Tag: Artificial Intelligence

#Podcast – NTN 83 – Especial NetCoreConf. Intelligencia Artificial, estado actual, futuro, ética y mates (si, mates!)

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Buenas!

El equipo de NetCoreConf nos permitió entrar al backstage del evento, y en una esquina del bar (virtual), tuvimos la suerte de hablar de muchos temas interesantes.

Por ejemplo, con Javier Menendez Pallo charlamos sobre Intelligencia Artificial. Bueno esa era la idea, en realidad empezamos hablando sobre tomar mate, y después sobre ética en AI, el futuro de AI y mucho más!

Como la grabación ha sido en el bar, desde ya os pido disculpas por la calidad del audio y la improvisación del guion. Como sabéis, la planificación del guion es fundamental en NTN!

Happy Coding!

Speakers

  • Javier Menendez Pallo es Helping companies improve their results by using Artificial Intelligence in any of their areas. | Microsoft AI MVP (LinkedIn)
  • Juan Carlos Quijano Abad es Microsoft Certified Trainer, Arquitecto de Soluciones en Azure, Consultor independiente en implantación de DevOps (LinkedIn)
  • Bruno Capuano es Canada Innovation Lead at Avanade and Microsoft AI MVP (LinkedIn)

Ir a descargar

Happy coding!

Greetings

El Bruno

¿Con ganas de ponerte al día?

En Lemoncode te ofrecemos formación online impartida por profesionales que se baten el cobre en consultoría:

#Event – “ML.Net and AutoML” at the MVP Fusion and Friends 2021 . Session Resources

Buy Me A Coffee

Hi !

I had a great time early in the day with some my friends from Europe.

As usual, time for slides and code:

Slides

Code

https://github.com/elbruno/events/tree/main/2021%2002%2023%20MVP%20Fussion%20MLNet

Resources

Happy coding!

Greetings

El Bruno

¿Con ganas de ponerte al día?

En Lemoncode te ofrecemos formación online impartida por profesionales que se baten el cobre en consultoría:

#Event – “Let’s code a drone to follow faces syncing everything with Azure IoT” at the Codegen 2021, Verona, Italy. Session Resources

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Hi !

I had a great time early in the day with some friend from Italy.

drone telemetry in azure IoT

As usual, time for slides and code:

Slides

Code

https://github.com/elbruno/events/tree/main/2021%2002%2013%20CodeGen%20Verona%20Italy%20Drone%20Azure%20IoT

My Session – Video

All Sessions – Videos

All +40 sessions here http://codegen2021.azurewebsites.net/videos

Resources

Coding4Fun Drone posts

  1. Introduction to DJI Tello
  2. Analyzing Python samples code from the official SDK
  3. Drone Hello World ! Takeoff and land
  4. Tips to connect to Drone WiFi in Windows 10
  5. Reading data from the Drone, Get battery level
  6. Sample for real time data read, Get Accelerometer data
  7. How the drone camera video feed works, using FFMPEG to display the feed
  8. Open the drone camera video feed using OpenCV
  9. Performance and OpenCV, measuring FPS
  10. Detect faces using the drone camera
  11. Detect a banana and land!
  12. Flip when a face is detected!
  13. How to connect to Internet and to the drone at the same time
  14. Video with real time demo using the drone, Python and Visual Studio Code
  15. Using custom vision to analyze drone camera images
  16. Drawing frames for detected objects in real-time in the drone camera feed
  17. Save detected objects to local files, images and JSON results
  18. Save the Drone camera feed into a local video file
  19. Overlay images into the Drone camera feed using OpenCV
  20. Instance Segmentation from the Drone Camera using OpenCV, TensorFlow and PixelLib
  21. Create a 3×3 grid on the camera frame to detect objects and calculate positions in the grid
  22. Create an Azure IoT Central Device Template to work with drone information
  23. Create a Drone Device for Azure IoT Central
  24. Send drone information to Azure IoT Central

Happy coding!

