/// <summary>
/// 测试小球OKNG的方法
/// </summary>
/// <param name="hv_liudao">小球所在位置</param>
/// <param name="hv_channels">流道信息</param>
/// <param name="hv_liudaoR">小球直径（像素值）</param>
/// <param name="hv_myLength">小球标准直径（像素值）</param>
/// <param name="hv_myThreashod">小球阈值范围</param>
/// <param name="hv_myResult">是否都是好的</param>
/// <param name="hv_NGLengths">NG小球直径</param>
/// <param name="hv_NGChls">NG小球所在流道</param>
public void testOKNG2(HTuple hv_liudao, HTuple hv_channels, HTuple hv_liudaoR,
HTuple hv_myLength, HTuple hv_myThreashod, out HTuple hv_myResult, out HTuple hv_NGLengths,
out HTuple hv_NGChls)
{
// Local iconic variables
// Local control variables
HTuple hv_num = null, hv_num2 = null, hv_Index1 = null;
HTuple hv_Index2 = new HTuple(), hv_lll = new HTuple();
HTuple hv_ff1 = new HTuple(), hv_ff2 = new HTuple(), hv_ff = new HTuple();
// Initialize local and output iconic variables
hv_myResult = 1;
hv_num = new HTuple(hv_liudao.TupleLength());
hv_num2 = new HTuple(hv_channels.TupleLength());
hv_NGChls = new HTuple();
hv_NGLengths = new HTuple();
HTuple end_val8 = hv_num - 1;
HTuple step_val8 = 1;
for (hv_Index1 = 0; hv_Index1.Continue(end_val8, step_val8); hv_Index1 = hv_Index1.TupleAdd(step_val8))
{
HTuple end_val9 = hv_num2;
HTuple step_val9 = 1;
for (hv_Index2 = 1; hv_Index2.Continue(end_val9, step_val9); hv_Index2 = hv_Index2.TupleAdd(step_val9))
{
if ((int)(new HTuple(((hv_liudao.TupleSelect(hv_Index1))).TupleLess(hv_channels.TupleSelect(
hv_Index2)))) != 0)
{
break;
}
}
hv_lll = hv_Index2 - 1;
hv_ff1 = new HTuple(((hv_liudaoR.TupleSelect(hv_Index1))).TupleGreater((hv_myLength.TupleSelect(
hv_lll)) - (hv_myThreashod.TupleSelect(hv_lll))));
hv_ff2 = new HTuple(((hv_liudaoR.TupleSelect(hv_Index1))).TupleLess((hv_myLength.TupleSelect(
hv_lll)) + (hv_myThreashod.TupleSelect(hv_lll))));
hv_ff = hv_ff1.TupleAnd(hv_ff2);
if ((int)(hv_ff) != 0)
{
}
else
{
hv_NGChls = hv_NGChls.TupleConcat(hv_Index2);
hv_NGLengths = hv_NGLengths.TupleConcat(hv_liudaoR.TupleSelect(hv_Index1));
}
hv_myResult = hv_myResult.TupleAnd(hv_ff);
}
return;
}
public void TrainProcess(HTuple hv_WindowHandle, string pretrained_DlClassifierName)
{
this.hv_WindowHandle = hv_WindowHandle;
//This example shows how to train a deep learning fruit classifier, along with a short overview of the necessary steps.
//
//Initialization.
//dev_open_window_fit_size(0, 0, hv_WindowWidth, hv_WindowHeight, -1, -1, out hv_WindowHandle);
set_display_font(hv_WindowHandle, 16, "mono", "true", "false");
//
//Some procedures use a random number generator. Set the seed for reproducibility.
HOperatorSet.SetSystem("seed_rand", 42);
//
HOperatorSet.ClearWindow(hv_WindowHandle);
//
//** TRAINING **
//
//Read one of the pretrained networks.
HOperatorSet.ReadDlClassifier(pretrained_DlClassifierName, out this.hv_Train_DLClassifierHandle);
//2) Split data into training, validation, and test set.
//
//Read the data, i.e., the paths of the images and their respective ground truth labels.
hv_ImageFiles.Dispose(); hv_Labels.Dispose(); hv_LabelsIndices.Dispose(); hv_Classes.Dispose();
read_dl_classifier_data_set(this.hv_PreprocessedFolder, "last_folder", out hv_ImageFiles,
out hv_Labels, out hv_LabelsIndices, out hv_Classes);
//
//Split the data into three subsets,
//Default for training 80%, validation 20%, and testing 0%.
hv_TrainingImages.Dispose(); hv_TrainingLabels.Dispose(); hv_ValidationImages.Dispose(); hv_ValidationLabels.Dispose(); hv_TestImages.Dispose(); hv_TestLabels.Dispose();
split_dl_classifier_data_set(hv_ImageFiles, hv_Labels, this.hv_TrainingPercent,
this.hv_ValidationPercent, out hv_TrainingImages, out hv_TrainingLabels, out hv_ValidationImages,
out hv_ValidationLabels, out hv_TestImages, out hv_TestLabels);
//
//Set training hyper-parameters.
//In order to retrain the neural network, we have to specify
//the class names of our classification problem.
HOperatorSet.SetDlClassifierParam(this.hv_Train_DLClassifierHandle, "classes", hv_Classes);
//Set the batch size.
HOperatorSet.SetDlClassifierParam(this.hv_Train_DLClassifierHandle, "batch_size", this.hv_BatchSize);
hv_RemovePreprocessingAfterExample.Dispose();
hv_RemovePreprocessingAfterExample = 0;
//Try to initialize the runtime environment.
try
{
HOperatorSet.SetDlClassifierParam(this.hv_Train_DLClassifierHandle, "runtime_init",
"immediately");
}
// catch (Exception)
catch (HalconException HDevExpDefaultException1)
{
HDevExpDefaultException1.ToHTuple(out hv_Exception);
HOperatorSet.DispText(hv_WindowHandle, "Failed to initialize the runtime environment.", "window", "bottom", "right", "red", "box", "false");
//dev_disp_error_text(hv_Exception);
if ((int)(hv_RemovePreprocessingAfterExample.TupleAnd(new HTuple(((hv_Exception.TupleSelect(
0))).TupleNotEqual(4104)))) != 0)
{
remove_dir_recursively(hv_PreprocessedFolder);
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
HOperatorSet.DispText(hv_WindowHandle, ("Preprocessed data in folder \"" + hv_PreprocessedFolder) + "\" have been deleted.",
"window", "top", "right", "red", new HTuple(), new HTuple());
}
}
return;
}
//For this data set, an initial learning rate of 0.001 has proven to yield good results.
HOperatorSet.SetDlClassifierParam(this.hv_Train_DLClassifierHandle, "learning_rate", this.hv_InitialLearningRate);
// stop(...); only in hdevelop
//
//Train the classifier.
HOperatorSet.DispText(hv_WindowHandle, "Training has started...",
"window", "top", "left", "black", new HTuple(), new HTuple());
Console.WriteLine("开始训练过程...");
_TrainingThread = new Thread(new ThreadStart(TrainingThread));
_TrainingThread.IsBackground = true;
_TrainingThread.Start();
//
//In this example, we reduce the learning rate by a factor of 1/10 every 5th epoch.
//We iterate 50 times over the full training set.
//ComputeConfusionMatrix(hv_WindowHandle);
}
