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1) The size of the good matches is good start point. 2) Typically > 7-8 good matches means that you found the object.

The implementation of train method for Brute Force Matcher is empty. It only makes sense for other matcher where you can train in advance without query descriptors.

Try this link: link It is really helpful for CascadeClassifier.

I supposed that createOptFlow(optFlow, useCuda); is the same function as in the super_resolution.cpp example. Did you try just like this to be sure that there is no mismatch:

Ptr<SuperResolution> superRes = createSuperResolution_BTVL1();
Ptr<DenseOpticalFlowExt> of = cv::superres::createOptFlow_DualTVL1();
superRes->set("opticalFlow", of);

If it still do not work, can you provide the whole error message (access conflict)?

String face_cascade_name = "C:\\haarcascade_frontalface_alt.xml";

is working fine on my side with the same configuration.

Did you try:

cout << "Point(x,y)=" << contours[i][j].x << "," << contours[i][j].y << endl;

I gave this code example because I wondered if there are other errors but I did not know that this algorithm cannot do real time processing as I wrote it. I saw it afterwards on this forum. However I wonder if other algorithms can be used for Real Time processing. And what is a reasonable maximum resolution for it?

Hi, I have read few other questions about Super Resolution and Video on this forum. But I did not find any tip about real time processing with super resolution. I wanted to test with a webcam but it seems that the algorithms, at least the BTV SuperResolution algorithm, need too many resources to be achieved real time and are supposed to have small input size.

1. Is there a new alternative for real time processing?
2. I tried the approach below but did not succeed. Did I make a programming mistake?

Code:

Mat newRawImage;
Ptr<SuperResolution> superRes;
Ptr<DenseOpticalFlowExt> of;
Ptr<FrameSource> frameSource;

And I call the following part in an infinite loop of a thread and use newRawImage:

if (of.empty())
{
  of = createOptFlow_DualTVL1_OCL();
}
if (!of.empty())
{
   superRes = createSuperResolution_BTVL1_OCL();
   superRes->set("opticalFlow", ext->of);
   superRes->set("scale", 4);
   superRes->set("iterations", 4);
   superRes->set("temporalAreaRadius", 4);
   if (frameSource.empty())
   {
     frameSource = createFrameSource_Camera(deviceId);
     superRes->setInput(frameSource);
   }
   cv::ocl::oclMat result_;
   superRes->nextFrame(result_);
   ocl::finish();
   if (!result_.empty())
   {
      result_.download(newRawImage);
   }
}