Question

我已经从源代码编译了OpenCV（2.4.6.1），在OS X Mavericks上启用了CUDA（6.0）（10.9.3）。

现在我想使用OpenCV和CUDA的混合创建自己的图像处理功能。让我们举一个简单的例子，我们有一个OpenCV Mat，并希望对每个元素做一些事情，并希望通过使用CUDA并行化来加快速度。在我们的例子中，我们只打印出每个Mat元素的值。不现实，但足以表明这个概念。

CUDA标头文件： print.cuh

#ifndef __PRINT_CUH__
#define __PRINT_CUH__

void print(const unsigned char * pixels, const int N);

#endif

CUDA源文件： print.cu

#include <stdio.h>

// The device version
__global__ void cuda_print(const unsigned char * pixels, const int N)
{
    int tidX = blockIdx.x * blockDim.x + threadIdx.x;

    if( tidX >= N ) {
        return;
    }

    printf("pixel value @ %d = %d\n", tidX, pixels[tidX]);
}

// The host version
void print(const unsigned char * pixels, const int N) {
    int num_blocks = 10;
    int num_threads = 128;

    unsigned char * d_pixels;
    cudaMalloc( &d_pixels, sizeof(char) * N );
    cudaMemcpy( d_pixels, pixels, sizeof(char)*N, cudaMemcpyHostToDevice);

    cuda_print<<<num_blocks, num_threads>>>(d_pixels, N);
    cudaDeviceSynchronize(); // The above call is asynchronous, wait until it
                             // finishes before exiting the program!

}

包含OpenCV和我们自己的CUDA代码的C ++代码： main.cpp

#include <opencv2/opencv.hpp>

#include "print.cuh"

int main(int argc, char ** argv )
{
    cv::Mat m(100,1,CV_8UC1, cv::Scalar(0));

    print(m.ptr(0), m.rows);

    return 0;
}

我们希望将自己的CUDA代码编译到共享库中，并将其包含在我们的主

中

CMAKE设置： CMakeLists.txt

# CUDA CMAKE TEST 
cmake_minimum_required(VERSION 2.8)

# project name
project(CUDA_CMAKE)

# find dependencies 
find_package(OpenCV REQUIRED)
find_package(CUDA REQUIRED)

# this is necessary on OS X since CUDA only support the older libstdc++
IF(APPLE)
    SET(CUDA_HOST_COMPILER /usr/bin/clang CACHE FILEPATH "Setting clang as the CUDA compiler" FORCE)
    SET(CUDA_NVCC_FLAGS "-Xcompiler -stdlib=libstdc++; -Xlinker -stdlib=libstdc++; -arch=sm_20" CACHE STRING "Setting NVCC compiler flags" FORCE)
ENDIF()

# build a shared library with our CUDA code
CUDA_ADD_LIBRARY(cudaPrint 
    SHARED
    print.cu
)
TARGET_LINK_LIBRARIES(cudaPrint
    ${CUDA_LIBRARIES}
)

# build the C++ code and link with the CUDA code
ADD_EXECUTABLE(cuda_test 
    main.cpp 
)
TARGET_LINK_LIBRARIES(cuda_test
    cudaPrint ${OpenCV_LIBS}
)

构建的第一步工作正常，并生成cudaPrint.dylib。但是，在尝试构建可执行文件时，我收到以下链接错误：

make all 
-- Configuring done
CMake Warning at CMakeLists.txt:29 (ADD_EXECUTABLE):
  Cannot generate a safe runtime search path for target cuda_test because
  there is a cycle in the constraint graph:

    dir 0 is [/Developer/NVIDIA/CUDA-5.5/lib]
      dir 1 must precede it due to runtime library [libcudart.dylib]
    dir 1 is [/usr/local/cuda/lib]
      dir 0 must precede it due to runtime library [libcudart.dylib]

  Some of these libraries may not be found correctly.


-- Generating done
-- Build files have been written to: /Users/navid/proj/CUDA/test_cuda_opencv/build
[ 50%] Built target cudaPrint
Linking CXX executable cuda_test
ld: can't map file, errno=22 file '/Developer/NVIDIA/CUDA-5.5/lib' for architecture x86_64
clang: error: linker command failed with exit code 1 (use -v to see invocation)
make[2]: *** [cuda_test] Error 1
make[1]: *** [CMakeFiles/cuda_test.dir/all] Error 2
make: *** [all] Error 2

看起来这个错误与OpenCV有关，还包括CUDA库。我不确定，但我有一个解决这个问题，我将在下面发布。

Answer 1

所以看起来链接错误是由于libopencv_ts通过${OpenCV_LIBS}传递给链接器，因为FIND_PACKAGE(OpenCV REQUIRED)将所有OpenCV库添加到${OpenCV_LIBS}变量。

如果libopencv_ts 不需要，一个简单的解决方法就是在我们要求CMAKE找到包时指定我们明确需要的OpenCV库，例如： FIND_PACKAGE(OpenCV REQUIRED COMPONENTS core highgui cuda)。

我不知道为什么libopencv_ts正在创建此循环错误以及如何绕过它。

将OpenCV（与Cuda）链接到实现CUDA函数的C ++代码会返回链接错误

1 个答案: