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Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning, 3rd
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By the end of this book, you'll have the skills you need to execute real-world computer vision projects.
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產品詳情
- Updated for OpenCV 4 and Python 3
- Covers depth cameras, 3D tracking, augmented reality, and deep neural networks
- Build powerful computer vision applications with OpenCV 4 and Python 3
- Learn fundamental concepts of image processing, object classification, and 2D and 3D tracking
- Train and use machine learning models like SVMs and neural networks
- Suitable for beginners and experts in computer vision, machine learning, and OpenCV 4 and Python 3
| Publisher | Packt Publishing |
| Publication date | February 20, 2020 |
| Edition | 3rd ed. |
| Language | English |
| Print length | 372 pages |
| ISBN-10 | 1789531616 |
| ISBN-13 | 978-1789531619 |
| Item Weight | 1.54 pounds (700 grams) |
| Dimensions | 7.5 x 0.84 x 9.25 inches (19.1 x 2.1 x 23.5 cm) |
Who Should Buy?
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Beginner Programmers
Ideal for those new to computer vision and Python, offering foundational knowledge and practical examples.
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Data Scientists
Great for data scientists wanting to implement computer vision techniques in their projects using Python.
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Students
Perfect for university students studying computer vision, providing relevant content and exercises for practical understanding.
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Advanced Users
Experienced developers may find the content too basic and not advanced enough for deep exploration.
產品敘述
Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning, 3rd Edition
About This Item
Title: Learning OpenCV 4 Computer Vision with Python 3: Dive into the World of Computer Vision and Machine Learning Description: Are you fascinated by the possibilities of computer vision and machine learning? Look no further! With "Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning, 3rd Edition," you'll embark on an exciting journey into the realm of computer vision. Whether you're a beginner or an experienced programmer, this comprehensive guide, now in its 3rd edition, will equip you with the knowledge and skills to master OpenCV 4, Python 3, and the techniques required for advanced computer vision applications. This book emphasizes hands-on learning, allowing you to quickly grasp the concepts and put them into practice. You'll start by exploring the basics of computer vision, learning the fundamentals and building a solid foundation. From there, you'll progress to more advanced topics, including deep learning with OpenCV 4, image processing, and machine learning for computer vision. Throughout the book, you'll find practical examples and code snippets that demonstrate how to implement the techniques and algorithms discussed.
You'll learn to tackle real-world challenges, such as object detection, face recognition, and image segmentation, using OpenCV 4 and Python 3. With "Learning OpenCV 4 Computer Vision with Python 3," you'll have a valuable resource at your fingertips. Whether you're a computer vision enthusiast, a machine learning expert, or a professional developer looking to enhance your skills, this book is essential for anyone interested in pushing the boundaries of computer vision. Revolutionize your understanding of computer vision and unlock its potential with OpenCV 4 and Python 3. Get your copy of "Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning, 3rd Edition" now, and embark on an exciting journey into the world of computer vision.
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Computer Vision & Pattern Recognition Editorial Review
The Learning OpenCV 4 Computer Vision with Python 3 book receives overall positive reviews from readers. However, there are some critiques on its teaching approach, particularly on the lack of explanation regarding certain details that could be relevant in using OpenCV, such as converting images to and from byteArrays and the importance of understanding image shape. Nevertheless, the book is well-written and engaging with excellent examples that cover all major topics under the OpenCV umbrella. The book's approach to building a suite of modules is also helpful to facilitate expansion of the program later. Some readers appreciate the guidance provided in detecting custom objects, while others are disappointed with the unclear explanations of certain algorithms. The last few chapters are noted to be somewhat fast-paced, and the suggestion is made to expand them in future versions. Despite some difficulties in finding and downloading public training datasets, the book is still Considered one of the best OpenCV resources available, recommended for those seeking to learn OpenCV.
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優點
- Well-written and engaging
- Excellent examples that cover all major topics under the OpenCV umbrella
- Provides guidance on building a suite of modules, including detecting custom objects
- Recommended for those seeking to learn OpenCV
缺點
- Lack of explanation regarding certain details that could be relevant in using OpenCV
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特色和優勢
- Learn OpenCV 4 and Python 3 for computer vision and machine learning.
- Build powerful applications using concise code.
- Understand the fundamental concepts of image processing, object classification, and 2D and 3D tracking.
- Tackle popular challenges such as face detection and recognition, 3D tracking, and augmented reality.
- Develop skills in machine learning models, including SVMs, ANNs, and DNNs.
- Execute real-world computer vision projects.
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