Image Processing#

Topics#

Detailed Description#

Classes#

Name	Description
`class` `cv::cuda::CannyEdgeDetector`	Base class for Canny Edge Detector. : View details
`class` `cv::cuda::TemplateMatching`	Base class for Template Matching. : View details

Enumerations#

enum cv::cuda::ConnectedComponentsAlgorithmsTypes {
    cv::cuda::CCL_DEFAULT = -1,
    cv::cuda::CCL_BKE = 0
}

Connected Components Algorithm. View details

Enumeration Type Documentation#

ConnectedComponentsAlgorithmsTypes#

enum cv::cuda::ConnectedComponentsAlgorithmsTypes

#include <opencv2/cudaimgproc.hpp>

Connected Components Algorithm.

Enumerator:

`CCL_DEFAULT`	BKE [13] algorithm for 8-way connectivity.
`CCL_BKE`	BKE [13] algorithm for 8-way connectivity.

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() )

#include <opencv2/cudaimgproc.hpp>

Performs bilateral filtering of passed image.

blendLinear()#

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

#include <opencv2/cudaimgproc.hpp>

Performs linear blending of two images.

Parameters

img1 — First image. Supports only CV_8U and CV_32F depth.
img2 — Second image. Must have the same size and the same type as img1 .
weights1 — Weights for first image. Must have tha same size as img1 . Supports only CV_32F type.
weights2 — Weights for second image. Must have tha same size as img2 . Supports only CV_32F type.
result — Destination image.
stream — Stream for the asynchronous version.

connectedComponents()#

void cv::cuda::connectedComponents(
InputArray image,
OutputArray labels,
int connectivity,
int ltype,
cv::cuda::ConnectedComponentsAlgorithmsTypes ccltype )

#include <opencv2/cudaimgproc.hpp>

Computes the Connected Components Labeled image of a binary image.

The function takes as input a binary image and performs Connected Components Labeling. The output is an image where each Connected Component is assigned a unique label (integer value). ltype specifies the output label image type, an important consideration based on the total number of labels or alternatively the total number of pixels in the source image. ccltype specifies the connected components labeling algorithm to use, currently BKE [13] is supported, see the ConnectedComponentsAlgorithmsTypes for details. Note that labels in the output are not required to be sequential.

Note

A sample program demonstrating Connected Components Labeling in CUDA can be found at

opencv_contrib_source_code/modules/cudaimgproc/samples/connected_components.cpp

Parameters

image — The 8-bit single-channel image to be labeled.
labels — Destination labeled image.
connectivity — Connectivity to use for the labeling procedure. 8 for 8-way connectivity is supported.
ltype — Output image label type. Currently CV_32S is supported.
ccltype — Connected components algorithm type (see the ConnectedComponentsAlgorithmsTypes).

connectedComponents()#

void cv::cuda::connectedComponents(
InputArray image,
OutputArray labels,
int connectivity = 8,
int ltype = CV_32S )

#include <opencv2/cudaimgproc.hpp>

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

image — The 8-bit single-channel image to be labeled.
labels — Destination labeled image.
connectivity — Connectivity to use for the labeling procedure. 8 for 8-way connectivity is supported.
ltype — Output image label type. Currently CV_32S is supported.

createCannyEdgeDetector()#

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

#include <opencv2/cudaimgproc.hpp>

Creates implementation for cuda::CannyEdgeDetector .

Parameters

low_thresh — First threshold for the hysteresis procedure.
high_thresh — Second threshold for the hysteresis procedure.
apperture_size — Aperture size for the Sobel operator.
L2gradient — Flag 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() )

#include <opencv2/cudaimgproc.hpp>

Creates implementation for cuda::TemplateMatching .

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

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() )

#include <opencv2/cudaimgproc.hpp>

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

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.

Parameters

src — Source image. Only CV_8UC4 images are supported for now.
dst — Destination image containing the color of mapped points. It has the same size and type as src .
sp — Spatial window radius.
sr — Color window radius.
criteria — Termination criteria. See TermCriteria.
stream — Stream for the asynchronous version.

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() )

#include <opencv2/cudaimgproc.hpp>

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

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() )

#include <opencv2/cudaimgproc.hpp>

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

Parameters

src — Source image. Only CV_8UC4 images are supported for now.
dst — Segmented image with the same size and type as src (host or gpu memory).
sp — Spatial window radius.
sr — Color window radius.
minsize — Minimum segment size. Smaller segments are merged.
criteria — Termination criteria. See TermCriteria.
stream — Stream for the asynchronous version.