480 lines
20 KiB
C#
480 lines
20 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.Data.SQLite;
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using System.Data;
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using System.Linq;
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using System.Text;
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using System.Text.RegularExpressions;
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using System.Threading.Tasks;
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using XKRS.Device.SimboVision.SimboHelper;
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using System.Diagnostics.Eventing.Reader;
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using static System.Runtime.InteropServices.JavaScript.JSType;
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using System.Drawing;
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using Microsoft.VisualBasic;
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namespace HisenceYoloDetection
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{
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public static class ManagerModelHelper
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{
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public static string RootPath = "D:\\Hisence\\SQLImages\\";
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/// <summary>
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/// 全图洗衣机 裁剪之后 OCR识别的结果
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/// </summary>
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/// <param name="saveimage"></param>
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/// <param name="CutMat"></param>
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/// <param name="currentMatC">全图图片</param>
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/// <param name="cam1TwoML">全局图片上的目标定位结果（包括定位矩形框）</param>
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/// <param name="cam1Button"></param>
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/// <param name="xK_HisenceWord"></param>
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/// <param name="strMatList">返回的定位框的结果</param>
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/// <param name="strMatRefList"></param>
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/// <param name="IOcrModel"></param>
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public static void InsertSqlRunDataButton(bool saveimage,ref Mat CutMat, ref Mat currentMatC, MLResult cam1TwoML, MLResult cam1Button, ref XK_HisenceWord xK_HisenceWord, /*ref List<string> strMatList, ref List<string> strMatRefList, */ref PaddleOcrModel IOcrModel)
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{
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#if true
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//try
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//{
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Mat mResultCut = currentMatC.Clone();
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Rect areaBlack=new Rect();
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//旋钮的位置
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if (cam1Button.ResultDetails.Count == 1)
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{
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Mat mResultCuti = mResultCut.Clone();
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int rectsx = cam1Button.ResultDetails[0].Rect.X;
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int rectsy = cam1Button.ResultDetails[0].Rect.Y;
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int rectsWidth = cam1Button.ResultDetails[0].Rect.Width;
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int rectsHeight = cam1Button.ResultDetails[0].Rect.Height;
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areaBlack = new Rect(rectsx, rectsy, rectsWidth, rectsHeight);
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}
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for (int i = 0; i < cam1TwoML.ResultDetails.Count; i++)
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{
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Mat mResultCuti = mResultCut.Clone();
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int rectsx = cam1TwoML.ResultDetails[i].Rect.X;
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int rectsy = cam1TwoML.ResultDetails[i].Rect.Y;
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int rectsWidth = cam1TwoML.ResultDetails[i].Rect.Width;
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int rectsHeight = cam1TwoML.ResultDetails[i].Rect.Height;
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string blockIndex = cam1TwoML.ResultDetails[i].LabelDisplay;
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Rect area2 = new Rect();
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if (blockIndex == "2")//根据旋钮扩大范围
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{
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areaBlack.X -= rectsx;
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areaBlack.Y -= rectsy;
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area2 = areaBlack;
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string TwoRectStr= CheckDiffSciHelper.rectChangeStr(area2);
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xK_HisenceWord.TwoRect = TwoRectStr;
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//Mat matCutblack = new Mat(mResultCuti, area2);
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//if((bool)xK_HisenceWord.TwoIFWhile)
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//{
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// matCutblack.SetTo(Scalar.Black);
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//}
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//else
