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opencv/modules/gpu/include/opencv2/gpu/gpu.hpp
Alexey Spizhevoy 6a9f934233 Fixed bug #1759
2012-04-28 11:45:29 +00:00

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/*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 GpuMaterials 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_GPU_HPP__
#define __OPENCV_GPU_HPP__
#ifndef SKIP_INCLUDES
#include <vector>
#endif
#include "opencv2/core/gpumat.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/features2d/features2d.hpp"
namespace cv { namespace gpu {
//////////////////////////////// CudaMem ////////////////////////////////
// CudaMem is limited cv::Mat with page locked memory allocation.
// Page locked memory is only needed for async and faster coping to GPU.
// It is convertable to cv::Mat header without reference counting
// so you can use it with other opencv functions.
// Page-locks the matrix m memory and maps it for the device(s)
CV_EXPORTS void registerPageLocked(Mat& m);
// Unmaps the memory of matrix m, and makes it pageable again.
CV_EXPORTS void unregisterPageLocked(Mat& m);
class CV_EXPORTS CudaMem
{
public:
enum { ALLOC_PAGE_LOCKED = 1, ALLOC_ZEROCOPY = 2, ALLOC_WRITE_COMBINED = 4 };
CudaMem();
CudaMem(const CudaMem& m);
CudaMem(int rows, int cols, int type, int _alloc_type = ALLOC_PAGE_LOCKED);
CudaMem(Size size, int type, int alloc_type = ALLOC_PAGE_LOCKED);
//! creates from cv::Mat with coping data
explicit CudaMem(const Mat& m, int alloc_type = ALLOC_PAGE_LOCKED);
~CudaMem();
CudaMem& operator = (const CudaMem& m);
//! returns deep copy of the matrix, i.e. the data is copied
CudaMem clone() const;
//! allocates new matrix data unless the matrix already has specified size and type.
void create(int rows, int cols, int type, int alloc_type = ALLOC_PAGE_LOCKED);
void create(Size size, int type, int alloc_type = ALLOC_PAGE_LOCKED);
//! decrements reference counter and released memory if needed.
void release();
//! returns matrix header with disabled reference counting for CudaMem data.
Mat createMatHeader() const;
operator Mat() const;
//! maps host memory into device address space and returns GpuMat header for it. Throws exception if not supported by hardware.
GpuMat createGpuMatHeader() const;
operator GpuMat() const;
//returns if host memory can be mapperd to gpu address space;
static bool canMapHostMemory();
// Please see cv::Mat for descriptions
bool isContinuous() const;
size_t elemSize() const;
size_t elemSize1() const;
int type() const;
int depth() const;
int channels() const;
size_t step1() const;
Size size() const;
bool empty() const;
// Please see cv::Mat for descriptions
int flags;
int rows, cols;
size_t step;
uchar* data;
int* refcount;
uchar* datastart;
uchar* dataend;
int alloc_type;
};
//////////////////////////////// CudaStream ////////////////////////////////
// Encapculates Cuda Stream. Provides interface for async coping.
// Passed to each function that supports async kernel execution.
// Reference counting is enabled
class CV_EXPORTS Stream
{
public:
Stream();
~Stream();
Stream(const Stream&);
Stream& operator=(const Stream&);
bool queryIfComplete();
void waitForCompletion();
//! downloads asynchronously.
// Warning! cv::Mat must point to page locked memory (i.e. to CudaMem data or to its subMat)
void enqueueDownload(const GpuMat& src, CudaMem& dst);
void enqueueDownload(const GpuMat& src, Mat& dst);
//! uploads asynchronously.
// Warning! cv::Mat must point to page locked memory (i.e. to CudaMem data or to its ROI)
void enqueueUpload(const CudaMem& src, GpuMat& dst);
void enqueueUpload(const Mat& src, GpuMat& dst);
void enqueueCopy(const GpuMat& src, GpuMat& dst);
void enqueueMemSet(GpuMat& src, Scalar val);
void enqueueMemSet(GpuMat& src, Scalar val, const GpuMat& mask);
// converts matrix type, ex from float to uchar depending on type
void enqueueConvert(const GpuMat& src, GpuMat& dst, int type, double a = 1, double b = 0);
static Stream& Null();
operator bool() const;
private:
void create();
void release();
struct Impl;
Impl *impl;
friend struct StreamAccessor;
explicit Stream(Impl* impl);
};
//////////////////////////////// Filter Engine ////////////////////////////////
/*!
The Base Class for 1D or Row-wise Filters
This is the base class for linear or non-linear filters that process 1D data.
In particular, such filters are used for the "horizontal" filtering parts in separable filters.
*/
class CV_EXPORTS BaseRowFilter_GPU
{
public:
BaseRowFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {}
virtual ~BaseRowFilter_GPU() {}
virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
int ksize, anchor;
};
/*!
The Base Class for Column-wise Filters
This is the base class for linear or non-linear filters that process columns of 2D arrays.
Such filters are used for the "vertical" filtering parts in separable filters.
*/
class CV_EXPORTS BaseColumnFilter_GPU
{
public:
BaseColumnFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {}
virtual ~BaseColumnFilter_GPU() {}
virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
int ksize, anchor;
};
/*!
The Base Class for Non-Separable 2D Filters.
This is the base class for linear or non-linear 2D filters.
*/
class CV_EXPORTS BaseFilter_GPU
{
public:
BaseFilter_GPU(const Size& ksize_, const Point& anchor_) : ksize(ksize_), anchor(anchor_) {}
virtual ~BaseFilter_GPU() {}
virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
Size ksize;
Point anchor;
};
/*!
The Base Class for Filter Engine.
The class can be used to apply an arbitrary filtering operation to an image.
It contains all the necessary intermediate buffers.
*/
class CV_EXPORTS FilterEngine_GPU
{
public:
virtual ~FilterEngine_GPU() {}
virtual void apply(const GpuMat& src, GpuMat& dst, Rect roi = Rect(0,0,-1,-1), Stream& stream = Stream::Null()) = 0;
};
//! returns the non-separable filter engine with the specified filter
CV_EXPORTS Ptr<FilterEngine_GPU> createFilter2D_GPU(const Ptr<BaseFilter_GPU>& filter2D, int srcType, int dstType);
//! returns the separable filter engine with the specified filters
CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter,
const Ptr<BaseColumnFilter_GPU>& columnFilter, int srcType, int bufType, int dstType);
CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter,
const Ptr<BaseColumnFilter_GPU>& columnFilter, int srcType, int bufType, int dstType, GpuMat& buf);
//! returns horizontal 1D box filter
//! supports only CV_8UC1 source type and CV_32FC1 sum type
CV_EXPORTS Ptr<BaseRowFilter_GPU> getRowSumFilter_GPU(int srcType, int sumType, int ksize, int anchor = -1);
//! returns vertical 1D box filter
//! supports only CV_8UC1 sum type and CV_32FC1 dst type
CV_EXPORTS Ptr<BaseColumnFilter_GPU> getColumnSumFilter_GPU(int sumType, int dstType, int ksize, int anchor = -1);
//! returns 2D box filter
//! supports CV_8UC1 and CV_8UC4 source type, dst type must be the same as source type
CV_EXPORTS Ptr<BaseFilter_GPU> getBoxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1, -1));
//! returns box filter engine
CV_EXPORTS Ptr<FilterEngine_GPU> createBoxFilter_GPU(int srcType, int dstType, const Size& ksize,
const Point& anchor = Point(-1,-1));
//! returns 2D morphological filter
//! only MORPH_ERODE and MORPH_DILATE are supported
//! supports CV_8UC1 and CV_8UC4 types
//! kernel must have CV_8UC1 type, one rows and cols == ksize.width * ksize.height
CV_EXPORTS Ptr<BaseFilter_GPU> getMorphologyFilter_GPU(int op, int type, const Mat& kernel, const Size& ksize,
Point anchor=Point(-1,-1));
//! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported.
CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat& kernel,
const Point& anchor = Point(-1,-1), int iterations = 1);
CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat& kernel, GpuMat& buf,
const Point& anchor = Point(-1,-1), int iterations = 1);
//! returns 2D filter with the specified kernel
//! supports CV_8U, CV_16U and CV_32F one and four channel image
CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
//! returns the non-separable linear filter engine
CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat& kernel,
Point anchor = Point(-1,-1), int borderType = BORDER_DEFAULT);
//! returns the primitive row filter with the specified kernel.
