C#调用Onnx模型

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us

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问答

xeliven

6822人浏览 · 2022-05-06 17:19:24

xeliven · 2022-05-06 17:19:24 发布

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 paths
            string 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 image
            using Image<Rgb24> image = SixLabors.ImageSharp.Image.Load<Rgb24>(imageFilePath);

            // Resize image
            float ratio = 800f / Math.Min(image.Width, image.Height);
            image.Mutate(x => x.Resize((int)(ratio * image.Width), (int)(ratio * image.Height)));

            // Preprocess image
            var 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 outputs
            var inputs = new List<NamedOnnxValue>
            {
                NamedOnnxValue.CreateFromTensor("image", input)
            };

            // Run inference
            using var session = new InferenceSession(modelFilePath);
            using IDisposableReadOnlyCollection<DisposableNamedOnnxValue> results = session.Run(inputs);

            // Postprocess to get predictions
            var 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 viewing
            using 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; }
    }
}