5 Exciting Computer Vision Applications With Relevant Datasets!

Overview

• Computer vision is the technology that allows the digital world to interact with the real world.
• Explore 5 of the hottest applications of Computer Vision

Introduction

I started using Facebook 10 years ago. Likewise, if you used that long ago you must remember the manual tagging of photographs. But now we do not have to tag these pictures manually. Facebook recognizes most of the people in the uploaded picture and provides suggestions to tag them. Similarly, you must have seen those hilarious filters on Snapchat where people use the dog filter and get a dog face on. Have you ever wondered how is all of it possible? How is our phone able to detect our face and add the filters over it? These are some of the computer vision applications.

Computer vision is one of the hottest research fields in the data science world. Moreover, it has become a part of our personal lives. Knowingly or unknowingly, we all use various features which have computer vision techniques running at the backend. For instance, we use the face unlock in our smartphones. The image below is efficiently explaining how does face detection works.

I choose face detection for starting this article since this is the one application of computer vision, we all have seen. But trust me computer vision is not limited to this. In this article, you will explore more interesting applications of computer vision.

If you are looking to master in computer vision, check out our course Computer Vision using Deep Learning 2.0

Table of Contents

• What is Computer Vision?
• Pose Estimation using Computer Vision
• Image transformation using Gans
• Computer Vision for developing Social distancing tools
• Converting 2D images into 3D models
• Medical Image analysis

What is Computer Vision?

Before entering into the world of computer vision applications, first, let’s understand what computer vision is? In short, Computer vision is a multidisciplinary branch of artificial intelligence trying to replicate the powerful capabilities of human vision.

If we go through the formal definition,

“Computer vision is a utility that makes useful decisions about real physical objects and scenes based on sensed images” (Sockman & Shapiro, 2001)

Computer vision works through visual recognition techniques like Image classification, object detection, Image segmentation, object tracking, optical character recognition, image captioning, etc. I know these are a lot of technical terms but understanding them is not tough. Just see the image below and you will understand many of these terminologies.

Let’s start with the first image. If I ask you what is there in the picture? Your answer will be, its a cat. This is classification. That means labelling the image based on what it consists of is classification. Here the class is ‘Cat’.

Now you know the class of the image. The next question comes where the object is situated in the image. When we identify the location of the object in the frame and create a bounding box around it, It is known as localization. In the second image, we have identified the location of the object and labeled it as a cat.

The next term is the object detection. In the previous two cases, we have a single object in the image but what if there are multiple objects present. Here we identify the instances present and their location via bounding boxes.

In object detection, we use a bounding box that is either square or rectangular in shape but it does not tell anything about the shape of the objects. Instance segmentation creates a pixel-wise mask around each object. Hence instance segmentation gives a deeper understanding of the image.

Check the following resources if you want to know more about Computer Vision-

• Computer Vision using Deep Learning 2.0 Course
• Certified Program: Computer Vision for Beginners
• Getting Started With Neural Networks (Free)
• Convolutional Neural Networks (CNN) from Scratch (Free)

Recent developments in deep learning approaches and advancements in technology have tremendously increased the capabilities of visual recognition systems. As a result, computer vision has been rapidly adopted by companies. Successful use-cases of computer vision can be seen across the industrial sectors leading to widening the applications and increased demand for computer vision tools.

Now without losing more time, let’s jump into the 5 exciting applications of computer vision.

Human Pose Estimation

Human Pose Estimation is an interesting application of Computer Vision. You must have heard about Posenet, which is an open-source model for Human pose estimation. In brief, pose estimation is a computer vision technique to infer the pose of a person or object present in the image/video.

Before discussing the working of pose estimation let us first understand ‘Human Pose Skeleton’. It is the set of coordinates to define the pose of a person. A pair of coordinates is known as the limb. Further, pose estimation is performed by identifying, locating, and tracking the key points of Humans pose skeleton in an Image or video.

#pose-estimation #computer-vision #gans #applications-of-ai #deep-learning #deep learning

What is GEEK

Buddha Community

5 Exciting Computer Vision Applications With Relevant Datasets!

Overview

• Computer vision is the technology that allows the digital world to interact with the real world.
• Explore 5 of the hottest applications of Computer Vision

Introduction

I started using Facebook 10 years ago. Likewise, if you used that long ago you must remember the manual tagging of photographs. But now we do not have to tag these pictures manually. Facebook recognizes most of the people in the uploaded picture and provides suggestions to tag them. Similarly, you must have seen those hilarious filters on Snapchat where people use the dog filter and get a dog face on. Have you ever wondered how is all of it possible? How is our phone able to detect our face and add the filters over it? These are some of the computer vision applications.

Computer vision is one of the hottest research fields in the data science world. Moreover, it has become a part of our personal lives. Knowingly or unknowingly, we all use various features which have computer vision techniques running at the backend. For instance, we use the face unlock in our smartphones. The image below is efficiently explaining how does face detection works.

I choose face detection for starting this article since this is the one application of computer vision, we all have seen. But trust me computer vision is not limited to this. In this article, you will explore more interesting applications of computer vision.

If you are looking to master in computer vision, check out our course Computer Vision using Deep Learning 2.0

Table of Contents

• What is Computer Vision?
• Pose Estimation using Computer Vision
• Image transformation using Gans
• Computer Vision for developing Social distancing tools
• Converting 2D images into 3D models
• Medical Image analysis

What is Computer Vision?