//! supports only CV_8UC1, CV_8UC4, CV_16SC1, CV_16SC2, CV_32SC1, CV_32FC1 source type.
//! there are two version of algorithm: NPP and OpenCV.
//! NPP calls when srcType == CV_8UC1 or srcType == CV_8UC4 and bufType == srcType,
//! otherwise calls OpenCV version.
//! NPP supports only BORDER_CONSTANT border type.
//! OpenCV version supports only CV_32F as buffer depth and
//! BORDER_REFLECT101, BORDER_REPLICATE and BORDER_CONSTANT border types.
CV_EXPORTS Ptr<BaseRowFilter_GPU> getLinearRowFilter_GPU(int srcType, int bufType, const Mat& rowKernel,
int anchor = -1, int borderType = BORDER_DEFAULT);
//! returns the primitive column filter with the specified kernel.
//! supports only CV_8UC1, CV_8UC4, CV_16SC1, CV_16SC2, CV_32SC1, CV_32FC1 dst type.
//! there are two version of algorithm: NPP and OpenCV.
//! NPP calls when dstType == CV_8UC1 or dstType == CV_8UC4 and bufType == dstType,
//! otherwise calls OpenCV version.
//! NPP supports only BORDER_CONSTANT border type.
//! OpenCV version supports only CV_32F as buffer depth and
//! BORDER_REFLECT101, BORDER_REPLICATE and BORDER_CONSTANT border types.
CV_EXPORTS Ptr<BaseColumnFilter_GPU> getLinearColumnFilter_GPU(int bufType, int dstType, const Mat& columnKernel,
int anchor = -1, int borderType = BORDER_DEFAULT);
//! returns the separable linear filter engine
CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel,
const Mat& columnKernel, const Point& anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT,
int columnBorderType = -1);
CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel,
const Mat& columnKernel, GpuMat& buf, const Point& anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT,
int columnBorderType = -1);
//! returns filter engine for the generalized Sobel operator
CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU(int srcType, int dstType, int dx, int dy, int ksize,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU(int srcType, int dstType, int dx, int dy, int ksize, GpuMat& buf,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
//! returns the Gaussian filter engine
CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, double sigma1, double sigma2 = 0,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, GpuMat& buf, double sigma1, double sigma2 = 0,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
//! returns maximum filter
CV_EXPORTS Ptr<BaseFilter_GPU> getMaxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1));
//! returns minimum filter
CV_EXPORTS Ptr<BaseFilter_GPU> getMinFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1));
//! smooths the image using the normalized box filter
//! supports CV_8UC1, CV_8UC4 types
CV_EXPORTS void boxFilter(const GpuMat& src, GpuMat& dst, int ddepth, Size ksize, Point anchor = Point(-1,-1), Stream& stream = Stream::Null());
//! a synonym for normalized box filter
static inline void blur(const GpuMat& src, GpuMat& dst, Size ksize, Point anchor = Point(-1,-1), Stream& stream = Stream::Null())
{
boxFilter(src, dst, -1, ksize, anchor, stream);
}
//! erodes the image (applies the local minimum operator)
CV_EXPORTS void erode(const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1);
CV_EXPORTS void erode(const GpuMat& src, GpuMat& dst, const Mat& kernel, GpuMat& buf,
Point anchor = Point(-1, -1), int iterations = 1,
Stream& stream = Stream::Null());
//! dilates the image (applies the local maximum operator)
CV_EXPORTS void dilate(const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1);
CV_EXPORTS void dilate(const GpuMat& src, GpuMat& dst, const Mat& kernel, GpuMat& buf,
Point anchor = Point(-1, -1), int iterations = 1,
Stream& stream = Stream::Null());
//! applies an advanced morphological operation to the image
CV_EXPORTS void morphologyEx(const GpuMat& src, GpuMat& dst, int op, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1);
CV_EXPORTS void morphologyEx(const GpuMat& src, GpuMat& dst, int op, const Mat& kernel, GpuMat& buf1, GpuMat& buf2,
Point anchor = Point(-1, -1), int iterations = 1, Stream& stream = Stream::Null());
//! applies non-separable 2D linear filter to the image
CV_EXPORTS void filter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernel, Point anchor=Point(-1,-1), int borderType = BORDER_DEFAULT, Stream& stream = Stream::Null());
//! applies separable 2D linear filter to the image
CV_EXPORTS void sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY,
Point anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
CV_EXPORTS void sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY, GpuMat& buf,
Point anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1,
Stream& stream = Stream::Null());
//! applies generalized Sobel operator to the image
CV_EXPORTS void Sobel(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, int ksize = 3, double scale = 1,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
CV_EXPORTS void Sobel(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, GpuMat& buf, int ksize = 3, double scale = 1,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());
//! applies the vertical or horizontal Scharr operator to the image
CV_EXPORTS void Scharr(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, double scale = 1,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
CV_EXPORTS void Scharr(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, GpuMat& buf, double scale = 1,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());
//! smooths the image using Gaussian filter.
CV_EXPORTS void GaussianBlur(const GpuMat& src, GpuMat& dst, Size ksize, double sigma1, double sigma2 = 0,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
CV_EXPORTS void GaussianBlur(const GpuMat& src, GpuMat& dst, Size ksize, GpuMat& buf, double sigma1, double sigma2 = 0,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());
//! applies Laplacian operator to the image
//! supports only ksize = 1 and ksize = 3
CV_EXPORTS void Laplacian(const GpuMat& src, GpuMat& dst, int ddepth, int ksize = 1, double scale = 1, int borderType = BORDER_DEFAULT, Stream& stream = Stream::Null());
////////////////////////////// Arithmetics ///////////////////////////////////
//! implements generalized matrix product algorithm GEMM from BLAS
CV_EXPORTS void gemm(const GpuMat& src1, const GpuMat& src2, double alpha,
const GpuMat& src3, double beta, GpuMat& dst, int flags = 0, Stream& stream = Stream::Null());
//! transposes the matrix
//! supports matrix with element size = 1, 4 and 8 bytes (CV_8UC1, CV_8UC4, CV_16UC2, CV_32FC1, etc)
CV_EXPORTS void transpose(const GpuMat& src1, GpuMat& dst, Stream& stream = Stream::Null());
//! reverses the order of the rows, columns or both in a matrix
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U, CV_32S or CV_32F depth
CV_EXPORTS void flip(const GpuMat& a, GpuMat& b, int flipCode, Stream& stream = Stream::Null());
//! transforms 8-bit unsigned integers using lookup table: dst(i)=lut(src(i))
//! destination array will have the depth type as lut and the same channels number as source
//! supports CV_8UC1, CV_8UC3 types
CV_EXPORTS void LUT(const GpuMat& src, const Mat& lut, GpuMat& dst, Stream& stream = Stream::Null());
//! makes multi-channel array out of several single-channel arrays
CV_EXPORTS void merge(const GpuMat* src, size_t n, GpuMat& dst, Stream& stream = Stream::Null());
//! makes multi-channel array out of several single-channel arrays
CV_EXPORTS void merge(const vector<GpuMat>& src, GpuMat& dst, Stream& stream = Stream::Null());
//! copies each plane of a multi-channel array to a dedicated array
CV_EXPORTS void split(const GpuMat& src, GpuMat* dst, Stream& stream = Stream::Null());
//! copies each plane of a multi-channel array to a dedicated array
CV_EXPORTS void split(const GpuMat& src, vector<GpuMat>& dst, Stream& stream = Stream::Null());
//! computes magnitude of complex (x(i).re, x(i).im) vector
//! supports only CV_32FC2 type
CV_EXPORTS void magnitude(const GpuMat& x, GpuMat& magnitude, Stream& stream = Stream::Null());
//! computes squared magnitude of complex (x(i).re, x(i).im) vector
//! supports only CV_32FC2 type
CV_EXPORTS void magnitudeSqr(const GpuMat& x, GpuMat& magnitude, Stream& stream = Stream::Null());
//! computes magnitude of each (x(i), y(i)) vector
//! supports only floating-point source
CV_EXPORTS void magnitude(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, Stream& stream = Stream::Null());
//! computes squared magnitude of each (x(i), y(i)) vector
//! supports only floating-point source
