Keras and deep learning on the Raspberry Pi

Building a Not Santa Detector using deep learning, Keras, Python, and the Raspberry Pi

Today’s blog post is the most fun I’ve EVER had writing a PyImageSearch tutorial.

It has everything we have been discussing the past few weeks, including:

  • Deep learning
  • Raspberry Pis
  • 3D Christmas trees
  • References to HBO’s Silicon Valley “Not Hotdog” detector
  • Me dressing up as Santa Clause!

In keeping with the Christmas and Holiday season, I’ll be demonstrating how to take a deep learning model (trained with Keras) and then deploy it to the Raspberry Pi.

But this isn’t any machine learning model…

This image classifier has been specifically trained to detect if Santa Claus is in our video stream.

And if we do detect Santa Claus…

Well. I won’t spoil the surprise (but it does involve a 3D Christmas tree and a jolly tune).

Enjoy the tutorial. Download the code. Hack with it.

And most of all, have fun!

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

Keras and deep learning on the Raspberry Pi

Today’s blog post is a complete guide to running a deep neural network on the Raspberry Pi using Keras.

I’ve framed this project as a Not Santa detector to give you a practical implementation (and have some fun along the way).

In the first part of this blog post, we’ll discuss what a Not Santa detector is (just in case you’re unfamiliar with HBO’s Silicon Valley “Not Hotdog” detector which has developed a cult following).

We’ll then configure our Raspberry Pi for deep learning by installing TensorFlow, Keras, and a number of other prerequisites.

Once our Raspberry Pi is configured for deep learning we’ll move on to building a Python script that can:

  1. Load our Keras model from disk
  2. Access our Raspberry Pi camera module/USB webcam
  3. Apply deep learning to detect if Santa Clause is in the frame
  4. Access our GPIO pins and play music if Santa is detected

These are my favorite types of blog posts to write here on PyImageSearch as they integrate a bunch of techniques we’ve discussed, including:

Let’s get started!

What is a Not Santa detector?

Figure 1: The Not Hotdog detector app from HBO’s Silicon Valley.

A Not Santa detector is a play off HBO’s Silicon Valley where the characters create a smartphone app that can determine if an input photo is a “hot dog” or is “not a hot dog”:

The show is clearly poking fun at the Silicon Valley startup culture in the United States by:

  1. Preying on the hype of machine learning/deep learning
  2. Satirically remaking on the abundance of smartphone applications that serve little purpose (but the creators are convinced their app will “change the world”)

I decided to have some fun myself.

Today we are creating a Not Santa detector that will detect if Santa Claus is in an image/video frame.

For those unfamiliar with Santa Claus (or simply Santa” for short), he is a jolly, portly, white-bearded, fictional western culture figure who delivers presents to boys and girls while they sleep Christmas Eve

However, this application is not totally just for fun and satire!

We’ll be learning some practical skills along the way, including how to:

  1. Configure your Raspberry Pi for deep learning
  2. Install Keras and TensorFlow on your Raspberry Pi
  3. Deploy a pre-trained Convolutional Neural Network (with Keras) to your Raspberry Pi
  4. Perform a given action once a positive detection has occurred

But before we can write a line of code, let’s first review the hardware we need.

What hardware do I need?

Figure 2: The Not Santa detector setup includes the Raspberry Pi 3, speakers, 3D Christmas tree, and a webcam (not pictured). The Pi has LeNet implemented with Keras in a Python script in order to detect Santa.

In order to follow along exactly with this tutorial (with no modifications) you’ll need:

Of course, you do not need all these parts.

If you have just a Raspberry Pi + camera module/USB camera you’ll be all set (but you will have to modify the code so it doesn’t try to access the GPIO pins or play music via the speakers).

Your setup should look similar to mine in Figure 2 above where I have connected my speakers, 3D Christmas Tree, and webcam (not pictured as it’s off camera).

