C# onnxruntime 部署LYT-Net轻量级低光图像增强

C# onnxruntime 部署LYT-Net轻量级低光图像增强

目录

说明

效果

模型信息

项目

代码

下载

说明

LYT-Net: Lightweight YUV Transformer-based Network for Low-Light Image Enhancement

参考：

https://github.com/albrateanu/LYT-Net

https://github.com/hpc203/Low-Light-Image-Enhancement-onnxrun

效果

模型信息

Model Properties
-------------------------
---------------------------------------------------------------

Inputs
-------------------------
name：input_1
tensor：Float[-1, 240, 320, 3]
---------------------------------------------------------------

Outputs
-------------------------
name：output_1
tensor：Float[-1, 240, 320, 3]
---------------------------------------------------------------

项目

代码

using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Drawing.Imaging;
using System.Linq;
using System.Windows.Forms;

namespace Onnx_Demo
{
    public partial class Form1 : Form
    {
        public Form1()
        {
            InitializeComponent();
        }

        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = "";
        string startupPath;
        DateTime dt1 = DateTime.Now;
        DateTime dt2 = DateTime.Now;
        string model_path;
        Mat image;
        Mat result_image;
        SessionOptions options;
        InferenceSession onnx_session;
        Tensor input_tensor;
        List input_container;
        IDisposableReadOnlyCollection result_infer;
        DisposableNamedOnnxValue[] results_onnxvalue;
        Tensor result_tensors;
        int inpHeight, inpWidth;

        private void button1_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.Filter = fileFilter;
            if (ofd.ShowDialog() != DialogResult.OK) return;
            pictureBox1.Image = null;
            image_path = ofd.FileName;
            pictureBox1.Image = new Bitmap(image_path);
            textBox1.Text = "";
            image = new Mat(image_path);
            pictureBox2.Image = null;
        }

        unsafe private void button2_Click(object sender, EventArgs e)
        {
            if (image_path == "")
            {
                return;
            }

            button2.Enabled = false;
            pictureBox2.Image = null;
            textBox1.Text = "";
            Application.DoEvents();

            //读图片
            image = new Mat(image_path);

            Mat dstimg = new Mat();

            Cv2.Resize(image, dstimg, new OpenCvSharp.Size(inpWidth, inpHeight));

            dstimg.ConvertTo(dstimg, MatType.CV_32FC3, 1 / 127.5, -1.0);

            float* pdata = (float*)dstimg.Data;
            float[] input_tensor_data = new float[1 * 3 * inpWidth * inpHeight];
            for (int i = 0; i < 1 * 3 * inpWidth * inpHeight; i++)
            {
                input_tensor_data[i] = pdata[i];
            }

            //输入Tensor
            input_tensor = new DenseTensor(input_tensor_data, new[] { 1, inpHeight, inpWidth, 3 });

            //将 input_tensor 放入一个输入参数的容器，并指定名称
            input_container.Add(NamedOnnxValue.CreateFromTensor("input_1", input_tensor));

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

            // 将输出结果转为DisposableNamedOnnxValue数组
            results_onnxvalue = result_infer.ToArray();

            // 读取第一个节点输出并转为Tensor数据
            result_tensors = results_onnxvalue[0].AsTensor();

            var result_array = result_tensors.ToArray();

            for (int i = 0; i < result_array.Length; i++)
            {
                result_array[i] = (result_array[i] + 1.0f) * 127.5f;
            }

            Mat result_image = new Mat(inpHeight, inpWidth, MatType.CV_32FC3, result_array);

            result_image.ConvertTo(result_image, MatType.CV_8UC3);

            pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
            textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";
            button2.Enabled = true;

        }

        private void Form1_Load(object sender, EventArgs e)
        {
            startupPath = System.Windows.Forms.Application.StartupPath;
            model_path = "model/lyt_net_lolv2_real_320x240.onnx";

            // 创建输出会话，用于输出模型读取信息
            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
            onnx_session = new InferenceSession(model_path, options);//model_path 为onnx模型文件的路径

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

            image_path = "test_img/1_1.JPG";
            pictureBox1.Image = new Bitmap(image_path);
            image = new Mat(image_path);

            inpHeight = 240;
            inpWidth = 320;

