// OPTIMIZE
mdarray_float convolve(const mdarray_float &src, const mdarray_float &kernel, int cx, int cy) {
mdarray_float convolved(false, src.length(0), src.length(1));
#pragma omp parallel
#pragma omp for
for (int y = 0; y < src.length(1); y++) for (int x = 0; x < src.length(0); x++) {
double s = 0.;
for (int yy = 0; yy < kernel.length(1); yy++) for (int xx = 0; xx < kernel.length(0); xx++) {
s += kernel(xx, yy) * src(x + xx - cx, y + yy - cy);
}
convolved(x, y) = s;
}
return convolved;
}
double MyModel_Pixels::calculate_total_flux(double time) const
{
double speed = 1.0/timescale;
double offset = x0 + speed*time;
int j = (int)floor((offset - (x_min + 0.5*dx))/dx);
return convolved(ni/2, j%nj);
}
std::vector<float> WindowFunction::convolve(
const std::vector<float>& input, const std::vector<float>& window
) const {
unsigned int inputSize = input.size();
unsigned int padding = window.size() / 2;
std::vector<float> convolved(inputSize, 0.0);
// TODO: this implements zero padding for boundary effects,
// write something mean-based later.
for (unsigned int sample = 0; sample < inputSize; sample++) {
float convolution = 0.0;
for (unsigned int k = 0; k < window.size(); k++) {
int frm = (signed)sample - (signed)padding + (signed)k;
if (frm >= 0 && frm < (signed)inputSize) // don't run off either end
convolution += input[frm] * window[k] / window.size();
}
convolved[sample] = convolution;
}
return convolved;
}
void MyModel_Pixels::calculate_obscurer_map()
{
// Obscurer image
for(size_t j=0; j<nj; ++j)
for(size_t i=0; i<ni; ++i)
obscurer_map(i, j) = exp(-sd*n(i, j));
// FFT of obscurer_map
arma::cx_mat A = arma::fft2(obscurer_map);
// (FFT of obscurer map)*(FFT of star map)
for(size_t j=0; j<nj; ++j)
for(size_t i=0; i<ni; ++i)
A(i, j) *= fft_of_star(i, j);
// obscurer map convolved with star map
A = arma::ifft2(A);
for(size_t i=0; i<ni; ++i)
for(size_t j=0; j<nj; ++j)
convolved(i, j) = A(i, j).real();
}
int main(int argc, char *argv[])
{
if (argc != 4)
{
cout << "Usage: " << argv[0] << " cpu|gpu out_func out_prefix" << endl;
return 1;
}
ImageParam input(UInt(8), 3, "input");
Func clamped("clamped");
Func convolved("convolved");
Func sharpen("sharpen");
Var c("c"), x("x"), y("y");
// Algorithm
clamped(x, y, c) = input(
clamp(x, 0, input.width()-1),
clamp(y, 0, input.height()-1),
c) / 255.f;
Image<int16_t> kernel(3, 3);
kernel(0, 0) = -1;
kernel(0, 1) = -1;
kernel(0, 2) = -1;
kernel(1, 0) = -1;
kernel(1, 1) = 8;
kernel(1, 2) = -1;
kernel(2, 0) = -1;
kernel(2, 1) = -1;
kernel(2, 2) = -1;
RDom r(kernel);
convolved(x, y, c) += kernel(r.x, r.y) * clamped(x + r.x - 1, y + r.y - 1, c);
sharpen(x, y, c) = u8(
clamp(convolved(x, y, c)/8 + clamped(x, y, c), 0, 1) * 255
);
// Channel order
input.set_stride(0, 4);
input.set_extent(2, 4);
sharpen.reorder_storage(c, x, y);
sharpen.output_buffer().set_stride(0, 4);
sharpen.output_buffer().set_extent(2, 4);
// Schedules
if (!strcmp(argv[1], "cpu"))
{
sharpen.parallel(y).vectorize(c, 4);
}
else if (!strcmp(argv[1], "gpu"))
{
sharpen.cuda_tile(x, y, 16, 4);
}
else
{
cout << "Invalid schedule type '" << argv[1] << "'" << endl;
return 1;
}
compile(sharpen, input, argv[2], argv[3]);
return 0;
}
int
main (int argc, char ** argv)
{
int viewport_source, viewport_convolved = 0;
int direction = -1;
int nb_threads = 0;
char border_policy = 'Z';
double threshold = 0.001;
pcl::filters::Convolution<pcl::PointXYZRGB, pcl::PointXYZRGB> convolution;
Eigen::ArrayXf gaussian_kernel(5);
gaussian_kernel << 1.f/16, 1.f/4, 3.f/8, 1.f/4, 1.f/16;
pcl::console::print_info ("convolution kernel:");
for (int i = 0; i < gaussian_kernel.size (); ++i)
pcl::console::print_info (" %f", gaussian_kernel[i]);
