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C# OnnxRuntime 部署 APISR 动漫超分辨率模型

news 2026/6/3 0:31:41

目录

效果

模型信息

项目

代码

下载

参考

效果

模型信息

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

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

Outputs
-------------------------
name：reconstruction
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; // 原始图像（BGR）
Mat result_image; // 超分结果（BGR）
SessionOptions options;
InferenceSession onnx_session;
Tensor<float> input_tensor;
List<NamedOnnxValue> input_container;
IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
DisposableNamedOnnxValue[] results_onnxvalue;
Tensor<float> result_tensor;

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();

// 读取原始图像（BGR）
image = new Mat(image_path);
int originalWidth = image.Cols;
int originalHeight = image.Rows;

// ------------------ 预处理 ------------------
// 1. 转换为RGB
Mat rgb = new Mat();
Cv2.CvtColor(image, rgb, ColorConversionCodes.BGR2RGB);

// 2. 调整尺寸使宽高均为4的倍数（APISR要求输入能被4整除）
int padHeight = (int)Math.Ceiling((double)rgb.Height / 4) * 4;
int padWidth = (int)Math.Ceiling((double)rgb.Width / 4) * 4;
Mat padded = new Mat();
Cv2.CopyMakeBorder(rgb, padded, 0, padHeight - rgb.Height, 0, padWidth - rgb.Width, BorderTypes.Constant, new Scalar(0, 0, 0));

// 3. 归一化到 [0,1] 并转换为浮点
padded.ConvertTo(padded, MatType.CV_32FC3, 1.0 / 255.0);

// 4. 构建 CHW 张量
int height = padded.Height;
int width = padded.Width;
Mat[] channels = Cv2.Split(padded); // 顺序：R, G, B
List<float> dataList = new List<float>();
for (int c = 0; c < 3; c++)
{
float[] channelData = new float[height * width];
System.Runtime.InteropServices.Marshal.Copy(channels[c].Data, channelData, 0, height * width);
dataList.AddRange(channelData);
}
float[] inputData = dataList.ToArray();
input_tensor = new DenseTensor<float>(inputData, new[] { 1, 3, height, width });

// 输入容器清空并添加
input_container.Clear();
input_container.Add(NamedOnnxValue.CreateFromTensor("pixel_values", input_tensor)); // 修改输入名称

// ------------------ 推理 ------------------
dt1 = DateTime.Now;
result_infer = onnx_session.Run(input_container);
dt2 = DateTime.Now;

// 获取输出（修改为 float 类型）
results_onnxvalue = result_infer.ToArray();
result_tensor = results_onnxvalue[0].AsTensor<float>(); // 改为 float

int[] outShape = result_tensor.Dimensions.ToArray();
int outChannels = outShape[1]; // 应为3
int outHeight = outShape[2];
int outWidth = outShape[3];

float[] predFloat = result_tensor.ToArray();

// 创建 OpenCV Mat 存储输出图像（RGB顺序）
Mat outputRgb = new Mat(outHeight, outWidth, MatType.CV_32FC3);
// 将 float 数组按 CHW 写入 Mat (OpenCV 是 HWC)
int index = 0;
for (int h = 0; h < outHeight; h++)
{
for (int w = 0; w < outWidth; w++)
{
Vec3f pixel;
pixel.Item0 = predFloat[index]; // R
pixel.Item1 = predFloat[index + outHeight * outWidth]; // G
pixel.Item2 = predFloat[index + 2 * outHeight * outWidth]; // B
outputRgb.Set<Vec3f>(h, w, pixel);
index++;
}
}

// 将像素值从 [0,1] 转换到 [0,255] 并转为 8UC3
outputRgb.ConvertTo(outputRgb, MatType.CV_8UC3, 255.0);

// 裁剪掉之前填充的部分（因为输出尺寸是填充后尺寸的4倍）
int cropHeight = originalHeight * 4;
int cropWidth = originalWidth * 4;
Rect roi = new Rect(0, 0, cropWidth, cropHeight);
Mat cropped = new Mat(outputRgb, roi);

