Intermediate Python Project – Driver Drowsiness Detection System with OpenCV & Keras

With this intermediate-level Python project, we will be making a drowsiness detecting device. A countless number of people drive on the highway day and night. Taxi drivers, bus drivers, truck drivers and people traveling long-distance suffer from lack of sleep. Due to which it becomes very dangerous to drive when feeling sleepy.

The majority of accidents happen due to the drowsiness of the driver. So, to prevent these accidents we will build a system using Python, OpenCV, and Keras which will alert the driver when he feels sleepy.

DataFlair has also published other Python project ideas with source code. You can check them from this Python projects list:

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  4. Breast Cancer Classification Python Project
  5. Age and Gender Detection Python Project
  6. Chatbot Python Project
  7. Drowsy Driver Safety Alert System Python Project
  8. Traffic Signs Recognition Python Project
  9. Image Caption Generator Python Project

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Intermediate Python Project on Drowsy Driver Alert System

Intermediate Python Project - Driver Drowsiness Detection System

Drowsiness detection is a safety technology that can prevent accidents that are caused by drivers who fell asleep while driving.

The objective of this intermediate Python project is to build a drowsiness detection system that will detect that a person’s eyes are closed for a few seconds. This system will alert the driver when drowsiness is detected.

Driver Drowsiness Detection System – About the Intermediate Python Project

In this Python project, we will be using OpenCV for gathering the images from webcam and feed them into a Deep Learning model which will classify whether the person’s eyes are ‘Open’ or ‘Closed’. The approach we will be using for this Python project is as follows :

Step 1 – Take image as input from a camera.

Step 2 – Detect the face in the image and create a Region of Interest (ROI).

Step 3 – Detect the eyes from ROI and feed it to the classifier.

Step 4 – Classifier will categorize whether eyes are open or closed.

Step 5 – Calculate score to check whether the person is drowsy.

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The Dataset

The dataset used for this model is created by us. To create the dataset, we wrote a script that captures eyes from a camera and stores in our local disk. We separated them into their respective labels ‘Open’ or ‘Closed’. The data was manually cleaned by removing the unwanted images which were not necessary for building the model. The data comprises around 7000 images of people’s eyes under different lighting conditions. After training the model on our dataset, we have attached the final weights and model architecture file “models/cnnCat2.h5”.

Now, you can use this model to classify if a person’s eye is open or closed.

The Model Architecture

The model we used is built with Keras using Convolutional Neural Networks (CNN). A convolutional neural network is a special type of deep neural network which performs extremely well for image classification purposes. A CNN basically consists of an input layer, an output layer and a hidden layer which can have multiple numbers of layers. A convolution operation is performed on these layers using a filter that performs 2D matrix multiplication on the layer and filter.

The CNN model architecture consists of the following layers:

  • Convolutional layer; 32 nodes, kernel size 3
  • Convolutional layer; 32 nodes, kernel size 3
  • Convolutional layer; 64 nodes, kernel size 3
  • Fully connected layer; 128 nodes

The final layer is also a fully connected layer with 2 nodes. In all the layers, a Relu activation function is used except the output layer in which we used Softmax.

Prerequisites

The requirement for this Python project is a webcam through which we will capture images. You need to have Python (3.6 version recommended) installed on your system, then using pip, you can install the necessary packages.

  1. OpenCV – pip install opencv-python (face and eye detection).
  2. TensorFlow – pip install tensorflow (keras uses TensorFlow as backend).
  3. Keras – pip install keras (to build our classification model).
  4. Pygame – pip install pygame (to play alarm sound).

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Python Project on Steps for Performing Driver Drowsiness Detection

Download the Python project source code from the zip and extract the files in your system:

Python Project Zip File

The contents of the zip are:

Project File Structure - Intermediate Python Project

  • The “haar cascade files” folder consists of the xml files that are needed to detect objects from the image. In our case, we are detecting the face and eyes of the person.
  • The models folder contains our model file “cnnCat2.h5” which was trained on convolutional neural networks.
  • We have an audio clip “alarm.wav” which is played when the person is feeling drowsy.
  • “Model.py” file contains the program through which we built our classification model by training on our dataset. You could see the implementation of convolutional neural network in this file.
  • “Drowsiness detection.py” is the main file of our project. To start the detection procedure, we have to run this file.

