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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

Generating image captions from the camera feed

Now that we have a clear idea about the image caption generator and have an application with a camera feed, we are ready to generate captions for the images from the camera feed. The logic to be followed is very simple. Images are captured from the live camera feed at a specific time interval and are stored in the device's local storage. Next, the stored pictures are retrieved and an HTTP POST request is created for the hosted model, passing in the retrieved image to fetch the generated captions, parsing the response, and displaying it on the screen.

Let's now look at the detailed steps, as follows: 

  1. We will first add an http dependency to the pubspec.yaml file to make http requests, as follows:
http: ^0.12.0

Install the dependency to the project using flutter pub get.

  1. To use the http package...
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