Before entering into the world of computer vision applications, first, let’s understand what computer vision is? In short, Computer vision is a multidisciplinary branch of artificial intelligence trying to replicate the powerful capabilities of human vision.

If we go through the formal definition,

“Computer vision is a utility that makes useful decisions about real physical objects and scenes based on sensed images” (Sockman & Shapiro, 2001)

Computer vision works through visual recognition techniques like Image classification, object detection, Image segmentation, object tracking, optical character recognition, image captioning, etc. I know these are a lot of technical terms but understanding them is not tough. Just see the image below and you will understand many of these terminologies.

Let’s start with the first image. If I ask you what is there in the picture? Your answer will be, its a cat. This is classification. That means labelling the image based on what it consists of is classification. Here the class is ‘Cat’.

Now you know the class of the image. The next question comes where the object is situated in the image. When we identify the location of the object in the frame and create a bounding box around it, It is known as localization. In the second image, we have identified the location of the object and labeled it as a cat.

The next term is the object detection. In the previous two cases, we have a single object in the image but what if there are multiple objects present. Here we identify the instances present and their location via bounding boxes.

In object detection, we use a bounding box that is either square or rectangular in shape but it does not tell anything about the shape of the objects. Instance segmentation creates a pixel-wise mask around each object. Hence instance segmentation gives a deeper understanding of the image.

Check the following resources if you want to know more about Computer Vision-

• Computer Vision using Deep Learning 2.0 Course
• Certified Program: Computer Vision for Beginners
• Getting Started With Neural Networks (Free)
• Convolutional Neural Networks (CNN) from Scratch (Free)

Recent developments in deep learning approaches and advancements in technology have tremendously increased the capabilities of visual recognition systems. As a result, computer vision has been rapidly adopted by companies. Successful use-cases of computer vision can be seen across the industrial sectors leading to widening the applications and increased demand for computer vision tools.

Now without losing more time, let’s jump into the 5 exciting applications of computer vision.

Human Pose Estimation

Human Pose Estimation is an interesting application of Computer Vision. You must have heard about Posenet, which is an open-source model for Human pose estimation. In brief, pose estimation is a computer vision technique to infer the pose of a person or object present in the image/video.

Before discussing the working of pose estimation let us first understand ‘Human Pose Skeleton’. It is the set of coordinates to define the pose of a person. A pair of coordinates is known as the limb. Further, pose estimation is performed by identifying, locating, and tracking the key points of Humans pose skeleton in an Image or video.

#pose-estimation #computer-vision #gans #applications-of-ai #deep-learning #deep learning

Vision: Datasets, Transforms and Models Specific to Computer Vision

Torchvision

The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.

Installation

We recommend Anaconda as Python package management system. Please refer to pytorch.org for the detail of PyTorch (torch) installation. The following is the corresponding torchvision versions and supported Python versions.

torch	torchvision	python
main / nightly	main / nightly	>=3.7, <=3.10
1.13.0	0.14.0	>=3.7, <=3.10
1.12.0	0.13.0	>=3.7, <=3.10
1.11.0	0.12.0	>=3.7, <=3.10
1.10.2	0.11.3	>=3.6, <=3.9
1.10.1	0.11.2	>=3.6, <=3.9
1.10.0	0.11.1	>=3.6, <=3.9
1.9.1	0.10.1	>=3.6, <=3.9
1.9.0	0.10.0	>=3.6, <=3.9
1.8.2	0.9.2	>=3.6, <=3.9
1.8.1	0.9.1	>=3.6, <=3.9
1.8.0	0.9.0	>=3.6, <=3.9
1.7.1	0.8.2	>=3.6, <=3.9
1.7.0	0.8.1	>=3.6, <=3.8
1.7.0	0.8.0	>=3.6, <=3.8
1.6.0	0.7.0	>=3.6, <=3.8
1.5.1	0.6.1	>=3.5, <=3.8
1.5.0	0.6.0	>=3.5, <=3.8
1.4.0	0.5.0	==2.7, >=3.5, <=3.8
1.3.1	0.4.2	==2.7, >=3.5, <=3.7
1.3.0	0.4.1	==2.7, >=3.5, <=3.7
1.2.0	0.4.0	==2.7, >=3.5, <=3.7
1.1.0	0.3.0	==2.7, >=3.5, <=3.7
<=1.0.1	0.2.2	==2.7, >=3.5, <=3.7

Anaconda:

conda install torchvision -c pytorch

pip:

pip install torchvision

From source:

python setup.py install
# or, for OSX
# MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install

We don't officially support building from source using pip, but if you do, you'll need to use the --no-build-isolation flag. In case building TorchVision from source fails, install the nightly version of PyTorch following the linked guide on the contributing page and retry the install.

By default, GPU support is built if CUDA is found and torch.cuda.is_available() is true. It's possible to force building GPU support by setting FORCE_CUDA=1 environment variable, which is useful when building a docker image.

Image Backend

Torchvision currently supports the following image backends:

• Pillow (default)
• Pillow-SIMD - a much faster drop-in replacement for Pillow with SIMD. If installed will be used as the default.
• accimage - if installed can be activated by calling torchvision.set_image_backend('accimage')
• libpng - can be installed via conda conda install libpng or any of the package managers for debian-based and RHEL-based Linux distributions.
• libjpeg - can be installed via conda conda install jpeg or any of the package managers for debian-based and RHEL-based Linux distributions. libjpeg-turbo can be used as well.