I would also recommend hooking up an HDMI monitor + keyboard to test your scripts and debug them:

Figure 3: My deep learning setup includes the Raspberry Pi and components along with a keyboard, mouse, and small HDMI display. With this setup, we will surely catch Santa delivering presents in front of my tree.

In the image above, you can see my Raspberry Pi, HDMI, keyboard, and Christmas critter friends keeping me company while I put together today’s tutorial.

How do I install TensorFlow and Keras on the Raspberry Pi?

Figure 4: We’re going to use Keras with the TensorFlow backend on the Raspberry Pi to implement a deep learning Not Santa detector.

Last week, we learned how to train a Convolutional Neural Network using Keras to determine if Santa was in an input image.

Today, we are going to take the pre-trained model and deploy it to the Raspberry Pi.

As I’ve mentioned before, the Raspberry Pi is not suitable for training a neural network (outside of “toy” examples). However, the Raspberry Pi can be used to deploy a neural network once it has already been trained (provided the model can fit into a small enough memory footprint, of course).

I’m going to assume you have already installed OpenCV on your Raspberry Pi.

If you have not installed OpenCV on your Raspberry Pi, start by using this tutorial where I demonstrate how to optimize your Raspberry Pi + OpenCV install for speed (leading to a 30%+ increase in performance).

Note: This guide will not work with Python 3 — you’ll instead need to use Python 2.7. I’ll explain why later in this section. Take the time now to configure your Raspberry Pi with Python 2.7 and OpenCV bindings. In Step #4 of the Raspberry Pi + OpenCV installation guide, be sure to make a virtual environment with the -p python2  switch.

From there, I recommend increasing the swap space on your Pi. Increasing the swap will enable you to use the Raspberry Pi SD card for additional memory (a critical step when trying to compile and install large libraries on the memory-limited Raspberry Pi).

To increase your swap space, open up /etc/dphys-swapfile  and then edit the CONF_SWAPSIZE  variable:

Notice that I am increasing the swap from 100MB to 1024MB.

From there, restart the swap service:

Note: Increasing swap size is a great way to burn out your memory card, so be sure to revert this change and restart the swap service when you’re done. You can read more about large sizes corrupting memory cards here.

Now that your swap size has been increased, let’s get started configuring our development environment.

To start, create a Python virtual environment named not_santa  using Python 2.7 (I’ll explain why Python 2.7 once we get to the TensorFlow install command):

Notice here how the -p  switch points to python2 , indicating that Python 2.7 will be used for the virtual environment.

If you are new to Python virtual environments, how they work, and why we use them, please refer to this guide to help get you up to speed as well as this excellent virtualenv primer from RealPython.

You’ll also want to make sure you have sym-linked your  bindings into your not_santa  virtual environment (if you have not done so yet):

Again, make sure you have compiled OpenCV with Python 2.7 bindings. You’ll want to double-check your path to your  file as well, just in case your install path is slightly different than mine.

If you compiled Python 3 + OpenCV bindings, created the sym-link, and then tried to import cv2  into your Python shell, you will get a confusing traceback saying that the import failed.

Important: For these next few pip commands, be sure that you’re in the not_santa  environment (or your Python environment of choice), otherwise you’ll be installing the packages to your Raspberry Pi’s system Python.

To enter the environment simply use the workon  command at the bash prompt:

From there, you’ll see “ (not_santa)” at the beginning of your bash prompt.

Ensure you have NumPy installed in the not_santa  environment using the following command:

Since we’ll be accessing the GPIO pins for this project we’ll need to install both RPi.GPIO and gpiozero:

We are now ready to install TensorFlow on your Raspberry Pi.

The problem is that there is not an official (Google released) TensorFlow distribution.

We can follow the long, arduous, painful process of compiling TensorFlow from scratch on the Raspberry Pi…

Or we can use the pre-compiled binaries created Sam Abrahams (published on GitHub).

