这几天在看opencl编程指南，照着书中的例子实现了sobel算法：
1.结合opencv读取图像，保存到缓冲区中；
2.编写和编译内核，并保存显示处理后的结果。
内核：
const sampler_t sampler = CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_NEAREST;
kernel void sobel_rgb(read_only image2d_t src,write_only image2d_t dst)
{
int x = (int)get_global_id(0);
int y = (int)get_global_id(1);
if (x >= get_image_width(src) || y >= get_image_height(src))
return;
float4 p00 = read_imagef(src, sampler, (int2)(x - 1, y - 1));
float4 p10 = read_imagef(src, sampler, (int2)(x, y - 1));
float4 p20 = read_imagef(src, sampler, (int2)(x + 1, y - 1));
float4 p01 = read_imagef(src, sampler, (int2)(x - 1, y));
float4 p21 = read_imagef(src, sampler, (int2)(x + 1, y));
float4 p02 = read_imagef(src, sampler, (int2)(x - 1, y + 1));
float4 p12 = read_imagef(src, sampler, (int2)(x, y + 1));
float4 p22 = read_imagef(src, sampler, (int2)(x + 1, y + 1));
float3 gx = -p00.xyz + p20.xyz + 2.0*(p21.xyz - p01.xyz) - p02.xyz + p22.xyz;
float3 gy = -p00.xyz + p02.xyz + 2.0*(p21.xyz - p10.xyz) - p20.xyz + p22.xyz;
float3 g = native_sqrt(gx*gx + gy*gy);
write_imagef(dst,(int2)(x,y),(float4)(g.x,g.y,g.z,1.0f));
}
// TODO: Add OpenCL kernel code here.//
// Book: OpenCL(R) Programming Guide
// Authors: Aaftab Munshi, Benedict Gaster, Timothy Mattson, James Fung, Dan Ginsburg
// ISBN-10: 0-321-74964-2
// ISBN-13: 978-0-321-74964-2
// Publisher: Addison-Wesley Professional
// URLs: http://safari.informit.com/9780132488006/
// http://www.openclprogrammingguide.com
//
// ImageFilter2D.cpp
//
// This example demonstrates performing gaussian filtering on a 2D image using
// OpenCL
//
// Requires FreeImage library for image I/O:
// http://freeimage.sourceforge.net/
#include <iostream>
#include <fstream>
#include <sstream>
#include <string.h>
#include <opencv.hpp>
#ifdef __APPLE__
#include <OpenCL/cl.h>
#else
#include <CL/cl.h>
#endif
#include "FreeImage.h"
///
// Create an OpenCL context on the first available platform using
// either a GPU or CPU depending on what is available.
//
cl_context CreateContext()
{
cl_int errNum;
cl_uint numPlatforms;
cl_platform_id firstPlatformId;
cl_context context = NULL;
// First, select an OpenCL platform to run on. For this example, we
// simply choose the first available platform. Normally, you would
// query for all available platforms and select the most appropriate one.
errNum = clGetPlatformIDs(1, &firstPlatformId, &numPlatforms);
if (errNum != CL_SUCCESS || numPlatforms <= 0)
{
std::cerr << "Failed to find any OpenCL platforms." << std::endl;
return NULL;
}
// Next, create an OpenCL context on the platform. Attempt to
// create a GPU-based context, and if that fails, try to create
// a CPU-based context.
