410 lines
14 KiB
C#
410 lines
14 KiB
C#
using Check.Main.Camera;
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using Check.Main.Common;
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using OpenCvSharp;
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using SkiaSharp;
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using System;
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using System.Collections.Generic;
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using System.Drawing;
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using System.Linq;
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using YoloDotNet;
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using YoloDotNet.Models;
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namespace Check.Main.Infer
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{
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/// <summary>
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/// YOLO 检测结果对象，包含标签、置信度与检测框
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/// </summary>
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public class YoloPrediction
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{
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public YoloLabel Label { get; set; }
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public float Score { get; set; }
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public BoundingBox BoundingBox { get; set; }
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}
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/// <summary>
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/// YOLO 类别标签
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/// </summary>
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public class YoloLabel
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{
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public string Name { get; set; }
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}
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/// <summary>
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/// 检测框坐标结构体
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/// </summary>
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public struct BoundingBox
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{
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public float Left { get; set; }
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public float Top { get; set; }
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public float Width { get; set; }
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public float Height { get; set; }
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public BoundingBox(float left, float top, float width, float height)
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{
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Left = left;
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Top = top;
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Width = width;
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Height = height;
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}
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}
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/// <summary>
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/// YOLO 目标检测器实现类
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/// </summary>
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public class YoloDetector : IDetector, IDisposable
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{
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private Yolo _yoloModel;
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private int _modelID;
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private float _confidenceThreshold = 0.25f;
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private float _nmsThreshold = 0.45f;
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/// <summary>
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/// 初始化 YOLO 检测器并加载模型
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/// </summary>
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/// <param name="modelIdStr">模型 ID 字符串</param>
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/// <param name="detectionSettings">可选检测参数</param>
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/// <exception cref="ArgumentException">ID 无效</exception>
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/// <exception cref="InvalidOperationException">模型未加载</exception>
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public void Initialize(string modelIdStr, object detectionSettings = null)
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{
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if (string.IsNullOrWhiteSpace(modelIdStr))
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throw new ArgumentException("模型ID字符串不能为空。", nameof(modelIdStr));
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if (!int.TryParse(modelIdStr, out _modelID))
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throw new ArgumentException("模型ID必须为有效整数。", nameof(modelIdStr));
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_yoloModel = YoloModelManager.GetModel(_modelID)
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?? throw new InvalidOperationException($"YOLO 模型 (ID: {_modelID}) 未加载或找不到。");
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if (detectionSettings is YoloDetectionSettings yoloSettings)
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{
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_confidenceThreshold = Math.Clamp(yoloSettings.ConfidenceThreshold, 0f, 1f);
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_nmsThreshold = Math.Clamp(yoloSettings.NmsThreshold, 0f, 1f);
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}
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}
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/// <summary>
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/// 执行检测，判断是否含有 logo 类对象
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/// </summary>
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public DetectionResult Detect(Bitmap image)
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{
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if (_yoloModel == null)
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throw new InvalidOperationException("YoloDetector 未初始化或模型未加载。");
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if (image == null)
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return new DetectionResult(false, "输入图像为空。");
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try
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{
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using var skBitmap = CameraProcessor.ToSKBitmapFast(image);
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if (skBitmap == null)
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return new DetectionResult(false, "图像转换失败。");
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using var skImage = SKImage.FromBitmap(skBitmap);
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if (skImage == null)
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return new DetectionResult(false, "无法生成 SKImage。");
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var predictions = _yoloModel.RunObjectDetection(
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skImage,
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confidence: _confidenceThreshold,
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iou: _nmsThreshold
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);
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// 检查是否检测到 logo
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bool foundLogo = predictions.Any(p =>
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p.Label.Name.Equals("logo", StringComparison.OrdinalIgnoreCase));
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return foundLogo
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? new DetectionResult(false, "NG")
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: new DetectionResult(true, "OK");
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}
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catch (Exception ex)
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{
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return new DetectionResult(false, $"检测失败: {ex.Message}");
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}
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}
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/// <summary>
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/// 在图像上绘制检测框与标签
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/// </summary>
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public Bitmap DrawYoloPredictions(Bitmap source, IEnumerable<YoloPrediction> predictions)
