前言

前段時間玩了把yolo,結果卡在用winform調用模型實識別的過程.從網絡上找了很長一段時間代碼,結果總不盡人意.今天隔離結束,回到工位,繼續擼代碼.

環境

vs2019,Net5,ML.OnnxRuntime,SixLabors.

準備

nuget下載如下包,

下載onnx權重文件

models/FasterRCNN-10.onnx at main · onnx/models · GitHub

代碼

using System; using System.Collections.Generic; using System.IO; using System.Linq; using Microsoft.ML.OnnxRuntime.Tensors; using SixLabors.ImageSharp; using SixLabors.ImageSharp.Formats; using SixLabors.ImageSharp.PixelFormats; using SixLabors.ImageSharp.Processing; using SixLabors.ImageSharp.Drawing.Processing; using SixLabors.Fonts; //using SixLabors.Fonts; //using System.Drawing;namespace Microsoft.ML.OnnxRuntime.FasterRcnnSample {class Program{public static void Main(string[] args){// Read pathsstring modelFilePath = @"D:\Test\2022\FasterRCNN-10.onnx";//string modelFilePath = @"D:\Test\2022\best.onnx";// string imageFilePath = @"D:\Test\2022\A.jpg";string imageFilePath = @"D:\Test\2022\B.png";string outImageFilePath = "outputs.jpg";//System.IO.Directory.CreateDirectory(outImageFilePath);// Read imageusing Image<Rgb24> image = SixLabors.ImageSharp.Image.Load<Rgb24>(imageFilePath);// Resize imagefloat ratio = 800f / Math.Min(image.Width, image.Height);image.Mutate(x => x.Resize((int)(ratio * image.Width), (int)(ratio * image.Height)));// Preprocess imagevar paddedHeight = (int)(Math.Ceiling(image.Height / 32f) * 32f);var paddedWidth = (int)(Math.Ceiling(image.Width / 32f) * 32f);Tensor<float> input = new DenseTensor<float>(new[] { 3, paddedHeight, paddedWidth });var mean = new[] { 102.9801f, 115.9465f, 122.7717f };for (int y = paddedHeight - image.Height; y < image.Height; y++){image.ProcessPixelRows(im =>{var pixelSpan = im.GetRowSpan(y);for (int x = paddedWidth - image.Width; x < image.Width; x++){input[0, y, x] = pixelSpan[x].B - mean[0];input[1, y, x] = pixelSpan[x].G - mean[1];input[2, y, x] = pixelSpan[x].R - mean[2];}});}// Setup inputs and outputsvar inputs = new List<NamedOnnxValue>{NamedOnnxValue.CreateFromTensor("image", input)};// Run inferenceusing var session = new InferenceSession(modelFilePath);using IDisposableReadOnlyCollection<DisposableNamedOnnxValue> results = session.Run(inputs);// Postprocess to get predictionsvar resultsArray = results.ToArray();float[] boxes = resultsArray[0].AsEnumerable<float>().ToArray();long[] labels = resultsArray[1].AsEnumerable<long>().ToArray();float[] confidences = resultsArray[2].AsEnumerable<float>().ToArray();var predictions = new List<Prediction>();var minConfidence = 0.7f;for (int i = 0; i < boxes.Length - 4; i += 4){var index = i / 4;if (confidences[index] >= minConfidence){predictions.Add(new Prediction{Box = new Box(boxes[i], boxes[i + 1], boxes[i + 2], boxes[i + 3]),Label = LabelMap.Labels[labels[index]],Confidence = confidences[index]});}}// Put boxes, labels and confidence on image and save for viewingusing var outputImage = File.OpenWrite(outImageFilePath);Font font = SystemFonts.CreateFont("Arial",16);foreach (var p in predictions){image.Mutate(x =>{x.DrawLines(Color.Red, 2f, new PointF[] {new SixLabors.ImageSharp.PointF(p.Box.Xmin, p.Box.Ymin),new SixLabors.ImageSharp.PointF(p.Box.Xmax, p.Box.Ymin),new PointF(p.Box.Xmax, p.Box.Ymin),new PointF(p.Box.Xmax, p.Box.Ymax),new PointF(p.Box.Xmax, p.Box.Ymax),new PointF(p.Box.Xmin, p.Box.Ymax),new PointF(p.Box.Xmin, p.Box.Ymax),new PointF(p.Box.Xmin, p.Box.Ymin)});x.DrawText($"{p.Label}, {p.Confidence:0.00}", font, Color.White, new PointF(p.Box.Xmin, p.Box.Ymin));});}image.SaveAsJpeg(outputImage);}}public class Prediction{public Box Box { set; get; }public string Label { set; get; }public float Confidence { set; get; }}public class Box{public Box(float xMin,float yMin,float xMax,float yMax){Xmin = xMin;Ymin = yMin;Xmax = xMax;Ymax = yMax;}public float Xmin { set; get; }public float Xmax { set; get; }public float Ymin { set; get; }public float Ymax { set; get; }}public static class LabelMap{static LabelMap(){Labels = new string[]{"","person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush"};}public static string[] Labels { set; get; }} }

說明

?1.NamedOnnxValue.CreateFromTensor("image", input),這里面的"image"可以從WinMLDashboard軟件中看到.

2.image.GetPixelRowSpan() 方法已經被?image.ProcessPixelRows()方法取代.這個東西找了半天.

3.Prediction,Box,LabelMap,是自己補全的.沒找到網上的源代碼.

效果

?隨便從網上找的圖片.如有侵權,請聯系本人.

引用

Mr.Q老師的博客

(20條消息) c# 部署onnx定位模型_Mr.Q的博客-CSDN博客_onnx模型部署

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