C# OpenVINO Yolov8-OBB 旋转目标检测

🕗 发布于 2024-02-29 09:26 openvino YOLO 目标检测 人工智能 opencv

效果

模型

项目

代码

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C# OpenVINO Yolov8-OBB 旋转目标检测

效果

模型

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'}
---------------------------------------------------------------

Inputs
-------------------------
name：images
tensor：Float[1, 3, 640, 640]
---------------------------------------------------------------

Outputs
-------------------------
name：output0
tensor：Float[1, 20, 8400]
---------------------------------------------------------------

项目

代码

using OpenCvSharp;
using OpenCvSharp.Dnn;
using Sdcb.OpenVINO;
using Sdcb.OpenVINO.Natives;
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Drawing;
using System.IO;
using System.Text;
using System.Windows.Forms;

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

string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
string image_path = "";
string classer_path;
string model_path;
Mat image;
Mat result_image;

string[] class_lables;

StringBuilder sb = new StringBuilder();

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 = "";
pictureBox2.Image = null;
}

unsafe private void button2_Click(object sender, EventArgs e)
{
if (pictureBox1.Image == null)
{
return;
}

pictureBox2.Image = null;
textBox1.Text = "";
sb.Clear();
Application.DoEvents();

Model rawModel = OVCore.Shared.ReadModel(model_path);
PrePostProcessor pp = rawModel.CreatePrePostProcessor();
PreProcessInputInfo inputInfo = pp.Inputs.Primary;

inputInfo.TensorInfo.Layout = Sdcb.OpenVINO.Layout.NHWC;
inputInfo.ModelInfo.Layout = Sdcb.OpenVINO.Layout.NCHW;

Model m = pp.BuildModel();
CompiledModel cm = OVCore.Shared.CompileModel(m, "CPU");
InferRequest ir = cm.CreateInferRequest();

Stopwatch stopwatch = new Stopwatch();

//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 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));

Mat f32 = new Mat();
resize_image.ConvertTo(f32, MatType.CV_32FC3, 1.0 / 255);

using (Tensor input = Tensor.FromRaw(
new ReadOnlySpan<byte>((void*)f32.Data, (int)((long)f32.DataEnd - (long)f32.DataStart)),
new Shape(1, f32.Rows, f32.Cols, 3),
ov_element_type_e.F32))
{
ir.Inputs.Primary = input;
}
double preprocessTime = stopwatch.Elapsed.TotalMilliseconds;
stopwatch.Restart();

ir.Run();
double inferTime = stopwatch.Elapsed.TotalMilliseconds;
stopwatch.Restart();

using (Tensor output = ir.Outputs.Primary)
{
ReadOnlySpan<float> data = output.GetData<float>();

Mat result_data = new Mat(20, 8400, MatType.CV_32F, data.ToArray());
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);
}

double postprocessTime = stopwatch.Elapsed.TotalMilliseconds;
stopwatch.Stop();
double totalTime = preprocessTime + inferTime + postprocessTime;

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);
}

sb.AppendLine($"Preprocess: {preprocessTime:F2}ms");
sb.AppendLine($"Infer: {inferTime:F2}ms");
sb.AppendLine($"Postprocess: {postprocessTime:F2}ms");
sb.AppendLine($"Total: {totalTime:F2}ms");

pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
textBox1.Text = sb.ToString();
}
}

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

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 = "2.png";
pictureBox1.Image = new Bitmap(image_path);
}
}
}

using OpenCvSharp;
using OpenCvSharp.Dnn;
using Sdcb.OpenVINO;
using Sdcb.OpenVINO.Natives;
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Drawing;
using System.IO;
using System.Text;
using System.Windows.Forms;

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

        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = "";
        string classer_path;
        string model_path;
        Mat image;
        Mat result_image;

        string[] class_lables;

        StringBuilder sb = new StringBuilder();

        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 = "";
            pictureBox2.Image = null;
        }

        unsafe private void button2_Click(object sender, EventArgs e)
        {
            if (pictureBox1.Image == null)
            {
                return;
            }

            pictureBox2.Image = null;
            textBox1.Text = "";
            sb.Clear();
            Application.DoEvents();

            Model rawModel = OVCore.Shared.ReadModel(model_path);
            PrePostProcessor pp = rawModel.CreatePrePostProcessor();
            PreProcessInputInfo inputInfo = pp.Inputs.Primary;

            inputInfo.TensorInfo.Layout = Sdcb.OpenVINO.Layout.NHWC;
            inputInfo.ModelInfo.Layout = Sdcb.OpenVINO.Layout.NCHW;

            Model m = pp.BuildModel();
            CompiledModel cm = OVCore.Shared.CompileModel(m, "CPU");
            InferRequest ir = cm.CreateInferRequest();
           
            Stopwatch stopwatch = new Stopwatch();

            //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 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));

            Mat f32 = new Mat();
            resize_image.ConvertTo(f32, MatType.CV_32FC3, 1.0 / 255);

            using (Tensor input = Tensor.FromRaw(
                 new ReadOnlySpan<byte>((void*)f32.Data, (int)((long)f32.DataEnd - (long)f32.DataStart)),
                new Shape(1, f32.Rows, f32.Cols, 3),
                ov_element_type_e.F32))
            {
                ir.Inputs.Primary = input;
            }
            double preprocessTime = stopwatch.Elapsed.TotalMilliseconds;
            stopwatch.Restart();

            ir.Run();
            double inferTime = stopwatch.Elapsed.TotalMilliseconds;
            stopwatch.Restart();

            using (Tensor output = ir.Outputs.Primary)
            {
                ReadOnlySpan<float> data = output.GetData<float>();

                Mat result_data = new Mat(20, 8400, MatType.CV_32F, data.ToArray());
                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);
                }

                double postprocessTime = stopwatch.Elapsed.TotalMilliseconds;
                stopwatch.Stop();
                double totalTime = preprocessTime + inferTime + postprocessTime;

                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);
                }

                sb.AppendLine($"Preprocess: {preprocessTime:F2}ms");
                sb.AppendLine($"Infer: {inferTime:F2}ms");
                sb.AppendLine($"Postprocess: {postprocessTime:F2}ms");
                sb.AppendLine($"Total: {totalTime:F2}ms");

                pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
                textBox1.Text = sb.ToString();
            }
        }

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

            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 = "2.png";
            pictureBox1.Image = new Bitmap(image_path);
        }
    }
}

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原文地址：https://blog.csdn.net/lw112190/article/details/136327087

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C# OpenVINO Yolov8-OBB 旋转目标检测

效果

模型

项目

代码

下载

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