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Learning OpenCV 4 Computer Vision with Python 3 - Third Edition

You're reading from  Learning OpenCV 4 Computer Vision with Python 3 - Third Edition

Product type Book
Published in Feb 2020
Publisher Packt
ISBN-13 9781789531619
Pages 372 pages
Edition 3rd Edition
Languages
Authors (2):
Joseph Howse Joseph Howse
Profile icon Joseph Howse
Joe Minichino Joe Minichino
Profile icon Joe Minichino
View More author details

Table of Contents (13) Chapters

Preface 1. Setting Up OpenCV 2. Handling Files, Cameras, and GUIs 3. Processing Images with OpenCV 4. Depth Estimation and Segmentation 5. Detecting and Recognizing Faces 6. Retrieving Images and Searching Using Image Descriptors 7. Building Custom Object Detectors 8. Tracking Objects 9. Camera Models and Augmented Reality 10. Introduction to Neural Networks with OpenCV 11. Other Book You May Enjoy Appendix A: Bending Color Space with the Curves Filter

Detecting and classifying objects with third-party DNNs

For this demo, we are going to capture frames from a webcam in real-time and use a DNN to detect and classify 20 kinds of objects that may be in any given frame. Yes, a single DNN can do all this in real-time on a typical laptop that a programmer might use!

Before delving into the code, let's introduce the DNN that we will use. It is a Caffe version of a model called MobileNet-SSD, which uses a hybrid of a framework from Google called MobileNet and another framework called Single Shot Detector (SSD) MultiBox. The latter framework has a GitHub repository at https://github.com/weiliu89/caffe/tree/ssd/. The training technique for the Caffe version of MobileNet-SSD is provided by a project on GitHub at https://github.com/chuanqi305/MobileNet-SSD/. Copies of the following MobileNet-SSD files can be found in this book&apos...

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