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(CuviImage* srcImage, CuviROI roi, CuviPointValue2D** outputFeatures, Cuvi32s* maxFeatures, CuviFeaturesCriteria criteria, CuviStream* stream = NULL);

Parameters

Name	Type	Description
srcImage	CuviImage*	Input 8 bit Gray Scale Image.
roi	CuviROI	Desired region of interest of input image. The function will return features from only that region of the image
outputFeatures	CuviPointValue2D**	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	CuviFeaturesCriteria	A structure containing various parameters that affect feature selection behavior
stream	CuviStream*	GPU stream ID for execution

Image Type Support

Input	Output
8uC1	CuviPointValue2D

Samples

Error creating thumbnail: Unable to save thumbnail to destination Input Image

Error creating thumbnail: Unable to save thumbnail to destination Selected Features

Example

//Create an 8-bit Grays-scale CuviImage CuviImage *gpu_image = new CuviImage(size,img->depth,img->nChannels); //Populate the Image gpu_image->upload(img->imageData,img->widthStep); CuviPointValue2D *features; //Desired number of features int featureCount = 100; //Selecting complete image for the process CuviROI roi = cuviROI(0,0,img->width,img->height); //Setting Feature selection parameters CuviFeaturesCriteria criteria = cuviFeaturesCriteria(CUVI_FEATURES_HARRIS_PETER,0.006f,15,3,-2.0f); //Calling GoodFeaturesToTrack cuvi::computerVision::goodFeaturesToTrack(&gimg,roi,&features,&featureCount,criteria);