CV_EXPORTS void magnitudeSqr(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, Stream& stream = Stream::Null());
//! computes angle (angle(i)) of each (x(i), y(i)) vector
//! supports only floating-point source
CV_EXPORTS void phase(const GpuMat& x, const GpuMat& y, GpuMat& angle, bool angleInDegrees = false, Stream& stream = Stream::Null());
//! converts Cartesian coordinates to polar
//! supports only floating-point source
CV_EXPORTS void cartToPolar(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, GpuMat& angle, bool angleInDegrees = false, Stream& stream = Stream::Null());
//! converts polar coordinates to Cartesian
//! supports only floating-point source
CV_EXPORTS void polarToCart(const GpuMat& magnitude, const GpuMat& angle, GpuMat& x, GpuMat& y, bool angleInDegrees = false, Stream& stream = Stream::Null());
//////////////////////////// Per-element operations ////////////////////////////////////
//! adds one matrix to another (c = a + b)
CV_EXPORTS void add(const GpuMat& a, const GpuMat& b, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
//! adds scalar to a matrix (c = a + s)
CV_EXPORTS void add(const GpuMat& a, const Scalar& sc, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
//! subtracts one matrix from another (c = a - b)
CV_EXPORTS void subtract(const GpuMat& a, const GpuMat& b, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
//! subtracts scalar from a matrix (c = a - s)
CV_EXPORTS void subtract(const GpuMat& a, const Scalar& sc, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
//! computes element-wise weighted product of the two arrays (c = scale * a * b)
CV_EXPORTS void multiply(const GpuMat& a, const GpuMat& b, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
//! weighted multiplies matrix to a scalar (c = scale * a * s)
CV_EXPORTS void multiply(const GpuMat& a, const Scalar& sc, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
//! computes element-wise weighted quotient of the two arrays (c = a / b)
CV_EXPORTS void divide(const GpuMat& a, const GpuMat& b, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
//! computes element-wise weighted quotient of matrix and scalar (c = a / s)
CV_EXPORTS void divide(const GpuMat& a, const Scalar& sc, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
//! computes element-wise weighted reciprocal of an array (dst = scale/src2)
CV_EXPORTS void divide(double scale, const GpuMat& src2, GpuMat& dst, int dtype = -1, Stream& stream = Stream::Null());
//! computes the weighted sum of two arrays (dst = alpha*src1 + beta*src2 + gamma)
CV_EXPORTS void addWeighted(const GpuMat& src1, double alpha, const GpuMat& src2, double beta, double gamma, GpuMat& dst,
int dtype = -1, Stream& stream = Stream::Null());
//! adds scaled array to another one (dst = alpha*src1 + src2)
static inline void scaleAdd(const GpuMat& src1, double alpha, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null())
{
addWeighted(src1, alpha, src2, 1.0, 0.0, dst, -1, stream);
}
//! computes element-wise absolute difference of two arrays (c = abs(a - b))
CV_EXPORTS void absdiff(const GpuMat& a, const GpuMat& b, GpuMat& c, Stream& stream = Stream::Null());
//! computes element-wise absolute difference of array and scalar (c = abs(a - s))
CV_EXPORTS void absdiff(const GpuMat& a, const Scalar& s, GpuMat& c, Stream& stream = Stream::Null());
//! computes absolute value of each matrix element
//! supports CV_16S and CV_32F depth
CV_EXPORTS void abs(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
//! computes square of each pixel in an image
//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
CV_EXPORTS void sqr(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
//! computes square root of each pixel in an image
//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
CV_EXPORTS void sqrt(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
//! computes exponent of each matrix element (b = e**a)
//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
CV_EXPORTS void exp(const GpuMat& a, GpuMat& b, Stream& stream = Stream::Null());
//! computes natural logarithm of absolute value of each matrix element: b = log(abs(a))
//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
CV_EXPORTS void log(const GpuMat& a, GpuMat& b, Stream& stream = Stream::Null());
//! computes power of each matrix element:
// (dst(i,j) = pow( src(i,j) , power), if src.type() is integer
// (dst(i,j) = pow(fabs(src(i,j)), power), otherwise
//! supports all, except depth == CV_64F
CV_EXPORTS void pow(const GpuMat& src, double power, GpuMat& dst, Stream& stream = Stream::Null());
//! compares elements of two arrays (c = a <cmpop> b)
CV_EXPORTS void compare(const GpuMat& a, const GpuMat& b, GpuMat& c, int cmpop, Stream& stream = Stream::Null());
//! performs per-elements bit-wise inversion
CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
//! calculates per-element bit-wise disjunction of two arrays
CV_EXPORTS void bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
//! calculates per-element bit-wise disjunction of array and scalar
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
CV_EXPORTS void bitwise_or(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
//! calculates per-element bit-wise conjunction of two arrays
CV_EXPORTS void bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
//! calculates per-element bit-wise conjunction of array and scalar
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
CV_EXPORTS void bitwise_and(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
//! calculates per-element bit-wise "exclusive or" operation
CV_EXPORTS void bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
//! calculates per-element bit-wise "exclusive or" of array and scalar
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
CV_EXPORTS void bitwise_xor(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
//! pixel by pixel right shift of an image by a constant value
//! supports 1, 3 and 4 channels images with integers elements
CV_EXPORTS void rshift(const GpuMat& src, Scalar_<int> sc, GpuMat& dst, Stream& stream = Stream::Null());
//! pixel by pixel left shift of an image by a constant value
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
CV_EXPORTS void lshift(const GpuMat& src, Scalar_<int> sc, GpuMat& dst, Stream& stream = Stream::Null());
//! computes per-element minimum of two arrays (dst = min(src1, src2))
CV_EXPORTS void min(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null());
//! computes per-element minimum of array and scalar (dst = min(src1, src2))
CV_EXPORTS void min(const GpuMat& src1, double src2, GpuMat& dst, Stream& stream = Stream::Null());
//! computes per-element maximum of two arrays (dst = max(src1, src2))
CV_EXPORTS void max(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null());
//! computes per-element maximum of array and scalar (dst = max(src1, src2))
CV_EXPORTS void max(const GpuMat& src1, double src2, GpuMat& dst, 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(const GpuMat& img1, const GpuMat& img2, GpuMat& dst, int alpha_op, Stream& stream = Stream::Null());
////////////////////////////// Image processing //////////////////////////////
//! DST[x,y] = SRC[xmap[x,y],ymap[x,y]]
//! supports only CV_32FC1 map type
CV_EXPORTS void remap(const GpuMat& src, GpuMat& dst, const GpuMat& xmap, const GpuMat& ymap,
int interpolation, int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar(),
Stream& stream = Stream::Null());
//! Does mean shift filtering on GPU.
CV_EXPORTS void meanShiftFiltering(const GpuMat& src, GpuMat& 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(const GpuMat& src, GpuMat& dstr, GpuMat& 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(const GpuMat& src, Mat& dst, int sp, int sr, int minsize,
TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
//! Does coloring of disparity image: [0..ndisp) -> [0..240, 1, 1] in HSV.
//! Supported types of input disparity: CV_8U, CV_16S.
//! Output disparity has CV_8UC4 type in BGRA format (alpha = 255).
CV_EXPORTS void drawColorDisp(const GpuMat& src_disp, GpuMat& dst_disp, int ndisp, Stream& stream = Stream::Null());
//! Reprojects disparity image to 3D space.
//! Supports CV_8U and CV_16S types of input disparity.
//! The output is a 3- or 4-channel floating-point matrix.
//! Each element of this matrix will contain the 3D coordinates of the point (x,y,z,1), computed from the disparity map.
//! Q is the 4x4 perspective transformation matrix that can be obtained with cvStereoRectify.
