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//---------------------------------------------------------------------------//
// Copyright (c) 2013-2014 Mageswaran.D <mageswaran1989@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 <iostream>
#include <string>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <boost/compute/system.hpp>
#include <boost/compute/interop/opencv/core.hpp>
#include <boost/compute/interop/opencv/highgui.hpp>
#include <boost/compute/utility/source.hpp>
#include <boost/program_options.hpp>
namespace compute = boost::compute;
namespace po = boost::program_options;
// Create convolution program
const char source[] = BOOST_COMPUTE_STRINGIZE_SOURCE (
__kernel void convolution(__read_only image2d_t sourceImage,
__write_only image2d_t outputImage,
__constant float* filter,
int filterWidth)
{
const sampler_t sampler = CLK_NORMALIZED_COORDS_FALSE |
CLK_ADDRESS_CLAMP_TO_EDGE |
CLK_FILTER_NEAREST;
// Store each work-item's unique row and column
int x = get_global_id(0);
int y = get_global_id(1);
// Half the width of the filter is needed for indexing
// memory later
int halfWidth = (int)(filterWidth/2);
// All accesses to images return data as four-element vector
// (i.e., float4).
float4 sum = {0.0f, 0.0f, 0.0f, 0.0f};
// Iterator for the filter
int filterIdx = 0;
// Each work-item iterates around its local area based on the
// size of the filter
int2 coords; // Coordinates for accessing the image
// Iterate the filter rows
for(int i = -halfWidth; i <= halfWidth; i++)
{
coords.y = y + i;
// Iterate over the filter columns
for(int j = -halfWidth; j <= halfWidth; j++)
{
coords.x = x + j;
float4 pixel;
// Read a pixel from the image.
// Work on a channel
pixel = read_imagef(sourceImage, sampler, coords);
sum.x += pixel.x * filter[filterIdx++];
//sum.y += pixel.y * filter[filterIdx++];
//sum.z += pixel.z * filter[filterIdx++];
}
}
barrier(CLK_GLOBAL_MEM_FENCE);
// Copy the data to the output image if the
// work-item is in bounds
if(y < get_image_height(sourceImage) &&
x < get_image_width(sourceImage))
{
coords.x = x;
coords.y = y;
//Same channel is copied in all three channels
//write_imagef(outputImage, coords,
// (float4)(sum.x,sum.x,sum.x,1.0f));
write_imagef(outputImage, coords, sum);
}
}
);
// This example shows how to read two images or use camera
// with OpenCV, transfer the frames to the GPU,
// and apply a convolution written in OpenCL
int main(int argc, char *argv[])
{
///////////////////////////////////////////////////////////////////////////
// setup the command line arguments
po::options_description desc;
desc.add_options()
("help", "show available options")
("camera", po::value<int>()->default_value(-1),
"if not default camera, specify a camera id")
("image", po::value<std::string>(), "path to image file");
// Parse the command lines
po::variables_map vm;
po::store(po::parse_command_line(argc, argv, desc), vm);
po::notify(vm);
//check the command line arguments
if(vm.count("help"))
{
std::cout << desc << std::endl;
return 0;
}
///////////////////////////////////////////////////////////////////////////
//OpenCV variables
cv::Mat cv_mat;
cv::VideoCapture cap; //OpenCV camera handle.
//Filter Variables
float filter[9] = {
-1.0, 0.0, 1.0,
-2.0, 0.0, 2.0,
-1.0, 0.0, 1.0,
};
// The convolution filter is 3x3
int filterWidth = 3;
//OpenCL variables
// Get default device and setup context
compute::device gpu = compute::system::default_device();
compute::context context(gpu);
compute::command_queue queue(context, gpu);
compute::buffer dev_filter(context, sizeof(filter),
compute::memory_object::read_only |
compute::memory_object::copy_host_ptr,
filter);
compute::program filter_program =
compute::program::create_with_source(source, context);
try
{
filter_program.build();
}
catch(compute::opencl_error e)
{
std::cout<<"Build Error: "<<std::endl
<<filter_program.build_log();
return -1;
}
// create fliter kernel and set arguments
compute::kernel filter_kernel(filter_program, "convolution");
///////////////////////////////////////////////////////////////////////////
//check for image paths
if(vm.count("image"))
{
// Read image with OpenCV
cv_mat = cv::imread(vm["image"].as<std::string>(),
CV_LOAD_IMAGE_COLOR);
if(!cv_mat.data){
std::cerr << "Failed to load image" << std::endl;
return -1;
}
}
else //by default use camera
{
//open camera
cap.open(vm["camera"].as<int>());
// read first frame
cap >> cv_mat;
if(!cv_mat.data){
std::cerr << "failed to capture frame" << std::endl;
return -1;
}
}
// Convert image to BGRA (OpenCL requires 16-byte aligned data)
cv::cvtColor(cv_mat, cv_mat, CV_BGR2BGRA);
// Transfer image/frame data to gpu
compute::image2d dev_input_image =
compute::opencv_create_image2d_with_mat(
cv_mat, compute::image2d::read_write, queue
);
// Create output image
// Be sure what will be your ouput image/frame size
compute::image2d dev_output_image(
context,
dev_input_image.width(),
dev_input_image.height(),
dev_input_image.format(),
compute::image2d::write_only
);
filter_kernel.set_arg(0, dev_input_image);
filter_kernel.set_arg(1, dev_output_image);
filter_kernel.set_arg(2, dev_filter);
filter_kernel.set_arg(3, filterWidth);
// run flip kernel
size_t origin[2] = { 0, 0 };
size_t region[2] = { dev_input_image.width(),
dev_input_image.height() };
///////////////////////////////////////////////////////////////////////////
queue.enqueue_nd_range_kernel(filter_kernel, 2, origin, region, 0);
//check for image paths
if(vm.count("image"))
{
// show host image
cv::imshow("Original Image", cv_mat);
// show gpu image
compute::opencv_imshow("Convoluted Image", dev_output_image, queue);
// wait and return
cv::waitKey(0);
}
else
{
char key = '\0';
while(key != 27) //check for escape key
{
cap >> cv_mat;
// Convert image to BGRA (OpenCL requires 16-byte aligned data)
cv::cvtColor(cv_mat, cv_mat, CV_BGR2BGRA);
// Update the device image memory with current frame data
compute::opencv_copy_mat_to_image(cv_mat,
dev_input_image,queue);
// Run the kernel on the device
queue.enqueue_nd_range_kernel(filter_kernel, 2, origin, region, 0);
// Show host image
cv::imshow("Camera Frame", cv_mat);
// Show GPU image
compute::opencv_imshow("Convoluted Frame", dev_output_image, queue);
// wait
key = cv::waitKey(10);
}
}
return 0;
}
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