Drowsiness detection with OpenCV

My Uncle John is a long haul tractor trailer truck driver.

For each new assignment, he picks his load up from a local company early in the morning and then sets off on a lengthy, enduring cross-country trek across the United States that takes him days to complete.

John is a nice, outgoing guy, who carries a smart, witty demeanor. He also fits the “cowboy of the highway” stereotype to a T, sporting a big ole’ trucker cap, red-checkered flannel shirt, and a faded pair of Levi’s that have more than one splotch of oil stain from quick and dirty roadside fixes. He also loves his country music.

I caught up with John a few weeks ago during a family dinner and asked him about his trucking job.

I was genuinely curious — before I entered high school I thought it would be fun to drive a truck or a car for a living (personally, I find driving to be a pleasurable, therapeutic experience).

But my question was also a bit self-motivated as well:

Earlier that morning I had just finished writing the code for this blog post and wanted to get his take on how computer science (and more specifically, computer vision) was affecting his trucking job.

The truth was this:

John was scared about his future employment, his livelihood, and his future.

The first five sentences out of his mouth included the words:

  • Tesla
  • Self-driving cars
  • Artificial Intelligence (AI)

Many proponents of autonomous, self-driving vehicles argue that the first industry that will be completely and totally overhauled by self-driving cars/trucks (even before consumer vehicles) is the long haul tractor trailer business.

If self-driving tractor trailers becomes a reality in the next few years, John has good reason to be worried — he’ll be out of a job, one that he’s been doing his entire life. He’s also getting close to retirement and needs to finish out his working years strong.

This isn’t speculation either: NVIDIA recently announced a partnership with PACCAR, a leading global truck manufacturer. The goal of this partnership is to make self-driving semi-trailers a reality.

After John and I were done discussing self-driving vehicles, I asked him the critical question that this very blog post hinges on:

Have you ever fallen asleep at the wheel?

I could tell instantly that John was uncomfortable. He didn’t look me in the eye. And when he finally did answer, it wasn’t a direct one — instead he recalled a story about his friend (name left out on purpose) who fell asleep after disobeying company policy on maximum number of hours driven during a 24 hour period.

The man ran off the highway, the contents of his truck spilling all over the road, blocking the interstate almost the entire night. Luckily, no one was injured, but it gave John quite the scare as he realized that if it could happen to other drivers, it could happen to him as well.

I then explained to John my work from earlier in the day — a computer vision system that can automatically detect driver drowsiness in a real-time video stream and then play an alarm if the driver appears to be drowsy.

While John said he was uncomfortable being directly video surveyed while driving, he did admit that it the technique would be helpful in the industry and ideally reduce the number of fatigue-related accidents.

Today, I am going to show you my implementation of detecting drowsiness in a video stream — my hope is that you’ll be able to use it in your own applications.

To learn more about drowsiness detection with OpenCV, just keep reading.

Looking for the source code to this post?
Jump right to the downloads section.

Drowsiness detection with OpenCV

Two weeks ago I discussed how to detect eye blinks in video streams using facial landmarks.

Today, we are going to extend this method and use it to determine how long a given person’s eyes have been closed for. If there eyes have been closed for a certain amount of time, we’ll assume that they are starting to doze off and play an alarm to wake them up and grab their attention.

To accomplish this task, I’ve broken down today’s tutorial into three parts.

In the first part, I’ll show you how I setup my camera in my car so I could easily detect my face and apply facial landmark localization to monitor my eyes.

I’ll then demonstrate how we can implement our own drowsiness detector using OpenCV, dlib, and Python.

Finally, I’ll hop in my car and go for a drive (and pretend to be falling asleep as I do).

As we’ll see, the drowsiness detector works well and reliably alerts me each time I start to “snooze”.

Rigging my car with a drowsiness detector

Figure 1: Mounting my camera to my car dash for drowsiness detection.

