C# OnnxRuntime 部署 DAViD 表面法线估计

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张建站

2026/4/21 2:37:01

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目录效果模型信息项目代码下载效果模型信息Model Properties-------------------------metadata{}---------------------------------------------------------------Inputs-------------------------nameinputtensorFloat[-1, 3, 512, 512]---------------------------------------------------------------Outputs-------------------------nameoutputtensorFloat[-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BGRMat normal_color_map; // 生成的法线彩色图SessionOptions options;InferenceSession onnx_session;Tensorfloat input_tensor;ListNamedOnnxValue input_container;IDisposableReadOnlyCollectionDisposableNamedOnnxValue 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BGRimage 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 512x512Mat 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 RGBMat[] channels Cv2.Split(resized); // channels[0]B, [1]G, [2]Rint channelSize inpHeight * inpWidth;float[] inputData new float[3 * channelSize];// 将 B,G,R 重新排列为 R,G,Bfor (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-0Array.Copy(channelData, 0, inputData, targetIndex * channelSize, channelSize);}// 4. 创建输入张量input_tensor new DenseTensorfloat(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).AsTensorfloat();var dimensions output.Dimensions.ToArray();int outChannels dimensions[1]; // 应为 3int 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×3Mat 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 normalRawMat[] 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 XG 对应 YB 对应 ZMat[] displayChannels Cv2.Split(normalDisplay);// 交换 R 和 BMat 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 ListNamedOnnxValue();// 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; Tensorfloat input_tensor; ListNamedOnnxValue input_container; IDisposableReadOnlyCollectionDisposableNamedOnnxValue 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 DenseTensorfloat(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).AsTensorfloat(); 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 XG 对应 YB 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 ListNamedOnnxValue(); // 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); } } }下载源码下载