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我正在尝试使用 NPP 互相关库实现图像置换方法。

我尝试通过在内存中生成几个简单的图像，输出 Npp8u 或 Npp32f 数组来创建一个简单的解决方案。然而，互相关库会产生无意义或无效（即 NAN）的结果。

int main(int argc, char* argv[])
{
    Npp8u* gpuImg1, * gpuImg2;
    Npp32f *gpuDest;

    cudaDeviceInit(argc, (const char**)argv);

    long dataSize1 = 128;
    auto err = cudaMalloc((void**)&gpuImg1, dataSize1 * dataSize1 * sizeof(unsigned char));
    auto img1Data = static_cast<unsigned char*>(malloc(dataSize1 * dataSize1 * sizeof(unsigned char)));
    memset(img1Data, 0, dataSize1 * dataSize1);

    for(auto y = 40; y < 60; y++)
    {
        for(auto x = 20; x < 40; x++)
        {
            img1Data[y * dataSize1 + x] = 0xff;
        }
    }

    long dataSize2 = 64;
    err = cudaMalloc((void**)&gpuImg2, dataSize2);
    auto img2data = static_cast<unsigned char*>(malloc(dataSize2 * dataSize2 * sizeof(unsigned char)));
    memset(img2data, 0, dataSize2 * dataSize2);

    for (auto y = 10; y < 30; y++)
    {
        for (auto x = 20; x < 40; x++)
        {
            img2data[y * dataSize2 + x] = 0xff;
        }
    }

    auto resSize = (dataSize1 - dataSize2) + 1;
    err = cudaMalloc((void**)&gpuDest, resSize * resSize * sizeof(Npp32f));
    auto resData = static_cast<Npp32f*>(malloc(resSize * resSize * sizeof(Npp32f)));

    NppiSize nppiSize1;
    nppiSize1.height = dataSize1;
    nppiSize1.width = dataSize1;

    NppiSize nppiSize2;
    nppiSize2.height = dataSize2;
    nppiSize2.width = dataSize2;

    err = cudaMemcpy(gpuImg1, img1Data, dataSize1, cudaMemcpyHostToDevice);
    err = cudaMemcpy(gpuImg2, img2data, dataSize2, cudaMemcpyHostToDevice);

    auto status = nppiCrossCorrValid_Norm_8u32f_C1R(gpuImg1, dataSize1, nppiSize1, gpuImg2, dataSize2, nppiSize2, gpuDest, resSize * sizeof(Npp32f));

    err = cudaMemcpy(resData, gpuDest, resSize * resSize * sizeof(Npp8u), cudaMemcpyDeviceToHost);

}

CUDA 方法和 NPP 方法都返回成功消息，所以我很确定这是我在设置互相关时做错的事情。任何人都可以帮助我朝着正确的方向寻求解决方案吗？

4

1 回答 1

5

您的代码似乎有两类问题。

首先，您在数据大小方面存在各种问题。不知道断开连接在哪里，因为其中一些你说得对，所以我只指出我看到的东西。

这个结构是正确的：

auto err = cudaMalloc((void**)&gpuImg1, dataSize1 * dataSize1 * sizeof(unsigned char));

你应该在这里做同样的事情：

err = cudaMalloc((void**)&gpuImg2, dataSize2);

这些都不正确。 cudaMemcpy, likememcpy和 likecudaMalloc采用字节为单位的大小参数：

err = cudaMemcpy(gpuImg1, img1Data, dataSize1, cudaMemcpyHostToDevice);
err = cudaMemcpy(gpuImg2, img2data, dataSize2, cudaMemcpyHostToDevice);
                                    ^^^^^^^^^

在从设备到主机的后续副本中，您几乎得到了正确的结果，除了您sizeof应该使用正确的类型：

err = cudaMemcpy(resData, gpuDest, resSize * resSize * sizeof(Npp8u), cudaMemcpyDeviceToHost);
                                                              ^^^^^

其次，您使用的是标准化版本的互相关。如果您研究文档，我相信您会发现当图像的大部分为零值时，分母可以计算为零的平方根。无论如何，当我将“背景”从 0 转换为 1 时，我得到了合理的结果。另一种选择是切换到函数 ( nppiCrossCorrValid_8u32f_C1R) 的非标准化版本，即使大面积为零“背景”，它也会产生非 NAN 结果。

