OpenCV: Computer Vision Projects with Python

More Information
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
About

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:

Features
  • Use OpenCV's Python bindings to capture video, manipulate images, and track objects
  • Learn about the different functions of OpenCV and their actual implementations.
  • Develop a series of intermediate to advanced projects using OpenCV and Python
Page Count 558
Course Length 16 hours 44 minutes
ISBN 9781787125490
Date Of Publication 23 Oct 2016

Authors

Joseph Howse

Joseph Howse lives in a Canadian fishing village with four cats; the cats like fish, but they prefer chicken.

Joseph provides computer vision expertise through his company, Nummist Media. His books include OpenCV 4 for Secret Agents, OpenCV 3 Blueprints, Android Application Programming with OpenCV 3, iOS Application Development with OpenCV 3, Learning OpenCV 3 Computer Vision with Python, and Python Game Programming by Example, published by Packt.

Prateek Joshi

Prateek Joshi is an artificial intelligence researcher, an author of several books, and a TEDx speaker. He has been featured in Forbes 30 Under 30, CNBC, TechCrunch, Silicon Valley Business Journal, and many more publications. He is the founder of Pluto AI, a venturefunded Silicon Valley start-up building an intelligence platform for water facilities. He graduated from the University of Southern California with a Master's degree specializing in Artificial Intelligence. He has previously worked at NVIDIA and Microsoft Research.

Michael Beyeler

Michael Beyeler is a Postdoctoral Fellow at the University of Washington in Seattle. His work lies at the intersection of neuroscience, computer vision, and machine learning. Michael is the author of two Packt books: OpenCV with Python Blueprints (2015) and Machine Learning for OpenCV (2017). He is an active contributor to several open-source software projects and has professional programming experience in Python, C/C++, CUDA, MATLAB, and Android. His technical blog can be found at www.askaswiss.com.