First let me open by saying projector-camera calibration is NOT EASY. But it's technically not complicated too.
It is however, an amalgamation of optimizations that accrue and accumulate error with each step, so that the end product is not far from a random guess.
So 3D reconstructions I was able to get from my calibrated pro-cam were just a distorted mess of points.
Use markings to determine the foreground and background color histograms (from the superpixels under the markings)
Setup a graph with a straightforward energy model: Smoothness term = K-L-Div between superpix histogram and neighbor superpix histogram, and Match term = inf if marked as BG or FG, or K-L-Div between SuperPix histogram and FG and BG.
To find neighbors I've used Delaunay tessellation (from scipy.spatial), for simplicity. But a full neighbor finding could be implemented by looking at all the neighbors on the superpix's boundary.
I spent an entire day getting OpenGL 4 to display data from a VAO with VBOs so I thought I'd share the results with you guys, save you some pain.
I'm using the excellent GL wrappers from Qt, and in particular QGLShaderProgram.
This is pretty straightforward, but the thing to remember is that OpenGL is looking for the vertices/other elements (color? tex coords?) to come from some bound GL buffer or from the host. So if your app is not working and nothing appears on screen, just make sure GL has a bound buffer and the shader locations match up and consistent (see the const int I have on the class here).
Using Poppler, of course!
Poppler is a very useful tool for handling PDF, so I've discovered lately. Having tried both muPDF and ImageMagick's Magick++ and failed, Poppler stepped up to the challenge and paid off.
So here's a small example of how work the API (with OpenCV, naturally):