GPU并行计算OpenCL(3)——图像处理
我们这一章来实现一下利用OpenCL完成一个简单的高斯过滤器处理图像，在实现图像处理之前，我们需要了解OpenCL中的图像对象和采样器对象。
图像对象
图像对象就是我们需要处理的图像，但是我们需要将其处理成OpenCL所理解的语言。这里我们可以使用FreeImage库来完成图像数据的处理，大大的减少了我们的工作量。图像对象封装了一个图像的多种信息：
图像大小：2维图像的高度和宽度。
图像格式：内存中像素的位数和布局。
内存访问标志：图像是可读还是可写。
坐标模式：图像获取数据所用的纹理坐标是否位规范化模式[0，1]。
寻地模式：当坐标超出图像边界的处理方式。
过滤模式：从图像获取数据时是单样本还是多样本过滤(如双线性过滤)。
创建图像对象
OpenCL中创建图像对象可以通过clCreateImage2D()完成。
| cl_context context, |
| cl_mem_flags flags, |
| const cl_image_format* image_format, |
| size_t *image_width, |
| size_t *image_height, |
| size_t *image_row_pitch, |
| void* host_ptr, |
| cl_int *errcode_ret) |
第一个参数为创建图像的上下文。第二个参数指定了图像可读可写性。第三个参数为图像格式。第四个参数为图像的长。第五个参数为图像的高。第六个参数为图像的各行的字节数(若设为0，则为image_width*(每个像素的字节数))。第七个参数为图像数据。第八个参数为错误信息。函数返回一个图像对象。
我们定一个LoadImage函数来获取图像对象。
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);
// 转变为32位
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);
// 创建OpenCL图像对象
cl_image_format clImageFormat;//图像格式属性
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;
}该段代码中，我们利用freeimage库来获取图像的信息，然后利用该信息通过函数clCreateImage2D获取图像对象。
采样器对象
OpenCL和OpenGL非常相似，处理图像对象时，都需要通过采样器对象对其进行操作。采样器对象指定来从图像获取数据时所使用的过滤，寻址和坐标模式。
过滤模式指定使用nearest(最近)采样还是linear(线性)采样，最近采样为选择离坐标最近的位置读取值，线性采样则获取周围4个最接近样本差值出最终值。
坐标模式指定图像读取数据是否使用规范化坐标[0,1]，寻址模式为超出坐标范围时要做什么。，这些模式在clCreateSampler()的描述中给出。
| cl_context context, |
| cl_bool normalized_coords, |
| cl_addressing_mode addressing_mode, |
| cl_filter_mode filter_mode, |
| cl_int *errcode_ret |
函数第一个参数为采样器对象的上下文。第二个参数为是否规范化坐标。第三个参数为寻址模式，如使用CL_ADDRESS_CLAMP则超出范围的坐标会使用图像的边界颜色。第四个参数为过滤模式，如CL_FILTER_NEAREST则选择最近的样本。第五个参数为错误信息。
高斯过滤器内核
我们接下来直接看我们的.cl文件(内核)。
__kernel void gaussian_filter(__read_only image2d_t srcImg,
__write_only image2d_t dstImg,
sampler_t sampler,
int width, int height)
{
// Gaussian Kernel is:
// 1 2 1
// 2 4 2
// 1 2 1
float kernelWeights[9] = { 1.0f, 2.0f, 1.0f,
2.0f, 4.0f, 2.0f,
1.0f, 2.0f, 1.0f };
int2 startImageCoord = (int2) (get_global_id(0) - 1, get_global_id(1) - 1);
int2 endImageCoord = (int2) (get_global_id(0) + 1, get_global_id(1) + 1);
int2 outImageCoord = (int2) (get_global_id(0), get_global_id(1));
if (outImageCoord.x < width && outImageCoord.y < height)
{
int weight = 0;
float4 outColor = (float4)(0.0f, 0.0f, 0.0f, 0.0f);
for( int y = startImageCoord.y; y <= endImageCoord.y; y++)
{
for( int x = startImageCoord.x; x <= endImageCoord.x; x++)
{
outColor += (read_imagef(srcImg, sampler, (int2)(x, y)) * (kernelWeights[weight] / 16.0f));
weight += 1;
}
}
//写入输出图像
//write_imagef(dstImg, outImageCoord, outColor);//正常的高斯模糊后的图像
write_imagef(dstImg, outImageCoord, (float4)(1.0f,outColor.yzw));//二次处理
}
}我们的核函数有5个参数，分别为只读的输入图像，只写的输出图像，采样器对象，图像宽和高。
之后定义卷积核kernelWeights用于下面的卷积计算，在计算每个新像素时，利用输入图像做卷积运算，函数read_imagef利用采样器sampler读取srcimg图像中int2(x,y)坐标下的颜色值。
计算完的颜色值保存在outColor中，我们可以直接输出，也可以在做二次处理。经过二次处理后效果为：
不仅图像有了高斯模糊的效果，还在R通道设定定值1.0。
完成内核的编写，接下来就是宿主机中的代码了。
完整代码
在编写OpenCL项目时，我们一定要非常熟悉其中的流程，首先选择平台，建立上下文：
cl_context CreateContext()
{
cl_int errNum;
cl_uint numPlatforms;
cl_platform_id firstPlatformId;
cl_context context = NULL;
// 选择第一个平台
errNum = clGetPlatformIDs(1, &firstPlatformId, &numPlatforms);
if (errNum != CL_SUCCESS || numPlatforms <= 0)
{
std::cerr << "Failed to find any OpenCL platforms." << std::endl;
return NULL;
}
// 接下来尝试通过GPU设备建立上下文
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;
}然后创建命令队列：
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;
// 首先获得设备的信息
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;
}
//为设备分配内存
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;
}
// 选择第一个设备并为其创建命令队列
commandQueue = clCreateCommandQueue(context, devices[0], 0, NULL);
if (commandQueue == NULL)
{
std::cerr << "Failed to create commandQueue for device 0";
return NULL;
}
//释放信息
*device = devices[0];
delete [] devices;
return commandQueue;
}然后利用内核和设备构建程序对象：
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)
{
// 输出错误信息