        }

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

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

        SaveFileDialog sdf = new SaveFileDialog();
        private void button3_Click(object sender, EventArgs e)
        {
            if (pictureBox2.Image == null)
            {
                return;
            }
            Bitmap output = new Bitmap(pictureBox2.Image);
            sdf.Title = "保存";
            sdf.Filter = "Images (*.jpg)|*.jpg|Images (*.png)|*.png|Images (*.bmp)|*.bmp|Images (*.emf)|*.emf|Images (*.exif)|*.exif|Images (*.gif)|*.gif|Images (*.ico)|*.ico|Images (*.tiff)|*.tiff|Images (*.wmf)|*.wmf";
            if (sdf.ShowDialog() == DialogResult.OK)
            {
                switch (sdf.FilterIndex)
                {
                    case 1:
                        {
                            output.Save(sdf.FileName, ImageFormat.Jpeg);
                            break;
                        }
                    case 2:
                        {
                            output.Save(sdf.FileName, ImageFormat.Png);
                            break;
                        }
                    case 3:
                        {
                            output.Save(sdf.FileName, ImageFormat.Bmp);
                            break;
                        }
                    case 4:
                        {
                            output.Save(sdf.FileName, ImageFormat.Emf);
                            break;
                        }
                    case 5:
                        {
                            output.Save(sdf.FileName, ImageFormat.Exif);
                            break;
                        }
                    case 6:
                        {
                            output.Save(sdf.FileName, ImageFormat.Gif);
                            break;
                        }
                    case 7:
                        {
                            output.Save(sdf.FileName, ImageFormat.Icon);
                            break;
                        }

                    case 8:
                        {
                            output.Save(sdf.FileName, ImageFormat.Tiff);
                            break;
                        }
                    case 9:
                        {
                            output.Save(sdf.FileName, ImageFormat.Wmf);
                            break;
                        }
                }
                MessageBox.Show("保存成功，位置：" + sdf.FileName);
            }
        }
    }
}

