[C++]使用C++部署yolov10目标检测的tensorrt模型支持图片视频推理windows测试通过

78 阅读 0 评论 0 点赞

【测试通过环境】
vs2019
cmake==3.24.3
cuda11.7.1+cudnn8.8.0
tensorrt==8.6.1.6
opencv==4.8.0

【部署步骤】
获取pt模型：https://github.com/THU-MIG/yolov10训练自己的模型或者直接使用yolov10官方预训练模型
下载源码：https://github.com/laugh12321/yolov10/tree/nms并安装到环境中
导出onnx：
yolo export model=yolov10n.pt format=onnx opset=13 simplify max_det=100 conf=0.25 iou=0.65 nms

注意导出模型和官方yolov10指令导出的onnx是不一样的，使用yolov10-nms导出模型结构如图

注意一定要是上面类似结构图才能用于这个C++项目
导出tensorrt：

等20分钟左右即可导出需要耐心等待一段时间视个人电脑性能决定，由于tensorrt依赖硬件不一样电脑可能无法共用tensorrt模型，所以必须要重新转换onnx模型到engine才可以运行。请勿直接使用提供的engine模型进行直接测试，否则可能无法正常运行
trtexec --onnx=yolov10n.onnx --saveEngine=yolov10n.engine --fp16

下载安装vs2019勾选桌面C++安装
git clone https://github.com/laugh12321/TensorRT-YOLO
cd TensorRT-YOLO
xmake f -k shared --tensorrt="C:/Program Files/NVIDIA GPU Computing Toolkit/TensorRT/v8.6.1.6"
xmake -P . -r
将编译好的deploy.dll和deploy.lib文件放到yolov10-tensorrt-cplus/lib文件夹
编译源码yolov10-tensorrt-cplus
运行命令：
推理图片：
yolov10.exe -e C:\Users\Administrator\Desktop\yolov10-tensorrt-cplus\yolov10n.engine -i C:\Users\Administrator\Desktop\yolov10-tensorrt-cplus\images\bus.jpg -o ./out -l C:\Users\Administrator\Desktop\yolov10-tensorrt-cplus\labels.txt

推理视频：
yolov10.exe -e C:\Users\Administrator\Desktop\yolov10-tensorrt-cplus\yolov10n.engine -i D:\car.mp4 -o ./out -l C:\Users\Administrator\Desktop\yolov10-tensorrt-cplus\labels.txt

【视频演示】

使用C++部署yolov10目标检测的tensorrt模型支持图片视频推理windows测试通过_哔哩哔哩_bilibili【测试通过环境】vs2019cmake==3.24.3cuda11.7.1+cudnn8.8.0tensorrt==8.6.1.6opencv==4.8.0更多信息参考博文：https://blog.csdn.net/FL1623863129/article/details/139693743, 视频播放量 3、弹幕量 0、点赞数 0、投硬币枚数 0、收藏人数 0、转发人数 0, 视频作者未来自主研究中心, 作者简介未来自主研究中心，相关视频：4060Ti 16G显卡安装Ollama+ChatTTS打造智能语音秘书（突破30秒限制），AI换脸最全面部细节演示：眨眼，捏眉毛，斗鸡眼，戳鼻子，做猪鼻子……认识ai换脸，警惕Ai换脸，Ai变声诈骗！，基于yolo的骑行速度检测，labelme json转yolo工具用于目标检测训练数据集使用教程，使用纯opencv部署yolov8目标检测模型onnx，rk3588 yolov5 目标检测推流rtsp，[深度学习][目标检测][面试提问]Batch Normalization批归一化，[数据集介绍][目标检测]城市街道垃圾数据集VOC格式5266张，GPT：可恶！我好不容易才建立的三观啊！，毕设项目—基于最新YOLOv10+ByteTrack+PaddleOCR实现交通状态分析 (功能：目标检测、轨迹跟踪、车牌检测、车牌号识别、单目测速及目标计数)https://www.bilibili.com/video/BV13S411P7XL/

【部分实现代码】

#include <CLI/CLI.hpp>
#include <chrono>
#include <filesystem>
#include <fstream>
#include <iostream>
#include <memory>
#include <opencv2/opencv.hpp>
#include <random>
#include <string>  
#include <algorithm> 
#include "deploy/utils/utils.hpp"
#include "deploy/vision/detection.hpp"
#include "deploy/vision/result.hpp"
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
using namespace cv;
using namespace std;

namespace fs = std::filesystem;
std::string getFileExtension(const std::string& filePath) {  
    size_t dotPosition = filePath.rfind('.'); // 从右向左查找'.'的位置  
    if (dotPosition == std::string::npos) {  
        // 没有找到'.'，返回空字符串  
        return "";  
    }  
    return filePath.substr(dotPosition + 1); // 返回'.'之后的所有字符  
}  
// Get image files in a directory
std::vector<std::string> getImagesInDirectory(const std::string &folderPath)
{
    std::vector<std::string> imageFiles;
    for (const auto &entry : fs::directory_iterator(folderPath))
    {
        const auto extension = entry.path().extension().string();
        if (fs::is_regular_file(entry) && (extension == ".jpg" || extension == ".png" || extension == ".jpeg" || extension == ".bmp"))
        {
            imageFiles.push_back(entry.path().string());
        }
    }
    return imageFiles;
}

