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imageGroup.cxx
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imageGroup.cxx
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#include <algorithm>
#include <assert.h>
#include <filesystem>
#include <iostream>
#include <numeric>
#include <omp.h>
#include <sstream>
#include <random>
#include <stdio.h>
#include <stdlib.h>
#include <vector>
#include <boost/iostreams/filtering_streambuf.hpp>
#include <boost/iostreams/copy.hpp>
#include <boost/iostreams/filter/gzip.hpp>
#include <vtkBoundingBox.h>
#include <vtkBSplineTransform.h>
#include <vtkImageData.h>
#include <vtkLandmarkTransform.h>
#include <vtkMath.h>
#include <vtkMatrixToLinearTransform.h>
#include <vtkNIFTIImageWriter.h>
#include <vtkPointData.h>
#include <vtkPoints.h>
#include "../tools/transformIO.h"
#include "imageGroup.h"
void ImageGroup::run() {
// read transform if there are fixed images
if ( this->numberOfFixedImages ) readAndApplyFixedImagesTransforms();
this->setupStats();
this->setupLinearTransforms();
this->transformPoints();
if ( this->useRANSAC && this->numberOfFixedImages ) {
for ( int i = this->numberOfFixedImages; i < this->images.size(); i++ )
this->RANSAC( i );
this->transformPoints();
this->updateStats();
if ( this->printStats ) displayStats();
if ( this->printLinear ) this->displayLinearTransforms();
} else {
// linear registration
cout << endl << "Linear registration" << endl;
for ( int iteration = 0; iteration < this->linearIterations; iteration++ ) {
cout << "Linear registration, iteration " << iteration + 1
<< "/" << this->linearIterations << endl;
if ( !( iteration % this->statIntervalUpdate ) ) this->updateStats();
if ( this->printStats ) displayStats();
float e = this->updateLinearTransforms();
if ( this->printLinear ) this->displayLinearTransforms();
this->transformPoints();
this->computeLandmarkDistances( e );
}
}
this->transformPoints( true );
this->saveDistanceHistograms( "histograms_linear.csv" );
if ( this->deformableLevels ) {
cout << endl << "Deformable registration" << endl;
std::vector< int > nGrids;
this->countInliers();
for ( int level = 0; level < this->deformableLevels; level++ ) {
cout << endl << "Level " << level + 1 << "/" << this->deformableLevels << endl;
this->setupDeformableTransforms( level );
this->transformPoints();
int numberOfGrids = 1;
float alpha = this->deformableAlpha;
cout << "alpha = " << alpha << endl;
int numberOfDiffeomorphicIterations = 0;
for ( int iteration = 0; iteration < this->deformableIterations; iteration++ ) {
cout << "Level " << level + 1 << "/" << this->deformableLevels
<< ", Iteration " << iteration + 1 <<"/"
<< this->deformableIterations << endl;
if ( !( iteration % this->statIntervalUpdate ) ) this->updateStats();
if ( this->printStats ) this->displayStats();
float e = this->updateDeformableTransforms( alpha );
if ( e < 0 ) {
if ( numberOfDiffeomorphicIterations == 0 ) {
alpha /= 2;
cout << "Halving alpha. New Value : " << alpha << endl;
}
cout << " creating new grid" << endl;
numberOfGrids++;
iteration--;
this->transformPoints( true );
this->setupDeformableTransforms( level );
this->transformPoints();
numberOfDiffeomorphicIterations = 0;
continue;
}
numberOfDiffeomorphicIterations++;
this->transformPoints();
this->computeLandmarkDistances( e );
}
this->countInliers();
cout << "Number of grids for this level : " << numberOfGrids << endl;
