OpenCV  3.3.0-rc
Open Source Computer Vision
Modules | Classes | Functions

Modules

 Color space processing
 
 Histogram Calculation
 
 Hough Transform
 
 Feature Detection
 

Classes

class  cv::cuda::CannyEdgeDetector
 Base class for Canny Edge Detector. : More...
 
class  cv::cuda::TemplateMatching
 Base class for Template Matching. : More...
 

Functions

void cv::cuda::bilateralFilter (InputArray src, OutputArray dst, int kernel_size, float sigma_color, float sigma_spatial, int borderMode=BORDER_DEFAULT, Stream &stream=Stream::Null())
 Performs bilateral filtering of passed image. More...
 
void cv::cuda::blendLinear (InputArray img1, InputArray img2, InputArray weights1, InputArray weights2, OutputArray result, Stream &stream=Stream::Null())
 Performs linear blending of two images. More...
 
Ptr< CannyEdgeDetectorcv::cuda::createCannyEdgeDetector (double low_thresh, double high_thresh, int apperture_size=3, bool L2gradient=false)
 Creates implementation for cuda::CannyEdgeDetector . More...
 
Ptr< TemplateMatchingcv::cuda::createTemplateMatching (int srcType, int method, Size user_block_size=Size())
 Creates implementation for cuda::TemplateMatching . More...
 
void cv::cuda::meanShiftFiltering (InputArray src, OutputArray dst, int sp, int sr, TermCriteria criteria=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 5, 1), Stream &stream=Stream::Null())
 Performs mean-shift filtering for each point of the source image. More...
 
void cv::cuda::meanShiftProc (InputArray src, OutputArray dstr, OutputArray dstsp, int sp, int sr, TermCriteria criteria=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 5, 1), Stream &stream=Stream::Null())
 Performs a mean-shift procedure and stores information about processed points (their colors and positions) in two images. More...
 
void cv::cuda::meanShiftSegmentation (InputArray src, OutputArray dst, int sp, int sr, int minsize, TermCriteria criteria=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 5, 1), Stream &stream=Stream::Null())
 Performs a mean-shift segmentation of the source image and eliminates small segments. More...
 

Detailed Description

Function Documentation

§ bilateralFilter()

void cv::cuda::bilateralFilter ( InputArray  src,
OutputArray  dst,
int  kernel_size,
float  sigma_color,
float  sigma_spatial,
int  borderMode = BORDER_DEFAULT,
Stream stream = Stream::Null() 
)

Performs bilateral filtering of passed image.

Parameters
srcSource image. Supports only (channles != 2 && depth() != CV_8S && depth() != CV_32S && depth() != CV_64F).
dstDestination imagwe.
kernel_sizeKernel window size.
sigma_colorFilter sigma in the color space.
sigma_spatialFilter sigma in the coordinate space.
borderModeBorder type. See borderInterpolate for details. BORDER_REFLECT101 , BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
streamStream for the asynchronous version.
See also
bilateralFilter

§ blendLinear()

void cv::cuda::blendLinear ( InputArray  img1,
InputArray  img2,
InputArray  weights1,
InputArray  weights2,
OutputArray  result,
Stream stream = Stream::Null() 
)

Performs linear blending of two images.

Parameters
img1First image. Supports only CV_8U and CV_32F depth.
img2Second image. Must have the same size and the same type as img1 .
weights1Weights for first image. Must have tha same size as img1 . Supports only CV_32F type.
weights2Weights for second image. Must have tha same size as img2 . Supports only CV_32F type.
resultDestination image.
streamStream for the asynchronous version.

§ createCannyEdgeDetector()

Ptr<CannyEdgeDetector> cv::cuda::createCannyEdgeDetector ( double  low_thresh,
double  high_thresh,
int  apperture_size = 3,
bool  L2gradient = false 
)

Creates implementation for cuda::CannyEdgeDetector .

Parameters
low_threshFirst threshold for the hysteresis procedure.
high_threshSecond threshold for the hysteresis procedure.
apperture_sizeAperture size for the Sobel operator.
L2gradientFlag indicating whether a more accurate \(L_2\) norm \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) should be used to compute the image gradient magnitude ( L2gradient=true ), or a faster default \(L_1\) norm \(=|dI/dx|+|dI/dy|\) is enough ( L2gradient=false ).

§ createTemplateMatching()

Ptr<TemplateMatching> cv::cuda::createTemplateMatching ( int  srcType,
int  method,
Size  user_block_size = Size() 
)

Creates implementation for cuda::TemplateMatching .

Parameters
srcTypeInput source type. CV_32F and CV_8U depth images (1..4 channels) are supported for now.
methodSpecifies the way to compare the template with the image.
user_block_sizeYou can use field user_block_size to set specific block size. If you leave its default value Size(0,0) then automatic estimation of block size will be used (which is optimized for speed). By varying user_block_size you can reduce memory requirements at the cost of speed.

The following methods are supported for the CV_8U depth images for now:

  • CV_TM_SQDIFF
  • CV_TM_SQDIFF_NORMED
  • CV_TM_CCORR
  • CV_TM_CCORR_NORMED
  • CV_TM_CCOEFF
  • CV_TM_CCOEFF_NORMED

The following methods are supported for the CV_32F images for now:

  • CV_TM_SQDIFF
  • CV_TM_CCORR
See also
matchTemplate

§ meanShiftFiltering()

void cv::cuda::meanShiftFiltering ( InputArray  src,
OutputArray  dst,
int  sp,
int  sr,
TermCriteria  criteria = TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 5, 1),
Stream stream = Stream::Null() 
)

Performs mean-shift filtering for each point of the source image.

Parameters
srcSource image. Only CV_8UC4 images are supported for now.
dstDestination image containing the color of mapped points. It has the same size and type as src .
spSpatial window radius.
srColor window radius.
criteriaTermination criteria. See TermCriteria.
streamStream for the asynchronous version.

It maps each point of the source image into another point. As a result, you have a new color and new position of each point.

§ meanShiftProc()

void cv::cuda::meanShiftProc ( InputArray  src,
OutputArray  dstr,
OutputArray  dstsp,
int  sp,
int  sr,
TermCriteria  criteria = TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 5, 1),
Stream stream = Stream::Null() 
)

Performs a mean-shift procedure and stores information about processed points (their colors and positions) in two images.

Parameters
srcSource image. Only CV_8UC4 images are supported for now.
dstrDestination image containing the color of mapped points. The size and type is the same as src .
dstspDestination image containing the position of mapped points. The size is the same as src size. The type is CV_16SC2 .
spSpatial window radius.
srColor window radius.
criteriaTermination criteria. See TermCriteria.
streamStream for the asynchronous version.
See also
cuda::meanShiftFiltering

§ meanShiftSegmentation()

void cv::cuda::meanShiftSegmentation ( InputArray  src,
OutputArray  dst,
int  sp,
int  sr,
int  minsize,
TermCriteria  criteria = TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 5, 1),
Stream stream = Stream::Null() 
)

Performs a mean-shift segmentation of the source image and eliminates small segments.

Parameters
srcSource image. Only CV_8UC4 images are supported for now.
dstSegmented image with the same size and type as src (host memory).
spSpatial window radius.
srColor window radius.
minsizeMinimum segment size. Smaller segments are merged.
criteriaTermination criteria. See TermCriteria.
streamStream for the asynchronous version.