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//{
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// matCutblack.SetTo(Scalar.Black);
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//}
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//rectsx -= rectsWidth;
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//rectsy -= 50;
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//rectsWidth = rectsWidth + 2 * rectsWidth;
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//rectsHeight = rectsHeight + 2 + 50;
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Rect area = new Rect(rectsx, rectsy, rectsWidth, rectsHeight);
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Mat matCut = new Mat(mResultCuti, area);
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CutMat = matCut.Clone();
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//Mat TwoCut = new Mat(mResultCuti, area2);
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//TwoCut.SetTo(Scalar.Black);
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//OCR识别裁剪图片
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MLRequest reqcut = new MLRequest();
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reqcut.currentMat = matCut.Clone();
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MLResult mLCut = IOcrModel.RunInferenceFixed(reqcut);
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//if (mLCut.IsSuccess)
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//{
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// DateTime dt = DateTime.Now;
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// mLCut.ResultMap.Save("D:\\Hisence\\detImages\\OCR" + dt.Year.ToString() + dt.Month.ToString() + dt.Day.ToString() + dt.Hour.ToString() + dt.Minute.ToString() + dt.Millisecond.ToString() + "2result.jpg");
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//}
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BlockChangeFun(saveimage, blockIndex, ref matCut, ref mLCut, ref xK_HisenceWord);
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}
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else
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{
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Rect area = new Rect(rectsx, rectsy, rectsWidth, rectsHeight);
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Mat matCut = new Mat(mResultCuti, area);
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//OCR识别裁剪图片
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MLRequest reqcut = new MLRequest();
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reqcut.currentMat = matCut.Clone();
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MLResult mLCut = IOcrModel.RunInferenceFixed(reqcut);
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//if (mLCut.IsSuccess)
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//{
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// DateTime dt = DateTime.Now;
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// mLCut.ResultMap.Save("D:\\Hisence\\detImages\\OCR" + dt.Year.ToString() + dt.Month.ToString() + dt.Day.ToString() + dt.Hour.ToString() + dt.Minute.ToString() + dt.Millisecond.ToString() + "2result.jpg");
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//}
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BlockChangeFun(saveimage, blockIndex, ref matCut, ref mLCut, ref xK_HisenceWord);
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}
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//插入数据库
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}
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//}
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//catch (Exception ex)
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//{
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//}
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#endif
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}
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/// <summary>
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/// 全图洗衣机 裁剪之后 OCR识别的结果
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/// </summary>
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/// <param name="currentMatC">全图图片</param>
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/// <param name="cam1TwoML">全局图片上的目标定位结果（包括定位矩形框）</param>
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/// <param name="strMatList">返回的定位框的结果</param>
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public static void InsertSqlRunData(bool saveimage, ref Mat currentMatC, MLResult cam1TwoML, ref XK_HisenceWord xK_HisenceWord, /*ref List<string> strMatList, ref List<string> strMatRefList,*/ ref PaddleOcrModel IOcrModel)
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{
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#if true
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//try
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//{
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Mat mResultCut = currentMatC.Clone();
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for (int i = 0; i < cam1TwoML.ResultDetails.Count; i++)
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{
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Mat mResultCuti = mResultCut.Clone();
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int rectsx = cam1TwoML.ResultDetails[i].Rect.X;
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int rectsy = cam1TwoML.ResultDetails[i].Rect.Y;
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int rectsWidth = cam1TwoML.ResultDetails[i].Rect.Width;
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int rectsHeight = cam1TwoML.ResultDetails[i].Rect.Height;
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string blockIndex = cam1TwoML.ResultDetails[i].LabelDisplay;
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Rect area = new Rect(rectsx, rectsy, rectsWidth, rectsHeight);