The camera I used for this project was a Logitech C920. I love this camera as it:

  • Is relatively affordable.
  • Can shoot in full 1080p.
  • Is plug-and-play compatible with nearly every device I’ve tried it with (including the Raspberry Pi).

I took this camera and mounted it to the top of my dash using some double-sided tape to keep it from moving around during the drive (Figure 1 above).

The camera was then connected to my MacBook Pro on the seat next to me:

Figure 2: I’ll be using my MacBook Pro to run the actual drowsiness detection algorithm.

Originally, I had intended on using my Raspberry Pi 3 due to (1) form factor and (2) the real-world implications of building a driver drowsiness detector using very affordable hardware; however, as last week’s blog post discussed, the Raspberry Pi isn’t quite fast enough for real-time facial landmark detection.

In a future blog post I’ll be discussing how to optimize the Raspberry Pi along with the dlib compile to enable real-time facial landmark detection. However, for the time being, we’ll simply use a standard laptop computer.

With all my hardware setup, I was ready to move on to building the actual drowsiness detector using computer vision techniques.

The drowsiness detector algorithm

The general flow of our drowsiness detection algorithm is fairly straightforward.

First, we’ll setup a camera that monitors a stream for faces:

Figure 3: Step #1 — Look for faces in the input video stream.

If a face is found, we apply facial landmark detection and extract the eye regions:

Figure 4: Step #2 — Apply facial landmark localization to extract the eye regions from the face.

Now that we have the eye regions, we can compute the eye aspect ratio (detailed here) to determine if the eyes are closed:

Figure 5: Step #3 — Compute the eye aspect ratio to determine if the eyes are closed.

If the eye aspect ratio indicates that the eyes have been closed for a sufficiently long enough amount of time, we’ll sound an alarm to wake up the driver:

Figure 6: Step #4 — Sound an alarm if the eyes have been closed for a sufficiently long enough time.

In the next section, we’ll implement the drowsiness detection algorithm detailed above using OpenCV, dlib, and Python.

Building the drowsiness detector with OpenCV

To start our implementation, open up a new file, name it detect_drowsiness.py , and insert the following code:

Lines 2-12 import our required Python packages.

We’ll need the SciPy package so we can compute the Euclidean distance between facial landmarks points in the eye aspect ratio calculation (not strictly a requirement, but you should have SciPy installed if you intend on doing any work in the computer vision, image processing, or machine learning space).

We’ll also need the imutils package, my series of computer vision and image processing functions to make working with OpenCV easier.

If you don’t already have imutils  installed on your system, you can install/upgrade imutils  via:

We’ll also import the Thread  class so we can play our alarm in a separate thread from the main thread to ensure our script doesn’t pause execution while the alarm sounds.

In order to actually play our WAV/MP3 alarm, we need the playsound library, a pure Python, cross-platform implementation for playing simple sounds.

The playsound  library is conveniently installable via pip :

However, if you are using macOS (like I did for this project), you’ll also want to install pyobjc, otherwise you’ll get an error related to AppKit  when you actually try to play the sound:

only tested playsound  on macOS, but according to both the documentation and Taylor Marks (the developer and maintainer of playsound ), the library should work on Linux and Windows as well.

Note: If you are having problems with playsound , please consult their documentation as I am not an expert on audio libraries.

To detect and localize facial landmarks we’ll need the dlib library which is imported on Line 11. If you need help installing dlib on your system, please refer to this tutorial.

Next, we need to define our sound_alarm  function which accepts a path  to an audio file residing on disk and then plays the file:

We also need to define the eye_aspect_ratio  function which is used to compute the ratio of distances between the vertical eye landmarks and the distances between the horizontal eye landmarks:

The return value of the eye aspect ratio will be approximately constant when the eye is open. The value will then rapid decrease towards zero during a blink.

If the eye is closed, the eye aspect ratio will again remain approximately constant, but will be much smaller than the ratio when the eye is open.

