#Coding4Fun – How to control your #drone with 20 lines of code! (9/N)

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Let’s take some Frames Per Second measurements on the UDP and OpenCV connection. It seems that working with simple movements, the values moves between 30 and 60 FPS.

showing FPS information with the drone camera

Just added a couple of lines in the main While, to calculate the FPS.

# open
i = 0
while True:
    i = i + 1
    start_time = time.time()

    sendReadCommand('battery?')
    print(f'battery: {battery} % - i: {i}')

    try:
        ret, frame = cap.read()
        img = cv2.resize(frame, (640, 480))

        if (time.time() - start_time ) > 0:
            fpsInfo = "FPS: " + str(1.0 / (time.time() - start_time)) # FPS = 1 / time to process loop
            font = cv2.FONT_HERSHEY_DUPLEX
            cv2.putText(img, fpsInfo, (10, 20), font, 0.4, (255, 255, 255), 1)

        cv2.imshow('@elbruno - DJI Tello Camera', img)
    except Exception as e:
        print(f'exc: {e}')
        pass

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

As a final note, just need to mention that I make some tests using different camera resolutions and the FPS averages are similar. I tested with 640 * 480 pixels and 1024*768 pixels.

Next posts, let’s do some Face Detection and rock some AI with the drone!

Happy coding!

Greetings

El Bruno

References

My Posts

#Coding4Fun – How to control your #drone with 20 lines of code! (8/N)

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Now that I started to understand how UDP works, I also did my research to find which are the best options to access an UDP video feed. Lucky for me, there are plenty of resources about doing this task using my old friend OpenCV.

Most of the OpenCV documentation is written in C++. However at the end, it all goes down to these basic lines of code

# open UDP
videoUDP = 'udp://192.168.10.1:11111'
cap = cv2.VideoCapture(videoUDP)

# read a frame from the feed
ret, frame = cap.read()
img = cv2.resize(frame, (320, 240))

# diplay the frame in openCV video window
cv2.imshow('Video', img)


Note: At the references section below I shared some of my posts with my experiences on how to install OpenCV in Windows 10.

Let’s go back to our sample Python App. Using the previous sample that display the battery level, I changed this code to be alive all the time and displaying the video feed in a 320*240 window.

The following video display how fast this works:

And of course the complete code with this notes:

  • Line 96-100. Open video feed and wait 2 seconds
  • Line 104-118. Main App.
    • Get and display battery level
    • Get UDP video frame
    • Resize frame to 320×240
    • Display frame
    • When Q key is pressed, exit app
  • Line 121. Close video stream

Happy coding!

Greetings

El Bruno

References

My Posts

#RaspberryPi – Install OpenCV

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Hi!

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

python

And then run the following lines

import cv2
cv2.__version__

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 4.1.0.25 with the command. We first uninstall the installed version (4.1.0.26) and install the specific version.

pip uninstall opencv-contrib
pip install opencv-contrib-python==4.1.0.25

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
opencv-contrib-python 4.1.0.25
pip                   20.0.1
setuptools            45.1.0
wheel                 0.33.6
(.venv) pi@rpidev5:~ $

Happy coding!

Greetings

El Bruno

References

My posts on Raspberry Pi

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

#AI – Getting started with #ComputerVision, #DeepLearning, and #OpenCV by Adrian Rosebrock @pyimagesearch

display face landmarks in python using face recognition
display face landmarks in python using face recognition
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Hi!

When you start to research the amazing world of Computer Vision, you find that there are plenty of courses, tutorials, videos and more resources. Something is kind of “too much”, and it’s not easy to choose where to start.

That’s why, when you arrive to one of the Adrian Rosebrock tutorials or articles, they’ll end in one of your favorites bookmarks. He has amazing detailed step by step tutorials, and I learned a lot of Raspberry Pi and OpenCV from his website.

