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Item #:
90041059
Learn OpenCV with Python by Examples: Implement Computer Vision Algorithms Provided by OpenCV with Python for Image Proc, (Hardcover)
Item #:
90041059
SBD 452
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By the end of the book, readers will have a solid understanding of the fundamentals of computer vision and be able to build their own computer vision applications with confidence.
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Product Details
- This book is a comprehensive guide to learning the basics of computer vision and machine learning using the powerful OpenCV library and the Python programming language. The book offers a practical, hands-on approach to learn the concepts and techniques of computer vision through practical example. All codes in this book are available at Github.Through a series of examples, the book covers a wide range of topics including image and video processing, feature detection, object detection and recognition, machine learning and deep neural networks. Each chapter includes detailed explanations of the concepts and techniques involved, as well as practical examples and code snippets that demonstrate how to implement them in Python. Throughout the book, readers will work through hands-on examples and projects, learning how to build image processing applications from scratch. Whether you are a beginner or an experienced programmer, this book provides a valuable resource for learning computer vision with OpenCV and Python. The clear and concise writing style makes it easy for readers to follow along, and the numerous examples ensure that readers can practice and apply what they have learned. By the end of the book, readers will have a solid understanding of the fundamentals of computer vision and be able to build their own computer vision applications with confidence. This book is an excellent resource for anyone looking to learn computer vision and machine learning using the OpenCV library and Python programming language.Table of Contents1. Introduction 52. Installation 13 2.1 Install on Windows 14 2.2 Install Python on Ubuntu 16 2.3 Configure PyCharm and Install OpenCV 183. OpenCV Basics 25 3.1 Load and Display Images 26 3.2 Load and Display Videos 30 3.3 Display Webcam 32 3.4 Image Fundamentals 35 3.5 Draw Shapes 42 3.6 Draw Texts 48 3.7 Draw an OpenCV-like Icon 504. User Interaction 52 4.1 Mouse Operations 53 4.2 Draw Circles with Mouse 56 4.3 Draw Polygon with Mouse 60 4.4 Crop an Image with Mouse 62 4.5 Input Values with Trackbars 645. Image Processing 70 5.1 Conversion of Color Spaces 72 5.2 Resize, Crop and Rotate an Image 77 5.3 Adjust Contrast and Brightness of an Image 83 5.4 Adjust Hue, Saturation and Value 87 5.5 Blend Image 91 5.6 Bitwise Operation 94 5.7 Warp Image 101 5.8 Blur Image 107 5.9 Histogram 1146. Object Detection 120 6.1 Canny Edge Detection 122 6.2 Dilation and Erosion 125 6.3 Shape Detection 129 6.4 Color Detection 139 6.5 Text Recogni
| Book format | Hardcover |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | May, 2023 |
| Pages | 316 |
| Subgenre | Artificial Intelligence |
| Series title | No Series |
| Edition | 2nd Edition |
| Publisher | James Chen |
| Original languages | English |
| Language | English |
| Edu focus | Technology |
| Is collectible | N |
| Binding type | Case Binding |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 6.00 x 0.75 x 9.00 Inches (15.2 x 1.9 x 22.9 cm) |
| Assembled product weight | 1.3 lb (590 grams) |
| Bisac subject heading | Computers |
Product Description
Learn OpenCV with Python by Examples: Implement Computer Vision Algorithms Provided by OpenCV with Python for Image Proc, (Hardcover)
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Question:
What prior knowledge do I need to start this book?
Answer: Basic knowledge of Python programming is recommended. -
Question:
Is there support for the examples and code in the book?
Answer: Yes, all code examples are available on GitHub for easy access. -
Question:
Can I use this book for self-study?
Answer: Absolutely! The book is designed for both beginners and experienced programmers for self-paced learning.
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Features & Benefits
- Comprehensive guide to computer vision and machine learning.
- Hands-on learning approach with practical examples in Python.
- Wide range of topics from image processing to deep neural networks.
- Clear and concise writing style for easy comprehension.
- Code snippets available on GitHub for practical application.
- Perfect for beginners and experienced programmers alike.
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