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());
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Parameters
| Name
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Type
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Description
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| src
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const CuviImage&
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Input 8 bit Gray Scale Image.
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| roi
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const CuviRect&
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Desired region of interest of input image. The function will return features from only that region of the image
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| output
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CuviPointValue2D<Cuvi32f,Cuvi32f>*&
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Output feature list that contains the coordinates & values of features/corners.
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| maxFeatures
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Cuvi32s&
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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
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| criteria
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const CuviFeaturesCriteria
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A structure containing various parameters that affect feature selection behavior
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| stream
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const CuviStream&
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GPU stream ID for execution
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Image Type Support
| Input
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Output
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| 8uC1
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CuviPointValue2D<Cuvi32f,Cuvi32f>
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=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);
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