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//---------------------------------------------------------------------------//
// Copyright (c) 2013-2014 Kyle Lutz <kyle.r.lutz@gmail.com>
//
// Distributed under the Boost Software License, Version 1.0
// See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt
//
// See http://boostorg.github.com/compute for more information.
//---------------------------------------------------------------------------//
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <boost/compute/system.hpp>
#include <boost/compute/container/vector.hpp>
#include <boost/compute/image/image2d.hpp>
#include <boost/compute/interop/opencv/core.hpp>
#include <boost/compute/interop/opencv/highgui.hpp>
#include <boost/compute/random/default_random_engine.hpp>
#include <boost/compute/random/uniform_real_distribution.hpp>
#include <boost/compute/utility/dim.hpp>
#include <boost/compute/utility/source.hpp>
namespace compute = boost::compute;
using compute::dim;
using compute::int_;
using compute::float_;
using compute::float2_;
// the k-means example implements the k-means clustering algorithm
int main()
{
// number of clusters
size_t k = 6;
// number of points
size_t n_points = 4500;
// height and width of image
size_t height = 800;
size_t width = 800;
// get default device and setup context
compute::device gpu = compute::system::default_device();
compute::context context(gpu);
compute::command_queue queue(context, gpu);
// generate random, uniformily-distributed points
compute::default_random_engine random_engine(queue);
compute::uniform_real_distribution<float_> uniform_distribution(0, 800);
compute::vector<float2_> points(n_points, context);
uniform_distribution.generate(
compute::make_buffer_iterator<float_>(points.get_buffer(), 0),
compute::make_buffer_iterator<float_>(points.get_buffer(), n_points * 2),
random_engine,
queue
);
// initialize all points to cluster 0
compute::vector<int_> clusters(n_points, context);
compute::fill(clusters.begin(), clusters.end(), 0, queue);
// create initial means with the first k points
compute::vector<float2_> means(k, context);
compute::copy_n(points.begin(), k, means.begin(), queue);
// k-means clustering program source
const char k_means_source[] = BOOST_COMPUTE_STRINGIZE_SOURCE(
__kernel void assign_clusters(__global const float2 *points,
__global const float2 *means,
const int k,
__global int *clusters)
{
const uint gid = get_global_id(0);
const float2 point = points[gid];
// find the closest cluster
float current_distance = 0;
int closest_cluster = -1;
// find closest cluster mean to the point
for(int i = 0; i < k; i++){
const float2 mean = means[i];
int distance_to_mean = distance(point, mean);
if(closest_cluster == -1 || distance_to_mean < current_distance){
current_distance = distance_to_mean;
closest_cluster = i;
}
}
// write new cluster
clusters[gid] = closest_cluster;
}
__kernel void update_means(__global const float2 *points,
const uint n_points,
__global float2 *means,
__global const int *clusters)
{
const uint k = get_global_id(0);
float2 sum = { 0, 0 };
float count = 0;
for(uint i = 0; i < n_points; i++){
if(clusters[i] == k){
sum += points[i];
count += 1;
}
}
means[k] = sum / count;
}
);
// build the k-means program
compute::program k_means_program =
compute::program::build_with_source(k_means_source, context);
// setup the k-means kernels
compute::kernel assign_clusters_kernel(k_means_program, "assign_clusters");
assign_clusters_kernel.set_arg(0, points);
assign_clusters_kernel.set_arg(1, means);
assign_clusters_kernel.set_arg(2, int_(k));
assign_clusters_kernel.set_arg(3, clusters);
compute::kernel update_means_kernel(k_means_program, "update_means");
update_means_kernel.set_arg(0, points);
update_means_kernel.set_arg(1, int_(n_points));
update_means_kernel.set_arg(2, means);
update_means_kernel.set_arg(3, clusters);
// run the k-means algorithm
for(int iteration = 0; iteration < 25; iteration++){
queue.enqueue_1d_range_kernel(assign_clusters_kernel, 0, n_points, 0);
queue.enqueue_1d_range_kernel(update_means_kernel, 0, k, 0);
}
// create output image
compute::image2d image(
context, width, height, compute::image_format(CL_RGBA, CL_UNSIGNED_INT8)
);
// program with two kernels, one to fill the image with white, and then
// one the draw to points calculated in coordinates on the image
const char draw_walk_source[] = BOOST_COMPUTE_STRINGIZE_SOURCE(
__kernel void draw_points(__global const float2 *points,
__global const int *clusters,
__write_only image2d_t image)
{
const uint i = get_global_id(0);
const float2 coord = points[i];
// map cluster number to color
uint4 color = { 0, 0, 0, 0 };
switch(clusters[i]){
case 0:
color = (uint4)(255, 0, 0, 255);
break;
case 1:
color = (uint4)(0, 255, 0, 255);
break;
case 2:
color = (uint4)(0, 0, 255, 255);
break;
case 3:
color = (uint4)(255, 255, 0, 255);
break;
case 4:
color = (uint4)(255, 0, 255, 255);
break;
case 5:
color = (uint4)(0, 255, 255, 255);
break;
}
// draw a 3x3 pixel point
for(int x = -1; x <= 1; x++){
for(int y = -1; y <= 1; y++){
if(coord.x + x > 0 && coord.x + x < get_image_width(image) &&
coord.y + y > 0 && coord.y + y < get_image_height(image)){
write_imageui(image, (int2)(coord.x, coord.y) + (int2)(x, y), color);
}
}
}
}
__kernel void fill_gray(__write_only image2d_t image)
{
const int2 coord = { get_global_id(0), get_global_id(1) };
if(coord.x < get_image_width(image) && coord.y < get_image_height(image)){
uint4 gray = { 15, 15, 15, 15 };
write_imageui(image, coord, gray);
}
}
);
// build the program
compute::program draw_program =
compute::program::build_with_source(draw_walk_source, context);
// fill image with dark gray
compute::kernel fill_kernel(draw_program, "fill_gray");
fill_kernel.set_arg(0, image);
queue.enqueue_nd_range_kernel(
fill_kernel, dim(0, 0), dim(width, height), dim(1, 1)
);
// draw points colored according to cluster
compute::kernel draw_kernel(draw_program, "draw_points");
draw_kernel.set_arg(0, points);
draw_kernel.set_arg(1, clusters);
draw_kernel.set_arg(2, image);
queue.enqueue_1d_range_kernel(draw_kernel, 0, n_points, 0);
// show image
compute::opencv_imshow("k-means", image, queue);
// wait and return
cv::waitKey(0);
return 0;
}
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