Practical OpenCV is a hands-on project book that shows you how to get the best results from OpenCV, the open-source computer vision library. Computer vision is key to technologies like object recognition, shape detection, and depth estimation. OpenCV is an open-source library with over 2500 algorithmsthat you can use to do all of these, as well as track moving objects,extract 3D models, and overlay augmented reality. It's used bymajor companies like Google (in its autonomous car), Intel, and Sony; and it is the backbone of the Robot Operating Systems computer vision capability. In short, if you're working with computer vision at all, you need to know OpenCV. With Practical OpenCV, you'll be able to: Get OpenCV up and running on Windows or Linux. Use OpenCV to control the camera board and run vision algorithms on Raspberry Pi. Understand what goes on behind the scenes in computer vision applications like object detection, image stitching, filtering, stereo vision, and more. Code complex computer vision projects for your class/hobby/robot/job, many of which can execute in real time on off-the-shelf processors. Combine different modules that you develop to create your own interactive computer vision app. What youll learn The ins and outs of OpenCV programming on Windows and Linux Transforming and filtering images Detecting corners, edges, lines, and circles in images and video Detecting pre-trained objects in images and video Making panoramas by stitching images together Getting depth information by using stereo cameras Basic machine learning techniques BONUS: Learn how to runOpenCV on Raspberry Pi Who this book is for This book is for programmers and makers withlittle or noprevious exposure to computer vision. Some proficiency with C++ is required. Table of ContentsPart 1: Getting comfortableChapter 1: Introduction to Computer Vision and OpenCVChapter 2: Setting up OpenCV on your computerChapter 3: CV Bling OpenCV inbuilt demosChapter 4: Basic operations on images and GUI windowsPart 2: Advanced computer vision problems and coding them in OpenCVChapter 5: Image filteringChapter 6: Shapes in imagesChapter 7: Image segmentation and histogramsChapter 8: Basic machine learning and keypoint-based object detectionChapter 9: Affine and Perspective transformations and their applications to image panoramasChapter 10: 3D geometry and stereo visionChapter 11: Embedded computer vision: Running OpenCV programs on the Raspberry Pi