Function:GoodFeaturesToTrack

Detecting interesting parts of an image is an important step in most computer vision applications. Itserves as the first low-level processing step in applications like image segmentation, object recognition, object tracking and motion estimation. CUVI offers feature detectors including KLT, Harris and Peter in a single function

Function

CuviStatus goodFeaturesToTrack(const CuviImage& src, const CuviRect& roi, CuviPointValue2D<Cuvi32f,Cuvi32f>*& output, Cuvi32s& maxFeatures, const CuviFeaturesCriteria criteria = CuviFeaturesCriteria(), const CuviStream& stream = CuviStream());

Parameters

Name	Type	Description
src	const CuviImage&	Input 8 bit Gray Scale Image.
roi	const CuviRect&	Desired region of interest of input image. The function will return features from only that region of the image
output	CuviPointValue2D<Cuvi32f,Cuvi32f>*&	Output feature list that contains the coordinates & values of features/corners.
maxFeatures	Cuvi32s&	Desired number of features. If the total features detected in the image or ROI are less than maxFeatures its value is updated to that number
criteria	const CuviFeaturesCriteria	A structure containing various parameters that affect feature selection behavior
stream	const CuviStream&	GPU stream ID for execution

Image Type Support

Input	Output
8uC1	CuviPointValue2D<Cuvi32f,Cuvi32f>

Samples

Input Image

Selected Features

Example

//Create an 8-bit Grays-scale CuviImage CuviImage gpu_image = cuvi::io::loadImage(path,CUVI_LOAD_IMAGE_GRAYSCALE); CuviPointValue2D<Cuvi32f,Cuvi32f> *features; //Desired number of features int featureCount = 100; //Selecting complete image for the process CuviRect roi(0,0,img.width(),img.height()); //Setting Feature selection parameters CuviFeaturesCriteria criteria(CUVI_FEATURES_HARRIS_PETER,0.006f,15,3,-2.0f); //Calling GoodFeaturesToTrack cuvi::computerVision::goodFeaturesToTrack(gimg,roi,features,featureCount,criteria);