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;
}这样我们的程序为：
#include <iostream>
#include <fstream>
#include <sstream>
#include <string.h>
#ifdef __APPLE__
#include <OpenCL/cl.h>
#else
#include <CL/cl.h>
#endif
#include <FreeImage.h>
//在第一个平台中创建只包括GPU的上下文
cl_context CreateContext()
{
cl_int errNum;
cl_uint numPlatforms;
cl_platform_id firstPlatformId;
cl_context context = NULL;
// 选择第一个平台
errNum = clGetPlatformIDs(1, &firstPlatformId, &numPlatforms);
if (errNum != CL_SUCCESS || numPlatforms <= 0)
{
std::cerr << "Failed to find any OpenCL platforms." << std::endl;
return NULL;
}
// 接下来尝试通过GPU设备建立上下文
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;
}
//在第一个设备上创建命令队列
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;
// 首先获得设备的信息
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;
}
//为设备分配内存
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;
}
// 选择第一个设备并为其创建命令队列
commandQueue = clCreateCommandQueue(context, devices[0], 0, NULL);
if (commandQueue == NULL)
{
std::cerr << "Failed to create commandQueue for device 0";
return NULL;
}
//释放信息
*device = devices[0];
delete [] devices;
return commandQueue;
}
// 创建OpenCL程序对象
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)
{
// 输出错误信息
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;
}
//清除资源
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);
}
///加载图像
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);
// 转变为32位
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);
// 创建OpenCL图像对象
cl_image_format clImageFormat;//图像格式属性
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;
}
// 利用freeimage库保存一张图片
//
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) == TRUE) ? true : false;
}
//获取最接近的倍数
size_t RoundUp(int groupSize, int globalSize)
{
int r = globalSize % groupSize;
if(r == 0)
{
return globalSize;
}
else
{
return globalSize + groupSize - r;
}
}
int main()
{
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;
// 创建上下文
context = CreateContext();
if (context == NULL)
{
std::cerr << "Failed to create OpenCL context." << std::endl;
return 1;
}
// 创建命令队列
commandQueue = CreateCommandQueue(context, &device);
if (commandQueue == NULL)
{
Cleanup(context, commandQueue, program, kernel, imageObjects, sampler);
return 1;
}
// 确保设备支持这种图像格式
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;
}
// 加载图像
int width, height;
imageObjects[0] = LoadImage(context, "123.png", width, height);
if (imageObjects[0] == 0)
{
std::cerr << "Error loading: " << std::string("123.png") << std::endl;
Cleanup(context, commandQueue, program, kernel, imageObjects, sampler);
return 1;
}
// 创建输出的图像对象
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;
}
// 创建采样器对象
sampler = clCreateSampler(context,
CL_FALSE, // 非规范化坐标
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;
}
// 创建OpenCL程序对象
program = CreateProgram(context, device, "ImageFilter2D.cl");
if (program == NULL)
{
Cleanup(context, commandQueue, program, kernel, imageObjects, sampler);
return 1;
}
// 创建OpenCL核
kernel = clCreateKernel(program, "gaussian_filter", NULL);
if (kernel == NULL)
{
std::cerr << "Failed to create kernel" << std::endl;
Cleanup(context, commandQueue, program, kernel, imageObjects, sampler);
return 1;
}
// 设定参数
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) };
// 将内核排队
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;
}
// 将输出缓冲区读回主机
char *buffer = new char [width * height * 4];
size_t origin[3] = { 0, 0, 0 };
size_t region[3] = { size_t(width), size_t(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;
//保存输出图像
if (!SaveImage("456.png", buffer, width, height))
{
std::cerr << "Error writing output image: " << "456.png" << std::endl;
Cleanup(context, commandQueue, program, kernel, imageObjects, sampler);
delete [] buffer;
return 1;
}
delete [] buffer;
Cleanup(context, commandQueue, program, kernel, imageObjects, sampler);
return 0;
}
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