CV_EXPORTS void reprojectImageTo3D(const GpuMat& disp, GpuMat& xyzw, const Mat& Q, int dst_cn = 4, Stream& stream = Stream::Null());
//! converts image from one color space to another
CV_EXPORTS void cvtColor(const GpuMat& src, GpuMat& dst, int code, int dcn = 0, 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(GpuMat& image, const int dstOrder[4], Stream& stream = Stream::Null());
//! applies fixed threshold to the image
CV_EXPORTS double threshold(const GpuMat& src, GpuMat& dst, double thresh, double maxval, int type, Stream& stream = Stream::Null());
//! resizes the image
//! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
CV_EXPORTS void resize(const GpuMat& src, GpuMat& dst, Size dsize, double fx=0, double fy=0, int interpolation = INTER_LINEAR, Stream& stream = Stream::Null());
//! warps the image using affine transformation
//! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
CV_EXPORTS void warpAffine(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags = INTER_LINEAR,
int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar(), Stream& stream = Stream::Null());
CV_EXPORTS void buildWarpAffineMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream = Stream::Null());
//! warps the image using perspective transformation
//! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
CV_EXPORTS void warpPerspective(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags = INTER_LINEAR,
int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar(), Stream& stream = Stream::Null());
CV_EXPORTS void buildWarpPerspectiveMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream = Stream::Null());
//! builds plane warping maps
CV_EXPORTS void buildWarpPlaneMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, const Mat &T, float scale,
GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null());
//! builds cylindrical warping maps
CV_EXPORTS void buildWarpCylindricalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, float scale,
GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null());
//! builds spherical warping maps
CV_EXPORTS void buildWarpSphericalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, float scale,
GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null());
//! rotates an image around the origin (0,0) and then shifts it
//! supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
//! supports 1, 3 or 4 channels images with CV_8U, CV_16U or CV_32F depth
CV_EXPORTS void rotate(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift = 0, double yShift = 0,
int interpolation = INTER_LINEAR, Stream& stream = Stream::Null());
//! copies 2D array to a larger destination array and pads borders with user-specifiable constant
CV_EXPORTS void copyMakeBorder(const GpuMat& src, GpuMat& dst, int top, int bottom, int left, int right, int borderType,
const Scalar& value = Scalar(), Stream& stream = Stream::Null());
//! computes the integral image
//! sum will have CV_32S type, but will contain unsigned int values
//! supports only CV_8UC1 source type
CV_EXPORTS void integral(const GpuMat& src, GpuMat& sum, Stream& stream = Stream::Null());
//! buffered version
CV_EXPORTS void integralBuffered(const GpuMat& src, GpuMat& sum, GpuMat& buffer, Stream& stream = Stream::Null());
//! computes squared integral image
//! result matrix will have 64F type, but will contain 64U values
//! supports source images of 8UC1 type only
CV_EXPORTS void sqrIntegral(const GpuMat& src, GpuMat& sqsum, Stream& stream = Stream::Null());
//! computes vertical sum, supports only CV_32FC1 images
CV_EXPORTS void columnSum(const GpuMat& src, GpuMat& sum);
//! computes the standard deviation of integral images
//! supports only CV_32SC1 source type and CV_32FC1 sqr type
//! output will have CV_32FC1 type
CV_EXPORTS void rectStdDev(const GpuMat& src, const GpuMat& sqr, GpuMat& dst, const Rect& rect, Stream& stream = Stream::Null());
//! computes Harris cornerness criteria at each image pixel
CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, double k, int borderType = BORDER_REFLECT101);
CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, double k, int borderType = BORDER_REFLECT101);
CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, GpuMat& buf, int blockSize, int ksize, double k,
int borderType = BORDER_REFLECT101, Stream& stream = Stream::Null());
//! computes minimum eigen value of 2x2 derivative covariation matrix at each pixel - the cornerness criteria
CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, int borderType=BORDER_REFLECT101);
CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, int borderType=BORDER_REFLECT101);
CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, GpuMat& buf, int blockSize, int ksize,
int borderType=BORDER_REFLECT101, Stream& stream = Stream::Null());
//! performs per-element multiplication of two full (not packed) Fourier spectrums
//! supports 32FC2 matrixes only (interleaved format)
CV_EXPORTS void mulSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, bool conjB=false, Stream& stream = Stream::Null());
//! performs per-element multiplication of two full (not packed) Fourier spectrums
//! supports 32FC2 matrixes only (interleaved format)
CV_EXPORTS void mulAndScaleSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, float scale, bool conjB=false, Stream& stream = Stream::Null());
//! Performs a forward or inverse discrete Fourier transform (1D or 2D) of floating point matrix.
//! Param dft_size is the size of DFT transform.
//!
//! If the source matrix is not continous, then additional copy will be done,
//! so to avoid copying ensure the source matrix is continous one. If you want to use
//! preallocated output ensure it is continuous too, otherwise it will be reallocated.
//!
//! Being implemented via CUFFT real-to-complex transform result contains only non-redundant values
//! in CUFFT's format. Result as full complex matrix for such kind of transform cannot be retrieved.
//!
//! For complex-to-real transform it is assumed that the source matrix is packed in CUFFT's format.
CV_EXPORTS void dft(const GpuMat& src, GpuMat& dst, Size dft_size, int flags=0, Stream& stream = Stream::Null());
struct CV_EXPORTS ConvolveBuf
{
Size result_size;
Size block_size;
Size user_block_size;
Size dft_size;
int spect_len;
GpuMat image_spect, templ_spect, result_spect;
GpuMat image_block, templ_block, result_data;
void create(Size image_size, Size templ_size);
static Size estimateBlockSize(Size result_size, Size templ_size);
};
//! computes convolution (or cross-correlation) of two images using discrete Fourier transform
//! supports source images of 32FC1 type only
//! result matrix will have 32FC1 type
CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr = false);
CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr, ConvolveBuf& buf, Stream& stream = Stream::Null());
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());
//! smoothes the source image and downsamples it
CV_EXPORTS void pyrDown(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
//! upsamples the source image and then smoothes it
CV_EXPORTS void pyrUp(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
//! 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());
struct CV_EXPORTS CannyBuf;
CV_EXPORTS void Canny(const GpuMat& image, GpuMat& edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
CV_EXPORTS void Canny(const GpuMat& image, CannyBuf& buf, GpuMat& edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
CV_EXPORTS void Canny(const GpuMat& dx, const GpuMat& dy, GpuMat& edges, double low_thresh, double high_thresh, bool L2gradient = false);
CV_EXPORTS void Canny(const GpuMat& dx, const GpuMat& dy, CannyBuf& buf, GpuMat& edges, double low_thresh, double high_thresh, bool L2gradient = false);
struct CV_EXPORTS CannyBuf
{
CannyBuf() {}
explicit CannyBuf(const Size& image_size, int apperture_size = 3) {create(image_size, apperture_size);}
CannyBuf(const GpuMat& dx_, const GpuMat& dy_);
void create(const Size& image_size, int apperture_size = 3);
void release();
GpuMat dx, dy;
GpuMat dx_buf, dy_buf;
GpuMat edgeBuf;
GpuMat trackBuf1, trackBuf2;
Ptr<FilterEngine_GPU> filterDX, filterDY;
};
class CV_EXPORTS ImagePyramid
{
public:
inline ImagePyramid() : nLayers_(0) {}
inline ImagePyramid(const GpuMat& img, int nLayers, Stream& stream = Stream::Null())
{
build(img, nLayers, stream);
}
void build(const GpuMat& img, int nLayers, Stream& stream = Stream::Null());
void getLayer(GpuMat& outImg, Size outRoi, Stream& stream = Stream::Null()) const;
inline void release()
{
layer0_.release();
pyramid_.clear();
nLayers_ = 0;
}
private:
GpuMat layer0_;
std::vector<GpuMat> pyramid_;
int nLayers_;
};
////////////////////////////// Matrix reductions //////////////////////////////
//! computes mean value and standard deviation of all or selected array elements
//! supports only CV_8UC1 type
CV_EXPORTS void meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev);
//! buffered version
CV_EXPORTS void meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev, GpuMat& buf);
//! computes norm of array
//! supports NORM_INF, NORM_L1, NORM_L2
//! supports all matrices except 64F
CV_EXPORTS double norm(const GpuMat& src1, int normType=NORM_L2);
//! computes norm of array
//! supports NORM_INF, NORM_L1, NORM_L2
//! supports all matrices except 64F
CV_EXPORTS double norm(const GpuMat& src1, int normType, GpuMat& buf);
//! computes norm of the difference between two arrays
//! supports NORM_INF, NORM_L1, NORM_L2
//! supports only CV_8UC1 type
CV_EXPORTS double norm(const GpuMat& src1, const GpuMat& src2, int normType=NORM_L2);
//! computes sum of array elements
//! supports only single channel images
CV_EXPORTS Scalar sum(const GpuMat& src);
//! computes sum of array elements
//! supports only single channel images
CV_EXPORTS Scalar sum(const GpuMat& src, GpuMat& buf);
//! computes sum of array elements absolute values
//! supports only single channel images
CV_EXPORTS Scalar absSum(const GpuMat& src);
//! computes sum of array elements absolute values
//! supports only single channel images
CV_EXPORTS Scalar absSum(const GpuMat& src, GpuMat& buf);
//! computes squared sum of array elements
//! supports only single channel images
CV_EXPORTS Scalar sqrSum(const GpuMat& src);
//! computes squared sum of array elements
//! supports only single channel images
CV_EXPORTS Scalar sqrSum(const GpuMat& src, GpuMat& buf);
//! finds global minimum and maximum array elements and returns their values
CV_EXPORTS void minMax(const GpuMat& src, double* minVal, double* maxVal=0, const GpuMat& mask=GpuMat());
//! finds global minimum and maximum array elements and returns their values
CV_EXPORTS void minMax(const GpuMat& src, double* minVal, double* maxVal, const GpuMat& mask, GpuMat& buf);
//! finds global minimum and maximum array elements and returns their values with locations
CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal=0, Point* minLoc=0, Point* maxLoc=0,
const GpuMat& mask=GpuMat());
//! finds global minimum and maximum array elements and returns their values with locations
CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
const GpuMat& mask, GpuMat& valbuf, GpuMat& locbuf);
//! counts non-zero array elements
CV_EXPORTS int countNonZero(const GpuMat& src);
//! counts non-zero array elements
CV_EXPORTS int countNonZero(const GpuMat& src, GpuMat& buf);
//! reduces a matrix to a vector
CV_EXPORTS void reduce(const GpuMat& mtx, GpuMat& vec, int dim, int reduceOp, int dtype = -1, Stream& stream = Stream::Null());
///////////////////////////// Calibration 3D //////////////////////////////////
CV_EXPORTS void transformPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec,
GpuMat& dst, Stream& stream = Stream::Null());
CV_EXPORTS void projectPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec,
const Mat& camera_mat, const Mat& dist_coef, GpuMat& dst,
Stream& stream = Stream::Null());
CV_EXPORTS void solvePnPRansac(const Mat& object, const Mat& image, const Mat& camera_mat,
const Mat& dist_coef, Mat& rvec, Mat& tvec, bool use_extrinsic_guess=false,
int num_iters=100, float max_dist=8.0, int min_inlier_count=100,
std::vector<int>* inliers=NULL);
//////////////////////////////// Image Labeling ////////////////////////////////
//!performs labeling via graph cuts of a 2D regular 4-connected graph.
CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& bottom, GpuMat& labels,
GpuMat& buf, Stream& stream = Stream::Null());
//!performs labeling via graph cuts of a 2D regular 8-connected graph.
CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& topLeft, GpuMat& topRight,
GpuMat& bottom, GpuMat& bottomLeft, GpuMat& bottomRight,
GpuMat& labels,
GpuMat& buf, Stream& stream = Stream::Null());
////////////////////////////////// Histograms //////////////////////////////////
//! Compute levels with even distribution. levels will have 1 row and nLevels cols and CV_32SC1 type.
CV_EXPORTS void evenLevels(GpuMat& 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(const GpuMat& src, GpuMat& hist, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());
CV_EXPORTS void histEven(const GpuMat& src, GpuMat& hist, GpuMat& buf, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());
//! 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(const GpuMat& src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null());
CV_EXPORTS void histEven(const GpuMat& src, GpuMat hist[4], GpuMat& buf, int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null());
//! 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(const GpuMat& src, GpuMat& hist, const GpuMat& levels, Stream& stream = Stream::Null());
CV_EXPORTS void histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels, GpuMat& buf, Stream& stream = Stream::Null());
//! 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(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], Stream& stream = Stream::Null());
CV_EXPORTS void histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], GpuMat& buf, Stream& stream = Stream::Null());
//! Calculates histogram for 8u one channel image
//! Output hist will have one row, 256 cols and CV32SC1 type.
CV_EXPORTS void calcHist(const GpuMat& src, GpuMat& hist, Stream& stream = Stream::Null());
CV_EXPORTS void calcHist(const GpuMat& src, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null());
//! normalizes the grayscale image brightness and contrast by normalizing its histogram
CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, Stream& stream = Stream::Null());
CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null());
//////////////////////////////// StereoBM_GPU ////////////////////////////////
class CV_EXPORTS StereoBM_GPU
{
public:
enum { BASIC_PRESET = 0, PREFILTER_XSOBEL = 1 };
enum { DEFAULT_NDISP = 64, DEFAULT_WINSZ = 19 };
//! the default constructor
StereoBM_GPU();
//! the full constructor taking the camera-specific preset, number of disparities and the SAD window size. ndisparities must be multiple of 8.
StereoBM_GPU(int preset, int ndisparities = DEFAULT_NDISP, int winSize = DEFAULT_WINSZ);
//! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair
//! Output disparity has CV_8U type.
void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null());
//! Some heuristics that tries to estmate
// if current GPU will be faster than CPU in this algorithm.
// It queries current active device.
static bool checkIfGpuCallReasonable();
int preset;
int ndisp;
int winSize;
// If avergeTexThreshold == 0 => post procesing is disabled
// If avergeTexThreshold != 0 then disparity is set 0 in each point (x,y) where for left image
// SumOfHorizontalGradiensInWindow(x, y, winSize) < (winSize * winSize) * avergeTexThreshold
// i.e. input left image is low textured.
float avergeTexThreshold;
private:
GpuMat minSSD, leBuf, riBuf;
};
////////////////////////// StereoBeliefPropagation ///////////////////////////
// "Efficient Belief Propagation for Early Vision"
// P.Felzenszwalb
class CV_EXPORTS StereoBeliefPropagation
{
public:
enum { DEFAULT_NDISP = 64 };
enum { DEFAULT_ITERS = 5 };
enum { DEFAULT_LEVELS = 5 };
static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels);
//! the default constructor
explicit StereoBeliefPropagation(int ndisp = DEFAULT_NDISP,
int iters = DEFAULT_ITERS,
int levels = DEFAULT_LEVELS,
int msg_type = CV_32F);
//! the full constructor taking the number of disparities, number of BP iterations on each level,
//! number of levels, truncation of data cost, data weight,
//! truncation of discontinuity cost and discontinuity single jump
//! DataTerm = data_weight * min(fabs(I2-I1), max_data_term)
//! DiscTerm = min(disc_single_jump * fabs(f1-f2), max_disc_term)
//! please see paper for more details
StereoBeliefPropagation(int ndisp, int iters, int levels,
float max_data_term, float data_weight,
float max_disc_term, float disc_single_jump,
int msg_type = CV_32F);
//! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair,
//! if disparity is empty output type will be CV_16S else output type will be disparity.type().
void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null());
//! version for user specified data term
void operator()(const GpuMat& data, GpuMat& disparity, Stream& stream = Stream::Null());
int ndisp;
int iters;
int levels;
float max_data_term;
float data_weight;
float max_disc_term;
float disc_single_jump;
int msg_type;
private:
GpuMat u, d, l, r, u2, d2, l2, r2;
std::vector<GpuMat> datas;
GpuMat out;
};
/////////////////////////// StereoConstantSpaceBP ///////////////////////////
// "A Constant-Space Belief Propagation Algorithm for Stereo Matching"
// Qingxiong Yang, Liang Wang, Narendra Ahuja
// http://vision.ai.uiuc.edu/~qyang6/
class CV_EXPORTS StereoConstantSpaceBP
{
public:
enum { DEFAULT_NDISP = 128 };
enum { DEFAULT_ITERS = 8 };
enum { DEFAULT_LEVELS = 4 };
enum { DEFAULT_NR_PLANE = 4 };
static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels, int& nr_plane);
//! the default constructor
explicit StereoConstantSpaceBP(int ndisp = DEFAULT_NDISP,
int iters = DEFAULT_ITERS,
int levels = DEFAULT_LEVELS,
int nr_plane = DEFAULT_NR_PLANE,
int msg_type = CV_32F);
//! the full constructor taking the number of disparities, number of BP iterations on each level,
//! number of levels, number of active disparity on the first level, truncation of data cost, data weight,
//! truncation of discontinuity cost, discontinuity single jump and minimum disparity threshold
StereoConstantSpaceBP(int ndisp, int iters, int levels, int nr_plane,
float max_data_term, float data_weight, float max_disc_term, float disc_single_jump,
int min_disp_th = 0,
int msg_type = CV_32F);
//! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair,
//! if disparity is empty output type will be CV_16S else output type will be disparity.type().
void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null());
int ndisp;
int iters;
int levels;
int nr_plane;
float max_data_term;
float data_weight;
float max_disc_term;
float disc_single_jump;
int min_disp_th;
int msg_type;
bool use_local_init_data_cost;
private:
GpuMat messages_buffers;
GpuMat temp;
GpuMat out;
};
/////////////////////////// DisparityBilateralFilter ///////////////////////////
// Disparity map refinement using joint bilateral filtering given a single color image.
