public bool Initialize(Stream modelData, bool useNumThreads)
{
using (var builder = new TextRecognizer.Builder(MainActivity.context))
{
txtRecognizer = builder.Build();
}
using (var ms = new MemoryStream())
{
modelData.CopyTo(ms);
model = new FlatBufferModel(ms.ToArray());
}
if (!model.CheckModelIdentifier())
{
return(false);
}
var op = new BuildinOpResolver();
interpreter = new Interpreter(model, op);
if (useNumThreads)
{
interpreter.SetNumThreads(Environment.ProcessorCount);
}
var allocateTensorStatus = interpreter.AllocateTensors();
if (allocateTensorStatus == Status.Error)
{
return(false);
}
var input = interpreter.GetInput();
inputTensor = interpreter.GetTensor(input[0]);
var output = interpreter.GetOutput();
var outputIndex = output[0];
outputTensors = new Tensor[output.Length];
for (var i = 0; i < output.Length; i++)
{
outputTensors[i] = interpreter.GetTensor(outputIndex + i);
}
return(true);
}
public void Recognize(int[] colors)
{
if (!initialized)
{
throw new Exception("Initialize TensorflowLiteService first");
}
MessagingCenter.Instance.Send(this, nameof(AR.InputTensorMessage), new InputTensorMessage()
{
Colors = colors,
});
using (var op = new BuildinOpResolver())
{
using (var interpreter = new Interpreter(model, op))
{
InvokeInterpreter(colors, interpreter);
}
}
}
private void onDownloadCompleted(object sender, System.ComponentModel.AsyncCompletedEventArgs e)
{
String localFileName = _downloadManager.Files[0].LocalFile;
if (_labels == null)
{
_labels = File.ReadAllLines(_downloadManager.Files[1].LocalFile);
}
System.Diagnostics.Debug.Assert(File.Exists(localFileName), "File doesn't exist");
FileInfo file = new FileInfo(localFileName);
if (_model == null)
{
_model = new FlatBufferModel(localFileName);
if (!_model.CheckModelIdentifier())
{
throw new Exception("Model indentifier check failed");
}
}
if (_resolver == null)
{
_resolver = new BuildinOpResolver();
}
if (_interpreter == null)
{
_interpreter = new Interpreter(_model, _resolver);
Status allocateTensorStatus = _interpreter.AllocateTensors();
if (allocateTensorStatus == Status.Error)
{
throw new Exception("Failed to allocate tensor");
}
}
}
private void onDownloadCompleted(object sender, System.ComponentModel.AsyncCompletedEventArgs e)
{
String localFileName = _downloadManager.Files[0].LocalFile;
if (_labels == null)
{
_labels = File.ReadAllLines(_downloadManager.Files[1].LocalFile);
}
System.Diagnostics.Debug.Assert(File.Exists(localFileName), "File doesn't exist");
FileInfo file = new FileInfo(localFileName);
if (_model == null)
{
_model = new FlatBufferModel(localFileName);
if (!_model.CheckModelIdentifier())
{
throw new Exception("Model indentifier check failed");
}
}
if (_resolver == null)
{
_resolver = new BuildinOpResolver();
}
if (_interpreter == null)
{
_interpreter = new Interpreter(_model, _resolver);
Status allocateTensorStatus = _interpreter.AllocateTensors();
if (allocateTensorStatus == Status.Error)
{
throw new Exception("Failed to allocate tensor");
}
}
int[] input = _interpreter.GetInput();
int[] output = _interpreter.GetOutput();
Tensor inputTensor = _interpreter.GetTensor(input[0]);
Tensor outputTensor = _interpreter.GetTensor(output[0]);
NativeImageIO.ReadImageFileToTensor(_image[0], inputTensor.DataPointer, 224, 224, 128.0f, 1.0f / 128.0f);
Stopwatch watch = Stopwatch.StartNew();
_interpreter.Invoke();
watch.Stop();
float[] probability = outputTensor.GetData() as float[];
String resStr = String.Empty;
if (probability != null)
{
float maxVal = 0;
int maxIdx = 0;
for (int i = 0; i < probability.Length; i++)
{
if (probability[i] > maxVal)
{
maxVal = probability[i];
maxIdx = i;
}
}
resStr = String.Format("Object is {0} with {1}% probability. Recognition completed in {2} milliseconds.", _labels[maxIdx], maxVal * 100, watch.ElapsedMilliseconds);
}
SetImage(_image[0]);
SetMessage(resStr);
}
