300 lines
9.1 KiB
C#
300 lines
9.1 KiB
C#
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using OpenCvSharp;
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using System.ComponentModel;
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using System.Drawing;
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using static OpenCvSharp.AgastFeatureDetector;
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using System.Text.RegularExpressions;
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using System.Text;
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namespace DH.Devices.Vision
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{
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public enum MLModelType
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{
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[Description("图像分类")]
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ImageClassification = 1,
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[Description("目标检测")]
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ObjectDetection = 2,
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//[Description("图像分割")]
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//ImageSegmentation = 3
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[Description("语义分割")]
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SemanticSegmentation = 3,
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[Description("实例分割")]
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InstanceSegmentation = 4,
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[Description("目标检测GPU")]
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ObjectGPUDetection = 5
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}
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public class MLRequest
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{
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public int ImageChannels = 3;
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public Mat mImage;
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public int ResizeWidth;
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public int ResizeHeight;
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public float confThreshold;
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public float iouThreshold;
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//public int ImageResizeCount;
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public bool IsCLDetection;
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public int ProCount;
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public string in_node_name;
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public string out_node_name;
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public string in_lable_path;
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public int ResizeImageSize;
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public int segmentWidth;
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public int ImageWidth;
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// public List<labelStringBase> OkClassTxtList;
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// public List<ModelLabel> LabelNames;
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public float Score;
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}
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public enum ResultState
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{
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[Description("检测NG")]
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DetectNG = -3,
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//[Description("检测不足TBD")]
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// ShortageTBD = -2,
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[Description("检测结果TBD")]
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ResultTBD = -1,
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[Description("OK")]
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OK = 1,
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// [Description("NG")]
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// NG = 2,
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//统计结果
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[Description("A类NG")]
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A_NG = 25,
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[Description("B类NG")]
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B_NG = 26,
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[Description("C类NG")]
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C_NG = 27,
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}
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/// <summary>
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/// 深度学习 识别结果明细 面向业务：detect 面向深度学习：Recongnition、Inference
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/// </summary>
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public class DetectionResultDetail
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{
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public string LabelBGR { get; set; }//识别到对象的标签BGR
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public int LabelNo { get; set; } // 识别到对象的标签索引
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public string LabelName { get; set; }//识别到对象的标签名称
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public double Score { get; set; }//识别目标结果的可能性、得分
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public string LabelDisplay { get; set; }//识别到对象的 显示信息
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public double Area { get; set; }//识别目标的区域面积
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public Rectangle Rect { get; set; }//识别目标的外接矩形
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public RotatedRect MinRect { get; set; }//识别目标的最小外接矩形(带角度)
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public ResultState InferenceResult { get; set; }//只是模型推理 label的结果
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public double DistanceToImageCenter { get; set; } //计算矩形框到图像中心的距离
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public ResultState FinalResult { get; set; }//模型推理+其他视觉、逻辑判断后 label结果
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}
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public class MLResult
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{
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public bool IsSuccess = false;
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public string ResultMessage;
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public Bitmap ResultMap;
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public List<DetectionResultDetail> ResultDetails = new List<DetectionResultDetail>();
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}
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public class MLInit
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{
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public string ModelFile;
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public string InferenceDevice;
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public int InferenceWidth;
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public int InferenceHeight;
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public string InputNodeName;
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public int SizeModel;
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public bool bReverse;//尺寸测量正反面
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//目标检测Gpu
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public bool IsGPU;
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public int GPUId;
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public float Score_thre;
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public MLInit(string modelFile, bool isGPU, int gpuId, float score_thre)
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{
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ModelFile = modelFile;
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IsGPU = isGPU;
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GPUId = gpuId;
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Score_thre = score_thre;
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}
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public MLInit(string modelFile, string inputNodeName, string inferenceDevice, int inferenceWidth, int inferenceHeight)
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{
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ModelFile = modelFile;
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InferenceDevice = inferenceDevice;
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InferenceWidth = inferenceWidth;
