hisence-yolo-detection/HisenceYoloDetection/PaddleOcrModelCountry.cs


using Newtonsoft.Json;

using OpenCvSharp;
using OpenCvSharp.Extensions;
using PaddleOCRSharp;
using STTech.BytesIO.Core;
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Linq;
using System.Runtime.ExceptionServices;
using System.Runtime.InteropServices;
using System.Text;
using System.Text.RegularExpressions;
using System.Threading.Tasks;

using static System.Net.Mime.MediaTypeNames;

using Point = OpenCvSharp.Point;

namespace XKRS.Device.SimboVision.SimboHelper
{
    public class SegResultCountry
    {
        public List<ResultCountry> SegmentResult;
        public class ResultCountry
        {

            public double fScore;
            public int classId;
            public string classname;

            public double area;
            public List<List<int>> rect;


        }


    }



    public class PaddleOcrModelCountry
    {
        IntPtr Model;
        public  bool Load(string ModelFile,string Device)
        {
            bool res = false;
            try
            {


                Model = OcrEngine.InitModel(ModelFile, Device);
                res = true;

#if USE_MULTI_THREAD

                IsCreated = true;
                if (IsCreated)
                {
                    if (_runHandleBefore == null)
                    {
                        _runHandleBefore = new AutoResetEvent(false);
                    }

                    if (_runHandleAfter == null)
                    {
                        _runHandleAfter = new ManualResetEvent(false);
                    }

                    if (_runTask == null)
                    {
                        _runTask = Task.Factory.StartNew(() =>
                        {
                            while (IsCreated)
                            {
                                _runHandleBefore.WaitOne();

                                if (IsCreated)
                                {
                                    _result = RunInferenceFixed(_req);
                                    _runHandleAfter.Set();
                                }
                            }
                        }, TaskCreationOptions.LongRunning);
                    }
                }
#endif
            }
            catch (Exception ex)
            {
                throw ex;
            }
            return res;
        }



#if USE_MULTI_THREAD
        MLRequest _req = null;
        MLResult _result = null;


        public bool IsCreated { get; set; } = false;
        Task _runTask = null;
        AutoResetEvent _runHandleBefore = new AutoResetEvent(false);
        ManualResetEvent _runHandleAfter = new ManualResetEvent(false);
        object _runLock = new object();
#endif

        [HandleProcessCorruptedStateExceptions]
        public MLResult RunInference(MLRequest req)
        {
#if USE_MULTI_THREAD
            MLResult mlResult = null;
            lock (_runLock)
            {
                _result = new MLResult();

                _req = req;

                _runHandleAfter.Reset();
                _runHandleBefore.Set();
                _runHandleAfter.WaitOne();

                mlResult = _result;
            }

            return mlResult;
#else
            return RunInferenceFixed(req);
#endif




        }


       

            //string pattern = @"^[A-Za-z0-9]+$";  //@意思忽略转义，+匹配前面一次或多次，$匹配结尾

          //  Match match = Regex.Match(str, pattern);


      

            private void ConvertJsonResult(string json, ref MLResult result)
        {
            // json = "{\"FastDetResult\":[{\"cls_id\":0,\"cls\":\"liewen\",\"fScore\":0.654843,\"rect\":[175,99,110,594]},{\"cls_id\":0,\"cls\":\"liewen\",\"fScore\":0.654589,\"rect\":[2608,19,104,661]},{\"cls_id\":0,\"cls\":\"liewen\",\"fScore\":0.654285,\"rect\":[1275,19,104,662]},{\"cls_id\":0,\"cls\":\"liewen\",\"fScore\":0.620762,\"rect\":[1510,95,107,600]},{\"cls_id\":0,\"cls\":\"liewen\",\"fScore\":0.617812,\"rect\":[2844,93,106,602]}]}";
            //
            Console.WriteLine("检测结果JSON：" + json);
            SegResult detResult = JsonConvert.DeserializeObject<SegResult>(json);
            if (detResult == null)
            {
                return;
            }

            int iNum = detResult.SegmentResult.Count;
            int IokNum = 0;
            for (int ix = 0; ix < iNum; ix++)
            {
                var det = detResult.SegmentResult[ix];

                var rect = det.rect;
                List<Point> points = new List<Point>();
                List<float> maxYs = new List<float>();
                //把字体打印在矩形框的下面，考虑矩形框最靠下的角点Y值不能超过边框值 否则就往上写
                for (int n = 0; n < det.rect.Count(); n++)
                {
                    points.Add(new Point(det.rect[n][0], det.rect[n][1]));
                    maxYs.Add(det.rect[n][1]);
                }
                // 定义矩形左上角和右下角的坐标
                Point topLeft = points[0];
                Point bottomRight = points[2];

