opencv/modules/video/src/tvl1flow.cpp

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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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// * Redistribution's of source code must retain the above copyright notice,
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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 <limits>
using namespace cv;
namespace {
class OpticalFlowDual_TVL1 : public DenseOpticalFlow
{
public:
OpticalFlowDual_TVL1();
void calc(InputArray I0, InputArray I1, InputOutputArray flow);
void collectGarbage();
AlgorithmInfo* info() const;
protected:
double tau;
double lambda;
double theta;
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);
std::vector<Mat_<float> > I0s;
std::vector<Mat_<float> > I1s;
std::vector<Mat_<float> > u1s;
std::vector<Mat_<float> > u2s;
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> p11_buf;
Mat_<float> p12_buf;
Mat_<float> p21_buf;
Mat_<float> p22_buf;
Mat_<float> div_p1_buf;
Mat_<float> div_p2_buf;
Mat_<float> u1x_buf;
Mat_<float> u1y_buf;
Mat_<float> u2x_buf;
Mat_<float> u2y_buf;
};
OpticalFlowDual_TVL1::OpticalFlowDual_TVL1()
{
tau = 0.25;
lambda = 0.15;
theta = 0.3;
nscales = 5;
warps = 5;
epsilon = 0.01;
innerIterations = 30;
outerIterations = 10;
useInitialFlow = false;
medianFiltering = 5;
scaleStep = 0.8;
}
void OpticalFlowDual_TVL1::calc(InputArray _I0, InputArray _I1, InputOutputArray _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 );
// allocate memory for the pyramid structure
I0s.resize(nscales);
I1s.resize(nscales);
u1s.resize(nscales);
u2s.resize(nscales);
I0.convertTo(I0s[0], I0s[0].depth(), I0.depth() == CV_8U ? 1.0 : 255.0);
I1.convertTo(I1s[0], I1s[0].depth(), I1.depth() == CV_8U ? 1.0 : 255.0);
u1s[0].create(I0.size());
u2s[0].create(I0.size());
if (useInitialFlow)
{
Mat_<float> mv[] = {u1s[0], u2s[0]};
split(_flow.getMat(), mv);
}
I1x_buf.create(I0.size());
I1y_buf.create(I0.size());
flowMap1_buf.create(I0.size());
flowMap2_buf.create(I0.size());
I1w_buf.create(I0.size());
I1wx_buf.create(I0.size());
I1wy_buf.create(I0.size());
grad_buf.create(I0.size());
rho_c_buf.create(I0.size());
v1_buf.create(I0.size());
v2_buf.create(I0.size());
p11_buf.create(I0.size());
p12_buf.create(I0.size());
p21_buf.create(I0.size());
p22_buf.create(I0.size());
div_p1_buf.create(I0.size());
div_p2_buf.create(I0.size());
u1x_buf.create(I0.size());
u1y_buf.create(I0.size());
u2x_buf.create(I0.size());
u2y_buf.create(I0.size());
// create the scales
for (int s = 1; s < nscales; ++s)
{
resize(I0s[s-1], I0s[s], Size(), scaleStep, scaleStep);
resize(I1s[s-1], I1s[s], Size(), scaleStep, scaleStep);
if (I0s[s].cols < 16 || I0s[s].rows < 16)
{
nscales = s;
break;
}
if (useInitialFlow)
{
resize(u1s[s-1], u1s[s], Size(), scaleStep, scaleStep);
resize(u2s[s-1], u2s[s], Size(), scaleStep, scaleStep);
multiply(u1s[s], Scalar::all(scaleStep), u1s[s]);
multiply(u2s[s], Scalar::all(scaleStep), u2s[s]);
}
else
{
u1s[s].create(I0s[s].size());
u2s[s].create(I0s[s].size());
}
}
if (!useInitialFlow)
{
u1s[nscales-1].setTo(Scalar::all(0));
u2s[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(I0s[s], I1s[s], u1s[s], u2s[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(u1s[s], u1s[s - 1], I0s[s - 1].size());
resize(u2s[s], u2s[s - 1], I0s[s - 1].size());
// scale the optical flow with the appropriate zoom factor
multiply(u1s[s - 1], Scalar::all(1/scaleStep), u1s[s - 1]);
multiply(u2s[s - 1], Scalar::all(1/scaleStep), u2s[s - 1]);
}
Mat uxy[] = {u1s[0], u2s[0]};
merge(uxy, 2, _flow);
}
////////////////////////////////////////////////////////////
