Difference between revisions of "CUVI by Example"

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<p>CUVI library comes with a lot of image processing building blocks that can be used to build countless applications. For example the Computer Vision module of CUVI can be used for motion detection in a live video stream, intrusion detection and tracking an object of interest throughout a video stream or series of cameras. The processing pipeline for motion detection goes as follows:</p>
<p>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:</p>
*Read a frame from the camera stream
*Read a frame from the camera stream
*Select Strong Features in that Frame using CUVI
*Select Strong Features in that Frame using CUVI
*Read next frame
*Read next frame
*Track features of first frame in the second frame using CUVI
*Track features of first frame in the second frame using CUVI
*Set alarm if motion is detected


<p>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</p>
<p>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</p>
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}
}


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


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


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


CuviPointValue2D *features1, *features2;
CuviPointValue2D *features1, *features2;
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do
do
{
{
                 //Read Frame
                 //Read a Video Frame and populate GPU image with it
gFrame->upload(frame->imageData);
gFrame->upload(frame->imageData);
cuvi::colorConvert(gFrame,gimg1);
                //Converting to Gray Image for computations
                cuvi::colorOperations::RGB2Gray(gFrame,gimg1);


//Read Next Frame
//Do the same with next, adjacent frame
gFrame->upload(frame->imageData);
gFrame->upload(frame->imageData);
cuvi::colorConvert(gFrame,gimg2);
cuvi::colorConvert(gFrame,gimg2);
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feature_count = RequestedFeatures; //Reset feature count to original
feature_count = RequestedFeatures; //Reset feature count to original


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


 
                //Use this option if the images contain fair amount of noise
if(SmoothBeforeSelecting){
if(SmoothBeforeSelecting){
//Apply Gaussian Smoothing Filter On Both The Images
//Apply Gaussian Smoothing Filter On Both The Images
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}
}
//Call A Feature Detector ( KLT | HARRIS | PETER )
//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);
cuvi::computerVision::goodFeaturesToTrack(gimg1,roi,features1,&feature_count,CUVI_FEATURES_HARRIS,FeatureQuality,FeatureMinDistance,3,k);
//Track Features Using KLT Method
//Track Features Using of Frame#1 onto Frame#2 using KLT Tracker
cuvi::trackFeatures(gimg1,gimg2,features1,features2,feature_count,PyramidLevels,TrackingWindow,Residue,Iterations);
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
//At this point you can indetify whether the selected features in frame one moved in frame two or not

Revision as of 19:08, 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

<syntaxhighlight lang="c">

  1. 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();

}