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CUDA小白 - NPP(4) 图像处理 Data Exchange and Initialization（1）
GPU架构近些年也有不少的变化，具体的可以参考别的博主的介绍，都比较详细。还有一些cuda中的专有名词的含义，可以参考《详解CUDA的Context、Stream、Warp、SM、SP、Kernel、Block、Grid》
常见的NppStatus，可以看这里。
如有问题，请指出，谢谢
Image Set Operations
当前模块主要功能是set图像中的像素值，主要分为三个大类：将ROI区域内的所有像素设置为一个特殊的值（Set），mask赋值（Masked Set），以及单通道赋值（Channel Set）。
三个大类分别以一个三通道的uint8_t为例子简单介绍一下。// ROI区域内的三通道设置为aValue NppStatus nppiSet_8u_C3R(const Npp8u aValue[3], Npp8u *pDst, int nDstStep, NppiSize oSizeROI); // 通过mask控制ROI区域内的那些像素会被set NppStatus nppiSet_8u_C3MR(const Npp8u aValue[3], Npp8u *pDst, int nDstStep, NppiSize oSizeROI, const Npp8u *pMask, int nMaskStep); // 通过pointer的起始位置区别，选择某通道设置为固定值 NppStatus nppiSet_8u_C3CR(Npp8u nValue, Npp8u *pDst, int nDstStep, NppiSize oSizeROI);- 1
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code
#include#include #include #include #define CUDA_FREE(ptr) { if (ptr != nullptr) { cudaFree(ptr); ptr = nullptr; } } int main() { std::string directory = "../"; // =============== load image =============== cv::Mat image_dog = cv::imread(directory + "dog.png"); int image_width = image_dog.cols; int image_height = image_dog.rows; int image_size = image_width * image_height; // =============== device memory =============== uint8_t *out_ptr1, *out_ptr2, *out_ptr3; cudaMalloc((void**)&out_ptr1, image_size * 3 * sizeof(uint8_t)); cudaMalloc((void**)&out_ptr2, image_size * 3 * sizeof(uint8_t)); cudaMalloc((void**)&out_ptr3, image_size * 3 * sizeof(uint8_t)); cudaMemcpy(out_ptr1, image_dog.data, image_size * 3 * sizeof(uint8_t), cudaMemcpyHostToDevice); cudaMemcpy(out_ptr2, image_dog.data, image_size * 3 * sizeof(uint8_t), cudaMemcpyHostToDevice); cudaMemcpy(out_ptr3, image_dog.data, image_size * 3 * sizeof(uint8_t), cudaMemcpyHostToDevice); cv::Mat mask = cv::Mat::zeros(image_height, image_width, CV_8UC1); cv::Mat mask1 = cv::Mat::ones(image_height * 3 / 4, image_width * 3 / 4, CV_8UC1); cv::Rect rc1 = cv::Rect(image_width / 4, image_height / 4, image_width * 3 / 4, image_height * 3 / 4); mask1.copyTo(mask(rc1)); uint8_t *gpu_mask; cudaMalloc((void**)&gpu_mask, image_size * sizeof(uint8_t)); cudaMemcpy(gpu_mask, mask.data, image_size * sizeof(uint8_t), cudaMemcpyHostToDevice); NppiSize roi1, roi2; roi1.width = image_width; roi1.height = image_height; roi2.width = image_width / 2; roi2.height = image_height / 2; cv::Mat out_image = cv::Mat::zeros(image_height, image_width, CV_8UC3); NppStatus status; // =============== nppiSet_8u_C3R =============== uint8_t value[3] = { 255, 0, 0 }; status = nppiSet_8u_C3R(value, out_ptr1, image_width * 3, roi1); if (status != NPP_SUCCESS) { std::cout << "[GPU] ERROR nppiSet_8u_C3R failed, status = " << status << std::endl; return false; } cudaMemcpy(out_image.data, out_ptr1, image_size * 3, cudaMemcpyDeviceToHost); cv::imwrite(directory + "set.jpg", out_image); // =============== nppiSet_8u_C3R =============== uint8_t value2[3] = { 0, 0, 255 }; status = nppiSet_8u_C3MR(value2, out_ptr2, image_width * 3, roi1, gpu_mask, image_width); if (status != NPP_SUCCESS) { std::cout << "[GPU] ERROR nppiSet_8u_C3MR failed, status = " << status << std::endl; return false; } cudaMemcpy(out_image.data, out_ptr2, image_size * 3, cudaMemcpyD eviceToHost); cv::imwrite(directory + "set_mask.jpg", out_image); // green status = nppiSet_8u_C3CR(255, out_ptr3 + image_width * 3 * 200 + 1, image_width * 3, roi1); if (status != NPP_SUCCESS) { std::cout << "[GPU] ERROR nppiSet_8u_C3CR failed, status = " << status << std::endl; return false; } cudaMemcpy(out_image.data, out_ptr3, image_size * 3, cudaMemcpyDeviceToHost); cv::imwrite(directory + "set_channel.jpg", out_image); // free CUDA_FREE(out_ptr1) CUDA_FREE(out_ptr2) CUDA_FREE(out_ptr3) } - 1
