461 lines
21 KiB
C++
461 lines
21 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
|
// If you do not agree to this license, do not download, install,
|
|
// copy or use the software.
|
|
//
|
|
//
|
|
// License Agreement
|
|
// For Open Source Computer Vision Library
|
|
//
|
|
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
|
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
|
// Third party copyrights are property of their respective owners.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
|
// are permitted provided that the following conditions are met:
|
|
//
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
//
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other materials provided with the distribution.
|
|
//
|
|
// * The name of the copyright holders may not be used to endorse or promote products
|
|
// derived from this software without specific prior written permission.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or implied warranties, including, but not limited to, the implied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
|
|
#ifndef __OPENCV_GPUIMGPROC_HPP__
|
|
#define __OPENCV_GPUIMGPROC_HPP__
|
|
|
|
#ifndef __cplusplus
|
|
# error gpuimgproc.hpp header must be compiled as C++
|
|
#endif
|
|
|
|
#include "opencv2/core/gpu.hpp"
|
|
#include "opencv2/imgproc.hpp"
|
|
|
|
#if defined __GNUC__
|
|
#define __OPENCV_GPUIMGPROC_DEPR_BEFORE__
|
|
#define __OPENCV_GPUIMGPROC_DEPR_AFTER__ __attribute__ ((deprecated))
|
|
#elif (defined WIN32 || defined _WIN32)
|
|
#define __OPENCV_GPUIMGPROC_DEPR_BEFORE__ __declspec(deprecated)
|
|
#define __OPENCV_GPUIMGPROC_DEPR_AFTER__
|
|
#else
|
|
#define __OPENCV_GPUIMGPROC_DEPR_BEFORE__
|
|
#define __OPENCV_GPUIMGPROC_DEPR_AFTER__
|
|
#endif
|
|
|
|
namespace cv { namespace gpu {
|
|
|
|
/////////////////////////// Color Processing ///////////////////////////
|
|
|
|
//! converts image from one color space to another
|
|
CV_EXPORTS void cvtColor(InputArray src, OutputArray dst, int code, int dcn = 0, Stream& stream = Stream::Null());
|
|
|
|
enum
|
|
{
|
|
// Bayer Demosaicing (Malvar, He, and Cutler)
|
|
COLOR_BayerBG2BGR_MHT = 256,
|
|
COLOR_BayerGB2BGR_MHT = 257,
|
|
COLOR_BayerRG2BGR_MHT = 258,
|
|
COLOR_BayerGR2BGR_MHT = 259,
|
|
|
|
COLOR_BayerBG2RGB_MHT = COLOR_BayerRG2BGR_MHT,
|
|
COLOR_BayerGB2RGB_MHT = COLOR_BayerGR2BGR_MHT,
|
|
COLOR_BayerRG2RGB_MHT = COLOR_BayerBG2BGR_MHT,
|
|
COLOR_BayerGR2RGB_MHT = COLOR_BayerGB2BGR_MHT,
|
|
|
|
COLOR_BayerBG2GRAY_MHT = 260,
|
|
COLOR_BayerGB2GRAY_MHT = 261,
|
|
COLOR_BayerRG2GRAY_MHT = 262,
|
|
COLOR_BayerGR2GRAY_MHT = 263
|
|
};
|
|
CV_EXPORTS void demosaicing(InputArray src, OutputArray dst, int code, int dcn = -1, Stream& stream = Stream::Null());
|
|
|
|
//! swap channels
|
|
//! dstOrder - Integer array describing how channel values are permutated. The n-th entry
|
|
//! of the array contains the number of the channel that is stored in the n-th channel of
|
|
//! the output image. E.g. Given an RGBA image, aDstOrder = [3,2,1,0] converts this to ABGR
|
|
//! channel order.