The problem is that there are only two types of pre-compiled TensorFlow binaries:

  1. One for Python 2.7
  2. And another for Python 3.4

The Raspbian Stretch distribution (the latest release of the Raspbian operating system at the time of this writing) ships with Python 3.5 — we, therefore, have a version mismatch.

To avoid any headaches between Python 3.4 and Python 3.5, I decided to stick with the Python 2.7 install.

While I would have liked to use Python 3 for this guide, the install process would have become more complicated (I could easily write multiple posts on installing TensorFlow + Keras on the Raspberry Pi, but since installation is not the main focus of this tutorial, I decided to keep it more straightforward).

Let’s go ahead and install TensorFlow for Python 2.7 using the following commands:

Note: You will need to expand the code block above to copy the full URL. I recommend pressing the “<>” button before you copy the commands.

Once TensorFlow compiles and installs (which took around an hour on my Raspberry Pi) you’ll need to install HDF5 and h5py. These libraries will allow us to load our pre-trained model from disk:

I Install HDF5 + h5py without the time  command running so I can’t remember the exact amount of time it took to install, but I believe it was around 30-45 minutes.

And finally, let’s install Keras and the other prerequisites required for this project:

The SciPy install in particular will take a few hours so make sure you let the install run overnight/while you’re at work.

To test your configuration, open up a Python shell (while in the not_santa  environment) and execute the following commands:

If all goes as planned you should see Keras imported using the TensorFlow backend.

As the output above demonstrates, you should also double-check that your OpenCV bindings ( cv2 ) can be imported as well.

Finally, don’t forget to reduce your swap size from 1024MB back down to 100MB by:

  1. Opening up /etc/dphys-swapfile .
  2. Resetting CONF_SWAPSIZE  to 100MB.
  3. Restarting the swap service (as we discussed earlier in this post).

As mentioned in the note above, setting your swap size back to 100MB is important for memory card longevity. If you skip this step, you may encounter memory corruption issues and a decreased lifespan of the card.

Running a Keras + deep learning model on the Raspberry Pi

Figure 5: Running a deep learning model on the Raspberry Pi using Keras and Python.

We are now ready to code our Not Santa detector using Keras, TensorFlow, and the Raspberry Pi.

Again, I’ll be assuming you have the same hardware setup as I do (i.e., 3D Christmas Tree and speakers) so if your setup is different you’ll need to hack up the code below.

To get started, open a new file, name it , and insert the following code:

Lines 2-12 handle our imports, notably:

  • keras  is used to preprocess input frames for classification and to load our pre-trained model from disk.
  • gpiozero  is used to access the 3D Christmas tree.
  • imutils  is used to access the video stream (whether Raspberry Pi camera module or USB).
  • threading  is used for non-blocking operations, in particular when we want to light the Christmas tree or play music while not blocking execution of the main thread.

From there, we’ll define a function to light our 3D Christmas tree:

Our light_tree  function accepts a tree  argument (which is assumed be an LEDBoard  object).

First, we loop over all LEDs in the tree  and randomly light each of the LEDs to create a “twinkling” effect (Lines 17-19).

We leave the lights on for a period of time for some holiday spirit (Line 23) and then we loop over the LEDs again, this time turning them off (Lines 26-28).

An example of the 3D Christmas tree lights turned on can be seen below:

Figure 6: The 3D Christmas tree for the Raspberry Pi. You can get yours from Pi Hut (photo credit: Pi Hut).

Our next function handles playing music when Santa is detected:

In the play_christmas_music  function, we make a system call to the aplay  command which enables us to play a music file as we would from the command line.

Using the os.system  call is a bit of a hack, but playing the audio file via pure Python (using a library such as Pygame) is overkill for this project.

From there, let’s hardcode the configurations we’ll use:

Lines 38 and 39 hardcode paths to our pre-trained Keras model and audio file. Be sure to use the “Downloads” section of this blog post to grab the files.

We also initialize parameters used for detection which include TOTAL_CONSEC  and TOTAL_THRESH . These two values represent the number of frames containing Santa and the threshold at which we’ll both play music and turn on the tree respectively (Lines 43 and 44).

