float math_util::get_variance(std::vector<float> distribution_)
{
// Initialize an Eigen vector representing the distribution
// In the following the formula for variance is implemented:
// Var(x) = 1/N * SUM((x-mean(x))^2)
Eigen::VectorXf distribution;
distribution = Eigen::Map<Eigen::VectorXf>(&distribution_[0],distribution_.size(),1);
// Retrieve mean value, mean(x)
float distribution_mean = distribution.mean();
// Get deviation vector. (x-mean(x))^2
distribution = (distribution.array() - distribution_mean) * (distribution.array() - distribution_mean);
// Get variance from deviation vector. Var(x)
return distribution.mean();
}
void
mutualproximity::set_normfacts(
musly_trackid trackid,
Eigen::VectorXf& sim)
{
double mu = sim.mean();
Eigen::VectorXd sim_mu = sim.cast<double>().array() - mu;
double std = (sim_mu.transpose() * sim_mu);
std /= (static_cast<double>(sim.size()) - 1.0);
// allocate space
if (trackid >= (int)norm_facts.size()) {
norm_facts.resize(trackid+1);
norm_facts[trackid].mu = mu;
norm_facts[trackid].std = sqrt(std);
}
}
int main(int argc, char** argv) {
if (argc < 5) {
std::cerr << "From the 3 images representing fiber orientation estimate angles and calculate angle homogeneity in the neighborhoods." << std::endl;
std::cerr << "Usage: CalculateEntropyFiberAngles coordx_image coordy_image coordz_image out_image neigborhood_radius" << std::endl;
return EXIT_FAILURE;
}
//parsing parameters
int c = 1;
const char* imagex_filename = argv[c++];
const char* imagey_filename = argv[c++];
const char* imagez_filename = argv[c++];
const char* output_filename = argv[c++];
int neighborhood_radius = atoi(argv[c++]);
const unsigned int Dimension = 3;
std::cout << "Input x: " << imagex_filename << std::endl;
std::cout << "Input y: " << imagey_filename << std::endl;
std::cout << "Input z: " << imagez_filename << std::endl;
std::cout << "Output: " << output_filename << std::endl;
std::cout << "Neighborhood radius: " << neighborhood_radius << std::endl;
//main program
typedef float InputPixelType;
typedef float OutputPixelType;
typedef itk::Image< InputPixelType, Dimension > InputImageType;
typedef itk::Image< OutputPixelType, Dimension > OutputImageType;
typedef itk::ImageFileReader< InputImageType > ReaderType;
typedef itk::ImageFileWriter< OutputImageType > WriterType;
ReaderType::Pointer readerx = ReaderType::New();
ReaderType::Pointer readery = ReaderType::New();
ReaderType::Pointer readerz = ReaderType::New();
WriterType::Pointer writer = WriterType::New();
readerx->SetFileName(imagex_filename);
readery->SetFileName(imagex_filename);
readerz->SetFileName(imagex_filename);
writer->SetFileName(output_filename);
std::cout<<"Reading X"<<std::endl;
readerx->Update();
std::cout<<"Reading Y"<<std::endl;
readery->Update();
std::cout<<"Reading Z"<<std::endl;
readerz->Update();
const InputImageType * inputx = readerx->GetOutput();
const InputImageType * inputy = readery->GetOutput();
const InputImageType * inputz = readerz->GetOutput();
//create the output image
OutputImageType::Pointer image = OutputImageType::New();
image->SetRegions(inputx->GetLargestPossibleRegion());
image->SetSpacing(inputx->GetSpacing());
image->SetOrigin(inputx->GetOrigin());
image->Allocate();
image->FillBuffer(0);
InputImageType::SizeType radius;
radius[0] = neighborhood_radius;
radius[1] = neighborhood_radius;
radius[2] = neighborhood_radius;
itk::ConstNeighborhoodIterator<InputImageType>
iteratorx(radius, inputx, image->GetRequestedRegion());
itk::ConstNeighborhoodIterator<InputImageType>
iteratory(radius, inputy, image->GetRequestedRegion());
itk::ConstNeighborhoodIterator<InputImageType>
iteratorz(radius, inputz, image->GetRequestedRegion());
itk::ImageRegionIterator<OutputImageType> it( image, image->GetRequestedRegion() );
iteratorx.GoToBegin();
iteratory.GoToBegin();
iteratorz.GoToBegin();
it.GoToBegin();
const unsigned int nneighbors = (neighborhood_radius*2+1)*(neighborhood_radius*2+1)*(neighborhood_radius*2+1);
Eigen::MatrixXf neighbors( nneighbors-1, 3 ); //to store the vectors
Eigen::Vector3f central_vector;
int c_id = (int) (nneighbors / 2); // get offset of center pixel
//check image size for the progress indicator
OutputImageType::RegionType region = image->GetLargestPossibleRegion();
OutputImageType::SizeType size = region.GetSize();
const unsigned long int npixels = size[0]*size[1]*size[2];
unsigned long int k = 0;
while ( ! iteratorx.IsAtEnd() )
{
if(k%10000 == 0)
{
std::cout<<"Processed "<<k<<"/"<<npixels<<"\r"<<std::flush;
}
k++;
unsigned int j = 0;
for (unsigned int i = 0; i < nneighbors; ++i)
{
if(i!=c_id) //exclude the center
{
neighbors(j, 0) = iteratorx.GetPixel(i);
neighbors(j, 1) = iteratory.GetPixel(i);
neighbors(j, 2) = iteratorz.GetPixel(i);
j++;
}
else
{
central_vector(0) = iteratorx.GetPixel(i);
central_vector(1) = iteratory.GetPixel(i);
central_vector(2) = iteratorz.GetPixel(i);
}
}
// calculate the angles
Eigen::VectorXf dp = neighbors*central_vector;
const float variance = ((dp.array()-dp.mean()).pow(2)).mean();
it.Set(variance);
++iteratorx;
++iteratory;
++iteratorz;
++it;
}
std::cout<<std::endl;
// The output of the resampling filter is connected to a writer and the
// execution of the pipeline is triggered by a writer update.
try {
writer->SetInput(image);
writer->Update();
} catch (itk::ExceptionObject & excep) {
std::cerr << "Exception caught !" << std::endl;
std::cerr << excep << std::endl;
}
return EXIT_SUCCESS;
}
