說明
官網(wǎng)地址：
https://github.com/JianghaiSCU/Diffusion-Low-Light
代碼實現(xiàn)參考：
https://github.com/hpc203/Diffusion-Low-Light-onnxrun
效果
模型信息
Model Properties
-------------------------
---------------------------------------------------------------
Inputs
-------------------------
name：input
tensor：Float[1, 3, 192, 320]
---------------------------------------------------------------
Outputs
-------------------------
name：output
tensor：Float[1, 3, 192, 320]
---------------------------------------------------------------
項目
代碼
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 = 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 = '';
Application.DoEvents();
//讀圖片
image = new Mat(image_path);
int cols = image.Cols;
int rows = image.Rows;
Mat dstimg = new Mat();
Cv2.CvtColor(image, dstimg, ColorConversionCodes.BGR2RGB);
Cv2.Resize(dstimg, dstimg, new OpenCvSharp.Size(inpWidth, inpHeight));
dstimg.ConvertTo(dstimg, MatType.CV_32FC3, 1 / 255.0f);
//輸入Tensor
input_tensor = new DenseTensor<float>(Common.ExtractMat(dstimg), new[] { 1, 3, inpHeight, inpWidth });
//將 input_tensor 放入一個輸入?yún)?shù)的容器，并指定名稱
input_container.Add(NamedOnnxValue.CreateFromTensor('input', input_tensor));
dt1 = DateTime.Now;
//運行 Inference 并獲取結(jié)果
result_infer = onnx_session.Run(input_container);
dt2 = DateTime.Now;
// 將輸出結(jié)果轉(zhuǎn)為DisposableNamedOnnxValue數(shù)組
results_onnxvalue = result_infer.ToArray();
// 讀取第一個節(jié)點輸出并轉(zhuǎn)為Tensor數(shù)據(jù)
result_tensors = results_onnxvalue[0].AsTensor<float>();
int[] out_shape = result_tensors.Dimensions.ToArray();
int out_h = out_shape[2];
int out_w = out_shape[3];
float[] pred = result_tensors.ToArray();
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(pred, temp_b, out_h * out_w);
Array.Copy(pred, out_h * out_w, temp_g, 0, out_h * out_w);
Array.Copy(pred, out_h * out_w * 2, temp_r, 0, out_h * out_w);
int channel_step = out_h * out_w;
Mat bmat = new Mat(out_h, out_w, MatType.CV_32FC1, temp_b);
Mat gmat = new Mat(out_h, out_w, MatType.CV_32FC1, temp_g);
Mat rmat = new Mat(out_h, out_w, MatType.CV_32FC1, temp_r);
bmat *= 255.0f;
gmat *= 255.0f;
rmat *= 255.0f;
Mat[] channel_mats = new Mat[] { rmat, gmat, bmat };
dstimg = new Mat();
Cv2.Merge(channel_mats.ToArray(), dstimg);
dstimg.ConvertTo(dstimg, MatType.CV_8UC3);
Cv2.Resize(dstimg, dstimg, new OpenCvSharp.Size(cols, rows));
pictureBox2.Image = new Bitmap(dstimg.ToMemoryStream());
textBox1.Text = '推理耗時:' + (dt2 - dt1).TotalMilliseconds + 'ms';
button2.Enabled = true;
}
private void Form1_Load(object sender, EventArgs e)
{
startupPath = System.Windows.Forms.Application.StartupPath;
model_path = 'model/diffusion_low_light_1x3x192x320.onnx';
// 創(chuàng)建輸出會話，用于輸出模型讀取信息
options = new SessionOptions();
options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
options.AppendExecutionProvider_CPU(0);// 設置為CPU上運行
// 創(chuàng)建推理模型類，讀取本地模型文件
onnx_session = new InferenceSession(model_path, options);
// 創(chuàng)建輸入容器
input_container = new List<NamedOnnxValue>();
image_path = 'test_img/1.png';
pictureBox1.Image = new Bitmap(image_path);
image = new Mat(image_path);
inpHeight = 192;
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);
}
}
}
}
