C# yolov8 TensorRT Demo

2024-05-28 17:12
文章标签 c# yolov8 .net demo tensorrt netcore

本文主要是介绍C# yolov8 TensorRT Demo，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

C# yolov8 TensorRT Demo

目录

效果

说明 

项目

代码

下载

效果

说明 

环境

NVIDIA GeForce RTX 4060 Laptop GPU

cuda12.1+cudnn 8.8.1+TensorRT-8.6.1.6

版本和我不一致的需要重新编译TensorRtExtern.dll，TensorRtExtern源码地址：https://github.com/guojin-yan/TensorRT-CSharp-API/tree/TensorRtSharp2.0/src/TensorRtExtern

Windows版 CUDA安装参考：https://blog.csdn.net/lw112190/article/details/137049845

项目

代码

Form2.cs

using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Drawing;
using System.IO;
using System.Threading;
using System.Windows.Forms;
using TensorRtSharp.Custom;

namespace yolov8_TensorRT_Demo
{
    public partial class Form2 : Form
    {
        public Form2()
        {
            InitializeComponent();
        }

        string imgFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";

        YoloV8 yoloV8;
        Mat image;

        string image_path = "";
        string model_path;

        string video_path = "";
        string videoFilter = "*.mp4|*.mp4;";
        VideoCapture vcapture;
        VideoWriter vwriter;
        bool saveDetVideo = false;


        /// <summary>
        /// 单图推理
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void button2_Click(object sender, EventArgs e)
        {

            if (image_path == "")
            {
                return;
            }

            button2.Enabled = false;
            pictureBox2.Image = null;
            textBox1.Text = "";

            Application.DoEvents();

            image = new Mat(image_path);

            List<DetectionResult> detResults = yoloV8.Detect(image);

            //绘制结果
            Mat result_image = image.Clone();
            foreach (DetectionResult r in detResults)
            {
                Cv2.PutText(result_image, $"{r.Class}:{r.Confidence:P0}", new OpenCvSharp.Point(r.Rect.TopLeft.X, r.Rect.TopLeft.Y - 10), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);
                Cv2.Rectangle(result_image, r.Rect, Scalar.Red, thickness: 2);
            }

            if (pictureBox2.Image != null)
            {
                pictureBox2.Image.Dispose();
            }
            pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
            textBox1.Text = yoloV8.DetectTime();

            button2.Enabled = true;

        }

        /// <summary>
        /// 窗体加载，初始化
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void Form1_Load(object sender, EventArgs e)
        {
            image_path = "test/zidane.jpg";
            pictureBox1.Image = new Bitmap(image_path);

            model_path = "model/yolov8n.engine";

            if (!File.Exists(model_path))
            {
                //有点耗时，需等待
                Nvinfer.OnnxToEngine("model/yolov8n.onnx", 20);
            }

            yoloV8 = new YoloV8(model_path, "model/lable.txt");

        }

        /// <summary>
        /// 选择图片
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void button1_Click_1(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.Filter = imgFilter;
            if (ofd.ShowDialog() != DialogResult.OK) return;

            pictureBox1.Image = null;

            image_path = ofd.FileName;
            pictureBox1.Image = new Bitmap(image_path);

            textBox1.Text = "";
            pictureBox2.Image = null;
        }

        /// <summary>
        /// 选择视频
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void button4_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.Filter = videoFilter;
            ofd.InitialDirectory = Application.StartupPath + "\\test";

            if (ofd.ShowDialog() != DialogResult.OK) return;

            video_path = ofd.FileName;

            button3_Click(null, null);

        }

        /// <summary>
        /// 视频推理
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void button3_Click(object sender, EventArgs e)
        {
            if (video_path == null)
            {
                return;
            }

            textBox1.Text = "开始检测";

            Application.DoEvents();

