Yolov8训练方式以及C#中读取yolov8+onnx模型进行目标检测.NET 6.0

首先你要定义v8的模型特征文件

下方是完整的模型编写函数

然后你要在控件窗体中定义应用这些方法以及函数

一、定义你的标签

二、下方是定义模块和坐标的方法

三、画框

完整推理函数代码块

效果展示

完整源码

训练其实和yolov10差不多因为v10就是在v8的基础上做了优化

YoloV10 训练自己的数据集（推理，转化，C#部署）_c# yolo-CSDN博客

首先你要定义v8的模型特征文件

下方是完整的模型编写函数

protected List<Prediction> ParseOutput(DenseTensor<float> output, Image image)
{DenseTensor<float> output2 = output;ConcurrentBag<Prediction> result = new ConcurrentBag<Prediction>();int width = image.Width;int height = image.Height;int w = width;int h = height;float num = (float)base.ModelInputWidth / (float)w;float num2 = (float)base.ModelInputHeight / (float)h;float val = num;float val2 = num2;float gain = Math.Min(val, val2);num = ((float)base.ModelInputWidth - (float)w * gain) / 2f;float num3 = ((float)base.ModelInputHeight - (float)h * gain) / 2f;float xPad = num;float yPad = num3;Parallel.For(0, output2.Dimensions[0], delegate (int i){Parallel.For(0, (int)(output2.Length / output2.Dimensions[1]), delegate (int j){float xMin = (output2[new int[3] { i, 0, j }] - output2[new int[3] { i, 2, j }] / 2f - xPad) / gain;float yMin = (output2[new int[3] { i, 1, j }] - output2[new int[3] { i, 3, j }] / 2f - yPad) / gain;float xMax = (output2[new int[3] { i, 0, j }] + output2[new int[3] { i, 2, j }] / 2f - xPad) / gain;float yMax = (output2[new int[3] { i, 1, j }] + output2[new int[3] { i, 3, j }] / 2f - yPad) / gain;xMin = Utils.Clamp(xMin, 0f, w);yMin = Utils.Clamp(yMin, 0f, h);xMax = Utils.Clamp(xMax, 0f, w - 1);yMax = Utils.Clamp(yMax, 0f, h - 1);Parallel.For(0, base.ModelOutputDimensions - 4, delegate (int l){float num4 = output2[new int[3]{i,4 + l,j}];if (!(num4 < base.Confidence)){result.Add(new Prediction{Label = base.Labels[l],Score = num4,Rectangle = new RectangleF(xMin, yMin, xMax - xMin, yMax - yMin)});}});});});return result.ToList();
}

然后你要在控件窗体中定义应用这些方法以及函数

一、定义你的标签

标签你也可以将其写成txt文件再用ReadAllText方法进行读取也是可以的

二、下方是定义模块和坐标的方法

三、画框

完整推理函数代码块

private void showimg(OpenFileDialog dialog)
{string imgpath = dialog.FileName;Image image = System.Drawing.Image.FromFile(imgpath);Form form = new Form();form.Text = dialog.SafeFileName + " " + image.Width + "x" + image.Height;PictureBox pictureBox = new PictureBox();pictureBox.Parent = form;//图像预测var predictions = yolov8.Predict(image);// Draw your boxes//using var graphics = Graphics.FromImage(image);foreach (var pred in predictions){var originalImageHeight = image.Height;var originalImageWidth = image.Width;var x = Math.Max(pred.Rectangle.X, 0);var y = Math.Max(pred.Rectangle.Y, 0);Console.WriteLine(x);Console.WriteLine(y);var width = Math.Min(originalImageWidth - x, pred.Rectangle.Width);var height = Math.Min(originalImageHeight - y, pred.Rectangle.Height);// 框定义string text = $"{pred.Label.Name} [{pred.Score}]";using (Graphics graphics = Graphics.FromImage(image)){graphics.CompositingQuality = CompositingQuality.HighQuality;graphics.SmoothingMode = SmoothingMode.HighQuality;graphics.InterpolationMode = InterpolationMode.HighQualityBicubic;// Font drawFont = new Font("consolas", 11, FontStyle.Regular);SizeF size = graphics.MeasureString(text, drawFont);SolidBrush fontBrush = new SolidBrush(Color.Black);Point atPoint = new Point((int)x, (int)y - (int)size.Height - 1);// Pen pen = new Pen(Color.Yellow, 2.0f);SolidBrush colorBrush = new SolidBrush(Color.Yellow);// 画框graphics.FillRectangle(colorBrush, (int)x, (int)(y - size.Height - 1), (int)size.Width, (int)size.Height);graphics.DrawString(text, drawFont, fontBrush, atPoint);// Draw bounding box on imagegraphics.DrawRectangle(pen, x, y, width, height);}}pictureBox.Width = image.Width;pictureBox.Height = image.Height;pictureBox1.Image = (Bitmap)image.Clone();}