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opencv/modules/video/src/tvl1flow.cpp
Maksim Shabunin da383e65e2 Remove deprecated methods from cv::Algorithm
2015-02-16 15:28:54 +03:00

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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.
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//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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/*
//
// This implementation is based on Javier Sánchez Pérez <jsanchez@dis.ulpgc.es> implementation.
// Original BSD license:
//
// Copyright (c) 2011, Javier Sánchez Pérez, Enric Meinhardt Llopis
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice, this
// list of conditions and the following disclaimer.
//
// * Redistributions 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.
//
// 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 COPYRIGHT HOLDER 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.
//
*/
#include "precomp.hpp"
#include "opencl_kernels_video.hpp"
#include <limits>
#include <iomanip>
#include <iostream>
#include "opencv2/core/opencl/ocl_defs.hpp"
using namespace cv;
namespace {
class OpticalFlowDual_TVL1 : public DualTVL1OpticalFlow
{
public:
OpticalFlowDual_TVL1();
void calc(InputArray I0, InputArray I1, InputOutputArray flow);
void collectGarbage();
CV_IMPL_PROPERTY(double, Tau, tau)
CV_IMPL_PROPERTY(double, Lambda, lambda)
CV_IMPL_PROPERTY(double, Theta, theta)
CV_IMPL_PROPERTY(double, Gamma, gamma)
CV_IMPL_PROPERTY(int, ScalesNumber, nscales)
CV_IMPL_PROPERTY(int, WarpingsNumber, warps)
CV_IMPL_PROPERTY(double, Epsilon, epsilon)
CV_IMPL_PROPERTY(int, InnerIterations, innerIterations)
CV_IMPL_PROPERTY(int, OuterIterations, outerIterations)
CV_IMPL_PROPERTY(bool, UseInitialFlow, useInitialFlow)
CV_IMPL_PROPERTY(double, ScaleStep, scaleStep)
CV_IMPL_PROPERTY(int, MedianFiltering, medianFiltering)
protected:
double tau;
double lambda;
double theta;
double gamma;
int nscales;
int warps;
double epsilon;
int innerIterations;
int outerIterations;
bool useInitialFlow;
double scaleStep;
int medianFiltering;
private:
void procOneScale(const Mat_<float>& I0, const Mat_<float>& I1, Mat_<float>& u1, Mat_<float>& u2, Mat_<float>& u3);
bool procOneScale_ocl(const UMat& I0, const UMat& I1, UMat& u1, UMat& u2);
bool calc_ocl(InputArray I0, InputArray I1, InputOutputArray flow);
struct dataMat
{
std::vector<Mat_<float> > I0s;
std::vector<Mat_<float> > I1s;
std::vector<Mat_<float> > u1s;
std::vector<Mat_<float> > u2s;
std::vector<Mat_<float> > u3s;
Mat_<float> I1x_buf;
Mat_<float> I1y_buf;
Mat_<float> flowMap1_buf;
Mat_<float> flowMap2_buf;
Mat_<float> I1w_buf;
Mat_<float> I1wx_buf;
Mat_<float> I1wy_buf;
Mat_<float> grad_buf;
Mat_<float> rho_c_buf;
Mat_<float> v1_buf;
Mat_<float> v2_buf;
Mat_<float> v3_buf;
Mat_<float> p11_buf;
Mat_<float> p12_buf;
Mat_<float> p21_buf;
Mat_<float> p22_buf;
Mat_<float> p31_buf;
Mat_<float> p32_buf;
Mat_<float> div_p1_buf;
Mat_<float> div_p2_buf;
Mat_<float> div_p3_buf;
Mat_<float> u1x_buf;
Mat_<float> u1y_buf;
Mat_<float> u2x_buf;
Mat_<float> u2y_buf;
Mat_<float> u3x_buf;
Mat_<float> u3y_buf;
} dm;
struct dataUMat
{
std::vector<UMat> I0s;
std::vector<UMat> I1s;
std::vector<UMat> u1s;
std::vector<UMat> u2s;
UMat I1x_buf;
UMat I1y_buf;
UMat I1w_buf;
UMat I1wx_buf;
UMat I1wy_buf;
UMat grad_buf;
UMat rho_c_buf;
UMat p11_buf;
UMat p12_buf;
UMat p21_buf;
UMat p22_buf;
UMat diff_buf;
UMat norm_buf;
} dum;
};
namespace cv_ocl_tvl1flow
{
bool centeredGradient(const UMat &src, UMat &dx, UMat &dy);
bool warpBackward(const UMat &I0, const UMat &I1, UMat &I1x, UMat &I1y,
UMat &u1, UMat &u2, UMat &I1w, UMat &I1wx, UMat &I1wy,
UMat &grad, UMat &rho);
bool estimateU(UMat &I1wx, UMat &I1wy, UMat &grad,
UMat &rho_c, UMat &p11, UMat &p12,
UMat &p21, UMat &p22, UMat &u1,
UMat &u2, UMat &error, float l_t, float theta, char calc_error);
bool estimateDualVariables(UMat &u1, UMat &u2,
UMat &p11, UMat &p12, UMat &p21, UMat &p22, float taut);
}
bool cv_ocl_tvl1flow::centeredGradient(const UMat &src, UMat &dx, UMat &dy)
{
size_t globalsize[2] = { src.cols, src.rows };
ocl::Kernel kernel;
if (!kernel.create("centeredGradientKernel", cv::ocl::video::optical_flow_tvl1_oclsrc, ""))
return false;
int idxArg = 0;
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadOnly(src));//src mat
idxArg = kernel.set(idxArg, (int)(src.cols));//src mat col
idxArg = kernel.set(idxArg, (int)(src.rows));//src mat rows
idxArg = kernel.set(idxArg, (int)(src.step / src.elemSize()));//src mat step
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrWriteOnly(dx));//res mat dx
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrWriteOnly(dy));//res mat dy
idxArg = kernel.set(idxArg, (int)(dx.step/dx.elemSize()));//res mat step
return kernel.run(2, globalsize, NULL, false);
}
bool cv_ocl_tvl1flow::warpBackward(const UMat &I0, const UMat &I1, UMat &I1x, UMat &I1y,
