298 lines
10 KiB
C#
298 lines
10 KiB
C#
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using OpenCvSharp;
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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.Runtime.InteropServices;
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public class XK_HisenceWord
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{
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public string? OcrBar;
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public string? OneblockPath;
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public string? OneblockMainWord;
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public string? OneblockText;
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public string? TwoRect;
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public bool? TwoIFWhile;
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public string? TwoblockPath;
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public string? TwoblockMainWord;
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public string? TwoblockText;
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public string? ThreeblockPath;
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public string? ThreeblockMainWord;
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public string? ThreeblockText;
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public string? FourblockPath;
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public string? FourblockMainWord;
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public string? FourblockText;
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public string? FiveblockPath;
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public string? FiveblockMainWord;
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public string? FiveblockText;
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public string? SixblockPath;
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public string? SixblockMainWord;
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public string? SixblockText;
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public string? SevenblockPath;
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public string? SevenblockMainWord;
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public string? SevenblockText;
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public string? EightblockPath;
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public string? EightblockMainWord;
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public string? EightblockText;
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public XK_HisenceWord()
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{
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}
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public XK_HisenceWord(string? ocrBar, string? oneblockPath, string? oneblockMainWord, string? oneblockText, string? twoRect, bool? twoIFWhile, string? twoblockPath, string? twoblockMainWord, string? twoblockText, string? threeblockPath, string? threeblockMainWord, string? threeblockText, string? fourblockPath, string? fourblockMainWord, string? fourblockText, string? fiveblockPath, string? fiveblockMainWord, string? fiveblockText, string? sixblockPath, string? sixblockMainWord, string? sixblockText, string? sevenblockPath, string? sevenblockMainWord, string? sevenblockText, string? eightblockPath, string? eightblockMainWord, string? eightblockText)
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{
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OcrBar = ocrBar;
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OneblockPath = oneblockPath;
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OneblockMainWord = oneblockMainWord;
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OneblockText = oneblockText;
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TwoRect = twoRect;
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TwoIFWhile = twoIFWhile;
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TwoblockPath = twoblockPath;
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TwoblockMainWord = twoblockMainWord;
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TwoblockText = twoblockText;
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ThreeblockPath = threeblockPath;
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ThreeblockMainWord = threeblockMainWord;
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ThreeblockText = threeblockText;
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FourblockPath = fourblockPath;
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FourblockMainWord = fourblockMainWord;
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FourblockText = fourblockText;
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FiveblockPath = fiveblockPath;
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FiveblockMainWord = fiveblockMainWord;
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FiveblockText = fiveblockText;
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SixblockPath = sixblockPath;
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SixblockMainWord = sixblockMainWord;
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SixblockText = sixblockText;
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SevenblockPath = sevenblockPath;
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SevenblockMainWord = sevenblockMainWord;
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SevenblockText = sevenblockText;
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EightblockPath = eightblockPath;
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EightblockMainWord = eightblockMainWord;
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EightblockText = eightblockText;
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}
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}
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public class XKHisence
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{
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public int Number;
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public string ?Type;
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public string ?OcrBar;
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public int MoveX;
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public int MoveY;
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public int MoveZ;
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public string ?Detect;
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public string ?OcrText;
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public int MoveTwoX;
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public int MoveTwoY;
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public int MoveTwoZ;
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public string ?OcrParm;
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public string ?Language;
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public string? FuzzyOcrText;
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public XKHisence()
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{
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}
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public XKHisence(string type,string ocrBar,int MoveX,int MoveY,int MoveZ,string Detect,string ocrText,int MoveTwoX,int MoveTwoY,int MoveTwoZ,string OcrParm,string Language,string FuzzyOcrText)
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{
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this.Type = type;
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this.OcrBar = ocrBar;
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this.MoveX = MoveX;
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this.MoveY = MoveY;
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this.MoveZ= MoveZ;
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this.Detect = Detect;
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this.OcrText= ocrText;
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this.MoveTwoX = MoveTwoX;
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this.MoveTwoY = MoveTwoY;
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this.MoveTwoZ = MoveTwoZ;
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this.OcrParm = OcrParm;
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this.Language = Language;
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this.FuzzyOcrText = FuzzyOcrText;
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}
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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 currentMat;
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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 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 float Score;
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public MLRequest()
