Name: Learning OpenCV
ISBN: 9780596516130

Learning OpenCV

"This library is useful for practitioners, and is an excellent tool for those entering the field: it is a set of computer vision algorithms that work as advertised." -William T. Freeman, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to "see" and make decisions based on that data. Computer vision is everywhere-in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It stitches Google maps and Google Earth together, checks the pixels on LCD screens, and makes sure the stitches in your shirt are sewn properly. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time. Learning OpenCV will teach any developer or hobbyist to use the framework quickly with the help of hands-on exercises in each chapter.

What Hacker News says

joshvmNov 2017

The "official" O'Reilly books are pretty good, Learning OpenCV by Kaehler and Bradski (I say official because Gary Bradski was one of the original maintainers). Make sure you get the new one for OpenCV 3. They cover a lot of the algorithms behind the code which is nice.

There's also https://www.pyimagesearch.com/ which is great. It's ultimately a hook to get you to buy the course, and I think the teaching style may put off some people, but everything is thorouhgly explained and there's a lot of cool mini projects.

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mendezaJun 2017

Here is a guide I have developed over 6 years when I dove into copmuter vision around 2011. My path has been self taught until recently I took a graduate course.

I started from wanting to develop AR apps during my undergrad, Here are the best resources I have found to date:

Computer Vision is very theoretical and experimental, so the more hands on, the better! My approach has been to go top-down, overview the landscape and slowly progress deeper.

Begin with the best library for CV in my opinion: OpenCV. The tutorials are amazing!

Python tutorials: http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_tutorial...

C++ tutorials: http://docs.opencv.org/3.0-beta/doc/tutorials/tutorials.html

Immerse yourself in these and build any apps you think of!

Then go into: pyimagesearch tutorials http://www.pyimagesearch.com/ and aishack.in http://aishack.in/,

tons of great tutorials to learn different topics of vision with coding walkthroughs. Understand the examples and rewrite applications.

Then Dive Deep:

Get the new OpenCV3 book, a nice deep overview of many topics in computer vision. https://www.amazon.com/Learning-OpenCV-Computer-Vision-Libra...

And watch this course on youtube:

https://www.youtube.com/watch?v=skaQfPQFSyY&list=PL4B3F8D4A5...

I feel like then, you will have so much exposure that when you dive into formal classes and textbooks, you will really understand and be enlightened.

This was the general way I learned computer vision, and recently I completed a cv internship for nanit.com . I was not hired for my formal knowledge, but they were impressed by all the various projects ive done and knowledge I had on many vision topics.

I also recently took a formal course of vision at Cornell: http://www.cs.cornell.edu/courses/cs5670/2017sp/

All the assignments have starter code in python and opencv. This was an amazing class as it dove deep into 3D computer vision, which is so relevant to augmented reality!

Also, here is a link opencv examples for iOS: https://github.com/Itseez/opencv_for_ios_book_samples

here are links for opencv example for Android: https://web.stanford.edu/class/ee368/Android/

Hope this helps! Shoot me a dm if you or anyone has more questions!

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lovelearningDec 2016

I'd suggest one of the 'Learning OpenCV' editions for that. They (or atleast the first edition which is 8 or 9 years old now) cover a lot of OpenCV algorithms and more importantly, the underlying intuition/math. It's the kind of book you need to make time for and study, but will make you feel good when you do :D. There's a 3rd edition [1] now, which I haven't read but has one of the original authors as a co-author and the TOC seems as comprehensive as the 1st edition.

If you develop a taste for CV theory and math, then I suggest Richard Szeliski's [2] book. It's very readable.

Neither book covers any of the latest deep learning stuff. You have to look elsewhere for that.

[1] http://shop.oreilly.com/product/0636920044765.do

[2] http://szeliski.org/Book/

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joshvmMar 2016

A good follow on from this is the Learning OpenCV book (O'Reilly), written by a couple of the lead developers. It goes into detail on the mathematics, but it's not heavy or verbose at all. I found it far more useful than a lot of introductory image processing books simply for its theoretical content.

Don't forget scikit-image and scipy.ndimage too.

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femtoMar 2013

Have you looked at the book "Learning OpenCV", by Bradski and Kaehler, published by O'Reilly? I haven't done much work with OpenCV, but I have read the entire book and it seemed to be a good introduction. A companion collection of examples is available online, consisting of a set of programs that can be compiled and used as a starting point for further hacking. A Google search on the title, happens to yield a link to the full text.

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ahelwerMar 2012

Before this post inspires people to go on a crazy I-can-do-computer-vision-the-hard-way binge and impulse-buy O'Reilly's (excellent) "Learning OpenCV", the second edition is being released sometime this month so you may want to hold off:

http://shop.oreilly.com/product/0636920022497.do

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