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Mat matCut = new Mat(mResultCuti, area);
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//OCR识别裁剪图片
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MLRequest reqcut = new MLRequest();
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reqcut.currentMat = matCut.Clone();
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MLResult mLCut = IOcrModel.RunInferenceFixed(reqcut);
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//if (mLCut.IsSuccess)
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//{
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// DateTime dt = DateTime.Now;
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// mLCut.ResultMap.Save("D:\\Hisence\\detImages\\OCR" + dt.Year.ToString() + dt.Month.ToString() + dt.Day.ToString() + dt.Hour.ToString() + dt.Minute.ToString() + dt.Millisecond.ToString() + "2result.jpg");
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//}
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BlockChangeFun(saveimage, blockIndex, ref matCut, ref mLCut, ref xK_HisenceWord);
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//插入数据库
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}
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//}
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//catch (Exception ex)
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//{
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//}
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#endif
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}
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/// <summary>
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///
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/// </summary>
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/// <param name="saveimage">是否保存</param>
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/// <param name="blockIndex">裁剪的一块索引</param>
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/// <param name="CutBlockMat">裁剪的一张图片</param>
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/// <param name="mLcut">裁剪图片的一些信息</param>
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/// <param name="xK_HisenceWord">要存储入数据库的东西</param>
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public static void BlockChangeFun(bool saveimage, string blockIndex, ref Mat CutBlockMat, ref MLResult mLcut, ref XK_HisenceWord xK_HisenceWord)
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{
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string ocrBar = xK_HisenceWord.OcrBar;
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//存放关键字和所有字符串
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List<string> OcrTextinsert = new List<string>();//存放关键字
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List<string> OcrFuzzyTextInsert = new List<string>();//存放模糊字
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string CutSavePath = "";
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CombineMessage(saveimage, ocrBar, blockIndex, ref CutBlockMat, ref mLcut, ref OcrTextinsert, ref OcrFuzzyTextInsert, ref CutSavePath);
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switch (blockIndex)
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{
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case "1"://完全匹配 重量信息
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{
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xK_HisenceWord.OneblockPath = CutSavePath;
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xK_HisenceWord.OneblockMainWord = OcrTextinsert.Join("##");
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xK_HisenceWord.OneblockText = OcrFuzzyTextInsert.Join("##");
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}
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break;
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case "2"://控制面板 匹配
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{
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xK_HisenceWord.TwoblockPath = CutSavePath;
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xK_HisenceWord.TwoblockMainWord = OcrTextinsert.Join("##");
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xK_HisenceWord.TwoblockText = OcrFuzzyTextInsert.Join("##");
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}
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break;
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case "3"://第三块板匹配
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{
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xK_HisenceWord.ThreeblockPath = CutSavePath;
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xK_HisenceWord.ThreeblockMainWord = OcrTextinsert.Join("##");
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xK_HisenceWord.ThreeblockText = OcrFuzzyTextInsert.Join("##");
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}
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break;
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case "4"://贴纸匹配
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{
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xK_HisenceWord.FourblockPath = CutSavePath;
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xK_HisenceWord.FourblockMainWord = OcrTextinsert.Join("##");
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xK_HisenceWord.FourblockText = OcrFuzzyTextInsert.Join("##");
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}
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break;
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case "5"://贴纸匹配
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{
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xK_HisenceWord.FiveblockPath = CutSavePath;
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xK_HisenceWord.FiveblockMainWord = OcrTextinsert.Join("##");
