OpenCV is a native, cross-platform C++ library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. OpenCV has C++/C, Python, and Java interfaces, with support for Windows, Linux, Mac, iOS, and Android. Developers who use OpenCV build applications to process visual data; this can include live streaming data such as photographs or videos from a device such as a camera. However, as developers move beyond their first computer vision applications, they might find it difficult to come up with solutions that are well-optimized, robust, and scalable for real-world scenarios.
This book demonstrates how to develop a series of intermediate to advanced projects using OpenCV and Python, rather than teaching the core concepts of OpenCV in theoretical lessons. The working projects developed in this book teach you how to apply your theoretical knowledge to topics such as image manipulation, augmented reality, object tracking, 3D scene reconstruction, statistical learning, and object categorization.
By the end of this book, you will be an OpenCV expert, and your newly gained experience will allow you to develop your own advanced computer vision applications.
Chapter 1, Fun with Filters, explores a number of interesting image filters (such as a black-and-white pencil sketch, warming/cooling filters, and a cartoonizer effect), and we apply them to the video stream of a webcam in real time.
Chapter 2, Hand Gesture Recognition Using a Kinect Depth Sensor, helps you develop an app to detect and track simple hand gestures in real time using the output of a depth sensor, such as a Microsoft Kinect 3D Sensor or Asus Xtion.
Chapter 3, Finding Objects via Feature Matching and Perspective Transforms, is where you develop an app to detect an arbitrary object of interest in the video stream of a webcam, even if the object is viewed from different angles or distances, or under partial occlusion.
Chapter 4, 3D Scene Reconstruction Using Structure from Motion, shows you how to reconstruct and visualize a scene in 3D by inferring its geometrical features from camera motion.
Chapter 5, Tracking Visually Salient Objects, helps you develop an app to track multiple visually salient objects in a video sequence (such as all the players on the field during a soccer match) at once.
Chapter 6, Learning to Recognize Traffic Signs, shows you how to train a support vector machine to recognize traffic signs from the German Traffic Sign Recognition Benchmark (GTSRB) dataset.
Chapter 7, Learning to Recognize Emotions on Faces, is where you develop an app that is able to both detect faces and recognize their emotional expressions in the video stream of a webcam in real time.