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{
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if (source == null)
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throw new ArgumentNullException(nameof(source));
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if (predictions == null || !predictions.Any())
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return (Bitmap)source.Clone();
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Bitmap output = (Bitmap)source.Clone();
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using var graphics = Graphics.FromImage(output);
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using var pen = new Pen(Color.Yellow, 2);
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using var font = new Font("Arial", 10, FontStyle.Bold);
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using var brush = new SolidBrush(Color.Yellow);
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foreach (var pred in predictions)
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{
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var box = pred.BoundingBox;
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var rect = new RectangleF(box.Left, box.Top, box.Width, box.Height);
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// 绘制检测框
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graphics.DrawRectangle(pen, rect.X, rect.Y, rect.Width, rect.Height);
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// 绘制标签
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string label = $"{pred.Label?.Name ?? "unknown"} ({pred.Score:P1})";
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graphics.DrawString(label, font, brush, rect.X, rect.Y - 15);
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}
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return output;
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}
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/// <summary>
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/// 无需显式释放模型，资源由 YoloModelManager 管理
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/// </summary>
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public void Dispose()
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{
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//_yoloModel = null;
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}
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}
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}
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//总结，区分好YoloModelManager.cs和YoloDetector.cs各自的职能，谁负责模型管理，谁负责yolo算法的执行，现在这两个文件是交织在一起比较乱的，有时间去处理一下
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//看上面的定义的一些结构体胡总和类，就可以调用啦
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//using Check.Main.Camera;
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//using Check.Main.Common;
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//using SkiaSharp;
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//using System;
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//using System.Collections.Generic;
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//using System.Drawing;
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//using System.Linq;
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//using System.Text;
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//using System.Threading.Tasks;
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//using YoloDotNet;
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//using YoloDotNet.Models;
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//namespace Check.Main.Infer
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//{
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// public class YoloDetector : IDetector
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// {
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// private Yolo _yoloModel;
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// private int _modelID; // 需要知道模型ID来从 YoloModelManager 获取
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// private float _confidenceThreshold = 0.25f;
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// private float _nmsThreshold = 0.45f;
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// public void Initialize(string modelIdStr, object detectionSettings = null)
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// {
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// if (!int.TryParse(modelIdStr, out _modelID))
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// {
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// throw new ArgumentException("YoloDetector 初始化需要有效的模型ID字符串。", nameof(modelIdStr));
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// }
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// _yoloModel = YoloModelManager.GetModel(_modelID);
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// if (_yoloModel == null)
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// {
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// throw new InvalidOperationException($"YOLO 模型 (ID: {_modelID}) 未加载或找不到。");
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// }
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// if (detectionSettings is YoloDetectionSettings yoloSettings)
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// {
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// _confidenceThreshold = yoloSettings.ConfidenceThreshold;
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// _nmsThreshold = yoloSettings.NmsThreshold;
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// // 注意：YOLO模型的置信度和NMS阈值最好在YoloModelManager加载时设置
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// // 这里如果需要运行时调整，可能需要Yolo.SetThresholds方法
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// }
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// }
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// public DetectionResult Detect(Bitmap image)
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// {
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// if (_yoloModel == null)
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// throw new InvalidOperationException("YoloDetector 未初始化或模型未加载。");
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// using (var skImage = CameraProcessor.ToSKBitmapFast(image)) // 使用 CameraProcessor 的静态方法
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// {
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// if (skImage == null)
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// {
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// return new DetectionResult(false, "图像转换失败");
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// }
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// // 在这里可以应用运行时阈值，如果Yolo模型支持
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// // _yoloModel.Confidence = _confidenceThreshold;
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// // _yoloModel.Nms = _nmsThreshold;
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// var predictions = _yoloModel.RunObjectDetection(skImage);
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// bool isOk = !predictions.Any(); // 假设没有检测到任何目标为 OK
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// string message = isOk ? "OK" : "NG";
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// List<RectangleF> boundingBoxes = predictions.Select(p => new RectangleF(p.Rectangle.X, p.Rectangle.Y, p.Rectangle.Width, p.Rectangle.Height)).ToList();
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// Bitmap resultImage = DrawYoloPredictions(image, predictions);
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// return new DetectionResult(isOk, message, 0, boundingBoxes, resultImage);
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// }
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// }
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// private Bitmap DrawYoloPredictions(Bitmap originalImage, IEnumerable<YoloPrediction> predictions)
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// {
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// Bitmap resultBmp = (Bitmap)originalImage.Clone();
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// using (Graphics g = Graphics.FromImage(resultBmp))
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// {
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// Pen ngPen = new Pen(Color.Red, 3);
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// Font font = new Font("Arial", 12, FontStyle.Bold);
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// Brush brush = new SolidBrush(Color.Red);
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// foreach (var p in predictions)
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// {
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// Rectangle rect = new Rectangle((int)p.Rectangle.X, (int)p.Rectangle.Y, (int)p.Rectangle.Width, (int)p.Rectangle.Height);