            Thread thread = new Thread(new ThreadStart(VideoDetection));

            thread.Start();
            thread.Join();

            textBox1.Text = "检测完成！";
        }

        void VideoDetection()
        {
            vcapture = new VideoCapture(video_path);
            if (!vcapture.IsOpened())
            {
                MessageBox.Show("打开视频文件失败");
                return;
            }

            Mat frame = new Mat();
            List<DetectionResult> detResults;

            // 获取视频的fps
            double videoFps = vcapture.Get(VideoCaptureProperties.Fps);
            // 计算等待时间（毫秒）
            int delay = (int)(1000 / videoFps);
            Stopwatch _stopwatch = new Stopwatch();

            if (checkBox1.Checked)
            {
                vwriter = new VideoWriter("out.mp4", FourCC.X264, vcapture.Fps, new OpenCvSharp.Size(vcapture.FrameWidth, vcapture.FrameHeight));
                saveDetVideo = true;
            }
            else {
                saveDetVideo = false;
            }

            while (vcapture.Read(frame))
            {
                if (frame.Empty())
                {
                    MessageBox.Show("读取失败");
                    return;
                }

                _stopwatch.Restart();

                delay = (int)(1000 / videoFps);

                detResults = yoloV8.Detect(frame);

                //绘制结果
                foreach (DetectionResult r in detResults)
                {
                    Cv2.PutText(frame, $"{r.Class}:{r.Confidence:P0}", new OpenCvSharp.Point(r.Rect.TopLeft.X, r.Rect.TopLeft.Y - 10), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);
                    Cv2.Rectangle(frame, r.Rect, Scalar.Red, thickness: 2);
                }
                Cv2.PutText(frame, "preprocessTime:" + yoloV8.preprocessTime.ToString("F2")+"ms", new OpenCvSharp.Point(10, 30), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);
                Cv2.PutText(frame, "inferTime:" + yoloV8.inferTime.ToString("F2") + "ms", new OpenCvSharp.Point(10, 70), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);
                Cv2.PutText(frame, "postprocessTime:" + yoloV8.postprocessTime.ToString("F2") + "ms", new OpenCvSharp.Point(10, 110), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);
                Cv2.PutText(frame, "totalTime:" + yoloV8.totalTime.ToString("F2") + "ms", new OpenCvSharp.Point(10, 150), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);
                Cv2.PutText(frame, "video fps:" + videoFps.ToString("F2"), new OpenCvSharp.Point(10, 190), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);
                Cv2.PutText(frame, "det fps:" + yoloV8.detFps.ToString("F2"), new OpenCvSharp.Point(10, 230), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);

                if (saveDetVideo)
                {
                    vwriter.Write(frame);
                }

                Cv2.ImShow("DetectionResult", frame);

                // for test
                // delay = 1;

                delay = (int)(delay - _stopwatch.ElapsedMilliseconds);
                if (delay <= 0)
                {
                    delay = 1;
                }
                //Console.WriteLine("delay:" + delay.ToString()) ;
                if (Cv2.WaitKey(delay) == 27)
                {
                    break; // 如果按下ESC，退出循环
                }
            }

            Cv2.DestroyAllWindows();
            vcapture.Release();
            if (saveDetVideo)
            {
                vwriter.Release();
            }