// Qingxiong Yang, Liang Wang, Narendra Ahuja
// http://vision.ai.uiuc.edu/~qyang6/
class CV_EXPORTS DisparityBilateralFilter
{
public:
enum { DEFAULT_NDISP = 64 };
enum { DEFAULT_RADIUS = 3 };
enum { DEFAULT_ITERS = 1 };
//! the default constructor
explicit DisparityBilateralFilter(int ndisp = DEFAULT_NDISP, int radius = DEFAULT_RADIUS, int iters = DEFAULT_ITERS);
//! the full constructor taking the number of disparities, filter radius,
//! number of iterations, truncation of data continuity, truncation of disparity continuity
//! and filter range sigma
DisparityBilateralFilter(int ndisp, int radius, int iters, float edge_threshold, float max_disc_threshold, float sigma_range);
//! the disparity map refinement operator. Refine disparity map using joint bilateral filtering given a single color image.
//! disparity must have CV_8U or CV_16S type, image must have CV_8UC1 or CV_8UC3 type.
void operator()(const GpuMat& disparity, const GpuMat& image, GpuMat& dst, Stream& stream = Stream::Null());
private:
int ndisp;
int radius;
int iters;
float edge_threshold;
float max_disc_threshold;
float sigma_range;
GpuMat table_color;
GpuMat table_space;
};
//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
struct CV_EXPORTS HOGDescriptor
{
enum { DEFAULT_WIN_SIGMA = -1 };
enum { DEFAULT_NLEVELS = 64 };
enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };
HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16),
Size block_stride=Size(8, 8), Size cell_size=Size(8, 8),
int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA,
double threshold_L2hys=0.2, bool gamma_correction=true,
int nlevels=DEFAULT_NLEVELS);
size_t getDescriptorSize() const;
size_t getBlockHistogramSize() const;
void setSVMDetector(const vector<float>& detector);
static vector<float> getDefaultPeopleDetector();
static vector<float> getPeopleDetector48x96();
static vector<float> getPeopleDetector64x128();
void detect(const GpuMat& img, vector<Point>& found_locations,
double hit_threshold=0, Size win_stride=Size(),
Size padding=Size());
void detectMultiScale(const GpuMat& img, vector<Rect>& found_locations,
double hit_threshold=0, Size win_stride=Size(),
Size padding=Size(), double scale0=1.05,
int group_threshold=2);
void getDescriptors(const GpuMat& img, Size win_stride,
GpuMat& descriptors,
int descr_format=DESCR_FORMAT_COL_BY_COL);
Size win_size;
Size block_size;
Size block_stride;
Size cell_size;
int nbins;
double win_sigma;
double threshold_L2hys;
bool gamma_correction;
int nlevels;
protected:
void computeBlockHistograms(const GpuMat& img);
void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle);
double getWinSigma() const;
bool checkDetectorSize() const;
static int numPartsWithin(int size, int part_size, int stride);
static Size numPartsWithin(Size size, Size part_size, Size stride);
// Coefficients of the separating plane
float free_coef;
GpuMat detector;
// Results of the last classification step
GpuMat labels, labels_buf;
Mat labels_host;
// Results of the last histogram evaluation step
GpuMat block_hists, block_hists_buf;
// Gradients conputation results
GpuMat grad, qangle, grad_buf, qangle_buf;
// returns subbuffer with required size, reallocates buffer if nessesary.
static GpuMat getBuffer(const Size& sz, int type, GpuMat& buf);
static GpuMat getBuffer(int rows, int cols, int type, GpuMat& buf);
std::vector<GpuMat> image_scales;
};
////////////////////////////////// BruteForceMatcher //////////////////////////////////
class CV_EXPORTS BruteForceMatcher_GPU_base
{
public:
enum DistType {L1Dist = 0, L2Dist, HammingDist};
explicit BruteForceMatcher_GPU_base(DistType distType = L2Dist);
// Add descriptors to train descriptor collection
void add(const std::vector<GpuMat>& descCollection);
// Get train descriptors collection
const std::vector<GpuMat>& getTrainDescriptors() const;
// Clear train descriptors collection
void clear();
// Return true if there are not train descriptors in collection
bool empty() const;
// Return true if the matcher supports mask in match methods
bool isMaskSupported() const;
// Find one best match for each query descriptor
void matchSingle(const GpuMat& query, const GpuMat& train,
GpuMat& trainIdx, GpuMat& distance,
const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
// Download trainIdx and distance and convert it to CPU vector with DMatch
static void matchDownload(const GpuMat& trainIdx, const GpuMat& distance, std::vector<DMatch>& matches);
// Convert trainIdx and distance to vector with DMatch
static void matchConvert(const Mat& trainIdx, const Mat& distance, std::vector<DMatch>& matches);
// Find one best match for each query descriptor
void match(const GpuMat& query, const GpuMat& train, std::vector<DMatch>& matches, const GpuMat& mask = GpuMat());
// Make gpu collection of trains and masks in suitable format for matchCollection function
void makeGpuCollection(GpuMat& trainCollection, GpuMat& maskCollection, const std::vector<GpuMat>& masks = std::vector<GpuMat>());
// Find one best match from train collection for each query descriptor
void matchCollection(const GpuMat& query, const GpuMat& trainCollection,
GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
const GpuMat& masks = GpuMat(), Stream& stream = Stream::Null());
// Download trainIdx, imgIdx and distance and convert it to vector with DMatch
static void matchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, std::vector<DMatch>& matches);
// Convert trainIdx, imgIdx and distance to vector with DMatch
static void matchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, std::vector<DMatch>& matches);
// Find one best match from train collection for each query descriptor.
void match(const GpuMat& query, std::vector<DMatch>& matches, const std::vector<GpuMat>& masks = std::vector<GpuMat>());
// Find k best matches for each query descriptor (in increasing order of distances)
void knnMatchSingle(const GpuMat& query, const GpuMat& train,
GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k,
const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
// Download trainIdx and distance and convert it to vector with DMatch
// compactResult is used when mask is not empty. If compactResult is false matches
// vector will have the same size as queryDescriptors rows. If compactResult is true
// matches vector will not contain matches for fully masked out query descriptors.
static void knnMatchDownload(const GpuMat& trainIdx, const GpuMat& distance,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
// Convert trainIdx and distance to vector with DMatch
static void knnMatchConvert(const Mat& trainIdx, const Mat& distance,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
// Find k best matches for each query descriptor (in increasing order of distances).
// compactResult is used when mask is not empty. If compactResult is false matches
// vector will have the same size as queryDescriptors rows. If compactResult is true
// matches vector will not contain matches for fully masked out query descriptors.
void knnMatch(const GpuMat& query, const GpuMat& train,
std::vector< std::vector<DMatch> >& matches, int k, const GpuMat& mask = GpuMat(),
bool compactResult = false);
// Find k best matches from train collection for each query descriptor (in increasing order of distances)
void knnMatch2Collection(const GpuMat& query, const GpuMat& trainCollection,
GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
const GpuMat& maskCollection = GpuMat(), Stream& stream = Stream::Null());
// Download trainIdx and distance and convert it to vector with DMatch
// compactResult is used when mask is not empty. If compactResult is false matches
// vector will have the same size as queryDescriptors rows. If compactResult is true
// matches vector will not contain matches for fully masked out query descriptors.
static void knnMatch2Download(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
// Convert trainIdx and distance to vector with DMatch
static void knnMatch2Convert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
// Find k best matches for each query descriptor (in increasing order of distances).
// compactResult is used when mask is not empty. If compactResult is false matches
// vector will have the same size as queryDescriptors rows. If compactResult is true
// matches vector will not contain matches for fully masked out query descriptors.
void knnMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches, int k,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);
// Find best matches for each query descriptor which have distance less than maxDistance.
// nMatches.at<int>(0, queryIdx) will contain matches count for queryIdx.
// carefully nMatches can be greater than trainIdx.cols - it means that matcher didn't find all matches,
// because it didn't have enough memory.
// If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nTrain / 100), 10),
// otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
// Matches doesn't sorted.
void radiusMatchSingle(const GpuMat& query, const GpuMat& train,
GpuMat& trainIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
// Download trainIdx, nMatches and distance and convert it to vector with DMatch.