|
|
CV_EXPORTS void swapChannels(InputOutputArray image, const int dstOrder[4], Stream& stream = Stream::Null());
|
|
|
|
//! Routines for correcting image color gamma
|
|
CV_EXPORTS void gammaCorrection(InputArray src, OutputArray dst, bool forward = true, Stream& stream = Stream::Null());
|
|
|
|
enum { ALPHA_OVER, ALPHA_IN, ALPHA_OUT, ALPHA_ATOP, ALPHA_XOR, ALPHA_PLUS, ALPHA_OVER_PREMUL, ALPHA_IN_PREMUL, ALPHA_OUT_PREMUL,
|
|
ALPHA_ATOP_PREMUL, ALPHA_XOR_PREMUL, ALPHA_PLUS_PREMUL, ALPHA_PREMUL};
|
|
|
|
//! Composite two images using alpha opacity values contained in each image
|
|
//! Supports CV_8UC4, CV_16UC4, CV_32SC4 and CV_32FC4 types
|
|
CV_EXPORTS void alphaComp(InputArray img1, InputArray img2, OutputArray dst, int alpha_op, Stream& stream = Stream::Null());
|
|
|
|
////////////////////////////// Histogram ///////////////////////////////
|
|
|
|
//! Calculates histogram for 8u one channel image
|
|
//! Output hist will have one row, 256 cols and CV32SC1 type.
|
|
CV_EXPORTS void calcHist(InputArray src, OutputArray hist, Stream& stream = Stream::Null());
|
|
|
|
//! normalizes the grayscale image brightness and contrast by normalizing its histogram
|
|
CV_EXPORTS void equalizeHist(InputArray src, OutputArray dst, InputOutputArray buf, Stream& stream = Stream::Null());
|
|
|
|
static inline void equalizeHist(InputArray src, OutputArray dst, Stream& stream = Stream::Null())
|
|
{
|
|
GpuMat buf;
|
|
gpu::equalizeHist(src, dst, buf, stream);
|
|
}
|
|
|
|
class CV_EXPORTS CLAHE : public cv::CLAHE
|
|
{
|
|
public:
|
|
using cv::CLAHE::apply;
|
|
virtual void apply(InputArray src, OutputArray dst, Stream& stream) = 0;
|
|
};
|
|
CV_EXPORTS Ptr<gpu::CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8));
|
|
|
|
//! Compute levels with even distribution. levels will have 1 row and nLevels cols and CV_32SC1 type.
|
|
CV_EXPORTS void evenLevels(OutputArray levels, int nLevels, int lowerLevel, int upperLevel);
|
|
|
|
//! Calculates histogram with evenly distributed bins for signle channel source.
|
|
//! Supports CV_8UC1, CV_16UC1 and CV_16SC1 source types.
|
|
//! Output hist will have one row and histSize cols and CV_32SC1 type.
|
|
CV_EXPORTS void histEven(InputArray src, OutputArray hist, InputOutputArray buf, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());
|
|
|
|
static inline void histEven(InputArray src, OutputArray hist, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null())
|
|
{
|
|
GpuMat buf;
|
|
gpu::histEven(src, hist, buf, histSize, lowerLevel, upperLevel, stream);
|
|
}
|
|
|
|
//! Calculates histogram with evenly distributed bins for four-channel source.
|
|
//! All channels of source are processed separately.
|
|
//! Supports CV_8UC4, CV_16UC4 and CV_16SC4 source types.
|
|
//! Output hist[i] will have one row and histSize[i] cols and CV_32SC1 type.
|
|
CV_EXPORTS void histEven(InputArray src, GpuMat hist[4], InputOutputArray buf, int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null());
|
|
|
|
static inline void histEven(InputArray src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null())
|
|
{
|
|
GpuMat buf;
|
|
gpu::histEven(src, hist, buf, histSize, lowerLevel, upperLevel, stream);
|
|
}
|
|
|
|
//! Calculates histogram with bins determined by levels array.
|
|
//! levels must have one row and CV_32SC1 type if source has integer type or CV_32FC1 otherwise.
|
|
//! Supports CV_8UC1, CV_16UC1, CV_16SC1 and CV_32FC1 source types.
