Form1.cs

using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Windows.Forms;

// 追加(NuGetでMicrosoft.ML.OnnxRuntimeの追加が必要)
using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;

namespace WindowsFormsApp1
{
    public partial class Form1 : Form
    {
        private string[] _category_names;
        private Color[] _category_colors;
        private Font _labelFont;

        public Form1()
        {
            InitializeComponent();

            // カテゴリ名
            // 参考 https://pytorch.org/vision/main/models.html#object-detection-instance-segmentation-and-person-keypoint-detection
            _category_names = new string[] {
                "__background__", "person", "bicycle", "car", "motorcycle", "airplane", "bus",
                "train", "truck", "boat", "traffic light", "fire hydrant", "N/A", "stop sign",
                "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
                "elephant", "bear", "zebra", "giraffe", "N/A", "backpack", "umbrella", "N/A", "N/A",
                "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball",
                "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket",
                "bottle", "N/A", "wine glass", "cup", "fork", "knife", "spoon", "bowl",
                "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza",
                "donut", "cake", "chair", "couch", "potted plant", "bed", "N/A", "dining table",
                "N/A", "N/A", "toilet", "N/A", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
                "microwave", "oven", "toaster", "sink", "refrigerator", "N/A", "book",
                "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"
            };

            // 表示色を用意
            _category_colors = new Color[91];
            var rnd = new System.Random();
            for (int i = 0; i < _category_colors.Length; i++)
            {
                _category_colors[i] = Color.FromArgb(rnd.Next(0, 256), rnd.Next(0, 256), rnd.Next(0, 256));
            }
            // ラベルのフォント
            _labelFont = new Font("Arial", 20);
        }

        /// <summary>
        /// 画像データをHWC→CHWへ変換し0～1のfloatの一次元配列に変換する
        /// </summary>
        /// <param name="filename">画像ファイルのファイル名</param>
        /// <param name="bmp">Bitmapクラスオブジェクト</param>
        /// <param name="width">Bitmapの幅</param>
        /// <param name="height">Bitmapの高さ</param>
        /// <param name="channel">Bitmapのチャンネル数</param>
        /// <returns>floatの一次元配列</returns>
        private float[] GetImageFloatData(string filename, out Bitmap bmp, out int width, out int height, out int channel)
        {
            float[] imageData;

            bmp = new Bitmap(filename);

            imageData = GetImageFloatData(bmp, out width, out height, out channel);

            return imageData;
        }

        /// <summary>
        /// 画像データをHWC→CHWへ変換し0～1のfloatの一次元配列に変換する
        /// </summary>
        /// <param name="bmp">Bitmapクラスオブジェクト</param>
        /// <param name="width">Bitmapの幅</param>
        /// <param name="height">Bitmapの高さ</param>
        /// <param name="channel">Bitmapのチャンネル数</param>
        /// <returns>floatの一次元配列</returns>
        private float[] GetImageFloatData(Bitmap bmp, out int width, out int height, out int channel)
        {

            width = bmp.Width;
            height = bmp.Height;

            // 画像の１画素あたりのビット数の取得（8,24,32など）
            var bitCount = Bitmap.GetPixelFormatSize(bmp.PixelFormat);
            channel = bitCount / 8;

            // Bitmapをロック
            var bmpData = bmp.LockBits(
                    new Rectangle(0, 0, width, height),
                    System.Drawing.Imaging.ImageLockMode.ReadOnly,
                    bmp.PixelFormat
                );

            // メモリの幅のバイト数を取得
            var stride = Math.Abs(bmpData.Stride);

            // 画像データ（輝度値データ）のバイト配列
            var byteData = new byte[stride * height];

            // Bitmapデータを配列へコピー 
            System.Runtime.InteropServices.Marshal.Copy(
                bmpData.Scan0,
                byteData,
                0,
                stride * height
                );

            // アンロック
            bmp.UnlockBits(bmpData);

            var imageSize = height * width;

