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Added flow estimation using DCT basis

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pull/710/head
Vladislav Samsonov 9 years ago
parent 7d13c8f9aa
commit 25b2958eb5
3 changed files with 341 additions and 39 deletions
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  1. 4
      modules/optflow/include/opencv2/optflow.hpp
  2. 81
      modules/optflow/include/opencv2/optflow/pcaflow.hpp
  3. 295
      modules/optflow/src/pcaflow.cpp

4
modules/optflow/include/opencv2/optflow.hpp
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@ -43,6 +43,8 @@ the use of this software, even if advised of the possibility of such damage.
#include "opencv2/core.hpp"
#include "opencv2/video.hpp"
#include "opencv2/optflow/pcaflow.hpp"
/**
@defgroup optflow Optical Flow Algorithms
@ -193,8 +195,6 @@ CV_EXPORTS_W Ptr<DenseOpticalFlow> createOptFlow_SparseToDense();
CV_EXPORTS_W Ptr<DenseOpticalFlow> createOptFlow_BlockMatching();
CV_EXPORTS_W Ptr<DenseOpticalFlow> createOptFlow_PCAFlow();
//! @}
} //optflow

81
modules/optflow/include/opencv2/optflow/pcaflow.hpp
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@ -0,0 +1,81 @@
/*
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
(3-clause BSD License)
Copyright (C) 2013, OpenCV Foundation, 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:
* 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.
* Neither the names of the copyright holders nor the names of the contributors
may 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 copyright holders 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.
*/
#ifndef __OPENCV_OPTFLOW_PCAFLOW_HPP__
#define __OPENCV_OPTFLOW_PCAFLOW_HPP__
#include "opencv2/core.hpp"
#include "opencv2/video.hpp"
namespace cv
{
namespace optflow
{
class OpticalFlowPCAFlow : public DenseOpticalFlow
{
protected:
const Size basisSize;
const float sparseRate; // (0 .. 0.1)
const float retainedCornersFraction; // [0 .. 1]
const float occlusionsThreshold;
public:
OpticalFlowPCAFlow( Size _basisSize = Size( 18, 14 ), float _sparseRate = 0.02, float _retainedCornersFraction = 1.0,
float _occlusionsThreshold = 0.00002 );
void calc( InputArray I0, InputArray I1, InputOutputArray flow );
void collectGarbage();
private:
void findSparseFeatures( Mat &from, Mat &to, std::vector<Point2f> &features,
std::vector<Point2f> &predictedFeatures ) const;
void removeOcclusions( Mat &from, Mat &to, std::vector<Point2f> &features,
std::vector<Point2f> &predictedFeatures ) const;
void getSystem( OutputArray AOut, OutputArray b1Out, OutputArray b2Out, const std::vector<Point2f> &features,
const std::vector<Point2f> &predictedFeatures, const Size size );
};
CV_EXPORTS_W Ptr<DenseOpticalFlow> createOptFlow_PCAFlow();
}
}
#endif

295
modules/optflow/src/pcaflow.cpp
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@ -41,31 +41,50 @@
//M*/
#include "precomp.hpp"
//#include <iostream>
// using std::cout;
// using std::endl;
namespace cv
{
namespace optflow
{
class OpticalFlowPCAFlow : public DenseOpticalFlow