The last initialization is SANTA = False , a boolean (Line 47). We’ll use the SANTA  variable later in the script as a status flag to aid in our logic.

Next, we’ll load our pre-trained Keras model and initialize our Christmas tree:

Keras allows us to save models to disk for future use. Last week, we saved our Not Santa model to disk and this week we’re going to load it up on our Raspberry Pi. We load the model on Line 51 with the Keras load_model  function.

Our tree  object is instantiated on Line 54. As shown, tree  is an LEDBoard  object from the gpiozero  package.

Now we’ll initialize our video stream:

To access the camera, we’ll use VideoStream  from my imutils package (you can find the documentation to VideoStream here) on either Line 58 or 59.

Important: If you’d like to use the PiCamera module (instead of a USB camera) for this project, simply comment Line 58 and uncomment Line 59.

We sleep  for a brief 2 seconds so our camera can warm up (Line 60) before we begin looping over the frames:

On Line 63 we start looping over video frames until the stop condition is met (shown later in the script).

First, we’ll grab a frame  by calling  (Line 66).

Then we resize  frame  to width=400 , maintaining the aspect ratio (Line 67). We’ll be preprocessing this frame  prior to sending it through our neural network model. Later on we’ll be displaying the frame to the screen along with a text label.

From there let’s preprocess the image and pass it through our Keras + deep learning model for prediction:

Lines 70-73 preprocess the image  and prepare it for classification. To learn more about preprocessing for deep learning, be sure to check out the Starter Bundle of my latest book, Deep Learning for Computer Vision with Python.

From there, we query  model.predict  with our image  as the argument. This sends the image  through the neural network, returning a tuple containing class probabilities (Line 77).

We initialize the label  to “Not Santa” (we’ll revisit label  later) and the probability, proba , to the value of  notSanta  on Lines 78 and 79.

Let’s check to see if Santa is in the image:

On Line 83 we check if the probability of santa  is greater than notSanta . If that is the case, we proceed to update the label  and proba  followed by incrementing TOTAL_CONSEC  (Lines 85-90).

Provided enough consecutive “Santa” frames have passed, we need to trigger the Santa alarm:

We have two actions to perform if SANTA  is False  and if the TOTAL_CONSEC  hits the TOTAL_THRESH  threshold:

  1. Create and start a treeThread  to twinkle the Christmas tree lights (Lines 98-100).
  2. Create and start a musicThread  to play music in the background (Lines 103-106).

These threads will run independently without stopping the forward execution of the script (i.e., a non-blocking operation).

You can also see that, on Line 95, we set our SANTA  status flag to True , implying that we have found Santa in the input frame. In the next pass of the loop, we’ll be looking at this value as we did on Line 93.

Otherwise ( SANTA  is True  or the TOTAL_THRESH  is not met), we reset TOTAL_CONSEC  to zero and SANTA  to False :

Finally, we display the frame to our screen with the generated text label:

The probability value is appended to the label  containing either “Santa” or “Not Santa” (Line 115).

Then using OpenCV’s cv2.putText , we can write the label (in Christmas-themed green) on the top of the frame before we display the frame to the screen (Lines 116-120).

The exit condition of our infinite while loop is when the ‘q’ key is pressed on the keyboard (Lines 121-125).

If the loop’s exit condition is met, we break  and perform some cleanup on Lines 129 and 130 before the script itself exits.

That’s all there is to it. Take a look back at the 130 lines we reviewed together — this framework/template can easily be used for other deep learning projects on the Raspberry Pi as well.

Now let’s catch that fat, bearded, jolly man on camera!

Deep learning + Keras + Raspberry Pi results

In last week’s blog post we tested our Not Santa deep learning model using stock images gathered from the web.

But that’s no fun — and certainly not sufficient for this blog post.

