cuda小白
原始API链接 NPP
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
Logical Operations
逻辑操作主要就是与、或、异或、右移、左移，非等逻辑操作，同样还是分为两个大类，一个是基于单张图像和常数的，另外一个是基于多张图像的。
AndC
第一大类以AndC为例子，主要是就是比较图像与提供的constant（每个通道一个值）进行与操作之后的结果。
cpp
// 有无I的区别在于是否直接对图像进行操作
NppStatus nppiAndC_8u_C3R(const Npp8u *pSrc1,
int nSrc1Step,
const Npp8u aConstants[3],
Npp8u *pDst,
int nDstStep,
NppiSize oSizeROI);
NppStatus nppiAndC_8u_C3IR(const Npp8u aConstants[3],
Npp8u *pSrcDst,
int nSrcDstStep,
NppiSize oSizeROI);code
cpp
#include <iostream>
#include <cuda_runtime.h>
#include <npp.h>
#include <opencv2/opencv.hpp>
#define PRINT_VALUE(value) { \
std::cout << "[GPU] " << #value << " = " << value << std::endl; }
#define CUDA_FREE(ptr) { if (ptr != nullptr) { cudaFree(ptr); ptr = nullptr; } }
int main() {
std::string directory = "../";
// =============== load image ===============
cv::Mat image = cv::Mat(500, 500, CV_8UC3, cv::Scalar(255, 255, 255));
cv::Rect rc1 = cv::Rect(150, 150, 200, 200);
cv::Rect rc2 = cv::Rect(200, 200, 200, 200);
cv::Rect rc3 = cv::Rect(300, 0, 100, 200);
cv::Rect rc4 = cv::Rect(0, 0, 200, 100);
cv::Mat(200, 200, CV_8UC3, cv::Scalar(75, 75, 75)).copyTo(image(rc1));
cv::Mat(200, 200, CV_8UC3, cv::Scalar(100, 100, 100)).copyTo(image(rc2));
cv::Mat(200, 100, CV_8UC3, cv::Scalar(125, 125, 125)).copyTo(image(rc3));
cv::Mat(100, 200, CV_8UC3, cv::Scalar(150, 150, 150)).copyTo(image(rc4));
cv::imwrite(directory + "orin.jpg", image);
int image_width = image.cols;
int image_height = image.rows;
int image_size = image_width * image_height * 3;
std::cout << "Image info : image_width = " << image_width
<< ", image_height = " << image_height << std::endl;
// =============== malloc && cpy ===============
uint8_t *in_ptr;
cudaMalloc((void**)&in_ptr, image_size * sizeof(uint8_t));
cudaMemcpy(in_ptr, image.data, image_size, cudaMemcpyHostToDevice);
uint8_t *out_ptr, *out_ptr1;
cudaMalloc((void**)&out_ptr, image_size * sizeof(uint8_t));
cudaMalloc((void**)&out_ptr1, image_size * sizeof(uint8_t));
NppiSize roi1, roi2;
roi1.width = image_width;
roi1.height = image_height;
roi2.width = image_width / 2;
roi2.height = image_height / 2;
uint8_t constant[3] = { (uint8_t)100, (uint8_t)100, (uint8_t)100 };
// nppiAdd_8u_C3RSfs
cv::Mat out_image = cv::Mat::zeros(image_height, image_width, CV_8UC3);
cv::Mat out_image1 = cv::Mat::zeros(image_height, image_width, CV_8UC3);
NppStatus status;
status = nppiAndC_8u_C3R(in_ptr, image_width * 3, constant, out_ptr,
image_width * 3, roi1);
if (status != NPP_SUCCESS) {
std::cout << "[GPU] ERROR nppiAndC_8u_C3R failed, status = " << status << std::endl;
return false;
}
cudaMemcpy(out_image.data, out_ptr, image_size, cudaMemcpyDeviceToHost);
cv::imwrite(directory + "and.jpg", out_image);
status = nppiAndC_8u_C3R(in_ptr, image_width * 3, constant, out_ptr1,
image_width * 3, roi2);
if (status != NPP_SUCCESS) {
std::cout << "[GPU] ERROR nppiAndC_8u_C3R failed, status = " << status << std::endl;
return false;
}
cudaMemcpy(out_image1.data, out_ptr1, image_size, cudaMemcpyDeviceToHost);
cv::imwrite(directory + "and_roi.jpg", out_image1);
// free
CUDA_FREE(in_ptr)
CUDA_FREE(out_ptr)
CUDA_FREE(out_ptr1)
}make
cpp
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}