UMat &u1, UMat &u2, UMat &I1w, UMat &I1wx, UMat &I1wy,
UMat &grad, UMat &rho)
{
size_t globalsize[2] = { I0.cols, I0.rows };
ocl::Kernel kernel;
if (!kernel.create("warpBackwardKernel", cv::ocl::video::optical_flow_tvl1_oclsrc, ""))
return false;
int idxArg = 0;
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadOnly(I0));//I0 mat
int I0_step = (int)(I0.step / I0.elemSize());
idxArg = kernel.set(idxArg, I0_step);//I0_step
idxArg = kernel.set(idxArg, (int)(I0.cols));//I0_col
idxArg = kernel.set(idxArg, (int)(I0.rows));//I0_row
ocl::Image2D imageI1(I1);
ocl::Image2D imageI1x(I1x);
ocl::Image2D imageI1y(I1y);
idxArg = kernel.set(idxArg, imageI1);//image2d_t tex_I1
idxArg = kernel.set(idxArg, imageI1x);//image2d_t tex_I1x
idxArg = kernel.set(idxArg, imageI1y);//image2d_t tex_I1y
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadOnly(u1));//const float* u1
idxArg = kernel.set(idxArg, (int)(u1.step / u1.elemSize()));//int u1_step
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadOnly(u2));//const float* u2
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrWriteOnly(I1w));///float* I1w
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrWriteOnly(I1wx));//float* I1wx
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrWriteOnly(I1wy));//float* I1wy
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrWriteOnly(grad));//float* grad
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrWriteOnly(rho));//float* rho
idxArg = kernel.set(idxArg, (int)(I1w.step / I1w.elemSize()));//I1w_step
idxArg = kernel.set(idxArg, (int)(u2.step / u2.elemSize()));//u2_step
int u1_offset_x = (int)((u1.offset) % (u1.step));
u1_offset_x = (int)(u1_offset_x / u1.elemSize());
idxArg = kernel.set(idxArg, (int)u1_offset_x );//u1_offset_x
idxArg = kernel.set(idxArg, (int)(u1.offset/u1.step));//u1_offset_y
int u2_offset_x = (int)((u2.offset) % (u2.step));
u2_offset_x = (int) (u2_offset_x / u2.elemSize());
idxArg = kernel.set(idxArg, (int)u2_offset_x);//u2_offset_x
idxArg = kernel.set(idxArg, (int)(u2.offset / u2.step));//u2_offset_y
return kernel.run(2, globalsize, NULL, false);
}
bool cv_ocl_tvl1flow::estimateU(UMat &I1wx, UMat &I1wy, UMat &grad,
UMat &rho_c, UMat &p11, UMat &p12,
UMat &p21, UMat &p22, UMat &u1,
UMat &u2, UMat &error, float l_t, float theta, char calc_error)
{
size_t globalsize[2] = { I1wx.cols, I1wx.rows };
ocl::Kernel kernel;
if (!kernel.create("estimateUKernel", cv::ocl::video::optical_flow_tvl1_oclsrc, ""))
return false;
int idxArg = 0;
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadOnly(I1wx)); //const float* I1wx
idxArg = kernel.set(idxArg, (int)(I1wx.cols)); //int I1wx_col
idxArg = kernel.set(idxArg, (int)(I1wx.rows)); //int I1wx_row
idxArg = kernel.set(idxArg, (int)(I1wx.step/I1wx.elemSize())); //int I1wx_step
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadOnly(I1wy)); //const float* I1wy
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadOnly(grad)); //const float* grad
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadOnly(rho_c)); //const float* rho_c
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadOnly(p11)); //const float* p11
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadOnly(p12)); //const float* p12
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadOnly(p21)); //const float* p21
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadOnly(p22)); //const float* p22
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadWrite(u1)); //float* u1
idxArg = kernel.set(idxArg, (int)(u1.step / u1.elemSize())); //int u1_step
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadWrite(u2)); //float* u2
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrWriteOnly(error)); //float* error
idxArg = kernel.set(idxArg, (float)l_t); //float l_t
idxArg = kernel.set(idxArg, (float)theta); //float theta
idxArg = kernel.set(idxArg, (int)(u2.step / u2.elemSize()));//int u2_step
int u1_offset_x = (int)(u1.offset % u1.step);
u1_offset_x = (int) (u1_offset_x / u1.elemSize());
idxArg = kernel.set(idxArg, (int)u1_offset_x); //int u1_offset_x
idxArg = kernel.set(idxArg, (int)(u1.offset/u1.step)); //int u1_offset_y
int u2_offset_x = (int)(u2.offset % u2.step);
u2_offset_x = (int)(u2_offset_x / u2.elemSize());
idxArg = kernel.set(idxArg, (int)u2_offset_x ); //int u2_offset_x
idxArg = kernel.set(idxArg, (int)(u2.offset / u2.step)); //int u2_offset_y
idxArg = kernel.set(idxArg, (char)calc_error); //char calc_error
return kernel.run(2, globalsize, NULL, false);
}
bool cv_ocl_tvl1flow::estimateDualVariables(UMat &u1, UMat &u2,
UMat &p11, UMat &p12, UMat &p21, UMat &p22, float taut)
{
size_t globalsize[2] = { u1.cols, u1.rows };
ocl::Kernel kernel;
if (!kernel.create("estimateDualVariablesKernel", cv::ocl::video::optical_flow_tvl1_oclsrc, ""))
return false;
int idxArg = 0;
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadOnly(u1));// const float* u1
