Home | Amazing | Today | Tags | Publishers | Years | Account | Search 
Mastering OpenCV 4 with Python: A practical guide covering topics from image processing, augmented reality to deep learning with OpenCV 4 and Python 3.7

Buy

Create advanced applications with Python and OpenCV, exploring the potential of facial recognition, machine learning, deep learning, web computing and augmented reality.

Key Features

  • Develop your computer vision skills by mastering algorithms in Open Source Computer Vision 4 (OpenCV 4)and Python
  • Apply machine learning and deep learning techniques with TensorFlow, Keras, and PyTorch
  • Discover the modern design patterns you should avoid when developing efficient computer vision applications

Book Description

OpenCV is considered to be one of the best open source computer vision and machine learning software libraries. It helps developers build complete projects in relation to image processing, motion detection, or image segmentation, among many others. OpenCV for Python enables you to run computer vision algorithms smoothly in real time, combining the best of the OpenCV C++ API and the Python language.

In this book, you'll get started by setting up OpenCV and delving into the key concepts of computer vision. You'll then proceed to study more advanced concepts and discover the full potential of OpenCV. The book will also introduce you to the creation of advanced applications using Python and OpenCV, enabling you to develop applications that include facial recognition, target tracking, or augmented reality. Next, you'll learn machine learning techniques and concepts, understand how to apply them in real-world examples, and also explore their benefits, including real-time data production and faster data processing. You'll also discover how to translate the functionality provided by OpenCV into optimized application code projects using Python bindings. Toward the concluding chapters, you'll explore the application of artificial intelligence and deep learning techniques using the popular Python libraries TensorFlow, and Keras.

By the end of this book, you'll be able to develop advanced computer vision applications to meet your customers' demands.

What you will learn

  • Handle files and images, and explore various image processing techniques
  • Explore image transformations, including translation, resizing, and cropping
  • Gain insights into building histograms
  • Brush up on contour detection, filtering, and drawing
  • Work with Augmented Reality to build marker-based and markerless applications
  • Work with the main machine learning algorithms in OpenCV
  • Explore the deep learning Python libraries and OpenCV deep learning capabilities
  • Create computer vision and deep learning web applications

Who this book is for

This book is designed for computer vision developers, engineers, and researchers who want to develop modern computer vision applications. Basic experience of OpenCV and Python programming is a must.

Table of Contents

  1. Setting up OpenCV
  2. Image basics in OpenCV
  3. Handling files and images
  4. Constructing basic shapes in OpenCV
  5. Image processing techniques
  6. Constructing and Building Histograms
  7. Thresholding techniques
  8. Contours Detection, filtering, and drawing
  9. Augmented reality and 3D Visualization
  10. Machine Learning and Deep Learning in OpenCV
  11. Face detection, tracking and recognition
  12. Introduction to deep learning
  13. Mobile and web computer vision with Python and OpenCV
(HTML tags aren't allowed.)

Success in Programming: How to Gain Recognition, Power, and Influence Through Personal Branding
Success in Programming: How to Gain Recognition, Power, and Influence Through Personal Branding

Why should you, a competent software developer or programmer, care about your own brand? After all, it’s not like you're an actor or musician.

In fact, as Success in Programming: How to Gain Recognition, Power, and Influence Through Personal Branding demonstrates in...

Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras
Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras

Explore various Generative Adversarial Network architectures using the Python ecosystem

Key Features

  • Use different datasets to build advanced projects in the Generative Adversarial Network domain
  • Implement projects ranging from generating 3D shapes to a face aging...
Soft Computing for Business Intelligence (Studies in Computational Intelligence)
Soft Computing for Business Intelligence (Studies in Computational Intelligence)

The book Soft Computing for Business Intelligence is the remarkable output of a program based on the idea of joint trans-disciplinary research as supported by the Eureka Iberoamerica Network and the University of Oldenburg.

It contains twenty-seven papers allocated to three sections: Soft Computing, Business...


Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition
Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms

Key Features

  • Explore statistics and complex mathematics for data-intensive applications
  • Discover new developments in EM algorithm, PCA, and bayesian...
TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning
TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning

Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects...

Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras
Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras

Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem

Key Features

  • Build deep learning models with transfer learning principles in Python
  • implement transfer learning to solve real-world research...
©2019 LearnIT (support@pdfchm.net) - Privacy Policy