            // データの並び替え、1/255にする
            float[] imageFloatData = new float[channel * imageSize];

            var w = width;
            var c = channel;

            Parallel.For(0, height, y =>
            {
                for (int x = 0; x < w; x++)
                {
                    for (int ch = 0; ch < c; ch++)
                    {
                        // BGRをR,G,Bのプレーンの順番に変更し、0～255を0～1のfloatへ
                        imageFloatData[imageSize * (c - 1 - ch) + w * y + x]
                            = byteData[stride * y + c * x + ch] / 255.0f;
                    }
                }
            });
            return imageFloatData;
        }

        /// <summary>
        /// 物体認識の結果表示
        /// </summary>
        /// <param name="pic">表示先のPictureBox</param>
        /// <param name="bmp">画像のBitmapオブジェクト</param>
        /// <param name="results">推論結果</param>
        private void DrawResult(PictureBox pic, Bitmap bmp, IDisposableReadOnlyCollection<DisposableNamedOnnxValue> results)
        {
            var g = Graphics.FromImage(bmp);

            // 結果をListに変換
            var values = results.ToList<DisposableNamedOnnxValue>();

            var boxes = values[0].AsTensor<float>().ToArray(); // 左、上、右、下、・・・
            var labels = values[1].AsTensor<Int64>().ToArray();// ラベルのインデックス番号
            var scores = values[2].AsTensor<float>().ToArray();// スコア

            for (int i = 0; i < scores.Length; i++)
            {
                if (scores[i] < 0.5) continue;

                // 検出領域の描画
                g.DrawRectangle(
                    new Pen(_category_colors[labels[i]], 3),
                    new Rectangle(
                        (int)boxes[i * 4],
                        (int)boxes[i * 4 + 1],
                        (int)(boxes[i * 4 + 2] - boxes[i * 4]),
                        (int)(boxes[i * 4 + 3] - boxes[i * 4 + 1]
                    )));
                // カテゴリ名の描画
                g.DrawString(
                    _category_names[labels[i]],
                    _labelFont,
                    new SolidBrush(_category_colors[labels[i]]),
                    new PointF(boxes[i * 4], boxes[i * 4 + 1])
                    );
            }

            pic.Image = bmp;
        }

        private void button1_Click(object sender, EventArgs e)
        {
            // 画像データをプレーン分離したRGBのfloat(0.0～1.0)の配列で取得
            int width, height, channel;
            Bitmap bmp;

            // 画像データをHWC→CHWへ変換し0～1のfloatの一次元配列に変換する
            var imageFloatData = GetImageFloatData("image.jpg", out bmp, out width, out height, out channel);

            // オプションの設定
            var opts = new SessionOptions();
            opts.ExecutionMode = ExecutionMode.ORT_PARALLEL;

            // ONNXファイルの読み込み
            using (var session = new InferenceSession("fasterrcnn_mobilenet_v3_large_320_fpn.onnx", opts))  // Microsoft.ML.OnnxRuntime.InferenceSession
            {
                var inputName = session.InputMetadata.First().Key;　　// 入力データの名前
                var inputDimensions = session.InputMetadata.First().Value.Dimensions;  // 入力データの次元　1, 3, 480, 640
                var inputTensor = new DenseTensor<float>(   //Microsoft.ML.OnnxRuntime.Tensors.DenseTensor
                    imageFloatData, // 画像データ(テンソル相当、floatの一次元配列)
                    inputDimensions // 入力データの次元（今回は1, 3, 480, 640）
                    );

                var namedOnnxValues = new List<NamedOnnxValue>   // Name と Value のリスト
                {
                    NamedOnnxValue.CreateFromTensor(inputName, inputTensor)
                };

                // 推論の実行
                using (var results = session.Run(namedOnnxValues))
                {
                    // 結果の表示
                    DrawResult(pictureBox1, bmp, results);
                }
            }
        }

    }
}