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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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