|
|
//! Output hist will have one row and (levels.cols-1) cols and CV_32SC1 type.
|
|
CV_EXPORTS void histRange(InputArray src, OutputArray hist, InputArray levels, InputOutputArray buf, Stream& stream = Stream::Null());
|
|
|
|
static inline void histRange(InputArray src, OutputArray hist, InputArray levels, Stream& stream = Stream::Null())
|
|
{
|
|
GpuMat buf;
|
|
gpu::histRange(src, hist, levels, buf, stream);
|
|
}
|
|
|
|
//! Calculates histogram with bins determined by levels array.
|
|
//! All levels must have one row and CV_32SC1 type if source has integer type or CV_32FC1 otherwise.
|
|
//! All channels of source are processed separately.
|
|
//! Supports CV_8UC4, CV_16UC4, CV_16SC4 and CV_32FC4 source types.
|
|
//! Output hist[i] will have one row and (levels[i].cols-1) cols and CV_32SC1 type.
|
|
CV_EXPORTS void histRange(InputArray src, GpuMat hist[4], const GpuMat levels[4], InputOutputArray buf, Stream& stream = Stream::Null());
|
|
|
|
static inline void histRange(InputArray src, GpuMat hist[4], const GpuMat levels[4], Stream& stream = Stream::Null())
|
|
{
|
|
GpuMat buf;
|
|
gpu::histRange(src, hist, levels, buf, stream);
|
|
}
|
|
|
|
//////////////////////////////// Canny ////////////////////////////////
|
|
|
|
class CV_EXPORTS CannyEdgeDetector : public Algorithm
|
|
{
|
|
public:
|
|
virtual void detect(InputArray image, OutputArray edges) = 0;
|
|
virtual void detect(InputArray dx, InputArray dy, OutputArray edges) = 0;
|
|
|
|
virtual void setLowThreshold(double low_thresh) = 0;
|
|
virtual double getLowThreshold() const = 0;
|
|
|
|
virtual void setHighThreshold(double high_thresh) = 0;
|
|
virtual double getHighThreshold() const = 0;
|
|
|
|
virtual void setAppertureSize(int apperture_size) = 0;
|
|
virtual int getAppertureSize() const = 0;
|
|
|
|
virtual void setL2Gradient(bool L2gradient) = 0;
|
|
virtual bool getL2Gradient() const = 0;
|
|
};
|
|
|
|
CV_EXPORTS Ptr<CannyEdgeDetector> createCannyEdgeDetector(double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
|
|
|
|
// obsolete
|
|
|
|
__OPENCV_GPUIMGPROC_DEPR_BEFORE__ void Canny(InputArray image, OutputArray edges,
|
|
double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false) __OPENCV_GPUIMGPROC_DEPR_AFTER__;
|
|
inline void Canny(InputArray image, OutputArray edges, double low_thresh, double high_thresh, int apperture_size, bool L2gradient)
|
|
{
|
|
gpu::createCannyEdgeDetector(low_thresh, high_thresh, apperture_size, L2gradient)->detect(image, edges);
|
|
}
|
|
|
|
__OPENCV_GPUIMGPROC_DEPR_BEFORE__ void Canny(InputArray dx, InputArray dy, OutputArray edges,
|
|
double low_thresh, double high_thresh, bool L2gradient = false) __OPENCV_GPUIMGPROC_DEPR_AFTER__;
|
|
inline void Canny(InputArray dx, InputArray dy, OutputArray edges, double low_thresh, double high_thresh, bool L2gradient)
|
|
{
|
|
gpu::createCannyEdgeDetector(low_thresh, high_thresh, 3, L2gradient)->detect(dx, dy, edges);
|
|
}
|
|
|
|
/////////////////////////// Hough Transform ////////////////////////////
|
|
|
|
//////////////////////////////////////
|
|
// HoughLines
|
|
|
|
class CV_EXPORTS HoughLinesDetector : public Algorithm
|
|
{
|
|
public:
|
|
virtual void detect(InputArray src, OutputArray lines) = 0;
|
|
