C# OnnxRuntime 部署 DAViD 表面法线估计

目录

效果

模型信息

项目

代码

下载

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效果

模型信息

Model Properties

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metadata：{}

---

Inputs

---

name：input

tensor：Float[-1, 3, 512, 512]

---

Outputs

---

name：output

tensor：Float[-1, 3, 512, 512]

---

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

{

// ----- 法线估计专用字段 -----

string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.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 normal_color_map; // 生成的法线彩色图

SessionOptions options;

InferenceSession onnx_session;

Tensor<float> input_tensor;

List<NamedOnnxValue> input_container;

IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;

int inpHeight = 512, inpWidth = 512;

public Form1()

{

InitializeComponent();

}

// ----- 按钮1：选择图片 -----

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;

normal_color_map = null;

}

// ----- 按钮2：执行法线估计推理 -----

private void button2_Click(object sender, EventArgs e)

{

if (string.IsNullOrEmpty(image_path))

{

MessageBox.Show("请先选择图片！");

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. 缩放至模型输入尺寸 512x512

Mat resized = new Mat();

Cv2.Resize(image, resized, new OpenCvSharp.Size(inpWidth, inpHeight));

// 2. 转换为浮点型并归一化到 [0,1]

resized.ConvertTo(resized, MatType.CV_32FC3, 1.0 / 255.0);

// 3. 分离 BGR 通道，并按 RGB 顺序填充（模型预期 RGB）

Mat[] channels = Cv2.Split(resized); // channels[0]=B, [1]=G, [2]=R

int channelSize = inpHeight * inpWidth;

float[] inputData = new float[3 * channelSize];

// 将 B,G,R 重新排列为 R,G,B

for (int c = 0; c < 3; c++)

{

float[] channelData = new float[channelSize];

System.Runtime.InteropServices.Marshal.Copy(channels[c].Data, channelData, 0, channelSize);

int targetIndex = (c == 0) ? 2 : (c == 2) ? 0 : 1; // B->2, G->1, R->0

Array.Copy(channelData, 0, inputData, targetIndex * channelSize, channelSize);

}

// 4. 创建输入张量

input_tensor = new DenseTensor<float>(inputData, new[] { 1, 3, inpHeight, inpWidth });

input_container.Clear();

input_container.Add(NamedOnnxValue.CreateFromTensor("input", input_tensor));

// ------------------ 推理 ------------------

dt1 = DateTime.Now;

result_infer = onnx_session.Run(input_container);

dt2 = DateTime.Now;

// 获取输出

var output = result_infer.First(x => x.Name == "output").AsTensor<float>();

var dimensions = output.Dimensions.ToArray();

int outChannels = dimensions[1]; // 应为 3

int outH = dimensions[2];

int outW = dimensions[3];

float[] normalFloat = output.ToArray();

// 创建三通道法线 Mat (CV_32FC3)，形状 H×W×3

Mat normalRaw = new Mat(outH, outW, MatType.CV_32FC3);

// 注意：输出张量布局为 [N,C,H,W]，需要转换为 H,W,C 存储

int planeSize = outH * outW;

for (int c = 0; c < outChannels; c++)

{

float[] channelData = new float[planeSize];

Array.Copy(normalFloat, c * planeSize, channelData, 0, planeSize);

// 将每个通道的数据填充到 Mat 的对应通道

Mat channelMat = new Mat(outH, outW, MatType.CV_32FC1);

System.Runtime.InteropServices.Marshal.Copy(channelData, 0, channelMat.Data, planeSize);

// 将单通道合并到 normalRaw

Mat[] destChannels = Cv2.Split(normalRaw);

channelMat.CopyTo(destChannels[c]);

Cv2.Merge(destChannels, normalRaw);

}

// ------------------ 后处理 ------------------

// 1. 双线性插值恢复原始尺寸

Mat normalResized = new Mat();

Cv2.Resize(normalRaw, normalResized, new OpenCvSharp.Size(originalWidth, originalHeight), interpolation: InterpolationFlags.Linear);

