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[OpenCL] 读取一张raw图像(NV12格式)，并转为jpg图像（rgb格式）

对于raw文件而言，读取其中的NV12格式的数据，需要利用二进制进行读取，再经过GPU处理后，保存输出的数据为jpg文件。

首先读取和保存图像接口：

1.data_io.h


#ifdef APPLE        //平台相关代码
#include <OpenCL/cl.h>
#else
#include <CL/cl.h>
#endif

#include <opencv2/opencv.hpp>
#include <opencv2/highgui.hpp>


cl_int Load_image_from_NV12(const std::string &filename,cl_mem *imageObjects,cl_context context,int &img_h,int &img_w);


void Save_pic_by_iostream(const std::string &filename,unsigned char  *rd,int &img_h,int &img_w);

data_io.cpp

#include "data_io.h"

#include <iostream>
#include <fstream>
#include <sstream>


cv::Mat src_Image,dst_Image;

u_char * get_NV12_buffer(const std::string &filePath, int width, int height) 
{
    std::ifstream file(filePath, std::ios::binary | std::ios::ate);
    
    if (!file.is_open()) {
        std::cerr << "Error: Unable to open NV12 file " << filePath << std::endl;
        return NULL;
    }
 
    std::streamsize size = file.tellg();
    file.seekg(0, std::ios::beg);

    size=width*height*3/2;
    char *buffer=new char [size];
    
    if (!file.read(buffer, size)) {
        std::cerr << "Error: Unable to read NV12 data from file " << filePath << std::endl;
        return NULL;
    }
    
    return (u_char *)buffer;
}



cl_int Load_image_from_NV12(const std::string &filename,cl_mem *imageObjects,cl_context context,int &img_h,int &img_w)
{

    cl_int errNum;
    img_w=3840;
    img_h=2176;

    u_char * nv12data= get_NV12_buffer(filename, img_w, img_h);
     

    if(NULL==nv12data)
        return 1;

    int nv12_size=sizeof(u_char)*img_h*img_w*3/2;
    
    imageObjects[0] = clCreateBuffer(context, CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR, 
                                    nv12_size, nv12data, &errNum);    //CL_MEM_COPY_HOST_PTR指定创建缓存对象后拷贝数据  
    return errNum;
}



void Save_pic_by_iostream(const std::string &filename,unsigned char  *rd,int &img_h,int &img_w)
{
    
    std::ofstream out;   // 读取图像
	out.open(filename, std::ios::out | std::ios::binary);
	
	//从buffer中写数据到out指向的文件中
	out.write((const char*)rd, img_w*3*img_h* sizeof(char));
	//关闭文件指针，释放buffer内存
	out.close();
	delete[]rd;


    std::cout <<"if:"<< filename <<std::endl;
    std::ifstream is(filename, std::ifstream::in | std::ios::binary);
    // 2. 计算图片长度
    is.seekg(0, is.end);
    int length = is.tellg();
    std::cout << length <<std::endl;
    is.seekg(0, is.beg);
    // 3. 创建内存缓存区
    char * buffer = new char[length];
    // 4. 读取图片
    is.read(buffer, length);
    // 到此，图片已经成功的被读取到内存（buffer）中
 
   //二、另存为 223.jpg
  cv::Mat dst_Image =cv::Mat(img_h, img_w, CV_8UC3, (void*)buffer);
  cv::imwrite("../data/dst_rgb_if.jpg", dst_Image);

}

其次，主函数：

3.main.cp

#include <iostream>
#include <fstream>
#include <sstream>


#ifdef APPLE        //平台相关代码
#include <OpenCL/cl.h>
#else
#include <CL/cl.h>
#endif

#include "data_io.h"

extern cv::Mat src_Image,dst_Image;

//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;
    }
 
    //cout<<"numPlatforms="<<numPlatforms<<endl; 1

    // 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;
}


//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;
    }
}


//清除资源
void Cleanup(cl_context context, cl_command_queue commandQueue,
             cl_program program, cl_kernel kernel, cl_mem imageObjects[2])
{
    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 (context != 0)
        clReleaseContext(context);
 
}

int main()
{

    
    //initial varibles
    cl_int errNum=0;
    cl_device_id device = 0;
    cl_context context = 0;
    cl_program program = 0;
    cl_kernel kernel = 0;
    cl_command_queue commandQueue = 0;
    cl_sampler sampler = 0;
    cl_mem imageObjects[2] = { 0, 0 };
    
    int img_w = 3840;
    int img_h = 2176; 
    int new_w=3840;
    int new_h=2176;
    size_t read_size=img_w*img_h*3;

    size_t g_w=3840;
    size_t g_h=2176;
    
    std::string input_file,output_jpgfile;
    char *kernel_file=new char [100];
    char *kernel_name=new char [100];
   

     input_file="../data/test.raw";
     output_jpgfile="../data/dst_rgb_of.jpg";
          
     strcpy(kernel_file,"nv12_to_rgb.cl");
     strcpy(kernel_name,"kernel_nv12_to_rgb");

    
     // 1.选择platform,创建contex上下文
    context = CreateContext();
    if (context == NULL)
    {
        std::cerr << "Failed to create OpenCL context." << std::endl;
        return 1;
    }
    