OpticalFlowPCAFlow::OpticalFlowPCAFlow( Size _basisSize, float _sparseRate, float _retainedCornersFraction,
float _occlusionsThreshold )
: basisSize( _basisSize ), sparseRate( _sparseRate ), retainedCornersFraction( _retainedCornersFraction ),
occlusionsThreshold( _occlusionsThreshold )
{
protected:
float sparseRate;
public:
OpticalFlowPCAFlow() : sparseRate( 0.02 ){};
CV_Assert( sparseRate > 0 && sparseRate <= 0.1 );
CV_Assert( retainedCornersFraction >= 0 && retainedCornersFraction <= 1.0 );
CV_Assert( occlusionsThreshold > 0 );
}
void calc( InputArray I0, InputArray I1, InputOutputArray flow );
void collectGarbage();
inline float eDistSq( const Point2f &p1, const Point2f &p2 )
{
const float dx = p1.x - p2.x;
const float dy = p1.y - p2.y;
return dx * dx + dy * dy;
}
private:
void findSparseFeatures( Mat &from, Mat &to, std::vector<Point2f> &features,
std::vector<Point2f> &predictedFeatures );
};
inline float eNormSq( const Point2f &v ) { return v.x * v.x + v.y * v.y; }
void OpticalFlowPCAFlow::findSparseFeatures( Mat &from, Mat &to, std::vector<Point2f> &features,
std::vector<Point2f> &predictedFeatures )
std::vector<Point2f> &predictedFeatures ) const
{
Size size = from.size();
const unsigned maxFeatures = size.area() * sparseRate;
goodFeaturesToTrack( from, features, maxFeatures * retainedCornersFraction, 0.005, 3 );
// Add points along the grid if not enough features
if ( maxFeatures > features.size() )
{
const unsigned missingPoints = maxFeatures - features.size();
const unsigned blockSize = sqrt( (float)size.area() / missingPoints );
for ( int x = blockSize / 2; x < size.width; x += blockSize )
for ( int y = blockSize / 2; y < size.height; y += blockSize )
features.push_back( Point2f( x, y ) );
}
std::vector<uchar> predictedStatus;
std::vector<float> predictedError;
calcOpticalFlowPyrLK( from, to, features, predictedFeatures, predictedStatus, predictedError );
@ -84,11 +103,204 @@ void OpticalFlowPCAFlow::findSparseFeatures( Mat &from, Mat &to, std::vector<Poi
predictedFeatures.resize( j );
}
void OpticalFlowPCAFlow::calc( InputArray I0, InputArray I1, InputOutputArray flow_out )
void OpticalFlowPCAFlow::removeOcclusions( Mat &from, Mat &to, std::vector<Point2f> &features,
std::vector<Point2f> &predictedFeatures ) const
{
Size size = I0.size();
std::vector<uchar> predictedStatus;
std::vector<float> predictedError;
std::vector<Point2f> backwardFeatures;
calcOpticalFlowPyrLK( to, from, predictedFeatures, backwardFeatures, predictedStatus, predictedError );
size_t j = 0;
const float threshold = occlusionsThreshold * from.size().area();
for ( size_t i = 0; i < predictedFeatures.size(); ++i )
{
if ( predictedStatus[i] )
{
Point2f flowDiff = features[i] - backwardFeatures[i];
if ( eNormSq( flowDiff ) < threshold )
{
features[j] = features[i];
predictedFeatures[j] = predictedFeatures[i];
++j;
}
}
}
features.resize( j );
predictedFeatures.resize( j );
}
void OpticalFlowPCAFlow::getSystem( OutputArray AOut, OutputArray b1Out, OutputArray b2Out,
const std::vector<Point2f> &features, const std::vector<Point2f> &predictedFeatures,
const Size size )
{