// Get file name from file path
std::string getFileName(const std::string &filePath)
{
    return fs::path(filePath).filename().string();
}

// Create output directory
void createOutputDirectory(const std::string &outputPath)
{
    if (!fs::exists(outputPath) && !fs::create_directories(outputPath))
    {
        std::cerr << "Failed to create output directory: " << outputPath << std::endl;
        exit(1);
    }
    else if (!fs::is_directory(outputPath))
    {
        std::cerr << "Output path exists but is not a directory: " << outputPath << std::endl;
        exit(1);
    }
}

// Generate label and color pairs
std::vector<std::pair<std::string, cv::Scalar>> generateLabelColorPairs(const std::string &labelFile)
{
    std::vector<std::pair<std::string, cv::Scalar>> labelColorPairs;
    std::ifstream file(labelFile);
    if (!file.is_open())
    {
        std::cerr << "Failed to open labels file: " << labelFile << std::endl;
        return labelColorPairs;
    }

    auto generateRandomColor = []()
    {
        std::random_device rd;
        std::mt19937 gen(rd());
        std::uniform_int_distribution<int> dis(0, 255);
        return cv::Scalar(dis(gen), dis(gen), dis(gen));
    };

    std::string label;
    while (std::getline(file, label))
    {
        labelColorPairs.emplace_back(label, generateRandomColor());
    }
    return labelColorPairs;
}

// Visualize detection results
void visualize(cv::Mat &image, const deploy::DetectionResult &result, const std::vector<std::pair<std::string, cv::Scalar>> &labelColorPairs)
{
    for (size_t i = 0; i < result.num; ++i)
    {
        const auto &box = result.boxes[i];
        int cls = result.classes[i];
        float score = result.scores[i];
        const auto &label = labelColorPairs[cls].first;
        const auto &color = labelColorPairs[cls].second;
        std::string labelText = label + " " + cv::format("%.2f", score);

        // Draw rectangle and label
        cv::rectangle(image, cv::Point(box.left, box.top), cv::Point(box.right, box.bottom), color, 2, cv::LINE_AA);
        int baseLine;
        cv::Size labelSize = cv::getTextSize(labelText, cv::FONT_HERSHEY_SIMPLEX, 0.6, 1, &baseLine);
        cv::rectangle(image, cv::Point(box.left, box.top - labelSize.height), cv::Point(box.left + labelSize.width, box.top), color, -1);
        cv::putText(image, labelText, cv::Point(box.left, box.top), cv::FONT_HERSHEY_SIMPLEX, 0.6, cv::Scalar(255, 255, 255), 1);
    }
}

// Process a single image
void processSingleImage(const std::string &imagePath, const std::shared_ptr<deploy::DeployDet> &model, const std::string &outputPath, const std::vector<std::pair<std::string, cv::Scalar>> &labels)
{
    cv::Mat cvimage = cv::imread(imagePath, cv::IMREAD_COLOR);
    if (cvimage.empty())
    {
        std::cerr << "Failed to read image: " << imagePath << std::endl;
        return;
    }
    // cv::cvtColor(cvimage, cvimage, cv::COLOR_BGR2RGB);  // It is better to use RGB images, but the impact of using BGR on the results is not significant.
    deploy::Image image(cvimage.data, cvimage.cols, cvimage.rows);
    std::cout << "start inference\n";
    auto result = model->predict(image);
    std::cout << "inference over!\n";
    if (!outputPath.empty())
    {
        std::cout << "show result\n";
        // cv::cvtColor(cvimage, cvimage, cv::COLOR_RGB2BGR);
        visualize(cvimage, result, labels);
        cv::imwrite(outputPath + "/" + getFileName(imagePath), cvimage);
    }
    else
    {
        std::cout << "outputPath is empty\n";
    }
}

void processVideo(const std::string &videoPath, const std::shared_ptr<deploy::DeployDet> &model, const std::vector<std::pair<std::string, cv::Scalar>> &labels)
{

    VideoCapture capture(videoPath);
    if (!capture.isOpened())
        return;
    double fps = capture.get(cv::CAP_PROP_FPS);          // 帧率
    int width = capture.get(cv::CAP_PROP_FRAME_WIDTH);   // 视频帧宽度
    int height = capture.get(cv::CAP_PROP_FRAME_HEIGHT); // 视频帧高度
    while (1)
    {
        Mat frame;
        capture >> frame; // 从相机读取新一帧
        if (frame.empty())
        {
            std::cout << "read over!\n";
            break;
        }
        deploy::Image image(frame.data, frame.cols, frame.rows);
        auto result = model->predict(image);
        // cv::cvtColor(cvimage, cvimage, cv::COLOR_RGB2BGR);
        visualize(frame, result, labels);
        cv::imshow("result", frame);
        if (char(waitKey(2)) == 'q')
        {

