前言

前段时间玩了把yolo,结果卡在用winform调用模型实识别的过程.从网络上找了很长一段时间代码,结果总不尽人意.今天隔离结束,回到工位,继续撸代码.

环境

vs2019,Net5,ML.OnnxRuntime,SixLabors.

准备

nuget下载如下包,

下载onnx权重文件

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

代码

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; }}
}

说明

1.NamedOnnxValue.CreateFromTensor("image", input),这里面的"image"可以从WinMLDashboard软件中看到.

2.image.GetPixelRowSpan() 方法已经被 image.ProcessPixelRows()方法取代.这个东西找了半天.

3.Prediction,Box,LabelMap,是自己补全的.没找到网上的源代码.

效果

随便从网上找的图片.如有侵权,请联系本人.

引用

Mr.Q老师的博客

(20条消息) 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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C#调用Onnx模型

前言

环境

准备

代码

说明

效果

引用

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