C# Onnx Yolov8-OBB 旋转目标检测-编程知识

效果

模型信息

项目

代码

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C# Onnx Yolov8-OBB 旋转目标检测

效果

模型信息

Model Properties
-------------------------
date：2024-02-26T08:38:44.171849
description：Ultralytics YOLOv8s-obb model trained on runs/DOTAv1.0-ms.yaml
author：Ultralytics
task：obb
license：AGPL-3.0 https://ultralytics.com/license
version：8.1.18
stride：32
batch：1
imgsz：[640, 640]
names：{0: 'plane', 1: 'ship', 2: 'storage tank', 3: 'baseball diamond', 4: 'tennis court', 5: 'basketball court', 6: 'ground track field', 7: 'harbor', 8: 'bridge', 9: 'large vehicle', 10: 'small vehicle', 11: 'helicopter', 12: 'roundabout', 13: 'soccer ball field', 14: 'swimming pool'}
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Inputs
-------------------------
name：images
tensor：Float[1, 3, 640, 640]
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Outputs
-------------------------
name：output0
tensor：Float[1, 20, 8400]
---------------------------------------------------------------

项目

代码

using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using OpenCvSharp;
using OpenCvSharp.Dnn;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Drawing.Imaging;
using System.IO;
using System.Linq;
using System.Numerics;
using System.Windows.Forms;

namespace Onnx_Yolov8_Demo
{
public partial class Form1 : Form
{
public Form1()
{
InitializeComponent();
}

string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
string image_path = "";
string classer_path;
DateTime dt1 = DateTime.Now;
DateTime dt2 = DateTime.Now;
string model_path;
Mat image;
Mat result_image;
public string[] class_lables;
SessionOptions options;
InferenceSession onnx_session;
Tensor<float> input_tensor;
List<NamedOnnxValue> input_container;
IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
DisposableNamedOnnxValue[] results_onnxvalue;

Tensor<float> result_tensors;

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();

//图片缩放
image = new Mat(image_path);
int max_image_length = image.Cols > image.Rows ? image.Cols : image.Rows;
Mat max_image = Mat.Zeros(new OpenCvSharp.Size(max_image_length, max_image_length), MatType.CV_8UC3);
Rect roi = new Rect(0, 0, image.Cols, image.Rows);
image.CopyTo(new Mat(max_image, roi));

float[] result_array;
float factor = (float)(max_image_length / 640.0);

// 将图片转为RGB通道
Mat image_rgb = new Mat();
Cv2.CvtColor(max_image, image_rgb, ColorConversionCodes.BGR2RGB);
Mat resize_image = new Mat();
Cv2.Resize(image_rgb, resize_image, new OpenCvSharp.Size(640, 640));

// 输入Tensor
for (int y = 0; y < resize_image.Height; y++)
{
for (int x = 0; x < resize_image.Width; x++)
{
input_tensor[0, 0, y, x] = resize_image.At<Vec3b>(y, x)[0] / 255f;
input_tensor[0, 1, y, x] = resize_image.At<Vec3b>(y, x)[1] / 255f;
input_tensor[0, 2, y, x] = resize_image.At<Vec3b>(y, x)[2] / 255f;
}
}

//将 input_tensor 放入一个输入参数的容器，并指定名称
input_container.Add(NamedOnnxValue.CreateFromTensor("images", input_tensor));

dt1 = DateTime.Now;
//运行 Inference 并获取结果
result_infer = onnx_session.Run(input_container);
dt2 = DateTime.Now;

// 将输出结果转为DisposableNamedOnnxValue数组
results_onnxvalue = result_infer.ToArray();

// 读取第一个节点输出并转为Tensor数据
result_tensors = results_onnxvalue[0].AsTensor<float>();

result_array = result_tensors.ToArray();

Mat result_data = new Mat(20, 8400, MatType.CV_32F, result_array);
result_data = result_data.T();
List<Rect2d> position_boxes = new List<Rect2d>();
List<int> class_ids = new List<int>();
List<float> confidences = new List<float>();
List<float> rotations = new List<float>();
// Preprocessing output results
for (int i = 0; i < result_data.Rows; i++)
{
Mat classes_scores = new Mat(result_data, new Rect(4, i, 15, 1));
OpenCvSharp.Point max_classId_point, min_classId_point;
double max_score, min_score;
// Obtain the maximum value and its position in a set of data
Cv2.MinMaxLoc(classes_scores, out min_score, out max_score,
out min_classId_point, out max_classId_point);
// Confidence level between 0 ~ 1
// Obtain identification box information
if (max_score > 0.25)
{
float cx = result_data.At<float>(i, 0);
float cy = result_data.At<float>(i, 1);
float ow = result_data.At<float>(i, 2);
float oh = result_data.At<float>(i, 3);
double x = (cx - 0.5 * ow) * factor;
double y = (cy - 0.5 * oh) * factor;
double width = ow * factor;
double height = oh * factor;
Rect2d box = new Rect2d();
box.X = x;
box.Y = y;
box.Width = width;
box.Height = height;
position_boxes.Add(box);
class_ids.Add(max_classId_point.X);
confidences.Add((float)max_score);
rotations.Add(result_data.At<float>(i, 19));
}
}