cl_context_properties contextProperties[] =
{
CL_CONTEXT_PLATFORM,
(cl_context_properties)firstPlatformId,
0
};
context = clCreateContextFromType(contextProperties, CL_DEVICE_TYPE_GPU,
NULL, NULL, &errNum);
if (errNum != CL_SUCCESS)
{
std::cout << "Could not create GPU context, trying CPU..." << std::endl;
context = clCreateContextFromType(contextProperties, CL_DEVICE_TYPE_CPU,
NULL, NULL, &errNum);
if (errNum != CL_SUCCESS)
{
std::cerr << "Failed to create an OpenCL GPU or CPU context." << std::endl;
return NULL;
}
}
return context;
}
///
// Create a command queue on the first device available on the
// context
//
cl_command_queue CreateCommandQueue(cl_context context, cl_device_id *device)
{
cl_int errNum;
cl_device_id *devices;
cl_command_queue commandQueue = NULL;
size_t deviceBufferSize = -1;
// First get the size of the devices buffer
errNum = clGetContextInfo(context, CL_CONTEXT_DEVICES, 0, NULL, &deviceBufferSize);
if (errNum != CL_SUCCESS)
{
std::cerr << "Failed call to clGetContextInfo(...,GL_CONTEXT_DEVICES,...)";
return NULL;
}
if (deviceBufferSize <= 0)
{
std::cerr << "No devices available.";
return NULL;
}
// Allocate memory for the devices buffer
devices = new cl_device_id[deviceBufferSize / sizeof(cl_device_id)];
errNum = clGetContextInfo(context, CL_CONTEXT_DEVICES, deviceBufferSize, devices, NULL);
if (errNum != CL_SUCCESS)
{
std::cerr << "Failed to get device IDs";
return NULL;
}
// In this example, we just choose the first available device. In a
// real program, you would likely use all available devices or choose
// the highest performance device based on OpenCL device queries
commandQueue = clCreateCommandQueue(context, devices[0], 0, NULL);
if (commandQueue == NULL)
{
std::cerr << "Failed to create commandQueue for device 0";
return NULL;
}
*device = devices[0];
delete[] devices;
return commandQueue;
}
///
// Create an OpenCL program from the kernel source file
//
cl_program CreateProgram(cl_context context, cl_device_id device, const char* fileName)
{
cl_int errNum;
cl_program program;
std::ifstream kernelFile(fileName, std::ios::in);
if (!kernelFile.is_open())
{
std::cerr << "Failed to open file for reading: " << fileName << std::endl;
return NULL;
}
std::ostringstream oss;
oss << kernelFile.rdbuf();
std::string srcStdStr = oss.str();
const char *srcStr = srcStdStr.c_str();
program = clCreateProgramWithSource(context, 1,
(const char**)&srcStr,
NULL, NULL);
if (program == NULL)
{
std::cerr << "Failed to create CL program from source." << std::endl;
return NULL;
}
errNum = clBuildProgram(program, 0, NULL, NULL, NULL, NULL);
if (errNum != CL_SUCCESS)
{
// Determine the reason for the error
char buildLog[16384];
clGetProgramBuildInfo(program, device, CL_PROGRAM_BUILD_LOG,
sizeof(buildLog), buildLog, NULL);
std::cerr << "Error in kernel: " << std::endl;
std::cerr << buildLog;
clReleaseProgram(program);
return NULL;
}
return program;
}
///
// Cleanup any created OpenCL resources
//
void Cleanup(cl_context context, cl_command_queue commandQueue,
cl_program program, cl_kernel kernel, cl_mem imageObjects[2],
cl_sampler sampler)
{
for (int i = 0; i < 2; i++)
{
if (imageObjects[i] != 0)
clReleaseMemObject(imageObjects[i]);
}
if (commandQueue != 0)
clReleaseCommandQueue(commandQueue);
if (kernel != 0)
clReleaseKernel(kernel);
if (program != 0)
clReleaseProgram(program);
if (sampler != 0)
clReleaseSampler(sampler);
if (context != 0)
clReleaseContext(context);
}
///
// Load an image using the FreeImage library and create an OpenCL
// image out of it
//
cl_mem LoadImage(cl_context context, char *fileName, int &width, int &height)
{