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make
cmake_minimum_required(VERSION 3.20) project(test) find_package(OpenCV REQUIRED) include_directories(${OpenCV_INCLUDE_DIRS}) find_package(CUDA REQUIRED) include_directories(${CUDA_INCLUDE_DIRS}) file(GLOB CUDA_LIBS "/usr/local/cuda/lib64/*.so") add_executable(test test.cpp) target_link_libraries(test ${OpenCV_LIBS} ${CUDA_LIBS} )- 1
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result
注意：- mask使用的是单通道的，仅表示那些像素需要进行set，那些不需要
- 通道的set，通过指针来表示是针对那个通道进行转换。输入指针表示开始set的起始位置，对于三通道的图像而言，则是隔两个set一次。
Image Copy Operations
除了比较常见的copy操作（copy，masked copy，channel copy）之外，还有一些planar和packed之间的来回拷贝，拷贝的同时伴随着border，以及Copy Sub-pixel（没接触过）
// 单纯的拷贝 NppStatus nppiCopy_8u_C3R(const Npp8u *pSrc, int nSrcStep, Npp8u *pDst, int nDstStep, NppiSize oSizeROI); // 依据mask有选择性的进行拷贝 NppStatus nppiCopy_8u_C3MR(const Npp8u *pSrc, int nSrcStep, Npp8u *pDst, int nDstStep, NppiSize oSizeROI, const Npp8u *pMask, int nMaskStep); // Channel Copy, 将一个多通道的某个通道拷贝到另外一个多通道图像的某一个channel NppStatus nppiCopy_8u_C3CR(const Npp8u *pSrc, int nSrcStep, Npp8u *pDst, int nDstStep, NppiSize oSizeROI); // Extract Channel Copy, 将一个多通道的某个通道拷贝到另外一个单通道的图像 NppStatus nppiCopy_8u_C3C1R(const Npp8u * pSrc, int nSrcStep, Npp8u *pDst, int nDstStep, NppiSize oSizeROI); // Insert Channel Copy, 一个单通道的图像拷贝到多通道中的某一个通道 NppStatus nppiCopy_8u_C1C3R(const Npp8u * pSrc, int nSrcStep, Npp8u * pDst, int nDstStep, NppiSize oSizeROI); // 剩下的接口平时接触较少，所以暂时不做详细介绍- 1
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code
#include#include #include #include #define CUDA_FREE(ptr) { if (ptr != nullptr) { cudaFree(ptr); ptr = nullptr; } } int main() { std::string directory = "../"; // =============== load image =============== cv::Mat image_dog = cv::imread(directory + "dog.png"); cv::Mat image_dog_gray; cv::cvtColor(image_dog, image_dog_gray, CV_RGB2GRAY); int image_width = image_dog.cols; int image_height = image_dog.rows; int image_size = image_width * image_height; // =============== device memory =============== uint8_t *in_image, *in_img_gray; cudaMalloc((void**)&in_image, image_size * 3 * sizeof(uint8_t)); cudaMalloc((void**)&in_img_gray, image_size * sizeof(uint8_t)); cudaMemcpy(in_image, image_dog.data, image_size * 3 * sizeof(uint8_t), cudaMemcpyHostToDevice); cudaMemcpy(in_img_gray, image_dog_gray.data, image_size * sizeof(uint8_t), cudaMemcpyHostToDevice); uint8_t *out_ptr1, *out_ptr2, *out_ptr3, *out_ptr4, *out_ptr5; cudaMalloc((void**)&out_ptr1, image_size * 3 * sizeof(uint8_t)); // 三通道 cudaMalloc((void**)&out_ptr2, image_size * 3 * sizeof(uint8_t)); // 三通道 cudaMalloc((void**)&out_ptr3, image_size * 3 * sizeof(uint8_t)); // 三通道 cudaMalloc((void**)&out_ptr4, image_size * sizeof(uint8_t)); // 单通道 cudaMalloc((void**)&out_ptr5, image_size * 3 * sizeof(uint8_t)); // 三通道 // mask cv::Mat mask = cv::Mat::zeros(image_height, image_width, CV_8UC1); cv::Mat mask1 = cv::Mat::ones(image_height * 3 / 4, image_width * 3 / 4, CV_8UC1); cv::Rect rc1 = cv::Rect(image_width / 4, image_height / 4, image_width * 3 / 4, image_height * 3 / 4); mask1.copyTo(mask(rc1)); uint8_t *gpu_mask; cudaMalloc((void**)&gpu_mask, image_size * sizeof(uint8_t)); cudaMemcpy(gpu_mask, mask.data, image_size * sizeof(uint8_t), cudaMemcpyHostToDevice); NppiSize roi1, roi2; roi1.width = image_width; roi1.height = image_height; roi2.width = image_width / 2; roi2.height = image_height / 2; cv::Mat out_image = cv::Mat::zeros(image_height, image_width, CV_8UC3); cv::Mat out_single = cv::Mat::zeros(image_height, image_width, CV_8UC1); NppStatus status; // =============== nppiCopy_8u_C3R =============== status = nppiCopy_8u_C3R(in_image, image_width * 3, out_ptr1, image_width * 3, roi1); if (status != NPP_SUCCESS) { std::cout << "[GPU] ERROR nppiCopy_8u_C3R failed, status = " << status << std::endl; return false; } cudaMemcpy(out_image.data, out_ptr1, image_size * 3, cudaMemcpyDeviceToHost); cv::imwrite(directory + "copy.jpg", out_image); // =============== nppiCopy_8u_C3MR =============== status = nppiCopy_8u_C3MR(in_image, image_width * 3, out_ptr2, image_width * 3, roi1, gpu_mask, image_width); if (status != NPP_SUCCESS) { std::cout << "[GPU] ERROR nppiCopy_8u_C3MR failed, status = " << status << std::endl; return false; } cudaMemcpy(out_image.data, out_ptr2, image_size * 3, cudaMemcpyDeviceToHost); cv::imwrite(directory + "copy_mask.jpg", out_image); // =============== nppiCopy_8u_C3CR =============== status = nppiCopy_8u_C3CR(in_image, image_width * 3, out_ptr3, image_width * 3, roi1); if (status != NPP_SUCCESS) { std::cout << "[GPU] ERROR nppiCopy_8u_C3CR failed, status = " << status << std::endl; return false; } cudaMemcpy(out_image.data, out_ptr3, image_size * 3, cudaMemcpyDeviceToHost); cv::imwrite(directory + "copy_channel.jpg", out_image); // =============== nppiCopy_8u_C3C1R =============== status = nppiCopy_8u_C3C1R(in_image, image_width * 3, out_ptr4, image_width, roi1); if (status != NPP_SUCCESS) { std::cout << "[GPU] ERROR nppiCopy_8u_C3C1R failed, status = " << status << std::endl; return false; } cudaMemcpy(out_single.data, out_ptr4, image_size, cudaMemcpyDeviceToHost); cv::imwrite(directory + "copy_channel_extract.jpg", out_single); // =============== nppiCopy_8u_C1C3R =============== status = nppiCopy_8u_C1C3R(in_img_gray, image_width, out_ptr5, image_width * 3, roi1); if (status != NPP_SUCCESS) { std::cout << "[GPU] ERROR nppiCopy_8u_C1C3R failed, status = " << status << std::endl; return false; } cudaMemcpy(out_image.data, out_ptr5, image_size * 3, cudaMemcpyDeviceToHost); cv::imwrite(directory + "copy_channel_insert.jpg", out_image); // free CUDA_FREE(in_image) CUDA_FREE(in_img_gray) CUDA_FREE(out_ptr1) CUDA_FREE(out_ptr2) CUDA_FREE(out_ptr3) CUDA_FREE(out_ptr4) CUDA_FREE(out_ptr5) } - 1
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make
cmake_minimum_required(VERSION 3.20) project(test) find_package(OpenCV REQUIRED) include_directories(${OpenCV_INCLUDE_DIRS}) find_package(CUDA REQUIRED) include_directories(${CUDA_INCLUDE_DIRS}) file(GLOB CUDA_LIBS "/usr/local/cuda/lib64/*.so") add_executable(test test.cpp) target_link_libraries(test ${OpenCV_LIBS} ${CUDA_LIBS} )- 1
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result
注意：- 由于提取三个通道进行copy，存图的时候只有单个通道，因此呈现出来的结果是灰色的。
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- 原文地址：https://blog.csdn.net/u011732139/article/details/132606436
