C# OpenCvSharp DNN Yolov8-OBB 旋转目标检测

2024-02-27 07:44
文章标签 c# 目标 检测 yolov8 旋转 .net dnn netcore opencvsharp obb

本文主要是介绍C# OpenCvSharp DNN Yolov8-OBB 旋转目标检测，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

目录

效果

模型信息

项目

代码

下载

C# OpenCvSharp DNN 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'}
---------------------------------------------------------------

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 System;
using System.Collections.Generic;
using System.Drawing;
using System.IO;
using System.Linq;
using System.Windows.Forms;

namespace OpenCvSharp_DNN_Demo
{
    public partial class frmMain : Form
    {
        public frmMain()
        {
            InitializeComponent();
        }

        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = "";

        DateTime dt1 = DateTime.Now;
        DateTime dt2 = DateTime.Now;

        string modelpath;
        string classer_path;

        List<string> class_names;

        Net opencv_net;
        Mat BN_image;

        Mat image;
        Mat result_image;

        string[] class_lables;

        private void button1_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.Filter = fileFilter;
            if (ofd.ShowDialog() != DialogResult.OK) return;

            pictureBox1.Image = null;
            pictureBox2.Image = null;
            textBox1.Text = "";

            image_path = ofd.FileName;
            pictureBox1.Image = new Bitmap(image_path);
            image = new Mat(image_path);
        }

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

            opencv_net = CvDnn.ReadNetFromOnnx(modelpath);

            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);

        }

        private  void button2_Click(object sender, EventArgs e)
        {
            if (image_path == "")
            {
                return;
            }
            textBox1.Text = "检测中，请稍等……";
            pictureBox2.Image = null;
            button2.Enabled = false;
            Application.DoEvents();

            image = new Mat(image_path);

            //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));

            BN_image = CvDnn.BlobFromImage(resize_image, 1 / 255.0, new OpenCvSharp.Size(640, 640), new Scalar(0, 0, 0), true, false);

            //配置图片输入数据
            opencv_net.SetInput(BN_image);

            //模型推理，读取推理结果
            Mat[] outs = new Mat[1] { new Mat() };
            string[] outBlobNames = opencv_net.GetUnconnectedOutLayersNames().ToArray();

            dt1 = DateTime.Now;

            opencv_net.Forward(outs, outBlobNames);

            dt2 = DateTime.Now;

            int num_proposal = outs[0].Size(1);
            int nout = outs[0].Size(2);

            if (outs[0].Dims > 2)
            {
                outs[0] = outs[0].Reshape(0, num_proposal);
            }

            Mat result_data = new Mat(20, 8400, MatType.CV_32F);
            result_data = outs[0].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 pictureBox2_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox2.Image);
        }

        private void pictureBox1_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox1.Image);
        }
    }
}

using OpenCvSharp;
using OpenCvSharp.Dnn;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.IO;
using System.Linq;
using System.Windows.Forms;namespace OpenCvSharp_DNN_Demo
{public partial class frmMain : Form{public frmMain(){InitializeComponent();}string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";string image_path = "";DateTime dt1 = DateTime.Now;DateTime dt2 = DateTime.Now;string modelpath;string classer_path;List<string> class_names;Net opencv_net;Mat BN_image;Mat image;Mat result_image;string[] class_lables;private void button1_Click(object sender, EventArgs e){OpenFileDialog ofd = new OpenFileDialog();ofd.Filter = fileFilter;if (ofd.ShowDialog() != DialogResult.OK) return;pictureBox1.Image = null;pictureBox2.Image = null;textBox1.Text = "";image_path = ofd.FileName;pictureBox1.Image = new Bitmap(image_path);image = new Mat(image_path);}private void Form1_Load(object sender, EventArgs e){modelpath = "model/yolov8s-obb.onnx";classer_path = "model/lable.txt";opencv_net = CvDnn.ReadNetFromOnnx(modelpath);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);}private  void button2_Click(object sender, EventArgs e){if (image_path == ""){return;}textBox1.Text = "检测中，请稍等……";pictureBox2.Image = null;button2.Enabled = false;Application.DoEvents();image = new Mat(image_path);//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));BN_image = CvDnn.BlobFromImage(resize_image, 1 / 255.0, new OpenCvSharp.Size(640, 640), new Scalar(0, 0, 0), true, false);//配置图片输入数据opencv_net.SetInput(BN_image);//模型推理，读取推理结果Mat[] outs = new Mat[1] { new Mat() };string[] outBlobNames = opencv_net.GetUnconnectedOutLayersNames().ToArray();dt1 = DateTime.Now;opencv_net.Forward(outs, outBlobNames);dt2 = DateTime.Now;int num_proposal = outs[0].Size(1);int nout = outs[0].Size(2);if (outs[0].Dims > 2){outs[0] = outs[0].Reshape(0, num_proposal);}Mat result_data = new Mat(20, 8400, MatType.CV_32F);result_data = outs[0].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 pictureBox2_DoubleClick(object sender, EventArgs e){Common.ShowNormalImg(pictureBox2.Image);}private void pictureBox1_DoubleClick(object sender, EventArgs e){Common.ShowNormalImg(pictureBox1.Image);}}
}