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InferenceHeight = inferenceHeight;
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InputNodeName = inputNodeName;
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}
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}
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public class DetectStationResult
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{
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public string Pid { get; set; }
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public string TempPid { get; set; }
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/// <summary>
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/// 检测工位名称
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/// </summary>
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public string DetectName { get; set; }
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/// <summary>
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/// 深度学习 检测结果
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/// </summary>
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public List<DetectionResultDetail> DetectDetails = new List<DetectionResultDetail>();
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/// <summary>
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/// 工位检测结果
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/// </summary>
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public ResultState ResultState { get; set; } = ResultState.ResultTBD;
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public double FinalResultfScore { get; set; } = 0.0;
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public string ResultLabel { get; set; } = "";// 多个ng时，根据label优先级，设定当前检测项的label
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public string ResultLabelCategoryId { get; set; } = "";// 多个ng时，根据label优先级，设定当前检测项的label
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public int PreTreatState { get; set; }
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public bool IsPreTreatDone { get; set; } = true;
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public bool IsAfterTreatDone { get; set; } = true;
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public bool IsMLDetectDone { get; set; } = true;
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/// <summary>
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/// 预处理阶段已经NG
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/// </summary>
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public bool IsPreTreatNG { get; set; } = false;
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/// <summary>
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/// 目标检测NG
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/// </summary>
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public bool IsObjectDetectNG { get; set; } = false;
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public DateTime EndTime { get; set; }
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public int StationDetectElapsed { get; set; }
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public static string NormalizeAndClean(string input)
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{
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if (input == null) return null;
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// Step 1: 标准化字符编码为 Form C (规范组合)
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string normalizedString = input.Normalize(NormalizationForm.FormC);
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// Step 2: 移除所有空白字符，包括制表符和换行符
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string withoutWhitespace = Regex.Replace(normalizedString, @"\s+", "");
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// Step 3: 移除控制字符 (Unicode 控制字符，范围 \u0000 - \u001F 和 \u007F)
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string withoutControlChars = Regex.Replace(withoutWhitespace, @"[\u0000-\u001F\u007F]+", "");
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// Step 4: 移除特殊的不可见字符（如零宽度空格等）
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string cleanedString = Regex.Replace(withoutControlChars, @"[\u200B\u200C\u200D\uFEFF]+", "");
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return cleanedString;
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}
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}
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public class RelatedCamera
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{
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[Category("关联相机")]
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[DisplayName("关联相机")]
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[Description("关联相机描述")]
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//[TypeConverter(typeof(CollectionCountConvert))]
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public string CameraSourceId { get; set; } = "";
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}
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public class VisionEngine
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{
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[ReadOnly(true)]
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public string Id { get; set; } = Guid.NewGuid().ToString();
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[Category("检测配置")]
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[DisplayName("检测配置名称")]
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[Description("检测配置名称")]
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public string Name { get; set; }
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[Category("关联相机")]
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[DisplayName("关联相机")]
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[Description("关联相机描述")]
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public string CameraSourceId { get; set; } = "";
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[Category("关联相机集合")]
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[DisplayName("关联相机集合")]
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[Description("关联相机描述")]
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//[TypeConverter(typeof(DeviceIdSelectorConverter<CameraBase>))]
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public List<RelatedCamera> CameraCollects { get; set; } = new List<RelatedCamera>();
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[Category("启用配置")]
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[DisplayName("是否启用GPU检测")]
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[Description("是否启用GPU检测")]
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public bool IsEnableGPU { get; set; } = false;
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[Category("2.中检测（深度学习）")]
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[DisplayName("中检测-模型类型")]
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[Description("模型类型：ImageClassification-图片分类；ObjectDetection：目标检测；Segmentation-图像分割")]
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//[TypeConverter(typeof(EnumDescriptionConverter<MLModelType>))]
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public MLModelType ModelType { get; set; } = MLModelType.ObjectDetection;
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//[Category("2.中检测（深度学习）")]
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//[DisplayName("中检测-GPU索引")]
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//[Description("GPU索引")]
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//public int GPUIndex { get; set; } = 0;
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[Category("2.中检测（深度学习）")]
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[DisplayName("中检测-模型文件路径")]
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[Description("中处理 深度学习模型文件路径，路径中不可含有中文字符，一般情况可以只配置中检测模型，当需要先用预检测过滤一次时，请先配置好与预检测相关配置")]
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public string ModelPath { get; set; }
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public VisionEngine(string name, MLModelType modelType, string modelPath, bool isEnableGPU,string sCameraSourceId)
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{
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ModelPath = modelPath ?? string.Empty;
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Name = name;
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ModelType = modelType;
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IsEnableGPU = isEnableGPU;
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Id = Guid.NewGuid().ToString();
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CameraSourceId = sCameraSourceId;
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}
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}
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}
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