using NumSharp;
using System.Collections.Generic;
using System.Diagnostics;
using System.Drawing;
using System.IO;
using Tensorflow;
using Tensorflow.Keras.Utils;
using static Tensorflow.Binding;
using Console = Colorful.Console;

namespace TensorFlowNET.Examples
{
 /// <summary>
 /// Inception v3 is a widely-used image recognition model 
 /// that has been shown to attain greater than 78.1% accuracy on the ImageNet dataset. 
 /// The model is the culmination of many ideas developed by multiple researchers over the years.
 /// </summary>
 public class ImageRecognitionInception : SciSharpExample, IExample
 {
 string dir = "ImageRecognitionInception";
 string pbFile = "tensorflow_inception_graph.pb";
 string labelFile = "imagenet_comp_graph_label_strings.txt";
 List<NDArray> file_ndarrays = new List<NDArray>();

 public ExampleConfig InitConfig()
 => Config = new ExampleConfig
 {
 Name = "Image Recognition Inception",
 Enabled = true,
 IsImportingGraph = false
 };

 public bool Run()
 {
 tf.compat.v1.disable_eager_execution();

 PrepareData();

 var graph = new Graph();
 //import GraphDef from pb file
 graph.Import(Path.Join(dir, pbFile));

 var input_name = "input";
 var output_name = "output";

 var input_operation = graph.OperationByName(input_name);
 var output_operation = graph.OperationByName(output_name);

 var labels = File.ReadAllLines(Path.Join(dir, labelFile));
 var result_labels = new List<string>();
 var sw = new Stopwatch();

 using (var sess = tf.Session(graph))
 {
 foreach (var nd in file_ndarrays)
 {
 sw.Restart();

 var results = sess.run(output_operation.outputs[0], (input_operation.outputs[0], nd));
 results = np.squeeze(results);
 int idx = np.argmax(results);

 Console.WriteLine($"{labels[idx]}{results[idx]} in {sw.ElapsedMilliseconds}ms", Color.Tan);
 result_labels.Add(labels[idx]);
 }
 }

 return result_labels.Contains("military uniform");
 }

 private NDArray ReadTensorFromImageFile(string file_name,
 int input_height = 224,
 int input_width = 224,
 int input_mean = 117,
 int input_std = 1)
 {
 var graph = tf.Graph().as_default();

 var file_reader = tf.io.read_file(file_name, "file_reader");
 var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: 3, name: "DecodeJpeg");
 var cast = tf.cast(decodeJpeg, tf.float32);
 var dims_expander = tf.expand_dims(cast, 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 });

 using (var sess = tf.Session(graph))
 return sess.run(normalized);
 }

 public override void PrepareData()
 {
 Directory.CreateDirectory(dir);

 // get model file
 string url = "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip";

 Web.Download(url, dir, "inception5h.zip");

 Compress.UnZip(Path.Join(dir, "inception5h.zip"), dir);

 // download sample picture
 Directory.CreateDirectory(Path.Join(dir, "img"));
 url = $"https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/label_image/data/grace_hopper.jpg";
 Web.Download(url, Path.Join(dir, "img"), "grace_hopper.jpg");

 url = $"https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/data/shasta-daisy.jpg";
 Web.Download(url, Path.Join(dir, "img"), "shasta-daisy.jpg");

 // load image file
 var files = Directory.GetFiles(Path.Join(dir, "img"));
 for (int i = 0; i < files.Length; i++)
 {
 var nd = ReadTensorFromImageFile(files[i]);
 file_ndarrays.Add(nd);
 }
 }
 }
}