OpenCV and Computer Vision, where do we stand now?

Question

I want to do a project involving Computer Vision. Mostly object detection/identification. After some research, I keep coming back to OpenCV. But all of the tutorials are from 2008 (I guess it was big for a bit then). It doesn't compile in Python on the mac apparently. I'm using the C++ framework right out of Xcode, but none of the tutorials work as they're outdated and the documentation sucks from what I can parse.

Is there a better solution for what I'm doing, and does anyone have any suggestions as to learning how to to use OpenCV?

Thanks

Answer 1

I have had similar problems getting started with OpenCV and from my experience this is actually the biggest hurdle to learning it. Here is what worked for me:

1. This book: "OpenCV 2 Computer Vision Application Programming Cookbook." It's the most up-to-date book and has examples on how to solve different Computer Vision problems (You can see the table of contents on Amazon with "Look Inside!"). It really helped ease me into OpenCV and get comfortable with how the library works.

2. Like have others have said, the samples are very helpful. For things that the book skips or covers only briefly you can usually find more detailed examples when looking through the samples. You can also find different ways of solving the same problem between the book and the samples. For example, for finding keypoints/features, the book shows an example using FAST features:

   vector<KeyPoint> keypoints;
FastFeatureDetector fast(40);
fast.detect(image, keypoints);

   But in the samples you will find a much more flexible way (if you want to have the option of choosing which keypoint detection algorithm to use):

   vector<KeyPoint> keypoints;
Ptr<FeatureDetector> featureDetector = FeatureDetector::create("FAST");
featureDetector->detect(image, keypoints);

From my experience things eventually start to click and for more specific questions you start finding up-to-date information on blogs or right here on StackOverflow.