OpenCV is for image processing. OpenCV has both python and C++ interfaces. The best language for computer vision is C++. We can do image processing operations, building GUI, video analysis, 3D reconstruction, feature extraction, object detection, machine learning, etc with OpenCV.
Here you will get some of the Best OpenCV books
Learning OpenCV 3 is a great reference book that provides a thorough introduction for developers, academics, roboticists, and hobbyists. After reading this best OpenCV books you’ll learn OpenCV data types, array types, and array operations. You can capture and store still and video images with HighGUI and explore pattern recognition, including face detection. Again you’ll be able to reconstruct 3D images from stereo vision.
This book includes information about the following information
- The basics of OpenCV
- Users of OpenCV
- Computer vision
- The origin of OpenCV
- Downloading and installing OpenCV
- Getting the latest OpenCV via Git
- More OpenCV documentation
- OpenCV contribution repository.
- Generate real-time visual effects using different filters and image manipulation techniques such as dodging and burning
- Create computer vision applications
- Image processing and machine learning for OpenCV with multiple examples.
- Recognize hand gestures in real-time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor
- Learn feature extraction and feature matching for tracking arbitrary objects of interest
- Reconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniques
- Track visually salient objects by searching for and focusing on important regions of an image
- Detect faces using a cascade classifier and recognize emotional expressions in human faces using multi-layer perceptrons (MLPs)
- Recognize street signs using a multi-class adaptation of support vector machines (SVMs)
- Strengthen your OpenCV2 skills and learn how to use new OpenCV3 features.
Machine Learning for OpenCV is one of the Best OpenCV books for machine learning. It includes a great explanation of fundamental machine learning. ThisOpenCV book is the perfect guidance for ML and CV. It loads, store, edit, and visualize data using OpenCV and Python. Again evaluate, compare, and choose the right algorithm for any task. After reading this book you’ll be able to grasp the fundamental concepts of classification, regression, and clustering.
Key contents are
- A test of machine learning
- Working with data in OpenCV and Python
- First steps in supervised learning
- Representing data and engineering features
- Using decision trees to make a medical diagnosis
- Detecting pedestrians with support vector machines
- Implementing a spam filter with Bayesian learning
- Discovering hidden structures with unsupervised learning
- Using deep learning to classify handwritten digits
- Combining different algorithms into an ensemble
- Selecting the right model with hyperparameter tuning
- Wrapping up.
Mastering OpenCV with Practical Computer Vision Projects contains a variety of interesting projects. Some are mini, some simple an some are extremely complicated. Each chapter contains its own example with various computer vision techniques. The author of this book upload all the codes on the online. This book is for computer science graduates, researchers, and computer vision experts widening their expertise. Again people who have basic OpenCV and C/C++ programming experience will find this book efficient.
This book will
- Allow anyone with basic OpenCV experience to rapidly obtain skills in many computer vision topics, for research or commercial use
- Provide you a step-by-step tutorial and full source-code, using the C++ interface of OpenCV
- Give you rapid training in nine computer vision areas with useful projects.
- Help you to perform face analysis including the simple face, eye & skin detection, Fisher faces face recognition, 3D head orientation, complex facial feature tracking.
- Able you to learn Augmented Reality for desktop and iPhone or iPad using simple artificial markers or complex markerless natural images
- Help you to generate a 3D object model by moving a plain 2D camera, using 3D Structure from Motion (SfM) camera reprojection methods
- Redesign desktop real-time computer vision applications to more suitable Android & iOS mobile apps
- Use simple image filter effects including cartoon, sketch, paint, and alien effects
- Execute Human-Computer Interaction with an Xbox Kinect sensor using the whole body as a dynamic input.
OpenCV for Secret Agents is a good intro for OpenCV features. The contents and examples are really good. It helps you to build OpenCV apps for the desktop, the Raspberry Pi, Android, and the Unity game engine. You will also learn real-time techniques that can be used to classify images, detecting and recognizing any person or animal, and studying motion and distance with superhuman precision.
What you’ll learn
- Control a phone app with your suave gestures
- Equip your car with a rearview camera and hazard detection
- To design hands-free interfaces that are practical in home automation, in cars, and in discreet surveillance
- Install OpenCV, a Python development environment, and an Android development environment on Windows, Mac, or Linux
- Install a Unity development environment on Windows or Mac
- Detect car headlights, estimate distances to them, and provide feedback to the driver
- Spot and recognize human faces and cat faces as a means of controlling an alarm
- Amplify motion in a real-time video so that a person’s heartbeat and breathing become clearly visible
- Integrate OpenCV with other libraries, as well as popular frameworks for GUI apps and games.
After reading this book you’ll be able to
- Apply effects to images
- Detect basic features in images
- Perform image transformations such as changing color, space, resizing, applying filters like Gaussian blur, and likes
- Explore face detection, object detection, and image stitching in OpenCV Android programming
- Tracking objects in videos
- Work with image alignment and stitching
- Learn to debug applications and create optimal custom algorithms by understanding how data is stored internally
- Develop a document scanning app
- Build smarter applications by using machine learning algorithms.