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...

Among its services, ML Kit provides a face detection API: you can use it to detect faces in an image, identify single parts of a face, such as eyes, mouth, and nose, get the contours of detected faces and parts, and identify whether a face is smiling and has the eyes open or closed.  

ML Kit provides a face detection service, not a face recognition one. This means that while you can identify faces in a picture, you cannot recognize people. 

There are several use cases to implement face detection: among others, you can create avatars, edit the pictures, or categorize your images. 

In this recipe, after taking a picture, you identified the faces in the image and gave your user some information about them: whether they had their eyes open and were smiling.  

Face detection is performed on the device, and no connection is required to use it.

You used a pattern similar to the previous recipes in this chapter: you got an image, sent it...