public void ScanImage(byte[] bytes)
{
var tensor = _interpreter.GetInputTensor(0);
var shape = tensor.Shape();
var width = shape[1];
var height = shape[2];
var byteBuffer = ConvertImageByteArrayToByteBuffer(bytes, width, height);
var outputLocation = new float[1][] { new float[_labels.Count] };
var outputs = Java.Lang.Object.FromArray(outputLocation);
_interpreter.Run(byteBuffer, outputs);
var classificationResult = outputs.ToArray <float[]>();
var result = new List <ImageClassification>();
for (var i = 0; i < _labels.Count; i++)
{
var label = _labels[i];
if (classificationResult != null)
{
result.Add(new ImageClassification(label, classificationResult[0][i]));
}
}
ClassificationCompleted?.Invoke(this, new ClassificationEventArgs(result));
}
private void HandleVNRequest(VNRequest request, NSError error)
{
if (error != null)
{
ClassificationCompleted?.Invoke(this, new ClassificationEventArgs(new List <Classification>()));
}
var result = request.GetResults <VNClassificationObservation>();
var classifications = result.OrderByDescending(x => x.Confidence).Select(x => new Classification(x.Identifier, x.Confidence)).ToList();
ClassificationCompleted?.Invoke(this, new ClassificationEventArgs(classifications));
}
private void HandleVNRequest(VNRequest request, NSError error)
{
if (error != null)
{
ClassificationCompleted?.Invoke(this, new ClassificationEventArgs(new Dictionary <string, float>()));
}
var result = request.GetResults <VNClassificationObservation>();
var classifications = result.OrderByDescending(x => x.Confidence).ToDictionary(x => x.Identifier, x => x.Confidence);
ClassificationCompleted?.Invoke(this, new ClassificationEventArgs(classifications));
}
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 void Classify(byte[] bytes)
{
if ((interpreter == null || tensor == null) && !isInitingModel)
{
InitModel();
}
else
{
return;
}
if (isClassifying)
{
return;
}
isClassifying = true;
new Thread(new ThreadStart(async() =>
{
var shape = tensor.Shape();
var width = shape[1];
var height = shape[2];
var byteBuffer = await GetByteBufferFromPhoto(bytes, width, height);
if (byteBuffer == null)
{
ClassificationCompleted?.Invoke(this, new ClassificationEventArgs(new List <Classification>()));
return;
}
if (List_Labels == null)
{
ClassificationCompleted?.Invoke(this, new ClassificationEventArgs(new List <Classification>()));
return;
}
var outputLocations = new float[1][] { new float[List_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 < List_Labels.Count; i++)
{
var label = List_Labels[i];
result.Add(new Classification {
TagName = label, Probability = classificationResult[0][i]
});
}
ClassificationCompleted?.Invoke(this, new ClassificationEventArgs(result));
isClassifying = false;
})).Start();
}
public void Classify(byte[] bytes)
{
var assets = Application.Context.Assets;
var inferenceInterface = new TensorFlowInferenceInterface(assets, "people-or-not-model.pb");
var streamReader = new StreamReader(assets.Open("people-or-not-labels.txt"));
var labels = streamReader
.ReadToEnd()
.Split('\n')
.Select(s => s.Trim())
.Where(s => !string.IsNullOrEmpty(s))
.ToList();
//page 354
var bitmap = BitmapFactory.DecodeByteArray(bytes, 0, bytes.Length);
var resizedBitMap = Bitmap.CreateScaledBitmap(bitmap, 227, 227, false).Copy(Bitmap.Config.Argb8888, false);
var floatValues = new float[227 * 227 * 3];
var intValues = new int[227 * 227 * 3];
resizedBitMap.GetPixels(intValues, 0, 227, 0, 0, 227, 227);
for (int i = 0; i < intValues.Length; i++)
{
var val = intValues[i];
floatValues[i * 3 + 0] = ((val & 0xFF) - 104);
floatValues[i * 3 + 1] = ((val & 8) - 117);
floatValues[i * 3 + 2] = ((val & 16) - 123);
}
var outputs = new float[labels.Count];
inferenceInterface.Feed("Placeholder", floatValues, 1, 227, 227, 3);
inferenceInterface.Run(new[] { "loss" });
inferenceInterface.Fetch("loss", outputs);
var results = new Dictionary <string, float>();
for (var i = 0; i < labels.Count; i++)
{
var label = labels[i];