To visualize this, consider the following figure from Soukupová and Čech’s 2016 paper, Real-Time Eye Blink Detection using Facial Landmarks:

Figure 7: Top-left: A visualization of eye landmarks when then the eye is open. Top-right: Eye landmarks when the eye is closed. Bottom: Plotting the eye aspect ratio over time. The dip in the eye aspect ratio indicates a blink (Figure 1 of Soukupová and Čech).

On the top-left we have an eye that is fully open with the eye facial landmarks plotted. Then on the top-right we have an eye that is closed. The bottom then plots the eye aspect ratio over time.

As we can see, the eye aspect ratio is constant (indicating the eye is open), then rapidly drops to zero, then increases again, indicating a blink has taken place.

In our drowsiness detector case, we’ll be monitoring the eye aspect ratio to see if the value falls but does not increase again, thus implying that the person has closed their eyes.

You can read more about blink detection and the eye aspect ratio in my previous post.

Next, let’s parse our command line arguments:

Our drowsiness detector requires one command line argument followed by two optional ones, each of which is detailed below:

  • --shape-predictor : This is the path to dlib’s pre-trained facial landmark detector. You can download the detector along with the source code to this tutorial by using the “Downloads” section at the bottom of this blog post.
  • --alarm : Here you can optionally specify the path to an input audio file to be used as an alarm.
  • --webcam : This integer controls the index of your built-in webcam/USB camera.

Now that our command line arguments have been parsed, we need to define a few important variables:

Line 48 defines the EYE_AR_THRESH . If the eye aspect ratio falls below this threshold, we’ll start counting the number of frames the person has closed their eyes for.

If the number of frames the person has closed their eyes in exceeds EYE_AR_CONSEC_FRAMES  (Line 49), we’ll sound an alarm.

Experimentally, I’ve found that an EYE_AR_THRESH  of 0.3  works well in a variety of situations (although you may need to tune it yourself for your own applications).

I’ve also set the EYE_AR_CONSEC_FRAMES  to be 48 , meaning that if a person has closed their eyes for 48 consecutive frames, we’ll play the alarm sound.

You can make the drowsiness detector more sensitive by decreasing the EYE_AR_CONSEC_FRAMES  — similarly, you can make the drowsiness detector less sensitive by increasing it.

Line 53 defines COUNTER , the total number of consecutive frames where the eye aspect ratio is below EYE_AR_THRESH .

If COUNTER  exceeds EYE_AR_CONSEC_FRAMES , then we’ll update the boolean ALARM_ON  (Line 54).

The dlib library ships with a Histogram of Oriented Gradients-based face detector along with a facial landmark predictor — we instantiate both of these in the following code block:

The facial landmarks produced by dlib are an indexable list, as I describe here:

Figure 8: Visualizing the 68 facial landmark coordinates from the iBUG 300-W dataset (larger resolution).

Therefore, to extract the eye regions from a set of facial landmarks, we simply need to know the correct array slice indexes:

Using these indexes, we’ll easily be able to extract the eye regions via an array slice.

We are now ready to start the core of our drowsiness detector:

On Line 69 we instantiate our VideoStream  using the supplied --webcam  index.

We then pause for a second to allow the camera sensor to warm up (Line 70).

On Line 73 we start looping over frames in our video stream.

Line 77 reads the next frame , which we then preprocess by resizing it to have a width of 450 pixels and converting it to grayscale (Lines 78 and 79).

Line 82 applies dlib’s face detector to find and locate the face(s) in the image.

The next step is to apply facial landmark detection to localize each of the important regions of the face:

We loop over each of the detected faces on Line 85 — in our implementation (specifically related to driver drowsiness), we assume there is only one face — the driver — but I left this for  loop in here just in case you want to apply the technique to videos with more than one face.

For each of the detected faces, we apply dlib’s facial landmark detector (Line 89) and convert the result to a NumPy array (Line 90).