这是一个更正的版本，我认为它会给你非 NAN 结果：

# cat t14.cu
#include <npp.h>
#include <iostream>
int main(int argc, char* argv[])
{
    Npp8u* gpuImg1, * gpuImg2;
    Npp32f *gpuDest;

   // cudaDeviceInit(argc, (const char**)argv);

    long dataSize1 = 128;
    auto err = cudaMalloc((void**)&gpuImg1, dataSize1 * dataSize1 * sizeof(unsigned char));
    unsigned char *img1Data = static_cast<unsigned char*>(malloc(dataSize1 * dataSize1 * sizeof(unsigned char)));
    memset(img1Data, 1, dataSize1 * dataSize1);

    for(auto y = 40; y < 60; y++)
    {
        for(auto x = 20; x < 40; x++)
        {
            img1Data[y * dataSize1 + x] = 0xff;
        }
    }

    long dataSize2 = 64;
    err = cudaMalloc((void**)&gpuImg2, dataSize2*dataSize2 *sizeof(unsigned char));
    unsigned char *img2data = static_cast<unsigned char*>(malloc(dataSize2 * dataSize2 * sizeof(unsigned char)));
    memset(img2data, 1, dataSize2 * dataSize2);

    for (auto y = 10; y < 30; y++)
    {
        for (auto x = 20; x < 40; x++)
        {
            img2data[y * dataSize2 + x] = 0xff;
        }
    }

    auto resSize = (dataSize1 - dataSize2) + 1;
    err = cudaMalloc((void**)&gpuDest, resSize * resSize * sizeof(Npp32f));
    auto resData = static_cast<Npp32f*>(malloc(resSize * resSize * sizeof(Npp32f)));

    NppiSize nppiSize1;
    nppiSize1.height = dataSize1;
    nppiSize1.width = dataSize1;

    NppiSize nppiSize2;
    nppiSize2.height = dataSize2;
    nppiSize2.width = dataSize2;

    err = cudaMemcpy(gpuImg1, img1Data, dataSize1*dataSize1*sizeof(unsigned char), cudaMemcpyHostToDevice);
    err = cudaMemcpy(gpuImg2, img2data, dataSize2*dataSize2*sizeof(unsigned char), cudaMemcpyHostToDevice);

    auto status = nppiCrossCorrValid_Norm_8u32f_C1R(gpuImg1, dataSize1, nppiSize1, gpuImg2, dataSize2, nppiSize2, gpuDest, resSize * sizeof(Npp32f));

    err = cudaMemcpy(resData, gpuDest, resSize * resSize * sizeof(Npp32f), cudaMemcpyDeviceToHost);
    for (int i = 0; i < resSize*2; i++)
      std::cout << resData[i] << ",";
    std::cout << std::endl;
}
# nvcc -std=c++11 -o t14 t14.cu -lnppc -lnppist
# cuda-memcheck ./t14
========= CUDA-MEMCHECK
0.00796924,0.00796924,0.00796924,0.00796924,0.00796924,0.00796924,0.00796924,0.00796924,0.00796924,0.00796924,0.00796924,0.00796924,0.00796924,0.00796925,0.00796925,0.00796925,0.00796925,0.00796925,0.00796925,0.00796925,0.00796925,0.00797587,0.00798853,0.00800826,0.00803633,0.00807432,0.00812423,0.00818861,0.00827071,0.00837505,0.00850754,0.00867648,0.00889385,0.00917761,0.00955609,0.0100771,0.0108291,0.0119988,0.0140744,0.0190166,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.00796925,0.00796925,0.00796925,0.00796925,0.00796925,0.00796925,0.00796925,0.00796925,0.00796925,0.00796925,0.00796925,0.00796925,0.00796925,0.00796925,0.00796925,0.00796925,0.00796925,0.00796926,0.00796926,0.00796926,0.00796926,0.00797588,0.00798854,0.00800827,0.00803634,0.00807434,0.00812425,0.00818863,0.00827071,0.00837505,0.00850754,0.00867648,0.00889385,0.00917761,0.00955609,0.0100771,0.0108291,0.0119988,0.0140744,0.0190166,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,0.323817,
========= ERROR SUMMARY: 0 errors
#
于 2019-07-08T13:47:39.190 回答