TensorFlow.NET/test/TensorFlowNET.Examples/LabelImage.cs at master · mrliban/TensorFlow.NET · GitHub

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using ICSharpCode.SharpZipLib.GZip;

using ICSharpCode.SharpZipLib.Tar;

using NumSharp.Core;

using System;

using System.Collections.Generic;

using System.IO;

using System.Linq;

using System.Net;

using System.Text;

using System.Threading;

using System.Threading.Tasks;

using Tensorflow;

namespace TensorFlowNET.Examples

{

/// <summary>

/// Port from tensorflow\examples\label_image\label_image.py

/// </summary>

public class LabelImage : Python, IExample

{

string dir = "label_image_data";

string pbFile = "inception_v3_2016_08_28_frozen.pb";

string labelFile = "imagenet_slim_labels.txt";

string picFile = "grace_hopper.jpg";

int input_height = 299;

int input_width = 299;

int input_mean = 0;

int input_std = 255;

string input_name = "import/input";

string output_name = "import/InceptionV3/Predictions/Reshape_1";

public void Run()

{

PrepareData();

var labels = LoadLabels(Path.Join(dir, labelFile));

var nd = ReadTensorFromImageFile(Path.Join(dir, picFile),

input_height: input_height,

input_width: input_width,

input_mean: input_mean,

input_std: input_std);

var graph = LoadGraph(Path.Join(dir, pbFile));

var input_operation = graph.get_operation_by_name(input_name);

var output_operation = graph.get_operation_by_name(output_name);

var results = with<Session, NDArray>(tf.Session(graph),

sess => sess.run(output_operation.outputs[0],

new FeedItem(input_operation.outputs[0], nd)));

results = np.squeeze(results);

var argsort = results.argsort<float>();

var top_k = argsort.Data<float>()

.Skip(results.size - 5)

.Reverse()

.ToArray();

foreach (float idx in top_k)

Console.WriteLine($"{picFile}: {idx} {labels[(int)idx]}, {results[(int)idx]}");

}

private string[] LoadLabels(string file)

{

return File.ReadAllLines(file);

}

private Graph LoadGraph(string modelFile)

{

var graph = tf.Graph().as_default();

var graph_def = GraphDef.Parser.ParseFrom(File.ReadAllBytes(modelFile));

tf.import_graph_def(graph_def);

return graph;

}

private NDArray ReadTensorFromImageFile(string file_name,

int input_height = 299,

int input_width = 299,

int input_mean = 0,

int input_std = 255)

{

return with<Graph, NDArray>(tf.Graph().as_default(), graph =>

{

var file_reader = tf.read_file(file_name, "file_reader");

var image_reader = tf.image.decode_jpeg(file_reader, channels: 3, name: "jpeg_reader");

var caster = tf.cast(image_reader, tf.float32);

var dims_expander = tf.expand_dims(caster, 0);

var resize = tf.constant(new int[] { input_height, input_width });

var bilinear = tf.image.resize_bilinear(dims_expander, resize);

var sub = tf.subtract(bilinear, new float[] { input_mean });

var normalized = tf.divide(sub, new float[] { input_std });

return with<Session, NDArray>(tf.Session(graph), sess => sess.run(normalized));

});

}

private void PrepareData()

{

Directory.CreateDirectory(dir);

// get model file

string url = "https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz";

string zipFile = Path.Join(dir, $"{pbFile}.tar.gz");

Utility.Web.Download(url, zipFile);

Utility.Compress.ExtractTGZ(zipFile, dir);

// download sample picture

string pic = "grace_hopper.jpg";

Utility.Web.Download($"https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/label_image/data/{pic}", Path.Join(dir, pic));

}

TensorFlow.NET/test/TensorFlowNET.Examples/LabelImage.cs at master · mrliban/TensorFlow.NET · GitHub

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