Opencv – Disable Auto Focus in Video Input Library or OpenCV

The Hercules cameras are UVC compliant, so they should work with the DirectShow Interface IAMCameraControl. You can set the focus to a specific value, and use the flags to set that you do not want it to be automatic. You can use IAMCameraControl::Get to poll the current state, because not all cameras do support turning off the focus.

IAMCameraControl *pCameraControl; 
HRESULT hr; 
hr = pFilter->QueryInterface(IID_IAMCameraControl, (void **)&pCameraControl); 
if (hr == S_OK) {
  long defaultFocusValue;
  hr = pCameraControl->GetRange(CameraControl_Focus,
                                NULL, // min
                                NULL, // max
                                NULL, // minstep
                                &defaultFocusValue, // default
                                NULL); // capflags
  hr = pCameraControl->Set(CameraControl_Focus, // property
                           defaultFocusValue, // value
                           CameraControl_Flags_Manual); 
} 

Focus has a range which is defined by each camera separately, so you should query it as shown to find the default value and the min, max if you want. In this example the pFilter is a pointer to the input filter that you have from DirectShow. You can get it by enumerating the devices and finding the one you want.

Can the screenshot or icon be transformed (scaled, rotated, skewed ...)? There are quite a few methods on top of my head that could possibly help you:

• Simple euclidean distance as mentioned by @carlosdc (doesn't work with transformed images and you need a threshold).
• (Normalized) Cross Correlation - a simple metrics which you can use for comparison of image areas. It's more robust than the simple euclidean distance but doesn't work on transformed images and you will again need a threshold.
• Histogram comparison - if you use normalized histograms, this method works well and is not affected by affine transforms. The problem is determining the correct threshold. It is also very sensitive to color changes (brightness, contrast etc.). You can combine it with the previous two.
• Detectors of salient points/areas - such as MSER (Maximally Stable Extremal Regions), SURF or SIFT. These are very robust algorithms and they might be too complicated for your simple task. Good thing is that you do not have to have an exact area with only one icon, these detectors are powerful enough to find the right match. A nice evaluation of these methods is in this paper: Local invariant feature detectors: a survey.

Most of these are already implemented in OpenCV - see for example the cvMatchTemplate method (uses histogram matching): http://dasl.mem.drexel.edu/~noahKuntz/openCVTut6.html. The salient point/area detectors are also available - see OpenCV Feature Detection.