#RaspberryPi – Install OpenCV

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After some posts about how to setup a Raspberry Pi, today I’ll share the steps I follow to install OpenCV.

Disclaimer: if you are looking for a detailed step by step on how to install or even build OpenCV in a Raspberry Pi, I strongly recommend to read the post “Install OpenCV on Raspberry Pi 4” by Adrian Rosebrock.

Ok, so let’s start. I assume that you read my posts and your Raspbian image is up and running.

Install Python 3 and Update device

1st step will be to install Python 3 with the following command

sudo apt-get install python3-dev

And run and update for all the installed software

sudo -- sh -c 'apt-get update; apt-get upgrade -y; apt-get dist-upgrade -y; apt-get autoremove -y; apt-get autoclean -y'
Install and use Virtual Environments

This will give us the base image to start working. And, in case we need to install different versions or different apps, I’ll use virtual environments to work with Python.

Let’s install VirtualEnv with the command

#create virtual environment
sudo pip3 install virtualenv

Now let’s create a new virtual environment named “venv” with the command

virtualenv -p python3 .venv

And let’s activate the environment with the command

source .venv/bin/activate

At this moment, the terminal should change and add a prefix (venv) in the bash.

raspberry pi install and activate a virtual environment
Install prerequisites

Let’s update again

sudo apt-get update

And install prerequisites with the commands

sudo apt-get install gfortran 
sudo apt-get install libopenblas-dev 
sudo apt-get install liblapack-dev
sudo apt-get install libatlas-base-dev
sudo apt-get install libjasper-dev
sudo apt-get install libqtgui4
sudo apt-get install python3-pyqt5
sudo apt-get install libqt4-test

or in a single command

sudo apt-get install gfortran libopenblas-dev liblapack-dev libatlas-base-dev libjasper-dev libqtgui4 python3-pyqt5 libqt4-test -y

This process will take some minutes, so this is time 1 to get a coffee!

Install OpenCV and switch to right Raspberry Pi version!

And now the magic command to install OpenCV

sudo apt-get install libopencv-dev

And this process is the one who take most of the time, so coffee number 2. Take a look at all the dependencies for this

And after a couple of minutes the process is done. We can test the OpenCV version running 2 simple python commands. First let’s start python with the command


And then run the following lines

import cv2

This should display the current OpenCV version.

However, with the latest version we have an error: ModuleNotFoundError: No module named ‘cv2’

The current installed version have some issues running in the raspberry py, so we need to make a downgrade to the version with the command. We first uninstall the installed version ( and install the specific version.

pip uninstall opencv-contrib
pip install opencv-contrib-python==

Now, we launch python again, run our 2 lines, and we got OpenCV up and running!

Bonus: Installed Packages

Finally, this is the current list of packages installed in the virtual environments and the version of each package

(.venv) pi@rpidev5:~ $ pip3 list
Package               Version
--------------------- --------
numpy                 1.18.1
pip                   20.0.1
setuptools            45.1.0
wheel                 0.33.6
(.venv) pi@rpidev5:~ $

Happy coding!


El Bruno


My posts on Raspberry Pi

Dev posts for Raspberry Pi
Tools and Apps for Raspberry Pi
Setup the device

#Python – How to fix “ERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none)” on #Windows10 #TensorFlow

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Ok, I’ll write this down. I face this issue from time to time, and then after some searching and reading, I found the solution (again!) and I realize I’ve been done this before.

So, I’m installing Tensorflow on Windows with the amazing single command

# Requires the latest pip
pip install --upgrade pip
# Current stable release for CPU and GPU
pip install tensorflow

And then I get this error

ERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none) 
ERROR: No matching distribution found for tensorflow
error installing tensor flow on windows

So, I decided to see what’s happened and I realize that I only have installed Python 3.8. And there is no official TF version for Python 3.8. So, I need to downgrade Python to 3.7.

Time to install earlier Python version

python current version 3.7.6

and then, try to install TensorFlow again. Now, it’s installing

installing tensorflow with current python version

and after installation, test current TF version

tensorflow installed and tested on windows terminal

So, remember: Using the latest Python version, does not warranty to have all the desired packed up to date. Specially with TensorFlow.

Happy coding!


El Bruno


#Ebook – Code the Classics, amazing book for programming games in #Python. From the #RaspberryPi library.

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My son is a crack and he already passed the Scratch stage. He is part of an amazing Code Ninja programming program and he is asking for some new challenges.

Last month, when I see the release of the Code the Classics – Volume 1 book, I get one for us. And it’s amazing. I mean, he needs to understand Python now, so it’s another challenge. But you know me, any excuse to keep him away from JavaScript!

