群晖如何做网站服务器/推广技巧

在使用opencv中的remap函数时，发现运行时间太长了，如果使用视频流进行重映射时根本不能实时，因此只能加速

1.使用opencv里的cv::cuda::remap函数

cv::cuda::remap函数头文件是#include <opencv2/cudawarping.hpp>，编译opencv时需要用cuda进行编译

    //1.重映射矩阵转成cuda处理的数据格式//map_x,map_y是重映射表，数据类型是CV_32FC1cv::cuda::GpuMat m_mapx = ::cv::cuda::GpuMat(map_x);cv::cuda::GpuMat m_mapy = ::cv::cuda::GpuMat(map_y);//2.原图像转成cuda处理的数据格式cv::cuda::GpuMat src(img);//3.计算结果cv::cuda::GpuMat gpuMat2;cv::cuda::remap(src, gpuMat2, m_mapx, m_mapy, cv::INTER_LINEAR);//4.结果转成Matcv::Mat dstimage;		gpuMat2.download(dstimage);

示例

#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/cudawarping.hpp>using namespace cv;int main(int argc, char** argv) 
{Mat img = imread("image.jpg", IMREAD_COLOR);if (img.empty()){std::cout << "Could not open the input image" << std::endl;exit(1);}int in_width = img.cols;int in_height = img.rows;Mat map_x(in_height, in_width, CV_32FC1);Mat map_y(in_height, in_width, CV_32FC1);// 创建重映射映射表for (int y = 0; y < in_height; y++) {for (int x = 0; x < in_width; x++) {map_x.at<float>(y, x) = (x + 20) / (float)in_width * in_width;map_y.at<float>(y, x) = y / (float)in_height * in_height;}}cv::cuda::GpuMat m_mapx = ::cv::cuda::GpuMat(map_x);cv::cuda::GpuMat m_mapy = ::cv::cuda::GpuMat(map_y);cv::cuda::GpuMat gpuMat1(img);double time0 = static_cast<double>(cv::getTickCount());//记录起始时间cv::cuda::GpuMat gpuMat2;cv::cuda::remap(gpuMat1, gpuMat2, m_mapx, m_mapy, cv::INTER_LINEAR);cv::Mat GPUimage;gpuMat2.download(GPUimage);	time0 = ((double)cv::getTickCount() - time0) / cv::getTickFrequency();std::cout << "GPU运行remap函数的时间为：" << time0 * 1000 << "ms" << std::endl;double time1 = static_cast<double>(cv::getTickCount());//记录起始时间cv::Mat CPUimage;cv::remap(img, CPUimage, map_x, map_y, cv::INTER_LINEAR);time1 = ((double)cv::getTickCount() - time1) / cv::getTickFrequency();std::cout << "CPU运行remap函数的时间为：" << time1 * 1000 << "ms" << std::endl;return 0;
}

经过实际运行，在我电脑上速度快了15倍左右

2.在cuda上重写remap函数

这是在csdn上看到的一篇文章上写的代码，在我的实际应用中变换的结果是错误的，由于我实际的应用时，我的图像输入尺寸和输出尺寸是不相同的，因此运行错误，但是在输入输出是相同尺寸时是正确的，因为使用了cv::cuda::remap,我也没修改这个程序。
建立.cu文件，可以生成静态库使用，也可以不生成使用

