yolo26已经正式发布了，因此使用C#代码实现YOLO26-pose姿态估计的onnx模型部署，首先看yolo11n-pose网络结构，发现输出shape是1x56x8400
再来看看yolo26n-pose网络结构输出，输出shape是1x300x57
可见yolo11和yolo26输出是不一样的是不能共用代码。
模型使用官方yolo26n-pose.pt转换成的onnx，转换命令
yolo export model=yolo26n-pose.pt format=onnx opset=12
如果你是自己训练的模型可以替换即可，但是需要yolo26-pose框架才行
测试环境：
vs2019
CPU推理，无需安装cuda+cudnn
onnxruntime==1.22.1
opecvsharp==4.11.0
.net framework4.8.0
ultralytics==8.4.0
实现界面代码和调用代码：
using System; using System.Collections.Generic; using System.ComponentModel; using System.Data; using System.Diagnostics; using System.Drawing; using System.Linq; using System.Text; using System.Threading.Tasks; using System.Windows.Forms; using OpenCvSharp; namespace FIRC { public partial class Form1 : Form { OpenCvSharp.Mat src = new OpenCvSharp.Mat(); Yolo26PoseManager ym = new Yolo26PoseManager(); public Form1() { InitializeComponent(); } private void button1_Click(object sender, EventArgs e) { OpenFileDialog openFileDialog = new OpenFileDialog(); openFileDialog.Filter = "图文件(*.*)|*.jpg;*.png;*.jpeg;*.bmp"; openFileDialog.RestoreDirectory = true; openFileDialog.Multiselect = false; if (openFileDialog.ShowDialog() == DialogResult.OK) { src = Cv2.ImRead(openFileDialog.FileName); pictureBox1.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(src); } } private void button2_Click(object sender, EventArgs e) { if(pictureBox1.Image==null) { return; } Stopwatch sw = new Stopwatch(); sw.Start(); var result = ym.Inference(src); sw.Stop(); this.Text = "耗时" + sw.Elapsed.TotalSeconds + "秒"; var resultMat = ym.DrawImage(src,result); pictureBox2.Image= OpenCvSharp.Extensions.BitmapConverter.ToBitmap(resultMat); //Mat转Bitmap } private void Form1_Load(object sender, EventArgs e) { ym.LoadWeights(Application.StartupPath+ "\\weights\\yolo26n-pose.onnx"); } private void btn_video_Click(object sender, EventArgs e) { var detector = new Yolo26PoseManager(); detector.LoadWeights(Application.StartupPath + "\\weights\\yolo26n-pose.onnx"); VideoCapture capture = new VideoCapture(0); if (!capture.IsOpened()) { Console.WriteLine("video not open!"); return; } Mat frame = new Mat(); var sw = new Stopwatch(); int fps = 0; while (true) { capture.Read(frame); if (frame.Empty()) { Console.WriteLine("data is empty!"); break; } sw.Start(); var result = detector.Inference(frame); var resultImg = detector.DrawImage(frame,result); sw.Stop(); fps = Convert.ToInt32(1 / sw.Elapsed.TotalSeconds); sw.Reset(); Cv2.PutText(resultImg, "FPS=" + fps, new OpenCvSharp.Point(30, 30), HersheyFonts.HersheyComplex, 1.0, new Scalar(255, 0, 0), 3); //显示结果 Cv2.ImShow("Result", resultImg); int key = Cv2.WaitKey(10); if (key == 27) break; } capture.Release(); } } }最后测试效果：
