Book Image

Flutter Cookbook

By : Simone Alessandria, Brian Kayfitz
4 (1)
Book Image

Flutter Cookbook

4 (1)
By: Simone Alessandria, Brian Kayfitz

Overview of this book

“Anyone interested in developing Flutter applications for Android or iOS should have a copy of this book on their desk.” – Amazon 5* Review Lauded as the ‘Flutter bible’ for new and experienced mobile app developers, this recipe-based guide will teach you the best practices for robust app development, as well as how to solve cross-platform development issues. From setting up and customizing your development environment to error handling and debugging, The Flutter Cookbook covers the how-tos as well as the principles behind them. As you progress, the recipes in this book will get you up to speed with the main tasks involved in app development, such as user interface and user experience (UI/UX) design, API design, and creating animations. Later chapters will focus on routing, retrieving data from web services, and persisting data locally. A dedicated section also covers Firebase and its machine learning capabilities. The last chapter is specifically designed to help you create apps for the web and desktop (Windows, Mac, and Linux). Throughout the book, you’ll also find recipes that cover the most important features needed to build a cross-platform application, along with insights into running a single codebase on different platforms. By the end of this Flutter book, you’ll be writing and delivering fully functional apps with confidence.
Table of Contents (17 chapters)
About Packt

How it works...

The Image Labeling service allows recognizing different objects in an image. These include people, places, animals, and plants. There are two kinds of ImageLabeler: one is on the device, and we used it in this recipe. Or, if you want to connect to the Firebase cloud services, you can use CloudImageLabeler instead.  

When using an on-device image labeler you can recognize over 400 labels, but with the cloud services, there are over 10,000 available labels. 

Use cases for image labeling are almost limitless; you could use it to automatically categorize your user’s pictures, or use it for content moderation, or more specific tasks.  

The pattern is similar to the previous recipes in this chapter: you need to get an image, send it to the API, and retrieve and show the results. 

In this case, the object you used was ImageLabeler

ImageLabeler labeler = vision.imageLabeler(); 

The method to call to get the labels of the...