void BipartiteDenseCRF::map ( int n_iterations, short* result1, short* result2, float relax ) {
// Run inference
float * prob[2];
runInference( n_iterations, prob, relax );
dense_crfs_[0]->currentMap( result1 );
dense_crfs_[1]->currentMap( result2 );
}
void DenseCRF::inference ( int n_iterations, float* result, float relax ) {
// Run inference
float * prob = runInference( n_iterations, relax );
// Copy the result over
for( int i=0; i<N_; i++ )
memcpy( result+i*M_, prob+i*M_, M_*sizeof(float) );
}
///////////////////////
///// Inference /////
///////////////////////
void BipartiteDenseCRF::inference ( int n_iterations, float* result1, float* result2, float relax ) {
// Run inference
float * prob[2];
runInference( n_iterations, prob, relax );
// Copy the result over
float* r[2] = {result1, result2};
for( int k=0; k<2; k++ )
for( int i=0; i<N_[k]; i++ )
memcpy( r[k]+i*M_, prob[k]+i*M_, M_*sizeof(float) );
}
void
pcl::DenseCrf::mapInference (int n_iterations, std::vector<int> &result, float relax)
{
// Start inference
// Initialize using the unary energies
expAndNormalize (current_, unary_, -1);
for (int i = 0; i < n_iterations; i++)
{
runInference (relax);
std::cout << "iteration: " << i+1 << " - DONE" << std::endl;
}
// Find the map
for (int i = 0; i < N_; i++)
{
const int prob_idx = i * M_;
// Find the max
float p_label = current_[prob_idx];
int idx = 0;
for (int j = 1; j < M_; j++)
{
if (p_label < current_[prob_idx + j])
{
p_label = current_[prob_idx + j];
idx = j;
}
}
result[i] = idx;
}
/*
for( int i = 0; i < N_; i++ ){
const float * p = prob + i*M_;
// Find the max and subtract it so that the exp doesn't explode
float mx = p[0];
int imx = 0;
for( int j=1; j<M_; j++ )
if( mx < p[j] ){
mx = p[j];
imx = j;
}
result[i] = imx
}
*/
}
void
pcl::DenseCrf::inference (int n_iterations, std::vector<float> &result, float relax)
{
// Start inference
// Initialize using the unary energies
expAndNormalize (current_, unary_, -1);
for (int i = 0; i < n_iterations; i++)
{
runInference (relax);
std::cout << "iteration: " << i+1 << " - DONE" << std::endl;
}
// Copy the data into the result vector
result = current_;
}
void DenseCRF::map ( int n_iterations, short* result, float relax ) {
// Run inference
prob = runInference( n_iterations, relax );
// Find the map
for( int i=0; i<N_; i++ ){
const float * p = prob + i*M_;
// Find the max probability in all the probability maps and only preserve the
float mx = p[0];
int imx = 0;
for( int j=1; j<M_; j++ )
if( mx < p[j] ){
mx = p[j];
imx = j;
}
result[i] = imx;
}
}
void DenseCRF::map ( int n_iterations, short* result, float relax ) {
// Run inference
float * prob = runInference( n_iterations, relax );
// Find the map
for( int i=0; i<N_; i++ ){
const float * p = prob + i*M_;
// Find the max and subtract it so that the exp doesn't explode
float mx = p[0];
int imx = 0;
for( int j=1; j<M_; j++ )
if( mx < p[j] ){
mx = p[j];
imx = j;
}
result[i] = imx;
}
}