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xK_HisenceWord.FiveblockText = OcrFuzzyTextInsert.Join("##");
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}
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break;
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case "6"://贴纸匹配
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{
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xK_HisenceWord.SixblockPath = CutSavePath;
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xK_HisenceWord.SixblockMainWord = OcrTextinsert.Join("##");
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xK_HisenceWord.SixblockText = OcrFuzzyTextInsert.Join("##");
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}
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break;
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case "7"://贴纸匹配
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{
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xK_HisenceWord.SevenblockPath = CutSavePath;
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xK_HisenceWord.SevenblockMainWord = OcrTextinsert.Join("##");
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xK_HisenceWord.SevenblockText = OcrFuzzyTextInsert.Join("##");
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}
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break;
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case "8"://贴纸匹配
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{
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xK_HisenceWord.EightblockPath = CutSavePath;
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xK_HisenceWord.EightblockMainWord = OcrTextinsert.Join("##");
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xK_HisenceWord.EightblockText = OcrFuzzyTextInsert.Join("##");
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}
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break;
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}
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}
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/// <summary>
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///
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/// </summary>
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/// <param name="saveimage">是否保存本地图片</param>
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/// <param name="OcrBar">唯一条形码</param>
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/// <param name="blockIndex">区块号</param>
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/// <param name="CutBlockMat">图像</param>
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/// <param name="mLcut">图像上的数据</param>
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/// <param name="OcrTextinsert">关键字</param>
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/// <param name="OcrFuzzyTextInsert">所有字</param>
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/// <param name="cutSavepath">图片保存路径</param>
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public static void CombineMessage(bool saveimage, string OcrBar, string blockIndex, ref Mat CutBlockMat, ref MLResult mLcut, ref List<string> OcrTextinsert, ref List<string> OcrFuzzyTextInsert, ref string cutSavepath)
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{
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//在这里面找到带数字的关键字 将所有字也存放在数据库中
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for (int j = 0; j < mLcut.ResultDetails.Count; j++)
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{
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string jdetial = mLcut.ResultDetails[j].LabelDisplay;
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string result = Regex.Replace(jdetial, "[ \\[ \\] \\^ \\-_*×――(^)$%~!@#$…&%￥—+=<>《》!！??？:：?`·、。，；,.;/\"‘’“”-]", "");
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if (Regex.IsMatch(result, @"\d"))
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{
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OcrTextinsert.Add(result);
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}
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OcrFuzzyTextInsert.Add(result);
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}
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if (saveimage)
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{
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DateTime dt = DateTime.Now;
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string namecutSavepath = OcrBar + "\\" + blockIndex + "\\" + OcrBar + "result.jpg";
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cutSavepath = Path.Combine(RootPath, namecutSavepath);
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//得到目录
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if (!Directory.Exists(Path.GetDirectoryName(cutSavepath)))
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{
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Directory.CreateDirectory(Path.GetDirectoryName(cutSavepath));
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}
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Cv2.ImWrite(cutSavepath, CutBlockMat);
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}
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}
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public static List<XK_HisenceWord> GetModeWordFromBar(string SkBar)
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{
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List<XK_HisenceWord> cslist = SQLiteHelper.ExecuteQuery($"select * from XK_HisenceWord where OCRBar='{SkBar}' ", r => new XK_HisenceWord
|
|||
|
|
{
|
|||
|
|
OcrBar = r["OcrBar"].ToString(),
|
|||
|
|
OneblockPath = r["OneblockPath"].ToString(),
|
|||
|
|
OneblockMainWord = r["OneblockMainWord"].ToString(),
|
|||
|
|
OneblockText = r["OneblockText"].ToString(),
|
|||
|
|
TwoRect = r["TwoRect"].ToString(),
