// Convert the image in filename to a Tensor suitable as input to the Inception model.
public static TFTensor CreateTensorFromImageFile(string file, TFDataType destinationDataType = TFDataType.Float)
{
var contents = File.ReadAllBytes(file);
// DecodeJpeg uses a scalar String-valued tensor as input.
using (var tensor = TFTensor.CreateString(contents))
// Construct a graph to normalize the image
using (var graph = ConstructGraphToNormalizeImage(out TFOutput input, out TFOutput output, destinationDataType))
// Execute that graph to normalize this one image
using (var session = new TFSession(graph))
{
var normalized = session.Run(
inputs: new[] { input },
inputValues: new[] { tensor },
outputs: new[] { output });
Console.WriteLine(normalized.Length);
return(normalized[0]);
}
}
/// <summary>
/// Create a constant tensor based on a shape
/// Used by Zeros and Ones
/// </summary>
/// <param name="value">Value for tensor</param>
/// <param name="tfshape">Shape of the tensor</param>
/// <param name="dtype">Optional Type of the Zero value. Default: Double</param>
/// <param name="operName">Operation name, optional.</param>
/// <returns></returns>
/// see https://github.com/tensorflow/tensorflow/blob/r1.1/tensorflow/python/framework/constant_op.py
public TFOutput Constant(object value, TFShape tfshape, TFDataType dtype = TFDataType.Double, string operName = null)
{
if (tfshape.NumDimensions <= 0)
{
TFTensor tensor = TFTensor.Create1DTensor(dtype, value);
return(Const(tensor, tensor.TensorType, operName));
}
//convert the .net type to relevant tensorflow type
object dtvalue = TFTensor.FetchSimple(dtype, value);
var shape = tfshape.ToArray();
var idx = new int [shape.Length];
for (int i = 0; i < shape.Length; i++)
{
if (shape [i] > Int32.MaxValue)
{
throw new ArgumentOutOfRangeException("Shape can not be longer than 32 bits");
}
}
Array data = null;
if (tfshape.IsLongArray)
{
data = Array.CreateInstance(dtvalue.GetType(), tfshape.ToArray());
}
else
{
data = Array.CreateInstance(dtvalue.GetType(), tfshape.ToIntArray());
}
TFTensor.Set(data, dtype, shape, idx, 0, value);
TFTensor tensor_value = new TFTensor(data);
return(Const(tensor_value, tensor_value.TensorType, operName));
}
// Additional pointers for using TensorFlow & CustomVision together
// Python: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/label_image/label_image.py
// C++: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/label_image/main.cc
// Java: https://github.com/Azure-Samples/cognitive-services-android-customvision-sample/blob/master/app/src/main/java/demo/tensorflow/org/customvision_sample/MSCognitiveServicesClassifier.java
private static TFGraph ConstructGraphToNormalizeImage(out TFOutput input, out TFOutput output, TFDataType destinationDataType = TFDataType.Float)
{
const int W = 227;
const int H = 227;
const float Scale = 1;
// Depending on your CustomVision.ai Domain - set appropriate Mean Values (RGB)
// https://github.com/Azure-Samples/cognitive-services-android-customvision-sample for RGB values (in BGR order)
var bgrValues = new TFTensor(new float[] { 104.0f, 117.0f, 123.0f }); // General (Compact) & Landmark (Compact)
//var bgrValues = new TFTensor(0f); // Retail (Compact)
var graph = new TFGraph();
input = graph.Placeholder(TFDataType.String);
var caster = graph.Cast(graph.DecodeJpeg(contents: input, channels: 3), DstT: TFDataType.Float);
var dims_expander = graph.ExpandDims(caster, graph.Const(0, "batch"));
var resized = graph.ResizeBilinear(dims_expander, graph.Const(new int[] { H, W }, "size"));
var resized_mean = graph.Sub(resized, graph.Const(bgrValues, "mean"));
var normalised = graph.Div(resized_mean, graph.Const(Scale));
output = normalised;
return(graph);
}
public static TensorFlow.TFOutput Const(this TFGraph graph, TensorFlow.TFTensor value, TensorFlow.TFDataType dtype, string operName = null)
{
return(graph.Const(value, dtype, operName));
}
/// <summary>
/// Creates a constant operation from a TFTensor or constant
/// </summary>
/// <param name="value">Value.</param>
/// <param name="operName">Oper name.</param>
/// <remarks>
/// Since TFTensor have implicit conversion operators, you can call this method with
/// a constant like this: graph.Const (23)
/// </remarks>
public TFOutput Const(TFTensor value, string operName = null)
{
return(Const(value, value.TensorType, operName));
}
public static TensorFlow.TFOutput ImageSummary(this TFGraph graph, TensorFlow.TFOutput tag, TensorFlow.TFOutput tensor, long?max_images = null, TensorFlow.TFTensor bad_color = null, string operName = null)
{
return(graph.ImageSummary(tag, tensor, max_images, bad_color, operName));
}