// NMS
int[] indexes = new int[position_boxes.Count];
CvDnn.NMSBoxes(position_boxes, confidences, 0.25f, 0.7f, out indexes);
List<RotatedRect> rotated_rects = new List<RotatedRect>();
for (int i = 0; i < indexes.Length; i++)
{
int index = indexes[i];
float w = (float)position_boxes[index].Width;
float h = (float)position_boxes[index].Height;
float x = (float)position_boxes[index].X + w / 2;
float y = (float)position_boxes[index].Y + h / 2;
float r = rotations[index];
float w_ = w > h ? w : h;
float h_ = w > h ? h : w;
r = (float)((w > h ? r : (float)(r + Math.PI / 2)) % Math.PI);
RotatedRect rotate = new RotatedRect(new Point2f(x, y), new Size2f(w_, h_), (float)(r * 180.0 / Math.PI));
rotated_rects.Add(rotate);
}

result_image = image.Clone();

for (int i = 0; i < indexes.Length; i++)
{
int index = indexes[i];
Point2f[] points = rotated_rects[i].Points();

for (int j = 0; j < 4; j++)
{
Cv2.Line(result_image, (OpenCvSharp.Point)points[j], (OpenCvSharp.Point)points[(j + 1) % 4], new Scalar(0, 255, 0), 2);
}

Cv2.PutText(result_image, class_lables[class_ids[index]] + "-" + confidences[index].ToString("0.00"),
(OpenCvSharp.Point)points[0], HersheyFonts.HersheySimplex, 0.8, new Scalar(0, 0, 255), 2);
}

pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";

button2.Enabled = true;
}

private void Form1_Load(object sender, EventArgs e)
{
model_path = "model/yolov8s-obb.onnx";
classer_path = "model/lable.txt";

// 创建输出会话，用于输出模型读取信息
options = new SessionOptions();
options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行

// 创建推理模型类，读取本地模型文件
onnx_session = new InferenceSession(model_path, options);//model_path 为onnx模型文件的路径

// 输入Tensor
input_tensor = new DenseTensor<float>(new[] { 1, 3, 640, 640 });
// 创建输入容器
input_container = new List<NamedOnnxValue>();

List<string> str = new List<string>();
StreamReader sr = new StreamReader(classer_path);
string line;
while ((line = sr.ReadLine()) != null)
{
str.Add(line);
}
class_lables = str.ToArray();

image_path = "test_img/1.png";
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 (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);
}
}
}
}

