using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Data.SQLite;
using System.Data;
using System.Linq;
using System.Text;
using System.Text.RegularExpressions;
using System.Threading.Tasks;
using XKRS.Device.SimboVision.SimboHelper;
using System.Diagnostics.Eventing.Reader;
using static System.Runtime.InteropServices.JavaScript.JSType;
using System.Drawing;
using Microsoft.VisualBasic;

namespace HisenceYoloDetection
{

    public static class ManagerModelHelper
    {
        public static string RootPath = "D:\\Hisence\\SQLImages\\";
       



        /// <summary>
        /// 全图洗衣机 裁剪之后 OCR识别的结果
        /// </summary>
        /// <param name="saveimage"></param>
        /// <param name="CutMat"></param>
        /// <param name="currentMatC">全图图片</param>
        /// <param name="cam1TwoML">全局图片上的目标定位结果（包括定位矩形框）</param>
        /// <param name="cam1Button"></param>
        /// <param name="xK_HisenceWord"></param>
        /// <param name="strMatList">返回的定位框的结果</param>
        /// <param name="strMatRefList"></param>
        /// <param name="IOcrModel"></param>
        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)
        {
#if true
            //try
            //{
            
            Mat mResultCut = currentMatC.Clone();
            Rect areaBlack=new Rect();
            //旋钮的位置
            if (cam1Button.ResultDetails.Count == 1)
            {
                Mat mResultCuti = mResultCut.Clone();
                int rectsx = cam1Button.ResultDetails[0].Rect.X;
                int rectsy = cam1Button.ResultDetails[0].Rect.Y;
                int rectsWidth = cam1Button.ResultDetails[0].Rect.Width;
                int rectsHeight = cam1Button.ResultDetails[0].Rect.Height;
                areaBlack = new Rect(rectsx, rectsy, rectsWidth, rectsHeight);

            }
            for (int i = 0; i < cam1TwoML.ResultDetails.Count; i++)
            {
                Mat mResultCuti = mResultCut.Clone();
                int rectsx = cam1TwoML.ResultDetails[i].Rect.X;
                int rectsy = cam1TwoML.ResultDetails[i].Rect.Y;
                int rectsWidth = cam1TwoML.ResultDetails[i].Rect.Width;
                int rectsHeight = cam1TwoML.ResultDetails[i].Rect.Height;

                string blockIndex = cam1TwoML.ResultDetails[i].LabelDisplay;
                Rect area2 = new Rect();
                if (blockIndex == "2")//根据旋钮扩大范围
                {
                    areaBlack.X -= rectsx;
                    areaBlack.Y -= rectsy;
                    area2 = areaBlack;
                  string TwoRectStr=  CheckDiffSciHelper.rectChangeStr(area2);
                    xK_HisenceWord.TwoRect = TwoRectStr;
                    //Mat matCutblack = new Mat(mResultCuti, area2);
                    //if((bool)xK_HisenceWord.TwoIFWhile)
                    //{
                    //    matCutblack.SetTo(Scalar.Black);
                    //}
                    //else
                    //{
                    //    matCutblack.SetTo(Scalar.Black);
                    //}


                    //rectsx -= rectsWidth;
                    //rectsy -= 50;
                    //rectsWidth = rectsWidth + 2 * rectsWidth;
                    //rectsHeight = rectsHeight + 2 + 50;

                    Rect area = new Rect(rectsx, rectsy, rectsWidth, rectsHeight);

                    Mat matCut = new Mat(mResultCuti, area);
                    CutMat = matCut.Clone();

                    //Mat TwoCut = new Mat(mResultCuti, area2);
                    //TwoCut.SetTo(Scalar.Black);
                    //OCR识别裁剪图片
                    MLRequest reqcut = new MLRequest();
                    reqcut.currentMat = matCut.Clone();
                    MLResult mLCut = IOcrModel.RunInferenceFixed(reqcut);
                   
                    //if (mLCut.IsSuccess)
                    //{
                    //    DateTime dt = DateTime.Now;
                    //    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");