idxArg = kernel.set(idxArg, (int)(u1.cols)); //int u1_col
idxArg = kernel.set(idxArg, (int)(u1.rows)); //int u1_row
idxArg = kernel.set(idxArg, (int)(u1.step/u1.elemSize())); //int u1_step
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadOnly(u2)); // const float* u2
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadWrite(p11)); // float* p11
idxArg = kernel.set(idxArg, (int)(p11.step/p11.elemSize())); //int p11_step
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadWrite(p12)); // float* p12
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadWrite(p21)); // float* p21
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrReadWrite(p22)); // float* p22
idxArg = kernel.set(idxArg, (float)(taut)); //float taut
idxArg = kernel.set(idxArg, (int)(u2.step/u2.elemSize())); //int u2_step
int u1_offset_x = (int)(u1.offset % u1.step);
u1_offset_x = (int)(u1_offset_x / u1.elemSize());
idxArg = kernel.set(idxArg, u1_offset_x); //int u1_offset_x
idxArg = kernel.set(idxArg, (int)(u1.offset / u1.step)); //int u1_offset_y
int u2_offset_x = (int)(u2.offset % u2.step);
u2_offset_x = (int)(u2_offset_x / u2.elemSize());
idxArg = kernel.set(idxArg, u2_offset_x); //int u2_offset_x
idxArg = kernel.set(idxArg, (int)(u2.offset / u2.step)); //int u2_offset_y
return kernel.run(2, globalsize, NULL, false);
}
OpticalFlowDual_TVL1::OpticalFlowDual_TVL1()
{
tau = 0.25;
lambda = 0.15;
theta = 0.3;
nscales = 5;
warps = 5;
epsilon = 0.01;
gamma = 0.;
innerIterations = 30;
outerIterations = 10;
useInitialFlow = false;
medianFiltering = 5;
scaleStep = 0.8;
}
void OpticalFlowDual_TVL1::calc(InputArray _I0, InputArray _I1, InputOutputArray _flow)
{
CV_OCL_RUN(_flow.isUMat() &&
ocl::Image2D::isFormatSupported(CV_32F, 1, false),
calc_ocl(_I0, _I1, _flow))
Mat I0 = _I0.getMat();
Mat I1 = _I1.getMat();
CV_Assert( I0.type() == CV_8UC1 || I0.type() == CV_32FC1 );
CV_Assert( I0.size() == I1.size() );
CV_Assert( I0.type() == I1.type() );
CV_Assert( !useInitialFlow || (_flow.size() == I0.size() && _flow.type() == CV_32FC2) );
CV_Assert( nscales > 0 );
bool use_gamma = gamma != 0;
// allocate memory for the pyramid structure
dm.I0s.resize(nscales);
dm.I1s.resize(nscales);
dm.u1s.resize(nscales);
dm.u2s.resize(nscales);
dm.u3s.resize(nscales);
I0.convertTo(dm.I0s[0], dm.I0s[0].depth(), I0.depth() == CV_8U ? 1.0 : 255.0);
I1.convertTo(dm.I1s[0], dm.I1s[0].depth(), I1.depth() == CV_8U ? 1.0 : 255.0);
dm.u1s[0].create(I0.size());
dm.u2s[0].create(I0.size());
if (use_gamma) dm.u3s[0].create(I0.size());
if (useInitialFlow)
{
Mat_<float> mv[] = { dm.u1s[0], dm.u2s[0] };
split(_flow.getMat(), mv);
}
dm.I1x_buf.create(I0.size());
dm.I1y_buf.create(I0.size());
dm.flowMap1_buf.create(I0.size());
dm.flowMap2_buf.create(I0.size());
dm.I1w_buf.create(I0.size());
dm.I1wx_buf.create(I0.size());
dm.I1wy_buf.create(I0.size());
dm.grad_buf.create(I0.size());
dm.rho_c_buf.create(I0.size());
dm.v1_buf.create(I0.size());
dm.v2_buf.create(I0.size());
dm.v3_buf.create(I0.size());
dm.p11_buf.create(I0.size());
dm.p12_buf.create(I0.size());
dm.p21_buf.create(I0.size());
dm.p22_buf.create(I0.size());
dm.p31_buf.create(I0.size());
dm.p32_buf.create(I0.size());
dm.div_p1_buf.create(I0.size());
dm.div_p2_buf.create(I0.size());
dm.div_p3_buf.create(I0.size());
dm.u1x_buf.create(I0.size());
dm.u1y_buf.create(I0.size());
dm.u2x_buf.create(I0.size());
dm.u2y_buf.create(I0.size());
dm.u3x_buf.create(I0.size());
dm.u3y_buf.create(I0.size());
// create the scales
for (int s = 1; s < nscales; ++s)
{
resize(dm.I0s[s - 1], dm.I0s[s], Size(), scaleStep, scaleStep);
resize(dm.I1s[s - 1], dm.I1s[s], Size(), scaleStep, scaleStep);
if (dm.I0s[s].cols < 16 || dm.I0s[s].rows < 16)
{
nscales = s;
break;
}
if (useInitialFlow)
{
resize(dm.u1s[s - 1], dm.u1s[s], Size(), scaleStep, scaleStep);
resize(dm.u2s[s - 1], dm.u2s[s], Size(), scaleStep, scaleStep);
multiply(dm.u1s[s], Scalar::all(scaleStep), dm.u1s[s]);
multiply(dm.u2s[s], Scalar::all(scaleStep), dm.u2s[s]);
}
else
{
dm.u1s[s].create(dm.I0s[s].size());
dm.u2s[s].create(dm.I0s[s].size());
}
if (use_gamma) dm.u3s[s].create(dm.I0s[s].size());
}
if (!useInitialFlow)
{
dm.u1s[nscales - 1].setTo(Scalar::all(0));
dm.u2s[nscales - 1].setTo(Scalar::all(0));
}
if (use_gamma) dm.u3s[nscales - 1].setTo(Scalar::all(0));
// pyramidal structure for computing the optical flow
for (int s = nscales - 1; s >= 0; --s)
{
// compute the optical flow at the current scale
procOneScale(dm.I0s[s], dm.I1s[s], dm.u1s[s], dm.u2s[s], dm.u3s[s]);
// if this was the last scale, finish now
if (s == 0)
break;
// otherwise, upsample the optical flow
// zoom the optical flow for the next finer scale
resize(dm.u1s[s], dm.u1s[s - 1], dm.I0s[s - 1].size());
resize(dm.u2s[s], dm.u2s[s - 1], dm.I0s[s - 1].size());
if (use_gamma) resize(dm.u3s[s], dm.u3s[s - 1], dm.I0s[s - 1].size());
// scale the optical flow with the appropriate zoom factor (don't scale u3!)