using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using OpenCvSharp;
using OpenCvSharp.Dnn;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Drawing.Imaging;
using System.IO;
using System.Linq;
using System.Numerics;
using System.Windows.Forms;namespace Onnx_Yolov8_Demo
{public partial class Form1 : Form{public Form1(){InitializeComponent();}string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";string image_path = "";string classer_path;DateTime dt1 = DateTime.Now;DateTime dt2 = DateTime.Now;string model_path;Mat image;Mat result_image;public string[] class_lables;SessionOptions options;InferenceSession onnx_session;Tensor<float> input_tensor;List<NamedOnnxValue> input_container;IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;DisposableNamedOnnxValue[] results_onnxvalue;Tensor<float> result_tensors;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();//图片缩放image = new Mat(image_path);int max_image_length = image.Cols > image.Rows ? image.Cols : image.Rows;Mat max_image = Mat.Zeros(new OpenCvSharp.Size(max_image_length, max_image_length), MatType.CV_8UC3);Rect roi = new Rect(0, 0, image.Cols, image.Rows);image.CopyTo(new Mat(max_image, roi));float[] result_array;float factor = (float)(max_image_length / 640.0);// 将图片转为RGB通道Mat image_rgb = new Mat();Cv2.CvtColor(max_image, image_rgb, ColorConversionCodes.BGR2RGB);Mat resize_image = new Mat();Cv2.Resize(image_rgb, resize_image, new OpenCvSharp.Size(640, 640));// 输入Tensorfor (int y = 0; y < resize_image.Height; y++){for (int x = 0; x < resize_image.Width; x++){input_tensor[0, 0, y, x] = resize_image.At<Vec3b>(y, x)[0] / 255f;input_tensor[0, 1, y, x] = resize_image.At<Vec3b>(y, x)[1] / 255f;input_tensor[0, 2, y, x] = resize_image.At<Vec3b>(y, x)[2] / 255f;}}//将 input_tensor 放入一个输入参数的容器，并指定名称input_container.Add(NamedOnnxValue.CreateFromTensor("images", input_tensor));dt1 = DateTime.Now;//运行 Inference 并获取结果result_infer = onnx_session.Run(input_container);dt2 = DateTime.Now;// 将输出结果转为DisposableNamedOnnxValue数组results_onnxvalue = result_infer.ToArray();// 读取第一个节点输出并转为Tensor数据result_tensors = results_onnxvalue[0].AsTensor<float>();result_array = result_tensors.ToArray();Mat result_data = new Mat(20, 8400, MatType.CV_32F, result_array);result_data = result_data.T();List<Rect2d> position_boxes = new List<Rect2d>();List<int> class_ids = new List<int>();List<float> confidences = new List<float>();List<float> rotations = new List<float>();// Preprocessing output resultsfor (int i = 0; i < result_data.Rows; i++){Mat classes_scores = new Mat(result_data, new Rect(4, i, 15, 1));OpenCvSharp.Point max_classId_point, min_classId_point;double max_score, min_score;// Obtain the maximum value and its position in a set of dataCv2.MinMaxLoc(classes_scores, out min_score, out max_score,out min_classId_point, out max_classId_point);// Confidence level between 0 ~ 1// Obtain identification box informationif (max_score > 0.25){float cx = result_data.At<float>(i, 0);float cy = result_data.At<float>(i, 1);float ow = result_data.At<float>(i, 2);float oh = result_data.At<float>(i, 3);double x = (cx - 0.5 * ow) * factor;double y = (cy - 0.5 * oh) * factor;double width = ow * factor;double height = oh * factor;Rect2d box = new Rect2d();box.X = x;box.Y = y;box.Width = width;box.Height = height;position_boxes.Add(box);class_ids.Add(max_classId_point.X);confidences.Add((float)max_score);rotations.Add(result_data.At<float>(i, 19));}}// NMS int[] indexes = new int[position_boxes.Count];CvDnn.NMSBoxes(position_boxes, confidences, 0.25f, 0.7f, out indexes);List<RotatedRect> rotated_rects = new List<RotatedRect>();for (int i = 0; i < indexes.Length; i++){int index = indexes[i];float w = (float)position_boxes[index].Width;float h = (float)position_boxes[index].Height;float x = (float)position_boxes[index].X + w / 2;float y = (float)position_boxes[index].Y + h / 2;float r = rotations[index];float w_ = w > h ? w : h;float h_ = w > h ? h : w;r = (float)((w > h ? r : (float)(r + Math.PI / 2)) % Math.PI);RotatedRect rotate = new RotatedRect(new Point2f(x, y), new Size2f(w_, h_), (float)(r * 180.0 / Math.PI));rotated_rects.Add(rotate);}result_image = image.Clone();for (int i = 0; i < indexes.Length; i++){int index = indexes[i];Point2f[] points = rotated_rects[i].Points();for (int j = 0; j < 4; j++){Cv2.Line(result_image, (OpenCvSharp.Point)points[j], (OpenCvSharp.Point)points[(j + 1) % 4], new Scalar(0, 255, 0), 2);}Cv2.PutText(result_image, class_lables[class_ids[index]] + "-" + confidences[index].ToString("0.00"),(OpenCvSharp.Point)points[0], HersheyFonts.HersheySimplex, 0.8, new Scalar(0, 0, 255), 2);}pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";button2.Enabled = true;}private void Form1_Load(object sender, EventArgs e){model_path = "model/yolov8s-obb.onnx";classer_path = "model/lable.txt";// 创建输出会话，用于输出模型读取信息options = new SessionOptions();options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行// 创建推理模型类，读取本地模型文件onnx_session = new InferenceSession(model_path, options);//model_path 为onnx模型文件的路径// 输入Tensorinput_tensor = new DenseTensor<float>(new[] { 1, 3, 640, 640 });// 创建输入容器input_container = new List<NamedOnnxValue>();List<string> str = new List<string>();StreamReader sr = new StreamReader(classer_path);string line;while ((line = sr.ReadLine()) != null){str.Add(line);}class_lables = str.ToArray();image_path = "test_img/1.png";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 (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);}}}
}