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spatiallyInvariantGaussianKernel.cpp
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spatiallyInvariantGaussianKernel.cpp
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// Download a Halide distribution from halide-lang.org and untar it in
// the current directory. Then you should be able to compile this
// file with:
//
//
//Using FITS images on os x:
// g++ Gaussian2D.cpp -g -I ../include -L ../bin -lHalide -lcfitsio `libpng-config --cflags --ldflags` -o Gaussian2D -std=c++11
// DYLD_LIBRARY_PATH=../bin ./Gaussian2D
//
//Using FITS images on linux:
// g++ Gaussian2D.cpp -g -I ../include -L ../bin -lHalide -lpthread -ldl -lcfitsio `libpng-config --cflags --ldflags` -o Gaussian2D -std=c++11
// LD_LIBRARY_PATH=../bin ./Gaussian2D
//
//
// You'll also need a multi-megapixel png image to run this on. Name
// it input.png and put it in this directory.
// Include the Halide language
// On os x:
// g++ spatiallyInvariantGaussianKernel.cpp -g -I ./include -I DarwinX86/pex_policy/10.1+1/include/ -I DarwinX86/daf_persistence/10.1+1/include/ -I DarwinX86/utils/10.1+1/include/ -I DarwinX86/daf_base/10.1+2/include/ -I DarwinX86/base/10.1+1/include/ -I DarwinX86/ndarray/10.1+2/include/ -I DarwinX86/pex_exceptions/10.1+1/include/ -I DarwinX86/eigen/3.2.0/include/ -I DarwinX86/afw/10.1+1/include -L ./bin -L DarwinX86/afw/10.1+1/lib -L DarwinX86/daf_base/10.1+2/lib/ -L DarwinX86/daf_persistence/10.1+1/lib/ -L DarwinX86/boost/1.55.0.1.lsst2+3/lib/ -lHalide -lafw -ldaf_base -ldaf_persistence -lboost_system `libpng-config --cflags --ldflags` -o spatiallyInvariantGaussianKernel -std=c++11
//
// DYLD_LIBRARY_PATH=./bin:DarwinX86/afw/10.1+1/lib/:DarwinX86/daf_persistence/10.1+1/lib/:DarwinX86/daf_base/10.1+2/lib/:DarwinX86/boost/1.55.0.1.lsst2+3/lib/:DarwinX86/xpa/2.1.15.lsst2/lib/:DarwinX86/pex_policy/10.1+1/lib/:DarwinX86/pex_logging/10.1+1/lib/:DarwinX86/utils/10.1+1/lib/:DarwinX86/pex_exceptions/10.1+1/lib/:DarwinX86/base/10.1+1/lib/ ./spatiallyInvariantGaussianKernel
#include "lsst/afw/image.h"
#include <stdio.h>
#include "Halide.h"
#include <bitset>
#include "clock.h"
using namespace std;
using namespace Halide;
using Halide::Image;
namespace afwImage = lsst::afw::image;
namespace afwMath = lsst::afw::math;
int main(int argc, char **argv) {
auto im = afwImage::MaskedImage<float>("../calexp-004207-g3-0123.fits");
printf("Loaded: %d x %d\n", im.getWidth(), im.getHeight());
//store image data in img_var(x, y, 0) and variance data in img_var(x, y, 1)
Image<float> img_var(im.getWidth(), im.getHeight(), 2);