Using NumPy array slicing we can extract the (x, y)-coordinates of the left and right eye, respectively (Lines 94 and 95).

Given the (x, y)-coordinates for both eyes, we then compute their eye aspect ratios on Line 96 and 97.

Soukupová and Čech recommend averaging both eye aspect ratios together to obtain a better estimation (Line 100).

We can then visualize each of the eye regions on our frame  by using the cv2.drawContours  function below — this is often helpful when we are trying to debug our script and want to ensure that the eyes are being correctly detected and localized:

Finally, we are now ready to check to see if the person in our video stream is starting to show symptoms of drowsiness:

On Line 111 we make a check to see if the eye aspect ratio is below the “blink/closed” eye threshold, EYE_AR_THRESH .

If it is, we increment COUNTER , the total number of consecutive frames where the person has had their eyes closed.

If COUNTER exceeds EYE_AR_CONSEC_FRAMES  (Line 116), then we assume the person is starting to doze off.

Another check is made, this time on Line 118 and 119 to see if the alarm is on — if it’s not, we turn it on.

Lines 124-128 handle playing the alarm sound, provided an --alarm  path was supplied when the script was executed. We take special care to create a separate thread responsible for calling sound_alarm  to ensure that our main program isn’t blocked until the sound finishes playing.

Lines 131 and 132 draw the text DROWSINESS ALERT!  on our frame  — again, this is often helpful for debugging, especially if you are not using the playsound  library.

Finally, Lines 136-138 handle the case where the eye aspect ratio is larger than EYE_AR_THRESH , indicating the eyes are open. If the eyes are open, we reset COUNTER  and ensure the alarm is off.

The final code block in our drowsiness detector handles displaying the output frame  to our screen:

To see our drowsiness detector in action, proceed to the next section.

Testing the OpenCV drowsiness detector

To start, make sure you use the “Downloads” section below to download the source code + dlib’s pre-trained facial landmark predictor + example audio alarm file utilized in today’s blog post.

I would then suggest testing the detect_drowsiness.py  script on your local system in the comfort of your home/office before you start to wire up your car for driver drowsiness detection.

In my case, once I was sufficiently happy with my implementation, I moved my laptop + webcam out to my car (as detailed in the “Rigging my car with a drowsiness detector” section above), and then executed the following command:

I have recorded my entire drive session to share with you — you can find the results of the drowsiness detection implementation below:

Note: The actual alarm.wav  file came from this website, credited to Matt Koenig.

As you can see from the screencast, once the video stream was up and running, I carefully started testing the drowsiness detector in the parking garage by my apartment to ensure it was indeed working properly.

After a few tests, I then moved on to some back roads and parking lots were there was very little traffic (it was a major holiday in the United States, so there were very few cars on the road) to continue testing the drowsiness detector.

Remember, driving with your eyes closed, even for a second, is dangerous, so I took extra special precautions to ensure that the only person who could be harmed during the experiment was myself.

As the results show, our drowsiness detector is able to detect when I’m at risk of dozing off and then plays a loud alarm to grab my attention.

The drowsiness detector is even able to work in a variety of conditions, including direct sunlight when driving on the road and low/artificial lighting while in the concrete parking garage.


In today’s blog post I demonstrated how to build a drowsiness detector using OpenCV, dlib, and Python.

Our drowsiness detector hinged on two important computer vision techniques:

  • Facial landmark detection
  • Eye aspect ratio

Facial landmark prediction is the process of localizing key facial structures on a face, including the eyes, eyebrows, nose, mouth, and jawline.

Specifically, in the context of drowsiness detection, we only needed the eye regions (I provide more detail on how to extract each facial structure from a face here).

Once we have our eye regions, we can apply the eye aspect ratio to determine if the eyes are closed. If the eyes have been closed for a sufficiently long enough period of time, we can assume the user is at risk of falling asleep and sound an alarm to grab their attention. More details on the eye aspect ratio and how it was derived can be found in my previous tutorial on blink detection.

