mirror of
https://github.com/opencv/opencv.git
synced 2024-11-26 20:20:20 +08:00
396 lines
14 KiB
C++
396 lines
14 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_CUDAOPTFLOW_HPP__
|
||
#define __OPENCV_CUDAOPTFLOW_HPP__
|
||
|
||
#ifndef __cplusplus
|
||
# error cudaoptflow.hpp header must be compiled as C++
|
||
#endif
|
||
|
||
#include "opencv2/core/cuda.hpp"
|
||
|
||
/**
|
||
@addtogroup cuda
|
||
@{
|
||
@defgroup cudaoptflow Optical Flow
|
||
@}
|
||
*/
|
||
|
||
namespace cv { namespace cuda {
|
||
|
||
//! @addtogroup cudaoptflow
|
||
//! @{
|
||
|
||
/** @brief Class computing the optical flow for two images using Brox et al Optical Flow algorithm
|
||
(@cite Brox2004). :
|
||
*/
|
||
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;
|
||
};
|
||
|
||
/** @brief Class used for calculating an optical flow.
|
||
|
||
The class can calculate an optical flow for a sparse feature set or dense optical flow using the
|
||
iterative Lucas-Kanade method with pyramids.
|
||
|
||
@sa calcOpticalFlowPyrLK
|
||
|
||
@note
|
||
- An example of the Lucas Kanade optical flow algorithm can be found at
|
||
opencv_source_code/samples/gpu/pyrlk_optical_flow.cpp
|
||
*/
|
||
class CV_EXPORTS PyrLKOpticalFlow
|
||
{
|
||
public:
|
||
PyrLKOpticalFlow();
|
||
|
||
/** @brief Calculate an optical flow for a sparse feature set.
|
||
|
||
@param prevImg First 8-bit input image (supports both grayscale and color images).
|
||
@param nextImg Second input image of the same size and the same type as prevImg .
|
||
@param prevPts Vector of 2D points for which the flow needs to be found. It must be one row matrix
|
||
with CV_32FC2 type.
|
||
@param nextPts Output vector of 2D points (with single-precision floating-point coordinates)
|
||
containing the calculated new positions of input features in the second image. When useInitialFlow
|
||
is true, the vector must have the same size as in the input.
|
||
@param status Output status vector (CV_8UC1 type). Each element of the vector is set to 1 if the
|
||
flow for the corresponding features has been found. Otherwise, it is set to 0.
|
||
@param err Output vector (CV_32FC1 type) that contains the difference between patches around the
|
||
original and moved points or min eigen value if getMinEigenVals is checked. It can be NULL, if not
|
||
needed.
|
||
|
||
@sa calcOpticalFlowPyrLK
|
||
*/
|
||
void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts,
|
||
GpuMat& status, GpuMat* err = 0);
|
||
|
||
/** @brief Calculate dense optical flow.
|
||
|
||
@param prevImg First 8-bit grayscale input image.
|
||
@param nextImg Second input image of the same size and the same type as prevImg .
|
||
@param u Horizontal component of the optical flow of the same size as input images, 32-bit
|
||
floating-point, single-channel
|
||
@param v Vertical component of the optical flow of the same size as input images, 32-bit
|
||
floating-point, single-channel
|
||
@param err Output vector (CV_32FC1 type) that contains the difference between patches around the
|
||
original and moved points or min eigen value if getMinEigenVals is checked. It can be NULL, if not
|
||
needed.
|
||
*/
|
||
void dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err = 0);
|
||
|
||
/** @brief Releases inner buffers memory.
|
||
*/
|
||
void releaseMemory();
|
||
|
||
Size winSize;
|
||
int maxLevel;
|
||
int iters;
|
||
bool useInitialFlow;
|
||
|
||
private:
|
||
std::vector<GpuMat> prevPyr_;
|
||
std::vector<GpuMat> nextPyr_;
|
||
|
||
GpuMat buf_;
|
||
|
||
GpuMat uPyr_[2];
|
||
GpuMat vPyr_[2];
|
||
};
|
||
|
||
/** @brief Class computing a dense optical flow using the Gunnar Farneback’s algorithm. :
|
||
*/
|
||
class CV_EXPORTS FarnebackOpticalFlow
|
||
{
|
||
public:
|
||
FarnebackOpticalFlow()
|
||
{
|
||
numLevels = 5;
|
||
pyrScale = 0.5;
|
||
fastPyramids = false;
|
||
winSize = 13;
|
||
numIters = 10;
|
||
polyN = 5;
|
||
polySigma = 1.1;
|
||
flags = 0;
|
||
}
|
||
|
||
int numLevels;
|
||
double pyrScale;
|
||
bool fastPyramids;
|
||
int winSize;
|
||
int numIters;
|
||
int polyN;
|
||
double polySigma;
|
||
int flags;
|
||
|
||
/** @brief Computes a dense optical flow using the Gunnar Farneback’s algorithm.
|
||
|
||
@param frame0 First 8-bit gray-scale input image
|
||
@param frame1 Second 8-bit gray-scale input image
|
||
@param flowx Flow horizontal component
|
||
@param flowy Flow vertical component
|
||
@param s Stream
|
||
|
||
@sa calcOpticalFlowFarneback
|
||
*/
|
||
void operator ()(const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s = Stream::Null());
|
||
|
||
/** @brief Releases unused auxiliary memory buffers.
