C# OnnxRuntime 部署 APISR 动漫超分辨率模型

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);
            }
        }
    }
}

下载

源码下载

参考

https://github.com/Kiteretsu77/APISR