// 2. 归一化法线向量（确保每个像素的向量长度为1，防止模型输出未严格归一化）

Mat[] normChannels = Cv2.Split(normalResized);

Mat normSq = new Mat();

Cv2.Pow(normChannels[0], 2, normSq);

Mat tmp = new Mat();

Cv2.Pow(normChannels[1], 2, tmp);

Cv2.Add(normSq, tmp, normSq);

Cv2.Pow(normChannels[2], 2, tmp);

Cv2.Add(normSq, tmp, normSq);

Mat norm = new Mat();

Cv2.Sqrt(normSq, norm);

norm += 1e-8; // 避免除零

for (int i = 0; i < 3; i++)

{

Cv2.Divide(normChannels[i], norm, normChannels[i]);

}

Mat normalizedNormal = new Mat();

Cv2.Merge(normChannels, normalizedNormal);

// 3. 将法线从 [-1,1] 映射到 [0,255] 并转为 8UC3 用于显示

Mat normalDisplay = new Mat();

normalizedNormal.ConvertTo(normalDisplay, MatType.CV_32FC3, 127.5, 127.5); // 0.5*255 = 127.5, 映射后值域[0,255]

normalDisplay.ConvertTo(normalDisplay, MatType.CV_8UC3);

// 注意：OpenCV 默认 BGR 顺序，而法线 RGB 直接显示可能会颜色偏差，若需要保持 RGB 可交换 R 和 B

// 这里为了视觉效果，交换 R 和 B 通道使显示更自然（法线常见可视化中 R 对应 X，G 对应 Y，B 对应 Z）

Mat[] displayChannels = Cv2.Split(normalDisplay);

// 交换 R 和 B

Mat temp = displayChannels[0].Clone();

displayChannels[0] = displayChannels[2];

displayChannels[2] = temp;

Cv2.Merge(displayChannels, normalDisplay);

normal_color_map = normalDisplay.Clone();

// 显示结果

pictureBox2.Image = new Bitmap(normal_color_map.ToMemoryStream());

textBox1.Text = $"推理耗时: {(dt2 - dt1).TotalMilliseconds:F2} ms";

button2.Enabled = true;

}

// ----- 按钮3：保存法线彩色图 -----

private void button3_Click(object sender, EventArgs e)

{

if (normal_color_map == null || normal_color_map.Empty())

{

MessageBox.Show("请先执行法线估计！");

return;

}

SaveFileDialog sdf = new SaveFileDialog();

sdf.Title = "保存法线彩色图";

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)

{

Cv2.ImWrite(sdf.FileName, normal_color_map);

MessageBox.Show($"保存成功: {sdf.FileName}");

}

}

// ----- 窗体加载：初始化 ONNX 模型 -----

private void Form1_Load(object sender, EventArgs e)

{

startupPath = Application.StartupPath;

// 法线估计模型路径（请根据实际位置修改）

model_path = System.IO.Path.Combine(startupPath, "model", "normal-model-vitb16_384.onnx");

if (!System.IO.File.Exists(model_path))

{

MessageBox.Show($"模型文件不存在: {model_path}\n请将模型放置于 {startupPath}\\model\\ 目录下");

return;

}

options = new SessionOptions();

options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;

options.AppendExecutionProvider_CPU(0);

// 若需 CUDA，可取消注释

// options.AppendExecutionProvider_CUDA(0);

onnx_session = new InferenceSession(model_path, options);

input_container = new List<NamedOnnxValue>();

// 可选默认测试图片

string testImg = System.IO.Path.Combine(startupPath, "test_img", "0.jpg");

if (System.IO.File.Exists(testImg))

{

image_path = testImg;

pictureBox1.Image = new Bitmap(image_path);

image = new Mat(image_path);

}

}

// ----- 双击图片放大（保留原功能，假设存在 Common 类）-----

private void pictureBox1_DoubleClick(object sender, EventArgs e)