I’ve always wanted to dress up like good ole’ St. Nicholas, so I ordered a cheap Santa Claus costume last week:

Figure 7: Me, Adrian Rosebrock, dressed up as Santa. I’ll be personally testing our Not Santa detector, built using deep learning, Keras, Python, and and OpenCV.

I’m a far cry from the real Santa, but the costume should do the trick.

I then pointed my camera attached to the Raspberry Pi at the Christmas tree in my apartment:

Figure 8: My very own Christmas tree will serve as the background for testing our Not Santa detector deep learning model which has been deployed to the Raspberry Pi.

If Santa comes by to put out some presents for the good boys and girls I want to make sure he feels welcome by twinkling the 3D Christmas tree lights and playing some Christmas tunes.

I then started the Not Santa deep learning + Keras detector using the following command:

To follow along, make sure you use “Downloads” section below to download the source code + pre-trained model + audio file used in this guide.

Once the Not Santa detector was up and running, I slipped into action:

Figure 9: Successfully detecting Santa in a video stream using deep learning, Python, Keras, and a Raspberry Pi.

Whenever Santa is detected the 3D Christmas tree lights up and music starts playing! (which you cannot hear since this is a sample GIF animation).

To see the full Not Santa detector (with sound), take a look at the video below:

Whenever Santa enters the scene you’ll see the 3D Christmas tree display turn on followed by a jolly laugh emitting from the Raspberry Pi speakers (audio credit to SoundBible).

Our deep learning model is surprisingly accurate and robust given the small network architecture.

I’ve been good this year, so I’m sure that Santa is stopping at my apartment.

I’m also more confident than I’ve ever been about seeing Santa bring some presents with my Not Santa detector.

Before Christmas, I’ll probably hack this script (with a call to cv2.imwrite , or better yet, save the video clip) to make sure that I save some frames of Santa to disk, as proof. If it is someone else that puts presents under my tree, I’ll certainly know.

Dear Santa: If you’re reading this, just know that I’ve got my Pi on you!


In today’s blog post you learned how to run a Keras deep learning model on the Raspberry Pi.

To accomplish this, we first trained our Keras deep learning model to detect if an image contains “Santa” or “Not Santa” on our laptop/desktop.

We then installed TensorFlow and Keras on our Raspberry Pi, enabling us to take our trained deep learning image classifier and then deploy it to our Raspberry Pi. While the Raspberry Pi isn’t suitable for training deep neural networks, it can be used for deploying them — and provided the network architecture is simplistic enough, we can even run our models in real-time.

To demonstrate this, we created a Not Santa detector on our Raspberry Pi that classifies each individual input frame from a video stream.

If Santa is detected, we access our GPIO pins to light up a 3D Christmas tree and play holiday tunes.

What now?

I hope you had fun learning how to build a Not Santa app using deep learning!

If you want to continue studying deep learning and:

  • Master the fundamentals of machine learning and neural networks…
  • Study deep learning in more detail…
  • Train your own Convolutional Neural Networks from scratch…

…then you’ll want to take a look at my new book, Deep Learning for Computer Vision with Python.

Inside my book you’ll find:

  • Super practical walkthroughs.
  • Hands-on tutorials (with lots of code).
  • Detailed, thorough guides to help you replicate state-of-the-art results from popular deep learning publications.

To learn more about my new book (and start your journey to deep learning mastery), just click here.

Otherwise, be sure to enter your email address in the form below to be notified when new deep learning post are published here on PyImageSearch.


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!

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39 Responses to Keras and deep learning on the Raspberry Pi

  1. Barath kumar December 18, 2017 at 11:06 am #

    Wow, thank you for the christmas gift..merry christmas…!!

    • Adrian Rosebrock December 18, 2017 at 11:33 am #

      Merry Christmas to you as well, Barath!

  2. Horelvis December 18, 2017 at 12:12 pm #

    hahaha, I have laughed very much!!!, because my daughters were planning to hunt Santa Claus this year, I hope they do not read the post for the sake of Santa!!! 😉

    • Adrian Rosebrock December 18, 2017 at 12:15 pm #

      Otherwise you might have to dress up as Santa 😉 Merry Christmas and Happy Holidays to you and your daughters.