Image<uint16_t> mask(im.getWidth(), im.getHeight());
//Read image in
for (int y = 0; y < im.getHeight(); y++) {
afwImage::MaskedImage<float, lsst::afw::image::MaskPixel, lsst::afw::image::VariancePixel>::x_iterator inPtr = im.x_at(0, y);
for (int x = 0; x < im.getWidth(); x++){
img_var(x, y, 0) = (*inPtr).image();
img_var(x, y, 1) = (*inPtr).variance();
mask(x, y) = (*inPtr).mask();
inPtr++;
}
}
int boundingBox = 2;
Var x, y, c, y0, yi, i, j;
//Kernel #1
Func kernel1;
float sigmaX1 = 2.0f;
float sigmaY1 = 2.0f;
float theta1 = 0.0f; //rotation of sigmaX axis
kernel1(i, j) = (exp(-((i*cos(theta1) +j*sin(theta1))*(i*cos(theta1) +j*sin(theta1)))
/(2*sigmaX1*sigmaX1)) / (sqrtf(2*M_PI)*sigmaX1))
*(exp(-((j*cos(theta1) - i*sin(theta1))*(j*cos(theta1) - i*sin(theta1)))
/(2*sigmaY1*sigmaY1)) / (sqrtf(2*M_PI)*sigmaY1));
//Kernel #2
Func kernel2;
float sigmaX2 = 0.5f;
float sigmaY2 = 4.0f;
float theta2 = 0.0f; //rotation of sigmaX axis
kernel2(i, j) = (exp(-((i*cos(theta2) +j*sin(theta2))*(i*cos(theta2) +j*sin(theta2)))
/(2*sigmaX2*sigmaX2)) / (sqrtf(2*M_PI)*sigmaX2))
*(exp(-((j*cos(theta2) - i*sin(theta2))*(j*cos(theta2) - i*sin(theta2)))
/(2*sigmaY2*sigmaY2)) / (sqrtf(2*M_PI)*sigmaY2));
//Kernel #3
Func kernel3;
float sigmaX3 = 0.5f;
float sigmaY3 = 4.0f;
float theta3 = 3.14159f/4; //rotation of sigmaX axis
kernel3(i, j) = (exp(-((i*cos(theta3) +j*sin(theta3))*(i*cos(theta3) +j*sin(theta3)))
/(2*sigmaX3*sigmaX3)) / (sqrtf(2*M_PI)*sigmaX3))
*(exp(-((j*cos(theta3) - i*sin(theta3))*(j*cos(theta3) - i*sin(theta3)))
/(2*sigmaY3*sigmaY3)) / (sqrtf(2*M_PI)*sigmaY3));
//Kernel #4
Func kernel4;
float sigmaX4 = 0.5f;
float sigmaY4 = 4.0f;
float theta4 = 3.14159f/2; //rotation of sigmaX axis
kernel4(i, j) = (exp(-((i*cos(theta4) +j*sin(theta4))*(i*cos(theta4) +j*sin(theta4)))
/(2*sigmaX4*sigmaX4)) / (sqrtf(2*M_PI)*sigmaX4))
*(exp(-((j*cos(theta4) - i*sin(theta4))*(j*cos(theta4) - i*sin(theta4)))
/(2*sigmaY4*sigmaY4)) / (sqrtf(2*M_PI)*sigmaY4));
//Kernel #5
Func kernel5;
float sigmaX5 = 4.0f;
float sigmaY5 = 4.0f;
float theta5 = 0.0; //rotation of sigmaX axis
kernel5(i, j) = (exp(-((i*cos(theta5) +j*sin(theta5))*(i*cos(theta5) +j*sin(theta5)))
/(2*sigmaX5*sigmaX5)) / (sqrtf(2*M_PI)*sigmaX5))
*(exp(-((j*cos(theta5) - i*sin(theta5))*(j*cos(theta5) - i*sin(theta5)))
/(2*sigmaY5*sigmaY5)) / (sqrtf(2*M_PI)*sigmaY5));
Func totalKernel;
totalKernel(i, j) = kernel1(i, j) + kernel2(i, j) + kernel3(i, j) + kernel4(i, j) + kernel5(i, j);
float norm = 0.0f;
float curKernelPos;
for(int i = -boundingBox; i <= boundingBox; i++){