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{
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}
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}
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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 static class MLEngine
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{
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//private const string sPath = @"D:\\C#\磁环项目\\OpenVinoYolo\\openvino_Yolov5_v7_v2.0\\openvino_Yolov5_v7\\Program\ConsoleProject\\x64\\Release\\QuickSegmentDynamic.dll";
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[DllImport("QuickSegmentDynamic.dll", EntryPoint = "InitModel")]
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public static extern IntPtr InitModel(string model_filename, string inferenceDevice, string input_node_name, int bacth, int inferenceChannels, int InferenceWidth, int InferenceHeight);
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/// <summary>
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/// 分割
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/// </summary>
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/// <param name="model"></param>
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/// <param name="img"></param>
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/// <param name="W"></param>
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/// <param name="H"></param>
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/// <param name="C"></param>
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/// <param name="labelText"></param>
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/// <param name="conf_threshold"></param>
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/// <param name="IOU_THRESHOLD"></param>
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/// <param name="fScoreThre"></param>
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/// <param name="segmentWidth"></param>
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/// <param name="Mask_output"></param>
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/// <param name="label"></param>
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/// <returns></returns>
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[DllImport("QuickSegmentDynamic.dll", EntryPoint = "seg_ModelPredict")]
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public static extern bool seg_ModelPredict(IntPtr model, byte[] img, int W, int H, int C,
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string labelText, float conf_threshold, float IOU_THRESHOLD, float fScoreThre, int segmentWidth,
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ref byte Mask_output, ref byte label);
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/// <summary>
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/// 目标检测
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/// </summary>
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/// <param name="model"></param>
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/// <param name="img"></param>
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/// <param name="W"></param>
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/// <param name="H"></param>
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/// <param name="C"></param>
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/// <param name="nodes"></param>
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/// <param name="labelText"></param>
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/// <param name="conf_threshold"></param>
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/// <param name="IOU_THRESHOLD"></param>
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/// <param name="Mask_output"></param>
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/// <param name="label"></param>
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[DllImport("QuickSegmentDynamic.dll", EntryPoint = "det_ModelPredict")]
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public static extern bool det_ModelPredict(IntPtr model, byte[] img, int W, int H, int C,
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string nodes,// ++++++++++++++++++++++++++++++++++++
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string labelText, float conf_threshold, float IOU_THRESHOLD,
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ref byte Mask_output, ref byte label);
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[DllImport("QuickSegmentDynamic.dll", EntryPoint = "FreePredictor")]
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public static extern void FreePredictor(IntPtr model);
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}
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public static class OcrEngine
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{
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// private const string sPath = @"F:\OOOCR\PaddleOCRsourcecodeGPU\PROJECTS\OcrDetForm\bin\Release\net7.0-windows\ocrInference.dll";
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[DllImport("ocrInference.dll", EntryPoint = "InitModel")]
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public static extern IntPtr InitModel(string model_ParaPath, string device_id);
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[DllImport("ocrInference.dll", EntryPoint = "Inference")]
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public static extern bool Inference(IntPtr model, byte[] img, int W, int H, int C,
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ref byte Mask_output, ref byte label);
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[DllImport("ocrInference.dll", EntryPoint = "FreePredictor")]
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public static extern void FreePredictor(IntPtr model);
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}
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public static class MLEngine1
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{
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/**********************************************************************/
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/***************** 1.推理DLL导入实现 ****************/
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/**********************************************************************/
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//private const string sPath = @"D:\M018_NET7.0\src\Debug\model_infer.dll";
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// 加载推理相关方法
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[DllImport("model_infer.dll", EntryPoint = "InitModel")] // 模型统一初始化方法: 需要yml、pdmodel、pdiparams
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//[DllImport(sPath, EntryPoint = "InitModel")] // 模型统一初始化方法: 需要yml、pdmodel、pdiparams
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public static extern IntPtr InitModel(string model_type, string model_filename, string params_filename, string cfg_file, bool use_gpu, int gpu_id, ref byte paddlex_model_type);
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[DllImport("model_infer.dll", EntryPoint = "Det_ModelPredict")] // PaddleDetection模型推理方法
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public static extern bool Det_ModelPredict(IntPtr model, byte[] img, int W, int H, int C, IntPtr output, int[] BoxesNum, ref byte label);
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[DllImport("model_infer.dll", EntryPoint = "Seg_ModelPredict")] // PaddleSeg模型推理方法
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public static extern bool Seg_ModelPredict(IntPtr model, byte[] img, int W, int H, int C, ref byte output);
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[DllImport("model_infer.dll", EntryPoint = "Cls_ModelPredict")] // PaddleClas模型推理方法
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public static extern bool Cls_ModelPredict(IntPtr model, byte[] img, int W, int H, int C, ref float score, ref byte category, ref int category_id);
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[DllImport("model_infer.dll", EntryPoint = "Mask_ModelPredict")] // Paddlex的MaskRCNN模型推理方法
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public static extern bool Mask_ModelPredict(IntPtr model, byte[] img, int W, int H, int C, IntPtr output, ref byte Mask_output, int[] BoxesNum, ref byte label);
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//public static extern bool Mask_ModelPredict(IntPtr model, IntPtr img, int W, int H, int C, IntPtr output, ref byte Mask_output, int[] BoxesNum, ref byte label);
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[DllImport("model_infer.dll", EntryPoint = "DestructModel")] // 分割、检测、识别模型销毁方法
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public static extern void DestructModel(IntPtr model);
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}
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