        }
    }

}
 

using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Drawing;
using System.IO;
using System.Threading;
using System.Windows.Forms;
using TensorRtSharp.Custom;namespace yolov8_TensorRT_Demo
{public partial class Form2 : Form{public Form2(){InitializeComponent();}string imgFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";YoloV8 yoloV8;Mat image;string image_path = "";string model_path;string video_path = "";string videoFilter = "*.mp4|*.mp4;";VideoCapture vcapture;VideoWriter vwriter;bool saveDetVideo = false;/// <summary>/// 单图推理/// </summary>/// <param name="sender"></param>/// <param name="e"></param>private void button2_Click(object sender, EventArgs e){if (image_path == ""){return;}button2.Enabled = false;pictureBox2.Image = null;textBox1.Text = "";Application.DoEvents();image = new Mat(image_path);List<DetectionResult> detResults = yoloV8.Detect(image);//绘制结果Mat result_image = image.Clone();foreach (DetectionResult r in detResults){Cv2.PutText(result_image, $"{r.Class}:{r.Confidence:P0}", new OpenCvSharp.Point(r.Rect.TopLeft.X, r.Rect.TopLeft.Y - 10), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);Cv2.Rectangle(result_image, r.Rect, Scalar.Red, thickness: 2);}if (pictureBox2.Image != null){pictureBox2.Image.Dispose();}pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());textBox1.Text = yoloV8.DetectTime();button2.Enabled = true;}/// <summary>/// 窗体加载，初始化/// </summary>/// <param name="sender"></param>/// <param name="e"></param>private void Form1_Load(object sender, EventArgs e){image_path = "test/zidane.jpg";pictureBox1.Image = new Bitmap(image_path);model_path = "model/yolov8n.engine";if (!File.Exists(model_path)){//有点耗时，需等待Nvinfer.OnnxToEngine("model/yolov8n.onnx", 20);}yoloV8 = new YoloV8(model_path, "model/lable.txt");}/// <summary>/// 选择图片/// </summary>/// <param name="sender"></param>/// <param name="e"></param>private void button1_Click_1(object sender, EventArgs e){OpenFileDialog ofd = new OpenFileDialog();ofd.Filter = imgFilter;if (ofd.ShowDialog() != DialogResult.OK) return;pictureBox1.Image = null;image_path = ofd.FileName;pictureBox1.Image = new Bitmap(image_path);textBox1.Text = "";pictureBox2.Image = null;}/// <summary>/// 选择视频/// </summary>/// <param name="sender"></param>/// <param name="e"></param>private void button4_Click(object sender, EventArgs e){OpenFileDialog ofd = new OpenFileDialog();ofd.Filter = videoFilter;ofd.InitialDirectory = Application.StartupPath + "\\test";if (ofd.ShowDialog() != DialogResult.OK) return;video_path = ofd.FileName;button3_Click(null, null);}/// <summary>/// 视频推理/// </summary>/// <param name="sender"></param>/// <param name="e"></param>private void button3_Click(object sender, EventArgs e){if (video_path == null){return;}textBox1.Text = "开始检测";Application.DoEvents();Thread thread = new Thread(new ThreadStart(VideoDetection));thread.Start();thread.Join();textBox1.Text = "检测完成！";}void VideoDetection(){vcapture = new VideoCapture(video_path);if (!vcapture.IsOpened()){MessageBox.Show("打开视频文件失败");return;}Mat frame = new Mat();List<DetectionResult> detResults;// 获取视频的fpsdouble videoFps = vcapture.Get(VideoCaptureProperties.Fps);// 计算等待时间（毫秒）int delay = (int)(1000 / videoFps);Stopwatch _stopwatch = new Stopwatch();if (checkBox1.Checked){vwriter = new VideoWriter("out.mp4", FourCC.X264, vcapture.Fps, new OpenCvSharp.Size(vcapture.FrameWidth, vcapture.FrameHeight));saveDetVideo = true;}else {saveDetVideo = false;}while (vcapture.Read(frame)){if (frame.Empty()){MessageBox.Show("读取失败");return;}_stopwatch.Restart();delay = (int)(1000 / videoFps);detResults = yoloV8.Detect(frame);//绘制结果foreach (DetectionResult r in detResults){Cv2.PutText(frame, $"{r.Class}:{r.Confidence:P0}", new OpenCvSharp.Point(r.Rect.TopLeft.X, r.Rect.TopLeft.Y - 10), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);Cv2.Rectangle(frame, r.Rect, Scalar.Red, thickness: 2);}Cv2.PutText(frame, "preprocessTime:" + yoloV8.preprocessTime.ToString("F2")+"ms", new OpenCvSharp.Point(10, 30), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);Cv2.PutText(frame, "inferTime:" + yoloV8.inferTime.ToString("F2") + "ms", new OpenCvSharp.Point(10, 70), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);Cv2.PutText(frame, "postprocessTime:" + yoloV8.postprocessTime.ToString("F2") + "ms", new OpenCvSharp.Point(10, 110), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);Cv2.PutText(frame, "totalTime:" + yoloV8.totalTime.ToString("F2") + "ms", new OpenCvSharp.Point(10, 150), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);Cv2.PutText(frame, "video fps:" + videoFps.ToString("F2"), new OpenCvSharp.Point(10, 190), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);Cv2.PutText(frame, "det fps:" + yoloV8.detFps.ToString("F2"), new OpenCvSharp.Point(10, 230), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);if (saveDetVideo){vwriter.Write(frame);}Cv2.ImShow("DetectionResult", frame);// for test// delay = 1;delay = (int)(delay - _stopwatch.ElapsedMilliseconds);if (delay <= 0){delay = 1;}//Console.WriteLine("delay:" + delay.ToString()) ;if (Cv2.WaitKey(delay) == 27){break; // 如果按下ESC，退出循环}}Cv2.DestroyAllWindows();vcapture.Release();if (saveDetVideo){vwriter.Release();}}}}