//FREE_IMAGE_FORMAT format = FreeImage_GetFileType(fileName, 0);
//FIBITMAP* image = FreeImage_Load(format, fileName);
//// Convert to 32-bit image
//FIBITMAP* temp = image;
//image = FreeImage_ConvertTo32Bits(image);
//FreeImage_Unload(temp);
//width = FreeImage_GetWidth(image);
//height = FreeImage_GetHeight(image);
/*char *buffer = new char[width * height * 4];
memcpy(buffer, FreeImage_GetBits(image), width * height * 4);
FreeImage_Unload(image);*/
cv::Mat image1 = cv::imread(fileName);
width = image1.cols;
height = image1.rows;
char *buffer = new char[width * height * 4];
int w = 0;
for (int v = height - 1; v >= 0; v--)
{
for (int u = 0; u <width; u++)
{
buffer[w++] = image1.at<cv::Vec3b>(v, u)[0];
buffer[w++] = image1.at<cv::Vec3b>(v, u)[1];
buffer[w++] = image1.at<cv::Vec3b>(v, u)[2];
w++;
}
}
// Create OpenCL image
cl_image_format clImageFormat;
clImageFormat.image_channel_order = CL_RGBA;
clImageFormat.image_channel_data_type = CL_UNORM_INT8;
cl_int errNum;
cl_mem clImage;
clImage = clCreateImage2D(context,
CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR,
&clImageFormat,
width,
height,
0,
buffer,
&errNum);
if (errNum != CL_SUCCESS)
{
std::cerr << "Error creating CL image object" << std::endl;
return 0;
}
return clImage;
}
///
// Save an image using the FreeImage library
//
bool SaveImage(char *fileName, char *buffer, int width, int height)
{
FREE_IMAGE_FORMAT format = FreeImage_GetFIFFromFilename(fileName);
FIBITMAP *image = FreeImage_ConvertFromRawBits((BYTE*)buffer, width,
height, width * 4, 32,
0xFF000000, 0x00FF0000, 0x0000FF00);
return FreeImage_Save(format, image, fileName);
}
///
// Round up to the nearest multiple of the group size
//
size_t RoundUp(int groupSize, int globalSize)
{
int r = globalSize % groupSize;
if (r == 0)
{
return globalSize;
}
else
{
return globalSize + groupSize - r;
}
}
///
// main() for HelloBinaryWorld example
//
int main(int argc, char** argv)
{
cl_context context = 0;
cl_command_queue commandQueue = 0;
cl_program program = 0;
cl_device_id device = 0;
cl_kernel kernel = 0;
cl_mem imageObjects[2] = { 0, 0 };
cl_sampler sampler = 0;
cl_int errNum;
/*if (argc != 3)
{
std::cerr << "USAGE: " << argv[0] << " <inputImageFile> <outputImageFiles>" << std::endl;
return 1;
}*/
// Create an OpenCL context on first available platform
context = CreateContext();
if (context == NULL)
{
std::cerr << "Failed to create OpenCL context." << std::endl;
return 1;
}
// Create a command-queue on the first device available
// on the created context
commandQueue = CreateCommandQueue(context, &device);
if (commandQueue == NULL)
{
Cleanup(context, commandQueue, program, kernel, imageObjects, sampler);
return 1;
}
// Make sure the device supports images, otherwise exit
cl_bool imageSupport = CL_FALSE;
clGetDeviceInfo(device, CL_DEVICE_IMAGE_SUPPORT, sizeof(cl_bool),
&imageSupport, NULL);
if (imageSupport != CL_TRUE)
{
std::cerr << "OpenCL device does not support images." << std::endl;
Cleanup(context, commandQueue, program, kernel, imageObjects, sampler);
return 1;
}
// Load input image from file and load it into
// an OpenCL image object
int width, height;
char *src0 = "C:/Users/jiang/Desktop/image/tu1.jpg";
imageObjects[0] = LoadImage(context, src0, width, height);
if (imageObjects[0] == 0)
{
std::cerr << "Error loading: " << std::string(src0) << std::endl;
Cleanup(context, commandQueue, program, kernel, imageObjects, sampler);
return 1;
}
// Create ouput image object
cl_image_format clImageFormat;
clImageFormat.image_channel_order = CL_RGBA;
clImageFormat.image_channel_data_type = CL_UNORM_INT8;
imageObjects[1] = clCreateImage2D(context,
CL_MEM_WRITE_ONLY,