            break;
        }
    }
    capture.release();
    cv::destroyAllWindows();
}

// Process a batch of images
void processBatchImages(const std::vector<std::string> &imageFiles, const std::shared_ptr<deploy::DeployDet> &model, const std::string &outputPath, const std::vector<std::pair<std::string, cv::Scalar>> &labels)
{
    const size_t batchSize = model->batch;
    deploy::GpuTimer gpuTimer;
    deploy::CpuTimer<std::chrono::high_resolution_clock> cpuTimer;
    int count = 0;

    for (size_t i = 0; i < imageFiles.size(); i += batchSize)
    {
        std::vector<cv::Mat> images;
        std::vector<std::string> imgNameBatch;

        for (size_t j = i; j < i + batchSize && j < imageFiles.size(); ++j)
        {
            cv::Mat image = cv::imread(imageFiles[j], cv::IMREAD_COLOR);
            if (image.empty())
            {
                std::cerr << "Failed to read image: " << imageFiles[j] << std::endl;
                continue;
            }
            // cv::cvtColor(image, image, cv::COLOR_BGR2RGB); // It is better to use RGB images, but the impact of using BGR on the results is not significant.
            images.push_back(image);
            imgNameBatch.push_back(getFileName(imageFiles[j]));
        }

        if (images.empty())
            continue;

        std::vector<deploy::Image> imgBatch;
        for (const auto &image : images)
        {
            imgBatch.emplace_back(image.data, image.cols, image.rows);
        }

        if (i > 5)
        {
            cpuTimer.start();
            gpuTimer.start();
        }

        auto results = model->predict(imgBatch);

        if (i > 5)
        {
            cpuTimer.stop();
            gpuTimer.stop();
            count++;
        }

        if (!outputPath.empty())
        {
            for (size_t j = 0; j < images.size(); ++j)
            {
                // cv::cvtColor(images[j], images[j], cv::COLOR_RGB2BGR);
                visualize(images[j], results[j], labels);
                cv::imwrite(outputPath + "/" + imgNameBatch[j], images[j]);
            }
        }
    }

    if (count > 0)
    {
        std::cout << "Average infer CPU elapsed time: " << cpuTimer.microseconds() / 1000 / count << " ms" << std::endl;
        std::cout << "Average infer GPU elapsed time: " << gpuTimer.microseconds() / 1000 / count << " ms" << std::endl;
    }
}

int main(int argc, char **argv)
{
    CLI::App app{"YOLO Series Inference Script"};

    std::string enginePath, inputPath, outputPath, labelPath;
    app.add_option("-e,--engine", enginePath, "Serialized TensorRT engine")->required()->check(CLI::ExistingFile);
    app.add_option("-i,--input", inputPath, "Path to image or directory")->required()->check(CLI::ExistingPath);
    app.add_option("-o,--output", outputPath, "Directory to save results");
    app.add_option("-l,--labels", labelPath, "File to use for reading the class labels from")->check(CLI::ExistingFile);

    CLI11_PARSE(app, argc, argv);
    std::cout << "load engine...\n";
    auto model = std::make_shared<deploy::DeployDet>(enginePath);
    std::cout << "engine loaded!\n";
    std::vector<std::pair<std::string, cv::Scalar>> labels;
    if (!outputPath.empty())
    {
        labels = generateLabelColorPairs(labelPath);
        createOutputDirectory(outputPath);
    }

    if (fs::is_regular_file(inputPath))
    {
        if(getFileExtension(inputPath)=="mp4")
        {
         processVideo(inputPath, model, labels);
        }else{
        std::cout << "start process image file\n";
        processSingleImage(inputPath, model, outputPath, labels);
        }
      
    }
    else
    {
        std::cout << "start process image directory\n";
        auto imageFiles = getImagesInDirectory(inputPath);
        if (!imageFiles.empty())
        {
            processBatchImages(imageFiles, model, outputPath, labels);
        }
        else
        {
            std::cerr << "No images found in directory: " << inputPath << std::endl;
            return 1;
        }
    }

    std::cout << "Inference completed." << std::endl;
    return 0;
}

【源码下载】https://download.csdn.net/download/FL1623863129/89436042

本站资源均来自互联网，仅供研究学习，禁止违法使用和商用，产生法律纠纷本站概不负责！如果侵犯了您的权益请与我们联系！

转载请注明出处：免费源码网-免费的源码资源网站 » [C++]使用C++部署yolov10目标检测的tensorrt模型支持图片视频推理windows测试通过

点赞(0) 打赏

本文分类：文章资讯
本文标签：[C++]使用C++部署yolov10目标检测的tensorrt模型支持图片视频推理windows测试通过
浏览次数：78 次浏览
本文链接：https://freeymw.com/article/12802.html

上一篇 > 如何从戴尔笔记本电脑恢复数据？
下一篇 > CleanMyMac中文版2024官方正式版下载！你的电脑清洁专家！

评论列表共有 0 条评论

暂无评论

[C++]使用C++部署yolov10目标检测的tensorrt模型支持图片视频推理windows测试通过

评论列表 共有 0 条评论

发表评论 取消回复

评论列表共有 0 条评论

发表评论取消回复