private static void HKCameraCltr_HardTriggerEvent(object sender, HObject e)
{
string camName = (string)sender;
HObject inImage = e;
SoftwareOnceEvent?.Invoke(null, new OutImage(camName, inImage));
foreach (var testItem in UseTestItems)
{
if (testItem.camName == camName)
{
testItem.Find(inImage);
testItem.Show(inImage, out HObject resultImage);
OutResult outResult = new OutResult()
{
TestItemName = testItem.name,
CamName = testItem.camName,
IsOK = testItem.isOK,
ResultMessage = testItem.outMessage,
OriginalImage = new OutImage(testItem.camName, inImage),
EffectImage = new OutImage(testItem.camName, resultImage)
};
DetectionOnceEvent?.Invoke(null, outResult);
}
}
inImage.Dispose();
}
/// <summary>
/// 触发检测，更具当前切换的型号模板进行拍照识别
/// 阻塞线程直到拍照识别结束，返回TriggerDetection的结果
/// 同一时刻只能有一个TriggerDetection在运行
/// </summary>
/// <param name="outResult"></param>
/// <returns></returns>
public static bool TriggerDetection(out List <OutResult> outResults)
{
outResults = null;
if (!MyRun.havInit)
{
StrErrorMsg = "视觉模块未初始化";
return(false);
}
if (DetectionThread != null && DetectionThread.IsAlive)
{
StrErrorMsg = "上一次拍照识别并未结束";
return(false);
}
OutResults.Clear();
ConcurrentDictionary <string, HObject> camImages = new ConcurrentDictionary <string, HObject>();
bool hav = true;
while (true)
{
Parallel.ForEach(UseCamsName.ToArray(), camName =>
{
if (MyRun.TriggerCamera(camName, out HObject camImage))
{
if (camImage != null)
{
//待升级:这里添加对图片质量的判断，如果图片模糊，撕裂则将图片丢弃
camImages.AddOrUpdate(camName, camImage, (k, v) => v = camImage);
}
}
else
{
StrErrorMsg = MyRun.StrErrorMsg;
}
});
//待升级:这里添加对产品是否到位的判断，通过特征检测的方式实现，不符合要求的则将图片丢弃
//待升级:这里添加模板匹配矫正图片的功能，检测项检测时使用的是矫正后的图片，
//要将矫正的变换参数保存，界面显示的需要是把ROI区域等检测效果进行矫正的图片
Parallel.ForEach(UseTestItems.ToArray(), testItem =>
{
if (camImages.TryGetValue(testItem.camName, out HObject camImage))
{
testItem.Find(camImage);
testItem.Show(camImage, out HObject resultImage);
testItem.hav = true;
OutResult outResult = new OutResult()
{
TestItemName = testItem.name,
CamName = testItem.camName,
IsOK = testItem.isOK,
ResultMessage = testItem.outMessage,
OriginalImage = new OutImage(testItem.camName, camImage),
EffectImage = new OutImage(testItem.camName, resultImage)
};
DetectionOnceEvent?.Invoke(null, outResult);
OutResults.Add(outResult);
}
});
foreach (var camImage in camImages.Values)
{
camImage.Dispose();
}
hav = true;
foreach (var testItem in UseTestItems.ToArray())
{
hav &= testItem.hav;
}
if (hav)
{
foreach (var testItem in UseTestItems.ToArray())
{
testItem.hav = false;
}
break;
}
if (stopDetectionSignal.WaitOne(20))
{
break;
}
Thread.Sleep(20);
}
outResults = OutResults;
return(true);
}
/// <summary>
/// 触发检测，更具当前切换的型号模板进行拍照识别
/// 该方法会开辟新线程
/// 该方法不会返回识别结果，识别结果需要在调用WaitDetectionResult获取
/// 同一时刻只能有一个TriggerDetection在运行
/// </summary>
/// <returns></returns>
public static bool TriggerDetection()
{
if (!MyRun.havInit)
{
StrErrorMsg = "视觉模块未初始化";
return(false);
}
if (DetectionThread != null && DetectionThread.IsAlive)
{
StrErrorMsg = "上一次拍照识别并未结束";
return(false);
}
DetectionThread = new Thread(() =>