)result
注意点：
- 该函数是将图像的三个通道分别于Constant的值进行按位与的操作，测试的例子中分别使用了255，75， 100， 125， 150三种像素，与100与之后分别为100，4，4，100，100，4。
- 由于roi的存在，可以仅保存roi区域内的结果，也就是说输出的地址其可以仅申请roi的区域的大小。
And
针对两张图的操作，包含与、或、非、异或。
cpp
NppStatus nppiAnd_8u_C3R(const Npp8u *pSrc1,
int nSrc1Step,
const Npp8u *pSrc2,
int nSrc2Step,
Npp8u *pDst,
int nDstStep,
NppiSize oSizeROI);
NppStatus nppiAnd_8u_C3IR(const Npp8u *pSrc,
int nSrcStep,
Npp8u *pSrcDst,
int nSrcDstStep,
NppiSize oSizeROI);code
cpp
#include <iostream>
#include <cuda_runtime.h>
#include <npp.h>
#include <opencv2/opencv.hpp>
#define PRINT_VALUE(value) { \
std::cout << "[GPU] " << #value << " = " << value << std::endl; }
#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 * 3;
cv::Mat image = cv::Mat(image_height, image_width, CV_8UC3, cv::Scalar(100, 125, 150));
std::cout << "Image info : image_width = " << image_width
<< ", image_height = " << image_height << std::endl;
// =============== malloc && cpy ===============
uint8_t *in_ptr, *mask;
cudaMalloc((void**)&in_ptr, image_size * sizeof(uint8_t));
cudaMalloc((void**)&mask, image_size * sizeof(uint8_t));
cudaMemcpy(in_ptr, image_dog.data, image_size, cudaMemcpyHostToDevice);
cudaMemcpy(mask, image.data, image_size, cudaMemcpyHostToDevice);
uint8_t *out_ptr, *out_ptr1;
cudaMalloc((void**)&out_ptr, image_size * sizeof(uint8_t));
cudaMalloc((void**)&out_ptr1, image_size * sizeof(uint8_t));
NppiSize roi1, roi2;
roi1.width = image_width;
roi1.height = image_height;
roi2.width = image_width / 2;
roi2.height = image_height / 2;
// nppiAdd_8u_C3RSfs
cv::Mat out_image = cv::Mat::zeros(image_height, image_width, CV_8UC3);
cv::Mat out_image1 = cv::Mat::zeros(image_height, image_width, CV_8UC3);
NppStatus status;
status = nppiAnd_8u_C3R(in_ptr, image_width * 3, mask, image_width * 3, out_ptr,
image_width * 3, roi1);
if (status != NPP_SUCCESS) {
std::cout << "[GPU] ERROR nppiAnd_8u_C3R failed, status = " << status << std::endl;
return false;
}
cudaMemcpy(out_image.data, out_ptr, image_size, cudaMemcpyDeviceToHost);
cv::imwrite(directory + "and.jpg", out_image);
status = nppiAnd_8u_C3R(in_ptr, image_width * 3, mask, image_width * 3, out_ptr1,
image_width * 3, roi2);
if (status != NPP_SUCCESS) {
std::cout << "[GPU] ERROR nppiAnd_8u_C3R failed, status = " << status << std::endl;
return false;
}
cudaMemcpy(out_image1.data, out_ptr1, image_size, cudaMemcpyDeviceToHost);
cv::imwrite(directory + "and_roi.jpg", out_image1);
// free
CUDA_FREE(in_ptr)
CUDA_FREE(out_ptr)
CUDA_FREE(out_ptr1)
}make
cpp
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}
)result
Alpha Composition
主要功能是图像的合成（AlphaComp）以及图像的不透明度调整（AlphaPremulC）。
AlphaCompC
该接口主要完成的两张图像（单通道，三通道，四通道）的合成，主要是操作是根据NppiAlphaOp来完成一定的操作。
cpp
NppStatus nppiAlphaCompC_8u_C3R(const Npp8u *pSrc1,
int nSrc1Step,
Npp8u nAlpha1,
const Npp8u *pSrc2,
int nSrc2Step,
Npp8u nAlpha2,
Npp8u *pDst,
int nDstStep,
NppiSize oSizeROI,
NppiAlphaOp eAlphaOp);AlphaComp
该接口主要完成的两张单通道或者四通道的图像的合成。主要是操作是根据NppiAlphaOp来完成一定的操作。
cpp
NppStatus nppiAlphaComp_8u_AC1R(const Npp8u *pSrc1,
int nSrc1Step,
const Npp8u *pSrc2,
int nSrc2Step,
Npp8u *pDst,
int nDstStep,
NppiSize oSizeROI,
NppiAlphaOp eAlphaOp);与AlphaCompC的区别在于，AlphaCompC可以指定每个输入图像的比例来完成对应的Operation，而AlphaComp则是没有。