Difference between revisions of "Function:GoodFeaturesToTrack"

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__NOTOC__
__NOTOC__
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
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===
====Function====
{|
{|
|style="font-size:150%;"|
|style="font-size:100%;"|
<syntaxhighlight lang="cpp">
<syntaxhighlight lang="cpp">
CuviStatus goodFeaturesToTrack(CuviImage* srcImage
CuviStatus goodFeaturesToTrack(const CuviImage& src,
                               CuviROI roi,
                               const CuviRect& roi,
                               CuviPointValue2D** outputFeatures,
                               CuviPointValue2D<Cuvi32f,Cuvi32f>*& output,
                               Cuvi32s* maxFeatures,
                               Cuvi32s& maxFeatures,
                               CuviFeaturesCriteria criteria,
                               const CuviFeaturesCriteria criteria = CuviFeaturesCriteria(),
                               CuviStream* stream = NULL);
                               const CuviStream& stream = CuviStream());
</syntaxhighlight>
</syntaxhighlight>
|}
|}


===Parameters===
====Parameters====
{|
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{|class="wikitable"
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! Description
! Description
|-
|-
| srcImage
| src
| CuviImage*
| const CuviImage&
| Input 8 bit Gray Scale Image.
| Input 8 bit Gray Scale Image.
|-
|-
| roi
| roi
| CuviROI
| const CuviRect&
| Desired region of interest of input image. The function will return features from only that region of the image
| Desired region of interest of input image. The function will return features from only that region of the image
|-
|-
| outputFeatures
| output
| CuviPointValue2D**
| CuviPointValue2D<Cuvi32f,Cuvi32f>*&
| Output feature list that contains the coordinates & values of features/corners.
| Output feature list that contains the coordinates & values of features/corners.
|-
|-
| maxFeatures
| maxFeatures
| Cuvi32s*
| 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
| 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
| criteria
| CuviFeaturesCriteria
| const CuviFeaturesCriteria
| Output image doesn’t use the values above this point from the intensity map
| A structure containing various parameters that affect feature selection behavior
|-
|-
| stream
| stream
| CuviStream*
| const CuviStream&
| Output image doesn’t use the values above this point from the intensity map
| GPU stream ID for execution
|}
|}
 
====Image Type Support====
{|
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{| class="wikitable"
|-
! Input
! Output
|-
| 8uC1
| CuviPointValue2D<Cuvi32f,Cuvi32f>
|}
|}
 
====Samples===
[[File:Window.jpg|none|frame|Input Image]]
<br/>
[[File:Features.jpg|none|frame| Selected Features]]
<br/>
 
====Example====
{|
|style="font-size:100%;"|
<syntaxhighlight lang="cpp">
 
//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);
</syntaxhighlight>
|}
|}

Latest revision as of 22:13, 18 October 2022

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

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 = 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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