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Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

You're reading from   Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter Build scalable real-world projects to implement end-to-end neural networks on Android and iOS

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Product type Paperback
Published in Apr 2020
Publisher Packt
ISBN-13 9781789611212
Length 380 pages
Edition 1st Edition
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Authors (2):
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Rimjhim Bhadani Rimjhim Bhadani
Author Profile Icon Rimjhim Bhadani
Rimjhim Bhadani
Anubhav Singh Anubhav Singh
Author Profile Icon Anubhav Singh
Anubhav Singh
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Deep Learning for Mobile 2. Mobile Vision - Face Detection Using On-Device Models FREE CHAPTER 3. Chatbot Using Actions on Google 4. Recognizing Plant Species 5. Generating Live Captions from a Camera Feed 6. Building an Artificial Intelligence Authentication System 7. Speech/Multimedia Processing - Generating Music Using AI 8. Reinforced Neural Network-Based Chess Engine 9. Building an Image Super-Resolution Application 10. Road Ahead 11. Other Books You May Enjoy Appendix

Getting pictures from the device's local storage

In this section, we will add the functionality of FloatingActionButton to let the user choose an image from the gallery of the device. This will eventually be sent to the server so that we can receive a response.

The following steps describe how to launch the gallery and let the user choose an image:

  1. To allow the user to choose an image from the device's gallery, we will use the image_picker library. This launches the gallery and stores the image file selected by the user. We will start by adding a dependency in the pubspec.yaml file:
image_picker: 0.4.12+1

Also, we fetch the library by running flutter pub get on the Terminal.

  1. Next, we import the library inside the image_super_resolution.dart file:
import 'package:image_picker/image_picker.dart';
  1. Now, let's define the pickImage() ...
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