virtual void downloadResults(InputArray d_lines, OutputArray h_lines, OutputArray h_votes = noArray()) = 0;
|
|
|
|
virtual void setRho(float rho) = 0;
|
|
virtual float getRho() const = 0;
|
|
|
|
virtual void setTheta(float theta) = 0;
|
|
virtual float getTheta() const = 0;
|
|
|
|
virtual void setThreshold(int threshold) = 0;
|
|
virtual int getThreshold() const = 0;
|
|
|
|
virtual void setDoSort(bool doSort) = 0;
|
|
virtual bool getDoSort() const = 0;
|
|
|
|
virtual void setMaxLines(int maxLines) = 0;
|
|
virtual int getMaxLines() const = 0;
|
|
};
|
|
|
|
CV_EXPORTS Ptr<HoughLinesDetector> createHoughLinesDetector(float rho, float theta, int threshold, bool doSort = false, int maxLines = 4096);
|
|
|
|
// obsolete
|
|
|
|
__OPENCV_GPUIMGPROC_DEPR_BEFORE__ void HoughLines(InputArray src, OutputArray lines, float rho, float theta, int threshold,
|
|
bool doSort = false, int maxLines = 4096) __OPENCV_GPUIMGPROC_DEPR_AFTER__;
|
|
|
|
inline void HoughLines(InputArray src, OutputArray lines, float rho, float theta, int threshold, bool doSort, int maxLines)
|
|
{
|
|
gpu::createHoughLinesDetector(rho, theta, threshold, doSort, maxLines)->detect(src, lines);
|
|
}
|
|
|
|
//////////////////////////////////////
|
|
// HoughLinesP
|
|
|
|
//! finds line segments in the black-n-white image using probabalistic Hough transform
|
|
class CV_EXPORTS HoughSegmentDetector : public Algorithm
|
|
{
|
|
public:
|
|
virtual void detect(InputArray src, OutputArray lines) = 0;
|
|
|
|
virtual void setRho(float rho) = 0;
|
|
virtual float getRho() const = 0;
|
|
|
|
virtual void setTheta(float theta) = 0;
|
|
virtual float getTheta() const = 0;
|
|
|
|
virtual void setMinLineLength(int minLineLength) = 0;
|
|
virtual int getMinLineLength() const = 0;
|
|
|
|
virtual void setMaxLineGap(int maxLineGap) = 0;
|
|
virtual int getMaxLineGap() const = 0;
|
|
|
|
virtual void setMaxLines(int maxLines) = 0;
|
|
virtual int getMaxLines() const = 0;
|
|
};
|
|
|
|
CV_EXPORTS Ptr<HoughSegmentDetector> createHoughSegmentDetector(float rho, float theta, int minLineLength, int maxLineGap, int maxLines = 4096);
|
|
|
|
// obsolete
|
|
|
|
__OPENCV_GPUIMGPROC_DEPR_BEFORE__ void HoughLinesP(InputArray src, OutputArray lines,
|
|
float rho, float theta, int minLineLength, int maxLineGap, int maxLines = 4096) __OPENCV_GPUIMGPROC_DEPR_AFTER__;
|
|
|
|
inline void HoughLinesP(InputArray src, OutputArray lines, float rho, float theta, int minLineLength, int maxLineGap, int maxLines)
|
|
{
|
|
gpu::createHoughSegmentDetector(rho, theta, minLineLength, maxLineGap, maxLines)->detect(src, lines);
|
|
}
|
|
|
|
//////////////////////////////////////
|
|
// HoughCircles
|
|
|
|
class CV_EXPORTS HoughCirclesDetector : public Algorithm
|
|
{
|
|
public:
|
|
virtual void detect(InputArray src, OutputArray circles) = 0;
|
|
|
|
virtual void setDp(float dp) = 0;
|
|
virtual float getDp() const = 0;
|
|
|
|
virtual void setMinDist(float minDist) = 0;
|
|
virtual float getMinDist() const = 0;
|
|
|
|
virtual void setCannyThreshold(int cannyThreshold) = 0;
|
|
virtual int getCannyThreshold() const = 0;
|
|
|
|
virtual void setVotesThreshold(int votesThreshold) = 0;
|
|
virtual int getVotesThreshold() const = 0;
|
|
|
|
virtual void setMinRadius(int minRadius) = 0;
|
|
virtual int getMinRadius() const = 0;