nGrids.push_back( numberOfGrids );
this->transformPoints( true );
}
int totalNumberOfGrids = 0;
cout << "Grids per level : ";
for ( int i = 0; i < nGrids.size(); i++ ) {
totalNumberOfGrids +=nGrids[ i ];
cout << nGrids[ i ] << " ";
}
cout << endl << "Total number of grids : " << totalNumberOfGrids << endl;
this->saveErrorMaps();
}
this->displayStats();
this->saveDistanceHistograms( "histograms.csv" );
this->saveMeasures( this->outputFileName );
this->saveTransforms();
this->saveBoundingBox();
this->saveLandmarkDistances();
this->saveTransformedLandmarks();
if ( this->writePairs ) this->writeLinksDistances();
std::fstream fs;
fs.open( "bbox.json", fstream::out | fstream::trunc );
fs << picojson::value( this->stats ).serialize();
fs.close();
}
void ImageGroup::setupDeformableTransforms( int level ) {
double size = this->initialGridSize / pow( 2, level ); // grid size
double spacing[ 3 ];
double length[ 3 ];
int dims[ 3 ];
double origin[ 3 ];
vtkBoundingBox box;
this->getBoundingBox( box );
box.ScaleAboutCenter( 1 + 2 * this->boundingBoxMargin );
for ( int k = 0; k < 3; k++ ) {
length[ k ] = box.GetLength( k );
dims[ k ] = round( length[ k ] / size );
if ( dims[ k ] < 1 ) dims[ k ] = 1;
spacing[ k ] = length[ k ] / dims[ k ];
origin[ k ] = box.GetBound( 2 * k ) - spacing[ k ];
dims[ k ] += 3;
}
cout << "Bounding box : ";
double bbox[ 6 ];
box.GetBounds( bbox );
print( bbox, 6 );
cout << "Box length : ";
print( length, 3 );
cout << "Grid origin : ";
print( origin, 3 );
cout << "Grid spacing : ";
print( spacing, 3 );
cout << "Grid dimensions (control points): ";
print( dims, 3 );
#pragma omp parallel for
for ( int i = this->numberOfFixedImages; i < this->images.size(); i++ ) {
vtkNew<vtkImageData> coeffs;
coeffs->SetOrigin( origin );
coeffs->SetSpacing( spacing );
coeffs->SetDimensions( dims );
coeffs->AllocateScalars( VTK_FLOAT, 3 );
coeffs->GetPointData()->GetScalars()->Fill( 0 );
vtkNew<vtkBSplineTransform> transform;
transform->SetCoefficientData( coeffs );
transform->SetBorderModeToZero();
Image &image = this->images[ i ];
image.allTransforms->Concatenate( transform );
image.transform = transform;
vtkNew<vtkImageData> gradient;
gradient->SetOrigin( origin );
gradient->SetSpacing( spacing );
gradient->SetDimensions( dims );
gradient->AllocateScalars( VTK_FLOAT, 4 );
image.gradient = gradient;
}
}
// inspired from vtkBSplineTransform.cxx
inline void vtkBSplineTransformWeights( double F[ 4 ], double f ) {
const double sixth = 1.0/6.0;
const double half = 0.5;
const double f2 = f * f;
F[ 3 ] = f2 * f * sixth;
F[ 0 ] = ( f2 - f ) * half - F[ 3 ] + sixth;
F[ 2 ] = f + F[ 0 ] - F[ 3 ] * 2;
F[ 1 ] = 1 - F[ 0 ] - F[ 2 ] - F[ 3 ];
}
double ImageGroup::updateDeformableTransforms( const float alpha ) {
double sDistances = 0, sWeights = 0;
const float constraintWeight = this->images.size() * this->landmarksConstraintsWeight;
#pragma omp parallel for reduction( +:sDistances, sWeights )
for ( int image1 = this->numberOfFixedImages; image1 < this->images.size(); image1++ ) {
Image &image = this->images[ image1 ];
float *gradient = ( float* ) image.gradient->GetScalarPointer();
const auto dims = image.gradient->GetDimensions();
const auto increments = image.gradient->GetIncrements();
const auto spacing = image.gradient->GetSpacing();
const auto origin = image.gradient->GetOrigin();
double weights[ 3 ][ 4 ];