Code The Classic - Volume 1

By the way, all the code is available in GitHub, and it will take just 5 minutes to set everything up to test the games. And, most important, the book is also available FOR FREE IN EBOOK FORMAT.

So kudos to the publisher, and let’s go with the formal book description.

This stunning 224-page hardback book not only tells the stories of some of the seminal video games of the 1970s and 1980s, but shows you how to create your own games inspired by them using Python and Pygame Zero, following examples programmed by Raspberry Pi founder Eben Upton.

In the first of two volumes, we remake five classic video games – ranging from Pong to Sensible Soccer, each represents a different genre. We interview the games’ original creators and learn from their example, as well as utilise the art and audio engineering skills of two of the 1980s’ most prolific games developers for our recreated versions of the games.

Get game design tips and tricks from the masters

– Explore the code listings and find out how they work

-Download and play game examples by Eben Upton

– Learn how to code your own games with Pygame Zero

– Read interviews with expert graphics and audio creators

Happy coding!


El Bruno


#RaspberryPi – 6 commands to install #OpenCV for #Python in #RaspberryPi4

Hi !

Quick post to remind me the 6 commands to install OpenCV in my Raspberry Pi

sudo apt-get install libhdf5-dev libhdf5-serial-dev libhdf5-100
sudo apt-get install libqtgui4 libqtwebkit4 libqt4-test
sudo apt-get install libatlas-base-dev
sudo apt-get install libjasper-dev
wget https://bootstrap.pypa.io/get-pip.py
sudo python3 get-pip.py
sudo pip3 install opencv-contrib-python 

There is an optional command to update pip, which is always nice.

Happy Coding!

Greetings @ Burlington

El Bruno


My posts on El Bruno

#VSCode – 20 lines to display a webcam camera feed with #Python using #OpenCV

Hi !

I always write this from scratch, so it seems that I’ll drop this one here. So next time I search for this, I’ll find myself.

And with some extra lines, we can even detect faces and display some face landmarks:

This is the base of some many image recognition scenarios, so I hope this will save me some local search time 😀

Happy coding!

Greetings @ Toronto

El Bruno


My posts on Face Recognition using Python

  1. Detecting Faces with 20 lines in Python
  2. Face Recognition with 20 lines in Python
  3. Detecting Facial Features with 20 lines in Python
  4. Facial Features and Face Recognition with 20 lines in Python
  5. Performance improvements with code
  6. More performance improvements, lowering the camera resolution

And some general Python posts

#Anaconda – My steps to install a virtual environment with #TensorFlow, #Keras and more


So today post is not a post, just a selfish reminder of the steps I do when I setup a new dev machine

  • Install Anaconda (see references). I use the default settings, and important: I don’t add Anaconda to Windows PATH.
  • Open Anaconda command prompt as administrator
open anaconda as administrator

Need to be open as Admin in order to install updates

  • Install updates with the command
conda update conda 
conda update –all
  • Create a new development environment named “tfEnv” with tensorflow. Activate the environment
conda create -n tfenv tensorflow 
conda activate tfenv
  • The command to install keras is
pip install

However, if it doesn’t work, I install keras with the following packages

pip install matplotlib 
pip install pillow
pip install tensorflow==1.14
conda install mingw libpython
pip install git+git://github.com/Theano/Theano.git
pip install git+git://github.com/fchollet/keras.git
  • Finally, install Jupyter notebook kernel and create a new kernel for the current virtual environment
pip install ipykernel 
ipython kernel install --user --name=tfEnv
  • There seems to be an issue to install OpenCV using pip with the command
pip install

So, I Install the OpenCV nonofficial package. 1st I download a compatible package from


Install with

pip install

Happy coding!

Greetings @ Toronto

El Bruno


#Anaconda – How to create a custom #Python virtual environment and use it in #Jupyter notebooks (a kernel!)


In yesterday post, I created a new virtual environment named [devtf] and in this environment I’ve installed a lot of tools that I need. Then I tried to launch a jupyter notebook from this environment, to use this tools and, of course, it didn’t work.

anaconda start virtual environment and error on launch jupyter notebook

It was time to read and learn how this works. So, when I finally get this I find this amazing article which really explain how this works “Using Virtual Environments in Jupyter Notebook and Python” (see references)

Jupyter Notebook makes sure that the IPython kernel is available, but you have to manually add a kernel with a different version of Python or a virtual environment. First, you need to activate your virtual environment. Next, install ipykernel which provides the IPython kernel for Jupyter. And finally, you can add your virtual environment to Jupyter.

