Book Image

Android Studio 4.0 Development Essentials - Kotlin Edition

By : Neil Smyth
Book Image

Android Studio 4.0 Development Essentials - Kotlin Edition

By: Neil Smyth

Overview of this book

Kotlin as an Android-compatible programming language is becoming increasingly popular. Fully updated for Android Studio 4.0, this book will teach you the skills necessary to develop Android-based applications using Kotlin. Starting with the basics, this book outlines the steps necessary to set up Android development and testing environments, and goes on to introduce you to programming in Kotlin. You’ll practice Java to Kotlin code conversion and explore data types, operators, expressions, loops, functions, as well as the basics of OOP in Kotlin. You’ll then learn about Android architecture components and advanced topics, such as intents, touchscreen handling, gesture recognition, multi-window support integration, and biometric authentication. As you make progress, you’ll explore Android Studio 4.0’s key features, including layout editor, direct reply notifications, and dynamic delivery. You’ll also delve into Android Jetpack and create a sample app project using ViewModel, the Android Jetpack component. Finally, you will upload your app to Google Play Console and model the build process using Gradle. By the end of this Android book, you’ll be fully prepared to develop applications using Android Studio 4.0 and Kotlin.
Table of Contents (97 chapters)
97
Index

36.4 Identifying Specific Gestures

When a gesture is detected, the onGesturePerformed callback method is called and passed as arguments a reference to the GestureOverlayView object on which the gesture was detected, together with a Gesture object containing information about the gesture.

With access to the Gesture object, the GestureLibrary can then be used to compare the detected gesture to those contained in the gestures file previously loaded into the application. The GestureLibrary reports the probability that the gesture performed by the user matches an entry in the gestures file by calculating a prediction score for each gesture. A prediction score of 1.0 or greater is generally accepted to be a good match between a gesture stored in the file and that performed by the user on the device display.