void GpuRule::generateDeclCode(Transform& trans, CodeGenerator& o, RuleFlavor rf) {
if(rf != RuleFlavor::SEQUENTIAL || isDisabled() || _done.find(_rule->id()) != _done.end())
return;
_done.insert(_rule->id());
generateKernel(trans, o, false);
if(_rule->canUseLocalMemory()) {
generateKernel(trans, o, true);
//o.createTunable(true, "system.flag.localmem", "rule_" + _rule->id() + "_localmem", 0, 0, 2);
//o.createTunable(true, "system.size.blocksize", "rule_" + _rule->id() + "_blocksize", 4, 0, 5);
}
//else {
//o.define("rule_" + _rule->id() + "_localmem", "0");
//}
}
EdgeDetect::EdgeDetect()
{
int rep = 1;
int radius = 1;
k_kernel = new Kernel(rep,radius);
generateKernel();
}
void MainWindow::convolution() {
statusBar()->showMessage("calculate convolution");
// ask user which kernel should be used
if(generateKernel() != 0) {
statusBar()->showMessage("error while convoluting");
return;
}
// covolution between the image and the given matrix
if(pgmImage->convolution(kernel, kSize, rotateKernel) != 0) {
statusBar()->showMessage("error while calculating convolution");
return;
}
// free memory
for(int i = 0; i < kSize; i++){
free(kernel[i]);
}
free(kernel);
// show chart
QPixmap pixmap(pgmImage->getTmpFilePath());
ui->imageLabel->setPixmap(pixmap);
statusBar()->showMessage("convolution calculated successfully",3000);
}
TBlur::TBlur()
{
int radius =1;
int rep = 1;
t_kernel = new Kernel(rep,radius);
generateKernel();
}
pcl::BRISK2DEstimation<PointInT, PointOutT, KeypointT, IntensityT>::BRISK2DEstimation ()
: rotation_invariance_enabled_ (true)
, scale_invariance_enabled_ (true)
, pattern_scale_ (1.0f)
, input_cloud_ (), keypoints_ (), scale_range_ (), pattern_points_ (), points_ ()
, n_rot_ (1024), scale_list_ (NULL), size_list_ (NULL)
, scales_ (64)
, scalerange_ (30)
, basic_size_ (12.0)
, strings_ (0), d_max_ (0.0f), d_min_ (0.0f), short_pairs_ (), long_pairs_ ()
, no_short_pairs_ (0), no_long_pairs_ (0)
, intensity_ ()
, name_ ("BRISK2Destimation")
{
// Since we do not assume pattern_scale_ should be changed by the user, we
// can initialize the kernel in the constructor
std::vector<float> r_list;
std::vector<int> n_list;
// this is the standard pattern found to be suitable also
r_list.resize (5);
n_list.resize (5);
const float f = 0.85f * pattern_scale_;
r_list[0] = f * 0.0f;
r_list[1] = f * 2.9f;
r_list[2] = f * 4.9f;
r_list[3] = f * 7.4f;
r_list[4] = f * 10.8f;
n_list[0] = 1;
n_list[1] = 10;
n_list[2] = 14;
n_list[3] = 15;
n_list[4] = 20;
generateKernel (r_list, n_list, 5.85f * pattern_scale_, 8.2f * pattern_scale_);
}