So the commands are

pip install --user ipykernel 
python -m ipykernel install --user --name=devtf

Where “devtf” is the name of the new kernel you want to create. Now, when I launch Jupyter Notebooks, the new kernel is available to be used

jupyter notebook change kernel to one with tensorflow

When I started to use this new kernel (virtual environment) I realized that I didn’t installed TensorFlow. You know, being happy about this, naming the kernel TF but not installing the core component. And, sure, my notebooks didn’t work.

jupyter notebook with kernel without tensorflow

I went to my terminal / command prompt and installed TensorFlow. Then I only need to restart the Kernel, and everything start working. I added a extra couple of lines in my notebook just to check the TensorFlow and keras versions.

jupyter notebook tf ok and test keras version

I find similar errors with another packages, so I pip installed the packages in the terminal and restart the kernel to have the notebook OK. So, my simple reminder for myself about how to do this!

Happy coding!

Greetings @ Mississauga

El Bruno


#Python – Can’t install TensorFlow on Anaconda, maybe is the Visual Studio distribution


This is the 2nd time I get a weird error when I install TensorFlow in my Anaconda distribution. And this is the 2nd time I realize that I’m using the Anaconda version that is preinstalled with Visual Studio. I’m not sure if the spaces in the path affects the creation of environments or it’s something else, however my current and big and amazing solution is:

  • Uninstall Anaconda
  • Install Anaconda again

And then, follow the simple commands in the official Anaconda and TensorFlow doc (see references)

conda create -n tensorflow_env tensorflow
conda activate tensorflow_env

Once tensorflow is installed, I usually test this in python

> Python 
import tensorflow as tf

Note: please ignore the typos!

anaconda start python and test anaconda version

Now TensorFlow is installed and it’s time to move forward with a new development environment.

Happy Coding!

Greetings @ Burlington

El Bruno


#Python – Let’s use a #FaceRecognition demo app for a performance comparison between #RaspberryPi3 and #RaspberryPi4

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I started to do some tests with the new Raspberry Pi 4 and the results are amazing. I’m not a performance expert, so I decided to pick up some of the demos / apps I’ve creating for the Raspberry Pi and run them in both models: Raspberry Pi 3 B+ and Raspberry Pi 4.

I started with an amazing set of tutorials on how to perform Face Recognition from Adrian Rosebrock (see references). I’ve been using his Face Recognition python package for this scenarios and it’s an amazing one.

I added some code to a custom version of Adrian’s Face Recognition sample, and it looks great. The main idea was to track in real-time the current FPS (similar to the work I did with the Image AI and Hololens sample a couple of days ago, see references).

This sample load a file with 15 trained faces and analyze frame by frame to

  • Detect faces in the frame.
  • If a face is detected, draw a frame around it.
  • For each detected frame analyze if the face is a trained face.
  • If the face is part of the trained dataset, the app will add the name of the person on top of the frame.

I display in real-time the FPS processed with a USB camera in a Raspberry Pi 3 B+. Doing a lot of tweaks and getting the best performance in the device I could never process 1FPS. The average processing data were between 0.6 and 0.9 FPS in a Raspberry Pi 3B+.

python face recognition in raspberry py 3 with FPS live

IMHO, these results are great for a small device like a Raspberry Pi 3B+. But now it was time to test it in the new Raspberry Pi 4. And an important note here is to remark that even if I did this tests in a Raspberry Pi 4 with 4GB of Rams, the performance results are similar to a RPI4 with just 1 GB of ram. We have more memory, however the processor improvements are quite significant in the new version.

I installed all the necessary software in the Raspberry Pi 4 and I got 3X better results. I’ve even tun this in a 1080p resolution to get a sense of the real processing time. The average processing data were between 2.3 and 2.4 FPS in a Raspberry Pi 4.

python face recognition in raspberry py 4 with FPS live

Amazing! In this scenario the Raspberry Pi 4 is almost 3 times faster than the Raspberry Pi 3. And again, these are amazing times for a 50USD device.

The sample source code is https://github.com/elbruno/Blog/tree/master/20190819%20Rpi%203%20vs%20Rpi%204%20Face%20Recognition

I even have time for some BBQ time with family and friends!

Happy coding!

Greetings @ Toronto

El Bruno


#Python – Detecting #Hololens in realtime in webcam feed using #ImageAI and #OpenCV with performance improvements


In my previous post I created a sample on how to use ImageAI and OpenCV to detect Hololens from a webcam frame (see references). I added some code to the last sample, and I found that the performance was not very good.

python using imageai to detect hololens less than 1 fps

With the previous sample code, I couldn’t process more than 1 frame per second. So, I started to make some improvements and I got this result

python using imageai to detect hololens little more than 1 fps

Not an amazing one, but still is nice to have more than 1 frame per second analyzed.

I even remove all the camera preview and still works in less than 1FPS.

python using imageai to detect hololens no opencv camera preview

So, now it’s time to read and learn of the deep code on ImageAI. Fun times!

Happy coding!

Greetings @ Burlington

El Bruno