  1. using Microsoft.ML.OnnxRuntime;
  2. using Microsoft.ML.OnnxRuntime.Tensors;
  3. using OpenCvSharp;
  4. using System;
  5. using System.Collections.Generic;
  6. using System.Drawing;
  7. using System.Drawing.Imaging;
  8. using System.Linq;
  9. using System.Windows.Forms;
  10.  
  11. namespace Onnx_Demo
  12. {
  13.     public partial class Form1 : Form
  14.     {
  15.         public Form1()
  16.         {
  17.             InitializeComponent();
  18.         }
  19.  
  20.         string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
  21.         string image_path = "";
  22.         string startupPath;
  23.         DateTime dt1 = DateTime.Now;
  24.         DateTime dt2 = DateTime.Now;
  25.         string model_path;
  26.         Mat image;
  27.         Mat result_image;
  28.         SessionOptions options;
  29.         InferenceSession onnx_session;
  30.         Tensor<float> input_tensor;
  31.         List<NamedOnnxValue> input_container;
  32.         IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
  33.         DisposableNamedOnnxValue[] results_onnxvalue;
  34.         Tensor<float> result_tensors;
  35.         int inpHeight, inpWidth;
  36.  
  37.         private void button1_Click(object sender, EventArgs e)
  38.         {
  39.             OpenFileDialog ofd = new OpenFileDialog();
  40.             ofd.Filter = fileFilter;
  41.             if (ofd.ShowDialog() != DialogResult.OK) return;
  42.             pictureBox1.Image = null;
  43.             image_path = ofd.FileName;
  44.             pictureBox1.Image = new Bitmap(image_path);
  45.             textBox1.Text = "";
  46.             image = new Mat(image_path);
  47.             pictureBox2.Image = null;
  48.         }
  49.  
  50.         unsafe private void button2_Click(object sender, EventArgs e)
  51.         {
  52.             if (image_path == "")
  53.             {
  54.                 return;
  55.             }
  56.  
  57.             button2.Enabled = false;
  58.             pictureBox2.Image = null;
  59.             textBox1.Text = "";
  60.             Application.DoEvents();
  61.  
  62.             //读图片
  63.             image = new Mat(image_path);
  64.  
  65.             Mat dstimg = new Mat();
  66.  
  67.             Cv2.Resize(image, dstimg, new OpenCvSharp.Size(inpWidth, inpHeight));
  68.  
  69.             dstimg.ConvertTo(dstimg, MatType.CV_32FC3, 1 / 127.5, -1.0);
  70.  
  71.             float* pdata = (float*)dstimg.Data;
  72.             float[] input_tensor_data = new float[1 * 3 * inpWidth * inpHeight];
  73.             for (int i = 0; i < 1 * 3 * inpWidth * inpHeight; i++)
  74.             {
  75.                 input_tensor_data[i] = pdata[i];
  76.             }
  77.  
  78.             //输入Tensor
  79.             input_tensor = new DenseTensor<float>(input_tensor_data, new[] { 1, inpHeight, inpWidth, 3 });
  80.  
  81.             //将 input_tensor 放入一个输入参数的容器，并指定名称
  82.             input_container.Add(NamedOnnxValue.CreateFromTensor("input_1", input_tensor));
  83.  
  84.             dt1 = DateTime.Now;
  85.             //运行 Inference 并获取结果
  86.             result_infer = onnx_session.Run(input_container);
  87.             dt2 = DateTime.Now;
  88.  
  89.             // 将输出结果转为DisposableNamedOnnxValue数组
  90.             results_onnxvalue = result_infer.ToArray();
  91.  
  92.             // 读取第一个节点输出并转为Tensor数据
  93.             result_tensors = results_onnxvalue[0].AsTensor<float>();
  94.  
  95.             var result_array = result_tensors.ToArray();
  96.  
  97.             for (int i = 0; i < result_array.Length; i++)
  98.             {
  99.                 result_array[i] = (result_array[i] + 1.0f) * 127.5f;
  100.             }
  101.  
  102.             Mat result_image = new Mat(inpHeight, inpWidth, MatType.CV_32FC3, result_array);
  103.  
  104.             result_image.ConvertTo(result_image, MatType.CV_8UC3);
  105.  
  106.             pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
  107.             textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";
  108.             button2.Enabled = true;
  109.  
  110.         }
  111.  
  112.         private void Form1_Load(object sender, EventArgs e)
  113.         {
  114.             startupPath = System.Windows.Forms.Application.StartupPath;
  115.             model_path = "model/lyt_net_lolv2_real_320x240.onnx";
  116.  
  117.             // 创建输出会话，用于输出模型读取信息
  118.             options = new SessionOptions();
  119.             options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
  120.             options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行
  121.  
  122.             // 创建推理模型类，读取本地模型文件
  123.             onnx_session = new InferenceSession(model_path, options);//model_path 为onnx模型文件的路径
  124.  
  125.             // 创建输入容器
  126.             input_container = new List<NamedOnnxValue>();
  127.  
  128.             image_path = "test_img/1_1.JPG";
  129.             pictureBox1.Image = new Bitmap(image_path);
  130.             image = new Mat(image_path);
  131.  
  132.             inpHeight = 240;
  133.             inpWidth = 320;
  134.  
  135.         }
  136.  
  137.         private void pictureBox1_DoubleClick(object sender, EventArgs e)
  138.         {
  139.             Common.ShowNormalImg(pictureBox1.Image);
  140.         }
  141.  
  142.         private void pictureBox2_DoubleClick(object sender, EventArgs e)
  143.         {
  144.             Common.ShowNormalImg(pictureBox2.Image);
  145.         }
  146.  
  147.         SaveFileDialog sdf = new SaveFileDialog();
  148.         private void button3_Click(object sender, EventArgs e)
  149.         {
  150.             if (pictureBox2.Image == null)
  151.             {
  152.                 return;
  153.             }
  154.             Bitmap output = new Bitmap(pictureBox2.Image);
  155.             sdf.Title = "保存";
  156.             sdf.Filter = "Images (*.jpg)|*.jpg|Images (*.png)|*.png|Images (*.bmp)|*.bmp|Images (*.emf)|*.emf|Images (*.exif)|*.exif|Images (*.gif)|*.gif|Images (*.ico)|*.ico|Images (*.tiff)|*.tiff|Images (*.wmf)|*.wmf";
  157.             if (sdf.ShowDialog() == DialogResult.OK)
  158.             {
  159.                 switch (sdf.FilterIndex)
  160.                 {
  161.                     case 1:
  162.                         {
  163.                             output.Save(sdf.FileName, ImageFormat.Jpeg);
  164.                             break;
  165.                         }
  166.                     case 2:
  167.                         {
  168.                             output.Save(sdf.FileName, ImageFormat.Png);
  169.                             break;
  170.                         }
  171.                     case 3:
  172.                         {
  173.                             output.Save(sdf.FileName, ImageFormat.Bmp);
  174.                             break;
  175.                         }
  176.                     case 4:
  177.                         {
  178.                             output.Save(sdf.FileName, ImageFormat.Emf);
  179.                             break;
  180.                         }
  181.                     case 5:
  182.                         {
  183.                             output.Save(sdf.FileName, ImageFormat.Exif);
  184.                             break;
  185.                         }
  186.                     case 6:
  187.                         {
  188.                             output.Save(sdf.FileName, ImageFormat.Gif);
  189.                             break;
  190.                         }
  191.                     case 7:
  192.                         {
  193.                             output.Save(sdf.FileName, ImageFormat.Icon);
  194.                             break;
  195.                         }
  196.  
  197.                     case 8:
  198.                         {
  199.                             output.Save(sdf.FileName, ImageFormat.Tiff);
  200.                             break;
  201.                         }
  202.                     case 9:
  203.                         {
  204.                             output.Save(sdf.FileName, ImageFormat.Wmf);
  205.                             break;
  206.                         }
  207.                 }
  208.                 MessageBox.Show("保存成功，位置：" + sdf.FileName);
  209.             }
  210.         }
  211.     }
  212. }

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