multiply(dm.u1s[s - 1], Scalar::all(1 / scaleStep), dm.u1s[s - 1]);
multiply(dm.u2s[s - 1], Scalar::all(1 / scaleStep), dm.u2s[s - 1]);
}
Mat uxy[] = { dm.u1s[0], dm.u2s[0] };
merge(uxy, 2, _flow);
}
bool OpticalFlowDual_TVL1::calc_ocl(InputArray _I0, InputArray _I1, InputOutputArray _flow)
{
UMat I0 = _I0.getUMat();
UMat I1 = _I1.getUMat();
CV_Assert(I0.type() == CV_8UC1 || I0.type() == CV_32FC1);
CV_Assert(I0.size() == I1.size());
CV_Assert(I0.type() == I1.type());
CV_Assert(!useInitialFlow || (_flow.size() == I0.size() && _flow.type() == CV_32FC2));
CV_Assert(nscales > 0);
// allocate memory for the pyramid structure
dum.I0s.resize(nscales);
dum.I1s.resize(nscales);
dum.u1s.resize(nscales);
dum.u2s.resize(nscales);
//I0s_step == I1s_step
double alpha = I0.depth() == CV_8U ? 1.0 : 255.0;
I0.convertTo(dum.I0s[0], CV_32F, alpha);
I1.convertTo(dum.I1s[0], CV_32F, I1.depth() == CV_8U ? 1.0 : 255.0);
dum.u1s[0].create(I0.size(), CV_32FC1);
dum.u2s[0].create(I0.size(), CV_32FC1);
if (useInitialFlow)
{
std::vector<UMat> umv;
umv.push_back(dum.u1s[0]);
umv.push_back(dum.u2s[0]);
cv::split(_flow,umv);
}
dum.I1x_buf.create(I0.size(), CV_32FC1);
dum.I1y_buf.create(I0.size(), CV_32FC1);
dum.I1w_buf.create(I0.size(), CV_32FC1);
dum.I1wx_buf.create(I0.size(), CV_32FC1);
dum.I1wy_buf.create(I0.size(), CV_32FC1);
dum.grad_buf.create(I0.size(), CV_32FC1);
dum.rho_c_buf.create(I0.size(), CV_32FC1);
dum.p11_buf.create(I0.size(), CV_32FC1);
dum.p12_buf.create(I0.size(), CV_32FC1);
dum.p21_buf.create(I0.size(), CV_32FC1);
dum.p22_buf.create(I0.size(), CV_32FC1);
dum.diff_buf.create(I0.size(), CV_32FC1);
// create the scales
for (int s = 1; s < nscales; ++s)
{
resize(dum.I0s[s - 1], dum.I0s[s], Size(), scaleStep, scaleStep);
resize(dum.I1s[s - 1], dum.I1s[s], Size(), scaleStep, scaleStep);
if (dum.I0s[s].cols < 16 || dum.I0s[s].rows < 16)
{
nscales = s;
break;
}
if (useInitialFlow)
{
resize(dum.u1s[s - 1], dum.u1s[s], Size(), scaleStep, scaleStep);
resize(dum.u2s[s - 1], dum.u2s[s], Size(), scaleStep, scaleStep);
//scale by scale factor
multiply(dum.u1s[s], Scalar::all(scaleStep), dum.u1s[s]);
multiply(dum.u2s[s], Scalar::all(scaleStep), dum.u2s[s]);
}
}
// pyramidal structure for computing the optical flow
for (int s = nscales - 1; s >= 0; --s)
{
// compute the optical flow at the current scale
if (!OpticalFlowDual_TVL1::procOneScale_ocl(dum.I0s[s], dum.I1s[s], dum.u1s[s], dum.u2s[s]))
return false;
// if this was the last scale, finish now
if (s == 0)
break;
// zoom the optical flow for the next finer scale
resize(dum.u1s[s], dum.u1s[s - 1], dum.I0s[s - 1].size());
resize(dum.u2s[s], dum.u2s[s - 1], dum.I0s[s - 1].size());
// scale the optical flow with the appropriate zoom factor
multiply(dum.u1s[s - 1], Scalar::all(1 / scaleStep), dum.u1s[s - 1]);
multiply(dum.u2s[s - 1], Scalar::all(1 / scaleStep), dum.u2s[s - 1]);
}
std::vector<UMat> uxy;
uxy.push_back(dum.u1s[0]);
uxy.push_back(dum.u2s[0]);
merge(uxy, _flow);
return true;
}
////////////////////////////////////////////////////////////
// buildFlowMap
struct BuildFlowMapBody : ParallelLoopBody
{
void operator() (const Range& range) const;
Mat_<float> u1;
Mat_<float> u2;
mutable Mat_<float> map1;
mutable Mat_<float> map2;
};
void BuildFlowMapBody::operator() (const Range& range) const
{
for (int y = range.start; y < range.end; ++y)
{
const float* u1Row = u1[y];
const float* u2Row = u2[y];
float* map1Row = map1[y];
float* map2Row = map2[y];
for (int x = 0; x < u1.cols; ++x)
{
map1Row[x] = x + u1Row[x];
map2Row[x] = y + u2Row[x];
}
}
}
void buildFlowMap(const Mat_<float>& u1, const Mat_<float>& u2, Mat_<float>& map1, Mat_<float>& map2)
{
CV_DbgAssert( u2.size() == u1.size() );
CV_DbgAssert( map1.size() == u1.size() );
CV_DbgAssert( map2.size() == u1.size() );
BuildFlowMapBody body;
body.u1 = u1;
body.u2 = u2;
body.map1 = map1;
body.map2 = map2;
parallel_for_(Range(0, u1.rows), body);
}
////////////////////////////////////////////////////////////
// centeredGradient
struct CenteredGradientBody : ParallelLoopBody
{
void operator() (const Range& range) const;
Mat_<float> src;
mutable Mat_<float> dx;
mutable Mat_<float> dy;
};
void CenteredGradientBody::operator() (const Range& range) const
{
const int last_col = src.cols - 1;
for (int y = range.start; y < range.end; ++y)
{
const float* srcPrevRow = src[y - 1];
const float* srcCurRow = src[y];
const float* srcNextRow = src[y + 1];
float* dxRow = dx[y];
float* dyRow = dy[y];
for (int x = 1; x < last_col; ++x)
{
dxRow[x] = 0.5f * (srcCurRow[x + 1] - srcCurRow[x - 1]);
dyRow[x] = 0.5f * (srcNextRow[x] - srcPrevRow[x]);
}
}
}
void centeredGradient(const Mat_<float>& src, Mat_<float>& dx, Mat_<float>& dy)
{
CV_DbgAssert( src.rows > 2 && src.cols > 2 );
CV_DbgAssert( dx.size() == src.size() );
CV_DbgAssert( dy.size() == src.size() );
const int last_row = src.rows - 1;
const int last_col = src.cols - 1;
// compute the gradient on the center body of the image
{
CenteredGradientBody body;
body.src = src;
body.dx = dx;
body.dy = dy;