YoloV8.cs

using OpenCvSharp;
using OpenCvSharp.Dnn;
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.IO;
using System.Linq;
using System.Text;
using TensorRtSharp.Custom;namespace yolov8_TensorRT_Demo
{public class YoloV8{float[] input_tensor_data;float[] outputData;List<DetectionResult> detectionResults;int input_height;int input_width;Nvinfer predictor;string[] class_names;int class_num;int box_num;float conf_threshold;float nms_threshold;float ratio_height;float ratio_width;public double preprocessTime;public double inferTime;public double postprocessTime;public double totalTime;public double detFps;public String DetectTime(){StringBuilder stringBuilder = new StringBuilder();stringBuilder.AppendLine($"Preprocess: {preprocessTime:F2}ms");stringBuilder.AppendLine($"Infer: {inferTime:F2}ms");stringBuilder.AppendLine($"Postprocess: {postprocessTime:F2}ms");stringBuilder.AppendLine($"Total: {totalTime:F2}ms");return stringBuilder.ToString();}public YoloV8(string model_path, string classer_path){predictor = new Nvinfer(model_path);class_names = File.ReadAllLines(classer_path, Encoding.UTF8);class_num = class_names.Length;input_height = 640;input_width = 640;box_num = 8400;conf_threshold = 0.25f;nms_threshold = 0.5f;detectionResults = new List<DetectionResult>();}void Preprocess(Mat image){//图片缩放int height = image.Rows;int width = image.Cols;Mat temp_image = image.Clone();if (height > input_height || width > input_width){float scale = Math.Min((float)input_height / height, (float)input_width / width);OpenCvSharp.Size new_size = new OpenCvSharp.Size((int)(width * scale), (int)(height * scale));Cv2.Resize(image, temp_image, new_size);}ratio_height = (float)height / temp_image.Rows;ratio_width = (float)width / temp_image.Cols;Mat input_img = new Mat();Cv2.CopyMakeBorder(temp_image, input_img, 0, input_height - temp_image.Rows, 0, input_width - temp_image.Cols, BorderTypes.Constant, 0);//归一化input_img.ConvertTo(input_img, MatType.CV_32FC3, 1.0 / 255);input_tensor_data = Common.ExtractMat(input_img);input_img.Dispose();temp_image.Dispose();}void Postprocess(float[] outputData){detectionResults.Clear();float[] data = Common.Transpose(outputData, class_num + 4, box_num);float[] confidenceInfo = new float[class_num];float[] rectData = new float[4];List<DetectionResult> detResults = new List<DetectionResult>();for (int i = 0; i < box_num; i++){Array.Copy(data, i * (class_num + 4), rectData, 0, 4);Array.Copy(data, i * (class_num + 4) + 4, confidenceInfo, 0, class_num);float score = confidenceInfo.Max(); // 获取最大值int maxIndex = Array.IndexOf(confidenceInfo, score); // 获取最大值的位置int _centerX = (int)(rectData[0] * ratio_width);int _centerY = (int)(rectData[1] * ratio_height);int _width = (int)(rectData[2] * ratio_width);int _height = (int)(rectData[3] * ratio_height);detResults.Add(new DetectionResult(maxIndex,class_names[maxIndex],new Rect(_centerX - _width / 2, _centerY - _height / 2, _width, _height),score));}//NMSCvDnn.NMSBoxes(detResults.Select(x => x.Rect), detResults.Select(x => x.Confidence), conf_threshold, nms_threshold, out int[] indices);detResults = detResults.Where((x, index) => indices.Contains(index)).ToList();detectionResults = detResults;}internal List<DetectionResult> Detect(Mat image){var t1 = Cv2.GetTickCount();Stopwatch stopwatch = new Stopwatch();stopwatch.Start();Preprocess(image);preprocessTime = stopwatch.Elapsed.TotalMilliseconds;stopwatch.Restart();predictor.LoadInferenceData("images", input_tensor_data);predictor.infer();inferTime = stopwatch.Elapsed.TotalMilliseconds;stopwatch.Restart();outputData = predictor.GetInferenceResult("output0");Postprocess(outputData);postprocessTime = stopwatch.Elapsed.TotalMilliseconds;stopwatch.Stop();totalTime = preprocessTime + inferTime + postprocessTime;detFps = (double)stopwatch.Elapsed.TotalSeconds / (double)stopwatch.Elapsed.Ticks;var t2 = Cv2.GetTickCount();detFps = 1 / ((t2 - t1) / Cv2.GetTickFrequency());return detectionResults;}}
}

