Book Image

OpenCV 4 with Python Blueprints - Second Edition

By : Dr. Menua Gevorgyan, Arsen Mamikonyan, Michael Beyeler
Book Image

OpenCV 4 with Python Blueprints - Second Edition

By: Dr. Menua Gevorgyan, Arsen Mamikonyan, Michael Beyeler

Overview of this book

OpenCV is a native cross-platform C++ library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. This book will get you hands-on with a wide range of intermediate to advanced projects using the latest version of the framework and language, OpenCV 4 and Python 3.8, instead of only covering the core concepts of OpenCV in theoretical lessons. This updated second edition will guide you through working on independent hands-on projects that focus on essential OpenCV concepts such as image processing, object detection, image manipulation, object tracking, and 3D scene reconstruction, in addition to statistical learning and neural networks. You’ll begin with concepts such as image filters, Kinect depth sensor, and feature matching. As you advance, you’ll not only get hands-on with reconstructing and visualizing a scene in 3D but also learn to track visually salient objects. The book will help you further build on your skills by demonstrating how to recognize traffic signs and emotions on faces. Later, you’ll understand how to align images, and detect and track objects using neural networks. By the end of this OpenCV Python book, you’ll have gained hands-on experience and become proficient at developing advanced computer vision apps according to specific business needs.
Table of Contents (14 chapters)
11
Profiling and Accelerating Your Apps
12
Setting Up a Docker Container

Classifying with CNNs

To start with the classification, first of all, we have to import the required modules:

import tensorflow.keras as K
from data import ds

We have to import our prepared dataset and Keras, which we will use to build our classifier.

However, before we build our classifier, let's first learn about convolutional networks, as we are going to use them to build our classifier.

Understanding CNNs

In Chapter 1, Fun with Filters, you learned about filters and convolution. In particular, you learned how filters can be used to create a pencil sketch image. In the pencil sketch, you could see the points in the image that had a sharp change in value, that is, they were darker than those that had a smooth change...