CUVI by Example

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:

• 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

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"> 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)); } CuviFilter* Gauss = Cuvi_Builtin_Filters::Gaussian(3,0.7f); //3x3 Gaussian Filter with Standard Deviation 0.7 void main() { //Read a Video Frame 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* gimg2 = new CuviImage(Width,Height,GetOpenCVPitch(Width,Height,8,1),8,1); CuviROI roi = cuviROI(0,0,Width,Height); CuviPointValue2D *features1, *features2; int feature_count = 0; do { //Read Frame gFrame->upload(frame->imageData); cuvi::colorConvert(gFrame,gimg1); //Read Next Frame gFrame->upload(frame->imageData); cuvi::colorConvert(gFrame,gimg2); feature_count = RequestedFeatures; //Reset feature count to original if(AdjustImage){ cuvi::colorOperations::adjust(gimg1); cuvi::colorOperations::adjust(gimg2); } if(SmoothBeforeSelecting){ //Apply Gaussian Smoothing Filter On Both The Images cuvi::imageFiltering::imageFilter(gimg1,roi,Gauss); cuvi::imageFiltering::imageFilter(gimg2,roi,Gauss); } //Call A Feature Detector ( KLT | HARRIS | PETER ) cuvi::computerVision::goodFeaturesToTrack(gimg1,roi,features1,&feature_count,CUVI_FEATURES_HARRIS,FeatureQuality,FeatureMinDistance,3,k); //Track Features Using KLT Method 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(); }