Let’s now understand how our algorithm works step by step.

Step 1 – Take Image as Input from a Camera

With a webcam, we will take images as input. So to access the webcam, we made an infinite loop that will capture each frame. We use the method provided by OpenCV, cv2.VideoCapture(0) to access the camera and set the capture object (cap). cap.read() will read each frame and we store the image in a frame variable.

Step 2 – Detect Face in the Image and Create a Region of Interest (ROI)

To detect the face in the image, we need to first convert the image into grayscale as the OpenCV algorithm for object detection takes gray images in the input. We don’t need color information to detect the objects. We will be using haar cascade classifier to detect faces. This line is used to set our classifier face = cv2.CascadeClassifier(‘ path to our haar cascade xml file’). Then we perform the detection using faces = face.detectMultiScale(gray). It returns an array of detections with x,y coordinates, and height, the width of the boundary box of the object. Now we can iterate over the faces and draw boundary boxes for each face.

for (x,y,w,h) in faces: 
        cv2.rectangle(frame, (x,y), (x+w, y+h), (100,100,100), 1 )

Step 3 – Detect the eyes from ROI and feed it to the classifier

The same procedure to detect faces is used to detect eyes. First, we set the cascade classifier for eyes in leye and reye respectively then detect the eyes using left_eye = leye.detectMultiScale(gray). Now we need to extract only the eyes data from the full image. This can be achieved by extracting the boundary box of the eye and then we can pull out the eye image from the frame with this code.

l_eye = frame[ y : y+h, x : x+w ]

l_eye only contains the image data of the eye. This will be fed into our CNN classifier which will predict if eyes are open or closed. Similarly, we will be extracting the right eye into r_eye.

Step 4 – Classifier will Categorize whether Eyes are Open or Closed

We are using CNN classifier for predicting the eye status. To feed our image into the model, we need to perform certain operations because the model needs the correct dimensions to start with. First, we convert the color image into grayscale using r_eye = cv2.cvtColor(r_eye, cv2.COLOR_BGR2GRAY). Then, we resize the image to 24*24 pixels as our model was trained on 24*24 pixel images cv2.resize(r_eye, (24,24)). We normalize our data for better convergence r_eye = r_eye/255 (All values will be between 0-1). Expand the dimensions to feed into our classifier. We loaded our model using model = load_model(‘models/cnnCat2.h5’) . Now we predict each eye with our model
lpred = model.predict_classes(l_eye). If the value of lpred[0] = 1, it states that eyes are open, if value of lpred[0] = 0 then, it states that eyes are closed.

Step 5 – Calculate Score to Check whether Person is Drowsy

The score is basically a value we will use to determine how long the person has closed his eyes. So if both eyes are closed, we will keep on increasing score and when eyes are open, we decrease the score. We are drawing the result on the screen using cv2.putText() function which will display real time status of the person.

cv2.putText(frame, “Open”, (10, height-20), font, 1, (255,255,255), 1, cv2.LINE_AA )

A threshold is defined for example if score becomes greater than 15 that means the person’s eyes are closed for a long period of time. This is when we beep the alarm using sound.play()

The Source Code of our main file looks like this:

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()

Python Project Example

Let’s start our project and see the working of our project. To start the project, you need to open a command prompt, go to the directory where our main file “drowsiness detection.py” exists. Run the script with this command.

python “drowsiness detection.py”

It may take a few seconds to open the webcam and start detection.

