public void Detect(byte[] image)
{
var mappedByteBuffer = GetModelAsMappedByteBuffer();
var interpreter = new Xamarin.TensorFlow.Lite.Interpreter(mappedByteBuffer);
var tensor = interpreter.GetInputTensor(0);
var tensor2 = interpreter.GetOutputTensor(0);
var shape1 = tensor2.Shape();
var shape = tensor.Shape();
var width = shape[1];
var height = shape[2];
var byteBuffer = GetPhotoAsByteBuffer(image, width, height);
var streamReader = new StreamReader(Android.App.Application.Context.Assets.Open("labels.txt"));
var labels = streamReader.ReadToEnd().Split('\n').Select(s => s.Trim()).Where(s => !string.IsNullOrEmpty(s)).ToList();
//Convert our two-dimensional array into a Java.Lang.Object, the required input for Xamarin.TensorFlow.List.Interpreter
var outputLocations = new float[1, 13, 13, 50];
var output = ByteBuffer.AllocateDirect(33800);
try
{
interpreter.Run(byteBuffer, output);
}
catch (Exception ex)
{
var x = ex.Message;
}
}
public async Task Classify(List <List <float> > dataframe)
{
var mappedByteBuffer = GetModelAsMappedByteBuffer();
var interpreter = new Xamarin.TensorFlow.Lite.Interpreter(mappedByteBuffer);
var tensor = interpreter.GetInputTensor(0);
var shape = tensor.Shape();
var width = shape[1];
var height = shape[2];
var sr = new StreamReader(Application.Context.Assets.Open("labels.txt"));
var labels = sr.ReadToEnd().Split('\n').Select(s => s.Trim()).Where(s => !string.IsNullOrEmpty(s)).ToList();
var outputLocations = new float[1][] { new float[labels.Count] };
var outputs = Java.Lang.Object.FromArray(outputLocations);
//interpreter.Run(dataframe, outputs);
var classificationResult = outputs.ToArray <float[]>();
}
public async Task Classify(byte[] bytes)
{
var modelByteBuffer = LoadModel("dog-identification.tflite");
var interpreter = new Xamarin.TensorFlow.Lite.Interpreter(modelByteBuffer);
var inputTensor = interpreter.GetInputTensor(0);
var shape = inputTensor.Shape();
var width = shape[1];
var height = shape[2];
List <String> labels = new List <String>();
var labelsStream = Application.Context.Assets.Open("labels.txt");
using (StreamReader sr = new StreamReader(labelsStream))
{
string line;
while ((line = sr.ReadLine()) != null)
{
labels.Add(line);
}
}
var input = GetPhotoAsByteBuffer(bytes, width, height);
var outputLocations = new float[1][] { new float[labels.Count] };
var outputs = Java.Lang.Object.FromArray(outputLocations);
interpreter.Run(input, outputs);
var classificationResults = outputs.ToArray <float[]>();
System.Console.WriteLine("I got results: ");
for (int i = 0; i < classificationResults[0].Length; i++)
{
System.Console.WriteLine($"{labels[i]} : {classificationResults[0][i]}");
}
var results = new List <Classification>();
for (var i = 0; i < labels.Count; i++)
{
var label = labels[i];
results.Add(new Classification(label, classificationResults[0][i]));
}
ClassificationCompleted?.Invoke(this, new ClassificationEventArgs(results));
}
public async Task Classify(byte[] bytes)
{
var mappedByteBuffer = GetModelAsMappedByteBuffer();
// The following line with fail with compilation error
// Error CS0234 The type or namespace name 'GPU' does not exist in the namespace 'Xamarin.TensorFlow.Lite' (are you missing an assembly reference?)
var gpuDelegate = new Xamarin.TensorFlow.Lite.GPU.GpuDelegate();
Xamarin.TensorFlow.Lite.Interpreter.Options options = new Xamarin.TensorFlow.Lite.Interpreter.Options().AddDelegate(gpuDelegate);
var interpreter = new Xamarin.TensorFlow.Lite.Interpreter(mappedByteBuffer, options);
// If we comment 3 lines above and uncomment 1 line below everything works fine.