A couple of weeks ago, Adrian released an amazing resource for Computer Vision enthusiasts:

Need help getting started with Computer Vision, Deep Learning, and OpenCV?

No matter if you are starting from zero, have some knowledge or you are already an expert; you must look at this amazing compile of resources. I’ll copy and paste the main topics

And I can’t thanks enough Adrian for his amazing work and also, for sharing all of this!

Happy coding!

Greetings @ Toronto

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

References

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

References

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

Hi!

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
keras

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
opencv-python

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

https://www.lfd.uci.edu/~gohlke/pythonlibs/#pyopencl

Install with

pip install
c:\temp\opencv_python-4.1.1-cp36-cp36m-win_amd64.whl

Happy coding!

Greetings @ Toronto

El Bruno

References

#Python –Detecting #Hololens in realtime in webcam feed using #ImageAI and #OpenCV (thanks to @OlafenwaMoses)

Hi!

Let’s start with a very quick intro:

During the past months, I’ve been playing around with several Image Analysis tools. And ImageAI (see references) is one that deserves a full series of posts. Please take a look at the product and the source code in GitHub, and also please thank the one behind this: Moses Olafenwa (@OlafenwaMoses).

And now, my 2 cents. I’ve started to test ImageAI to create my own image detection models. Most of the times, this is a hard path to do, however ImageAI show me an interesting option.

… with the latest release of ImageAI v2.1.0, support for training your custom YOLOv3 models to detect literally any kind and number of objects is now fully supported, …

Wow! That’s mean that I can pick up my own set of images dataset and train on top of a YOLOv3 and use it as a trained model. Again, this is amazing.

So, I started to read the article [Train Object Detection AI with 6 lines of code, see references] where Olafenwa explains how to do this using a data set with almost 500 rows with images for Hololens and Oculus Rift.

The code is very simple and easy to read. There are also examples on how to analyze a single file, or a video, or even a camera feed. The output for the analysis can be also in a new file, in a processed video or even a full log file with the detected information.

I started to read the code samples and I realized that I’m missing a scenario:

Display the realtime feed from a webcam, analyze each webcam frame and if a device is found, add a frame to the realtime feed to display this.

I use OpenCV to access to my camera, and it took me some time to figure out how to convert my OpenCV2 camera frame to the format needed by ImageAI. At the end, thanks to the GitHub code I manage to create this (very slow but working) demo

As usual in this scenario, now it’s time to improve the performance and start testing with some tweaks to get a decent up and running App.

And of course, the code

Happy coding!

Greetings @ Toronto

El Bruno

Resources

#VSCode – Let’s do some #FaceRecognition with 20 lines in #Python (6/N)

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Hi!

In my last post I share some lines of code which allowed me to run some of the face recognition demos 6 times faster. I added a Frames per Second (FPS) feature in my samples. Later, thinking about performance, I realize that I don’t need to work with a full HD picture (1920 x 1080), so I added some code to resize the photo before the face detection process.

However, while I was coding around this solution I also realized that I may want to initialize my camera to start in a lower resolution. So, I searched online on how to do this with OpenCV and I found 3 beautiful lines of code.

open camera with opencv with lower resolution

So, I manage to improve my processing code from 20FPS to +30FPS … which is very good ! Later on this posts I’ll try to do some similar FPS tests on a smaller device and I’ll see and share how this works.

Happy Coding!

Greetings @ Burlington

El Bruno

Resources

#VSCode – Let’s do some #FaceRecognition with 20 lines in #Python (4/N)

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Hi!

Quick post today. I’ll pickup yesterday demo, showing the facial features and adding Face Recognition on top of that. In other words, we’ll move from this

To this

With a couple of extra lines for face recognition

There is some room for performance improvement, so I’ll focus on this in next posts.

The complete project is available here https://github.com/elbruno/Blog/tree/master/20190528%20Python%20FaceRecognition

Happy Coding!

Greetings @ Burlington

El Bruno

Resources

My Posts

  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