{
//防止之前暂停的信号影响到这次检测
stopDetectionSignal.Reset();
OutResults.Clear();
ConcurrentDictionary <string, HObject> camImages = new ConcurrentDictionary <string, HObject>();
ConcurrentDictionary <string, HObject> matchImages = new ConcurrentDictionary <string, HObject>();
ConcurrentDictionary <string, HTuple> matchHomMat2D = new ConcurrentDictionary <string, HTuple>();
bool hav = true;
while (true)
{
Parallel.ForEach(UseCamsName.ToArray(), camName =>
{
if (MyRun.TriggerCamera(camName, out HObject camImage))
{
if (camImage != null)
{
//待升级:这里添加对图片质量的判断，如果图片模糊，撕裂则将图片丢弃
camImages.AddOrUpdate(camName, camImage, (k, v) => v = camImage);
}
}
else
{
StrErrorMsg = MyRun.StrErrorMsg;
}
});
//待升级:这里添加对产品是否到位的判断，通过特征检测的方式实现，不符合要求的则将图片丢弃
//待升级:这里添加模板匹配矫正图片的功能，检测项检测时使用的是矫正后的图片
//要将矫正的变换参数保存，界面显示的需要是把ROI区域等检测效果进行矫正的图片
Parallel.ForEach(UseMatchingFun.ToArray(), matchingFun =>
{
if (!matchingFun.matching.Name.Equals("无模板定位") && camImages.TryGetValue(matchingFun.matching.CamName, out HObject camImage))
{
int nRet = matchingFun.Find(camImage, out HObject matchImage);
if (nRet == 0)
{
matchImages.AddOrUpdate(matchingFun.matching.Name, matchImage, (k, v) => v = matchImage);
matchHomMat2D.AddOrUpdate(matchingFun.matching.Name, matchingFun.homMat2D, (k, v) => v = matchingFun.homMat2D);
}
SoftwareOnceEvent?.Invoke(null, new OutImage(matchingFun.matching.CamName, camImage));
}
});
Parallel.ForEach(UseTestItems.ToArray(), testItem =>
{
if (testItem.hav)
{
return;
}
HObject inImage = null;
if (testItem.matchName.Equals("无模板定位"))
{
if (camImages.TryGetValue(testItem.camName, out inImage))
{
testItem.Find(inImage);
testItem.Show(inImage, out HObject resultImage);
testItem.hav = true;
OutResult outResult = new OutResult()
{
TestItemName = testItem.name,
CamName = testItem.camName,
IsOK = testItem.isOK,
ResultMessage = testItem.outMessage,
OriginalImage = new OutImage(testItem.camName, inImage),
EffectImage = new OutImage(testItem.camName, resultImage)
};
DetectionOnceEvent?.Invoke(null, outResult);
OutResults.Add(outResult);
}
}
else
{
if (matchImages.TryGetValue(testItem.matchName, out inImage))
{
testItem.Find(inImage);
camImages.TryGetValue(testItem.camName, out inImage);
testItem.Show(inImage, matchHomMat2D[testItem.matchName], out HObject resultImage);
testItem.hav = true;
OutResult outResult = new OutResult()
{
TestItemName = testItem.name,
CamName = testItem.camName,
IsOK = testItem.isOK,
ResultMessage = testItem.outMessage,
OriginalImage = new OutImage(testItem.camName, inImage),
EffectImage = new OutImage(testItem.camName, resultImage)
};
DetectionOnceEvent?.Invoke(null, outResult);
OutResults.Add(outResult);
}
}
});
foreach (var camImage in camImages.Values)
{
camImage.Dispose();
}
hav = true;
foreach (var testItem in UseTestItems.ToArray())
{
hav &= testItem.hav;
}
if (hav)
{
foreach (var testItem in UseTestItems.ToArray())
{
testItem.hav = false;
}
break;
}
if (stopDetectionSignal.WaitOne(20))
{
break;
}
Thread.Sleep(20);
}
})
{
IsBackground = true
};
DetectionThread.Start();
return(true);
}