|
|||
|
|
TwoIFWhile = r["TwoIFWhile"].ToBool(),
|
|||
|
|
TwoblockPath = r["TwoblockPath"].ToString(),
|
|||
|
|
TwoblockMainWord = r["TwoblockMainWord"].ToString(),
|
|||
|
|
TwoblockText = r["TwoblockText"].ToString(),
|
|||
|
|
ThreeblockPath = r["ThreeblockPath"].ToString(),
|
|||
|
|
ThreeblockMainWord = r["ThreeblockMainWord"].ToString(),
|
|||
|
|
ThreeblockText = r["ThreeblockText"].ToString(),
|
|||
|
|
FourblockPath = r["FourblockPath"].ToString(),
|
|||
|
|
FourblockMainWord = r["FourblockMainWord"].ToString(),
|
|||
|
|
FourblockText = r["FourblockText"].ToString(),
|
|||
|
|
FiveblockPath = r["FiveblockPath"].ToString(),
|
|||
|
|
FiveblockMainWord = r["FiveblockMainWord"].ToString(),
|
|||
|
|
FiveblockText = r["FiveblockText"].ToString(),
|
|||
|
|
SixblockPath = r["SixblockPath"].ToString(),
|
|||
|
|
SixblockMainWord = r["SixblockMainWord"].ToString(),
|
|||
|
|
SixblockText = r["SixblockText"].ToString(),
|
|||
|
|
SevenblockPath = r["SevenblockPath"].ToString(),
|
|||
|
|
SevenblockMainWord = r["SevenblockMainWord"].ToString(),
|
|||
|
|
SevenblockText = r["SevenblockText"].ToString(),
|
|||
|
|
EightblockPath = r["EightblockPath"].ToString(),
|
|||
|
|
EightblockMainWord = r["EightblockMainWord"].ToString(),
|
|||
|
|
EightblockText = r["EightblockText"].ToString()
|
|||
|
|
|
|||
|
|
});
|
|||
|
|
return cslist;
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
public static bool IsMatchSQLText(ref Mat detMat, ref XK_HisenceWord XKSQL, ref XK_HisenceWord XKDet)
|
|||
|
|
{
|
|||
|
|
|
|||
|
|
try
|
|||
|
|
{
|
|||
|
|
string TwoRectstr=XKSQL.TwoRect;
|
|||
|
|
string oneBlockWordSql = XKSQL.OneblockMainWord;
|
|||
|
|
string twoBlockWordSql = XKSQL.TwoblockMainWord;
|
|||
|
|
string threeBlockWordSql = XKSQL.ThreeblockMainWord;
|
|||
|
|
string fourBlockWordSql = XKSQL.FourblockMainWord;
|
|||
|
|
string fiveBlockWordSql = XKSQL.FiveblockMainWord;
|
|||
|
|
string sixBlockWordSql = XKSQL.SixblockMainWord;
|
|||
|
|
string sevenBlockWordSql = XKSQL.SevenblockMainWord;
|
|||
|
|
string eightBlockWordSql = XKSQL.EightblockMainWord;
|
|||
|
|
|
|||
|
|
string oneBlockWordDet = XKDet.OneblockMainWord;
|
|||
|
|
string twoBlockWordDet = XKDet.TwoblockMainWord;
|
|||
|
|
string threeBlockWordDet = XKDet.ThreeblockMainWord;
|
|||
|
|
string fourBlockWordDet = XKDet.FourblockMainWord;
|
|||
|
|
string fiveBlockWordDet = XKDet.FiveblockMainWord;
|
|||
|
|
string sixBlockWordDet = XKDet.SixblockMainWord;
|
|||
|
|
string sevenBlockWordDet = XKDet.SevenblockMainWord;
|
|||
|
|
string eightBlockWordDet = XKDet.EightblockMainWord;
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
bool OneIF = isMatchStr(oneBlockWordSql, oneBlockWordDet);
|
|||
|
|
bool TwoIF = isMatchStr(twoBlockWordSql, twoBlockWordDet);
|
|||
|
|
bool ThreeIF = isMatchStr(threeBlockWordSql, threeBlockWordDet);
|
|||
|
|
bool FourIF = isMatchStr(fourBlockWordSql, fourBlockWordDet);
|
|||
|
|
bool FiveIF = isMatchStr(fiveBlockWordSql, fiveBlockWordDet);
|
|||
|
|
bool SixIF = isMatchStr(sixBlockWordSql, sixBlockWordDet);
|
|||
|
|
bool SenvenIF = isMatchStr(sevenBlockWordSql, sevenBlockWordDet);
|
|||
|
|
bool EightIF = isMatchStr(eightBlockWordSql, eightBlockWordDet);
|
|||
|
|
//第二快 卷积匹配
|
|||
|
|
string PathSql = XKSQL.TwoblockPath;
|
|||
|
|
//
|
|||
|
|
Rect rectsql = CheckDiffSciHelper.strChangeRect(TwoRectstr);
|
|||
|
|
Rect rectDet = CheckDiffSciHelper.strChangeRect(XKDet.TwoRect);
|
|||
|
|
bool twoif2 = CheckDiffSciHelper.CheckDiffSci(PathSql, detMat, rectsql, rectDet,(bool)XKSQL.TwoIFWhile, "D://");
|
|||
|
|
//第三块区域一直都是false
|
|||
|
|
if (OneIF && TwoIF && ThreeIF && FourIF && FiveIF && SixIF && SenvenIF && EightIF&& twoif2)
|
|||
|
|
{
|
|||
|
|
return true;
|
|||
|
|
}
|
|||
|
|
else
|
|||
|
|
{
|
|||
|
|
return false;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
catch (Exception ex)
|
|||
|
|
{
|
|||
|
|
return false;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
public static bool isMatchStr(string SqlText, string DetText)
|
|||
|
|
{
|
|||
|
|
if (SqlText.Contains("##") && DetText.Contains("##"))
|
|||
|
|
{
|
|||
|
|
// 计算Levenshtein距离
|
|||
|
|
int distance = LevenshteinDistance(SqlText, DetText);
|
|||
|
|
|
|||
|
|
// 计算相似度（相似度等于1减去标准化的Levenshtein距离）
|
|||
|
|
double similarity = 1 - ((double)distance / Math.Max(SqlText.Length, DetText.Length));
|
|||
|
|
bool areEqual = false;
|
|||
|
|
if (similarity<0.5)
|
|||
|
|
{
|
|||
|
|
areEqual = false;
|
|||
|
|
}
|
|||
|
|
else
|
|||
|
|
{
|
|||
|
|
areEqual = true;
|
|||
|
|
}
|
|||
|
|
//string[] sArraysql = Regex.Split(SqlText, "##", RegexOptions.IgnoreCase);
|
|||
|
|
//string[] sArraydet = Regex.Split(DetText, "##", RegexOptions.IgnoreCase);
|
|||
|
|
//bool areEqual = sArraysql.OrderBy(x => x).SequenceEqual(sArraydet.OrderBy(x => x));
|
|||
|
|
return areEqual;
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
else if ((SqlText == "" || SqlText == null) && (DetText == "" || DetText == null))
|
|||
|
|
{
|
|||
|
|
return true;
|
|||
|
|
}
|
|||
|
|
else
|
|||
|
|
{
|
|||
|
|
return false;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
// 计算Levenshtein距离
|
|||
|
|
static int LevenshteinDistance(string string1, string string2)
|
|||
|
|
{
|
|||
|
|
int[,] dp = new int[string1.Length + 1, string2.Length + 1];
|
|||
|
|
|
|||
|
|
for (int i = 0; i <= string1.Length; i++)
|
|||
|
|
dp[i, 0] = i;
|
|||
|
|
|
|||
|
|
for (int j = 0; j <= string2.Length; j++)
|
|||
|
|
dp[0, j] = j;
|
|||
|
|
|
|||
|
|
for (int i = 1; i <= string1.Length; i++)
|
|||
|
|
{
|
|||
|
|
for (int j = 1; j <= string2.Length; j++)
|
|||
|
|
{
|
|||
|
|
int cost = string1[i - 1] == string2[j - 1] ? 0 : 1;
|
|||
|
|
|
|||
|
|
dp[i, j] = Math.Min(Math.Min(dp[i - 1, j] + 1, dp[i, j - 1] + 1), dp[i - 1, j - 1] + cost);
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
return dp[string1.Length, string2.Length];
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
}
|