#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <cuda_runtime_api.h>
#include <stdio.h>
#include <math.h>__global__ void remap_kernel(const unsigned char* src, int src_width, int src_height,unsigned char* dst, int dst_width, int dst_height,const float* map_x, const float* map_y) 
{int x = threadIdx.x + blockIdx.x * blockDim.x;int y = threadIdx.y + blockIdx.y * blockDim.y;if (x < dst_width && y < dst_height) {int index = (y * dst_width + x) * 3;float src_x = map_x[index / 3];float src_y = map_y[index / 3];if (src_x >= 0 && src_x < src_width - 1 && src_y >= 0 && src_y < src_height - 1) {int x0 = floorf(src_x);int y0 = floorf(src_y);int x1 = x0 + 1;int y1 = y0 + 1;float tx = src_x - x0;float ty = src_y - y0;int src_index00 = (y0 * src_width + x0) * 3;int src_index10 = (y0 * src_width + x1) * 3;int src_index01 = (y1 * src_width + x0) * 3;int src_index11 = (y1 * src_width + x1) * 3;for (int i = 0; i < 3; i++) {float value00 = src[src_index00 + i];float value10 = src[src_index10 + i];float value01 = src[src_index01 + i];float value11 = src[src_index11 + i];float value0 = value00 * (1.0f - tx) + value10 * tx;float value1 = value01 * (1.0f - tx) + value11 * tx;float value = value0 * (1.0f - ty) + value1 * ty;dst[index + i] = static_cast<unsigned char>(value);}}}
}extern "C"  void remap_gpu(const unsigned char* in, int in_width, int in_height,unsigned char* out, int out_width, int out_height,const float* map_x, const float* map_y) {unsigned char* d_in, * d_out;float* d_map_x, * d_map_y;cudaMalloc((void**)&d_in, in_width * in_height * 3);cudaMalloc((void**)&d_out, out_width * out_height * 3);cudaMalloc((void**)&d_map_x, out_width * out_height * sizeof(float));cudaMalloc((void**)&d_map_y, out_width * out_height * sizeof(float));cudaMemcpy(d_in, in, in_width * in_height * 3, cudaMemcpyHostToDevice);cudaMemcpy(d_map_x, map_x, out_width * out_height * sizeof(float), cudaMemcpyHostToDevice);cudaMemcpy(d_map_y, map_y, out_width * out_height * sizeof(float), cudaMemcpyHostToDevice);dim3 block(32, 32, 1);dim3 grid((out_width + block.x - 1) / block.x, (out_height + block.y - 1) / block.y, 1);remap_kernel << <grid, block >> > (d_in, in_width, in_height, d_out, out_width, out_height, d_map_x, d_map_y);cudaMemcpy(out, d_out, out_width * out_height * 3, cudaMemcpyDeviceToHost);cudaFree(d_in);cudaFree(d_out);cudaFree(d_map_x);cudaFree(d_map_y);
}

重新新建一个.cpp文件

#include <iostream>
#include <opencv2/opencv.hpp>using namespace cv;extern "C"  void remap_gpu(const unsigned char* in, int in_width, int in_height,unsigned char* out, int out_width, int out_height,const float* map_x, const float* map_y);int main(int argc, char** argv) 
{cv::Mat img = imread("image.jpg", IMREAD_COLOR);if (img.empty()) {std::cout << "Could not open the input image" << std::endl;exit(1);}int in_width = img.cols;int in_height = img.rows;cv::Mat map_x(in_height, in_width, CV_32FC1);cv::Mat map_y(in_height, in_width, CV_32FC1);// 创建重映射映射表for (int y = 0; y < in_height; y++) {for (int x = 0; x < in_width; x++) {map_x.at<float>(y, x) = (x + 20) / (float)in_width * in_width;map_y.at<float>(y, x) = y / (float)in_height * in_height;}}double time0 = static_cast<double>(cv::getTickCount());//记录起始时间cv::Mat CPUimage;remap(img, CPUimage, map_x, map_y, cv::INTER_LINEAR, cv::BORDER_CONSTANT, cv::Scalar(0, 0, 0));time0 = ((double)cv::getTickCount() - time0) / cv::getTickFrequency();std::cout << "CPU 运行remap函数时间为：" << time0 * 1000 << "ms" << std::endl;int out_width = in_width;int out_height = in_height;	unsigned char* out = (unsigned char*)malloc(out_width * out_height * 3);double time1 = static_cast<double>(cv::getTickCount());//记录起始时间unsigned char* in = (unsigned char*)img.data;remap_gpu(in, in_width, in_height, out, out_width, out_height, (float*)map_x.data, (float*)map_y.data);cv::Mat GPUimage(out_height, out_width, CV_8UC3, out);time1 = ((double)cv::getTickCount() - time1) / cv::getTickFrequency();std::cout << "GPU 运行remap函数时间为：" << time1 * 1000 << "ms" << std::endl;free(out);return 0;
}

只运行一帧时cpu上运行的remap较快，运行多帧时，GPU上运行的remap函数要比CPU上运行快5倍左右

总结

如果自己编译的opencv带cuda，最好还是使用cv::cuda::remap函数，耗时较少