pcl::console::print_info ("\n");
if (argc < 3)
{
usage (argv);
return 1;
}
// check if user is requesting help
std::string arg (argv[1]);
if (arg == "--help" || arg == "-h")
{
usage (argv);
return 1;
}
// user don't need help find convolving direction
// convolve row
if (pcl::console::find_switch (argc, argv, "-r"))
direction = 0;
else
{
// convolve column
if (pcl::console::find_switch (argc, argv, "-c"))
direction = 1;
else
// convolve both
if (pcl::console::find_switch (argc, argv, "-s"))
direction = 2;
else
{
// wrong direction given print usage
usage (argv);
return 1;
}
}
// number of threads if any
if (pcl::console::parse_argument (argc, argv, "-t", nb_threads) != -1 )
{
if (nb_threads <= 0)
nb_threads = 1;
}
convolution.setNumberOfThreads (nb_threads);
// borders policy if any
if (pcl::console::parse_argument (argc, argv, "-p", border_policy) != -1 )
{
switch (border_policy)
{
case 'Z' :
convolution.setBordersPolicy (pcl::filters::Convolution<pcl::PointXYZRGB, pcl::PointXYZRGB>::BORDERS_POLICY_IGNORE);
break;
case 'M' :
convolution.setBordersPolicy (pcl::filters::Convolution<pcl::PointXYZRGB, pcl::PointXYZRGB>::BORDERS_POLICY_MIRROR);
break;
case 'D' :
convolution.setBordersPolicy (pcl::filters::Convolution<pcl::PointXYZRGB, pcl::PointXYZRGB>::BORDERS_POLICY_DUPLICATE);
break;
default :
{
usage (argv);
return (1);
}
}
}
else
convolution.setBordersPolicy (pcl::filters::Convolution<pcl::PointXYZRGB, pcl::PointXYZRGB>::BORDERS_POLICY_IGNORE);
// distance threshold if any
if (pcl::console::parse_argument (argc, argv, "-d", threshold) == -1 )
{
threshold = 0.01;
}
convolution.setDistanceThreshold (static_cast<float> (threshold));
// all set
// we have file name and convolving direction
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZRGB> ());
if (pcl::io::loadPCDFile (argv[1], *cloud) == -1)
{
pcl::console::print_error ("Couldn't read file %s \n", argv[1]);
return (-1);
}
cloud->is_dense = false;
convolution.setInputCloud (cloud);
convolution.setKernel (gaussian_kernel);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr convolved (new pcl::PointCloud<pcl::PointXYZRGB> ());
double t0;
pcl::console::print_info ("convolving %s along \n", argv[1]);
std::ostringstream convolved_label;
convolved_label << "convolved along ";
switch (direction)
{
case 0:
{
convolved_label << "rows... ";
t0 = pcl::getTime ();
convolution.convolveRows (*convolved);
break;
}
case 1:
{
convolved_label << "columns... ";
t0 = pcl::getTime ();
convolution.convolveCols (*convolved);
break;
}
case 2:
{
convolved_label << "rows and columns... ";
t0 = pcl::getTime ();
convolution.convolve (*convolved);
break;
}
}
convolved_label << pcl::getTime () - t0 << "s";
// Display
boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer (new pcl::visualization::PCLVisualizer ("Convolution"));
// viewport stuff
viewer->createViewPort (0, 0, 0.5, 1, viewport_source);
viewer->createViewPort (0.5, 0, 1, 1, viewport_convolved);
viewer->setBackgroundColor (0, 0, 0);
// Source
pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> color_handler_source (cloud);
viewer->addPointCloud<pcl::PointXYZRGB> (cloud, color_handler_source, "source", viewport_source);
viewer->addText ("source", 10, 10, "source_label", viewport_source);
// Convolved
pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> color_handler_convolved (convolved);
viewer->addPointCloud<pcl::PointXYZRGB> (convolved, color_handler_convolved, "convolved", viewport_convolved);
viewer->addText (convolved_label.str (), 10, 10, "convolved_label", viewport_convolved);
viewer->spin ();
pcl::PCDWriter writer;
writer.write<pcl::PointXYZRGB> ("convolved.pcd", *convolved, false);
}
