using Microsoft.ML.OnnxRuntime.Tensors;
 using Microsoft.ML.OnnxRuntime;
 using OpenCvSharp;
 using System;
 using System.Collections.Generic;
 using System.Windows.Forms;
 using System.Linq;
 using System.Drawing;
 using System.IO;
 using OpenCvSharp.Dnn;
 using System.Text;namespace Onnx_Demo
 {
     public partial class frmMain : Form
     {
         public frmMain()
         {
             InitializeComponent();
         }        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
         string image_path = "";        DateTime dt1 = DateTime.Now;
         DateTime dt2 = DateTime.Now;        float confThreshold = 0.5f;
         float nmsThreshold = 0.85f;         int inpWidth;
         int inpHeight;        Mat image;
        string model_path = "";
        SessionOptions options;
         InferenceSession onnx_session;
         Tensor<float> input_tensor;
         List<NamedOnnxValue> input_container;        IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
         DisposableNamedOnnxValue[] results_onnxvalue;        List<string> class_names;
         int nout;
         int num_proposal;
         int num_class;        StringBuilder sb = new StringBuilder();
        private void button1_Click(object sender, EventArgs e)
         {
             OpenFileDialog ofd = new OpenFileDialog();
             ofd.Filter = fileFilter;
             if (ofd.ShowDialog() != DialogResult.OK) return;            pictureBox1.Image = null;
             pictureBox2.Image = null;
             textBox1.Text = "";            image_path = ofd.FileName;
             pictureBox1.Image = new System.Drawing.Bitmap(image_path);
             image = new Mat(image_path);
         }        private void Form1_Load(object sender, EventArgs e)
         {
             // 创建输入容器
             input_container = new List<NamedOnnxValue>();            // 创建输出会话
             options = new SessionOptions();
             options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
             options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行            // 创建推理模型类，读取本地模型文件
             model_path = "model/damoyolo_tinynasL20_T_192x320.onnx";            inpHeight = 192;
             inpWidth = 320;            onnx_session = new InferenceSession(model_path, options);
            // 创建输入容器
             input_container = new List<NamedOnnxValue>();            image_path = "test_img/dog.jpg";
             pictureBox1.Image = new Bitmap(image_path);            class_names = new List<string>();
             StreamReader sr = new StreamReader("coco.names");
             string line;
             while ((line = sr.ReadLine()) != null)
             {
                 class_names.Add(line);
             }
             num_class = class_names.Count();        }
        private unsafe void button2_Click(object sender, EventArgs e)
         {
             if (image_path == "")
             {
                 return;
             }
             textBox1.Text = "检测中，请稍等……";
             pictureBox2.Image = null;
             sb.Clear();
             System.Windows.Forms.Application.DoEvents();            image = new Mat(image_path);
             //-----------------前处理--------------------------
             float ratio = Math.Min(inpHeight * 1.0f / image.Rows, inpWidth * 1.0f / image.Cols);
             int neww = (int)(image.Cols * ratio);
             int newh = (int)(image.Rows * ratio);
             Mat dstimg = new Mat();
             Cv2.CvtColor(image, dstimg, ColorConversionCodes.BGR2RGB);
             Cv2.Resize(dstimg, dstimg, new OpenCvSharp.Size(neww, newh));
             Cv2.CopyMakeBorder(dstimg, dstimg, 0, inpHeight - newh, 0, inpWidth - neww, BorderTypes.Constant, new Scalar(1));
             int row = dstimg.Rows;
             int col = dstimg.Cols;
             float[] input_tensor_data = new float[1 * 3 * row * col];
             for (int c = 0; c < 3; c++)
             {
                 for (int i = 0; i < row; i++)
                 {
                     for (int j = 0; j < col; j++)
                     {
                         byte pix = ((byte*)(dstimg.Ptr(i).ToPointer()))[j * 3 + c];
                         input_tensor_data[c * row * col + i * col + j] = pix;
                     }
                 }
             }
             input_tensor = new DenseTensor<float>(input_tensor_data, new[] { 1, 3, inpHeight, inpWidth });
             input_container.Add(NamedOnnxValue.CreateFromTensor("images", input_tensor));             //-----------------推理--------------------------