Greetings

El Bruno

#Event – Let’s code a drone ✈ to follow faces 😀 with #AzureIoT (x2!) Supporting the #GlobalAI Bootcamp Singapore and Germany!

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Hi !

Let’s start the 2021 with some drones events supporting 2 Global AI Bootcamp events

Please each event location for specific details on time and streaming options.

Let’s code a drone to follow faces syncing everything with Azure IoT

You can control a drone using 20 lines of code. That’s the easy part. However, adding extra features like face or object detection and program the drone to follow and object or a face requires … another 20 lines of code! During this workshop we will review how to connect to a drone, how to send and receive commands from the drone, how to read the camera video feed and how to apply AI on top of the camera feed to recognize objects or faces. We will use a simple house drone ($100) and Python. And, when we review some enterprise scenarios, we will use Azure IoT to sync the drone information in IoT mode. Let’s build this!

For this session I’ll explain how to create a SDK from zero to control a DJI Tello drone, and the last past will include some examples on how to connect the drone reported information to Azure IoT.

And this is probably the last one my Microsoft MVP room-office (see below), there are some changes in the near future, so let’ rock these 2 sessions !

Happy coding!

Greetings

El Bruno

¿Con ganas de ponerte al día?

En Lemoncode te ofrecemos formación online impartida por profesionales que se baten el cobre en consultoría:

#Event – Global AI Bootcamp Toronto on Dec 12 🤖🤖🤖 @GlobAICommunity

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Hi !

This started to be my annual tradition: Global AI Bootcamp is the last event of the year!
And this year, we had a set of amazing session planned with the following topics:

  • Introduction to AI and Cognitive Services for Developers and Information Workers
  • Understanding Autonomous systems, Machine Teaching and Bonsai Platform
  • Knowledge Mining and MLOps

We have speakers from all around Canada and a special guest from the South !

As usual the event is free, no food this year (sorry!), however you can spend a nice Saturday morning learning about AI in your chosen location !

Registration: https://www.meetup.com/metrotorontoug/events/267883977/

Full details here 👇👇👇

Intro and Welcome

Session. 10:00 – 10:10

Kick off of the amazing event

Introduction to AI and Cognitive Services for Developers and Information Workers

Session 1. 10:10 – 11:00

Speaker: Prashant G Bhoyar | Microsoft AI MVP

“Artificial Intelligence and Machine Learning are the new buzzwords in the industry. Microsoft’s vision is to make AI accessible to every enterprise, data scientist, developer, information worker, consumer and device everywhere in the world. AI has a big role to play in the enterprise space. The field of AI is progressing at a rapid pace. Without understanding the concepts behind these advanced technologies, developers and administrators will struggle to evaluate the potential impact of new tools and solutions. In this session, we will break down the concepts behind existing technologies, outline various tools available today, and discuss the direction of AI and ML for Developers. We will cover how developers, Power Users, and Information workers can take advantage of Microsoft’s AI and Cognitive Services offerings to build real-life enterprise solutions.

You will learn:
1) Overview of Microsoft AI Platform
2) What are the cognitive services?
3) What tools are available today?
4) How to use Cognitive Services to implement real-life business solutions?

Understanding Autonomous systems, Machine Teaching and Bonsai Platform

Session 2. 11:10 – 12:00

Speaker: Ivana Tilca | Microsoft AI MVP | 3XM Group Quality Manager

Innovations in AI are creating the next wave of disruption in industrial technology. Autonomous machines are more than an expansion of automated systems: They are an entirely new way to amplify human expertise. In this session you will take a look to how to speed the creation of AI-powered automation to improve production efficiency and reduce downtime – without requiring data scientists with Project Bonsai. We will also take a look at AirSim, a simulator for drones, cars and more, built on Unreal Engine. AirSim a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.