If you’ve enjoyed this blog post on drowsiness detection with OpenCV (and want to learn more about computer vision techniques applied to faces), be sure to enter your email address in the form below — I’ll be sure to notify you when new content is published here on the PyImageSearch blog.


If you would like to download the code and images used in this post, please enter your email address in the form below. Not only will you get a .zip of the code, I’ll also send you a FREE 11-page Resource Guide on Computer Vision and Image Search Engines, including exclusive techniques that I don’t post on this blog! Sound good? If so, enter your email address and I’ll send you the code immediately!

, , , , , , , ,

35 Responses to Drowsiness detection with OpenCV

  1. Lee Hoyoung May 8, 2017 at 11:06 am #

    Hello, Adrian.
    I’d like to ask you a few questions about this post.
    I use raspberry pies 3 and I’m using an SAMSUNG SPC-B900W webcam.
    You mentioned that you did not perform well in the raspberry Pie 3 article.
    I’d like to reduce the incidence of this phenomenon, but how can I solve this phenomenon?

    • Adrian Rosebrock May 8, 2017 at 12:14 pm #

      Please see my reply to “N.Trewartha” regarding the Raspberry Pi 3.

  2. N.Trewartha May 8, 2017 at 11:48 am #


    A super project.
    I will try to do this on a RPi 3 so I have a solution fpr the car.
    Any tips ?

    • Adrian Rosebrock May 8, 2017 at 12:13 pm #

      If you intend on using a Raspberry Pi for this, I would:

      1. Use Haar cascades rather than the HOG face detector. While Haar cascades are less accurate, they are also faster.
      2. Use skip frames and only detect faces in every N frames. This will also speedup the pipeline.

      • Vikram May 12, 2017 at 2:21 pm #

        Adrian, what exactly do you mean when you say,”Use skip frames”?Is this switch or an option that we can use?I am planning to implement this on R-Pi3 in my car and would love to understand more.Btw,fantastic article.A huge fan of your site and courses.

        Also any tips or articles on the precompilation of dlib libraries and perf tips for R-pi3?

        • Adrian Rosebrock May 15, 2017 at 8:54 am #

          By “skip frames” I mean literally only process every N frames for face detection (i.e., “skipping frames”). I plan on doing an updated blog post on how to optimize facial landmark detection for the Raspberry Pi, so stay tuned for that post.

          • SMITH May 17, 2017 at 12:28 am #

            I’m waiting for the results, too!

  3. Kenny May 8, 2017 at 11:51 am #

    Awesome Adrian! Fantastic post as usual. Looking forward to the release of your deep learning book!

    • Adrian Rosebrock May 8, 2017 at 12:12 pm #

      Thank you Kenny! 🙂

  4. Hitesh May 8, 2017 at 12:19 pm #

    How many FPS you can process ?

    • Fang May 10, 2017 at 3:56 am #

      This depends on what device you are running on.

  5. Balesh May 8, 2017 at 1:46 pm #

    How to detect when a driver wears shades.

  6. Gary Cao May 8, 2017 at 2:15 pm #

    Fantastic work!
    What if the driver wears sunglasses? Any ideas?

    • Adrian Rosebrock May 11, 2017 at 9:02 am #

      If the driver wears sunglasses and you cannot detect the eyes then you cannot apply this algorithm. I would suggest extending the approach to also monitor the head tilt of the driver as well.

  7. Carlos May 8, 2017 at 4:29 pm #

    Is this the most dangerous and risky software test you have ever made?

    • Adrian Rosebrock May 11, 2017 at 9:01 am #

      Off the top of my head, yes. But I was driving very slow (5-10 MPH) on uncrowded streets. The video made it seem like I was going much faster.

  8. Hermi May 8, 2017 at 4:55 pm #

    I love to read your great posts. Amazing work, very impressive.

    Greetings from germany.