                    //}

                    BlockChangeFun(saveimage, blockIndex, ref matCut, ref mLCut, ref xK_HisenceWord);
                }
                else
                {
                    Rect area = new Rect(rectsx, rectsy, rectsWidth, rectsHeight);

                    Mat matCut = new Mat(mResultCuti, area);
                    
                    //OCR识别裁剪图片
                    MLRequest reqcut = new MLRequest();
                    reqcut.currentMat = matCut.Clone();
                    MLResult mLCut = IOcrModel.RunInferenceFixed(reqcut);
                    //if (mLCut.IsSuccess)
                    //{
                    //    DateTime dt = DateTime.Now;
                    //    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");

                    //}

                    BlockChangeFun(saveimage, blockIndex, ref matCut, ref mLCut, ref xK_HisenceWord);
                }

               
                //插入数据库

            }


            //}
            //catch (Exception ex)
            //{

            //}
#endif


        }

        /// <summary>
        /// 全图洗衣机 裁剪之后 OCR识别的结果
        /// </summary>
        /// <param name="currentMatC">全图图片</param>
        /// <param name="cam1TwoML">全局图片上的目标定位结果（包括定位矩形框）</param>
        /// <param name="strMatList">返回的定位框的结果</param>
        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)
        {
#if true
            //try
            //{


            Mat mResultCut = currentMatC.Clone();

            for (int i = 0; i < cam1TwoML.ResultDetails.Count; i++)
            {
                Mat mResultCuti = mResultCut.Clone();
                int rectsx = cam1TwoML.ResultDetails[i].Rect.X;
                int rectsy = cam1TwoML.ResultDetails[i].Rect.Y;
                int rectsWidth = cam1TwoML.ResultDetails[i].Rect.Width;
                int rectsHeight = cam1TwoML.ResultDetails[i].Rect.Height;
                string blockIndex = cam1TwoML.ResultDetails[i].LabelDisplay;
               

                Rect area = new Rect(rectsx, rectsy, rectsWidth, rectsHeight);
                Mat matCut = new Mat(mResultCuti, area);

                //OCR识别裁剪图片
                MLRequest reqcut = new MLRequest();
                reqcut.currentMat = matCut.Clone();
                MLResult mLCut = IOcrModel.RunInferenceFixed(reqcut);
                //if (mLCut.IsSuccess)
                //{
                //    DateTime dt = DateTime.Now;
                //    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");

                //}

                BlockChangeFun(saveimage, blockIndex, ref matCut, ref mLCut, ref xK_HisenceWord);
                //插入数据库

            }


            //}
            //catch (Exception ex)
            //{

            //}
#endif


        }


        /// <summary>
        /// 
        /// </summary>
        /// <param name="saveimage">是否保存</param>
        /// <param name="blockIndex">裁剪的一块索引</param>
        /// <param name="CutBlockMat">裁剪的一张图片</param>
        /// <param name="mLcut">裁剪图片的一些信息</param>
        /// <param name="xK_HisenceWord">要存储入数据库的东西</param>
        public static void BlockChangeFun(bool saveimage, string blockIndex, ref Mat CutBlockMat, ref MLResult mLcut, ref XK_HisenceWord xK_HisenceWord)
        {
            string ocrBar = xK_HisenceWord.OcrBar;
            //存放关键字和所有字符串
            List<string> OcrTextinsert = new List<string>();//存放关键字
            List<string> OcrFuzzyTextInsert = new List<string>();//存放模糊字
            string CutSavePath = "";
            CombineMessage(saveimage, ocrBar, blockIndex, ref CutBlockMat, ref mLcut, ref OcrTextinsert, ref OcrFuzzyTextInsert, ref CutSavePath);

            switch (blockIndex)
            {
                case "1"://完全匹配 重量信息
                    {
                        xK_HisenceWord.OneblockPath = CutSavePath;
                        xK_HisenceWord.OneblockMainWord = OcrTextinsert.Join("##");
                        xK_HisenceWord.OneblockText = OcrFuzzyTextInsert.Join("##");