// 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> grad;
Mat_<float> rho_c;
mutable Mat_<float> v1;
mutable Mat_<float> v2;
float l_t;
};
void EstimateVBody::operator() (const Range& range) const
{
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* gradRow = grad[y];
const float* rhoRow = rho_c[y];
float* v1Row = v1[y];
float* v2Row = v2[y];
for (int x = 0; x < I1wx.cols; ++x)
{
const float rho = rhoRow[x] + (I1wxRow[x] * u1Row[x] + I1wyRow[x] * u2Row[x]);
float d1 = 0.0f;
float d2 = 0.0f;
if (rho < -l_t * gradRow[x])
{
d1 = l_t * I1wxRow[x];
d2 = l_t * I1wyRow[x];
}
else if (rho > l_t * gradRow[x])
{
d1 = -l_t * I1wxRow[x];
d2 = -l_t * I1wyRow[x];
}
else if (gradRow[x] > std::numeric_limits<float>::epsilon())
{
float fi = -rho / gradRow[x];
d1 = fi * I1wxRow[x];
d2 = fi * I1wyRow[x];
}
v1Row[x] = u1Row[x] + d1;
v2Row[x] = u2Row[x] + d2;
}
}
}
void estimateV(const Mat_<float>& I1wx, const Mat_<float>& I1wy, const Mat_<float>& u1, const Mat_<float>& u2, const Mat_<float>& grad, const Mat_<float>& rho_c,
Mat_<float>& v1, Mat_<float>& v2, float l_t)
{
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;
body.I1wx = I1wx;
body.I1wy = I1wy;
body.u1 = u1;
body.u2 = u2;
body.grad = grad;
body.rho_c = rho_c;
body.v1 = v1;
body.v2 = v2;
body.l_t = l_t;
parallel_for_(Range(0, I1wx.rows), body);
}
////////////////////////////////////////////////////////////
// estimateU
float estimateU(const Mat_<float>& v1, const Mat_<float>& v2, const Mat_<float>& div_p1, const Mat_<float>& div_p2, Mat_<float>& u1, Mat_<float>& u2, float theta)
{
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;
for (int y = 0; y < v1.rows; ++y)
{
const float* v1Row = v1[y];
const float* v2Row = v2[y];
const float* divP1Row = div_p1[y];
const float* divP2Row = div_p2[y];
float* u1Row = u1[y];
float* u2Row = u2[y];
for (int x = 0; x < v1.cols; ++x)
{
const float u1k = u1Row[x];
const float u2k = u2Row[x];
u1Row[x] = v1Row[x] + theta * divP1Row[x];
u2Row[x] = v2Row[x] + theta * divP2Row[x];
error += (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;
mutable Mat_<float> p11;
mutable Mat_<float> p12;
mutable Mat_<float> p21;
mutable Mat_<float> p22;
float taut;
};
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];
float* p11Row = p11[y];
float* p12Row = p12[y];
float* p21Row = p21[y];
float* p22Row = p22[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 ng1 = 1.0f + taut * g1;
const float ng2 = 1.0f + taut * g2;
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;
}
}
}
void estimateDualVariables(const Mat_<float>& u1x, const Mat_<float>& u1y, const Mat_<float>& u2x, const Mat_<float>& u2y,
Mat_<float>& p11, Mat_<float>& p12, Mat_<float>& p21, Mat_<float>& p22, float taut)
{
CV_DbgAssert( u1y.size() == u1x.size() );
CV_DbgAssert( u2x.size() == u1x.size() );
CV_DbgAssert( u2y.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() );
EstimateDualVariablesBody body;
body.u1x = u1x;
body.u1y = u1y;
body.u2x = u2x;
body.u2y = u2y;
body.p11 = p11;
body.p12 = p12;
body.p21 = p21;
body.p22 = p22;
body.taut = taut;
parallel_for_(Range(0, u1x.rows), body);
}
void OpticalFlowDual_TVL1::procOneScale(const Mat_<float>& I0, const Mat_<float>& I1, Mat_<float>& u1, Mat_<float>& u2)
{
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 = I1x_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> I1y = I1y_buf(Rect(0, 0, I0.cols, I0.rows));
centeredGradient(I1, I1x, I1y);
