各位友友，现在我有一个c++程序，是跑模型的代码，现在的问题是我用一个初始化一个模型用一个线程跑速度在100ms，但是我初始化两个模型用两个模型并行计算的时候每个模型的速度就增加到了200ms，两个一起的化就差不多400-500ms了，这个问题怎么解决呢
#include "SamBox.h"
#include <opencv2/core/cuda.hpp>
#include <opencv2/opencv.hpp>
using namespace cv;
#include <regex>
#include <vector>
#include <thread>
#include <future>
// 找中心点
std::vector<cv::Point> find_center(cv::Mat img)
{
std::vector<std::vector<cv::Point>> contours;
std::vector<cv::Vec4i> hierarchy;
cv::findContours(img, contours, hierarchy, cv::RETR_TREE, cv::CHAIN_APPROX_SIMPLE);
std::vector<cv::Point> pointList;
double area = 0;
for (const auto &cnt : contours)
{
area = cv::contourArea(cnt);
if (area > 9000)
{
cv::Moments M = cv::moments(cnt);
int cX = static_cast<int>(M.m10 / M.m00);
int cY = static_cast<int>(M.m01 / M.m00);
pointList.push_back(cv::Point(cX, cY));
}
}
return pointList;
}
void processImages(const std::vector<cv::String>& fileNames, SamBox* samBox) {
cv::Mat blendedImage;
cv::Mat mask;
for (const auto& filename : fileNames) {
double iou;
cv::Rect box_crop;
cv::Rect box_rect;
cv::Mat img = cv::imread(filename, -1);
size_t pos0 = filename.find_last_of("/");
std::string name = filename.substr(pos0+1);
std::string inputStr = filename;
int channels = img.channels();
if (channels == 3)
{
cv::cvtColor(img, img, cv::COLOR_BGR2GRAY);
}
// 使用正则表达式匹配数字
std::regex pattern(R"(\d+)");
std::smatch matches;
std::vector<std::string> numbers;
// 从字符串中找到所有匹配的数字，并保存到vector中
while (std::regex_search(inputStr, matches, pattern)) {
numbers.push_back(matches.str());
inputStr = matches.suffix();
}
// 存储后面的四个匹配的数字
int number1 = 0, number2 = 0, number3 = 0, number4 = 0;
if (numbers.size() >= 4) {
number1 = std::stoi(numbers[numbers.size() - 4]);
number2 = std::stoi(numbers[numbers.size() - 3]);
number3 = std::stoi(numbers[numbers.size() - 2]);
number4 = std::stoi(numbers[numbers.size() - 1]);
} else {
std::cout << "Not enough numbers found.";
}
cv::Rect box = {number1,number2,number3,number4};
auto start = std::chrono::high_resolution_clock::now();
// 计算原始矩形框的中心点
cv::Point center = cv::Point(box.x + box.width / 2, box.y + box.height / 2);
// 计算新的矩形框的宽度和高度
int newWidth = static_cast<int>(box.width * 2);
int newHeight = static_cast<int>(box.height * 1.5);
// 计算新的矩形框的左上角坐标
int newX = center.x - newWidth / 2;
int newY = center.y - newHeight / 2;
cv::Mat image;
box_crop = {newX, newY, newWidth, newHeight};
bool flag;
if( newX < 0 || newY < 0 || newWidth > img.size().width || newHeight > img.size().height ){
image = img;
flag = false;
box_rect = {box.x,box.y,box.width,box.height};
}
else
{
image = img(box_crop);
box_rect = {box.x-newX,box.y-newY,box.width,box.height};
flag = true;
}
auto results = samBox->segmentImgs(image, box_rect);
mask = results.first;
iou = results.second;
//将crop传入模型输出的mask还回原图尺寸大小
if(flag){
cv::Mat result_mask = cv::Mat::zeros(img.size(), img.type());
mask.copyTo(result_mask(box_crop));
mask = result_mask;
}
// 查找轮廓
std::vector<std::vector<cv::Point>> contours;
std::vector<cv::Vec4i> hierarchy;
cv::findContours(mask, contours, hierarchy, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);
//在图像上绘制矩形框
cv::rectangle(img,box.tl(),box.br(), cv::Scalar(0, 255, 0), 5);
cv::drawContours(img, contours, -1, cv::Scalar(0, 0, 255), 5);
cv::cvtColor(img, img, cv::COLOR_GRAY2BGR);
auto end = std::chrono::high_resolution_clock::now();
std::chrono::duration<double> duration = end - start;
std::cout << "all_time_2: " << duration.count() << " seconds" << std::endl;
cv::imwrite("/home/jacc/samC++/res_img/"+name, img);
std::cout << "............................................" << std::endl;
}
// }
}
int main(int argc, char **argv)
{
std::string folderPath = "/home/jacc/samC++/img_tray/60/";
std::string fileExtension = ".jpg";
std::vector<cv::String> fileNames;
cv::glob(folderPath + "*" + fileExtension, fileNames);
std::string folderPath1 = "/home/jacc/samC++/img_tray/70/";
std::string fileExtension1 = ".jpg";
std::vector<cv::String> fileNames1;
cv::glob(folderPath1 + "*" + fileExtension1, fileNames1);
// 初始化两个模型
SamBox* samBoxA = new SamBox();
SamBox* samBoxB = new SamBox();
samBoxA->initializeSam();
samBoxB->initializeSam();
// 启动两个线程并行处理不同类别的图像
std::thread threadA(processImages, fileNames, samBoxA);
std::thread threadB(processImages, fileNames1, samBoxB);
// 等待两个线程完成
threadA.join();
threadB.join();
// 释放模型资源
delete samBoxA;
delete samBoxB;
}