for(int j = -boundingBox; j <= boundingBox; j++){
curKernelPos =
((exp(-((i*cos(theta1) +j*sin(theta1))*(i*cos(theta1) +j*sin(theta1)))
/(2*sigmaX1*sigmaX1)) / (sqrtf(2*M_PI)*sigmaX1))
*(exp(-((j*cos(theta1) - i*sin(theta1))*(j*cos(theta1) - i*sin(theta1)))
/(2*sigmaY1*sigmaY1)) / (sqrtf(2*M_PI)*sigmaY1))) +
((exp(-((i*cos(theta2) +j*sin(theta2))*(i*cos(theta2) +j*sin(theta2)))
/(2*sigmaX2*sigmaX2)) / (sqrtf(2*M_PI)*sigmaX2))
*(exp(-((j*cos(theta2) - i*sin(theta2))*(j*cos(theta2) - i*sin(theta2)))
/(2*sigmaY2*sigmaY2)) / (sqrtf(2*M_PI)*sigmaY2))) +
((exp(-((i*cos(theta3) +j*sin(theta3))*(i*cos(theta3) +j*sin(theta3)))
/(2*sigmaX3*sigmaX3)) / (sqrtf(2*M_PI)*sigmaX3))
*(exp(-((j*cos(theta3) - i*sin(theta3))*(j*cos(theta3) - i*sin(theta3)))
/(2*sigmaY3*sigmaY3)) / (sqrtf(2*M_PI)*sigmaY3))) +
((exp(-((i*cos(theta4) +j*sin(theta4))*(i*cos(theta4) +j*sin(theta4)))
/(2*sigmaX4*sigmaX4)) / (sqrtf(2*M_PI)*sigmaX4))
*(exp(-((j*cos(theta4) - i*sin(theta4))*(j*cos(theta4) - i*sin(theta4)))
/(2*sigmaY4*sigmaY4)) / (sqrtf(2*M_PI)*sigmaY4))) +
((exp(-((i*cos(theta5) +j*sin(theta5))*(i*cos(theta5) +j*sin(theta5)))
/(2*sigmaX5*sigmaX5)) / (sqrtf(2*M_PI)*sigmaX5))
*(exp(-((j*cos(theta5) - i*sin(theta5))*(j*cos(theta5) - i*sin(theta5)))
/(2*sigmaY5*sigmaY5)) / (sqrtf(2*M_PI)*sigmaY5)))
;
norm += curKernelPos;
//debugging
cout << curKernelPos << "\t";
}
//debugging
cout << endl;
}
cout << "Norm = " << norm << endl;
Func in_bounded = BoundaryConditions::repeat_edge(img_var);
Func blur;
blur(x, y, c) = 0.0f;
//compute Image output
Expr blur_image_help = 0.0f;
for(int i = -boundingBox; i <= boundingBox; i++){
for(int j = -boundingBox; j <= boundingBox; j++){
blur_image_help += in_bounded(x + i, y + j, 0) * totalKernel(i, j);
}
}
blur_image_help = blur_image_help/norm;
blur(x, y, 0) = blur_image_help;
//compute Variance output
Expr blur_variance_help = 0.0f;
for(int i = -boundingBox; i <= boundingBox; i++){
for(int j = -boundingBox; j <= boundingBox; j++){
blur_variance_help += in_bounded(x + i, y + j, 1) * totalKernel(i, j) * totalKernel(i, j);
}
}
blur_variance_help = blur_variance_help/(norm*norm);
blur(x, y, 1) = blur_variance_help;
// totalKernel.compute_at(blur, y);
totalKernel.compute_root();
// blur.reorder(c, x, y);
// Split the y coordinate of the consumer into strips of 16 scanlines:
blur.split(y, y0, yi, 4);
blur.update(0).split(y, y0, yi, 1);
blur.update(1).split(y, y0, yi, 1);
// Compute the strips using a thread pool and a task queue.
blur.parallel(y0);
blur.update(0).parallel(y0);
blur.update(1).parallel(y0);
// Vectorize across x by a factor of four.