Mat_<float> flowMap1 = flowMap1_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> flowMap2 = flowMap2_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> I1w = I1w_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> I1wx = I1wx_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> I1wy = I1wy_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> grad = grad_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> rho_c = rho_c_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> v1 = v1_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> v2 = v2_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> p11 = p11_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> p12 = p12_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> p21 = p21_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> p22 = 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));
Mat_<float> div_p1 = div_p1_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> div_p2 = div_p2_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> u1x = u1x_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> u1y = u1y_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> u2x = u2x_buf(Rect(0, 0, I0.cols, I0.rows));
Mat_<float> u2y = u2y_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);
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, grad, rho_c, v1, v2, l_t);
// compute the divergence of the dual variable (p1, p2)
divergence(p11, p12, div_p1);
divergence(p21, p22, div_p2);
// estimate the values of the optical flow (u1, u2)
error = estimateU(v1, v2, div_p1, div_p2, u1, u2, static_cast<float>(theta));
// compute the gradient of the optical flow (Du1, Du2)
forwardGradient(u1, u1x, u1y);
forwardGradient(u2, u2x, u2y);
// estimate the values of the dual variable (p1, p2)
estimateDualVariables(u1x, u1y, u2x, u2y, p11, p12, p21, p22, taut);
}
}
}
}
void OpticalFlowDual_TVL1::collectGarbage()
{
I0s.clear();
I1s.clear();
u1s.clear();
u2s.clear();
I1x_buf.release();
I1y_buf.release();
flowMap1_buf.release();
flowMap2_buf.release();
I1w_buf.release();
I1wx_buf.release();
I1wy_buf.release();
grad_buf.release();
rho_c_buf.release();
v1_buf.release();
v2_buf.release();
p11_buf.release();
p12_buf.release();
p21_buf.release();
p22_buf.release();
div_p1_buf.release();
div_p2_buf.release();
u1x_buf.release();
u1y_buf.release();
u2x_buf.release();
u2y_buf.release();
}
CV_INIT_ALGORITHM(OpticalFlowDual_TVL1, "DenseOpticalFlow.DualTVL1",
obj.info()->addParam(obj, "tau", obj.tau, false, 0, 0,
"Time step of the numerical scheme");
obj.info()->addParam(obj, "lambda", obj.lambda, false, 0, 0,
"Weight parameter for the data term, attachment parameter");
obj.info()->addParam(obj, "theta", obj.theta, false, 0, 0,
"Weight parameter for (u - v)^2, tightness parameter");
obj.info()->addParam(obj, "nscales", obj.nscales, false, 0, 0,
"Number of scales used to create the pyramid of images");
obj.info()->addParam(obj, "warps", obj.warps, false, 0, 0,
"Number of warpings per scale");
obj.info()->addParam(obj, "medianFiltering", obj.medianFiltering, false, 0, 0,
"Median filter kernel size (1 = no filter) (3 or 5)");
obj.info()->addParam(obj, "scaleStep", obj.scaleStep, false, 0, 0,
"Step between scales (<1)");
obj.info()->addParam(obj, "epsilon", obj.epsilon, false, 0, 0,
"Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time");
obj.info()->addParam(obj, "innerIterations", obj.innerIterations, false, 0, 0,
"inner iterations (between outlier filtering) used in the numerical scheme");
obj.info()->addParam(obj, "outerIterations", obj.outerIterations, false, 0, 0,
"outer iterations (number of inner loops) used in the numerical scheme");
obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow));
} // namespace
Ptr<DenseOpticalFlow> cv::createOptFlow_DualTVL1()
{
2013-08-13 21:57:49 +08:00
return makePtr<OpticalFlowDual_TVL1>();
}