                // 计算矩形的长和宽
                int width = bottomRight.X - topLeft.X;//矩形宽度
                int height = bottomRight.Y - topLeft.Y;//矩形高度
                //下面定义一个矩形区域，以后在这个矩形里画上白底黑字
                float rectX = points[0].X;
                float rectY = points[0].Y;
                float rectWidth = width;
                float rectHeight = height;

                //string pattern = @"^[A-Za-z0-9]+$";  //@意思忽略转义，+匹配前面一次或多次，$匹配结尾

                 // bool match = Regex.IsMatch(det.classname, pattern);
                if (det.classname != ""&& rectWidth>0&& rectHeight>0)
                {
                    DetectionResultDetail detectionResultDetail = new DetectionResultDetail();
                    detectionResultDetail.LabelNo = det.classId;
                    //todo: 标签名相对应
                    detectionResultDetail.LabelDisplay = det.classname;
                    detectionResultDetail.Rect = new Rectangle((int)rectX, (int)rectY, (int)rectWidth, (int)rectHeight);
                    detectionResultDetail.Score = det.fScore;
                    detectionResultDetail.LabelName = det.classname;
                    detectionResultDetail.Area = det.area;
                    result.ResultDetails.Add(detectionResultDetail);
                }


            }

        }


        [HandleProcessCorruptedStateExceptions]
        public MLResult RunInferenceFixed(MLRequest req)
        {
            MLResult mlResult = new MLResult();
            Mat originMat = new Mat();

            try
            {
                originMat = req.currentMat;//1ms

                int iWidth = originMat.Cols;
                int iHeight = originMat.Rows;

                //输入数据转化为字节
                var inputByte = new byte[originMat.Total() * 3];//这里必须乘以通道数，不然数组越界，也可以用w*h*c，差不多
                Marshal.Copy(originMat.Data, inputByte, 0, inputByte.Length);

                byte[] labellist = new byte[20480];    //新建字节数组：label1_str label2_str 

                byte[] outputByte = new byte[originMat.Total() * 3];

                Stopwatch sw = new Stopwatch();
                sw.Start();
                unsafe
                {
                    mlResult.IsSuccess = OcrEngine.Inference(Model, inputByte, iWidth, iHeight, 3, ref outputByte[0], ref labellist[0]);
                   
                }
                sw.Stop();

                if (mlResult.IsSuccess)
                {
                    mlResult.ResultMessage = $"深度学习推理成功，耗时：{sw.ElapsedMilliseconds} ms";

                    Mat maskWeighted = new Mat(iHeight, iWidth, MatType.CV_8UC3, outputByte);

                    mlResult.ResultMap = BitmapConverter.ToBitmap(maskWeighted);//4ms
                    //将字节数组转换为字符串
                    mlResult.ResultMap = originMat.ToBitmap();//4ms
                    string strGet = System.Text.Encoding.Default.GetString(labellist, 0, labellist.Length);


                    Console.WriteLine("strGet:", strGet);


                    ConvertJsonResult(strGet, ref mlResult);

                    maskWeighted?.Dispose();
                    maskWeighted = null;

                    //解析json字符串

                    return mlResult;
                }
                else
                {
                    mlResult.ResultMessage = $"异常：深度学习执行推理失败！";
                    return mlResult;
                }
            }
            catch (Exception ex)
            {
                mlResult.ResultMessage = $"深度学习执行推理异常：{ex.Message}";
                return mlResult;
            }
            finally
            {
                
                //originMat?.Dispose();
                //originMat = null;


                // GC.Collect();
            }
        }

       
        public void FreeModel()
        {
          OcrEngine.FreePredictor(Model);
        }
    }
}