AOut.create( features.size(), basisSize.area(), CV_32F );
b1Out.create( features.size(), 1, CV_32F );
b2Out.create( features.size(), 1, CV_32F );
Mat A = AOut.getMat();
Mat b1 = b1Out.getMat();
Mat b2 = b2Out.getMat();
const Point2f scale =
Point2f( (float)basisSize.width / (float)size.width, (float)basisSize.height / (float)size.height );
for ( size_t i = 0; i < features.size(); ++i )
{
const Point2f p = Point2f( features[i].x * scale.x, features[i].y * scale.y );
for ( int n1 = 0; n1 < basisSize.width; ++n1 )
for ( int n2 = 0; n2 < basisSize.height; ++n2 )
{
const float c = cos( ( n1 * M_PI / basisSize.width ) * ( p.x + 0.5 ) ) *
cos( ( n2 * M_PI / basisSize.height ) * ( p.y + 0.5 ) );
A.at<float>( i, n1 * basisSize.height + n2 ) = c;
}
const Point2f flow = predictedFeatures[i] - features[i];
b1.at<float>( i ) = flow.x;
b2.at<float>( i ) = flow.y;
}
}
template <typename T> static inline int mathSign( T val ) { return ( T( 0 ) < val ) - ( val < T( 0 ) ); }
static inline void symOrtho( double a, double b, double &c, double &s, double &r )
{
if ( b == 0 )
{
c = mathSign( a );
s = 0;
r = std::abs( a );
}
else if ( a == 0 )
{
c = 0;
s = mathSign( b );
r = std::abs( b );
}
else if ( std::abs( b ) > std::abs( a ) )
{
const double tau = a / b;
s = mathSign( b ) / sqrt( 1 + tau * tau );
c = s * tau;
r = b / s;
}
else
{
const double tau = b / a;
c = mathSign( a ) / sqrt( 1 + tau * tau );
s = c * tau;
r = a / c;
}
}
static void solveLSQR( const Mat &A, const Mat &b, OutputArray xOut, const double damp = 0.0,
const unsigned iter_lim = 10 )
{
int m = A.size().height;
int n = A.size().width;
CV_Assert( m == b.size().height );
CV_Assert( A.type() == CV_32F );
CV_Assert( b.type() == CV_32F );
xOut.create( n, 1, CV_32F );
double anorm = 0;
const double dampsq = damp * damp;
double ddnorm = 0;
double res2 = 0;
double xxnorm = 0;
double z = 0;
double cs2 = -1;
double sn2 = 0;
Mat v( n, 1, CV_32F, 0.0f );
Mat u = b;
Mat x = xOut.getMat();
x = Mat::zeros( x.size(), x.type() );
double alfa = 0;
double beta = cv::norm( u, NORM_L2 );
Mat w( n, 1, CV_32F, 0.0f );
if ( beta > 0 )
{
u *= 1 / beta;
v = A.t() * u;
alfa = cv::norm( v, NORM_L2 );
}
if ( alfa > 0 )
{
v *= 1 / alfa;
w = v.clone();
}
double rhobar = alfa;
double phibar = beta;
double rnorm = beta;
double r1norm = rnorm;
double arnorm = alfa * beta;
if ( arnorm == 0 )
return;
for ( unsigned itn = 0; itn < iter_lim; ++itn )
{
u = A * v - alfa * u;
beta = cv::norm( u, NORM_L2 );
if ( beta > 0 )
{
u *= 1 / beta;
anorm = sqrt( anorm * anorm + alfa * alfa + beta * beta + damp * damp );
v = A.t() * u - beta * v;
alfa = cv::norm( v, NORM_L2 );
if ( alfa > 0 )
v = ( 1 / alfa ) * v;
}
double rhobar1 = sqrt( rhobar * rhobar + damp * damp );
double cs1 = rhobar / rhobar1;
double sn1 = damp / rhobar1;
double psi = sn1 * phibar;
phibar = cs1 * phibar;
double cs, sn, rho;
symOrtho( rhobar1, beta, cs, sn, rho );
double theta = sn * alfa;
rhobar = -cs * alfa;
double phi = cs * phibar;
phibar = sn * phibar;
double tau = sn * phi;
double t1 = phi / rho;
double t2 = -theta / rho;
Mat dk = ( 1 / rho ) * w;
x = x + t1 * w;
w = v + t2 * w;