//var interpreter = new Xamarin.TensorFlow.Lite.Interpreter(mappedByteBuffer);
var tensor = interpreter.GetInputTensor(0);
var shape = tensor.Shape();
var width = shape[1];
var height = shape[2];
var byteBuffer = GetPhotoAsByteBuffer(bytes, width, height);
var sr = new StreamReader(Application.Context.Assets.Open("labels.txt"));
var labels = sr.ReadToEnd().Split('\n').Select(s => s.Trim()).Where(s => !string.IsNullOrEmpty(s)).ToList();
var outputLocations = new float[1][] { new float[labels.Count] };
var outputs = Java.Lang.Object.FromArray(outputLocations);
interpreter.Run(byteBuffer, outputs);
var classificationResult = outputs.ToArray <float[]>();
var result = new List <Classification>();
for (var i = 0; i < labels.Count; i++)
{
var label = labels[i];
result.Add(new Classification(label, classificationResult[0][i]));
}
ClassificationCompleted?.Invoke(this, new ClassificationEventArgs(result));
}
public List <ImageClassificationModel> Classify(byte[] image)
{
MappedByteBuffer mappedByteBuffer = GetModelAsMappedByteBuffer();
// TODO: Find a solution that is not being deprecated.
Xamarin.TensorFlow.Lite.Interpreter interpreter = new Xamarin.TensorFlow.Lite.Interpreter(mappedByteBuffer);
//To resize the image, we first need to get its required width and height
Xamarin.TensorFlow.Lite.Tensor tensor = interpreter.GetInputTensor(0);
int[] shape = tensor.Shape();
int width = shape[1];
int height = shape[2];
ByteBuffer byteBuffer = GetPhotoAsByteBuffer(image, width, height);
//use StreamReader to import the labels from labels.txt
StreamReader streamReader = new StreamReader(Application.Context.Assets.Open("labels.txt"));
//Transform labels.txt into List<string>
List <string> labels = streamReader.ReadToEnd().Split('\n').Select(s => s.Trim()).Where(s => !string.IsNullOrEmpty(s)).ToList();
//Convert our two-dimensional array into a Java.Lang.Object, the required input for Xamarin.TensorFlow.List.Interpreter
float[][] outputLocations = new float[1][] { new float[labels.Count] };
Java.Lang.Object outputs = Java.Lang.Object.FromArray(outputLocations);
interpreter.Run(byteBuffer, outputs);
float[][] classificationResult = outputs.ToArray <float[]>();
//Map the classificationResult to the labels and sort the result to find which label has the highest probability
List <ImageClassificationModel> classificationModelList = new List <ImageClassificationModel>();
for (int i = 0; i < labels.Count; i++)
{
string label = labels[i]; classificationModelList.Add(new ImageClassificationModel(label, classificationResult[0][i]));
}
return(classificationModelList);
}
public void Classify(byte[] bytes)
{
var mappedByteBuffer = GetModelAsMappedByteBuffer();
var interpreter = new Xamarin.TensorFlow.Lite.Interpreter(mappedByteBuffer);
var tensor = interpreter.GetInputTensor(0);
var shape = tensor.Shape();
var width = shape[1];
var height = shape[2];
var sr = new StreamReader(Application.Context.Assets.Open("hotdog-or-not-labels.txt"));
var labels = sr.ReadToEnd().Split('\n').Select(s => s.Trim()).Where(s => !string.IsNullOrEmpty(s)).ToList();
var outputLocations = new float[1][] { new float[labels.Count] };
var outputs = Java.Lang.Object.FromArray(outputLocations);
var byteBuffer = GetPhotoAsByteBuffer(bytes, width, height);
interpreter.Run(byteBuffer, outputs);
var predictionResult = outputs.ToArray <float[]>();
var result = new Dictionary <string, float>();
for (var i = 0; i < labels.Count; i++)
{
var label = labels[i];
result.Add(label, predictionResult[0][i]);
}
ClassificationCompleted?.Invoke(this, new ClassificationEventArgs(result));
}