parallel_for_(Range(1, last_row), body);
}
// compute the gradient on the first and last rows
for (int x = 1; x < last_col; ++x)
{
dx(0, x) = 0.5f * (src(0, x + 1) - src(0, x - 1));
dy(0, x) = 0.5f * (src(1, x) - src(0, x));
dx(last_row, x) = 0.5f * (src(last_row, x + 1) - src(last_row, x - 1));
dy(last_row, x) = 0.5f * (src(last_row, x) - src(last_row - 1, x));
}
// compute the gradient on the first and last columns
for (int y = 1; y < last_row; ++y)
{
dx(y, 0) = 0.5f * (src(y, 1) - src(y, 0));
dy(y, 0) = 0.5f * (src(y + 1, 0) - src(y - 1, 0));
dx(y, last_col) = 0.5f * (src(y, last_col) - src(y, last_col - 1));
dy(y, last_col) = 0.5f * (src(y + 1, last_col) - src(y - 1, last_col));
}
// compute the gradient at the four corners
dx(0, 0) = 0.5f * (src(0, 1) - src(0, 0));
dy(0, 0) = 0.5f * (src(1, 0) - src(0, 0));
dx(0, last_col) = 0.5f * (src(0, last_col) - src(0, last_col - 1));
dy(0, last_col) = 0.5f * (src(1, last_col) - src(0, last_col));
dx(last_row, 0) = 0.5f * (src(last_row, 1) - src(last_row, 0));
dy(last_row, 0) = 0.5f * (src(last_row, 0) - src(last_row - 1, 0));
dx(last_row, last_col) = 0.5f * (src(last_row, last_col) - src(last_row, last_col - 1));
dy(last_row, last_col) = 0.5f * (src(last_row, last_col) - src(last_row - 1, last_col));
}
////////////////////////////////////////////////////////////
// forwardGradient
struct ForwardGradientBody : ParallelLoopBody
{
void operator() (const Range& range) const;
Mat_<float> src;
mutable Mat_<float> dx;
mutable Mat_<float> dy;
};
void ForwardGradientBody::operator() (const Range& range) const
{
const int last_col = src.cols - 1;
for (int y = range.start; y < range.end; ++y)
{
const float* srcCurRow = src[y];
const float* srcNextRow = src[y + 1];
float* dxRow = dx[y];
float* dyRow = dy[y];
for (int x = 0; x < last_col; ++x)
{
dxRow[x] = srcCurRow[x + 1] - srcCurRow[x];
dyRow[x] = srcNextRow[x] - srcCurRow[x];
}
}
}
void forwardGradient(const Mat_<float>& src, Mat_<float>& dx, Mat_<float>& dy)
{
CV_DbgAssert( src.rows > 2 && src.cols > 2 );
CV_DbgAssert( dx.size() == src.size() );
CV_DbgAssert( dy.size() == src.size() );
const int last_row = src.rows - 1;
const int last_col = src.cols - 1;
// compute the gradient on the central body of the image
{
ForwardGradientBody body;
body.src = src;
body.dx = dx;
body.dy = dy;
parallel_for_(Range(0, last_row), body);
}
// compute the gradient on the last row
for (int x = 0; x < last_col; ++x)
{
dx(last_row, x) = src(last_row, x + 1) - src(last_row, x);
dy(last_row, x) = 0.0f;
}
// compute the gradient on the last column
for (int y = 0; y < last_row; ++y)
{
dx(y, last_col) = 0.0f;
dy(y, last_col) = src(y + 1, last_col) - src(y, last_col);
}
dx(last_row, last_col) = 0.0f;
dy(last_row, last_col) = 0.0f;
}
////////////////////////////////////////////////////////////
// divergence
struct DivergenceBody : ParallelLoopBody
{
void operator() (const Range& range) const;
Mat_<float> v1;
Mat_<float> v2;
mutable Mat_<float> div;
};
void DivergenceBody::operator() (const Range& range) const
{
for (int y = range.start; y < range.end; ++y)
{
const float* v1Row = v1[y];
const float* v2PrevRow = v2[y - 1];
const float* v2CurRow = v2[y];
float* divRow = div[y];
for(int x = 1; x < v1.cols; ++x)
{
const float v1x = v1Row[x] - v1Row[x - 1];
const float v2y = v2CurRow[x] - v2PrevRow[x];
divRow[x] = v1x + v2y;
}
}
}
void divergence(const Mat_<float>& v1, const Mat_<float>& v2, Mat_<float>& div)
{
CV_DbgAssert( v1.rows > 2 && v1.cols > 2 );
CV_DbgAssert( v2.size() == v1.size() );
CV_DbgAssert( div.size() == v1.size() );
{
DivergenceBody body;
body.v1 = v1;
body.v2 = v2;
body.div = div;
parallel_for_(Range(1, v1.rows), body);
}
// compute the divergence on the first row
for(int x = 1; x < v1.cols; ++x)
div(0, x) = v1(0, x) - v1(0, x - 1) + v2(0, x);
// compute the divergence on the first column
for (int y = 1; y < v1.rows; ++y)
div(y, 0) = v1(y, 0) + v2(y, 0) - v2(y - 1, 0);
div(0, 0) = v1(0, 0) + v2(0, 0);
}
////////////////////////////////////////////////////////////
// calcGradRho
struct CalcGradRhoBody : ParallelLoopBody
{
void operator() (const Range& range) const;
Mat_<float> I0;
Mat_<float> I1w;
Mat_<float> I1wx;
Mat_<float> I1wy;
Mat_<float> u1;
Mat_<float> u2;
mutable Mat_<float> grad;
mutable Mat_<float> rho_c;
};
void CalcGradRhoBody::operator() (const Range& range) const
{
for (int y = range.start; y < range.end; ++y)
{
const float* I0Row = I0[y];
const float* I1wRow = I1w[y];
const float* I1wxRow = I1wx[y];
const float* I1wyRow = I1wy[y];
const float* u1Row = u1[y];
const float* u2Row = u2[y];
float* gradRow = grad[y];
float* rhoRow = rho_c[y];
for (int x = 0; x < I0.cols; ++x)
{
const float Ix2 = I1wxRow[x] * I1wxRow[x];
const float Iy2 = I1wyRow[x] * I1wyRow[x];
// store the |Grad(I1)|^2
gradRow[x] = Ix2 + Iy2;
// compute the constant part of the rho function
rhoRow[x] = (I1wRow[x] - I1wxRow[x] * u1Row[x] - I1wyRow[x] * u2Row[x] - I0Row[x]);
}
}
}
