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CUDA小白 - NPP(11) 图像处理 Comparison Operations
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。
Thresholding Operations
分通道，逐像素进行比较，根据指定的Operation，如果不符合则更新当前值。当前模块分为两大类，一个是直接原地址进行操作，另外一类则是指定不同的输出地址。
/* enum NppCmpOp { NPP_CMP_LESS, NPP_CMP_LESS_EQ, NPP_CMP_EQ, NPP_CMP_GREATER_EQ, NPP_CMP_GREATER }; */ // 通用的，如果满足比较条件，则 NppStatus nppiThreshold_8u_C3R(const Npp8u *pSrc, int nSrcStep, Npp8u *pDst, int nDstStep, NppiSize oSizeROI, const Npp8u rThresholds[3], NppCmpOp eComparisonOperation); // 大于 NPP_CMP_GREATER_EQ NppStatus nppiThreshold_GT_8u_C3R(const Npp8u *pSrc, int nSrcStep, Npp8u *pDst, int nDstStep, NppiSize oSizeROI, const Npp8u rThresholds[3]); // 小于 NPP_CMP_LESS_EQ NppStatus nppiThreshold_LT_8u_C3R(const Npp8u *pSrc, int nSrcStep, Npp8u *pDst, int nDstStep, NppiSize oSizeROI, const Npp8u rThresholds[3]); // 指定需要设置的值 NppStatus nppiThreshold_Val_8u_C3R(const Npp8u *pSrc, int nSrcStep, Npp8u *pDst, int nDstStep, NppiSize oSizeROI, const Npp8u rThresholds[3], const Npp8u rValues[3], NppCmpOp eComparisonOperation); NppStatus nppiThreshold_GTVal_8u_C3R(const Npp8u * pSrc, int nSrcStep, Npp8u *pDst, int nDstStep, NppiSize oSizeROI, const Npp8u rThresholds[3], const Npp8u rValues[3]); NppStatus nppiThreshold_LTVal_8u_C3R(const Npp8u *pSrc, int nSrcStep, Npp8u *pDst, int nDstStep, NppiSize oSizeROI, const Npp8u rThresholds[3], const Npp8u rValues[3]); // 设置上下界 NppStatus nppiThreshold_LTValGTVal_8u_C3R(const Npp8u *pSrc, int nSrcStep, Npp8u *pDst, int nDstStep, NppiSize oSizeROI, const Npp8u rThresholdsLT[3], const Npp8u rValuesLT[3], const Npp8u rThresholdsGT[3], const Npp8u rValuesGT[3]);- 1
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两边各选用一个接口作为示例
code
#include#include #include #include #define CUDA_FREE(ptr) { if (ptr != nullptr) { cudaFree(ptr); ptr = nullptr; } } int main() { std::string directory = "../"; 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 =============== // input uint8_t *in_image; cudaMalloc((void**)&in_image, image_size * 3 * sizeof(uint8_t)); cudaMemcpy(in_image, image_dog.data, image_size * 3 * sizeof(uint8_t), cudaMemcpyHostToDevice); // output uint8_t *out_ptr1, *out_ptr2; 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 NppiSize in_size; in_size.width = image_width; in_size.height = image_height; uint8_t threshold[3] = {150, 150, 150}; cv::Mat out_image = cv::Mat::zeros(image_height, image_width, CV_8UC3); // =============== nppiThreshold_GT_8u_C3R =============== NppStatus status; status = nppiThreshold_GT_8u_C3R(in_image, image_width * 3, out_ptr1, image_width * 3, in_size, threshold); if (status != NPP_SUCCESS) { std::cout << "[GPU] ERROR nppiThreshold_GT_8u_C3R failed, status = " << status << std::endl; return false; } cudaMemcpy(out_image.data, out_ptr1, image_size * 3, cudaMemcpyDeviceToHost); cv::imwrite(directory + "threshold_gt.jpg", out_image); // =============== nppiThreshold_GTVal_8u_C3R =============== uint8_t value[3] = {255, 255, 255}; status = nppiThreshold_GTVal_8u_C3R(in_image, image_width * 3, out_ptr2, image_width * 3, in_size, threshold, value); if (status != NPP_SUCCESS) { std::cout << "[GPU] ERROR nppiThreshold_GTVal_8u_C3R failed, status = " << status << std::endl; return false; } cudaMemcpy(out_image.data, out_ptr2, image_size * 3, cudaMemcpyDeviceToHost); cv::imwrite(directory + "threshold_gt_value.jpg", out_image); // free CUDA_FREE(in_image) CUDA_FREE(out_ptr1) CUDA_FREE(out_ptr2) } - 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