image.gradient->GetPointData()->GetScalars()->Fill( 0 );
Stats *statsA = &image.stats;
for ( auto const &point : image.points ) {
const float *pos = point.xyz;
const float *pA = point.xyz2;
float sWeight = 0;
float sDisp[ 3 ] = { 0, 0, 0 };
// compute point displacement
for ( auto const &link : point.links ) {
Image *image2 = &this->images[ link.image ];
Point *pointB = &image2->points[ link.point ];
float *pB = pointB->xyz2;
float dist2 = vtkMath::Distance2BetweenPoints( pA, pB );
float dist = sqrt( dist2 );
float probA = statsA->getInlierProbability( dist );
float probB = image2->stats.getInlierProbability( dist );
float weight = std::min( probA, probB );
float weight2 = weight * weight;
if ( weight < this->inlierThreshold ) continue;
sWeights += weight2;
sDistances += weight2 * dist2;
for ( int k = 0; k < 3; k++ )
sDisp[ k ] += weight2 * ( pB[ k ] - pA[ k ] );
sWeight += weight2;
}
for ( auto const &link : point.hardLinks ) {
Image *image2 = &this->images[ link.image ];
Point *pointB = &image2->points[ link.point ];
float *pB = pointB->xyz2;
float dist2 = vtkMath::Distance2BetweenPoints( pA, pB );
float weight2 = constraintWeight * constraintWeight;
sDistances += weight2 * dist2;
sWeights += weight2;
for ( int k = 0; k < 3; k++ )
sDisp[ k ] += weight2 * ( pB[ k ] - pA[ k ] );
sWeight += weight2;
}
if ( sWeight == 0 ) continue;
// add point displacement to gradient
int idZ = 0;
for ( int k = 0; k < 3; k++) {
float coord = ( pos[ k ] - origin[ k ] ) / spacing[ k ];
int intCoord = floor( coord );
vtkBSplineTransformWeights( weights[ k ], coord - intCoord );
idZ += ( intCoord - 1 ) * increments[ k ];
}
for ( int k = 0; k < 4; k++ ) {
int idY = idZ;
for ( int j = 0; j < 4; j++ ) {
int idX = idY;
for ( int i = 0; i < 4; i++ ) {
double w = weights[ 0 ][ i ]* weights[ 1 ][ j ] * weights[ 2 ][ k ];
for ( int l = 0; l < 3; l++ )
gradient[ idX + l ] += w * sDisp[ l ];
gradient[ idX + 3 ] += w * sWeight;
idX += increments[ 0 ];
}
idY += increments[ 1 ];
}
idZ += increments[ 2 ];
}
}
// apply gradient to transform
float *coeffs = ( float *) ( ( vtkBSplineTransform * ) image.transform )
->GetCoefficientData()->GetScalarPointer();
int gradientId = 0;
int coeffsId = 0;
for ( int i = 0; i < dims[ 0 ] * dims[ 1 ] * dims[ 2 ]; i++ ) {
const float coeff = gradient[ gradientId + 3 ];
if ( coeff > 0 ) {
for ( int j = 0; j < 3; j++ ) {
gradient[ gradientId + j ] = coeffs[ coeffsId + j ]
+ alpha * gradient[ gradientId + j ] / coeff;
}
} else {
for ( int j = 0; j < 3; j++ ) {
gradient[ gradientId + j ] = coeffs[ coeffsId + j ];
}
}
gradientId += 4;
coeffsId += 3;
}
}
// substract average displacement on all transforms;
const int nImages = this->images.size();
float *coeffs[ nImages ];
int dims[ 3 ];
double spacing[ 3 ];
for ( int image = this->numberOfFixedImages; image < nImages; image++ ) {
vtkImageData *gradient = this->images[ image ].gradient;
gradient->GetDimensions( dims );
gradient->GetSpacing( spacing );
coeffs[ image ] = ( float * ) gradient->GetScalarPointer();
}
const int nValues = dims[ 0 ] * dims[ 1 ] * dims[ 2 ];
int nBigCoeffs = 0;
int direction = 0;
const float maxD = this->maxDisplacementRatio;
const bool apply = this->numberOfFixedImages == 0;
#pragma omp parallel for reduction( + : nBigCoeffs )
for ( int i = 0; i < nValues; i++ ) {
int offset = i * 4;
for ( int j = 0; j < 3; j++ ) {
double sum = 0;
if ( apply ) {
