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 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 : Take image as input from a camera. Step 1 – Detect the face in the image and create a Region of Interest (ROI). Step 2 – Detect the eyes from ROI and feed it to the classifier. Step 3 – Classifier will categorize whether eyes are open or closed. Step 4 – Calculate score to check whether the person is drowsy. Step 5 – Now before starting with Prerequisites, Datasets and Model Architecture , If you are newbie I will suggest you to refer this Python Master sheet to Learn all necessary concepts of Python Programming language. 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. pip install (face and eye detection). OpenCV – opencv-python pip install tensorflow (keras uses TensorFlow as backend). TensorFlow – pip install keras (to build our classification model). Keras – pip install pygame (to play alarm sound). Pygame – 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 . 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. Convolutional Neural Networks (CNN) 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. 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: 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, to access the camera and set the capture object (cap). will read each frame and we store the image in a frame variable. cv2.VideoCapture(0) cap.read() 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 . Then we perform the detection using . 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. face = cv2.CascadeClassifier(‘ path to our haar cascade xml file’) faces = face.detectMultiScale(gray) 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 and respectively then detect the eyes using . 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. leye reye left_eye = leye.detectMultiScale(gray) l_eye = frame[ y : y+h, x : x+w ] 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 . l_eye r_eye Step 4 – Classifier will Categorise whether Eyes are Open or Closed We are using 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 . CNN 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 . We normalize our data for better convergence (All values will be between 0-1). Expand the dimensions to feed into our classifier. We loaded our model using . Now we predict each eye with our model . 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. cv2.resize(r_eye, (24,24)) r_eye = r_eye/255 model = load_model(‘models/cnnCat2.h5’) lpred = model.predict_classes(l_eye) 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: Output Screenshot: 1.Closed Eye Detection 2. Open Eyes Detection 3. 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. If you liked the Intermediate Python Project on Drowsiness Detection System, do share it on social media with your friends and colleagues. For more interesting Python projects please refer  - 14 Cool Python Project with Source Code!!

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