Month: February 2015

A busy month of OpenCV contracting for a number of clients, including some work in areas of OpenCV I’ve not used much, if at all, before (non-chargeable, of course – I only charge for productive time).

I am now more familiar than I ever thought I’d be with the HoughLines(P) and HoughCircles functions – the former of which is more complex than it first seems.  Like many things in computer vision, it takes some coaxing to get good results, and even more coaxing to get really robust results across a range of ‘real live’ images in the problem domain.

I have also worked a lot this month with the whole ‘camera calibration’ suite of functions, and then followed that up by gaining experience with the ‘project image points into the plane’ routines, which can lead to some interesting ‘augmented reality’ applications.  However, in my case, I’ve used them to simply determine exactly where (in the 2D image) a specific point in the 3D space would appear.  It works very well, and I have a project lined up ready to put this into action.

I’ve revisited one of my ‘favourite’ (i.e. most used) parts of the library:  contour finding, and associated pre- and post-processing, but this time all from Python.

During the last few days, I’ve started looking at 2D pose estimation:  specifically in this case, trying to determine the location of a known set of 2D points in a target image, given possible translation, rotation and scale invariance.  Not finished with that one, yet.

Last (but not least – this isn’t going to go away) I’ve been making an effort to learn Git.  I was pleased to find this simple guide, which at least let me get on with my work while I learn the rest.

I’ve spent a month or so trying to make an effort to learn Python, mostly by forcing myself to do any new ‘prototype’ vision / OpenCV work in the language.  This has cost me some money – I only charge for ‘productive’ time, not ‘learning’ time, and at times the temptation to go back to ‘nice familiar C++’ has been great.  But I’ve made good progress with Python, and I’m glad I’ve stuck at it.  Apart from anything else, the language itself isn’t hard to pick up.

The pros and cons from a computer vision perspective are roughly as expected.  It can be slower to run, but depending on how the code is written, it’s not a big difference.  Once ‘inside’ the OpenCV functions, the speed appears to be about the same (as you’d expect:  it’s just a wrapper for the same code), but any code run actually in Python needs careful planning, and if large amounts of compution were going to be done, C++ would no doubt still be the best bet.

But anything it lacks in runtime speed, it certainly makes up for in speed of development.  As a prototyping language, I think I’m already more productive in Python than C++ (and that’s after 20+ years of C++, and a month of part-time Python).  There will always be more to learn, of course, but I think I’m at the point where the learning curve is beginning to get less steep.