for ( int image = this->numberOfFixedImages; image < nImages; image++ ) {
sum += coeffs[ image ][ offset + j ];
}
sum /= nImages;
}
for ( int image = this->numberOfFixedImages; image < nImages; image++ ) {
coeffs[ image ][ offset + j ] -= sum;
if ( fabs( coeffs[ image ][ offset + j ] ) > maxD * spacing[ j ] )
nBigCoeffs++;
}
}
}
if ( this->guaranteeDiffeomorphism && ( nBigCoeffs > 0 ) ) {
cout << endl<< "Diffeomorphism is not guaranteed : Iteration canceled" << endl;
return -1;
}
#pragma omp parallel for
for ( int image1 = this->numberOfFixedImages; image1 < nImages; image1++ ) {
Image &image = this->images[ image1 ];
vtkBSplineTransform *transform = ( vtkBSplineTransform * ) image.transform;
vtkImageData *imageData = transform->GetCoefficientData();
float *disp = ( float * ) imageData->GetScalarPointer();
float *newDisp = coeffs[ image1 ];
int offset = 0;
int offset2 = 0;
for ( int i = 0; i < nValues; i++ ) {
for ( int j = 0; j < 3; j++ ) {
disp[ offset + j ] = newDisp[ offset2 + j ];
}
offset += 3;
offset2 += 4;
}
image.transform->Update();
}
return sqrt( sDistances / sWeights );
}
void ImageGroup::saveErrorMaps() {
const float constraintWeight = this->images.size() * this->landmarksConstraintsWeight;
std::filesystem::create_directory( this->errorMapsSubdirectory.c_str() );
#pragma omp parallel for
for ( int image1 = this->numberOfFixedImages; image1 < this->images.size(); image1++ ) {
Image &image = this->images[ image1 ];
float *gradient = ( float* ) image.gradient->GetScalarPointer();
const auto dims = image.gradient->GetDimensions();
const auto increments = image.gradient->GetIncrements();
const auto spacing = image.gradient->GetSpacing();
const auto origin = image.gradient->GetOrigin();
image.gradient->GetPointData()->GetScalars()->Fill( 0 );
Stats *statsA = &image.stats;
for ( auto const &point : image.points ) {
const float *pos = point.xyz;
const float *pA = point.xyz2;
float sWeight = 0;
float sDisp[ 3 ] = { 0, 0, 0 };
// compute point displacement
for ( auto const &link : point.links ) {
Image *image2 = &this->images[ link.image ];
Point *pointB = &image2->points[ link.point ];
float *pB = pointB->xyz2;
float dist2 = vtkMath::Distance2BetweenPoints( pA, pB );
float dist = sqrt( dist2 );
float probA = statsA->getInlierProbability( dist );
float probB = image2->stats.getInlierProbability( dist );
float weight = std::min( probA, probB );
float weight2 = weight * weight;
if ( weight < this->inlierThreshold ) continue;
for ( int k = 0; k < 3; k++ )
sDisp[ k ] += weight2 * ( pB[ k ] - pA[ k ] );
sWeight += weight2;
}
for ( auto const &link : point.hardLinks ) {
Image *image2 = &this->images[ link.image ];
Point *pointB = &image2->points[ link.point ];
float *pB = pointB->xyz2;
float dist2 = vtkMath::Distance2BetweenPoints( pA, pB );
float weight2 = constraintWeight * constraintWeight;
for ( int k = 0; k < 3; k++ )
sDisp[ k ] += weight2 * ( pB[ k ] - pA[ k ] );
sWeight += weight2;
}
if ( sWeight == 0 ) continue;
// add point displacement to mean image
int id = 0;
for ( int k = 0; k < 3; k++ ) {
float coord = ( pos[ k ] - origin[ k ] ) / spacing[ k ];
id += floor( coord ) * increments[ k ];
}
gradient[ id + 3 ] += sWeight;
for ( int k = 0; k < 3; k++ ) gradient[ id + k ] += sDisp[ k ];
}
const int nValues = dims[ 0 ] * dims[ 1 ] * dims[ 2 ];
for ( int i = 0; i < nValues; i++ ) {
const int id = 4 * i;
if ( gradient[ id + 3 ] > 0 )