Notes: libpng and libjpeg must be available at compilation time in order to be available. Make sure that it is available on the standard library locations, otherwise, add the include and library paths in the environment variables TORCHVISION_INCLUDE and TORCHVISION_LIBRARY, respectively.

Video Backend

Torchvision currently supports the following video backends:

• pyav (default) - Pythonic binding for ffmpeg libraries.
• video_reader - This needs ffmpeg to be installed and torchvision to be built from source. There shouldn't be any conflicting version of ffmpeg installed. Currently, this is only supported on Linux.

conda install -c conda-forge ffmpeg
python setup.py install

Using the models on C++

TorchVision provides an example project for how to use the models on C++ using JIT Script.

Installation From source:

mkdir build
cd build
# Add -DWITH_CUDA=on support for the CUDA if needed
cmake ..
make
make install

Once installed, the library can be accessed in cmake (after properly configuring CMAKE_PREFIX_PATH) via the TorchVision::TorchVision target:

find_package(TorchVision REQUIRED)
target_link_libraries(my-target PUBLIC TorchVision::TorchVision)

The TorchVision package will also automatically look for the Torch package and add it as a dependency to my-target, so make sure that it is also available to cmake via the CMAKE_PREFIX_PATH.

For an example setup, take a look at examples/cpp/hello_world.

Python linking is disabled by default when compiling TorchVision with CMake, this allows you to run models without any Python dependency. In some special cases where TorchVision's operators are used from Python code, you may need to link to Python. This can be done by passing -DUSE_PYTHON=on to CMake.

TorchVision Operators

In order to get the torchvision operators registered with torch (eg. for the JIT), all you need to do is to ensure that you #include <torchvision/vision.h> in your project.

Documentation

You can find the API documentation on the pytorch website: https://pytorch.org/vision/stable/index.html

Contributing

See the CONTRIBUTING file for how to help out.

Disclaimer on Datasets

This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.

If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!

Pre-trained Model License

The pre-trained models provided in this library may have their own licenses or terms and conditions derived from the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case.

More specifically, SWAG models are released under the CC-BY-NC 4.0 license. See SWAG LICENSE for additional details.

Download Details:

Author: Pytorch
Source Code: https://github.com/pytorch/vision 
License: BSD-3-Clause license

#machinelearning #computer #vision #dataset 

Inside ABCD, A Dataset To Build In-Depth Task-Oriented Dialogue Systems

According to a recent study, call centre agents’ spend approximately 82 percent of their total time looking at step-by-step guides, customer data, and knowledge base articles.

Traditionally, dialogue state tracking (DST) has served as a way to determine what a caller wants at a given point in a conversation. Unfortunately, these aspects are not accounted for in popular DST benchmarks. DST is the core part of a spoken dialogue system. It estimates the beliefs of possible user’s goals at every dialogue turn.

To reduce the burden on call centre agents and improve the SOTA of task-oriented dialogue systems, AI-powered customer service company ASAPP recently launched an action-based conversations dataset (ABCD). The dataset is designed to help develop task-oriented dialogue systems for customer service applications. ABCD consists of a fully labelled dataset with over 10,000 human dialogues containing 55 distinct user intents requiring sequences of actions constrained by company policies to accomplish tasks.

https://twitter.com/asapp/status/1397928363923177472

The dataset is currently available on GitHub.

#developers corner #asapp abcd dataset #asapp new dataset #build enterprise chatbot #chatbot datasets latest #customer support datasets #customer support model training #dataset for chatbots #dataset for customer datasets

Hangfire en ASP.NET Core 3.1

En este artículo, aprendamos sobre Hangfire en ASP.NET Core 3.1 y cómo integrarlo con sus aplicaciones principales. Una tarea de programación común a la que nos enfrentamos regularmente es ejecutar trabajos en segundo plano. Y ejecutar estos trabajos correctamente sin estropear su código no es una tarea fácil, pero tampoco es difícil. Solía ​​trabajar con los servicios de Windows para programar varias tareas dentro de mi aplicación C#. Luego, me encontré con esta biblioteca casi increíble: Hangfire, y nunca me fui.

Trabajos en segundo plano en ASP.NET Core

Básicamente, los trabajos en segundo plano son aquellos métodos o funciones que pueden tardar mucho tiempo en ejecutarse (cantidad de tiempo desconocida). Estos trabajos, si se ejecutan en el subproceso principal de nuestra aplicación, pueden o no bloquear la interacción del usuario y puede parecer que nuestra aplicación .NET Core se ha bloqueado y no responde. Esto es bastante crítico para las aplicaciones orientadas al cliente. Por lo tanto, tenemos trabajos en segundo plano, similares a los subprocesos múltiples, estos trabajos se ejecutan en otro subproceso, lo que hace que nuestra aplicación parezca bastante asíncrona.

También deberíamos tener la posibilidad de programarlos en un futuro cercano para que esté completamente automatizado. La vida de un desarrollador sería muy dura sin estas increíbles posibilidades.

¿Qué es Hangfire?

Hangfire es una biblioteca de código abierto que permite a los desarrolladores programar eventos en segundo plano con la mayor facilidad. Es una biblioteca altamente flexible que ofrece varias funciones necesarias para hacer que la tarea de programación de trabajos sea pan comido. Hangfire en ASP.NET Core es la única biblioteca que no puede perderse.