Comparison Operations
本文到此就只阐述比较简单的两个接口，其他的结果按需索取
NppStatus nppiCompare_8u_C3R(const Npp8u *pSrc1, int nSrc1Step, const Npp8u *pSrc2, int nSrc2Step, Npp8u *pDst, int nDstStep, NppiSize oSizeROI, NppCmpOp eComparisonOperation); NppStatus nppiCompareC_8u_C3R(const Npp8u *pSrc, int nSrcStep, const Npp8u *pConstants, Npp8u * pDst, int nDstStep, NppiSize oSizeROI, NppCmpOp eComparisonOperation);- 1
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目的就是比较两张图或者将一张图与一个constant进行比较，并且生成一个二进制的结果图像。二进制的结果图像类型是8UC1，如果是不同的话，则设置为0，反之表示uint8_t的最大值。
code
#include#include #include #include #define CUDA_FREE(ptr) { if (ptr != nullptr) { cudaFree(ptr); ptr = nullptr; } } int main() { std::string directory = "../"; 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 =============== // input uint8_t *in_image1, *in_image2; cudaMalloc((void**)&in_image1, image_size * 3 * sizeof(uint8_t)); cudaMalloc((void**)&in_image2, image_size * 3 * sizeof(uint8_t)); cudaMemcpy(in_image1, image_dog.data, image_size * 3 * sizeof(uint8_t), cudaMemcpyHostToDevice); cv::Mat mask = cv::Mat::zeros(image_height, image_width, CV_8UC3); int step = 4; int step_width = image_width / step; cv::Mat ones = cv::Mat::ones(image_height, step_width, CV_8UC3); for (int i = 1; i < step; ++i) { cv::Rect rc1 = cv::Rect(i * step_width, 0, step_width, image_height); mask(rc1) = ones.clone() * 50 * i; } cudaMemcpy(in_image2, mask.data, image_size * 3 * sizeof(uint8_t), cudaMemcpyHostToDevice); // output uint8_t *out_ptr1, *out_ptr2; cudaMalloc((void**)&out_ptr1, image_size * sizeof(uint8_t)); // 三通道 cudaMalloc((void**)&out_ptr2, image_size * sizeof(uint8_t)); // 三通道 NppiSize in_size; in_size.width = image_width; in_size.height = image_height; cv::Mat out_image = cv::Mat::zeros(image_height, image_width, CV_8UC1); NppStatus status; // =============== nppiCompare_8u_C3R =============== status = nppiCompare_8u_C3R(in_image1, image_width * 3, in_image2, image_width * 3, out_ptr1, image_width, in_size, NPP_CMP_GREATER); if (status != NPP_SUCCESS) { std::cout << "[GPU] ERROR nppiCompare_8u_C3R failed, status = " << status << std::endl; return false; } cudaMemcpy(out_image.data, out_ptr1, image_size, cudaMemcpyDeviceToHost); cv::imwrite(directory + "compare.jpg", out_image); // =============== nppiCompareC_8u_C3R =============== uint8_t constant[3] = {100, 100, 100}; status = nppiCompareC_8u_C3R(in_image1, image_width * 3, constant, out_ptr2, image_width, in_size, NPP_CMP_GREATER); if (status != NPP_SUCCESS) { std::cout << "[GPU] ERROR nppiCompareC_8u_C3R failed, status = " << status << std::endl; return false; } cudaMemcpy(out_image.data, out_ptr2, image_size, cudaMemcpyDeviceToHost); cv::imwrite(directory + "comparec.jpg", out_image); // free CUDA_FREE(in_image1) CUDA_FREE(in_image2) CUDA_FREE(out_ptr1) CUDA_FREE(out_ptr2) } - 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
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- 原文地址：https://blog.csdn.net/u011732139/article/details/132962984