{

Common.ShowNormalImg(pictureBox1.Image);

}

private void pictureBox2_DoubleClick(object sender, EventArgs e)

{

Common.ShowNormalImg(pictureBox2.Image);

}

}

}

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
    {
        // ----- 法线估计专用字段 -----
        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.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 normal_color_map;            // 生成的法线彩色图
        SessionOptions options;
        InferenceSession onnx_session;
        Tensor<float> input_tensor;
        List<NamedOnnxValue> input_container;
        IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
        int inpHeight = 512, inpWidth = 512;

        public Form1()
        {
            InitializeComponent();
        }

        // ----- 按钮1：选择图片 -----
        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;
            normal_color_map = null;
        }

        // ----- 按钮2：执行法线估计推理 -----
        private void button2_Click(object sender, EventArgs e)
        {
            if (string.IsNullOrEmpty(image_path))
            {
                MessageBox.Show("请先选择图片！");
                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. 缩放至模型输入尺寸 512x512
            Mat resized = new Mat();
            Cv2.Resize(image, resized, new OpenCvSharp.Size(inpWidth, inpHeight));

            // 2. 转换为浮点型并归一化到 [0,1]
            resized.ConvertTo(resized, MatType.CV_32FC3, 1.0 / 255.0);

            // 3. 分离 BGR 通道，并按 RGB 顺序填充（模型预期 RGB）
            Mat[] channels = Cv2.Split(resized);   // channels[0]=B, [1]=G, [2]=R
            int channelSize = inpHeight * inpWidth;
            float[] inputData = new float[3 * channelSize];

            // 将 B,G,R 重新排列为 R,G,B
            for (int c = 0; c < 3; c++)
            {
                float[] channelData = new float[channelSize];
                System.Runtime.InteropServices.Marshal.Copy(channels[c].Data, channelData, 0, channelSize);
                int targetIndex = (c == 0) ? 2 : (c == 2) ? 0 : 1; // B->2, G->1, R->0
                Array.Copy(channelData, 0, inputData, targetIndex * channelSize, channelSize);
            }

            // 4. 创建输入张量
            input_tensor = new DenseTensor<float>(inputData, new[] { 1, 3, inpHeight, inpWidth });
            input_container.Clear();
            input_container.Add(NamedOnnxValue.CreateFromTensor("input", input_tensor));

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

            // 获取输出
            var output = result_infer.First(x => x.Name == "output").AsTensor<float>();
            var dimensions = output.Dimensions.ToArray();
            int outChannels = dimensions[1];          // 应为 3
            int outH = dimensions[2];
            int outW = dimensions[3];
            float[] normalFloat = output.ToArray();

            // 创建三通道法线 Mat (CV_32FC3)，形状 H×W×3
            Mat normalRaw = new Mat(outH, outW, MatType.CV_32FC3);
            // 注意：输出张量布局为 [N,C,H,W]，需要转换为 H,W,C 存储
            int planeSize = outH * outW;
            for (int c = 0; c < outChannels; c++)
            {
                float[] channelData = new float[planeSize];
                Array.Copy(normalFloat, c * planeSize, channelData, 0, planeSize);
                // 将每个通道的数据填充到 Mat 的对应通道
                Mat channelMat = new Mat(outH, outW, MatType.CV_32FC1);
                System.Runtime.InteropServices.Marshal.Copy(channelData, 0, channelMat.Data, planeSize);
                // 将单通道合并到 normalRaw
                Mat[] destChannels = Cv2.Split(normalRaw);
                channelMat.CopyTo(destChannels[c]);
                Cv2.Merge(destChannels, normalRaw);
            }

            // ------------------ 后处理 ------------------
            // 1. 双线性插值恢复原始尺寸
            Mat normalResized = new Mat();
            Cv2.Resize(normalRaw, normalResized, new OpenCvSharp.Size(originalWidth, originalHeight), interpolation: InterpolationFlags.Linear);