// 将 RGB 转换为 BGR 以便 OpenCV 显示
Mat resultBgr = new Mat();
Cv2.CvtColor(cropped, resultBgr, ColorConversionCodes.RGB2BGR);
result_image = resultBgr.Clone();

// 显示结果
pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());

textBox1.Text = $"推理耗时:{(dt2 - dt1).TotalMilliseconds:F2}ms 输出尺寸:{cropWidth}x{cropHeight}";
button2.Enabled = true;
}

private void Form1_Load(object sender, EventArgs e)
{
startupPath = System.Windows.Forms.Application.StartupPath;
model_path = "model/4x_APISR_GRL_GAN_generator.onnx";

// 创建会话，使用 CPU（可根据需要改为 CUDA）
options = new SessionOptions();
options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
options.AppendExecutionProvider_CPU(0);
// 如需 GPU，取消下面注释并安装 Microsoft.ML.OnnxRuntime.Gpu
// options.AppendExecutionProvider_CUDA(0);

onnx_session = new InferenceSession(model_path, options);
input_container = new List<NamedOnnxValue>();

// 测试图片路径（可选）
image_path = "test_img/test01.png";
if (System.IO.File.Exists(image_path))
{
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 (result_image == null || result_image.Empty())
{
MessageBox.Show("请先进行推理！");
return;
}

sdf.Title = "保存超分图像";
sdf.Filter = "PNG图片 (*.png)|*.png|JPEG图片 (*.jpg)|*.jpg|BMP图片 (*.bmp)|*.bmp";
sdf.FilterIndex = 1;
if (sdf.ShowDialog() == DialogResult.OK)
{
string ext = System.IO.Path.GetExtension(sdf.FileName).ToLower();
ImageFormat format = ImageFormat.Png;
if (ext == ".jpg" || ext == ".jpeg")
format = ImageFormat.Jpeg;
else if (ext == ".bmp")
format = ImageFormat.Bmp;

using (var stream = result_image.ToMemoryStream())
using (var bitmap = new Bitmap(stream))
{
bitmap.Save(sdf.FileName, format);
}
MessageBox.Show("保存成功，位置：" + sdf.FileName);
}
}
}
}