      • Horelvis December 18, 2017 at 12:44 pm #

        Thank you Adrian! Merry Chrismas to you as well!

  3. siddharth December 18, 2017 at 12:22 pm #

    Really AWESOME, enjoyed reading your blog. Merry Christmas !

    • Adrian Rosebrock December 18, 2017 at 1:13 pm #

      I’m glad you enjoyed it Siddharth! A Merry Christmas to you as well.

  4. Ravindra Singh Rathore December 18, 2017 at 1:34 pm #

    its really awesome, keep it up.
    and now I’m also making a sign detector so your blog is really helpful for me. 🙂

    • Adrian Rosebrock December 18, 2017 at 1:40 pm #

      Great project Ravindra, I wish you the very best of luck with it 🙂

  5. choud December 18, 2017 at 2:57 pm #

    What a thoughtful gift. I really appreciate this!
    you keep feeding me.
    your biggest fan down here in Africa

    • Adrian Rosebrock December 18, 2017 at 4:42 pm #

      Thank you!

  6. Carlos Torres December 18, 2017 at 6:32 pm #

    Adrian, you keep giving us surprise after surprise. This post is a wonderful Christmas gift..
    (I was thinking on modifying the project to detect my boss at the office… ehhrggg I mean a Grinch!!!)

    Best Regards and Merry Christmas to you and all your fans!!!

    • Adrian Rosebrock December 19, 2017 at 10:44 am #

      Merry Christmas and Happy Holidays Carlos, thank you for the comment 🙂

  7. Behram December 19, 2017 at 1:27 am #

    Adrain you’re terrific !
    This is exactly the kind of training children ( & adults ) need to get into technology.

    I hope in 2018 we can see you on Udemy or other platforms.

    Have a merry Xmas
    ( dig the ‘pi on you’ joke )


    • Davi December 20, 2017 at 9:03 am #

      I also hope to see him on Udemy =}

      • Adrian Rosebrock December 20, 2017 at 9:17 am #

        Out of curiosity, why would you like to see me on Udemy versus publishing my content here on PyImageSearch?

  8. Phong December 19, 2017 at 6:10 am #

    Would you mind share your souce code which train the model on desktop/laptop ?

  9. Amare Mahtsentu December 19, 2017 at 7:04 pm #

    No word for the blog. if international banking was accepted in my country i will definitely donate for it.

    In this post what I really know is how can I proceed with my real world project in deep learning. that is all, thank you for real

  10. Amare Mahtsentu December 20, 2017 at 1:41 pm #

    I think Keras implementation of SSD or YOLO like what you did in LeNet is very important if you consider them in your next blogs because they are new and efficient object detection architectures.

    • Adrian Rosebrock December 22, 2017 at 7:03 am #

      I agree. I also cover object detection implementations using deep learning inside Deep Learning for Computer Vision with Python. I’ll be doing more object detection tutorials in the future as well, so stay tuned 🙂

  11. Bhargavi December 21, 2017 at 5:12 pm #

    Hi Adrian, it’s a very nice tutorial with clear explanation. How can I extend this and show where exactly the Santa is in the video frame? I am new to Keras.

    • Adrian Rosebrock December 22, 2017 at 6:46 am #

      What you are referring to is “object detection”. I would suggest starting with this blog post to familiarize yourself with object detection + deep learning. I cover object detection and deep learning in more detail inside my book, Deep Learning for Computer Vision with Python. This book will also help if you are new to Keras. I hope that helps!

  12. jack December 22, 2017 at 3:24 am #

    I made one. 3Q

  13. cristian benglenok December 23, 2017 at 8:14 am #

    I need to count how many times Santa came to my house in 2 hours for example. but I do not have internet access, is there any time library to do this?