Integración de Hangfire en ASP.NET Core 3.1

Para este tutorial, tengamos un escenario específico para que podamos explicar Hangfire y su potencial completo. Digamos que estamos desarrollando una API que se encarga de enviar correos al Usuario para diferentes escenarios. Tiene más sentido explicar Hangfire de esta manera. Hangfire es una de las bibliotecas más fáciles de adaptar, pero también muy poderosa. Es uno de los paquetes que ayuda por completo a crear aplicaciones de forma asíncrona y desacoplada.

Como mencioné anteriormente, Hangfire usa una base de datos para almacenar los datos del trabajo. Usaremos la base de datos del servidor MSSQL en esta demostración. Hangfire crea automáticamente las tablas requeridas durante la primera ejecución.

Configuración del proyecto ASP.NET Core

Comenzaremos creando un nuevo proyecto ASP.NET Core con la plantilla API seleccionada. Ahora cree un controlador de API vacío. Llamémoslo HangfireController. Estoy usando Visual Studio 2019 Community como mi IDE y POSTMAN para probar las API.

Instalación de los paquetes Hangfire

Instalando el único paquete que necesitarías para configurar Hangfire.

Install-Package Hangfire

Configuración de Hangfire

Una vez que haya instalado el paquete, ahora estamos listos para configurarlo para que sea compatible con nuestra aplicación ASP.NET Core API. Este es un paso bastante sencillo, además, una vez que instale el paquete, se le mostrará un Léame rápido que le muestra el paso para completar la configuración.

Navigate to Startup.cs / ConfigureServices so that it looks like the below code snippet.
public void ConfigureServices(IServiceCollection services)
{
services.AddHangfire(x => x.UseSqlServerStorage("<connection string>"));
services.AddHangfireServer();
services.AddControllers();
}

Explicación.
Línea #3 Agrega el servicio Hangfire a nuestra aplicación. También hemos mencionado el almacenamiento que se utilizará, el servidor MSSQL, junto con la cadena/nombre de conexión.
La línea n.º 4 en realidad enciende el servidor Hangfire, que es responsable del procesamiento de trabajos.

Una vez hecho esto, vayamos al método Configurar y agregue la siguiente línea.

app.UseHangfireDashboard("/mydashboard");

Explicación.
Lo que hace esta línea es que nos permite acceder al panel de hangfire en nuestra aplicación ASP.NET Core. El tablero estará disponible yendo a /URL de mi tablero. Iniciemos la aplicación.

Esquema de la base de datos Hangfire

Cuando inicia su aplicación ASP.NET Core por el momento, Hangfire verifica si tiene un esquema de Hangfire asociado disponible en su base de datos. Si no, creará un montón de tablas para ti. Así es como se vería su base de datos.

Panel de Hangfire

Después de cargar la aplicación, vaya a <localhost>/mydashboard. Podrá ver el panel de control de Hangfire.

Desde el tablero podrás monitorear los trabajos y sus estados. También le permite activar manualmente los trabajos disponibles. Esta es la característica ÚNICA que diferencia a Hangfire de otros programadores. Tablero incorporado. ¿Cuan genial es eso? La captura de pantalla anterior es la de la descripción general del panel. Exploremos también las otras pestañas.

Pestaña de trabajos.

Todos los trabajos que están disponibles en el almacén de datos (nuestro servidor MSSQL) se enumerarán aquí. Obtendrá una idea completa del estado de cada trabajo (En cola, Exitoso, Procesando, Fallido, etc.) en esta pantalla.

Pestaña Reintentos.

Los trabajos tienden a fallar de vez en cuando debido a factores externos. En nuestro caso, nuestra api intenta enviar un correo al usuario, pero hay un problema de conexión interna, lo que hace que el trabajo no se ejecute. Cuando falla un trabajo, Hangfire continúa intentándolo hasta que pasa. (configurable)

Pestaña de trabajos recurrentes.

¿Qué sucede si necesita enviar por correo el uso de su factura mensualmente? Esta es la característica principal de Hangfire, trabajos recurrentes. Esta pestaña le permite monitorear todos los trabajos configurados.

Pestaña Servidores.

Recuerde, al configurar Hangfire en la clase Startup.cs, lo hemos mencionado. services.AddHangfireServer().. Esta es la pestaña donde muestra todos los Hangfire Server activos. Estos servidores son responsables de procesar los trabajos. Digamos que no ha agregado los servicios. AddHangfireServer() en la clase de inicio, aún podría agregar trabajos Hangfire a la base de datos, pero no se ejecutarán hasta que inicie un servidor Hangfire.

Protección del panel Hangfire

Esta es una característica bastante obvia. Dado que el tablero puede exponer datos muy confidenciales como nombres de métodos, valores de parámetros, ID de correo electrónico, es muy importante que protejamos/restringamos este punto final. Hangfire, listo para usar, hace que el tablero sea seguro al permitir solo solicitudes locales. Sin embargo, puede cambiar esto implementando su propia versión de IDashboardAuthorizationFilter . Si ya implementó la Autorización en su API, puede implementarla para Hangfire. Consulte estos pasos para asegurar el tablero.

Tipos de trabajo en Hangfire

Los trabajos en segundo plano en ASP.NET Core (o digamos cualquier tecnología) pueden ser de muchos tipos según los requisitos. Repasemos los tipos de trabajo disponibles con Hangfire con la implementación adecuada y la explicación en nuestro proyecto ASP.NET Core API. Vamos a codificar.

