Opencv computer vision projects with python

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Computer Vision Projects with OpenCV and Python 3, published by Packt

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PacktPublishing/Computer-Vision-Projects-with-OpenCV-and-Python-3

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README.md

Computer Vision Projects with OpenCV and Python 3

This is the code repository for Computer Vision Projects with OpenCV and Python 3, published by Packt.

Six end-to-end projects built using machine learning with OpenCV, Python, and TensorFlow

Python is the ideal programming language for rapidly prototyping and developing production-grade codes for image processing and Computer Vision with its robust syntax and wealth of powerful libraries. This book will help you design and develop production-grade Computer Vision projects tackling real-world problems.

This book covers the following exciting features: Install and run major Computer Vision packages within Python Apply powerful support vector machines for simple digit classification Understand deep learning with TensorFlow Build a deep learning classifier for general images Use LSTMs for automated image captioning Read text from real-world images Extract human pose data from images

If you feel this book is for you, get your copy today!

Instructions and Navigations

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

testfile = 'test_images/dog.jpeg' figure() imshow(imread(testfile)) 

Following is what you need for this book: Python programmers and machine learning developers who wish to build exciting Computer Vision projects using the power of machine learning and OpenCV will find this book useful. The only prerequisite for this book is that you should have a sound knowledge of Python programming.

With the following software and hardware list you can run all code files present in the book (Chapter 1-7).

Software and Hardware List

Chapter	Software required	OS required
1-7	Anaconda, Python 3.x, Jupyter Notebook	Windows, Mac OS X, and Linux (Any)

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

Matthew Rever Matthew Rever is an image processing and computer vision engineer at a major national laboratory. He has years of experience in automating the analysis of complex scientific data, as well as in controlling sophisticated instruments. He has applied computer vision technology to save a great many hours of valuable human labor. He is also enthusiastic about making the latest developments in computer vision accessible to developers of all backgrounds.

Click here if you have any feedback or suggestions.

If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.

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Computer Vision Projects with OpenCV and Python 3, published by Packt

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PacktPublishing/OpenCV-Computer-Vision-Projects-with-Python

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README.md

OpenCV Computer Vision Projects with Python

This is the code repository for OpenCV-Computer-Vision-Projects-with-Python, published by Packt. It contains all the necessary code files.

• 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

If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.

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OpenCV: Computer Vision Projects with Python

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Book description

Get savvy with OpenCV and actualize cool computer vision applications

This learning path is for someone who has a working knowledge of Python and wants to try out OpenCV. This Learning Path will take you from a beginner to an expert in computer vision applications using OpenCV. OpenCV’s application are humongous and this Learning Path is the best resource to get yourself acquainted thoroughly with OpenCV.

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:

This course aims to create a smooth learning path that will teach you how to get started with will learn how to get started with OpenCV and OpenCV 3’s Python API, and develop superb computer vision applications. Through this comprehensive course, you’ll learn to create computer vision applications from scratch to finish and more!.

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OpenCV: Computer Vision Projects with Python

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

Michael Beyeler is a postdoctoral fellow in neuroengineering and data science at the University of Washington, where he is working on computational models of bionic vision in order to improve the perceptual experience of blind patients implanted with a retinal prosthesis (bionic eye).His work lies at the intersection of neuroscience, computer engineering, computer vision, and machine learning. He is also an active contributor to several open source software projects, and has professional programming experience in Python, C/C++, CUDA, MATLAB, and Android. Michael received a PhD in computer science from the University of California, Irvine, and an MSc in biomedical engineering and a BSc in electrical engineering from ETH Zurich, Switzerland. Browse publications by this author

Prateek Joshi is the founder of Plutoshift and a published author of 9 books on Artificial Intelligence. He has been featured on Forbes 30 Under 30, NBC, Bloomberg, CNBC, TechCrunch, and The Business Journals. He has been an invited speaker at conferences such as TEDx, Global Big Data Conference, Machine Learning Developers Conference, and Silicon Valley Deep Learning. Apart from Artificial Intelligence, some of the topics that excite him are number theory, cryptography, and quantum computing. His greater goal is to make Artificial Intelligence accessible to everyone so that it can impact billions of people around the world. Browse publications by this author

Joseph Howse lives in a Canadian fishing village, where he chats with his cats, crafts his books, and nurtures an orchard of hardy fruit trees. He is President of Nummist Media Corporation, which exists to support his books and to provide mentoring and consulting services, with a specialty in computer vision. On average, in 2015-2022, Joseph has written 1.4 new books or new editions per year for Packt. He also writes fiction, including an upcoming novel about the lives of a group of young people in the last days of the Soviet Union. Browse publications by this author

It is annoying/counterproductive, that Packt does not have space e.g. a forum or email to ask the authors questions.

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