    • Adrian Rosebrock May 11, 2017 at 9:01 am #

      Thank you Hermi, I hope all is well in Germany.

  9. mapembert May 9, 2017 at 11:09 am #

    Fantastic job Adrian! Both the results and the write up. I’m patiently waiting for a ultra dice counter. 🙂

    • Adrian Rosebrock May 11, 2017 at 8:54 am #

      I’m glad you enjoyed the blog post mapembert! What do you mean by an “ultra dice counter”?

  10. Oleh May 10, 2017 at 6:35 am #

    Nice tutorial and nice application for facial landmarks, thank you! Cool car! ( I am Subaru lover too 🙂

    • Adrian Rosebrock May 11, 2017 at 8:47 am #

      Thanks Oleh, I’m glad you enjoyed the tutorial! I also really love my Subaru as well. Living in the north-eastern part of the United States, it’s often help to have AWD drive to get around on snowy days 😉

  11. Rishabh Gupta May 13, 2017 at 4:28 am #

    Awesome work Adrian! A slight change from Blink detector but a nice application.

    I’ve a question regarding this.

    Dont you think you should also consider the moving state of tha car coz there’s no point of any alert if the car is stationary and driver is sleepy.

    I know we would need some sensor to detect the speed of the car for this. But i would like to know exactly what device do we need to use for this, how do we connect to our system and required modules for our code to incorporate this functionality.

    • Adrian Rosebrock May 15, 2017 at 8:51 am #

      I often get questions on how to build practical computer vision applications based on previous blog posts. This post on drowsiness detection, as you noted, is an extension of blink detection.

      As for considering the moving state of the car, absolutely — but that’s outside what we are focusing on: computer vision. If you were to implement this method in a factory for cars you would have sensors that could tell you if the car was moving, how fast, etc. Exactly how you access this information is dependent on the manufacturer of the car.

  12. Joseph Landau May 13, 2017 at 9:50 pm #

    In view of the importance of this application, would it not be sensible to use a faster single board computer, such as perhaps an Odroid? Or would that still be inadequate?

    • Adrian Rosebrock May 15, 2017 at 8:42 am #

      For an entirely self-contained project I would likely use a device from the NVIDIA TX series.

  13. heart May 14, 2017 at 8:53 am #

    Thank you.
    The connection was successful.
    Movement is about 5 seconds slower.
    What should I do if my camera is slow?

    • heart May 14, 2017 at 8:56 am #

      Thank you.
      The connection was successful.
      But there is no sound.
      Is there a solution?
      What should I download separately?

      • Adrian Rosebrock May 15, 2017 at 8:39 am #

        If there is no sound, then there is an issue with the playsound library. As I mentioned in the blog post, I’m not an expert on playing sounds with the Python programming language so you will need to consult the playsound documentation.

  14. Umar Yusuf May 16, 2017 at 11:48 am #

    Nice practical application of openCV. Am a huge fan of your blog, however my primary niche of interest is in Geo sciences field.

    Any chance you will venture into creating Geo related blog posts in future?

    I mean openCV in GIS, Remote Sensing, Geomatics, Geology, Geography etc…

    • Adrian Rosebrock May 17, 2017 at 9:56 am #

      Hi Umar — I personally don’t do any work with geo-related projects, but it’s something I would consider exploring in the future.

  15. Joseph Landau May 18, 2017 at 12:58 pm #

    Do you have any plans to support night driving?

    • Adrian Rosebrock May 21, 2017 at 5:20 am #

      At the present time no, but I will certainly consider it.

  16. Ömer Furkan May 20, 2017 at 10:06 am #

    Hi I work on raspberry pi 3 I think I did everything right but occur an error like that :

    usage: detect_drowsiness.py [-h] -p SHAPE_PREDICTOR [-a ALARM] [-w WEBCAM]
    detect_drowsiness.py: error: argument -p/–shape-predictor is required

Leave a Reply