void calcGradRho(const Mat_<float>& I0, const Mat_<float>& I1w, const Mat_<float>& I1wx, const Mat_<float>& I1wy, const Mat_<float>& u1, const Mat_<float>& u2,
Mat_<float>& grad, Mat_<float>& rho_c)
{
CV_DbgAssert( I1w.size() == I0.size() );
CV_DbgAssert( I1wx.size() == I0.size() );
CV_DbgAssert( I1wy.size() == I0.size() );
CV_DbgAssert( u1.size() == I0.size() );
CV_DbgAssert( u2.size() == I0.size() );
CV_DbgAssert( grad.size() == I0.size() );
CV_DbgAssert( rho_c.size() == I0.size() );
CalcGradRhoBody body;
body.I0 = I0;
body.I1w = I1w;
body.I1wx = I1wx;
body.I1wy = I1wy;
body.u1 = u1;
body.u2 = u2;
body.grad = grad;
body.rho_c = rho_c;
parallel_for_(Range(0, I0.rows), body);
}
////////////////////////////////////////////////////////////
// estimateV
struct EstimateVBody : ParallelLoopBody
{
void operator() (const Range& range) const;
Mat_<float> I1wx;
Mat_<float> I1wy;
Mat_<float> u1;
Mat_<float> u2;
Mat_<float> u3;
Mat_<float> grad;
Mat_<float> rho_c;
mutable Mat_<float> v1;
mutable Mat_<float> v2;
mutable Mat_<float> v3;
float l_t;
float gamma;
};
void EstimateVBody::operator() (const Range& range) const
{
bool use_gamma = gamma != 0;
for (int y = range.start; y < range.end; ++y)
{
const float* I1wxRow = I1wx[y];
const float* I1wyRow = I1wy[y];
const float* u1Row = u1[y];
const float* u2Row = u2[y];
const float* u3Row = use_gamma?u3[y]:NULL;
const float* gradRow = grad[y];
const float* rhoRow = rho_c[y];
float* v1Row = v1[y];
float* v2Row = v2[y];
float* v3Row = use_gamma ? v3[y]:NULL;
for (int x = 0; x < I1wx.cols; ++x)
{
const float rho = use_gamma ? rhoRow[x] + (I1wxRow[x] * u1Row[x] + I1wyRow[x] * u2Row[x]) + gamma * u3Row[x] :
rhoRow[x] + (I1wxRow[x] * u1Row[x] + I1wyRow[x] * u2Row[x]);
float d1 = 0.0f;
float d2 = 0.0f;
float d3 = 0.0f;
if (rho < -l_t * gradRow[x])
{
d1 = l_t * I1wxRow[x];
d2 = l_t * I1wyRow[x];
if (use_gamma) d3 = l_t * gamma;
}
else if (rho > l_t * gradRow[x])
{
d1 = -l_t * I1wxRow[x];
d2 = -l_t * I1wyRow[x];
if (use_gamma) d3 = -l_t * gamma;
}
else if (gradRow[x] > std::numeric_limits<float>::epsilon())
{
float fi = -rho / gradRow[x];
d1 = fi * I1wxRow[x];
d2 = fi * I1wyRow[x];
if (use_gamma) d3 = fi * gamma;
}
v1Row[x] = u1Row[x] + d1;
v2Row[x] = u2Row[x] + d2;
if (use_gamma) v3Row[x] = u3Row[x] + d3;
}
}
}
void estimateV(const Mat_<float>& I1wx, const Mat_<float>& I1wy, const Mat_<float>& u1, const Mat_<float>& u2, const Mat_<float>& u3, const Mat_<float>& grad, const Mat_<float>& rho_c,
Mat_<float>& v1, Mat_<float>& v2, Mat_<float>& v3, float l_t, float gamma)
{
CV_DbgAssert( I1wy.size() == I1wx.size() );
CV_DbgAssert( u1.size() == I1wx.size() );
CV_DbgAssert( u2.size() == I1wx.size() );
CV_DbgAssert( grad.size() == I1wx.size() );
CV_DbgAssert( rho_c.size() == I1wx.size() );
CV_DbgAssert( v1.size() == I1wx.size() );
CV_DbgAssert( v2.size() == I1wx.size() );
EstimateVBody body;
bool use_gamma = gamma != 0;
body.I1wx = I1wx;
body.I1wy = I1wy;
body.u1 = u1;
body.u2 = u2;
if (use_gamma) body.u3 = u3;
body.grad = grad;
body.rho_c = rho_c;
body.v1 = v1;
body.v2 = v2;
if (use_gamma) body.v3 = v3;
body.l_t = l_t;
body.gamma = gamma;
parallel_for_(Range(0, I1wx.rows), body);
}
////////////////////////////////////////////////////////////
// estimateU
float estimateU(const Mat_<float>& v1, const Mat_<float>& v2, const Mat_<float>& v3,
const Mat_<float>& div_p1, const Mat_<float>& div_p2, const Mat_<float>& div_p3,
Mat_<float>& u1, Mat_<float>& u2, Mat_<float>& u3,
float theta, float gamma)
{
CV_DbgAssert( v2.size() == v1.size() );
CV_DbgAssert( div_p1.size() == v1.size() );
CV_DbgAssert( div_p2.size() == v1.size() );
CV_DbgAssert( u1.size() == v1.size() );
CV_DbgAssert( u2.size() == v1.size() );
float error = 0.0f;
bool use_gamma = gamma != 0;
for (int y = 0; y < v1.rows; ++y)
{
const float* v1Row = v1[y];
const float* v2Row = v2[y];
const float* v3Row = use_gamma?v3[y]:NULL;
const float* divP1Row = div_p1[y];
const float* divP2Row = div_p2[y];
const float* divP3Row = use_gamma?div_p3[y]:NULL;
float* u1Row = u1[y];
float* u2Row = u2[y];
float* u3Row = use_gamma?u3[y]:NULL;
for (int x = 0; x < v1.cols; ++x)
{
const float u1k = u1Row[x];
const float u2k = u2Row[x];
const float u3k = use_gamma?u3Row[x]:0;
u1Row[x] = v1Row[x] + theta * divP1Row[x];
u2Row[x] = v2Row[x] + theta * divP2Row[x];
if (use_gamma) u3Row[x] = v3Row[x] + theta * divP3Row[x];
error += use_gamma?(u1Row[x] - u1k) * (u1Row[x] - u1k) + (u2Row[x] - u2k) * (u2Row[x] - u2k) + (u3Row[x] - u3k) * (u3Row[x] - u3k):
(u1Row[x] - u1k) * (u1Row[x] - u1k) + (u2Row[x] - u2k) * (u2Row[x] - u2k);
}
}
return error;
}
////////////////////////////////////////////////////////////
// estimateDualVariables
struct EstimateDualVariablesBody : ParallelLoopBody
{
void operator() (const Range& range) const;
Mat_<float> u1x;
Mat_<float> u1y;
Mat_<float> u2x;
Mat_<float> u2y;
Mat_<float> u3x;
Mat_<float> u3y;
mutable Mat_<float> p11;
mutable Mat_<float> p12;
mutable Mat_<float> p21;
mutable Mat_<float> p22;
mutable Mat_<float> p31;
mutable Mat_<float> p32;
float taut;
bool use_gamma;
};
