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Learning Microsoft Cognitive Services
Learning Microsoft Cognitive Services

Learning Microsoft Cognitive Services: Use Cognitive Services APIs to add AI capabilities to your applications , Third Edition

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Profile Icon Leif Larsen Henning Larsen
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zł197.99
Paperback Sep 2018 312 pages 3rd Edition
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Arrow left icon
Profile Icon Leif Larsen Henning Larsen
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zł197.99
Paperback Sep 2018 312 pages 3rd Edition
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zł59.99 zł158.99
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Learning Microsoft Cognitive Services

Chapter 2. Analyzing Images to Recognize a Face

"We can use the Computer Vision API to prove to our clients the reliability of the data, so they can be confident making important business decisions based on that information."

- Leendert de Voogd, CEO of Vigiglobe

In the previous chapter, you were briefly introduced to Microsoft Cognitive Services. Throughout this chapter, we will dive into image-based APIs from the vision API. We will learn how to perform image analysis. Moving on, we will dive deeper into the Face API, which we briefly looked at in the previous chapter, and we will learn how you can identify people. Next, we will learn how to use the Face API to recognize emotions in faces. Finally, we will learn about the different ways to moderate content.

In this chapter, we will cover the following topics:

  • Analyzing images to identify content, metadata, and adult ratings.
  • Recognizing celebrities in images and reading text in images.
  • Diving into the Face API:
    • Learning...

Analyze an image using the Computer Vision API

The Computer Vision API allows us to process an image and retrieve information about it. It relies on advanced algorithms to analyze the content of the image in different ways, based on our needs.

Throughout this section, we will learn how to take advantage of this API. We will look at the different ways to analyze an image through standalone examples. Some of the features we will cover will also be incorporated into our end-to-end application in a later chapter.

Calling any of the APIs will return one of the following response codes:

Code

Description

200

Information of the extracted features in JSON format.

400

Typically, this means bad request. It may be an invalid image URL, an image that is too small or too large, an invalid image format, or any other errors to do with the request body.

415

Unsupported media type.

500

Possible errors may include a failure to process the image, image processing timing out, or an internal...

Diving deep into the Face API

The Face API has two main features. The first one is face detection and the other is face recognition.

Face detection allows us to detect up to 64 faces in one image. We have already seen the basic usage. The features of face recognition are implied in its name: using it, we can detect whether two faces belong to the same person. We can find similar faces, or one in particular, and we can group similar faces. We will learn how to do all of this in the following sections.

When calling any of the APIs, it will respond with one of the following responses:

Code

Description

200

Successful call. It returns an array containing data related to the API call.

400

Request body is invalid. This can be a number of errors, depending on the API call. Typically, the request code is invalid.

401

Access denied because of an invalid subscription key. The key may be wrong or the account/subscription plan may be blocked.

403

Out of call volume data. You have made...

Adding identification to our smart-house application

As a part of our smart-house application, we want the application to recognize who we are. Doing so opens up the opportunity to get responses and actions from the application, tailored to you.

Creating our smart-house application

Create a new project for the smart-house application, based on the MVVM template we created earlier.

With the new project created, add the Microsoft.ProjectOxford.Face NuGet package.

As we will be building this application throughout this book, we will start small. In the MainView.xaml file, add a TabControl property containing two items. The two items should be two user controls, one called the AdministrationView.xaml file and the other called the HomeView.xaml file.

The administration control will be where we administer different parts of the application. The home control will be the starting point and the main control to use.

Add corresponding ViewModel instances to the Views. Make sure they are declared and created...

Knowing your mood using the Face API

The Face API allows you to recognize emotions from faces.

Research has shown that there are some key emotions that can be classified as cross-cultural. These are happiness, sadness, surprise, anger, fear, contempt, disgust, and neutral. All of these are detected by the API, which allows your applications to respond in a more personalized way by knowing the user's mood.

We will learn how to recognize emotions from images so that our smart-house application can know our mood.

Getting images from a web camera

Imagine that there are several cameras around your house. The smart-house application can see what your mood is at any time. By knowing this, it can utilize the mood to better predict your needs.

We are going to add web-camera capabilities to our application. If you do not have a web camera, you can follow along, but load images using the techniques we have already seen.

First we need to add a NuGet package to our smart-house application. Search for...

Automatically moderating user content

Using the content moderator API, we can add monitoring to user-generated content. The API is created to assist with flags and to assess and filter offensive and unwanted content.

