-drowsiness-detection
+```
+
+
+## Dataset
+
+We used a dataset downloaded from Kaggle.
+## Creating Virtual Environment
+
+Using a virtual environment isolates dependencies, manages library versions, keeps the global Python environment clean, and ensures consistent setups.
+
+### On Windows
+
+#### Creating a virtual environment:
+
+Open Command Prompt and navigate to the project directory
+
+```sh
+cd project/directory/
+
+```
+Create a Virtual Environment:
+```sh
+python -m venv env
+```
+To Activate the Virtual Environment:
+
+```sh
+.\env\Scripts\activate
+```
+
+### On mac/Linux
+
+#### Creating a virtual environment:
+Open terminal and navigate to the project directory
+
+```sh
+cd project/directory/
+
+```
+Create a Virtual Environment:
+```sh
+python -m venv env
+```
+To Activate the Virtual Environment:
+
+```sh
+source env/bin/activate
+```
+
+
+## Installing Required Packages
+
+Once the virtual environment is activated, install the required packages using the following commands:
+
+#### 1. Install pygame
+
+```sh
+pip install pygame==2.4.0
+```
+#### 2. Install openCV-Python
+
+```sh
+pip install opencv-python==4.6.0.66
+```
+#### 3. Install numpy
+
+```sh
+pip install numpy==1.23.0
+```
+#### 4. Install keras
+
+```sh
+pip install keras==2.12.0
+```
+#### 5. Install tensorflow
+
+```sh
+pip install tensorflow==2.13.0
+```
+
+
+## Execution
+After installing the packages required, the project can be executed using the following command.
+
+```sh
+python main.py 
+```
+
+This will initiate the application, and it may take a few moments to activate the webcam and begin detection.
\ No newline at end of file
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+  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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+  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,
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+                        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.
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+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
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+   * Redistribution's of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
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+   * 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.
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+   * The name of Intel Corporation may not be used to endorse or promote products
+     derived from this software without specific prior written permission.
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+ 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,
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+ loss of use, data, or profits; or business interruption) however caused
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diff --git a/MachineLearning Projects/Driver-Drowsiness-Detection/haar_cascade_files/haarcascade_lefteye_2splits.xml b/MachineLearning Projects/Driver-Drowsiness-Detection/haar_cascade_files/haarcascade_lefteye_2splits.xml
new file mode 100644
index 00000000..9a9ef58f
--- /dev/null
+++ b/MachineLearning Projects/Driver-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.
+-->
+<opencv_storage>
+<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
+  <stageParams>
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diff --git a/MachineLearning Projects/Driver-Drowsiness-Detection/haar_cascade_files/haarcascade_righteye_2splits.xml b/MachineLearning Projects/Driver-Drowsiness-Detection/haar_cascade_files/haarcascade_righteye_2splits.xml
new file mode 100644
index 00000000..db4571cd
--- /dev/null
+++ b/MachineLearning Projects/Driver-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>
+  <width>20</width>
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diff --git a/MachineLearning Projects/Driver-Drowsiness-Detection/main.py b/MachineLearning Projects/Driver-Drowsiness-Detection/main.py
new file mode 100644
index 00000000..daa11f1a
--- /dev/null
+++ b/MachineLearning Projects/Driver-Drowsiness-Detection/main.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
+
+import os
+print(os.path.abspath('haar cascade files/haarcascade_frontalface_alt.xml'))
+
+
+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 = np.argmax(model.predict(r_eye), axis=-1)
+        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 = np.argmax(model.predict(l_eye), axis=-1)
+        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):
+        cv2.imwrite(os.path.join(path,'image.jpg'),frame)
+        try:
+            sound.play()
+        except:  
+            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()
\ No newline at end of file
diff --git a/MachineLearning Projects/Driver-Drowsiness-Detection/model.py b/MachineLearning Projects/Driver-Drowsiness-Detection/model.py
new file mode 100644
index 00000000..7cc62c64
--- /dev/null
+++ b/MachineLearning Projects/Driver-Drowsiness-Detection/model.py	
@@ -0,0 +1,48 @@
+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)
+
+
+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)),
+    Conv2D(64, (3, 3), activation='relu'),
+    MaxPooling2D(pool_size=(1,1)),
+    Dropout(0.25),
+    Flatten(),
+    Dense(128, activation='relu'),
+    Dropout(0.5),
+    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)
+
+<<<<<<< HEAD
+model.save('models/cnnCat2.h5', overwrite=True)
+=======
+model.save('models/cnnCat2.h5', overwrite=True)
+model = load_model('model.h5')
+>>>>>>> 79e42d9 (Updated)
diff --git a/MachineLearning Projects/Driver-Drowsiness-Detection/models/cnnCat2.h5 b/MachineLearning Projects/Driver-Drowsiness-Detection/models/cnnCat2.h5
new file mode 100644
index 00000000..f89cd07d
Binary files /dev/null and b/MachineLearning Projects/Driver-Drowsiness-Detection/models/cnnCat2.h5 differ