void EstimateDualVariablesBody::operator() (const Range& range) const
{
for (int y = range.start; y < range.end; ++y)
{
const float* u1xRow = u1x[y];
const float* u1yRow = u1y[y];
const float* u2xRow = u2x[y];
const float* u2yRow = u2y[y];
const float* u3xRow = u3x[y];
const float* u3yRow = u3y[y];
float* p11Row = p11[y];
float* p12Row = p12[y];
float* p21Row = p21[y];
float* p22Row = p22[y];
float* p31Row = p31[y];
float* p32Row = p32[y];
for (int x = 0; x < u1x.cols; ++x)
{
const float g1 = static_cast<float>(hypot(u1xRow[x], u1yRow[x]));
const float g2 = static_cast<float>(hypot(u2xRow[x], u2yRow[x]));
const float g3 = static_cast<float>(hypot(u3xRow[x], u3yRow[x]));
const float ng1 = 1.0f + taut * g1;
const float ng2 = 1.0f + taut * g2;
const float ng3 = 1.0f + taut * g3;
p11Row[x] = (p11Row[x] + taut * u1xRow[x]) / ng1;
p12Row[x] = (p12Row[x] + taut * u1yRow[x]) / ng1;
p21Row[x] = (p21Row[x] + taut * u2xRow[x]) / ng2;
p22Row[x] = (p22Row[x] + taut * u2yRow[x]) / ng2;
if (use_gamma) p31Row[x] = (p31Row[x] + taut * u3xRow[x]) / ng3;
if (use_gamma) p32Row[x] = (p32Row[x] + taut * u3yRow[x]) / ng3;
}
}
}
void estimateDualVariables(const Mat_<float>& u1x, const Mat_<float>& u1y,
const Mat_<float>& u2x, const Mat_<float>& u2y,
const Mat_<float>& u3x, const Mat_<float>& u3y,
Mat_<float>& p11, Mat_<float>& p12,
Mat_<float>& p21, Mat_<float>& p22,
Mat_<float>& p31, Mat_<float>& p32,
float taut, bool use_gamma)
{
CV_DbgAssert( u1y.size() == u1x.size() );
CV_DbgAssert( u2x.size() == u1x.size() );
CV_DbgAssert( u3x.size() == u1x.size() );
CV_DbgAssert( u2y.size() == u1x.size() );
CV_DbgAssert( u3y.size() == u1x.size() );
CV_DbgAssert( p11.size() == u1x.size() );
CV_DbgAssert( p12.size() == u1x.size() );
CV_DbgAssert( p21.size() == u1x.size() );
CV_DbgAssert( p22.size() == u1x.size() );
CV_DbgAssert( p31.size() == u1x.size() );
CV_DbgAssert( p32.size() == u1x.size() );
EstimateDualVariablesBody body;
body.u1x = u1x;
body.u1y = u1y;
body.u2x = u2x;
body.u2y = u2y;
body.u3x = u3x;
body.u3y = u3y;
body.p11 = p11;
body.p12 = p12;
body.p21 = p21;
body.p22 = p22;
body.p31 = p31;
body.p32 = p32;
body.taut = taut;
body.use_gamma = use_gamma;
parallel_for_(Range(0, u1x.rows), body);
}
bool OpticalFlowDual_TVL1::procOneScale_ocl(const UMat& I0, const UMat& I1, UMat& u1, UMat& u2)
{
using namespace cv_ocl_tvl1flow;
const double scaledEpsilon = epsilon * epsilon * I0.size().area();
CV_DbgAssert(I1.size() == I0.size());
CV_DbgAssert(I1.type() == I0.type());
CV_DbgAssert(u1.empty() || u1.size() == I0.size());
CV_DbgAssert(u2.size() == u1.size());
if (u1.empty())
{
u1.create(I0.size(), CV_32FC1);
u1.setTo(Scalar::all(0));
u2.create(I0.size(), CV_32FC1);
u2.setTo(Scalar::all(0));
}
UMat I1x = dum.I1x_buf(Rect(0, 0, I0.cols, I0.rows));
UMat I1y = dum.I1y_buf(Rect(0, 0, I0.cols, I0.rows));
if (!centeredGradient(I1, I1x, I1y))
return false;
UMat I1w = dum.I1w_buf(Rect(0, 0, I0.cols, I0.rows));
UMat I1wx = dum.I1wx_buf(Rect(0, 0, I0.cols, I0.rows));
UMat I1wy = dum.I1wy_buf(Rect(0, 0, I0.cols, I0.rows));
UMat grad = dum.grad_buf(Rect(0, 0, I0.cols, I0.rows));
UMat rho_c = dum.rho_c_buf(Rect(0, 0, I0.cols, I0.rows));
UMat p11 = dum.p11_buf(Rect(0, 0, I0.cols, I0.rows));
UMat p12 = dum.p12_buf(Rect(0, 0, I0.cols, I0.rows));
UMat p21 = dum.p21_buf(Rect(0, 0, I0.cols, I0.rows));
UMat p22 = dum.p22_buf(Rect(0, 0, I0.cols, I0.rows));
p11.setTo(Scalar::all(0));
p12.setTo(Scalar::all(0));
p21.setTo(Scalar::all(0));
p22.setTo(Scalar::all(0));
UMat diff = dum.diff_buf(Rect(0, 0, I0.cols, I0.rows));
const float l_t = static_cast<float>(lambda * theta);
const float taut = static_cast<float>(tau / theta);
int n;
for (int warpings = 0; warpings < warps; ++warpings)
{
if (!warpBackward(I0, I1, I1x, I1y, u1, u2, I1w, I1wx, I1wy, grad, rho_c))
return false;
double error = std::numeric_limits<double>::max();
double prev_error = 0;
for (int n_outer = 0; error > scaledEpsilon && n_outer < outerIterations; ++n_outer)
{
if (medianFiltering > 1) {
cv::medianBlur(u1, u1, medianFiltering);
cv::medianBlur(u2, u2, medianFiltering);
}
for (int n_inner = 0; error > scaledEpsilon && n_inner < innerIterations; ++n_inner)
{
// some tweaks to make sum operation less frequently
n = n_inner + n_outer*innerIterations;
char calc_error = (n & 0x1) && (prev_error < scaledEpsilon);
if (!estimateU(I1wx, I1wy, grad, rho_c, p11, p12, p21, p22,
u1, u2, diff, l_t, static_cast<float>(theta), calc_error))
return false;
if (calc_error)
{
error = cv::sum(diff)[0];
prev_error = error;
}
else
{
error = std::numeric_limits<double>::max();
prev_error -= scaledEpsilon;
}
if (!estimateDualVariables(u1, u2, p11, p12, p21, p22, taut))
return false;
}
}
}
return true;
}
void OpticalFlowDual_TVL1::procOneScale(const Mat_<float>& I0, const Mat_<float>& I1, Mat_<float>& u1, Mat_<float>& u2, Mat_<float>& u3)
{
const float scaledEpsilon = static_cast<float>(epsilon * epsilon * I0.size().area());
CV_DbgAssert( I1.size() == I0.size() );
CV_DbgAssert( I1.type() == I0.type() );
CV_DbgAssert( u1.size() == I0.size() );
