El Bruno

Tag: Code Sample

#Coding4Fun – How to control your #drone with 20 lines of code! (3/N)

Hi! Today I’ll write the equivalent of a Hello World in the drone ecosystem. This is a very complex app which: take off the drone wait a couple of seconds lands the drone I’ve followed the Python code sample from the SDK and the final code is very complex (see below). And it deserves some… — read more

Feb 10, 2020
#Coding4Fun – How to control your #drone with 20 lines of code! (2/N)

Hi! I my previous posts I shared some links about the DJI Tello drone. One of them is the SDK 1.3.0.0. In this document we can find the main commands and descriptions of the specifics commands to use to communicate with the drone. The document also links a Python sample file with the following code:… — read more

Feb 4, 2020
#RaspberryPi – Performance differences in #FaceRecognition using #OpenVino (code with @code!)

Hi ! I’ve been looking to use the amazing Intel Neural Stick 2 for a while, and one of the 1st ideas that I have was to check how fast my Raspberry Pi 4 can run using this device. The Intel team released a nice step by step process installation for Raspberry Pi. And it… — read more

Jan 28, 2020
#CognitiveServices – Easy lines to convert CSV to JSON to be used on the #AnomalyDetector service

Hi! After the event “Building an Anomaly Detector System with a few or no lines of code” at MsftReactor, some people asked for the 2 lines that I used to convert a CSV file to JSON, to be used with Cognitive Services Anomaly Detector, so here they are. Important: you need Newtonsoft.Json to build the… — read more

Nov 25, 2019
#CustomVision – Sort and manage Json response estimation in a single line of code

Hi! On my Custom Vision samples, I usually send an image to a CustomVision.ai HTTP Endpoint, and I process the Json result. The results are very easy to understand, however, I created a C# converter class to help with the Custom Vision results. To create this class I navigate: http://json2csharp.com/, and paste a sample result… — read more

Oct 10, 2019
#Azure – Sending custom Telemetry and Event information from a #RaspberryPi device to #AzureIoT Central

Hi! Azure IoT Central is one of the amazing services we can use on Azure. I was wondering how easy is to use a Raspberry Pi using Raspbian and Azure IoT and here is my experience. Let’s assume we had a device up to date using Raspbian, our next step will be to create an… — read more

Sep 18, 2019
#Python – Let’s use a #FaceRecognition demo app for a performance comparison between #RaspberryPi3 and #RaspberryPi4

Hi! 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.… — read more

Aug 19, 2019
#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:… — read more

Aug 7, 2019
#VSCode – Let’s do some #FaceRecognition with 20 lines in #Python (5/N)

Hi! And after yesterday’s post I realize that the code is working, but there is room for performance improvement. So, I went back to my 1st sample, the one for face detection and I added some code to get some times for Frames per Second (FPS). In my initial code, the app was working processing… — read more

May 31, 2019
#VSCode – Let’s do some #FaceRecognition with 20 lines in #Python (4/N)

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… — read more

May 30, 2019
#VSCode – Let’s do some #FaceRecognition with 20 lines in #Python (3/N)

Hi! In my previous posts I explained how to detect faces and perform face recognition in python. Today I’ll explore another feature in the face_recognition package: Find Facial Features. The live camera output will be something like this: Note: Special thanks to my daughter who is always OK to help me with this. The main… — read more

May 29, 2019
#VSCode – Let’s do some #FaceRecognition with 20 lines in #Python (2/N)

Hi! Yesterday I explained how to write a couple of lines in Python to perform live face detection in a webcam feed [Post]. Check the resources section to find more about the tools I’m using. Today, I’ll add some more code to perform face recognition. And as usual, I’ll work with my kids to test… — read more

May 23, 2019
#VSCode – Let’s do some #FaceRecognition with 20 lines in #Python

Hi ! I’ve write a lot about how to use AI models in C# to perform tasks like Face recognition, speech analysis, and more. During the Chicago CodeCamp, someone ask me about how to perform Face Recognition in Python. I didn’t have any working sample to showcase this, and I failed in try to write… — read more

May 22, 2019
#MLNET – How to use the AutoML API in a Console App

Hi ! In my last posts I was testing AutoML using the Model Builder inside Visual Studio and also the CLI commands. There is also an API to use this in a .Net app, and the usage is very simple. It all start, of course, adding the [Microsoft.ML.AutoML] nuget package I read the documentation in… — read more

May 16, 2019
#Windows10 – Windows #VisionSkills sample UWP App

Hi! Yesterday the Windows Team announced the preview version of Windows Vision Skills. So today I was browsing the samples in Github and I’ve created a simplified version of the Skeleton tracker using a live feed from a webcam. Here are some notes about my GitHub sample The UWP App must be Windows 10 version… — read more

May 2, 2019
#Python – The best way to explain how jupyter notebooks works with Visual Studio Code @Code

Hi ! So, after my yesterday post [Edit and work with Jupyter notebooks in Visual Studio Code], today some people asked me how the Jupyter Notebooks and Python integration works. The best way to explain this is with a simple animated video with the following actions Create a cell using the prefix # %% Run… — read more

Apr 24, 2019
#Event – Resources used on my session at the largest Canada makeathon: @MakeUofT [How a PoC at home can scale to Enterprise Level using #CustomVision APIs]

Hi ! What an amazing time at the Canadian Largest Makeathon: MakeUofT (https://ieee.utoronto.ca/makeuoft/). The event, people and ideas are great. And now it’s time to share some of the materials used during my session How a PoC at home can scale to Enterprise Level using Custom Vision APIs It all started with a DIY project… — read more

Feb 19, 2019
#WinML – #CustomVision, reconocimiento de objectos utilizando Onnx en Windows10, calculando FPS

Buenas ! Hoy va un post rápido. Y es del tipo de ayuda mental, ya que siempre que tengo que mostrar información relacionada a proceso de frames por segundo, tengo que buscar en mis aplicaciones anteriores. En este caso agregare esta información al reconocimiento de imágenes con un modelo Onnx exportado desde Custom Vision. En… — read more

Jan 29, 2019
#WinML – #CustomVision, object recognition using Onnx in Windows10, calculate FPS

Hi ! Quick post today. And it’s mostly as a brain reminder on the best way to perform a Frames Per Second calculation when we are analyzing images using a ONNX model. In the final UWP app, I added a top right label displaying the current date and time, and the processed FPS And the… — read more

Jan 28, 2019
#Onnx – Reconocimiento de objetos con #CustomVision y ONNX desde aplicaciones Windows 10 con Windows ML, Frames de objetos detectados

Buenas ! Custom Vision nos permite crear modelos de reconocimiento de objetos. Una vez entrenados estos modelos, podemos analizar una imagen y el modelo nos ofrecerá como respuesta Una lista de objetos [Tags] detectados en cada imagen Para cada Tag tendremos también la probabilidad [score] asociado al mismo y una serie de valores numéricos con… — read more

Jan 24, 2019