|
||
*/
|
||
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_;
|
||
};
|
||
|
||
// Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method
|
||
//
|
||
// see reference:
|
||
// [1] C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
|
||
// [2] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
|
||
class CV_EXPORTS OpticalFlowDual_TVL1_CUDA
|
||
{
|
||
public:
|
||
OpticalFlowDual_TVL1_CUDA();
|
||
|
||
void operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy);
|
||
|
||
void collectGarbage();
|
||
|
||
/**
|
||
* Time step of the numerical scheme.
|
||
*/
|
||
double tau;
|
||
|
||
/**
|
||
* Weight parameter for the data term, attachment parameter.
|
||
* This is the most relevant parameter, which determines the smoothness of the output.
|
||
* The smaller this parameter is, the smoother the solutions we obtain.
|
||
* It depends on the range of motions of the images, so its value should be adapted to each image sequence.
|
||
*/
|
||
double lambda;
|
||
|
||
/**
|
||
* Weight parameter for (u - v)^2, tightness parameter.
|
||
* It serves as a link between the attachment and the regularization terms.
|
||
* In theory, it should have a small value in order to maintain both parts in correspondence.
|
||
* The method is stable for a large range of values of this parameter.
|
||
*/
|
||
|
||
double gamma;
|
||
/**
|
||
* parameter used for motion estimation. It adds a variable allowing for illumination variations
|
||
* Set this parameter to 1. if you have varying illumination.
|
||
* See: Chambolle et al, A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging
|
||
* Journal of Mathematical imaging and vision, may 2011 Vol 40 issue 1, pp 120-145
|
||
*/
|
||
double theta;
|
||
|
||
/**
|
||
* Number of scales used to create the pyramid of images.
|
||
*/
|
||
int nscales;
|
||
|
||
/**
|
||
* Number of warpings per scale.
|
||
* Represents the number of times that I1(x+u0) and grad( I1(x+u0) ) are computed per scale.
|
||
* This is a parameter that assures the stability of the method.
|
||
* It also affects the running time, so it is a compromise between speed and accuracy.
|
||
*/
|
||
int warps;
|
||
|
||
/**
|
||
* Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time.
|
||
* A small value will yield more accurate solutions at the expense of a slower convergence.
|
||
*/
|
||
double epsilon;
|
||
|
||
/**
|
||
* Stopping criterion iterations number used in the numerical scheme.
|
||
*/
|
||
int iterations;
|
||
|
||
double scaleStep;
|
||
|
||
bool useInitialFlow;
|
||
|
||
private:
|
||
void procOneScale(const GpuMat& I0, const GpuMat& I1, GpuMat& u1, GpuMat& u2, GpuMat& u3);
|
||
|
||
std::vector<GpuMat> I0s;
|
||
std::vector<GpuMat> I1s;
|
||
std::vector<GpuMat> u1s;
|
||
std::vector<GpuMat> u2s;
|
||
std::vector<GpuMat> u3s;
|
||
|
||
GpuMat I1x_buf;
|
||
GpuMat I1y_buf;
|
||
|
||
GpuMat I1w_buf;
|
||
GpuMat I1wx_buf;
|
||
GpuMat I1wy_buf;
|
||
|
||
GpuMat grad_buf;
|
||
GpuMat rho_c_buf;
|
||
|
||
GpuMat p11_buf;
|
||
GpuMat p12_buf;
|
||
GpuMat p21_buf;
|
||
GpuMat p22_buf;
|
||
GpuMat p31_buf;
|
||
GpuMat p32_buf;
|
||
|
||
GpuMat diff_buf;
|
||
GpuMat norm_buf;
|
||
};
|
||
|
||
//! Calculates optical flow for 2 images using block matching algorithm */
|
||
CV_EXPORTS void calcOpticalFlowBM(const GpuMat& prev, const GpuMat& curr,
|
||
Size block_size, Size shift_size, Size max_range, bool use_previous,
|
||
GpuMat& velx, GpuMat& vely, GpuMat& buf,
|
||
Stream& stream = Stream::Null());
|
||
|
||
class CV_EXPORTS FastOpticalFlowBM
|
||
{
|
||
public:
|
||
void operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy, int search_window = 21, int block_window = 7, Stream& s = Stream::Null());
|
||
|
||
private:
|
||
GpuMat buffer;
|
||
GpuMat extended_I0;
|
||
GpuMat extended_I1;
|
||
};
|
||
|
||
/** @brief Interpolates frames (images) using provided optical flow (displacement field).
|
||
|
||
@param frame0 First frame (32-bit floating point images, single channel).
|
||
@param frame1 Second frame. Must have the same type and size as frame0 .
|
||
@param fu Forward horizontal displacement.
|
||
@param fv Forward vertical displacement.
|
||
@param bu Backward horizontal displacement.
|
||
@param bv Backward vertical displacement.
|
||
@param pos New frame position.
|
||
@param newFrame Output image.
|
||
@param buf Temporary buffer, will have width x 6\*height size, CV_32FC1 type and contain 6
|
||
GpuMat: occlusion masks for first frame, occlusion masks for second, interpolated forward
|
||
horizontal flow, interpolated forward vertical flow, interpolated backward horizontal flow,
|
||
interpolated backward vertical flow.
|
||
@param stream Stream for the asynchronous version.
|
||
*/
|
||
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 cv { namespace cuda {
|
||
|
||
#endif /* __OPENCV_CUDAOPTFLOW_HPP__ */
|