CV_DbgAssert( u2.size() == u1.size() );
Mat_<float> I1x = dm.I1x_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> I1y = dm.I1y_buf(Rect(0, 0, I0.cols, I0.rows));
centeredGradient(I1, I1x, I1y);
Mat_<float> flowMap1 = dm.flowMap1_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> flowMap2 = dm.flowMap2_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> I1w = dm.I1w_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> I1wx = dm.I1wx_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> I1wy = dm.I1wy_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> grad = dm.grad_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> rho_c = dm.rho_c_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> v1 = dm.v1_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> v2 = dm.v2_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> v3 = dm.v3_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> p11 = dm.p11_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> p12 = dm.p12_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> p21 = dm.p21_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> p22 = dm.p22_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> p31 = dm.p31_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> p32 = dm.p32_buf(Rect(0, 0, I0.cols, I0.rows));
p11.setTo(Scalar::all(0));
p12.setTo(Scalar::all(0));
p21.setTo(Scalar::all(0));
p22.setTo(Scalar::all(0));
bool use_gamma = gamma != 0.;
if (use_gamma) p31.setTo(Scalar::all(0));
if (use_gamma) p32.setTo(Scalar::all(0));
Mat_<float> div_p1 = dm.div_p1_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> div_p2 = dm.div_p2_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> div_p3 = dm.div_p3_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> u1x = dm.u1x_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> u1y = dm.u1y_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> u2x = dm.u2x_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> u2y = dm.u2y_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> u3x = dm.u3x_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> u3y = dm.u3y_buf(Rect(0, 0, I0.cols, I0.rows));
const float l_t = static_cast<float>(lambda * theta);
const float taut = static_cast<float>(tau / theta);
for (int warpings = 0; warpings < warps; ++warpings)
{
// compute the warping of the target image and its derivatives
buildFlowMap(u1, u2, flowMap1, flowMap2);
remap(I1, I1w, flowMap1, flowMap2, INTER_CUBIC);
remap(I1x, I1wx, flowMap1, flowMap2, INTER_CUBIC);
remap(I1y, I1wy, flowMap1, flowMap2, INTER_CUBIC);
//calculate I1(x+u0) and its gradient
calcGradRho(I0, I1w, I1wx, I1wy, u1, u2, grad, rho_c);
float error = std::numeric_limits<float>::max();
for (int n_outer = 0; error > scaledEpsilon && n_outer < outerIterations; ++n_outer)
{
if (medianFiltering > 1) {
cv::medianBlur(u1, u1, medianFiltering);
cv::medianBlur(u2, u2, medianFiltering);
}
for (int n_inner = 0; error > scaledEpsilon && n_inner < innerIterations; ++n_inner)
{
// estimate the values of the variable (v1, v2) (thresholding operator TH)
estimateV(I1wx, I1wy, u1, u2, u3, grad, rho_c, v1, v2, v3, l_t, static_cast<float>(gamma));
// compute the divergence of the dual variable (p1, p2, p3)
divergence(p11, p12, div_p1);
divergence(p21, p22, div_p2);
if (use_gamma) divergence(p31, p32, div_p3);
// estimate the values of the optical flow (u1, u2)
error = estimateU(v1, v2, v3, div_p1, div_p2, div_p3, u1, u2, u3, static_cast<float>(theta), static_cast<float>(gamma));
// compute the gradient of the optical flow (Du1, Du2)
forwardGradient(u1, u1x, u1y);
forwardGradient(u2, u2x, u2y);
if (use_gamma) forwardGradient(u3, u3x, u3y);
// estimate the values of the dual variable (p1, p2, p3)
estimateDualVariables(u1x, u1y, u2x, u2y, u3x, u3y, p11, p12, p21, p22, p31, p32, taut, use_gamma);
}
}
}
}
void OpticalFlowDual_TVL1::collectGarbage()
{
//dataMat structure dm
dm.I0s.clear();
dm.I1s.clear();
dm.u1s.clear();
dm.u2s.clear();
dm.I1x_buf.release();
dm.I1y_buf.release();
dm.flowMap1_buf.release();
dm.flowMap2_buf.release();
dm.I1w_buf.release();
dm.I1wx_buf.release();
dm.I1wy_buf.release();
dm.grad_buf.release();
dm.rho_c_buf.release();
dm.v1_buf.release();
dm.v2_buf.release();
dm.p11_buf.release();
dm.p12_buf.release();
dm.p21_buf.release();
dm.p22_buf.release();
dm.div_p1_buf.release();
dm.div_p2_buf.release();
dm.u1x_buf.release();
dm.u1y_buf.release();
dm.u2x_buf.release();
dm.u2y_buf.release();
//dataUMat structure dum
dum.I0s.clear();
dum.I1s.clear();
dum.u1s.clear();
dum.u2s.clear();
dum.I1x_buf.release();
dum.I1y_buf.release();
dum.I1w_buf.release();
dum.I1wx_buf.release();
dum.I1wy_buf.release();
dum.grad_buf.release();
dum.rho_c_buf.release();
dum.p11_buf.release();
dum.p12_buf.release();
dum.p21_buf.release();
dum.p22_buf.release();
dum.diff_buf.release();
dum.norm_buf.release();
}
} // namespace
Ptr<DualTVL1OpticalFlow> cv::createOptFlow_DualTVL1()
{
return makePtr<OpticalFlowDual_TVL1>();
}
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