C# Onnx C2PNet 图像去雾 室外场景

介绍

github地址

https://github.com/YuZheng9/C2PNet

[CVPR 2023] Curricular Contrastive Regularization for Physics-aware Single Image Dehazing

效果

模型信息

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

Inputs
-------------------------
name：input
tensor：Float[1, 3, -1, -1]
---------------------------------------------------------------

Outputs
-------------------------
name：output
tensor：Float[1, 3, -1, -1]
---------------------------------------------------------------

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<float> input_tensor;
 List<NamedOnnxValue> input_container;
 IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
 DisposableNamedOnnxValue[] results_onnxvalue;
 Tensor<float> 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 = ;
 image_path = ofd.FileName;
 pictureBox1.Image = new Bitmap(image_path);
 textBox1.Text = "";
 image = new Mat(image_path);
 pictureBox2.Image = ;
 }

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

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

 //读图片
 image = new Mat(image_path);
 inpWidth = image.Width;
 inpHeight = image.Height;
 //将图片转为RGB通道
 Mat image_rgb = new Mat();
 Cv2.CvtColor(image, image_rgb, ColorConversionCodes.BGR2RGB);
 //输入Tensor
 input_tensor = new DenseTensor<float>(new[] { 1, 3, inpHeight, inpWidth });
for (int y = 0; y < image_rgb.Height; y++)
 {
for (int x = 0; x < image_rgb.Width; x++)
 {
 input_tensor[0, 0, y, x] = image_rgb.At<Vec3b>(y, x)[0] / 255f;
 input_tensor[0, 1, y, x] = image_rgb.At<Vec3b>(y, x)[1] / 255f;
 input_tensor[0, 2, y, x] = image_rgb.At<Vec3b>(y, x)[2] / 255f;
 }
 }

 //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", 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<float>();

 var result_array = result_tensors.ToArray();

for (int i = 0; i < result_array.Length; i++)
 {
 result_array[i] = result_array[i] * 255f;

if (result_array[i] < 0)
 {
 result_array[i] = 0;
 }
else if (result_array[i] > 255)
 {
 result_array[i] = 255;
 }
 }
 int out_h = result_tensors.Dimensions[2];
 int out_w = result_tensors.Dimensions[3];

float[] temp_r = new float[out_h * out_w];
float[] temp_g = new float[out_h * out_w];
float[] temp_b = new float[out_h * out_w];

 Array.Copy(result_array, temp_r, out_h * out_w);
 Array.Copy(result_array, out_h * out_w, temp_g, 0, out_h * out_w);
 Array.Copy(result_array, out_h * out_w * 2, temp_b, 0, out_h * out_w);

 Mat rmat = new Mat(out_h, out_w, MatType.CV_32FC1, temp_r);
 Mat gmat = new Mat(out_h, out_w, MatType.CV_32FC1, temp_g);
 Mat bmat = new Mat(out_h, out_w, MatType.CV_32FC1, temp_b);

 result_image = new Mat();
 Cv2.Merge(new Mat[] { bmat, gmat, rmat }, result_image);

 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/c2pnet_outdoor_HxW.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<NamedOnnxValue>();

 image_path = "test_img/0.jpg";
 pictureBox1.Image = new Bitmap(image_path);
 image = new Mat(image_path);

 }

 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 == )
 {
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);
 }
 }
 }
}