|
|
|
|
virtual void setMaxRadius(int maxRadius) = 0;
|
|
virtual int getMaxRadius() const = 0;
|
|
|
|
virtual void setMaxCircles(int maxCircles) = 0;
|
|
virtual int getMaxCircles() const = 0;
|
|
};
|
|
|
|
CV_EXPORTS Ptr<HoughCirclesDetector> createHoughCirclesDetector(float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096);
|
|
|
|
// obsolete
|
|
|
|
__OPENCV_GPUIMGPROC_DEPR_BEFORE__ void HoughCircles(InputArray src, OutputArray circles,
|
|
int method, float dp, float minDist, int cannyThreshold, int votesThreshold,
|
|
int minRadius, int maxRadius, int maxCircles = 4096) __OPENCV_GPUIMGPROC_DEPR_AFTER__;
|
|
|
|
inline void HoughCircles(InputArray src, OutputArray circles, int /*method*/, float dp, float minDist,
|
|
int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles)
|
|
{
|
|
gpu::createHoughCirclesDetector(dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius, maxCircles)->detect(src, circles);
|
|
}
|
|
|
|
//////////////////////////////////////
|
|
// GeneralizedHough
|
|
|
|
//! finds arbitrary template in the grayscale image using Generalized Hough Transform
|
|
//! Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122.
|
|
//! Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038.
|
|
class CV_EXPORTS GeneralizedHough : public Algorithm
|
|
{
|
|
public:
|
|
static Ptr<GeneralizedHough> create(int method);
|
|
|
|
//! set template to search
|
|
virtual void setTemplate(InputArray templ, int cannyThreshold = 100, Point templCenter = Point(-1, -1)) = 0;
|
|
virtual void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1)) = 0;
|
|
|
|
//! find template on image
|
|
virtual void detect(InputArray image, OutputArray positions, int cannyThreshold = 100) = 0;
|
|
virtual void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions) = 0;
|
|
|
|
virtual void downloadResults(InputArray d_positions, OutputArray h_positions, OutputArray h_votes = noArray()) = 0;
|
|
};
|
|
|
|
////////////////////////// Corners Detection ///////////////////////////
|
|
|
|
class CV_EXPORTS CornernessCriteria : public Algorithm
|
|
{
|
|
public:
|
|
virtual void compute(InputArray src, OutputArray dst, Stream& stream = Stream::Null()) = 0;
|
|
};
|
|
|
|
//! computes Harris cornerness criteria at each image pixel
|
|
CV_EXPORTS Ptr<CornernessCriteria> createHarrisCorner(int srcType, int blockSize, int ksize, double k, int borderType = BORDER_REFLECT101);
|
|
|
|
//! computes minimum eigen value of 2x2 derivative covariation matrix at each pixel - the cornerness criteria
|
|
CV_EXPORTS Ptr<CornernessCriteria> createMinEigenValCorner(int srcType, int blockSize, int ksize, int borderType = BORDER_REFLECT101);
|
|
|
|
// obsolete
|
|
|
|
__OPENCV_GPUIMGPROC_DEPR_BEFORE__ void cornerHarris(InputArray src, OutputArray dst,
|
|
int blockSize, int ksize, double k, int borderType = BORDER_REFLECT101,
|
|
Stream& stream = Stream::Null()) __OPENCV_GPUIMGPROC_DEPR_AFTER__;
|
|
|
|
inline void cornerHarris(InputArray src, OutputArray dst, int blockSize, int ksize, double k, int borderType, Stream& stream)
|
|
{
|
|
gpu::createHarrisCorner(src.type(), blockSize, ksize, k, borderType)->compute(src, dst, stream);
|
|
}
|
|
|
|
__OPENCV_GPUIMGPROC_DEPR_BEFORE__ void cornerMinEigenVal(InputArray src, OutputArray dst,
|
