Difference between revisions of "CUVI by Example"

From CUVI Wiki
Line 91: Line 91:


//You can also plot the tracked features on the screen
//You can also plot the tracked features on the screen
 
 
}
}


//Freeing GPU Memory
//Freeing GPU Memory

Revision as of 19:10, 30 April 2012

CUVI library comes with all the image processing essentials that can be used to build countless applications. For example the Computer Vision module of CUVI can be used for motion and intrusion detection in a live video stream and tracking an object of interest throughout series of cameras installed in a premises. The processing pipeline for motion detection goes as follows:

  • Read a frame from the camera stream
  • Select Strong Features in that Frame using CUVI
  • Read next frame
  • Track features of first frame in the second frame using CUVI
  • Set alarm if motion is detected

The CUVI functions used in this example are goodFeaturesToTrack() and trackFeatures(). For simplicity we have removed the I/O part on host side from the code

#include <cuvi.h>

static const int width = 640;	//Width of video frame
static const int height = 480;	//Height of video frame
static const int requestedFeatures = 150; //Number of features to look for
static const float featureQuality = 0.006f; //Quality of a feature
static const int featureMinDistance = 3;	 //Minimum distance between 2 features
static const float k = -2.0f;	//k for Harris Corner detector
static const int pyramidLevels = 3; //Level Of Scaling
static const CuviSize trackingWindow = cuviSize(30,30);	//Size of tracking window
static const float residue = 20.0f; //Absolute Difference Between Original Location Window & Tracked Location Window
static const int iterations = 10; //Maximum number of iterations before a feature is found
static const bool smoothBeforeSelecting = false; //Smooth Image Before Feature Selection & Tracking
static const bool adjustImage = false;	//Adjust Image Light Before Feature Selection
static const float movementThreshold = 0.33f; //Mark as motion if a feature moves 0.33 Pixels


//Checks if the feature has moved from is original location.
//It can be used in intrusion detection and the sensitivity can be set using 'threshold' parameter
bool featureHasMoved(CuviPointValue2D point1, CuviPointValue2D point2, float threshold){
        if(point2.val != 0.0f)	return false;
	return ((fabsf(point1.x - point2.x)>threshold) || (fabsf(point1.y - point2.y)>threshold));
}

//Smoothing filter  
CuviFilter* Gauss = Cuvi_Builtin_Filters::Gaussian(3,0.7f); //3x3 Gaussian Filter with Standard Deviation 0.7

void main()
{
        //Buffer Images on GPU
	CuviImage* gFrame = new CuviImage(width,height,pitch,8,3);
	CuviImage* gimg1 = new CuviImage(Width,Height,GetOpenCVPitch(Width,Height,8,1),8,1);
	CuviImage* gimg2 = new CuviImage(Width,Height,GetOpenCVPitch(Width,Height,8,1),8,1);

        //Region of Interest in the video frame
	CuviROI roi = cuviROI(0,0,width,height);

	CuviPointValue2D *features1, *features2;

	int feature_count = 0;

	do
	{
                //Read a Video Frame and populate GPU image with it
		gFrame->upload(frame->imageData);
		
                //Converting to Gray Image for computations
                cuvi::colorOperations::RGB2Gray(gFrame,gimg1);

		//Do the same with next, adjacent frame
		gFrame->upload(frame->imageData);
		cuvi::colorConvert(gFrame,gimg2);

		feature_count = RequestedFeatures; //Reset feature count to original

                //Use this option if the adjacent frames are lightening sensitive
		if(AdjustImage){
			cuvi::colorOperations::adjust(gimg1);
			cuvi::colorOperations::adjust(gimg2);
		}

                //Use this option if the images contain fair amount of noise
		if(SmoothBeforeSelecting){
			//Apply Gaussian Smoothing Filter On Both The Images
			cuvi::imageFiltering::imageFilter(gimg1,roi,Gauss);
			cuvi::imageFiltering::imageFilter(gimg2,roi,Gauss);
		}
		
//Call any Feature Detector on first Frame( KLT | HARRIS | PETER )
cuvi::computerVision::goodFeaturesToTrack(gimg1,roi,features1,&feature_count,CUVI_FEATURES_HARRIS,FeatureQuality,FeatureMinDistance,3,k);
		
//Track Features Using of Frame#1 onto Frame#2 using KLT Tracker
cuvi::trackFeatures(gimg1,gimg2,features1,features2,feature_count,pyramidLevels,trackingWindow,residue,iterations);


//At this point you can indetify whether the selected features in frame one moved in frame two or not
for(int i=0; i<feature_count; i++)
      if(FeatureHasMoved(features1[i],features2[i],MovementThreshold)) //Plot Only If The Feature Has Moved From Its Location

//You can also plot the tracked features on the screen
	
	}

//Freeing GPU Memory
gFrame->release();
gimg1->release();
gimg2->release();

}