Example Screenshot:

running drowsiness program - python project with source code

Output Screenshot:

closed eye detection python project for beginners

Closed Eye Detection

Open Eyes Detection - python open source projects

Open Eyes Detection

sleep alert - python open source project

Sleep Alert

Summary

In this Python project, we have built a drowsy driver alert system that you can implement in numerous ways. We used OpenCV to detect faces and eyes using a haar cascade classifier and then we used a CNN model to predict the status.

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29 Responses

  1. Khairul says:

    Nothing train and valid dataset. Can you give me a link for download it?

    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)

  2. Dr Shannon Kakungulu says:

    Cannot seem to open on mac-os using the command python “drowsiness detection.py”

  3. ‫Ali Desoki says:

    Is there an available link to the dataset?

  4. George says:

    Shouldn’t I learn deep learning and Image recognition if I am to truly understand the code. I’m a complete beginner, what is the point for me to do this project without actually having the knowledge preexisting requisites?

    • DataFlair Team says:

      Yes, before starting this Python project it is recommended to get some knowledge about the following prerequisites that we have mentioned –
      OpenCV is a Computer vision library while Keras is a deep learning framework. Learn the concepts first and then go for their libraries to build projects.

      • Rahul Gupta says:

        @DataFlairTeam
        Sir could you please share the data set which used to generate the cnnCat2.h5. It is very helpful for me. I want to learn more and expand this project further.

  5. Tom says:

    Hi, Thank you for this wonder full project, Can you please give the training data set and also the training architecture code for cnnCat2.h5

  6. John Hurtado says:

    Thank you for sharing your knowledge. Could you send me the link where the training data is to my email? Thank you

  7. Arnav AGGARWAL says:

    please share the dataset links I wanna update this projects to a new level.
    I have an awesome idea to integrate it with blockchain networking.
    if you are willing to share the dataset. please do share it in the comment section.
    waiting for your reply

  8. chaitali Tanaji ghadge says:

    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)

    File “C:\Users\dbda\AppData\Local\Continuum\anaconda3\lib\site-packages\keras_preprocessing\image\directory_iterator.py”, line 106, in __init__
    for subdir in sorted(os.listdir(directory)):

    FileNotFoundError: [WinError 3] The system cannot find the path specified: ‘data/train’
    How to solve this problem?

  9. Phalguni says:

    Thank you for this project. Can you please mail me the link for the dataset on which the model was trained?

  10. kusuma says:

    can you please mail the dataset on which the model was trained?

  11. Rahul Gupta says:

    sir could you share the dataset, based on the model was trained.

  12. Rahul Gupta says:

    please, sir, send me the data set, it is very helpful for extending this project and learn more on this.

  13. Rahul Gupta says:

    @DataFlairTeam
    Sir could you please share the data set which used to generate the cnnCat2.h5. It is very helpful for me. I want to learn more and expand this project further.

  14. Aishwarya D S says:

    can we run this program in ubuntu 14??

  15. Sumit says:

    Sir the code is not running the frame application . Only the light of camera opens after running the main file. It is only throwing the message that is” your cpu supports instructions that this tensorflow binary was not compiled to use avx2″ . Can you tell me what’s the issue i am facing

    • DataFlair Team says:

      Hi Sumit,

      This is the warning message that comes when your CPU doesn’t support the AVX. However, the program will execute further and it may take few seconds to load the TensorFlow library and start the webcam.
      If you still face the issue then you can try reinstalling the TensorFlow library or run the program on a different system.
      .

  16. prince kumar says:

    sir can i get the dataset of this project for looking towards it.it would be helpful for understanding and implementing it more.

    • DataFlair Team says:

      Hi Prince,
      The dataset used in this Python project is a private dataset. It is not available publicly. But you are free to use the trained model that is available in the project dataset folder.

  17. prince kumar says:

    sir can i please get the dataset behind this project to enhance it more and to get easily understandable.

  18. prince kumar says:

    sir can i get the data set behind this project to understand the performance more clearly.

  19. Mahesh roy says:

    sir can i get the data set behind this project to understand the performance more clearly.

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