Types of content moderation APIs

We will quickly go through the key features of the moderation APIs in this section.

Note

A reference to the documentation for all APIs can be found at https://docs.microsoft.com/nb-no/azure/cognitive-services/content-moderator/api-reference.

Image moderation

The image moderation API allows you to moderate images for adult and inappropriate content. It can also extract textual content and detect faces in images.

When using the API to evaluate inappropriate content, the API will take an image as input. Based on the image, it will return a Boolean value, indicating whether the image is appropriate or not. It will also contain a corresponding confidence score between 0 and 1. The Boolean value is set based on a set of default thresholds.

If the image contains...

Building your own image classifiers

The Custom Vision service allows you to build your own image classifiers. There might be cases where you require special images to use the image APIs. Such cases may be from a factory, where the equipment you need to recognize is not very available. You can start to build a prototype, using as little

Building a classifier

To build a classifier, you will need to create a new project. Doing so will allow you to specify what category the images will be in. You will also select the classification type and project type.

Moving on, you will need to upload images. This can be done through the web page or through a REST API. All images must be tagged so that the classifier will recognize similar images later.

Once all images (at least 50) are uploaded, you must train your model. Once the training is complete, you will be presented with a precision percentage per tag. This is a measurement of the accuracy of the model.

Improving the model

On the website, you can test...

Summary

In this chapter, we took a deep dive into a big part of the vision APIs. You first learned how to get good descriptions of images. Next, you learned how to recognize celebrities and text in images, and you learned how to generate thumbnails. Following this, we moved on to the Face API, where we got more information about detected faces. We found out how to verify whether two faces were the same. After this, you learned how to find similar faces and group similar faces. Then we added identification to our smart-house application, allowing it to know who we are. We also added the ability to recognize emotions in faces. We took a quick look into the content moderator to see how you can add automatic moderation to user-generated content. Finally, we briefly looked at the Custom Vision service, and how you can use it to generate specific prediction models.

The next chapter will continue with the final vision API. We will focus on videos, learning what the video indexer API has to offer...

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Key benefits

  • Build applications with computer vision, speech recognition, and language processing capabilities
  • Process and analyze data in the form of text, images, and videos
  • Build smarter applications in Visual Studio using real-world examples

Description

Microsoft Cognitive Services is a set of APIs for integrating artificial intelligence in your applications to solve logical business problems. If you’re new to developing applications with AI, Learning Microsoft Cognitive Services will give you a comprehensive introduction to Microsoft’s AI stack and get you up-to-speed in no time. The book introduces you to 24 APIs, including Emotion, Language, Vision, Speech, Knowledge, and Search. Using Visual Studio, you can develop applications with enhanced capabilities for image processing, speech recognition, text processing, and much more. Moving forward, you will work with datasets that enable your applications to process various data in the form of image, video, or text. By the end of the book, you’ll be able to confidently explore Cognitive Services APIs for building intelligent applications that can be deployed for real-world business uses.

Who is this book for?

If you’re a developer or machine learning enthusiast who wants to get started with building intelligent applications, this book is for you. Though you’re not expected to have much programming experience, some knowledge of .NET and Visual Studio will help you undertake the tasks explained in this book easily.

What you will learn

  • Identify a person through visual and audio inspection
  • Reduce user effort by utilizing AI capabilities
  • Understand how to analyze images and text in different ways
  • Add video and image analysis to applications using Vision APIs
  • Use the Search API to find anything you want from your database
  • Analyze text to extract information and explore text structure
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Table of Contents

13 Chapters
1. Getting Started with Microsoft Cognitive Services Chevron down icon Chevron up icon
2. Analyzing Images to Recognize a Face Chevron down icon Chevron up icon
3. Analyzing Videos Chevron down icon Chevron up icon
4. Letting Applications Understand Commands Chevron down icon Chevron up icon
5. Speaking with Your Application Chevron down icon Chevron up icon
6. Understanding Text Chevron down icon Chevron up icon
7. Building Recommendation Systems for Businesses Chevron down icon Chevron up icon
8. Querying Structured Data in a Natural Way Chevron down icon Chevron up icon
9. Adding Specialized Searches Chevron down icon Chevron up icon
10. Connecting the Pieces Chevron down icon Chevron up icon
A. LUIS Entities Chevron down icon Chevron up icon
B. License Information Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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