            // 2. 归一化法线向量（确保每个像素的向量长度为1，防止模型输出未严格归一化）
            Mat[] normChannels = Cv2.Split(normalResized);
            Mat normSq = new Mat();
            Cv2.Pow(normChannels[0], 2, normSq);
            Mat tmp = new Mat();
            Cv2.Pow(normChannels[1], 2, tmp);
            Cv2.Add(normSq, tmp, normSq);
            Cv2.Pow(normChannels[2], 2, tmp);
            Cv2.Add(normSq, tmp, normSq);
            Mat norm = new Mat();
            Cv2.Sqrt(normSq, norm);
            norm += 1e-8;   // 避免除零
            for (int i = 0; i < 3; i++)
            {
                Cv2.Divide(normChannels[i], norm, normChannels[i]);
            }
            Mat normalizedNormal = new Mat();
            Cv2.Merge(normChannels, normalizedNormal);

            // 3. 将法线从 [-1,1] 映射到 [0,255] 并转为 8UC3 用于显示
            Mat normalDisplay = new Mat();
            normalizedNormal.ConvertTo(normalDisplay, MatType.CV_32FC3, 127.5, 127.5); // 0.5*255 = 127.5, 映射后值域[0,255]
            normalDisplay.ConvertTo(normalDisplay, MatType.CV_8UC3);

            // 注意：OpenCV 默认 BGR 顺序，而法线 RGB 直接显示可能会颜色偏差，若需要保持 RGB 可交换 R 和 B
            // 这里为了视觉效果，交换 R 和 B 通道使显示更自然（法线常见可视化中 R 对应 X，G 对应 Y，B 对应 Z）
            Mat[] displayChannels = Cv2.Split(normalDisplay);
            // 交换 R 和 B
            Mat temp = displayChannels[0].Clone();
            displayChannels[0] = displayChannels[2];
            displayChannels[2] = temp;
            Cv2.Merge(displayChannels, normalDisplay);

            normal_color_map = normalDisplay.Clone();

            // 显示结果
            pictureBox2.Image = new Bitmap(normal_color_map.ToMemoryStream());
            textBox1.Text = $"推理耗时: {(dt2 - dt1).TotalMilliseconds:F2} ms";
            button2.Enabled = true;
        }

        // ----- 按钮3：保存法线彩色图 -----
        private void button3_Click(object sender, EventArgs e)
        {
            if (normal_color_map == null || normal_color_map.Empty())
            {
                MessageBox.Show("请先执行法线估计！");
                return;
            }

            SaveFileDialog sdf = new SaveFileDialog();
            sdf.Title = "保存法线彩色图";
            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)
            {
                Cv2.ImWrite(sdf.FileName, normal_color_map);
                MessageBox.Show($"保存成功: {sdf.FileName}");
            }
        }

        // ----- 窗体加载：初始化 ONNX 模型 -----
        private void Form1_Load(object sender, EventArgs e)
        {
            startupPath = Application.StartupPath;
            // 法线估计模型路径（请根据实际位置修改）
            model_path = System.IO.Path.Combine(startupPath, "model", "normal-model-vitb16_384.onnx");
            if (!System.IO.File.Exists(model_path))
            {
                MessageBox.Show($"模型文件不存在: {model_path}\n请将模型放置于 {startupPath}\\model\\ 目录下");
                return;
            }

            options = new SessionOptions();
            options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
            options.AppendExecutionProvider_CPU(0);
            // 若需 CUDA，可取消注释
            // options.AppendExecutionProvider_CUDA(0);

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

            // 可选默认测试图片
            string testImg = System.IO.Path.Combine(startupPath, "test_img", "0.jpg");
            if (System.IO.File.Exists(testImg))
            {
                image_path = testImg;
                pictureBox1.Image = new Bitmap(image_path);
                image = new Mat(image_path);
            }
        }

        // ----- 双击图片放大（保留原功能，假设存在 Common 类）-----
        private void pictureBox1_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox1.Image);
        }

        private void pictureBox2_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox2.Image);
        }
    }
}

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