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; // 原始图像（BGR） Mat result_image; // 超分结果（BGR） SessionOptions options; InferenceSession onnx_session; Tensor<float> input_tensor; List<NamedOnnxValue> input_container; IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer; DisposableNamedOnnxValue[] results_onnxvalue; Tensor<float> result_tensor; 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(); // 读取原始图像（BGR） image = new Mat(image_path); int originalWidth = image.Cols; int originalHeight = image.Rows; // ------------------ 预处理 ------------------ // 1. 转换为RGB Mat rgb = new Mat(); Cv2.CvtColor(image, rgb, ColorConversionCodes.BGR2RGB); // 2. 调整尺寸使宽高均为4的倍数（APISR要求输入能被4整除） int padHeight = (int)Math.Ceiling((double)rgb.Height / 4) * 4; int padWidth = (int)Math.Ceiling((double)rgb.Width / 4) * 4; Mat padded = new Mat(); Cv2.CopyMakeBorder(rgb, padded, 0, padHeight - rgb.Height, 0, padWidth - rgb.Width, BorderTypes.Constant, new Scalar(0, 0, 0)); // 3. 归一化到 [0,1] 并转换为浮点 padded.ConvertTo(padded, MatType.CV_32FC3, 1.0 / 255.0); // 4. 构建 CHW 张量 int height = padded.Height; int width = padded.Width; Mat[] channels = Cv2.Split(padded); // 顺序：R, G, B List<float> dataList = new List<float>(); for (int c = 0; c < 3; c++) { float[] channelData = new float[height * width]; System.Runtime.InteropServices.Marshal.Copy(channels[c].Data, channelData, 0, height * width); dataList.AddRange(channelData); } float[] inputData = dataList.ToArray(); input_tensor = new DenseTensor<float>(inputData, new[] { 1, 3, height, width }); // 输入容器清空并添加 input_container.Clear(); input_container.Add(NamedOnnxValue.CreateFromTensor("pixel_values", input_tensor)); // 修改输入名称 // ------------------ 推理 ------------------ dt1 = DateTime.Now; result_infer = onnx_session.Run(input_container); dt2 = DateTime.Now; // 获取输出（修改为 float 类型） results_onnxvalue = result_infer.ToArray(); result_tensor = results_onnxvalue[0].AsTensor<float>(); // 改为 float int[] outShape = result_tensor.Dimensions.ToArray(); int outChannels = outShape[1]; // 应为3 int outHeight = outShape[2]; int outWidth = outShape[3]; float[] predFloat = result_tensor.ToArray(); // 创建 OpenCV Mat 存储输出图像（RGB顺序） Mat outputRgb = new Mat(outHeight, outWidth, MatType.CV_32FC3); // 将 float 数组按 CHW 写入 Mat (OpenCV 是 HWC) int index = 0; for (int h = 0; h < outHeight; h++) { for (int w = 0; w < outWidth; w++) { Vec3f pixel; pixel.Item0 = predFloat[index]; // R pixel.Item1 = predFloat[index + outHeight * outWidth]; // G pixel.Item2 = predFloat[index + 2 * outHeight * outWidth]; // B outputRgb.Set<Vec3f>(h, w, pixel); index++; } } // 将像素值从 [0,1] 转换到 [0,255] 并转为 8UC3 outputRgb.ConvertTo(outputRgb, MatType.CV_8UC3, 255.0); // 裁剪掉之前填充的部分（因为输出尺寸是填充后尺寸的4倍） int cropHeight = originalHeight * 4; int cropWidth = originalWidth * 4; Rect roi = new Rect(0, 0, cropWidth, cropHeight); Mat cropped = new Mat(outputRgb, roi); // 将 RGB 转换为 BGR 以便 OpenCV 显示 Mat resultBgr = new Mat(); Cv2.CvtColor(cropped, resultBgr, ColorConversionCodes.RGB2BGR); result_image = resultBgr.Clone(); // 显示结果 pictureBox2.Image = new Bitmap(result_image.ToMemoryStream()); textBox1.Text = $"推理耗时:{(dt2 - dt1).TotalMilliseconds:F2}ms 输出尺寸:{cropWidth}x{cropHeight}"; button2.Enabled = true; } private void Form1_Load(object sender, EventArgs e) { startupPath = System.Windows.Forms.Application.StartupPath; model_path = "model/4x_APISR_GRL_GAN_generator.onnx"; // 创建会话，使用 CPU（可根据需要改为 CUDA） options = new SessionOptions(); options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO; options.AppendExecutionProvider_CPU(0); // 如需 GPU，取消下面注释并安装 Microsoft.ML.OnnxRuntime.Gpu // options.AppendExecutionProvider_CUDA(0); onnx_session = new InferenceSession(model_path, options); input_container = new List<NamedOnnxValue>(); // 测试图片路径（可选） image_path = "test_img/test01.png"; if (System.IO.File.Exists(image_path)) { 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 (result_image == null || result_image.Empty()) { MessageBox.Show("请先进行推理！"); return; } sdf.Title = "保存超分图像"; sdf.Filter = "PNG图片 (*.png)|*.png|JPEG图片 (*.jpg)|*.jpg|BMP图片 (*.bmp)|*.bmp"; sdf.FilterIndex = 1; if (sdf.ShowDialog() == DialogResult.OK) { string ext = System.IO.Path.GetExtension(sdf.FileName).ToLower(); ImageFormat format = ImageFormat.Png; if (ext == ".jpg" || ext == ".jpeg") format = ImageFormat.Jpeg; else if (ext == ".bmp") format = ImageFormat.Bmp; using (var stream = result_image.ToMemoryStream()) using (var bitmap = new Bitmap(stream)) { bitmap.Save(sdf.FileName, format); } MessageBox.Show("保存成功，位置：" + sdf.FileName); } } } }

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源码下载

参考

https://github.com/Kiteretsu77/APISR

http://www.jsqmd.com/news/576798/

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