for ( int k = 0; k < 3; k++ ) gradient[ id + k ] /= gradient[ id + 3 ];
}
std::ostringstream file;
file << this->errorMapsSubdirectory << "/" << image1 << ".nii.gz";
vtkNew< vtkNIFTIImageWriter > writer;
writer->SetInputData( image.gradient );
writer->SetFileName( file.str().c_str() );
writer->Write();
}
}
void ImageGroup::updateStats() {
// add samples to statistics
#pragma omp parallel for
for ( int image1 = 0; image1 < this->images.size(); image1++) {
Image *image = &this->images[ image1 ];
Stats *stats = &image->stats;
stats->reset();
for ( auto const &point : image->points ) {
const float *pA = point.xyz2;
for ( auto const &link : point.links ) {
const Image *image2 = &this->images[ link.image ];
const Point *pointB = &image2->points[ link.point ];
const float *pB = pointB->xyz2;
float dist2 = vtkMath::Distance2BetweenPoints( pA, pB );
stats->addSample( sqrt( dist2 ) );
}
}
stats->estimateDistribution();
}
}
void ImageGroup::displayLinearTransforms() {
for ( int i = this->numberOfFixedImages; i < this->images.size(); i++) {
cout << "Image " << i << ", translation=";
auto matrix = ( ( vtkMatrixToLinearTransform *) this->images[ i ].transform )->GetInput( );
for ( int k = 0; k < 3; k++ ) {
cout << matrix->GetElement( k, 3 );
if ( k < 2 ) cout << " ";
}
cout << endl << "scale=";
for ( int k = 0; k < 3; k++ ) {
cout << matrix->GetElement( k, k );
if ( k < 2 ) cout << " ";
}
cout << endl;
}
}
void ImageGroup::RANSAC( int imageId ) {
auto start = std::chrono::system_clock::now();
cout << "RANSAC registration for image " << imageId << ": "<<std::flush;
Image *image = &this->images[ imageId ];
int nBatches = omp_get_num_procs();
int batchIterations = numberOfRANSACIterations / nBatches;
std::vector < RANSACResult > results;
#pragma omp parallel for
for ( int i = 0; i < nBatches; i++ ) {
RANSACResult result = this->RANSACBatch( imageId, batchIterations, i );
#pragma omp critical
results.push_back( result );
}
vtkMatrixToLinearTransform *transform = ( vtkMatrixToLinearTransform *) image->transform;
auto matrix = transform->GetInput();
int maxNumberOfInliers = 0;
for ( auto res = results.begin(); res != results.end(); res++ ) {
int nInliers = res->first;
if ( nInliers > maxNumberOfInliers ) {
matrix->DeepCopy( res->second );
maxNumberOfInliers = nInliers;
}
res->second->Delete();
}
float transformed[ 3 ];
auto &pts = image->points;
vtkPoints *source = vtkPoints::New();
vtkPoints *target = vtkPoints::New();
source->SetDataTypeToFloat();
target->SetDataTypeToFloat();
vtkLandmarkTransform *trans = vtkLandmarkTransform::New();
trans->SetModeToSimilarity();
trans->SetSourceLandmarks( source );
trans->SetTargetLandmarks( target );
float maxDistance2 = pow( this->RANSACInlierDistance, 2 );
for ( auto const &pointA : pts ) {
transform->TransformPoint( pointA.xyz, transformed );
for ( auto const &link : pointA.links ) {
Image *image2 = &this->images[ link.image ];
Point *pointB = &image2->points[ link.point ];
float *pB = pointB->xyz2;
if ( vtkMath::Distance2BetweenPoints( transformed, pB ) < maxDistance2 ) {
source->InsertNextPoint( pointA.xyz );
target->InsertNextPoint( pB );
}
}
}
source->Modified();
target->Modified();
trans->Update();
matrix->DeepCopy( trans->GetMatrix() );
trans->Delete();
source->Delete();
target->Delete();
auto end = std::chrono::system_clock::now();
cout << maxNumberOfInliers << " inliers, computed in " << std::chrono::duration<float>(end - start).count() << "s" << endl;