results.Add(label, outputs[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 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));
}
public async Task Classify(byte[] bytes)
{
var client = new HttpClient();
client.Timeout = new TimeSpan(0, 0, 3);
client.DefaultRequestHeaders.Add("Prediction-Key", "<insert-key-here>");
string url = "https://southcentralus.api.cognitive.microsoft.com/customvision/v3.0/Prediction/80c96cf0-45ee-4887-9925-46422af8711d/classify/iterations/Iteration3/image";
HttpResponseMessage response;
using (var content = new ByteArrayContent(bytes))
{
content.Headers.ContentType = new MediaTypeHeaderValue("application/octet-stream");
response = await client.PostAsync(url, content);
var json = await response.Content.ReadAsStringAsync();
var result = JsonConvert.DeserializeObject <PredictionResult>(json);
ClassificationCompleted.Invoke(this, new ClassificationEventArgs(result.Predictions));
}
}
public void _onHandleResults(object error, ClassificationResult[] result) => ClassificationCompleted?.Invoke(error, result);
public async void Classification(byte[] bytes)
{
var assets = Application.Context.Assets;
var inferenceInterface = new TensorFlowInferenceInterface(assets, "model.pb");
var inputSize = (int)inferenceInterface.GraphOperation("Placeholder").Output(0).Shape().Size(1);
List <string> labels;
using (var streamReader = new StreamReader(assets.Open("labels.txt")))
{
labels = streamReader.ReadToEnd().Split('\n').Select(s => s.Trim()).Where(s => !string.IsNullOrEmpty(s))
.ToList();
}
var bitmap = await BitmapFactory.DecodeByteArrayAsync(bytes, 0, bytes.Length);
var resizedMap = Bitmap.CreateScaledBitmap(bitmap, inputSize, inputSize, false)
.Copy(Bitmap.Config.Argb8888, false);
var floatValues = new float[inputSize * inputSize * 3];
var intValues = new int[inputSize * inputSize];
resizedMap.GetPixels(intValues, 0, inputSize, 0, 0, inputSize, inputSize);
for (var i = 0; i < intValues.Length; ++i)
{
var intValue = intValues[i];
floatValues[i * 3 + 0] = (intValue & 0xFF) - 105f;
floatValues[i * 3 + 1] = ((intValue >> 8) & 0xFF) - 117f;
floatValues[i * 3 + 2] = ((intValue >> 16) & 0xFF) - 124f;
}
var operation = inferenceInterface.GraphOperation("loss");
var outputs = new float[labels.Count];
inferenceInterface.Feed("Placeholder", floatValues, 1, inputSize, inputSize, 3);
inferenceInterface.Run(new[] { "loss" }, true);
inferenceInterface.Fetch("loss", outputs);
var result = new Dictionary <string, float>();
for (var i = 0; i < labels.Count; i++)
{
var label = labels[i];
result.Add(label, outputs[i]);
}
var maxConf = 0f;
for (var i = 0; i < outputs.Length; ++i)
{
if (outputs[i] > maxConf)
{
maxConf = outputs[i];
}
}
ClassificationCompleted?.Invoke(this, new ClassificationEventArgs(result));
}
public async void Classification(byte[] bytes)
{
var file = await StorageFile.GetFileFromApplicationUriAsync(new Uri($"ms-appx:///Assets/{modelFileName}"));
petModel = await PetModelModel.CreateFromStreamAsync(file);
try
{
var newBitmap = new WriteableBitmap(255, 255);
using (var stream = new InMemoryRandomAccessStream())
{
await stream.WriteAsync(bytes.AsBuffer());
stream.Seek(0);
await newBitmap.SetSourceAsync(stream);
}
var outputBitmap = SoftwareBitmap.CreateCopyFromBuffer(
newBitmap.PixelBuffer,
BitmapPixelFormat.Bgra8,
newBitmap.PixelWidth,
newBitmap.PixelHeight
);
var frame = VideoFrame.CreateWithSoftwareBitmap(outputBitmap);
if (frame != null)
{
try
{
var inputData = new PetModelInput();
inputData.data = ImageFeatureValue.CreateFromVideoFrame(frame);
var results = await petModel.EvaluateAsync(inputData);
var loss = results.loss.ToList();
var catValue = loss.FirstOrDefault()["cat"];
var dogValue = loss.FirstOrDefault()["dog"];
var result = new Dictionary <string, float>();
if (catValue > dogValue)
{
result.Add("cat", catValue);
}
else
{
result.Add("dog", dogValue);
}
ClassificationCompleted?.Invoke(this, new ClassificationEventArgs(result));
}
catch (Exception ex)
{
Debug.WriteLine($"error: {ex.Message}");
}
}
}
catch (Exception e)
{
Console.WriteLine(e);
}
}
