OpenCV: Computer Vision Projects with Python by Michael Beyeler

Key FeaturesUse OpenCVs Python bindings to capture video, manipulate images, and track objectsLearn about the different functions of OpenCV and their actual implementations.Develop a series of intermediate to advanced projects using OpenCV and PythonBook DescriptionOpenCV is a state-of-art computer vision library that allows a great variety of image and video processing operations. OpenCV for Python enables us to run computer vision algorithms in real time. This learning path proposes to teach the following topics. First, we will learn how to get started with OpenCV and OpenCV3s Python API, and develop a computer vision application that tracks body parts. Then, we will build amazing intermediate-level computer vision applications such as making an object disappear from an image, identifying different shapes, reconstructing a 3D map from images , and building an augmented reality application, Finally, well move to more advanced projects such as hand gesture recognition, tracking visually salient objects, as well as recognizing traffic signs and emotions on faces using support vector machines and multi-layer perceptrons respectively.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:OpenCV Computer Vision with Python by Joseph HowseOpenCV with Python By Example by Prateek JoshiOpenCV with Python Blueprints by Michael BeyelerWhat you will learnInstall OpenCV and related software such as Python, NumPy, SciPy, OpenNI, and SensorKinect - all on Windows, Mac or UbuntuApply curves and other color transformations to simulate the look of old photos, movies, or video gamesApply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like imageRecognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensorReconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniquesDetect and recognize street signs using a cascade classifier and support vector machines (SVMs)Identify emotional expressions in human faces using convolutional neural networks (CNNs) and SVMsStrengthen your OpenCV2 skills and learn how to use new OpenCV3 featuresAbout the AuthorJoseph Howse (Joe) is fanciful. So to him, the virtual world always seemed to reach out into reality. One of his earliest memories is of watching an animated time-bomb on the screen of a Tandy Color Computer. The animation was programmed in BASIC by Joes older brother, Sam, who explained, Im making a bomb. Get ready!The bomb exploded in a rain of dots and a rumble of beeps as Joe and Sam ran to hide from the fallout. Today, Joe still fancies that a computer program can blast a tunnel into reality. As a hobby, he likes looking at reality through the tunnel of a digital cameras lens. As a career, he develops augmented reality software, which uses cameras and other sensors to composite real and virtual scenes interactively in real time. Joe holds a Master of Computer Science degree from Dalhousie University. He does research on software architecture as applied to augmented reality. Joe works at Ad-Dispatch, an augmented reality company, where he develops applications for mobile devices, kiosks, and the Web. Joe likes cats, kittens, oceans, and seas. Felines and saline water sustain him. He lives with his multi-species family in Halifax, on Canadas Atlantic coast.Prateek Joshi is a computer vision researcher with a primary focus on content-based analysis. He is particularly interested in intelligent algorithms that can understand images to produce scene descriptions in terms of constituent objects. He has a masters degree from the University of Southern California, specializing in computer vision. He was elected to become a member of the Honor Society for academic excellence and an ambassador for the School of Engineering. Over the course of his career, he has worked for companies such as Nvidia, Microsoft Research, Qualcomm, and a couple of early stage start-ups in Silicon Valley. His work in this field has resulted in multiple patents, tech demos, and research papers at major IEEE conferences. He has won many hackathons using a wide variety of technologies related to image recognition. He enjoys blogging about topics such as artificial intelligence, abstract mathematics, and cryptography. His blog has been visited by users in more than 200 countries, and he has been featured as a guest author in prominent tech magazines.Michael Beyeler is a PhD candidate in the department of computer science at the University of California, Irvine, where he is working on computational models of the brain as well as their integration into autonomous brain-inspired robots. His work on vision-based navigation, learning, and cognition has been presented at IEEE conferences and published in international journals. Currently, he is one of the main developers of CARLsim, an open source GPGPU spiking neural network simulator. This is his first technical book that, in contrast to his (or any) dissertation, might actually be read. Michael has professional programming experience in Python, C/C++, CUDA, MATLAB, and Android. Born and raised in Switzerland, he received a BSc degree in electrical engineering and information technology, as well as a MSc degree in biomedical engineering from ETH Zurich. When he is not nerding out on robots, he can be found on top of a snowy mountain, in front of a live band, or behind the piano.Table of ContentsSetting up OpenCVHandling Files, Cameras, and GUIsFiltering ImagesTracking Faces with Haar CascadesDetecting Foreground/Background Regions and DepthIntegrating with PygameGenerating Haar Cascades for Custom TargetsDetecting Edges and Applying Image FiltersCartoonizing an ImageDetecting and Tracking Different Body PartsExtracting Features from an ImageCreating a Panoramic ImageSeam CarvingDetecting Shapes and Segmenting an ImageObject TrackingObject RecognitionStereo Vision and 3D ReconstructionAugmented RealityFun with FiltersHand Gesture Recognition Using a Kinect Depth SensorFinding Objects via Feature Matching and Perspective Transforms3D Scene Reconstruction Using Structure from MotionTracking Visually Salient ObjectsLearning to Recognize Traffic SignsLearning to Recognize Emotions on FacesBibliography