ddnorm += cv::norm( dk, NORM_L2SQR );
double delta = sn2 * rho;
double gambar = -cs2 * rho;
double rhs = phi - delta * z;
double gamma = sqrt( gambar * gambar + theta * theta );
cs2 = gambar / gamma;
sn2 = theta / gamma;
z = rhs / gamma;
xxnorm = xxnorm + z * z;
double res1 = phibar * phibar;
res2 = res2 + psi * psi;
rnorm = sqrt( res1 + res2 );
arnorm = alfa * std::abs( tau );
double r1sq = rnorm * rnorm - dampsq * xxnorm;
r1norm = sqrt( std::abs( r1sq ) );
if ( r1sq < 0 )
r1norm = -r1norm;
}
}
void OpticalFlowPCAFlow::calc( InputArray I0, InputArray I1, InputOutputArray flowOut )
{
const Size size = I0.size();
CV_Assert( size == I1.size() );
CV_Assert( sparseRate > 0 && sparseRate < 0.1 );
Mat from, to;
if ( I0.channels() == 3 )
@ -114,31 +326,40 @@ void OpticalFlowPCAFlow::calc( InputArray I0, InputArray I1, InputOutputArray fl
CV_Assert( to.channels() == 1 );
std::vector<Point2f> features, predictedFeatures;
const unsigned maxFeatures = size.area() * sparseRate;
goodFeaturesToTrack( from, features, maxFeatures, 0.005, 3 );
// Add points along the grid if not enough features
{
const unsigned missingPoints = maxFeatures - features.size();
const unsigned blockSize = sqrt( (float)size.area() / missingPoints );
for ( int x = blockSize / 2; x < size.width; x += blockSize )
for ( int y = blockSize / 2; y < size.height; y += blockSize )
features.push_back( Point2f( x, y ) );
}
findSparseFeatures( from, to, features, predictedFeatures );
removeOcclusions( from, to, features, predictedFeatures );
// TODO: Remove occlusions
// from.convertTo( from, CV_32F );
// to.convertTo( to, CV_32F );
flow_out.create( size, CV_32FC2 );
Mat flow = flow_out.getMat();
for ( size_t i = 0; i < features.size(); ++i )
{
flow.at<Point2f>( features[i].y, features[i].x ) = predictedFeatures[i] - features[i];
}
flowOut.create( size, CV_32FC2 );
Mat flow = flowOut.getMat();
// interpolateSparseFlow(flow, features, predictedFeatures);
// for ( size_t i = 0; i < features.size(); ++i )
// flow.at<Point2f>( features[i].y, features[i].x ) = /*Point2f(10,10);*/ predictedFeatures[i] - features[i];
from.convertTo( from, CV_32F );
to.convertTo( to, CV_32F );
Mat A, b1, b2, w1, w2;
getSystem( A, b1, b2, features, predictedFeatures, size );
// solve( A, b1, w1, DECOMP_CHOLESKY | DECOMP_NORMAL );
// solve( A, b2, w2, DECOMP_CHOLESKY | DECOMP_NORMAL );
solveLSQR( A, b1, w1, 2 );
solveLSQR( A, b2, w2, 2 );
Mat flowSmall( basisSize, CV_32FC2 );
for ( int y = 0; y < basisSize.height; ++y )
for ( int x = 0; x < basisSize.width; ++x )
{
float sumX = 0, sumY = 0;
for ( int n1 = 0; n1 < basisSize.width; ++n1 )
for ( int n2 = 0; n2 < basisSize.height; ++n2 )
{
const float c = cos( ( n1 * M_PI / basisSize.width ) * ( x + 0.5 ) ) *
cos( ( n2 * M_PI / basisSize.height ) * ( y + 0.5 ) );
sumX += c * w1.at<float>( n1 * basisSize.height + n2 );
sumY += c * w2.at<float>( n1 * basisSize.height + n2 );
}
flowSmall.at<Point2f>( y, x ) = Point2f( sumX, sumY );
}
resize( flowSmall, flow, size, 0, 0, INTER_CUBIC );
}
void OpticalFlowPCAFlow::collectGarbage() {}

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