             dt1 = DateTime.Now;
             result_infer = onnx_session.Run(input_container);//运行 Inference 并获取结果
             dt2 = DateTime.Now;            //-----------------后处理--------------------------
             results_onnxvalue = result_infer.ToArray();
             num_proposal = results_onnxvalue[0].AsTensor<float>().Dimensions[1];
             nout = results_onnxvalue[0].AsTensor<float>().Dimensions[2];
             int n = 0, k = 0; ///cx,cy,w,h,box_score, class_score
             float[] pscores = results_onnxvalue[0].AsTensor<float>().ToArray();
             float[] pbboxes = results_onnxvalue[1].AsTensor<float>().ToArray();
             List<float> confidences = new List<float>();
             List<Rect> position_boxes = new List<Rect>();
             List<int> class_ids = new List<int>();
             for (n = 0; n < num_proposal; n++)   ///特征图尺度
             {
                 int max_ind = 0;
                 float class_socre = 0;
                 for (k = 0; k < num_class; k++)
                 {
                     if (pscores[k + n * nout] > class_socre)
                     {
                         class_socre = pscores[k + n * nout];
                         max_ind = k;
                     }
                 }                if (class_socre > confThreshold)
                 {
                     float xmin = pbboxes[0 + n * 4] / ratio;
                     float ymin = pbboxes[1 + n * 4] / ratio;
                     float xmax = pbboxes[2 + n * 4] / ratio;
                     float ymax = pbboxes[3 + n * 4] / ratio;                    Rect box = new Rect();
                     box.X = (int)xmin;
                     box.Y = (int)ymin;
                     box.Width = (int)(xmax - xmin);
                     box.Height = (int)(ymax - ymin);                    position_boxes.Add(box);
                    confidences.Add(class_socre);
                    class_ids.Add(max_ind);
                 }
             }            // NMS非极大值抑制
             int[] indexes = new int[position_boxes.Count];
             CvDnn.NMSBoxes(position_boxes, confidences, confThreshold, nmsThreshold, out indexes);            Result result = new Result();
            for (int i = 0; i < indexes.Length; i++)
             {
                 int index = indexes[i];
                 result.add(confidences[index], position_boxes[index], class_names[class_ids[index]]);
             }            if (pictureBox2.Image != null)
             {
                 pictureBox2.Image.Dispose();
             }            sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");
             sb.AppendLine("------------------------------");            // 将识别结果绘制到图片上
             Mat result_image = image.Clone();
             for (int i = 0; i < result.length; i++)
             {
                 Cv2.Rectangle(result_image, result.rects[i], new Scalar(0, 0, 255), 2, LineTypes.Link8);                Cv2.Rectangle(result_image, new OpenCvSharp.Point(result.rects[i].TopLeft.X - 1, result.rects[i].TopLeft.Y - 20),
                     new OpenCvSharp.Point(result.rects[i].BottomRight.X, result.rects[i].TopLeft.Y), new Scalar(0, 0, 255), -1);                Cv2.PutText(result_image, result.classes[i] + "-" + result.scores[i].ToString("0.00"),
                     new OpenCvSharp.Point(result.rects[i].X, result.rects[i].Y - 4),
                     HersheyFonts.HersheySimplex, 0.6, new Scalar(0, 0, 0), 1);                sb.AppendLine(string.Format("{0}：{1},({2},{3},{4},{5})"
                     , result.classes[i]
                     , result.scores[i].ToString("0.00")
                     , result.rects[i].TopLeft.X
                     , result.rects[i].TopLeft.Y
                     , result.rects[i].BottomRight.X
                     , result.rects[i].BottomRight.Y
                     ));
             }            textBox1.Text = sb.ToString();
             pictureBox2.Image = new System.Drawing.Bitmap(result_image.ToMemoryStream());            result_image.Dispose();
             dstimg.Dispose();
             image.Dispose();        }
        private void pictureBox2_DoubleClick(object sender, EventArgs e)
         {
             Common.ShowNormalImg(pictureBox2.Image);
         }        private void pictureBox1_DoubleClick(object sender, EventArgs e)
         {
             Common.ShowNormalImg(pictureBox1.Image);
         }
     }
 }