                    }
                    break;
                case "2"://控制面板 匹配
                    {
                        xK_HisenceWord.TwoblockPath = CutSavePath;
                        xK_HisenceWord.TwoblockMainWord = OcrTextinsert.Join("##");
                        xK_HisenceWord.TwoblockText = OcrFuzzyTextInsert.Join("##");
                    }
                    break;
                case "3"://第三块板匹配
                    {
                        xK_HisenceWord.ThreeblockPath = CutSavePath;
                        xK_HisenceWord.ThreeblockMainWord = OcrTextinsert.Join("##");
                        xK_HisenceWord.ThreeblockText = OcrFuzzyTextInsert.Join("##");
                    }
                    break;
                case "4"://贴纸匹配
                    {
                        xK_HisenceWord.FourblockPath = CutSavePath;
                        xK_HisenceWord.FourblockMainWord = OcrTextinsert.Join("##");
                        xK_HisenceWord.FourblockText = OcrFuzzyTextInsert.Join("##");
                    }
                    break;
                case "5"://贴纸匹配
                    {
                        xK_HisenceWord.FiveblockPath = CutSavePath;
                        xK_HisenceWord.FiveblockMainWord = OcrTextinsert.Join("##");
                        xK_HisenceWord.FiveblockText = OcrFuzzyTextInsert.Join("##");
                    }
                    break;
                case "6"://贴纸匹配
                    {
                        xK_HisenceWord.SixblockPath = CutSavePath;
                        xK_HisenceWord.SixblockMainWord = OcrTextinsert.Join("##");
                        xK_HisenceWord.SixblockText = OcrFuzzyTextInsert.Join("##");
                    }
                    break;
                case "7"://贴纸匹配
                    {
                        xK_HisenceWord.SevenblockPath = CutSavePath;
                        xK_HisenceWord.SevenblockMainWord = OcrTextinsert.Join("##");
                        xK_HisenceWord.SevenblockText = OcrFuzzyTextInsert.Join("##");
                    }
                    break;
                case "8"://贴纸匹配
                    {
                        xK_HisenceWord.EightblockPath = CutSavePath;
                        xK_HisenceWord.EightblockMainWord = OcrTextinsert.Join("##");
                        xK_HisenceWord.EightblockText = OcrFuzzyTextInsert.Join("##");
                    }
                    break;
            }

        }
        /// <summary>
        /// 
        /// </summary>
        /// <param name="saveimage">是否保存本地图片</param>
        /// <param name="OcrBar">唯一条形码</param>
        /// <param name="blockIndex">区块号</param>
        /// <param name="CutBlockMat">图像</param>
        /// <param name="mLcut">图像上的数据</param>
        /// <param name="OcrTextinsert">关键字</param>
        /// <param name="OcrFuzzyTextInsert">所有字</param>
        /// <param name="cutSavepath">图片保存路径</param>
        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)
        {
            //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
            for (int j = 0; j < mLcut.ResultDetails.Count; j++)
            {
                string jdetial = mLcut.ResultDetails[j].LabelDisplay;
                string result = Regex.Replace(jdetial, "[ \\[ \\] \\^ \\-_*×――(^)$%~!@#$…&%￥—+=<>《》!！??？:：?`·、。，；,.;/\"‘’“”-]", "");
                if (Regex.IsMatch(result, @"\d"))
                {
                    OcrTextinsert.Add(result);
                }
                OcrFuzzyTextInsert.Add(result);
            }

            if (saveimage)
            {
                DateTime dt = DateTime.Now;
                string namecutSavepath = OcrBar + "\\" + blockIndex + "\\" + OcrBar + "result.jpg";

                cutSavepath = Path.Combine(RootPath, namecutSavepath);
                //得到目录
                if (!Directory.Exists(Path.GetDirectoryName(cutSavepath)))
                {
                    Directory.CreateDirectory(Path.GetDirectoryName(cutSavepath));
                }

                Cv2.ImWrite(cutSavepath, CutBlockMat);
            }

        }

        public static List<XK_HisenceWord> GetModeWordFromBar(string SkBar)
        {
            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];
        }


    }
}