  14. David December 23, 2017 at 5:49 pm #

    Ggreat tutorial but what about if it gets the answer wrong, could we tell it yes/no and it updates and learns so it gets it correct next time?

    • Adrian Rosebrock December 26, 2017 at 4:28 pm #

      You could potentially fine-tuning over time but I wouldn’t recommend it using this model. Instead it would be best to gather additional training data and use a more robust model with higher resolution images.

  15. Reed December 25, 2017 at 12:08 pm #

    Hi Adrian
    I’m curious that if it is possible to throw the real-time streaming to your own PC and can supervise remotely on website or somewhere. Will you make that post? I would appreciate if you have that tutorial.

    • Adrian Rosebrock December 26, 2017 at 4:02 pm #

      Hey Reed — I do not have any tutorials on streaming the output of a particular script to a PC or website, but I’ll make sure to cover that in a future blog post.

  16. Mansoor December 25, 2017 at 12:25 pm #

    Adrian, a great tutorial as always.

    This tutorial is used to distinguish between only “Santa” and “Not Santa”, what if we have more than one class? How much code needs to be changed or is it even possible in this tutorial?

    Thank you for all your time.

    • Adrian Rosebrock December 26, 2017 at 4:01 pm #

      Change “binary_crossentropy” to “categorical_crossentropy” in the loss function. Provided you use the same directory structure as me nothing else needs to be changed. If you’re interested in learning more about training your own custom CNNs, take a look at Deep Learning for Computer Vision with Python.

  17. Alex Holsgrove December 29, 2017 at 6:21 pm #

    Adrian, thank you for another fantastic article. I hope you’ve had a good Christmas. I have setup my OpenCV environment on my Pi3, but have accidentally used Python 3 before I read down further to see you say to use Python 2.7. Can I just run something like mkvirtualenv cv2 -p python2 and setup another environment or will there be any issues going forward? Am I better to just start over?

    • Adrian Rosebrock December 31, 2017 at 9:45 am #

      You can either:

      1. Delete your original Python virtual environment via rmvirtualenv your_env_name
      2. Create a new Python virtual environment

      Either will work.

  18. Alex Holsgrove December 31, 2017 at 7:18 am #

    Adrian, I have just completed the Python 2.7 setup of open CV and then worked through this article as far as the “test your configuration”. I couldn’t import “gpiozero”, despite running “sudo pip install RPi.GPIO gpiozero” whist in the virtualenv. I checked the site-packages and couldn’t see it in there. When I re-ran the command, I left of “sudo” and that seemed to install it correctly ->”pip install RPi.GPIO gpiozero”. My OpenCV version is 3.4.0 and Keras is 2.1.2 . Not sure why other people haven’t run into this yet? Is there something wrong with my setup possibly?

    • Adrian Rosebrock December 31, 2017 at 9:31 am #

      If you use “sudo pip install” then your packages will be installed into the system version of Python and not your Python virtual environment. Make sure you use the “workon” command to access your Python virtual environment and then install:

  19. scott huang January 11, 2018 at 8:13 pm #

    What is the FPS of 1 Raspberry Pi 3 if the picture is 1080P? My company want to find a low-cost solution for object detection. Given tensorflow have published tensorflow lit and your post, maybe this is a way to go.

    • Adrian Rosebrock January 12, 2018 at 5:32 am #

      The Pi would certainly be low cost, but it’s not really fast enough for object detection as I discuss in this post.

  20. judson antu January 18, 2018 at 4:54 am #

    hey adrian,
    i think by using sudo-apt get install we can drastically reduce the time to install scipy and h5py

    • Adrian Rosebrock January 18, 2018 at 8:45 am #

      I do not recommend using sudo apt-get install. In most cases you’ll install older versions of the library. Furthermore, this method will not work if you use Python virtual environments (which we do extensively on the PyImageSearch blog). In newer versions of Raspbian Python wheels are being pre-built and cached so they can be installed faster. I highly recommending using pip install.

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