C# OpenVINO Yolov8-OBB 旋转目标检测

2024-03-09 23:28

本文主要是介绍C# OpenVINO 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

项目

代码

下载 


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 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);}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);}}
}

下载 

源码下载

这篇关于C# OpenVINO 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/792278

相关文章

2. c#从不同cs的文件调用函数

1.文件目录如下: 2. Program.cs文件的主函数如下 using System;using System.Collections.Generic;using System.Linq;using System.Threading.Tasks;using System.Windows.Forms;namespace datasAnalysis{internal static

综合安防管理平台LntonAIServer视频监控汇聚抖动检测算法优势

LntonAIServer视频质量诊断功能中的抖动检测是一个专门针对视频稳定性进行分析的功能。抖动通常是指视频帧之间的不必要运动,这种运动可能是由于摄像机的移动、传输中的错误或编解码问题导致的。抖动检测对于确保视频内容的平滑性和观看体验至关重要。 优势 1. 提高图像质量 - 清晰度提升:减少抖动,提高图像的清晰度和细节表现力,使得监控画面更加真实可信。 - 细节增强:在低光条件下,抖

C#实战|大乐透选号器[6]:实现实时显示已选择的红蓝球数量

哈喽,你好啊,我是雷工。 关于大乐透选号器在前面已经记录了5篇笔记,这是第6篇; 接下来实现实时显示当前选中红球数量,蓝球数量; 以下为练习笔记。 01 效果演示 当选择和取消选择红球或蓝球时,在对应的位置显示实时已选择的红球、蓝球的数量; 02 标签名称 分别设置Label标签名称为:lblRedCount、lblBlueCount

烟火目标检测数据集 7800张 烟火检测 带标注 voc yolo

一个包含7800张带标注图像的数据集,专门用于烟火目标检测,是一个非常有价值的资源,尤其对于那些致力于公共安全、事件管理和烟花表演监控等领域的人士而言。下面是对此数据集的一个详细介绍: 数据集名称:烟火目标检测数据集 数据集规模: 图片数量:7800张类别:主要包含烟火类目标,可能还包括其他相关类别,如烟火发射装置、背景等。格式:图像文件通常为JPEG或PNG格式;标注文件可能为X

用命令行的方式启动.netcore webapi

用命令行的方式启动.netcore web项目 进入指定的项目文件夹,比如我发布后的代码放在下面文件夹中 在此地址栏中输入“cmd”,打开命令提示符,进入到发布代码目录 命令行启动.netcore项目的命令为:  dotnet 项目启动文件.dll --urls="http://*:对外端口" --ip="本机ip" --port=项目内部端口 例: dotnet Imagine.M

poj 2187 凸包or旋转qia壳法

题意: 给n(50000)个点,求这些点与点之间距离最大的距离。 解析: 先求凸包然后暴力。 或者旋转卡壳大法。 代码: #include <iostream>#include <cstdio>#include <cstdlib>#include <algorithm>#include <cstring>#include <cmath>#include <s

基于 YOLOv5 的积水检测系统:打造高效智能的智慧城市应用

在城市发展中,积水问题日益严重,特别是在大雨过后,积水往往会影响交通甚至威胁人们的安全。通过现代计算机视觉技术,我们能够智能化地检测和识别积水区域,减少潜在危险。本文将介绍如何使用 YOLOv5 和 PyQt5 搭建一个积水检测系统,结合深度学习和直观的图形界面,为用户提供高效的解决方案。 源码地址: PyQt5+YoloV5 实现积水检测系统 预览: 项目背景

JavaFX应用更新检测功能(在线自动更新方案)

JavaFX开发的桌面应用属于C端,一般来说需要版本检测和自动更新功能,这里记录一下一种版本检测和自动更新的方法。 1. 整体方案 JavaFX.应用版本检测、自动更新主要涉及一下步骤: 读取本地应用版本拉取远程版本并比较两个版本如果需要升级,那么拉取更新历史弹出升级控制窗口用户选择升级时,拉取升级包解压,重启应用用户选择忽略时,本地版本标志为忽略版本用户选择取消时,隐藏升级控制窗口 2.

Android 10.0 mtk平板camera2横屏预览旋转90度横屏拍照图片旋转90度功能实现

1.前言 在10.0的系统rom定制化开发中,在进行一些平板等默认横屏的设备开发的过程中,需要在进入camera2的 时候,默认预览图像也是需要横屏显示的,在上一篇已经实现了横屏预览功能,然后发现横屏预览后,拍照保存的图片 依然是竖屏的,所以说同样需要将图片也保存为横屏图标了,所以就需要看下mtk的camera2的相关横屏保存图片功能, 如何实现实现横屏保存图片功能 如图所示: 2.mtk

C# dateTimePicker 显示年月日,时分秒

dateTimePicker默认只显示日期,如果需要显示年月日,时分秒,只需要以下两步: 1.dateTimePicker1.Format = DateTimePickerFormat.Time 2.dateTimePicker1.CustomFormat = yyyy-MM-dd HH:mm:ss Tips:  a. dateTimePicker1.ShowUpDown = t