private void ImageBinaryClassificationAndDuplicateWorker(object sender, DoWorkEventArgs e)
{
//should probably check for inputdirecttext is validated
_filesToProcess = Directory.GetFiles(InputDirText);
//I need property and feild for data binding of the text box for model location
//modelFilePath =
_predictedResults = new ObservableCollection <CustomTwoClassificationImagePredictionResults>();
_targetOutputDirectoryPath = OutputDirText;
//create output directories
Console.WriteLine("-----------------------------------");
Console.WriteLine(_targetOutputDirectoryPath);
var inventoryDir = System.IO.Path.Combine(_targetOutputDirectoryPath, "inventory");
Console.WriteLine(inventoryDir);
Console.WriteLine(inventoryDir);
var infrastructureDir = System.IO.Path.Combine(_targetOutputDirectoryPath, "infrastructure");
Console.WriteLine(infrastructureDir);
if (!System.IO.Directory.Exists(inventoryDir))
{
System.IO.Directory.CreateDirectory(inventoryDir);
}
if (!System.IO.Directory.Exists(infrastructureDir))
{
System.IO.Directory.CreateDirectory(infrastructureDir);
}
MLContext mlContext = new MLContext();
// ModelOutput mop = ConsumeModel.Predict(mip, ConsumeModel.ClassicationModelEnum.classtwo);
string modelPath = _modelInputFileText;
//// Load model & create prediction engine
ITransformer mlModel = mlContext.Model.Load(modelPath, out var modelInputSchema);
foreach (var item in _filesToProcess)
{
Console.WriteLine(_imageClassificationCount.ToString());
_imageClassificationCount++;
Console.WriteLine(_imageClassificationCount); //parse to model input
ModelInput mip = new ModelInput();
mip.Label = "none"; //useful for evaluation section
mip.ImageSource = item;
//##########################################################
var predEngine = mlContext.Model.CreatePredictionEngine <ModelInput, ModelOutput>(mlModel);
Console.WriteLine($"number of columns is ======= {modelInputSchema.Count.ToString()}");
foreach (var inputItem in modelInputSchema)
{
Console.WriteLine($"name of column is ========={inputItem.Name}");
}
// Console.WriteLine($"count is ======= {modelInputSchema.}");
// Use model to make prediction on input data
ModelOutput result = predEngine.Predict(mip);
string toprintDebugConsole = $"prediction class: {result.Prediction}|| score: {result.Score.FirstOrDefault()}";
//##########################################################
Console.WriteLine(toprintDebugConsole);
Console.WriteLine(result.Prediction.ToString());
Console.WriteLine(result.Score.FirstOrDefault());
Console.WriteLine("##########################");
Console.WriteLine(item);
var filename = System.IO.Path.GetFileName(item);
Console.WriteLine(filename);
string disclass = System.IO.Path.Combine(_targetOutputDirectoryPath, result.Prediction);
Console.WriteLine(disclass);
var destfile = System.IO.Path.Combine(disclass, filename);
Console.WriteLine(destfile);
System.IO.File.Copy(item, destfile, true);
//update ui with
var newPredictionToUpdateOutputStatus = new CustomTwoClassificationImagePredictionResults();
newPredictionToUpdateOutputStatus.PredictionId = _imageClassificationCount.ToString();
newPredictionToUpdateOutputStatus.ImageOriginalPath = item.ToString();
Console.WriteLine(item.ToString());
newPredictionToUpdateOutputStatus.ModelOutputscore = result.Score.FirstOrDefault().ToString();
newPredictionToUpdateOutputStatus.ModelOutputPrediction = result.Prediction;
Console.WriteLine("number of items in observable collection");
//from the backgroud thread in backgroudworker, I need to call the ui thread to update the listview
uiContext.Send(x => PredictionResults.Add(newPredictionToUpdateOutputStatus), null);
//PredictionResults.Add(newPredictionToUpdateOutputStatus);
Console.WriteLine(PredictionResults.Count.ToString());
}
}