// matches will be sorted in increasing order of distances.
// compactResult is used when mask is not empty. If compactResult is false matches
// vector will have the same size as queryDescriptors rows. If compactResult is true
// matches vector will not contain matches for fully masked out query descriptors.
static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& distance, const GpuMat& nMatches,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
// Convert trainIdx, nMatches and distance to vector with DMatch.
static void radiusMatchConvert(const Mat& trainIdx, const Mat& distance, const Mat& nMatches,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
// Find best matches for each query descriptor which have distance less than maxDistance
// in increasing order of distances).
void radiusMatch(const GpuMat& query, const GpuMat& train,
std::vector< std::vector<DMatch> >& matches, float maxDistance,
const GpuMat& mask = GpuMat(), bool compactResult = false);
// Find best matches for each query descriptor which have distance less than maxDistance.
// If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nQuery / 100), 10),
// otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
// Matches doesn't sorted.
void radiusMatchCollection(const GpuMat& query, GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(), Stream& stream = Stream::Null());
// Download trainIdx, imgIdx, nMatches and distance and convert it to vector with DMatch.
// matches will be sorted in increasing order of distances.
// compactResult is used when mask is not empty. If compactResult is false matches
// vector will have the same size as queryDescriptors rows. If compactResult is true
// matches vector will not contain matches for fully masked out query descriptors.
static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, const GpuMat& nMatches,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
// Convert trainIdx, nMatches and distance to vector with DMatch.
static void radiusMatchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, const Mat& nMatches,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
// Find best matches from train collection for each query descriptor which have distance less than
// maxDistance (in increasing order of distances).
void radiusMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches, float maxDistance,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);
DistType distType;
private:
std::vector<GpuMat> trainDescCollection;
};
template <class Distance>
class CV_EXPORTS BruteForceMatcher_GPU;
template <typename T>
class CV_EXPORTS BruteForceMatcher_GPU< L1<T> > : public BruteForceMatcher_GPU_base
{
public:
explicit BruteForceMatcher_GPU() : BruteForceMatcher_GPU_base(L1Dist) {}
explicit BruteForceMatcher_GPU(L1<T> /*d*/) : BruteForceMatcher_GPU_base(L1Dist) {}
};
template <typename T>
class CV_EXPORTS BruteForceMatcher_GPU< L2<T> > : public BruteForceMatcher_GPU_base
{
public:
explicit BruteForceMatcher_GPU() : BruteForceMatcher_GPU_base(L2Dist) {}
explicit BruteForceMatcher_GPU(L2<T> /*d*/) : BruteForceMatcher_GPU_base(L2Dist) {}
};
template <> class CV_EXPORTS BruteForceMatcher_GPU< Hamming > : public BruteForceMatcher_GPU_base
{
public:
explicit BruteForceMatcher_GPU() : BruteForceMatcher_GPU_base(HammingDist) {}
explicit BruteForceMatcher_GPU(Hamming /*d*/) : BruteForceMatcher_GPU_base(HammingDist) {}
};
////////////////////////////////// CascadeClassifier_GPU //////////////////////////////////////////
// The cascade classifier class for object detection.
class CV_EXPORTS CascadeClassifier_GPU
{
public:
CascadeClassifier_GPU();
CascadeClassifier_GPU(const std::string& filename);
~CascadeClassifier_GPU();
bool empty() const;
bool load(const std::string& filename);
void release();
/* returns number of detected objects */
int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size());
bool findLargestObject;
bool visualizeInPlace;
Size getClassifierSize() const;
private:
struct CascadeClassifierImpl;
CascadeClassifierImpl* impl;
};
////////////////////////////////// SURF //////////////////////////////////////////
class CV_EXPORTS SURF_GPU
{
public:
enum KeypointLayout
{
X_ROW = 0,
Y_ROW,
LAPLACIAN_ROW,
OCTAVE_ROW,
SIZE_ROW,
ANGLE_ROW,
HESSIAN_ROW,
ROWS_COUNT
};
//! the default constructor
SURF_GPU();
//! the full constructor taking all the necessary parameters
explicit SURF_GPU(double _hessianThreshold, int _nOctaves=4,
int _nOctaveLayers=2, bool _extended=false, float _keypointsRatio=0.01f, bool _upright = false);
//! returns the descriptor size in float's (64 or 128)
int descriptorSize() const;
//! upload host keypoints to device memory
void uploadKeypoints(const vector<KeyPoint>& keypoints, GpuMat& keypointsGPU);
//! download keypoints from device to host memory
void downloadKeypoints(const GpuMat& keypointsGPU, vector<KeyPoint>& keypoints);
//! download descriptors from device to host memory
void downloadDescriptors(const GpuMat& descriptorsGPU, vector<float>& descriptors);
//! finds the keypoints using fast hessian detector used in SURF
//! supports CV_8UC1 images
//! keypoints will have nFeature cols and 6 rows
//! keypoints.ptr<float>(X_ROW)[i] will contain x coordinate of i'th feature
//! keypoints.ptr<float>(Y_ROW)[i] will contain y coordinate of i'th feature
//! keypoints.ptr<float>(LAPLACIAN_ROW)[i] will contain laplacian sign of i'th feature
//! keypoints.ptr<float>(OCTAVE_ROW)[i] will contain octave of i'th feature
//! keypoints.ptr<float>(SIZE_ROW)[i] will contain size of i'th feature
//! keypoints.ptr<float>(ANGLE_ROW)[i] will contain orientation of i'th feature
//! keypoints.ptr<float>(HESSIAN_ROW)[i] will contain response of i'th feature
void operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints);
//! finds the keypoints and computes their descriptors.
//! Optionally it can compute descriptors for the user-provided keypoints and recompute keypoints direction
void operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors,
bool useProvidedKeypoints = false);
void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints);
void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints, GpuMat& descriptors,
bool useProvidedKeypoints = false);
void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints, std::vector<float>& descriptors,
bool useProvidedKeypoints = false);
void releaseMemory();
// SURF parameters
double hessianThreshold;
int nOctaves;
int nOctaveLayers;
bool extended;
bool upright;
//! max keypoints = min(keypointsRatio * img.size().area(), 65535)
float keypointsRatio;
GpuMat sum, mask1, maskSum, intBuffer;
GpuMat det, trace;
GpuMat maxPosBuffer;
};
////////////////////////////////// FAST //////////////////////////////////////////
class CV_EXPORTS FAST_GPU
{
public:
enum
{
LOCATION_ROW = 0,
RESPONSE_ROW,
ROWS_COUNT
};
// all features have same size
static const int FEATURE_SIZE = 7;
explicit FAST_GPU(int threshold, bool nonmaxSupression = true, double keypointsRatio = 0.05);
//! finds the keypoints using FAST detector
//! supports only CV_8UC1 images
void operator ()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints);
void operator ()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints);
//! download keypoints from device to host memory
void downloadKeypoints(const GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints);
//! convert keypoints to KeyPoint vector
void convertKeypoints(const Mat& h_keypoints, std::vector<KeyPoint>& keypoints);
//! release temporary buffer's memory
void release();
bool nonmaxSupression;
int threshold;
//! max keypoints = keypointsRatio * img.size().area()
double keypointsRatio;
//! find keypoints and compute it's response if nonmaxSupression is true
//! return count of detected keypoints
int calcKeyPointsLocation(const GpuMat& image, const GpuMat& mask);
//! get final array of keypoints
//! performs nonmax supression if needed
//! return final count of keypoints
int getKeyPoints(GpuMat& keypoints);
private:
GpuMat kpLoc_;
int count_;
GpuMat score_;
GpuMat d_keypoints_;
};
////////////////////////////////// ORB //////////////////////////////////////////
