c# 部署onnx定位模型

目录

1.环境

2.代码

3.效果

---

1.环境

gpu3060+cuda11.1+vs2019

+Microsoft.ML.OnnxRuntime

+SixLabors.ImageSharp

2.代码

权重https://github.com/onnx/models/blob/master/vision/object_detection_segmentation/faster-rcnn/model/FasterRCNN-10.onnxhttps://github.com/onnx/models/blob/master/vision/object_detection_segmentation/faster-rcnn/model/FasterRCNN-10.onnx

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;

namespace Microsoft.ML.OnnxRuntime.FasterRcnnSample
{
    class Program
    {
        public static void Main(string[] args)
        {
            // Read paths
            string modelFilePath = @"E:\code\Csharp\onnxruntime-master\csharp\sample\Microsoft.ML.OnnxRuntime.FasterRcnnSample\FasterRCNN-10.onnx";
            string imageFilePath = @"E:\code\Csharp\onnxruntime-master\csharp\sample\Microsoft.ML.OnnxRuntime.FasterRcnnSample\demo.jpg";
            string outImageFilePath = "outputs.jpg";
            //System.IO.Directory.CreateDirectory(outImageFilePath);


            // Read image
            using Image image = Image.Load(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 input = new DenseTensor(new[] { 3, paddedHeight, paddedWidth });
            var mean = new[] { 102.9801f, 115.9465f, 122.7717f };
            for (int y = paddedHeight - image.Height; y < image.Height; y++)
            {
                Span pixelSpan = image.GetPixelRowSpan(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.CreateFromTensor("image", input)
            };

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

            // Postprocess to get predictions
            var resultsArray = results.ToArray();
            float[] boxes = resultsArray[0].AsEnumerable().ToArray();
            long[] labels = resultsArray[1].AsEnumerable().ToArray();
            float[] confidences = resultsArray[2].AsEnumerable().ToArray();
            var predictions = new List();
            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 PointF(p.Box.Xmin, p.Box.Ymin),
                        new 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);
        }
    }
}

3.效果