本文介绍如何在.Net环境调用Yolov5的模型，把yolov5的pt模型转成onnx模型，接着通过Microsoft.ML.OnnxRuntime调用yolov5的onnx模型
1.安装yolov5
git clone https://github.com/ultralytics/yolov5.git
cd yolov5
python -m venv venv
.\venv\Scripts\activate
pip install -r requirements.txt
2.安装好环境以后运行示例
2.1下载yolov5模型放到项目根目录，地址：https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt
2.2 执行一下命令：
python detect.py --weights yolov5s.pt --source data/images/zidane.jpg
输出：
data\images\zidane.jpg: 384x640 2 persons, 2 ties, 140.7ms
Speed: 1.0ms pre-process, 140.7ms inference, 1.0ms NMS per image at shape (1, 3, 640, 640)
Results saved to runs\detect\exp
runs/detect/exp可以看输出结果
3.把yolov5.pt转成yolov5.onnx，以下命令会在根目录输出一个yolov5.onnx
python export.py --weights yolov5s.pt --img 640 --batch 1 --device cpu --include onnx
4.获取onnx模型但是输入输出参数，参数在.net项目会用到
import onnx
# 加载 ONNX 模型
model = onnx.load("yolov5s.onnx")print("=== 模型输入 ===")
for input_tensor in model.graph.input:name = input_tensor.nameelem_type = input_tensor.type.tensor_type.elem_typedims = [d.dim_value for d in input_tensor.type.tensor_type.shape.dim]print(f"Name: {name}")print(f" ElementType: {elem_type}")print(f" Dimensions: {dims}")print("\n=== 模型输出 ===")
for output_tensor in model.graph.output:name = output_tensor.nameelem_type = output_tensor.type.tensor_type.elem_typedims = [d.dim_value for d in output_tensor.type.tensor_type.shape.dim]print(f"Name: {name}")print(f" ElementType: {elem_type}")print(f" Dimensions: {dims}")
输出结果如下：
5.把onnx放到.net，然后运行项目
下面给出代码：
using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Linq;
using System.Threading.Channels;namespace YoloV5SoftNMS
{class Program{static void Main(string[] args){string modelPath = "yolov5s.onnx";string imagePath = "zidane.jpg";int inputWidth = 640;int inputHeight = 640;float confThreshold = 0.25f; // 保留小目标float iouThreshold = 0.45f; // Soft-NMS IOU阈值float sigma = 0.5f; // Soft-NMS 高斯衰减参数// 1️. 预处理var (inputTensor, padX, padY, scale, origWidth, origHeight) = PreprocessImage(imagePath, inputWidth, inputHeight);// 2️. ONNX 推理using var session = new InferenceSession(modelPath);var inputs = new List<NamedOnnxValue>{NamedOnnxValue.CreateFromTensor("images", inputTensor)//这里面images就是前面模型输入参数};using var results = session.Run(inputs);var outputTensor = results.First(r => r.Name == "output0").AsTensor<float>(); //这里面output0对应前面模型输出参数var output = outputTensor.ToArray();// 3️. 解析输出var predictions = ParseOutputs(output, 80, confThreshold);Console.WriteLine("=== NMS 前候选框 ===");foreach (var p in predictions)Console.WriteLine($"Class:{p.ClassId} Conf:{p.Confidence:0.00} X1:{p.X1:0.} Y1:{p.Y1:0.} X2:{p.X2:0.} Y2:{p.Y2:0.}");// 4️. Soft-NMSvar finalBoxes = SoftNMS(predictions, iouThreshold, sigma, confThreshold);Console.WriteLine("=== Soft-NMS 结果 ===");foreach (var p in finalBoxes)Console.WriteLine($"Class:{p.ClassId} Conf:{p.Confidence:0.00} X1:{p.X1:0.} Y1:{p.Y1:0.} X2:{p.X2:0.} Y2:{p.Y2:0.}");// 5️. 绘制回原图using var bitmap = new Bitmap(imagePath);using var g = Graphics.FromImage(bitmap);foreach (var p in finalBoxes){float x1 = (p.X1 - padX) / scale;float y1 = (p.Y1 - padY) / scale;float x2 = (p.X2 - padX) / scale;float y2 = (p.Y2 - padY) / scale;g.DrawRectangle(Pens.Red, x1, y1, x2 - x1, y2 - y1);g.DrawString($"ID:{p.ClassId} {p.Confidence:0.00}", new Font("Arial", 12), Brushes.Yellow, x1, y1 - 16);}bitmap.Save("result.jpg");Console.WriteLine("完成，结果保存为 result.jpg");Console.ReadKey();}/// <summary>/// 处理图片，把图片转成按CHW排布的一维数组/// </summary>/// <param name="imagePath"></param>/// <param name="targetWidth"></param>/// <param name="targetHeight"></param>/// <returns></returns>static (DenseTensor<float>, float, float, float, int, int) PreprocessImage(string imagePath, int targetWidth, int targetHeight){using var bitmap = new Bitmap(imagePath);int origW = bitmap.Width;int origH = bitmap.Height;float scale = Math.Min(targetWidth / (float)origW, targetHeight / (float)origH);int newW = (int)(origW * scale);int newH = (int)(origH * scale);float padX = (targetWidth - newW) / 2f;float padY = (targetHeight - newH) / 2f;using var resized = new Bitmap(targetWidth, targetHeight);using (var g = Graphics.FromImage(resized)){g.FillRectangle(Brushes.Black, 0, 0, targetWidth, targetHeight);g.InterpolationMode = System.Drawing.Drawing2D.InterpolationMode.HighQualityBicubic;g.DrawImage(bitmap, padX, padY, newW, newH);}resized.Save("resized_input.jpg");float[] data = new float[3 * targetWidth * targetHeight];for (int y = 0; y < targetHeight; y++){for (int x = 0; x < targetWidth; x++){var color = resized.GetPixel(x, y);int idx = y * targetWidth + x;data[idx] = color.R / 255f;data[targetWidth * targetHeight + idx] = color.G / 255f;data[2 * targetWidth * targetHeight + idx] = color.B / 255f;}}//DenseTensor 是 ML.NET / ONNX Runtime 用来表示张量的数据结构// 第一个参数 data → 一维浮点数组// 第二个参数[1, 3, H, W] → 张量形状：//1 → batch = 1，一次输入 1 张图片//3 → RGB 通道//targetHeight / targetWidth → 缩放后图片大小//张量会自动按照 CHW 排布，把一维数组映射成 4 维张量[batch, channel, height, width]，刚好对应 YOLOv5 ONNX 模型输入。var tensor = new DenseTensor<float>(data, new int[] { 1, 3, targetHeight, targetWidth });return (tensor, padX, padY, scale, origW, origH);}/// <summary>/// 对输出的可信度进行过滤/// </summary>/// <param name="output"></param>/// <param name="numClasses"></param>/// <param name="confThreshold"></param>/// <returns></returns>static List<YoloPrediction> ParseOutputs(float[] output, int numClasses, float confThreshold){int numBoxes = output.Length / (5 + numClasses);var predictions = new List<YoloPrediction>();for (int i = 0; i < numBoxes; i++){int offset = i * (5 + numClasses);float x = output[offset];float y = output[offset + 1];float w = output[offset + 2];float h = output[offset + 3];float objConf = output[offset + 4];float maxClassConf = 0;int classId = 0;for (int c = 0; c < numClasses; c++){float classConf = output[offset + 5 + c];if (classConf > maxClassConf){maxClassConf = classConf;classId = c;}}float conf = objConf * maxClassConf;if (conf > confThreshold){predictions.Add(new YoloPrediction{X1 = x - w / 2,Y1 = y - h / 2,X2 = x + w / 2,Y2 = y + h / 2,Confidence = conf,ClassId = classId});}}return predictions;}/// <summary>/// 对一组候选框执行 Soft Non-Maximum Suppression (Soft-NMS)，减少重叠框的冗余，但不像传统 NMS 那样直接丢弃，而是降低它们的置信度。/// </summary>/// <param name="boxes"></param>/// <param name="iouThreshold"></param>/// <param name="sigma"></param>/// <param name="confThreshold"></param>/// <returns></returns>static List<YoloPrediction> SoftNMS(List<YoloPrediction> boxes, float iouThreshold, float sigma, float confThreshold){var dets = boxes.OrderByDescending(b => b.Confidence).ToList();var keep = new List<YoloPrediction>();while (dets.Count > 0){var maxBox = dets[0];keep.Add(maxBox);dets.RemoveAt(0);for (int i = 0; i < dets.Count; i++){float iou = IoU(maxBox, dets[i]);if (iou > iouThreshold)dets[i].Confidence *= (float)Math.Exp(-iou * iou / sigma);}dets = dets.Where(b => b.Confidence > confThreshold).OrderByDescending(b => b.Confidence).ToList();}return keep;}/// <summary>/// 计算两个框之间的 IoU（Intersection over Union, 交并比）/// </summary>/// <param name="a"></param>/// <param name="b"></param>/// <returns></returns>static float IoU(YoloPrediction a, YoloPrediction b){float x1 = Math.Max(a.X1, b.X1);float y1 = Math.Max(a.Y1, b.Y1);float x2 = Math.Min(a.X2, b.X2);float y2 = Math.Min(a.Y2, b.Y2);float interArea = Math.Max(0, x2 - x1) * Math.Max(0, y2 - y1);float unionArea = (a.X2 - a.X1) * (a.Y2 - a.Y1) + (b.X2 - b.X1) * (b.Y2 - b.Y1) - interArea;return interArea / unionArea;}class YoloPrediction{public float X1, Y1, X2, Y2;public float Confidence;public int ClassId;}}
}
5.输出结果
6.输出对应的类别
yolov5模型可以识别80种类别，可以根据id找到对应的类别，把这个类别放到.Net项目就可以了，可以看到id为0是person，id为27是tie