Despedir y olvidar trabajos

Los trabajos de disparar y olvidar se ejecutan  solo una vez  y casi  inmediatamente  después de la creación. Crearemos nuestro primer trabajo de fondo. Abre el controlador Hangfire que habíamos creado. Crearemos un punto final POST que dé la bienvenida a un usuario con un correo electrónico (idealmente). Añade estos códigos.

[HttpPost]
[Route("welcome")]
public IActionResult Welcome(string userName)
{
var jobId = BackgroundJob.Enqueue(() => SendWelcomeMail(userName));
return Ok($"Job Id {jobId} Completed. Welcome Mail Sent!");
}

public void SendWelcomeMail(string userName)
{
//Logic to Mail the user
Console.WriteLine($"Welcome to our application, {userName}");
}

Explicación.
La línea #5 almacena JobId en una variable. Puede ver que en realidad estamos agregando un trabajo en segundo plano representado por una función ficticia SendWelcomeMail. El JobId se vuelve a publicar más tarde en el Panel de control de Hangfire. Cree la aplicación y ejecútela. Vamos a probarlo con Postman.

Tenga en cuenta la URL y cómo estoy pasando el nombre de usuario al controlador. Una vez que lo ejecute, obtendrá nuestra respuesta requerida. “Id. de trabajo 2 completado. ¡Correo de bienvenida enviado!”. Ahora veamos el tablero de Hangfire.

En la pestaña Correcto, puede ver el recuento de trabajos completados. También puede ver los detalles de cada trabajo, similar a la captura de pantalla anterior. Todos los parámetros y nombres de funciones se exponen aquí. ¿Quiere volver a ejecutar este trabajo con los mismos parámetros? Presiona el botón Volver a poner en cola. Vuelve a agregar su trabajo en la cola para que Hangfire lo procese. Sucede casi de inmediato.

Trabajos retrasados

Ahora, qué pasa si queremos enviar un correo a un usuario, no inmediatamente, sino después de 10 minutos. En tales casos, utilizamos trabajos retrasados. Veamos su implementación, después de lo cual lo explicaré en detalle. En el mismo controlador, agregue estas líneas de código. Es bastante similar a la variante anterior, pero le introducimos un factor de retraso.

[HttpPost]
[Route("delayedWelcome")]
public IActionResult DelayedWelcome(string userName)
{
var jobId = BackgroundJob.Schedule(() => SendDelayedWelcomeMail(userName),TimeSpan.FromMinutes(2));
return Ok($"Job Id {jobId} Completed. Delayed Welcome Mail Sent!");
}

public void SendDelayedWelcomeMail(string userName)
{
//Logic to Mail the user
Console.WriteLine($"Welcome to our application, {userName}");
}

Explicación.
La línea #5 programó el trabajo en un período de tiempo definido, en nuestro caso son 2 minutos. Eso significa que nuestro trabajo se ejecutará 2 minutos después de que Postman haya llamado a la acción. Abramos Postman de nuevo y probemos.

Puede ver que recibimos la respuesta esperada de Postman. Ahora. vuelva rápidamente al Panel de control de Hangfire y haga clic en la pestaña Trabajos/programados. Diría que el trabajo se ejecutará en un minuto. Ahí estás para. Ha creado su primer trabajo programado usando Hangfire con facilidad.

trabajos recurrentes

Nuestro Cliente tiene una suscripción a nuestro servicio. Obviamente, tendríamos que enviarle un recordatorio sobre el pago o la factura en sí. Esto llama la necesidad de un trabajo recurrente, donde puedo enviar correos electrónicos a mis clientes mensualmente. Esto es compatible con Hangfire mediante el programa CRON.
¿Qué es CRON? CRON es una utilidad basada en el tiempo que puede definir intervalos de tiempo. Veamos cómo lograr tal requisito.

[HttpPost]
[Route("invoice")]
public IActionResult Invoice(string userName)
{
RecurringJob.AddOrUpdate(() => SendInvoiceMail(userName), Cron.Monthly);
return Ok($"Recurring Job Scheduled. Invoice will be mailed Monthly for {userName}!");
}

public void SendInvoiceMail(string userName)
{
//Logic to Mail the user
Console.WriteLine($"Here is your invoice, {userName}");
}

La línea #5 establece claramente que estamos tratando de agregar/actualizar un trabajo recurrente, que llama a una función tantas veces como lo define el esquema CRON. Aquí enviaremos la factura al cliente mensualmente el primer día de cada mes. Ejecutemos la aplicación y cambiemos a Postman. Estoy ejecutando este código el 24 de mayo de 2020. Según nuestro requisito, este trabajo debe despedirse el 1 de junio de 2020, que es dentro de 7 días. Vamos a ver.

Entonces, esto funcionó. Pasemos a Hangfire Dashboard y vayamos a la pestaña Trabajos recurrentes.

¡Perfecto! Funciona como excepción. Puede pasar por varios esquemas CRON aquí que pueden coincidir con sus requisitos. Aquí hay una pequeña documentación agradable para comprender cómo se usan varias expresiones CRON.

Continuaciones

Este es un escenario más complicado. Permítanme tratar de mantenerlo muy simple. Un usuario decide darse de baja de su servicio. Después de que confirme su acción (tal vez haciendo clic en el botón para cancelar la suscripción), nosotros (la aplicación) tenemos que cancelar la suscripción del sistema y enviarle un correo de confirmación después de eso también. Entonces, el primer trabajo es realmente cancelar la suscripción del usuario. El segundo trabajo es enviar un correo confirmando la acción. El segundo trabajo debe ejecutarse solo después de que el primer trabajo se haya completado correctamente. Obtener el escenario?