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// g.DrawRectangle(ngPen, rect);
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// g.DrawString($"{p.Label} ({p.Confidence:P})", font, brush, rect.X, rect.Y - 20);
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// }
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// }
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// return resultBmp;
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// }
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// public void Dispose()
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// {
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// // YOLO 模型生命周期由 YoloModelManager 管理，这里不需要额外释放
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// }
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// }
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//}
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//public DetectionResult Detect(Bitmap image)
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//{
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// if (_yoloModel == null)
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// throw new InvalidOperationException("YoloDetector 未初始化或模型未加载。");
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// using (var skBitmap = CameraProcessor.ToSKBitmapFast(image))
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// using (var skImage = SKImage.FromBitmap(skBitmap))
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// {
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// //注意深拷贝浅拷贝的概念，复制的图片别忘了释放
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// var output = image.Clone();
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// if (skImage == null)
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// return new DetectionResult(false, "图像转换失败");
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// //var results = _yoloModel.RunObjectDetection(skImage, confidence: 0.4f, iou: 0.5f);
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// var predictions = _yoloModel.RunObjectDetection(
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// skImage,
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// confidence: _confidenceThreshold,
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// iou: _nmsThreshold
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// );
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// var ExistBool = predictions.FirstOrDefault(res => res.Label.Name.Equals("logo", StringComparison.OrdinalIgnoreCase));
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// if (ExistBool != null)
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// {
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// var box = ExistBool.BoundingBox;
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// var rect = new Rect(box.Left, box.Top, box.Width, box.Height);
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// //Cv2.Rectangle(output, rect, Scalar.Yellow, 2);
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// //Cv2.PutText(output, $"{logoResult.Label.Name}: {logoResult.Confidence:P2}", new CvPoint(rect.X, rect.Y - 10), HersheyFonts.HersheySimplex, 0.6, Scalar.Yellow, 2);
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// }
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// bool isOk = !predictions.Any(); // 没有检测结果为OK
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// string message = isOk ? "OK" : "NG";
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// //var boundingBoxes = predictions
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// // .Select(p => new RectangleF(p.Rectangle.X, p.Rectangle.Y, p.Rectangle.Width, p.Rectangle.Height))
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// // .ToList();
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// //Bitmap resultImage = DrawYoloPredictions(image, predictions);
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// //return new DetectionResult(isOk, message, 0, boundingBoxes, resultImage);
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// }
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//}
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//// 注意：如果你使用的是 YoloResult，请改为 IEnumerable<YoloResult> IEnumerable<YoloPrediction>
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//private Bitmap DrawYoloPredictions(Bitmap originalImage, IEnumerable<YoloPrediction> predictions)
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//{
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// Bitmap resultBmp = (Bitmap)originalImage.Clone();
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// using (Graphics g = Graphics.FromImage(resultBmp))
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// {
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// Pen boxPen = new Pen(Color.Red, 3);
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// Font font = new Font("Arial", 12, FontStyle.Bold);
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// Brush brush = new SolidBrush(Color.Red);
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// foreach (var p in predictions)
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// {
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// Rectangle rect = new Rectangle(
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// (int)p.Rectangle.X,
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// (int)p.Rectangle.Y,
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// (int)p.Rectangle.Width,
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// (int)p.Rectangle.Height
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// );
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// g.DrawRectangle(boxPen, rect);
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// g.DrawString($"{p.Label} ({p.Confidence:P0})", font, brush, rect.X, rect.Y - 20);
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// }
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// boxPen.Dispose();
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// font.Dispose();
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// brush.Dispose();
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// }
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// return resultBmp;
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//}
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// 错误②：CS0246 解决方法：确保 YoloPrediction 的完整命名空间正确引用。
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// 由于你的文件开头已经有了 `using YoloDotNet.Models;`
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// 所以这里直接使用 `YoloPrediction` 应该是正确的，除非 `YoloPrediction` 不在该命名空间下。
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// 如果问题依然存在，检查 YoloDotNet.Models 命名空间中 YoloPrediction 的具体定义。
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//private Bitmap DrawYoloPredictions(Bitmap originalImage, IEnumerable<YoloPrediction> predictions)
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//{
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// Bitmap resultBmp = (Bitmap)originalImage.Clone();
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// using (Graphics g = Graphics.FromImage(resultBmp))
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// {
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// Pen boxPen = new Pen(Color.Red, 3);
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// Font font = new Font("Arial", 12, FontStyle.Bold);
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// Brush brush = new SolidBrush(Color.Red);
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// foreach (var p in predictions)
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// {
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// Rectangle rect = new Rectangle(
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// (int)p.Rectangle.X,
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// (int)p.Rectangle.Y,
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// (int)p.Rectangle.Width,
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// (int)p.Rectangle.Height
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// );
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// g.DrawRectangle(boxPen, rect);
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// g.DrawString($"{p.Label} ({p.Confidence:P0})", font, brush, rect.X, rect.Y - 20);
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// }
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// boxPen.Dispose();
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// font.Dispose();
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// brush.Dispose();
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// }
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// return resultBmp;
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//}
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