using Microsoft.ML.OnnxRuntime.Tensors;
using Microsoft.ML.OnnxRuntime;
using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Windows.Forms;
using System.Linq;
using System.Drawing;
using System.IO;
using OpenCvSharp.Dnn;
using System.Text;

namespace Onnx_Demo
{
    public partial class frmMain : Form
    {
        public frmMain()
        {
            InitializeComponent();
        }

        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = "";

        DateTime dt1 = DateTime.Now;
        DateTime dt2 = DateTime.Now;

        float confThreshold = 0.5f;
        float nmsThreshold = 0.85f;


        int inpWidth;
        int inpHeight;

        Mat image;

        string model_path = "";

        SessionOptions options;
        InferenceSession onnx_session;
        Tensor<float> input_tensor;
        List<NamedOnnxValue> input_container;

        IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
        DisposableNamedOnnxValue[] results_onnxvalue;

        List<string> class_names;
        int nout;
        int num_proposal;
        int num_class;

        StringBuilder sb = new StringBuilder();

        private void button1_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.Filter = fileFilter;
            if (ofd.ShowDialog() != DialogResult.OK) return;

            pictureBox1.Image = null;
            pictureBox2.Image = null;
            textBox1.Text = "";

            image_path = ofd.FileName;
            pictureBox1.Image = new System.Drawing.Bitmap(image_path);
            image = new Mat(image_path);
        }

        private void Form1_Load(object sender, EventArgs e)
        {
            // 创建输入容器
            input_container = new List<NamedOnnxValue>();

            // 创建输出会话
            options = new SessionOptions();
            options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
            options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行

            // 创建推理模型类，读取本地模型文件
            model_path = "model/damoyolo_tinynasL20_T_192x320.onnx";

            inpHeight = 192;
            inpWidth = 320;

            onnx_session = new InferenceSession(model_path, options);

            // 创建输入容器
            input_container = new List<NamedOnnxValue>();

            image_path = "test_img/dog.jpg";
            pictureBox1.Image = new Bitmap(image_path);

            class_names = new List<string>();
            StreamReader sr = new StreamReader("coco.names");
            string line;
            while ((line = sr.ReadLine()) != null)
            {
                class_names.Add(line);
            }
            num_class = class_names.Count();

        }

        private unsafe void button2_Click(object sender, EventArgs e)
        {
            if (image_path == "")
            {
                return;
            }
            textBox1.Text = "检测中，请稍等……";
            pictureBox2.Image = null;
            sb.Clear();
            System.Windows.Forms.Application.DoEvents();

            image = new Mat(image_path);
            //-----------------前处理--------------------------
            float ratio = Math.Min(inpHeight * 1.0f / image.Rows, inpWidth * 1.0f / image.Cols);
            int neww = (int)(image.Cols * ratio);
            int newh = (int)(image.Rows * ratio);
            Mat dstimg = new Mat();
            Cv2.CvtColor(image, dstimg, ColorConversionCodes.BGR2RGB);
            Cv2.Resize(dstimg, dstimg, new OpenCvSharp.Size(neww, newh));
            Cv2.CopyMakeBorder(dstimg, dstimg, 0, inpHeight - newh, 0, inpWidth - neww, BorderTypes.Constant, new Scalar(1));
            int row = dstimg.Rows;
            int col = dstimg.Cols;
            float[] input_tensor_data = new float[1 * 3 * row * col];
            for (int c = 0; c < 3; c++)
            {
                for (int i = 0; i < row; i++)
                {
                    for (int j = 0; j < col; j++)
                    {
                        byte pix = ((byte*)(dstimg.Ptr(i).ToPointer()))[j * 3 + c];
                        input_tensor_data[c * row * col + i * col + j] = pix;
                    }
                }
            }
            input_tensor = new DenseTensor<float>(input_tensor_data, new[] { 1, 3, inpHeight, inpWidth });
            input_container.Add(NamedOnnxValue.CreateFromTensor("images", input_tensor)); //将 input_tensor 放入一个输入参数的容器，并指定名称

