305 lines
8.7 KiB
C#
305 lines
8.7 KiB
C#
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using OpenCvSharp;
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using OpenCvSharp.Extensions;
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using System;
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using System.Collections.Generic;
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using System.Diagnostics;
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using System.Drawing;
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using System.Linq;
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using System.Runtime.ExceptionServices;
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using System.Threading;
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using System.Threading.Tasks;
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using System.Runtime.InteropServices;
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using Newtonsoft.Json;
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public class SegResult
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{
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public List<Result> SegmentResult;
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public class Result
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{
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public double fScore;
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public int classId;
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public string classname;
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public double area;
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public List<int> rect;
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}
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}
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/// <summary>
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/// 实例分割 maskrcnn
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/// </summary>
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public class SimboObjectDetection
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{
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IntPtr Model;
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public bool Load(string ModelFile, string InferenceDevice, string InputNodeName, int InferenceWidth, int InferenceHeight)
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{
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bool res = false;
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try
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{
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Model = MLEngine.InitModel(ModelFile,
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InferenceDevice,
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InputNodeName,
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1, 3,
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InferenceWidth,
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InferenceHeight);
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res = true;
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#if USE_MULTI_THREAD
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IsCreated = true;
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if (IsCreated)
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{
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if (_runHandleBefore == null)
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{
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_runHandleBefore = new AutoResetEvent(false);
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}
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if (_runHandleAfter == null)
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{
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_runHandleAfter = new ManualResetEvent(false);
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}
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if (_runTask == null)
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{
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_runTask = Task.Factory.StartNew(() =>
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{
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while (IsCreated)
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{
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_runHandleBefore.WaitOne();
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if (IsCreated)
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{
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_result = RunInferenceFixed(_req);
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_runHandleAfter.Set();
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}
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}
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}, TaskCreationOptions.LongRunning);
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}
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}
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#endif
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}
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catch (Exception ex)
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{
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throw ex;
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}
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return res;
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}
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#if USE_MULTI_THREAD
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MLRequest _req = null;
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MLResult _result = null;
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public bool IsCreated { get; set; } = false;
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Task _runTask = null;
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AutoResetEvent _runHandleBefore = new AutoResetEvent(false);
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ManualResetEvent _runHandleAfter = new ManualResetEvent(false);
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object _runLock = new object();
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#endif
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[HandleProcessCorruptedStateExceptions]
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public MLResult RunInference(MLRequest req)
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{
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#if USE_MULTI_THREAD
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MLResult mlResult = null;
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lock (_runLock)
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{
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_result = new MLResult();
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_req = req;
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_runHandleAfter.Reset();
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_runHandleBefore.Set();
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_runHandleAfter.WaitOne();
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mlResult = _result;
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}
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return mlResult;
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#else
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return RunInferenceFixed(req);
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#endif
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}
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public static int LableCount(string json, string targetLabel)//出现的标签数
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{
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// 解析 JSON 结果
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SegResult detResult = JsonConvert.DeserializeObject<SegResult>(json);
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if (detResult == null)
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{
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throw new ArgumentException("Invalid JSON format.");
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}
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// 统计目标标签的数量
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int labelCount = detResult.SegmentResult
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.Count(det => det.classname.Equals(targetLabel, StringComparison.OrdinalIgnoreCase));
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return labelCount;
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}
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public static Rect RectMes(string json, string targetLabel)
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{
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SegResult detResult = JsonConvert.DeserializeObject<SegResult>(json);
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if (detResult == null)
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{
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throw new ArgumentException("Invalid JSON format.");
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}
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// 获取第一个指定标签的框信息
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var result = detResult.SegmentResult
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.FirstOrDefault(det => det.classname.Equals(targetLabel, StringComparison.OrdinalIgnoreCase));
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// 如果找到匹配的结果，返回其矩形框；否则返回 null
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return new Rect(result.rect[0], result.rect[1], result.rect[2], result.rect[3]);
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}
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private void ConvertJsonResult(string json, ref MLResult result)
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{
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// 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]}]}";
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//
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Console.WriteLine("检测结果JSON：" + json);
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SegResult detResult = JsonConvert.DeserializeObject<SegResult>(json);
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if (detResult == null)
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{
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return;
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}
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int iNum = detResult.SegmentResult.Count;
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int IokNum = 0;
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for (int ix = 0; ix < iNum; ix++)
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{
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var det = detResult.SegmentResult[ix];
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var rect = det.rect;
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DetectionResultDetail detectionResultDetail = new DetectionResultDetail();
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detectionResultDetail.LabelNo = det.classId;
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//todo: 标签名相对应
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detectionResultDetail.LabelDisplay = det.classname;
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detectionResultDetail.Rect = new Rectangle(rect[0], rect[1], rect[2], rect[3]);
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detectionResultDetail.Score = det.fScore;
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detectionResultDetail.LabelName = det.classname;
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detectionResultDetail.Area = det.area;
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// detectionResultDetail.InferenceResult = ResultState.NG;
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result.ResultDetails.Add(detectionResultDetail);
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}
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result.ResultDetails.Sort((s1, s2) => s1.Rect.X.CompareTo(s2.Rect.X));
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}
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[HandleProcessCorruptedStateExceptions]
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public MLResult RunInferenceFixed(MLRequest req)
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{
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MLResult mlResult = new MLResult();
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Mat originMat = new Mat();
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try
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{
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// resize
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originMat = req.currentMat;//1ms
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int iWidth = originMat.Cols;
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int iHeight = originMat.Rows;
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//输入数据转化为字节
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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
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Marshal.Copy(originMat.Data, inputByte, 0, inputByte.Length);
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byte[] labellist = new byte[20480]; //新建字节数组：label1_str label2_str
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byte[] outputByte = new byte[originMat.Total() * 3];
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Stopwatch sw = new Stopwatch();
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sw.Start();
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unsafe
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{
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mlResult.IsSuccess = MLEngine.det_ModelPredict(Model,
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inputByte,
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iWidth, iHeight, 3,
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req.out_node_name,
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req.in_lable_path,
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req.confThreshold, req.iouThreshold,
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ref outputByte[0],
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ref labellist[0]);
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//mlResult.IsSuccess = true;
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}
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sw.Stop();
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if (mlResult.IsSuccess)
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{
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mlResult.ResultMessage = $"深度学习推理成功，耗时：{sw.ElapsedMilliseconds} ms";
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Mat maskWeighted = new Mat(iHeight, iWidth, MatType.CV_8UC3, outputByte);
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mlResult.ResultMap = BitmapConverter.ToBitmap(maskWeighted);//4ms
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//将字节数组转换为字符串
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// mlResult.ResultMap = originMat.ToBitmap();//4ms
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mlResult.JsonString = System.Text.Encoding.Default.GetString(labellist, 0, labellist.Length);
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ConvertJsonResult(mlResult.JsonString, ref mlResult);
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maskWeighted?.Dispose();
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maskWeighted = null;
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// 解析json字符串
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return mlResult;
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}
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else
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{
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mlResult.ResultMessage = $"异常：深度学习执行推理失败！";
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return mlResult;
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}
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}
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catch (Exception ex)
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{
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//mlResult.ResultMessage = $"深度学习执行推理异常：{ex.GetExceptionMessage()}";
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return mlResult;
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}
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finally
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{
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// originMat?.Dispose();
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// originMat = null;
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// GC.Collect();
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}
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}
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}
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