Knowledge Mining and MLOps

Session 3. 12:10 – 13:00

Speakers: Amol Mane, Meghana Madhusudhan and Niloofar Nayebi

Knowledge mining is an emerging discipline in artificial intelligence (AI) that uses a combination of intelligent services to quickly learn from vast amounts of information. It allows organizations to deeply understand and easily explore information, uncover hidden insights, and find relationships and patterns at scale. This platform allows to use machine learning model to address any specific business need. The life cycle of the models can be managed using MLOps. MLOps, or DevOps for machine learning, enables data science and IT teams to collaborate and increase the pace of model development and deployment via monitoring, validation, and governance of machine learning models. These two solutions combined can create a strong platform to extract, enrich, and expose any hidden data within any organization.
In this session you are going to learn about three main steps in KM platform:

– Ingestion

– Enrichment

– Exploration

And three main pipelines in MLOps:

– Continues Integration

– Continues Delivery

– Deployment

Let’s meet there !

Happy coding!

Greetings

El Bruno

#Event – Resources used during the session “Hack a drone, hack the camera and use AI” at the Global AI Tour, Lahore Pakistan, 2020

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Hi !

I had a great time early in the day with my Microsoft Student Partners from Lahore Pakistan, for the Global AI Tour. As usual, time for slides and code:

Slides

Code

https://github.com/elbruno/events/tree/main/2020%2011%2030%20Global%20AI%20Tour%20Pakistan

Resources

Coding4Fun Drone posts

  1. Introduction to DJI Tello
  2. Analyzing Python samples code from the official SDK
  3. Drone Hello World ! Takeoff and land
  4. Tips to connect to Drone WiFi in Windows 10
  5. Reading data from the Drone, Get battery level
  6. Sample for real time data read, Get Accelerometer data
  7. How the drone camera video feed works, using FFMPEG to display the feed
  8. Open the drone camera video feed using OpenCV
  9. Performance and OpenCV, measuring FPS
  10. Detect faces using the drone camera
  11. Detect a banana and land!
  12. Flip when a face is detected!
  13. How to connect to Internet and to the drone at the same time
  14. Video with real time demo using the drone, Python and Visual Studio Code
  15. Using custom vision to analyze drone camera images
  16. Drawing frames for detected objects in real-time in the drone camera feed
  17. Save detected objects to local files, images and JSON results
  18. Save the Drone camera feed into a local video file
  19. Overlay images into the Drone camera feed using OpenCV
  20. Instance Segmentation from the Drone Camera using OpenCV, TensorFlow and PixelLib
  21. Create a 3×3 grid on the camera frame to detect objects and calculate positions in the grid
  22. Create an Azure IoT Central Device Template to work with drone information
  23. Create a Drone Device for Azure IoT Central
  24. Send drone information to Azure IoT Central

Happy coding!

Greetings

El Bruno

#AI – #Lobe, exporting to ONNX, and running in C# #csharp @lobe_ai

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Hi !

Follow up post after yesterday post on Lobe, and today focusing on ONNX and C# code. And, it all started because someone asked in twitter about an ETA to export the model to ONNX

I decided to give a try to the TensorFlow to Onnx tool, and it worked great ! (see references). I use the following command to convert my model

python -m tf2onnx.convert --saved-model model --output model.onnx

From the PB exported model from yesterday, and I got my 2 models

And, here I got an amazing surprise. Before I started to write some C# code, I found some NuGet packages available to use

  • lobe
  • lobe.Onnx
  • lobe.ImageSharp
lobe 100 install nuget packages

And, after a quick search I found some sample code in GitHub about how to use these packages. So, I pickup the original Code and make a few changes to perform estimations on 2 manual drawings.

Remember my model was trained to analyze drawings and detect: humans, fish and flowers.