            //-----------------推理--------------------------
            dt1 = DateTime.Now;
            result_infer = onnx_session.Run(input_container);//运行 Inference 并获取结果
            dt2 = DateTime.Now;

            //-----------------后处理--------------------------
            results_onnxvalue = result_infer.ToArray();
            num_proposal = results_onnxvalue[0].AsTensor<float>().Dimensions[1];
            nout = results_onnxvalue[0].AsTensor<float>().Dimensions[2];
            int n = 0, k = 0; ///cx,cy,w,h,box_score, class_score
            float[] pscores = results_onnxvalue[0].AsTensor<float>().ToArray();
            float[] pbboxes = results_onnxvalue[1].AsTensor<float>().ToArray();
            List<float> confidences = new List<float>();
            List<Rect> position_boxes = new List<Rect>();
            List<int> class_ids = new List<int>();
            for (n = 0; n < num_proposal; n++)   ///特征图尺度
            {
                int max_ind = 0;
                float class_socre = 0;
                for (k = 0; k < num_class; k++)
                {
                    if (pscores[k + n * nout] > class_socre)
                    {
                        class_socre = pscores[k + n * nout];
                        max_ind = k;
                    }
                }

                if (class_socre > confThreshold)
                {
                    float xmin = pbboxes[0 + n * 4] / ratio;
                    float ymin = pbboxes[1 + n * 4] / ratio;
                    float xmax = pbboxes[2 + n * 4] / ratio;
                    float ymax = pbboxes[3 + n * 4] / ratio;

                    Rect box = new Rect();
                    box.X = (int)xmin;
                    box.Y = (int)ymin;
                    box.Width = (int)(xmax - xmin);
                    box.Height = (int)(ymax - ymin);

                    position_boxes.Add(box);

                    confidences.Add(class_socre);

                    class_ids.Add(max_ind);
                }
            }

            // NMS非极大值抑制
            int[] indexes = new int[position_boxes.Count];
            CvDnn.NMSBoxes(position_boxes, confidences, confThreshold, nmsThreshold, out indexes);

            Result result = new Result();

            for (int i = 0; i < indexes.Length; i++)
            {
                int index = indexes[i];
                result.add(confidences[index], position_boxes[index], class_names[class_ids[index]]);
            }

            if (pictureBox2.Image != null)
            {
                pictureBox2.Image.Dispose();
            }

            sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");
            sb.AppendLine("------------------------------");

            // 将识别结果绘制到图片上
            Mat result_image = image.Clone();
            for (int i = 0; i < result.length; i++)
            {
                Cv2.Rectangle(result_image, result.rects[i], new Scalar(0, 0, 255), 2, LineTypes.Link8);

                Cv2.Rectangle(result_image, new OpenCvSharp.Point(result.rects[i].TopLeft.X - 1, result.rects[i].TopLeft.Y - 20),
                    new OpenCvSharp.Point(result.rects[i].BottomRight.X, result.rects[i].TopLeft.Y), new Scalar(0, 0, 255), -1);

                Cv2.PutText(result_image, result.classes[i] + "-" + result.scores[i].ToString("0.00"),
                    new OpenCvSharp.Point(result.rects[i].X, result.rects[i].Y - 4),
                    HersheyFonts.HersheySimplex, 0.6, new Scalar(0, 0, 0), 1);

                sb.AppendLine(string.Format("{0}：{1},({2},{3},{4},{5})"
                    , result.classes[i]
                    , result.scores[i].ToString("0.00")
                    , result.rects[i].TopLeft.X
                    , result.rects[i].TopLeft.Y
                    , result.rects[i].BottomRight.X
                    , result.rects[i].BottomRight.Y
                    ));
            }

            textBox1.Text = sb.ToString();
            pictureBox2.Image = new System.Drawing.Bitmap(result_image.ToMemoryStream());

            result_image.Dispose();
            dstimg.Dispose();
            image.Dispose();

        }

        private void pictureBox2_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox2.Image);
        }

        private void pictureBox1_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox1.Image);
        }
    }
}