[HttpPost]
[Route("unsubscribe")]
public IActionResult Unsubscribe(string userName)
{
var jobId = BackgroundJob.Enqueue(() => UnsubscribeUser(userName));
BackgroundJob.ContinueJobWith(jobId, () => Console.WriteLine($"Sent Confirmation Mail to {userName}"));
return Ok($"Unsubscribed");
}

public void UnsubscribeUser(string userName)
{
//Logic to Unsubscribe the user
Console.WriteLine($"Unsubscribed {userName}");
}

Explicación.
Línea #5 El primer trabajo que realmente contiene lógica para eliminar la suscripción del usuario.
Línea #6 Nuestro segundo trabajo que continuará después de que se ejecute el primer trabajo. Esto se hace pasando el Id. de trabajo del trabajo principal a los trabajos secundarios.

Iniciemos la aplicación y vayamos a Postman.

Ahora, vaya al Tablero y verifique el trabajo exitoso. Verá 2 nuevos trabajos ejecutados en el orden exacto que queríamos. Eso es todo por este tutorial. Espero que tengan claro estos conceptos y les resulte fácil integrar Hangfire en las aplicaciones ASP.NET Core.

Resumen

En esta guía detallada, hemos repasado los conceptos de trabajos en segundo plano, características e implementación de Hangfire en aplicaciones ASP.NET Core y varios tipos de trabajos en Hangfire. El código fuente utilizado para demostrar este tutorial está publicado en GitHub. Te dejo el enlace a continuación para que lo consultes. ¿Tienes experiencia con Hangfire? ¿Tienes alguna consulta/sugerencia? Siéntase libre de dejar a continuación en la sección de comentarios. ¡Feliz codificación!

Fuente: https://codewithmukesh.com/blog/hangfire-in-aspnet-core-3-1/

#aspdotnet #hangfire 

Substrate Knowledge Map For Hackathon Participants

Substrate Knowledge Map for Hackathon Participants

The Substrate Knowledge Map provides information that you—as a Substrate hackathon participant—need to know to develop a non-trivial application for your hackathon submission.

The map covers 6 main sections:

1. Introduction
2. Basics
3. Preliminaries
4. Runtime Development
5. Polkadot JS API
6. Smart Contracts

Each section contains basic information on each topic, with links to additional documentation for you to dig deeper. Within each section, you'll find a mix of quizzes and labs to test your knowledge as your progress through the map. The goal of the labs and quizzes is to help you consolidate what you've learned and put it to practice with some hands-on activities.

Introduction

One question we often get is why learn the Substrate framework when we can write smart contracts to build decentralized applications?

The short answer is that using the Substrate framework and writing smart contracts are two different approaches.

Smart contract development

Traditional smart contract platforms allow users to publish additional logic on top of some core blockchain logic. Since smart contract logic can be published by anyone, including malicious actors and inexperienced developers, there are a number of intentional safeguards and restrictions built around these public smart contract platforms. For example:

Fees: Smart contract developers must ensure that contract users are charged for the computation and storage they impose on the computers running their contract. With fees, block creators are protected from abuse of the network.

Sandboxed: A contract is not able to modify core blockchain storage or storage items of other contracts directly. Its power is limited to only modifying its own state, and the ability to make outside calls to other contracts or runtime functions.

Reversion: Contracts can be prone to undesirable situations that lead to logical errors when wanting to revert or upgrade them. Developers need to learn additional patterns such as splitting their contract's logic and data to ensure seamless upgrades.

These safeguards and restrictions make running smart contracts slower and more costly. However, it's important to consider the different developer audiences for contract development versus Substrate runtime development.

Building decentralized applications with smart contracts allows your community to extend and develop on top of your runtime logic without worrying about proposals, runtime upgrades, and so on. You can also use smart contracts as a testing ground for future runtime changes, but done in an isolated way that protects your network from any errors the changes might introduce.

In summary, smart contract development:

• Is inherently safer to the network.
• Provides economic incentives and transaction fee mechanisms that can't be directly controlled by the smart contract author.
• Provides computational overhead to support graceful logical failures.
• Has a low barrier to entry for developers and enables a faster pace of community interaction.

Substrate runtime development

Unlike traditional smart contract development, Substrate runtime development offers none of the network protections or safeguards. Instead, as a runtime developer, you have total control over how the blockchain behaves. However, this level of control also means that there is a higher barrier to entry.

Substrate is a framework for building blockchains, which almost makes comparing it to smart contract development like comparing apples and oranges. With the Substrate framework, developers can build smart contracts but that is only a fraction of using Substrate to its full potential.

With Substrate, you have full control over the underlying logic that your network's nodes will run. You also have full access for modifying and controlling each and every storage item across your runtime modules. As you progress through this map, you'll discover concepts and techniques that will help you to unlock the potential of the Substrate framework, giving you the freedom to build the blockchain that best suits the needs of your application.

You'll also discover how you can upgrade the Substrate runtime with a single transaction instead of having to organize a community hard-fork. Upgradeability is one of the primary design features of the Substrate framework.

In summary, runtime development:

• Provides low level access to your entire blockchain.
• Removes the overhead of built-in safety for performance.
• Has a higher barrier of entry for developers.
• Provides flexibility to customize full-stack application logic.

To learn more about using smart contracts within Substrate, refer to the Smart Contract - Overview page as well as the Polkadot Builders Guide.