I created a new C# Console App and

  • copy the generated [model.onnx]
  • copy the 2 test files: [fishy.png] and [human.png]
  • copy the original [signature.json] file generated on the Lobe TensorFlow export

I edited the [signature.json] file and change the values

  • format to onnx
  • filename to the generated exported filename

And I was ready to run my code:

using System;
using System.IO;
using SixLabors.ImageSharp;
using SixLabors.ImageSharp.PixelFormats;
using lobe.ImageSharp;
using lobe;
namespace ConsoleApp1
{
class Program
{
static void Main(string[] args)
{
var signatureFilePath = "signature.json";
ImageClassifier.Register("onnx", () => new OnnxImageClassifier());
using var classifier = ImageClassifier.CreateFromSignatureFile(
new FileInfo(signatureFilePath));
// Images
ShowResults("fishy.png", classifier);
ShowResults("human.png", classifier);
}
private static void ShowResults(string imagePath, ImageClassifier classifier)
{
var results = classifier.Classify(Image
.Load(imagePath).CloneAs<Rgb24>());
Console.WriteLine();
Console.WriteLine($"Image : {imagePath}");
Console.WriteLine($"Top Label: {results.Classification.Label}");
foreach (var res in results.Classifications)
{
Console.WriteLine($" – Label: {res.Label} – Confidence: {res.Confidence}");
}
}
}
}
view raw lobeonnxcsharpsample.cs hosted with ❤ by GitHub

And the output is fast and great, as we are used to do with Onnx

Lobe looks great !

Happy coding!

Greetings

El Bruno

References

#AI – #Lobe, desktop tool to train custom machine learning models for Computer Vision @lobe_ai

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Hi !

There are 2 ways to describe Lobe. You may want to use their official description

Lobe helps you train machine learning models with a free, easy to use tool.

Lobe AI Homepage

Or you may want to use the tool. Let’s review the 2nd one with a NNNN step tutorial.

Data Source Images

For this tutorial I’ll use a set of 24 drawings for fish, human and flowers. I have 8 drawings for each category in different folders.

Import and label Images

Ok, you probably have already installed the app, so let’s create a new project. I’ll name this project drawings.

lobe 02 new project name drawings

The import button, will display a set of options. For this demo, I’ll go to DataSet, where I need to have all my images in a structured set of folders.

The import option also describes how to organize the images in folder.

Once we import the images, we will have the option to automatically label our images based on the folder name

Train

This is the most user friendly step, we don’t need to train the model. I mean, once we have our images, Lobe will automatically start the training process in the background.

Depending the size of your training dateset, this may take some time. Once the model is trained, we can also see some output around the correct and incorrect predictions.

Test with new images

This is also cool. Once the model is trained we can test is in the Play section. In example, I’ve uploaded a custom fish model, and it’s predicted as a fish. I can confirm or assign the correct label in the Play area.

I’ve uploaded a couple more test images, and the background training process created a perfect model !!! (I know, I know …)

lobe 09 100 acurracy

Export generated model

And another great feature is the Export option.

lobe 10 export options

There are several options:

  • TensorFlow
  • TensorFlow Lite
  • Local API

I haven’t used options 2 and 3, however option 1 is good enough to play around. It will include the TensorFlow frozen model, some supporting files, and a sample to use this in Python !

Super easy to start and learn without coding !

Happy coding!

Greetings

El Bruno

References

#Personal – We have a “Learn to pronounce” feature in Google !

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Hi !

Some days ago, while I was searching for the meaning of a word, I found an amazing feature in Google:

Learn to pronounce

learn to pronounce feature in Google results

And it’s very basic and amazing at the same time.

  • You search for a definition of a word
  • In the result area, you get the speaker button, who reproduces the word
  • Once you play the word, a new feature will be available: [Learn to Pronounce]
  • This will open a new section, with the mouth movements for the American and British pronunciation

That’s it, super useful ! I could not find a lot of related information about this. It seems that it’s been around for over 2 years, based on these The Verge articles:

Besides some great AI in the back, this is so amazing 😀

Happy coding!

Greetings

El Bruno

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#Event – Let’s hack a drone, hack the camera and use AI! virtual with Microsoft Reactor @MSFTReactor

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Hi !

Today I’ll share a new version of my drone and Artificial Intelligence session, hosted by Microsoft Reactor Toronto.

The registration link and details are available here

https://developer.microsoft.com/en-us/reactor/Location/Toronto#filter?dayfrom=7-1-2020&id=7650

Happy coding!

Greetings

El Bruno