|
int blockSize, int ksize, int borderType = BORDER_REFLECT101,
|
|
Stream& stream = Stream::Null()) __OPENCV_GPUIMGPROC_DEPR_AFTER__;
|
|
|
|
inline void cornerMinEigenVal(InputArray src, OutputArray dst, int blockSize, int ksize, int borderType, Stream& stream)
|
|
{
|
|
gpu::createMinEigenValCorner(src.type(), blockSize, ksize, borderType)->compute(src, dst, stream);
|
|
}
|
|
|
|
////////////////////////// Corners Detection ///////////////////////////
|
|
|
|
class CV_EXPORTS CornersDetector : public Algorithm
|
|
{
|
|
public:
|
|
//! return 1 rows matrix with CV_32FC2 type
|
|
virtual void detect(InputArray image, OutputArray corners, InputArray mask = noArray()) = 0;
|
|
};
|
|
|
|
CV_EXPORTS Ptr<CornersDetector> createGoodFeaturesToTrackDetector(int srcType, int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 0.0,
|
|
int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04);
|
|
|
|
///////////////////////////// Mean Shift //////////////////////////////
|
|
|
|
//! Does mean shift filtering on GPU.
|
|
CV_EXPORTS void meanShiftFiltering(InputArray src, OutputArray dst, int sp, int sr,
|
|
TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1),
|
|
Stream& stream = Stream::Null());
|
|
|
|
//! Does mean shift procedure on GPU.
|
|
CV_EXPORTS void 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());
|
|
|
|
//! Does mean shift segmentation with elimination of small regions.
|
|
CV_EXPORTS void meanShiftSegmentation(InputArray src, OutputArray dst, int sp, int sr, int minsize,
|
|
TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
|
|
|
|
/////////////////////////// Match Template ////////////////////////////
|
|
|
|
struct CV_EXPORTS MatchTemplateBuf
|
|
{
|
|
Size user_block_size;
|
|
GpuMat imagef, templf;
|
|
std::vector<GpuMat> images;
|
|
std::vector<GpuMat> image_sums;
|
|
std::vector<GpuMat> image_sqsums;
|
|
};
|
|
|
|
//! computes the proximity map for the raster template and the image where the template is searched for
|
|
CV_EXPORTS void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, Stream &stream = Stream::Null());
|
|
|
|
//! computes the proximity map for the raster template and the image where the template is searched for
|
|
CV_EXPORTS void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, MatchTemplateBuf &buf, Stream& stream = Stream::Null());
|
|
|
|
////////////////////////// Bilateral Filter ///////////////////////////
|
|
|
|
//! Performa bilateral filtering of passsed image
|
|
CV_EXPORTS void bilateralFilter(const GpuMat& src, GpuMat& dst, int kernel_size, float sigma_color, float sigma_spatial,
|
|
int borderMode = BORDER_DEFAULT, Stream& stream = Stream::Null());
|
|
|
|
///////////////////////////// Blending ////////////////////////////////
|
|
|
|
//! performs linear blending of two images
|
|
//! to avoid accuracy errors sum of weigths shouldn't be very close to zero
|
|
CV_EXPORTS void blendLinear(const GpuMat& img1, const GpuMat& img2, const GpuMat& weights1, const GpuMat& weights2,
|
|
GpuMat& result, Stream& stream = Stream::Null());
|
|
|
|
}} // namespace cv { namespace gpu {
|
|
|
|
#undef __OPENCV_GPUIMGPROC_DEPR_BEFORE__
|
|
#undef __OPENCV_GPUIMGPROC_DEPR_AFTER__
|
|
|
|
#endif /* __OPENCV_GPUIMGPROC_HPP__ */
|