By Joseph Howse
Discover ways to seize video clips, control pictures, and tune gadgets with Python utilizing the OpenCV Library
* arrange OpenCV, its Python bindings, and non-compulsory Kinect drivers on home windows, Mac or Ubuntu
* Create an software that tracks and manipulates faces
* establish face areas utilizing basic colour photos and intensity images
Computer imaginative and prescient can succeed in shoppers in a number of contexts through webcams, digicam telephones and gaming sensors like Kinect. OpenCV's Python bindings may help builders meet those shopper calls for for purposes that trap photographs, switch their visual appeal and extract info from them, in a high-level language and in a standardized info layout that's interoperable with clinical libraries equivalent to NumPy and SciPy.
"OpenCV machine imaginative and prescient with Python" is a realistic, hands-on advisor that covers the basic projects of desktop vision—capturing, filtering and reading images—with step by step directions for writing either an software and reusable library classes.
"OpenCV computing device imaginative and prescient with Python" exhibits you ways to exploit the Python bindings for OpenCV. through following transparent and concise examples you are going to strengthen a working laptop or computer imaginative and prescient program that tracks faces in stay video and applies lighting tricks to them. when you've got continuously desired to study which model of those bindings to exploit, the best way to combine with cross-platform Kinect drivers and and the way to successfully approach photo facts with NumPy and SciPy then this publication is for you.
What you are going to study from this book
* set up OpenCV and similar software program reminiscent of Python, NumPy, SciPy, OpenNI, and SensorKinect—all on home windows, Mac or Ubuntu
* seize, exhibit, and keep photographs and real-time videos
* deal with window occasions and enter occasions utilizing OpenCV's HighGui module or Pygame
* comprehend OpenCV's picture structure and the way to accomplish effective operations on OpenCV photographs with NumPy and SciPy
* follow "curves" and different colour adjustments to simulate the glance of outdated images, videos or video games
* follow an impression in simple terms to edges in an image
* replica and resize segments of an image
* observe an impact purely to yes depths in a picture through the use of information from a intensity sensor comparable to Kinect
* song faces, eyes, noses and mouths through the use of prebuilt datasets
* tune arbitrary gadgets by way of growing unique datasets
A sensible, project-based educational for Python builders and hobbyists who are looking to start with laptop imaginative and prescient with OpenCV and Python.
Who this ebook is written for
OpenCV computing device imaginative and prescient with Python is written for Python builders who're new to computing device imaginative and prescient and wish a realistic advisor to educate them the necessities. a few knowing of picture facts (for instance, pixels and colour channels) will be worthy. At a minimal you'll want entry to one webcam. yes routines require extra like a moment webcam, a Microsoft Kinect or an OpenNI-compliant intensity sensor reminiscent of the Asus Xtion professional.
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Additional resources for OpenCV Computer Vision with Python
The number of cameras and their ordering is of course system-dependent. Unfortunately, OpenCV does not provide any means of querying the number of cameras or their properties. If an invalid index is used to construct a VideoCapture class, the VideoCapture class will not yield any frames; its read() method will return (false, None). The read() method is inappropriate when we need to synchronize a set of cameras or a multi-head camera (such as a stereo camera or a Kinect). Then, we use the grab() and retrieve() methods instead.
One use of channel mixing is to simulate some other, smaller color space inside RGB or BGR. By assigning equal values to any two channels, we can collapse part of the color space and create the impression that our palette is based on just two colors of light (blended additively) or two inks (blended subtractively). This type of effect can offer nostalgic value because early color films and early digital graphics had more limited palettes than digital graphics today. As examples, let's invent some notional color spaces that are reminiscent of Technicolor movies of the 1920s and CGA graphics of the 1980s.
This type of application covers a broad range of OpenCV's functionality and challenges us to create an efficient, effective implementation. Users would immediately notice flaws, such as a low frame rate or inaccurate tracking. To get the best results, we will try several approaches using conventional imaging and depth imaging. Specifically, our application will perform real-time facial merging. Given two streams of camera input (or, optionally, prerecorded video input), the application will superimpose faces from one stream atop faces in the other.