下载

源码下载

这篇关于C# OpenCvSharp DNN Yolov8-OBB 旋转目标检测的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！


http://www.chinasem.cn/article/751684。

相关文章

C#中lock关键字的使用小结

C#中lock关键字的使用小结

《C#中lock关键字的使用小结》在C#中,lock关键字用于确保当一个线程位于给定实例的代码块中时,其他线程无法访问同一实例的该代码块,下面就来介绍一下lock关键字的使用... 目录使用方式工作原理注意事项示例代码为什么不能lock值类型在C#中，lock关键字用于确保当一个线程位于给定实例的代码块中时
阅读更多...
C# $字符串插值的使用

C# $字符串插值的使用

《C#$字符串插值的使用》本文介绍了C#中的字符串插值功能,详细介绍了使用$符号的实现方式,文中通过示例代码介绍的非常详细,需要的朋友们下面随着小编来一起学习学习吧... 目录$ 字符使用方式创建内插字符串包含不同的数据类型控制内插表达式的格式控制内插表达式的对齐方式内插表达式中使用转义序列内插表达式中使用
阅读更多...
C#中的Converter的具体应用

C#中的Converter的具体应用

《C#中的Converter的具体应用》C#中的Converter提供了一种灵活的类型转换机制,本文详细介绍了Converter的基本概念、使用场景,具有一定的参考价值,感兴趣的可以了解一下... 目录Converter的基本概念1. Converter委托2. 使用场景布尔型转换示例示例1：简单的字符串到
阅读更多...
C#监听txt文档获取新数据方式

C#监听txt文档获取新数据方式

《C#监听txt文档获取新数据方式》文章介绍通过监听txt文件获取最新数据,并实现开机自启动、禁用窗口关闭按钮、阻止Ctrl+C中断及防止程序退出等功能,代码整合于主函数中,供参考学习... 目录前言一、监听txt文档增加数据二、其他功能1. 设置开机自启动2. 禁止控制台窗口关闭按钮3. 阻止Ctrl +
阅读更多...
C#解析JSON数据全攻略指南

C#解析JSON数据全攻略指南

《C#解析JSON数据全攻略指南》这篇文章主要为大家详细介绍了使用C#解析JSON数据全攻略指南,文中的示例代码讲解详细,感兴趣的小伙伴可以跟随小编一起学习一下... 目录一、为什么jsON是C#开发必修课？二、四步搞定网络JSON数据1. 获取数据 - HttpClient最佳实践2. 动态解析 - 快速
阅读更多...
C#连接SQL server数据库命令的基本步骤

C#连接SQL server数据库命令的基本步骤

《C#连接SQLserver数据库命令的基本步骤》文章讲解了连接SQLServer数据库的步骤,包括引入命名空间、构建连接字符串、使用SqlConnection和SqlCommand执行SQL操作,... 目录建议配合使用：如何下载和安装SQL server数据库-CSDN博客1. 引入必要的命名空间2.
阅读更多...
Linux系统性能检测命令详解

Linux系统性能检测命令详解

《Linux系统性能检测命令详解》本文介绍了Linux系统常用的监控命令（如top、vmstat、iostat、htop等）及其参数功能,涵盖进程状态、内存使用、磁盘I/O、系统负载等多维度资源监控,... 目录toppsuptimevmstatIOStatiotopslabtophtopdstatnmon
阅读更多...
C#读写文本文件的多种方式详解

C#读写文本文件的多种方式详解

《C#读写文本文件的多种方式详解》这篇文章主要为大家详细介绍了C#中各种常用的文件读写方式,包括文本文件,二进制文件、CSV文件、JSON文件等,有需要的小伙伴可以参考一下... 目录一、文本文件读写1. 使用 File 类的静态方法2. 使用 StreamReader 和 StreamWriter二、二进
阅读更多...
C#中Guid类使用小结

C#中Guid类使用小结

《C#中Guid类使用小结》本文主要介绍了C#中Guid类用于生成和操作128位的唯一标识符,用于数据库主键及分布式系统,支持通过NewGuid、Parse等方法生成,感兴趣的可以了解一下... 目录前言一、什么是 Guid二、生成 Guid1. 使用 Guid.NewGuid() 方法2. 从字符串创建
阅读更多...
C# 比较两个list 之间元素差异的常用方法

C# 比较两个list 之间元素差异的常用方法

《C#比较两个list之间元素差异的常用方法》：本文主要介绍C#比较两个list之间元素差异,本文通过实例代码给大家介绍的非常详细,对大家的学习或工作具有一定的参考借鉴价值,需要的朋友参考下吧... 目录1. 使用Except方法2. 使用Except的逆操作3. 使用LINQ的Join,GroupJoin
阅读更多...

Copyright China编程 23002807@qq.com

沪ICP备2023033072号-2