/// <summary>
/// Mixed Mode Analyze
/// Recommended
/// </summary>
private void PictureAnalysis(Bitmap bitmap)
{
this.localModel = new MLCustomLocalModel.Factory(ModelName).SetAssetPathFile(ModelFullName).Create();
this.remoteModel = new MLCustomRemoteModel.Factory(RemoteModelName).Create();
DownloadModels(remoteModel);
MLLocalModelManager.Instance
.IsModelExist(remoteModel)
.ContinueWithTask(new CustomModelContinuation(
delegate(Huawei.Hmf.Tasks.Task task)
{
if (!task.IsSuccessful)
{
throw task.Exception;
}
Java.Lang.Boolean isDownloaded = task.Result.JavaCast <Java.Lang.Boolean>();
MLModelExecutorSettings settings = null;
if ((bool)isDownloaded)
{
Toast.MakeText(this, "Executing Remote Model", ToastLength.Short).Show();
settings = new MLModelExecutorSettings.Factory(remoteModel).Create();
}
else
{
Toast.MakeText(this, "Model download failed. Executing Local Model", ToastLength.Long).Show();
settings = new MLModelExecutorSettings.Factory(localModel).Create();
}
try
{
this.modelExecutor = MLModelExecutor.GetInstance(settings);
ExecutorImpl(modelExecutor, bitmap);
}
catch (System.Exception e)
{
Log.Info(Tag, e.ToString());
}
}
));
}
/// <summary>
/// Execute Custom Model
/// classification
/// </summary>
public void ExecutorImpl(MLModelExecutor modelExecutor, Bitmap bitmap)
{
byte[][][][] convertedBitmap = ConvertBitmapToInputFormat(bitmap);
byte[] modelInputArray = FourDimenArrayToOneDimen(convertedBitmap);
MLModelInputs inputs = null;
try
{
inputs = new MLModelInputs.Factory().Add(modelInputArray).Create();
// If the model requires multiple inputs, you need to call Add() for multiple times so that image data can be input to the inference engine at a time.
}
catch (MLException e)
{
// Handle the input data formatting exception.
Log.Info(Tag, " input data format exception! " + e.Message);
}
MLModelInputOutputSettings inOutSettings = null;
try
{
// according to the model requirement, set the in and out format.
inOutSettings = new MLModelInputOutputSettings.Factory()
.SetInputFormat(0, MLModelDataType.Byte, new int[] { BitmapHeight *BitmapWidth * 3 })
.SetOutputFormat(0, MLModelDataType.Byte, new int[] { OutputSize })
.Create();
}
catch (MLException e)
{
Log.Info(Tag, "set input output format failed! " + e.Message);
}
modelExecutor.Exec(inputs, inOutSettings).AddOnSuccessListener(new OnSuccessListener(
delegate(Java.Lang.Object myobj)
{
Log.Info(Tag, "interpret get result");
MLModelOutputs mlModelOutputs = (MLModelOutputs)myobj;
if (mlModelOutputs.Outputs.Count != 0)
{
byte[] output = (byte[])mlModelOutputs.GetOutput(0); // index
var myFlo = GetFloatRest(output);
PrepareResult(myFlo);
// display the result
string totalResult = "";
foreach (var item in result)
{
if (item.Value != 0)
{
totalResult += item.Key.ToString() + ": " + item.Value.ToString();
totalResult += "\n";
}
}
DisplayResult(totalResult);
}
}
)).AddOnFailureListener(new OnFailureListener(
delegate(Java.Lang.Exception e)
{
Log.Info(Tag, " ModelExecutor.Exec() failed: " + e.Message);
}
));
}
