I love opencv for many reasons, but above all the reason that has me most taken is the extensive documentation provided (I use the python wrapper which is brilliant!). Looking through the numerous basic examples shipped out with the distribution, and given that image alignment and dense matching is a central theme running through my research, it occurred to me that sharing some of what it has to offer would be a good idea.
As such, I’ve thrown together a web based implementation of their dense stereo matcher using Python’s convenient CGI filetype. There’s lots wrong with it, it’s set up to mainly deal with the image sets from the Middlebury examples listed on the page for the moment. I have done it with my own images too which will work provided they’re the same size, I haven’t quite dealt with resizing images and the sort.
The inputs are two URLs to images (.jpg or .png) hosted somewhere, the output after processing is a disparity map generated using the block matching algorithm. It’s being hosted at my website here, and an example is presented below.
Note: It takes about 15 seconds for the disparity map to show
Note2: Now defunct, planning to make into a heroku app!