if ( this->stats[ "RANSAC" ].is<picojson::null>() )
this->stats[ "RANSAC" ] = picojson::value( picojson::array() );
picojson::object stats;
stats[ "image" ] = picojson::value( ( double ) imageId );
stats[ "threshold" ] = picojson::value( this->RANSACInlierDistance );
stats[ "inliers" ] = picojson::value( (double) maxNumberOfInliers );
this->stats[ "RANSAC" ].get<picojson::array>().push_back( picojson::value( stats ) );
}
ImageGroup::RANSACResult ImageGroup::RANSACBatch( int imageId, int nIterations, int batch ) {
int numberOfRansacPoints = 4;
vtkPoints *source = vtkPoints::New();
vtkPoints *target = vtkPoints::New();
source->SetDataTypeToFloat();
target->SetDataTypeToFloat();
vtkLandmarkTransform *trans = vtkLandmarkTransform::New();
trans->SetModeToSimilarity();
trans->SetSourceLandmarks( source );
trans->SetTargetLandmarks( target );
int maxNumberOfInliers = 0;
float maxDistance2 = pow( this->RANSACInlierDistance, 2 );
Image *image = &this->images[ imageId ];
vtkMatrix4x4 *matrix = vtkMatrix4x4::New();
auto matrix2 = trans->GetMatrix();
auto &pts = image->points;
int nPoints = pts.size();
std::mt19937 rng( batch * 1000 );
for ( int i = 0; i < nIterations; i++ ) {
source->Reset();
target->Reset();
for ( int j = 0; j < numberOfRansacPoints; j++ ) {
while ( true ) {
int pt = rng() % nPoints;
Point &point = pts[ pt ];
auto &links = point.links;
auto size = links.size();
if ( size == 0 ) continue;
source->InsertNextPoint( point.xyz );
int linkId = rng() % size;
auto &link = links[ linkId ];
target->InsertNextPoint(
this->images[ link.image ].points[ link.point ].xyz );
break;
}
}
source->Modified();
target->Modified();
trans->Update();
int nInliers = 0;
float transformed[ 3 ];
for ( auto const &pointA : pts ) {
trans->TransformPoint( pointA.xyz, transformed );
for ( auto const &link : pointA.links ) {
Image *image2 = &this->images[ link.image ];
Point *pointB = &image2->points[ link.point ];
float *pB = pointB->xyz2;
if ( vtkMath::Distance2BetweenPoints( transformed, pB ) < maxDistance2 )
nInliers++;
}
}
float determinant = fabs( matrix2->Determinant() );
if ( ( determinant > this->RANSACMaxScale ) || ( determinant < 1.0 / this->RANSACMaxScale ) ) continue;
if ( maxNumberOfInliers < nInliers ) {
maxNumberOfInliers = nInliers;
matrix->DeepCopy( matrix2 );
}
}
source->Delete();
target->Delete();
trans->Delete();
return std::make_pair( maxNumberOfInliers, matrix );
}
void ImageGroup::setupLinearTransforms() {
vtkBoundingBox box;
float anchors[ this->images.size() ][ 3 ];
float averageAnchor[ 3 ] = { 0, 0, 0 };
float *anchorPosition = this->linearInitializationAnchor;
for ( int i = 0; i < this->images.size(); i++ ) {
box.Reset();
this->images[ i ].addPoints( box );
for ( int j = 0; j < 3; j++ ) {
float c = anchorPosition[ j ];
float anchor = ( 1 - c ) * box.GetBound( 2 * j ) + c * box.GetBound( 1 + 2 * j );
anchors[ i ][ j ] = anchor;
if ( i < this->images.size() - this->numberOfFixedImages )
averageAnchor[ j ] += anchor / ( float ) ( this->images.size() - this->numberOfFixedImages );
}
}
// center volumes
for ( int i = this->numberOfFixedImages; i < this->images.size() ; i++) {
Image &image = this->images[ i ];
image.transform = vtkMatrixToLinearTransform::New();
image.allTransforms = vtkSmartPointer<vtkGeneralTransform>::New();
image.allTransforms->PostMultiply();
