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
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| Auteur principal: | |
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| Support: | E-Book |
| Langue: | Anglais |
| Publié: |
Birmingham :
Packt Publishing,
2019.
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| Autres localisations: | Voir dans le Sudoc |
| Résumé: | 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 and Keras. Discover the modern design patterns you should avoid when developing efficient computer vision applications. 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. |
| Accès en ligne: | Accès à l'E-book |
| Résumé: | Create advanced applications with Python and OpenCV, exploring the potential of facial recognition, machine learning, deep learning, web computing and augmented reality.<br>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 and Keras. Discover the modern design patterns you should avoid when developing efficient computer vision applications. 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.<br><br>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<br><br>* Create computer vision and deep learning web applications<br><br><br>Who this book is for<br>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. |
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| Description: | Couverture (https://static.cyberlibris.com/books_upload/136pix/9781789349757.jpg). Titre provenant de la page de titre du document numérique. La pagination de l'édition imprimée correspondante est de 517 p. |
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| ISBN: | 9781789349757 |
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