Navigating the documentation

If you need any community support, please join the following channels based on the area where you need help:

• Substrate Technical channel on Discord or Element.
• ink! Smart Contract channel on Discord or Element
• Frontier EVM Compatibility channel on Discord or Element
• PolkatodJS API channel on Discord

Alternatively, also look for support on Stackoverflow where questions are tagged with "substrate" or on the Parity Subport repo.

Use the following links to explore the sites and resources available on each:

Substrate Developer Hub has the most comprehensive all-round coverage about Substrate, from a "big picture" explanation of architecture to specific technical concepts. The site also provides tutorials to guide you as your learn the Substrate framework and the API reference documentation. You should check this site first if you want to look up information about Substrate runtime development. The site consists of:

Knowledge Base: Explaining the foundational concepts of building blockchain runtimes using Substrate.

Tutorials: Hand-on tutorials for developers to follow. The first SIX tutorials show the fundamentals in Substrate and are recommended for every Substrate learner to go through.

How-to Guides: These resources are like the O'Reilly cookbook series written in a task-oriented way for readers to get the job done. Some examples of the topics overed include:

• Setting up proper weight functions for extrinsic calls.
• Using off-chain workers to fetch HTTP requests.
• Writing tests for your pallets It can also be read from

API docs: Substrate API reference documentation.

Substrate Node Template provides a light weight, minimal Substrate blockchain node that you can set up as a local development environment.

Substrate Front-end template provides a front-end interface built with React using Polkadot-JS API to connect to any Substrate node. Developers are encouraged to start new Substrate projects based on these templates.

If you face any technical difficulties and need support, feel free to join the Substrate Technical matrix channel and ask your questions there.

Additional resources

Polkadot Wiki documents the specific behavior and mechanisms of the Polkadot network. The Polkadot network allows multiple blockchains to connect and pass messages to each other. On the wiki, you can learn about how Polkadot—built using Substrate—is customized to support inter-blockchain message passing.

Polkadot JS API doc: documents how to use the Polkadot-JS API. This JavaScript-based API allows developers to build custom front-ends for their blockchains and applications. Polkadot JS API provides a way to connect to Substrate-based blockchains to query runtime metadata and send transactions.

Quiz #1

👉 Submit your answers to Quiz #1

Basics

Set up your local development environment

Here you will set up your local machine to install the Rust compiler—ensuring that you have both stable and nightly versions installed. Both stable and nightly versions are required because currently a Substrate runtime is compiled to a native binary using the stable Rust compiler, then compiled to a WebAssembly (WASM) binary, which only the nightly Rust compiler can do.

Also refer to:

• Set up Substrate for Unix-based machines
• Set up Substrate for Windows

Lab #1

👉 Complete Lab #1: Run a Substrate node

Interact with a Substrate network using Polkadot-JS apps

Polkadot JS Apps is the canonical front-end to interact with any Substrate-based chain.

You can configure whichever endpoint you want it to connected to, even to your localhost running node. Refer to the following two diagrams.

1. Click on the top left side showing your currently connected network:

1. Scroll to the bottom of the menu, open DEVELOPMENT, and choose either Local Node or Custom to specify your own endpoint.

Quiz #2

👉 Complete Quiz #2

Lab #2

👉 Complete Lab #2: Using Polkadot-JS Apps

Notes: If you are connecting Apps to a custom chain (or your locally-running node), you may need to specify your chain's custom data types in JSON under Settings > Developer.

Polkadot-JS Apps only receives a series of bytes from the blockchain. It is up to the developer to tell it how to decode and interpret these custom data type. To learn more on this, refer to:

• Polkadot JS doc: Type basics
• Polkadot JS doc: Extending types

You will also need to create an account. To do so, follow these steps on account generation. You'll learn that you can also use the Polkadot-JS Browser Plugin (a Metamask-like browser extension to manage your Substrate accounts) and it will automatically be imported into Polkadot-JS Apps.

Notes: When you run a Substrate chain in development mode (with the --dev flag), well-known accounts (Alice, Bob, Charlie, etc.) are always created for you.

Lab #3

👉 Complete Lab #3: Create an Account

Preliminaries

You need to know some Rust programming concepts and have a good understanding on how blockchain technology works in order to make the most of developing with Substrate. The following resources will help you brush up in these areas.

Rust

You will need familiarize yourself with Rust to understand how Substrate is built and how to make the most of its capabilities.

If you are new to Rust, or need a brush up on your Rust knowledge, please refer to The Rust Book. You could still continue learning about Substrate without knowing Rust, but we recommend you come back to this section whenever in doubt about what any of the Rust syntax you're looking at means. Here are the parts of the Rust book we recommend you familiarize yourself with:

• ch 1 - 10: These chapters cover the foundational knowledge of programming in Rust
• ch 13: On iterators and closures
• ch 18 - 19: On advanced traits and advanced types. Learn a bit about macros as well. You will not necessarily be writing your own macros, but you'll be using a lot of Substrate and FRAME's built-in macros to write your blockchain runtime.

How blockchains work

Given that you'll be writing a blockchain runtime, you need to know what a blockchain is, and how it works. The **Web3 Blockchain Fundamental MOOC Youtube video series provides a good basis for understanding key blockchain concepts and how blockchains work.

The lectures we recommend you watch are: lectures 1 - 7 and lecture 10. That's 8 lectures, or about 4 hours of video.

Quiz #3

👉 Complete Quiz #3

Substrate runtime development

High level architecture

To know more about the high level architecture of Substrate, please go through the Knowledge Base articles on Getting Started: Overview and Getting Started: Architecture.