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C# double[] 转换成MWArray[], 直接赋值就行             MWNumericArray[] ma = new MWNumericArray[4];             double[] dT = new double[] { 0 };             double[] dT1 = new double[] { 0,2 };
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C# Hash算法之MD5、SHA

C# Hash算法之MD5、SHA

MD5我们用的还是比较多的，一般用来加密存储密码。但是现在很多人觉MD5可能不太安全了，所以都用上了SHA256等来做加密（虽然我觉得都差不多,MD5还是能玩）。 还是跟上一篇说的一样，当一个算法的复杂度提高的同时肯定会带来效率的降低，所以SHA和MD5比较起来的话，SHA更安全，MD5更高效。 由于HASH算法的不可逆性，所以我认为MD5和SHA主要还是应用在字符串的"加密"上。 由于
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C#线程系列(1)：BeginInvoke和EndInvoke方法

C#线程系列(1)：BeginInvoke和EndInvoke方法

一、线程概述 在操作系统中一个进程至少要包含一个线程，然后，在某些时候需要在同一个进程中同时执行多项任务，或是为了提供程序的性能，将要执行的任务分解成多个子任务执行。这就需要在同一个进程中开启多个线程。我们使用 C# 编写一个应用程序（控制台或桌面程序都可以），然后运行这个程序，并打开 windows 任务管理器，这时我们就会看到这个应用程序中所含有的线程数，如下图所示。
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C#设计模式(1)——单例模式（讲解非常清楚）

C#设计模式(1)——单例模式（讲解非常清楚）

一、引言 最近在学设计模式的一些内容，主要的参考书籍是《Head First 设计模式》，同时在学习过程中也查看了很多博客园中关于设计模式的一些文章的，在这里记录下我的一些学习笔记，一是为了帮助我更深入地理解设计模式，二同时可以给一些初学设计模式的朋友一些参考。首先我介绍的是设计模式中比较简单的一个模式——单例模式（因为这里只牵涉到一个类） 二、单例模式的介绍 说到单例模式,大家第一
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