HuaRui/SimboObjectDetection.cs
2024-08-17 14:06:39 +08:00

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using OpenCvSharp;
using OpenCvSharp.Extensions;
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Drawing;
using System.Linq;
using System.Runtime.ExceptionServices;
using System.Threading;
using System.Threading.Tasks;
using System.Runtime.InteropServices;
using Newtonsoft.Json;
public class SegResult
{
public List<Result> SegmentResult;
public class Result
{
public double fScore;
public int classId;
public string classname;
public double area;
public List<int> rect;
}
}
/// <summary>
/// 实例分割 maskrcnn
/// </summary>
public class SimboObjectDetection
{
IntPtr Model;
public bool Load(string ModelFile, string InferenceDevice, string InputNodeName, int InferenceWidth, int InferenceHeight)
{
bool res = false;
try
{
Model = MLEngine.InitModel(ModelFile,
InferenceDevice,
InputNodeName,
1, 3,
InferenceWidth,
InferenceHeight);
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
}
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;
DetectionResultDetail detectionResultDetail = new DetectionResultDetail();
detectionResultDetail.LabelNo = det.classId;
//todo: 标签名相对应
detectionResultDetail.LabelDisplay = det.classname;
detectionResultDetail.Rect = new Rectangle(rect[0], rect[1], rect[2], rect[3]);
detectionResultDetail.Score = det.fScore;
detectionResultDetail.LabelName = det.classname;
detectionResultDetail.Area = det.area;
result.ResultDetails.Add(detectionResultDetail);
// detectionResultDetail.InferenceResult = ResultState.NG;
}
}
[HandleProcessCorruptedStateExceptions]
public MLResult RunInferenceFixed(MLRequest req)
{
MLResult mlResult = new MLResult();
Mat originMat = new Mat();
try
{
// resize
originMat = req.currentMat;//1ms
int iWidth = originMat.Cols;
int iHeight = originMat.Rows;
int channels = originMat.Channels();
//输入数据转化为字节
var inputByte = new byte[originMat.Total() * channels];//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[204800]; //新建字节数组label1_str label2_str
byte[] outputByte = new byte[originMat.Total() * channels];
Stopwatch sw = new Stopwatch();
sw.Start();
unsafe
{
mlResult.IsSuccess = MLEngine.det_ModelPredict(Model,
inputByte,
iWidth, iHeight, channels,
req.out_node_name,
req.in_lable_path,
req.confThreshold, req.iouThreshold,
ref outputByte[0],
ref labellist[0]);
//mlResult.IsSuccess = true;
}
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);//CV_8UC3
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.GetExceptionMessage()}";
return mlResult;
}
finally
{
originMat?.Dispose();
originMat = null;
// GC.Collect();
}
}
}