class CV_EXPORTS ORB_GPU
{
public:
enum
{
X_ROW = 0,
Y_ROW,
RESPONSE_ROW,
ANGLE_ROW,
OCTAVE_ROW,
SIZE_ROW,
ROWS_COUNT
};
enum
{
DEFAULT_FAST_THRESHOLD = 20
};
//! Constructor
explicit ORB_GPU(int nFeatures = 500, float scaleFactor = 1.2f, int nLevels = 8, int edgeThreshold = 31,
int firstLevel = 0, int WTA_K = 2, int scoreType = 0, int patchSize = 31);
//! Compute the ORB features on an image
//! image - the image to compute the features (supports only CV_8UC1 images)
//! mask - the mask to apply
//! keypoints - the resulting keypoints
void operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints);
void operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints);
//! Compute the ORB features and descriptors on an image
//! image - the image to compute the features (supports only CV_8UC1 images)
//! mask - the mask to apply
//! keypoints - the resulting keypoints
//! descriptors - descriptors array
void operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints, GpuMat& descriptors);
void operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors);
//! download keypoints from device to host memory
void downloadKeyPoints(GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints);
//! convert keypoints to KeyPoint vector
void convertKeyPoints(Mat& d_keypoints, std::vector<KeyPoint>& keypoints);
//! returns the descriptor size in bytes
inline int descriptorSize() const { return kBytes; }
inline void setFastParams(int threshold, bool nonmaxSupression = true)
{
fastDetector_.threshold = threshold;
fastDetector_.nonmaxSupression = nonmaxSupression;
}
//! release temporary buffer's memory
void release();
//! if true, image will be blurred before descriptors calculation
bool blurForDescriptor;
private:
enum { kBytes = 32 };
void buildScalePyramids(const GpuMat& image, const GpuMat& mask);
void computeKeyPointsPyramid();
void computeDescriptors(GpuMat& descriptors);
void mergeKeyPoints(GpuMat& keypoints);
int nFeatures_;
float scaleFactor_;
int nLevels_;
int edgeThreshold_;
int firstLevel_;
int WTA_K_;
int scoreType_;
int patchSize_;
// The number of desired features per scale
std::vector<size_t> n_features_per_level_;
// Points to compute BRIEF descriptors from
GpuMat pattern_;
std::vector<GpuMat> imagePyr_;
std::vector<GpuMat> maskPyr_;
GpuMat buf_;
std::vector<GpuMat> keyPointsPyr_;
std::vector<int> keyPointsCount_;
FAST_GPU fastDetector_;
Ptr<FilterEngine_GPU> blurFilter;
GpuMat d_keypoints_;
};
////////////////////////////////// Optical Flow //////////////////////////////////////////
class CV_EXPORTS BroxOpticalFlow
{
public:
BroxOpticalFlow(float alpha_, float gamma_, float scale_factor_, int inner_iterations_, int outer_iterations_, int solver_iterations_) :
alpha(alpha_), gamma(gamma_), scale_factor(scale_factor_),
inner_iterations(inner_iterations_), outer_iterations(outer_iterations_), solver_iterations(solver_iterations_)
{
}
//! Compute optical flow
//! frame0 - source frame (supports only CV_32FC1 type)
//! frame1 - frame to track (with the same size and type as frame0)
//! u - flow horizontal component (along x axis)
//! v - flow vertical component (along y axis)
void operator ()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& u, GpuMat& v, Stream& stream = Stream::Null());
//! flow smoothness
float alpha;
//! gradient constancy importance
float gamma;
//! pyramid scale factor
float scale_factor;
//! number of lagged non-linearity iterations (inner loop)
int inner_iterations;
//! number of warping iterations (number of pyramid levels)
int outer_iterations;
//! number of linear system solver iterations
int solver_iterations;
GpuMat buf;
};
class CV_EXPORTS GoodFeaturesToTrackDetector_GPU
{
public:
explicit GoodFeaturesToTrackDetector_GPU(int maxCorners_ = 1000, double qualityLevel_ = 0.01, double minDistance_ = 0.0,
int blockSize_ = 3, bool useHarrisDetector_ = false, double harrisK_ = 0.04)
{
maxCorners = maxCorners_;
qualityLevel = qualityLevel_;
minDistance = minDistance_;
blockSize = blockSize_;
useHarrisDetector = useHarrisDetector_;
harrisK = harrisK_;
}
//! return 1 rows matrix with CV_32FC2 type
void operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask = GpuMat());
int maxCorners;
double qualityLevel;
double minDistance;
int blockSize;
bool useHarrisDetector;
double harrisK;
void releaseMemory()
{
Dx_.release();
Dy_.release();
buf_.release();
eig_.release();
minMaxbuf_.release();
tmpCorners_.release();
}
private:
GpuMat Dx_;
GpuMat Dy_;
GpuMat buf_;
GpuMat eig_;
GpuMat minMaxbuf_;
GpuMat tmpCorners_;
};
class CV_EXPORTS PyrLKOpticalFlow
{
public:
PyrLKOpticalFlow()
{
winSize = Size(21, 21);
maxLevel = 3;
iters = 30;
derivLambda = 0.5;
useInitialFlow = false;
minEigThreshold = 1e-4f;
getMinEigenVals = false;
isDeviceArch11_ = !DeviceInfo().supports(FEATURE_SET_COMPUTE_12);
}
void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts,
GpuMat& status, GpuMat* err = 0);
void dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err = 0);
Size winSize;
int maxLevel;
int iters;
double derivLambda;
bool useInitialFlow;
float minEigThreshold;
bool getMinEigenVals;
void releaseMemory()
{
dx_calcBuf_.release();
dy_calcBuf_.release();
prevPyr_.clear();
nextPyr_.clear();
dx_buf_.release();
dy_buf_.release();
uPyr_.clear();
vPyr_.clear();
}
private:
void calcSharrDeriv(const GpuMat& src, GpuMat& dx, GpuMat& dy);
void buildImagePyramid(const GpuMat& img0, vector<GpuMat>& pyr, bool withBorder);
GpuMat dx_calcBuf_;
GpuMat dy_calcBuf_;
vector<GpuMat> prevPyr_;
vector<GpuMat> nextPyr_;
GpuMat dx_buf_;
GpuMat dy_buf_;
vector<GpuMat> uPyr_;
vector<GpuMat> vPyr_;
bool isDeviceArch11_;
};
class CV_EXPORTS FarnebackOpticalFlow
{
public:
FarnebackOpticalFlow()
{
numLevels = 5;
pyrScale = 0.5;
fastPyramids = false;
winSize = 13;
numIters = 10;
polyN = 5;
polySigma = 1.1;
flags = 0;
isDeviceArch11_ = !DeviceInfo().supports(FEATURE_SET_COMPUTE_12);
}
int numLevels;
double pyrScale;
bool fastPyramids;
int winSize;
int numIters;
int polyN;
double polySigma;
int flags;
void operator ()(const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s = Stream::Null());
void releaseMemory()
{
frames_[0].release();
frames_[1].release();
pyrLevel_[0].release();
pyrLevel_[1].release();
M_.release();
bufM_.release();
R_[0].release();
R_[1].release();
blurredFrame_[0].release();
blurredFrame_[1].release();
pyramid0_.clear();
pyramid1_.clear();
}
private:
void prepareGaussian(
int n, double sigma, float *g, float *xg, float *xxg,
double &ig11, double &ig03, double &ig33, double &ig55);
void setPolynomialExpansionConsts(int n, double sigma);
void updateFlow_boxFilter(
const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat &flowy,
GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]);
void updateFlow_gaussianBlur(
const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat& flowy,
GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]);
GpuMat frames_[2];
GpuMat pyrLevel_[2], M_, bufM_, R_[2], blurredFrame_[2];
std::vector<GpuMat> pyramid0_, pyramid1_;
bool isDeviceArch11_;
};
//! Interpolate frames (images) using provided optical flow (displacement field).
//! frame0 - frame 0 (32-bit floating point images, single channel)
//! frame1 - frame 1 (the same type and size)
//! fu - forward horizontal displacement
//! fv - forward vertical displacement
//! bu - backward horizontal displacement
//! bv - backward vertical displacement
//! pos - new frame position
//! newFrame - new frame
//! buf - temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 GpuMat;
//! occlusion masks 0, occlusion masks 1,
//! interpolated forward flow 0, interpolated forward flow 1,
//! interpolated backward flow 0, interpolated backward flow 1
//!
CV_EXPORTS void interpolateFrames(const GpuMat& frame0, const GpuMat& frame1,
const GpuMat& fu, const GpuMat& fv,
const GpuMat& bu, const GpuMat& bv,
float pos, GpuMat& newFrame, GpuMat& buf,
Stream& stream = Stream::Null());
CV_EXPORTS void createOpticalFlowNeedleMap(const GpuMat& u, const GpuMat& v, GpuMat& vertex, GpuMat& colors);
} // namespace gpu
} // namespace cv
#endif /* __OPENCV_GPU_HPP__ */
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