    // 2.创建命令队列
    commandQueue = CreateCommandQueue(context, &device);
    if (commandQueue == NULL)
    {
        std::cerr <<"CreateCommandQueue failed"<<std::endl;
        Cleanup(context, commandQueue, program, kernel, imageObjects);
        return 1;
    }
    
    
    // 3. 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);
        
        return 1;
    }
    
     //4.准备输入数据 
    
     errNum=Load_image_from_NV12(input_file,imageObjects,context,img_h,img_w);
    
     if (errNum != CL_SUCCESS)
        { 
            std::cerr << "Load_image failed." << std::endl;
            Cleanup(context, commandQueue, program, kernel, imageObjects);
            return 1;

        }

    // 5.创建输出的图像对象
   
      new_w=img_w;
      new_h=img_h;
      read_size=sizeof(u_char)*img_h*img_w*3;
      
    imageObjects[1] = clCreateBuffer(context, CL_MEM_WRITE_ONLY, 
                                    read_size, NULL, &errNum);
    
     if (errNum != CL_SUCCESS)
    { 
        std::cerr << "create output imageobject failed." << std::endl;
        Cleanup(context, commandQueue, program, kernel, imageObjects);
        return 1;

    }

  
    //6.创建OpenCL-program对象
    
    program = CreateProgram(context, device, kernel_file);
    if (program == NULL)
    {
        std::cerr <<"CreateProgram failed"<<std::endl;
        Cleanup(context, commandQueue, program, kernel, imageObjects);
        return 1;
    }
    
    // 7.创建OpenCL核

    kernel = clCreateKernel(program, kernel_name, NULL);
    
    if (kernel == NULL)
    {
        std::cerr << "Failed to create kernel" << std::endl;
        Cleanup(context, commandQueue, program, kernel, imageObjects);
        return 1;
    }
 
  
    //8. 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_int), &img_w);
    errNum |= clSetKernelArg(kernel, 3, sizeof(cl_int), &img_h);
    
    
    if (errNum != CL_SUCCESS)
    {
        std::cerr << "Error setting kernel arguments." << std::endl;
        Cleanup(context, commandQueue, program, kernel, imageObjects);
        return 1;
    }
    
    g_w=new_w;
    g_h=new_h;
    
    size_t localWorkSize[2] = { 32, 32 };
    size_t globalWorkSize[2] =  {g_w,g_h};
   
    
    //9.启动内核，内核执行完成后，会将evt置为CL_SUCCESS/CL_COMPLETE
    cl_event evt;
    errNum = clEnqueueNDRangeKernel(commandQueue, kernel,  
                               2, 0, globalWorkSize, localWorkSize, 
                               0, NULL, &evt);  
    clWaitForEvents(1, &evt);   //等待命令事件发生
    clReleaseEvent(evt);
    
    //10.读回数据
      
    u_char *read_data = new u_char [read_size];
    errNum =clEnqueueReadBuffer(commandQueue, imageObjects[1], 
                            CL_TRUE, 
                            0, 
                            read_size, 
                            read_data, 0, NULL, NULL);
    if (errNum != CL_SUCCESS)
    {
        std::cerr << "Error reading result buffer enume." <<errNum<< std::endl;
        Cleanup(context, commandQueue, program, kernel, imageObjects);
        return 1;
    }
     
    if (read_data == NULL)
    {
        std::cerr << "Error reading result buffer null." <<errNum<< std::endl;
        Cleanup(context, commandQueue, program, kernel, imageObjects);
        return 1;
    }
    
    //显示图像
     Save_pic_by_iostream(output_jpgfile,read_data,img_h,img_w);
     
    
    Cleanup(context, commandQueue, program, kernel, imageObjects);
    
    std::cout<<"NV12 convert to rgb via opencl success!"<<std::endl;

    return 0;
}

最后，实际灰度图像的kernel函数

4.nv12_to_rgb.cl

__kernel void kernel_nv12_to_rgb(__global unsigned char * nv12data, 
                              __global unsigned char * rgbdata, 
                              int width, int height){
    
    int x = get_global_id(0); //width
    int y = get_global_id(1); //height
    unsigned char *ybase = nv12data;
    unsigned char *ubase = &nv12data[width * height];

    if(x < width && y < height)
    {
        int index = y * width + x;

        unsigned char Y = ybase[x + y * width];
        unsigned char U = ubase[y / 2 * width + (x / 2) * 2];
        unsigned char V = ubase[y / 2 * width + (x / 2) * 2 + 1];

        rgbdata[index*3] =  Y + 1.402 * (V - 128);//R
        rgbdata[index*3+1] = Y - 0.34413 * (U - 128) - 0.71414 * (V - 128);//G
        rgbdata[index*3+2] =Y + 1.772 * (U - 128);//B
        
    }
    
}

5.Makefile

demo : main.cpp data_io.cpp
	g++ `pkg-config opencv4 --cflags` main.cpp data_io.cpp -o demo `pkg-config opencv4 --libs` -D CL_TARGET_OPENCL_VERSION=100 -lOpenCL

验证是否转换正确，可以分别用opencv和自己生成的结果做对比。

悦读

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