目录
介绍
模型信息
效果
项目
代码
下载
C# Onnx 使用onnxruntime部署夜间雾霾图像的可见度增强
介绍
github地址：GitHub - jinyeying/nighttime_dehaze: [ACMMM2023] "Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution", https://arxiv.org/abs/2308.01738
[ACMMM2023] "Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution",
模型信息
Model Properties
-------------------------
---------------------------------------------------------------
Inputs
-------------------------
name：input
tensor：Float[1, 3, 512, 512]
---------------------------------------------------------------
Outputs
-------------------------
name：output
tensor：Float[1, 3, 512, 512]
---------------------------------------------------------------
效果
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_Yolov8_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;
float[] result_array;
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;
}
private void button2_Click(object sender, EventArgs e)
{
if (image_path == "")
{
return;
}
button2.Enabled = false;
pictureBox2.Image = null;
textBox1.Text = "正在运行，请稍后……";
Application.DoEvents();
image = new Mat(image_path);
int srch = image.Rows;
int srcw = image.Cols;
Mat resize_image = new Mat();
Cv2.Resize(image, resize_image, new OpenCvSharp.Size(512, 512));
Common.Normalize(resize_image);
float[] data= Common.ExtractMat(resize_image);
// 输入Tensor
input_tensor = new DenseTensor<float>(data,new[] { 1, 3, 512, 512 });
//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>();
result_array = result_tensors.ToArray();
int out_h = 512;
int out_w = 512;
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);
Cv2.Resize(result_image, result_image, new OpenCvSharp.Size(srcw, srch));
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/nighttime_dehaze_realnight_1x3x512x512.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/038.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 == 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);
}
}
}
}
下载
源码下载