blur.vectorize(x, 8);
blur.update(0).vectorize(x, 8);
blur.update(1).vectorize(x, 8);
// for(int i = 0; i < boundingBox*boundingBox; i++)
// blur.update(i).vectorize(x, 8);
// Print out pseudocode for the pipeline.
blur.compile_to_lowered_stmt("spatiallyInvariant.html", {img_var}, HTML);
blur.print_loop_nest();
//Compute mask
Func mask_bounded = BoundaryConditions::repeat_edge(mask);
Func maskOut;
Expr maskOutHelp = 0;
for(int i = -boundingBox; i <= boundingBox; i++)
for(int j = -boundingBox; j <= boundingBox; j++){
//bitwise OR in following line seems to cast result to int32, so mask_output must be typed int32, is this a problem?
maskOutHelp = select(totalKernel(i, j) == 0.0f, maskOutHelp, maskOutHelp | mask_bounded(x + i, y + j));
//maskOutHelp = select(totalKernel(i, j) == 0.0f, maskOutHelp, maskOutHelp + mask_bounded(x + i, y + j));
}
maskOut(x, y) = maskOutHelp;
// Split the y coordinate of the consumer into strips of 16 scanlines:
maskOut.split(y, y0, yi, 30);
// Compute the strips using a thread pool and a task queue.
maskOut.parallel(y0);
// Vectorize across x by a factor of four.
maskOut.vectorize(x, 8);
// Benchmark the pipeline.
Image<float> img_var_output(img_var.width(), img_var.height(), 2);
blur.realize(img_var_output);
Image<int32_t> mask_output(mask.width(), mask.height());
maskOut.realize(mask_output);
double average = 0;
double min;
double max;
int numberOfRuns = 5;
for (int i = 0; i < numberOfRuns; i++) {
double t1 = current_time();
blur.realize(img_var_output);
maskOut.realize(mask_output);
double t2 = current_time();
double curTime = (t2-t1);
average += curTime;
if(i == 0){
min = curTime;
max = curTime;
}
else{
if(curTime < min)
min = curTime;
if(curTime > max)
max = curTime;
}
}
average = average/numberOfRuns;
std::cout << "Average Time: " << average << ", Min = " <<
min << ", Max = " << max << ", with " << numberOfRuns <<
" runs" << '\n';
// blur_mask.realize(mask_output);
//write image out
auto imOut = afwImage::MaskedImage<float, lsst::afw::image::MaskPixel, lsst::afw::image::VariancePixel>(im.getWidth(), im.getHeight());
for (int y = 0; y < imOut.getHeight(); y++) {
afwImage::MaskedImage<float, lsst::afw::image::MaskPixel, lsst::afw::image::VariancePixel>::x_iterator inPtr = imOut.x_at(0, y);
for (int x = 0; x < imOut.getWidth(); x++){
afwImage::pixel::SinglePixel<float, lsst::afw::image::MaskPixel, lsst::afw::image::VariancePixel>
curPixel(img_var_output(x, y, 0), mask_output(x, y), img_var_output(x, y, 1));
(*inPtr) = curPixel;
inPtr++;
}
}
imOut.writeFits("./halideSpatiallyInvariantGaussian.fits");
/*
double average = 0;
double min;
double max;
int numberOfRuns = 20;
for (int i = 0; i < numberOfRuns; i++) {
double t1 = current_time();
blur.realize(output);
double t2 = current_time();
// std::cout << "Time: " << (t2 - t1) << '\n';
double curTime = (t2-t1);
average += curTime;
if(i == 0){
min = curTime;
max = curTime;
}
else{
if(curTime < min)
min = curTime;
if(curTime > max)
max = curTime;
}
}
average = average/numberOfRuns;
std::cout << "Average Time: " << average << ", Min = " <<
min << ", Max = " << max << ", with " << numberOfRuns <<
" runs" << '\n';
*/
return 0;
}