&clImageFormat,
width,
height,
0,
NULL,
&errNum);
if (errNum != CL_SUCCESS)
{
std::cerr << "Error creating CL output image object." << std::endl;
Cleanup(context, commandQueue, program, kernel, imageObjects, sampler);
return 1;
}
// Create sampler for sampling image object
sampler = clCreateSampler(context,
CL_FALSE, // Non-normalized coordinates
CL_ADDRESS_CLAMP_TO_EDGE,
CL_FILTER_NEAREST,
&errNum);
if (errNum != CL_SUCCESS)
{
std::cerr << "Error creating CL sampler object." << std::endl;
Cleanup(context, commandQueue, program, kernel, imageObjects, sampler);
return 1;
}
// Create OpenCL program
//program = CreateProgram(context, device, "ImageFilter2D.cl");
program = CreateProgram(context, device, "Sobel.cl");
if (program == NULL)
{
Cleanup(context, commandQueue, program, kernel, imageObjects, sampler);
return 1;
}
// Create OpenCL kernel
kernel = clCreateKernel(program, "sobel_rgb", NULL);
if (kernel == NULL)
{
std::cerr << "Failed to create kernel" << std::endl;
Cleanup(context, commandQueue, program, kernel, imageObjects, sampler);
return 1;
}
// Set the kernel arguments
errNum = clSetKernelArg(kernel, 0, sizeof(cl_mem), &imageObjects[0]);
errNum |= clSetKernelArg(kernel, 1, sizeof(cl_mem), &imageObjects[1]);
/*errNum |= clSetKernelArg(kernel, 2, sizeof(cl_sampler), &sampler);
errNum |= clSetKernelArg(kernel, 3, sizeof(cl_int), &width);
errNum |= clSetKernelArg(kernel, 4, sizeof(cl_int), &height);*/
if (errNum != CL_SUCCESS)
{
std::cerr << "Error setting kernel arguments." << std::endl;
Cleanup(context, commandQueue, program, kernel, imageObjects, sampler);
return 1;
}
size_t localWorkSize[2] = { 16, 16 };
size_t globalWorkSize[2] = { RoundUp(localWorkSize[0], width),
RoundUp(localWorkSize[1], height) };
// Queue the kernel up for execution
errNum = clEnqueueNDRangeKernel(commandQueue, kernel, 2, NULL,
globalWorkSize, localWorkSize,
0, NULL, NULL);
if (errNum != CL_SUCCESS)
{
std::cerr << "Error queuing kernel for execution." << std::endl;
Cleanup(context, commandQueue, program, kernel, imageObjects, sampler);
return 1;
}
// Read the output buffer back to the Host
char *buffer = new char[width * height * 4];
size_t origin[3] = { 0, 0, 0 };
size_t region[3] = { width, height, 1 };
errNum = clEnqueueReadImage(commandQueue, imageObjects[1], CL_TRUE,
origin, region, 0, 0, buffer,
0, NULL, NULL);
if (errNum != CL_SUCCESS)
{
std::cerr << "Error reading result buffer." << std::endl;
Cleanup(context, commandQueue, program, kernel, imageObjects, sampler);
return 1;
}
std::cout << std::endl;
std::cout << "Executed program succesfully." << std::endl;
//memset(buffer, 0xff, width * height * 4);
// Save the image out to disk
char *saveImage = "C:/Users/jiang/Desktop/image/tu2.jpg";
//std::cout << buffer << std::endl;
cv::Mat imageColor = cv::imread(src0);
cv::Mat imageColor2;
imageColor2.create(imageColor.rows, imageColor.cols, imageColor.type());
int w = 0;
for (int v = imageColor2.rows-1; v >=0; v--)
{
for (int u =0 ; u <imageColor2.cols; u++)
{
imageColor2.at<cv::Vec3b>(v, u)[0] = buffer[w++];
imageColor2.at<cv::Vec3b>(v, u)[1] = buffer[w++];
imageColor2.at<cv::Vec3b>(v, u)[2] = buffer[w++];
w++;
}
}
cv::imshow("image", imageColor2);
cv::imwrite(saveImage, imageColor2);
cv::waitKey(0);
/*if (!SaveImage(saveImage, buffer, width, height))
{
std::cerr << "Error writing output image: " << saveImage<< std::endl;
Cleanup(context, commandQueue, program, kernel, imageObjects, sampler);
delete[] buffer;
return 1;
}*/
delete[] buffer;
Cleanup(context, commandQueue, program, kernel, imageObjects, sampler);
return 0;
}
原文地址：http://blog.csdn.net/u012361418/article/details/46651239