image.allTransforms->Concatenate( image.transform );
vtkNew<vtkMatrix4x4> matrix;
matrix->Identity();
for ( int j = 0; j < 3; j++ )
matrix->SetElement( j, 3, averageAnchor[ j ] - anchors[ i ][ j ] );
( ( vtkMatrixToLinearTransform *) image.transform )->SetInput( matrix );
}
}
void ImageGroup::saveDistanceHistograms( const char *file ) {
std::fstream fs;
fs.open( file, fstream::out | fstream::trunc );
int maxSize = 0;
for ( int i = 0; i < this->images.size(); i++) {
Stats *stats = &this->images[ i ].stats;
stats->getHistogram();
maxSize =std:: max( maxSize, ( int ) stats->histogram.size() );
fs << "image " << i ;
if ( i < this->images.size() - 1 ) fs << ",";
else fs << endl;
}
for ( int d = 0; d < maxSize; d++ ) {
for ( int i = 0; i < this->images.size(); i++) {
Stats *stats = &this->images[ i ].stats;
if ( stats->histogram.size() <= d ) fs << 0;
else fs << stats->histogram[ d ];
if ( i < this->images.size() - 1 ) fs << ",";
else fs << endl;
}
}
fs.close();
}
void ImageGroup::saveIndividualDistanceHistograms() {
for ( int i = 0; i < this->images.size(); i++) {
std::ostringstream file;
file << "histogram" << i << ".csv";
this->images[ i ].stats.saveHistogram( file.str().c_str() );
}
}
void ImageGroup::displayStats() {
for ( int i = 0; i < this->images.size() ; i++) {
cout << "Stats " << i << ":";
this->images[ i ].stats.displayParameters();
}
}
void ImageGroup::transformPoints( const bool &apply ) {
#pragma omp parallel for
for ( int i = this->numberOfFixedImages; i < this->images.size() ; i++ )
this->images[ i ].transformPoints( apply );
}
bool comparePairs( const std::vector< float > &p1, const std::vector< float > & p2) {
return ( p1[ 4 ] < p2[ 4 ] );
}
void ImageGroup::writeLinksDistances() {
std::vector < std::vector< float > > pairs;
for ( int i1 = 0; i1 < this->images.size(); i1++ ) {
Image &image = this->images[ i1 ];
Stats *stats = &image.stats;
for ( int p1 = 0; p1 != image.points.size(); p1++ ) {
auto pointA = &image.points[ p1 ];
float *pA = pointA->xyz2;
for ( auto const &link : pointA->links ) {
int i2 = link.image;
Image *image2 = &this->images[ i2 ];
int p2 = link.point;
Point *pointB = &image2->points[ p2 ];
float *pB = pointB->xyz2;
float dist = sqrt( vtkMath::Distance2BetweenPoints( pA, pB ) );
std::vector< float > pair;
pair.push_back( i1 );
pair.push_back( p1 );
pair.push_back( i2 );
pair.push_back( p2 );
pair.push_back( dist );
pair.push_back( stats->getInlierProbability( dist ) );
pairs.push_back( pair );
}
}
}
sort( pairs.begin(), pairs.end(), comparePairs );
std::ofstream file( "pairs.csv.gz", std::ios_base::out | std::ios_base::binary);
boost::iostreams::filtering_streambuf<boost::iostreams::output> outbuf;
outbuf.push(boost::iostreams::gzip_compressor());
outbuf.push(file);
std::ostream out(&outbuf);
for ( int i = 0; i < pairs.size(); i++ ) {
std::vector< float > &pair = pairs[ i ];
for ( int j = 0; j < pair.size(); j++ ) {
out << pair[ j ];
if ( j < pair.size() - 1 ) out << ",";
}
if ( i < pairs.size() - 1 ) out << "\n";
}
boost::iostreams::close( outbuf );
file.close();
}
void ImageGroup::countInliers() {
int nPairs = 0, nInliers = 0, nOutliers = 0;
picojson::array imagesStats;
this->stats[ "images" ] = picojson::value( imagesStats );
// setup json stats record
for ( int i = 0; i < this->images.size(); i++ ) {
picojson::object obj;
this->stats[ "images" ].get<picojson::array>().push_back( picojson::value( obj ) );
}