In this document, we assume you will develop a Substrate runtime with FRAME (v2). This is what a Substrate node consists of.

Each node has many components that manage things like the transaction queue, communicating over a P2P network, reaching consensus on the state of the blockchain, and the chain's actual runtime logic (aka the blockchain runtime). Each aspect of the node is interesting in its own right, and the runtime is particularly interesting because it contains the business logic (aka "state transition function") that codifies the chain's functionality. The runtime contains a collection of pallets that are configured to work together.

On the node level, Substrate leverages libp2p for the p2p networking layer and puts the transaction pool, consensus mechanism, and underlying data storage (a key-value database) on the node level. These components all work "under the hood", and in this knowledge map we won't cover them in detail except for mentioning their existence.

Quiz #4

👉 Complete Quiz #4

Runtime development topics

In our Developer Hub, we have a thorough coverage on various subjects you need to know to develop with Substrate. So here we just list out the key topics and reference back to Developer Hub. Please go through the following key concepts and the directed resources to know the fundamentals of runtime development.

Key Concept: Runtime, this is where the blockchain state transition function (the blockchain application-specific logic) is defined. It is about composing multiple pallets (can be understood as Rust modules) together in the runtime and hooking them up together.

Runtime Development: Execution, this article describes how a block is produced, and how transactions are selected and executed to reach the next "stage" in the blockchain.

Runtime Develpment: Pallets, this article describes what the basic structure of a Substrate pallet is consists of.

Runtime Development: FRAME, this article gives a high level overview of the system pallets Substrate already implements to help you quickly develop as a runtime engineer. Have a quick skim so you have a basic idea of the different pallets Substrate is made of.

Lab #4

👉 Complete Lab #4: Adding a Pallet into a Runtime

Runtime Development: Storage, this article describes how data is stored on-chain and how you could access them.

Runtime Development: Events & Errors, this page describe how external parties know what has happened in the blockchain, via the emitted events and errors when executing transactions.

Notes: All of the above concepts we leverage on the #[pallet::*] macro to define them in the code. If you are interested to learn more about what other types of pallet macros exist go to the FRAME macro API documentation and this doc on some frequently used Substrate macros.

Lab #5

👉 Complete Lab #5: Building a Proof-of-Existence dApp

• Writing Tests for Your Pallet: learn to build up a mock runtime and write test cases for your pallet logics.

Lab #6

👉 Complete Lab #6: Building a Substrate Kitties dApp

Quiz #5

👉 Complete Quiz #5

Polkadot JS API

Polkadot JS API is the javascript API for Substrate. By using it you can build a javascript front end or utility and interact with any Substrate-based blockchain.

The Substrate Front-end Template is an example of using Polkadot JS API in a React front-end.

• Runtime Development: Metadata, this article describes the API allowing external parties to query what API is open for the chain. Polkadot JS API makes use of a chain's metadata to know what queries and functions are available from a chain to call.

Lab #7

👉 Complete Lab #7: Using Polkadot-JS API

Quiz #6

👉 Complete Quiz #6: Using Polkadot-JS API

Smart contracts

Learn about the difference between smart contract development vs Substrate runtime development, and when to use each here.

In Substrate, you can program smart contracts using ink!.

• Learn about smart contract development in Substrate
• Learn about Smart contract development using ink!

Quiz #7

👉 Complete Quiz #7: Using ink!

What we do not cover

A lot 😄

On-chain runtime upgrades. We have a tutorial on On-chain (forkless) Runtime Upgrade. This tutorial introduces how to perform and schedule a runtime upgrade as an on-chain transaction.

About transaction weight and fee, and benchmarking your runtime to determine the proper transaction cost.

Off-chain Features

There are certain limits to on-chain logic. For instance, computation cannot be too intensive that it affects the block output time, and computation must be deterministic. This means that computation that relies on external data fetching cannot be done on-chain. In Substrate, developers can run these types of computation off-chain and have the result sent back on-chain via extrinsics.

Tightly- and Loosely-coupled pallets, calling one pallet's functions from another pallet via trait specification.

Blockchain Consensus Mechansim, and a guide on customizing it to proof-of-work here.

Parachains: one key feature of Substrate is the capability of becoming a parachain for relay chains like Polkadot. You can develop your own application-specific logic in your chain and rely on the validator community of the relay chain to secure your network, instead of building another validator community yourself. Learn more with the following resources:

• Cross-chain Message Passing (XCMP), how parachain and relay-chain communicate to each others
• Workshop: Using cumulus to build your parachain

Terms clarification

• Substrate: the blockchain development framework built for writing highly customized, domain-specific blockchains.
• Polkadot: Polkadot is the relay chain blockchain, built with Substrate.
• Kusama: Kusama is Polkadot's canary network, used to launch features before these features are launched on Polkadot. You could view it as a beta-network with real economic value where the state of the blockchain is never reset.
• Web 3.0: is the decentralized internet ecosystem that, instead of apps being centrally stored in a few servers and managed by a sovereign party, it is an open, trustless, and permissionless network when apps are not controlled by a centralized entity.
• Web3 Foundation: A foundation setup to support the development of decentralized web software protocols. Learn more about what they do on thier website.

Others

• Answers to quizzes and labs

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Author: substrate-developer-hub
Source Code: https://github.com/substrate-developer-hub/hackathon-knowledge-map
License: 

#blockchain #substrate