CPP项目中使用CUDA(opencv+cuda or pybind+cuda)

该文展示了如何使用CMake构建结合CUDA和OpenCV的C++项目。它包括设置C++标准、查找CUDA和OpenCV库、定义NVCC标志、编译CUDA源码以及链接OpenCV库。此外,还提及了与Python绑定的PyBind11库的使用。

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cmake_minimum_required(VERSION 2.8.12)
project(flow LANGUAGES CXX)


set(CMAKE_CXX_STANDARD 11)
set(CMAKE_CXX_STANDARD_REQUIRED ON)

include_directories(include
        ${CUDA_INCLUDE_DIRS}
        ${OpenCV_INCLUDE_DIRS}
        )

link_directories(${OpenCV_LIBRARY_DIRS})

find_package(CUDA REQUIRED)
find_package(OpenCV REQUIRED)


FILE(GLOB SOURCES "*.cu" "*.cpp" "*.cuh" "*.h" )

set(CUDA_NVCC_FLAGS "-g -G")

CUDA_ADD_EXECUTABLE(flow hell0.cu example.cpp)  # add_executable(flow example.cpp)

target_link_libraries(flow ${OpenCV_LIBS})
#include "hell0.cuh"
int main(){
    cuda_main();
    return 0;
}
#include <stdio.h>

__global__ void cuda_hello(){
    printf("gpu\n");
}



extern "C" void cuda_main() {
    cuda_hello<<<1,10>>>();
    cudaDeviceSynchronize();//witout this no output
}

extern "C"
void cuda_main();

opencv+cuda

cmake_minimum_required(VERSION 3.5)

project(cuda_cv LANGUAGES CXX)

set(CMAKE_CXX_STANDARD 11)
set(CMAKE_CXX_STANDARD_REQUIRED ON)

include_directories(include
        ${CUDA_INCLUDE_DIRS}
        ${OpenCV_INCLUDE_DIRS}
        )

link_directories(${OpenCV_LIBRARY_DIRS})

find_package(CUDA REQUIRED)
find_package(OpenCV REQUIRED)

#INCLUDE(/home/psdz/cmake-3.9.0/Modules/FindCUDA.cmake)

FILE(GLOB SOURCES "*.cu" "*.cpp" "*.c" "*.h")

set(CUDA_NVCC_FLAGS "-g -G")

CUDA_ADD_EXECUTABLE(cuda_cv main.cu)

target_link_libraries(cuda_cv ${OpenCV_LIBS})

pybind+cuda

cmake_minimum_required(VERSION 2.8.12)
project(example LANGUAGES CXX)
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(PYBIND11_PYTHON_VERSION "3.7")

#include_directories("/usr/include/python3.10/")

include_directories(include
        ${CUDA_INCLUDE_DIRS}
        ${OpenCV_INCLUDE_DIRS}
        )
link_directories(${OpenCV_LIBRARY_DIRS})

find_package(CUDA REQUIRED)
find_package(OpenCV REQUIRED)
include_directories("/home/pdd/anaconda3/envs/yolocopy/bin/")


FILE(GLOB SOURCES "*.cu" "*.cpp" "*.cuh" "*.h" )

set(CUDA_NVCC_FLAGS "-g -G")

add_subdirectory(pybind11)
pybind11_add_module(example example.cpp)
#add_executable(untitled example.cpp)

#target_link_libraries(${PROJECT_NAME} INTERFACE
#        ${OpenCV_LIBRARIES}
##        pybind11::pybind11
#        )
target_link_libraries(${PROJECT_NAME} PRIVATE
       ${OpenCV_LIBRARIES}
        pybind11::pybind11
        )
#link_libraries(${OpenCV_LIBRARIES})
OpenCV中结合CUDA实现多线程并行处理图像色彩转换(如BGR到RGB)通常涉及以下几个步骤: 1. **引入库**[^1]: ```cpp #include <opencv2/opencv.hpp> #include <opencv2/gpu/gpu.hpp> ``` 2. **创建CUDA设备上下文**: ```cpp cv::cuda::setDevice(0); // 设定要使用的GPU设备 ``` 3. **将图像复制到GPU**: ```cpp cv::Mat src, dst; cv::cvtColor(inputImage, src, cv::COLOR_BGR2RGB); // BGR to RGB转换 cv::cuda::GpuMat d_src(src), d_dst; // GPU上的输入和输出矩阵 d_src.upload(src); ``` 4. **定义CUDA内核函数**: ```cpp __global__ void colorConvertKernel(const float* srcData, float* dstData, int width, int height) { int x = blockIdx.x * blockDim.x + threadIdx.x; int y = blockIdx.y * blockDim.y + threadIdx.y; if (x >= width || y >= height) return; // CUDA内核执行色彩转换 dstData[y * width + x] = ...; // 根据具体颜色模型计算新的像素值 } ``` 5. **设置CUDA线程块大小和网格大小**: ```cpp const int blockSize = 32; // 或者根据GPU架构选择合适的大小 const int gridSize = cv::cuda::getGridSize(d_src.cols, blockSize); ``` 6. **调用CUDA内核**: ```cpp colorConvertKernel<<<gridSize, blockSize>>>(d_src.data, d_dst.data, d_src.cols, d_src.rows); ``` 7. **下载结果到CPU**: ```cpp d_dst.download(dst); ``` 8. **显示或保存结果**: ```cpp imshow("Result", dst); waitKey(); ``` 请注意,由于CUDA编程的具体细节,上述代码中的`...`应替换实际的颜色转换公式。这一步可能涉及复杂的数学运算,具体取决于你想要的目标色彩空间。
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