OpenCV: Computer Vision Projects with Python

English | 2016 | ISBN: 978-1787125490 | 558 Pages | PDF, EPUB, AZW3 | 36 MB

Get savvy with OpenCV and actualize cool computer vision applications
OpenCV 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 OpenCV3’s 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, we’ll 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 Howse
• OpenCV with Python By Example by Prateek Joshi
• OpenCV with Python Blueprints by Michael Beyeler

What You Will Learn

• Install OpenCV and related software such as Python, NumPy, SciPy, OpenNI, and SensorKinect – all on Windows, Mac or Ubuntu
• Apply “curves” and other color transformations to simulate the look of old photos, movies, or video games
• Apply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like image
• Recognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor
• Reconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniques
• Detect 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 SVMs
• Strengthen your OpenCV2 skills and learn how to use new OpenCV3 features