|
// |
|
// Based on the code from: https://github.com/migueldeicaza/TensorFlowSharp/tree/master/Examples/ExampleInceptionInference |
|
// |
|
// An example for using the TensorFlow C# API for image recognition |
|
// using a pre-trained inception model (http://arxiv.org/abs/1512.00567). |
|
// |
|
using System; |
|
using System.Collections.Concurrent; |
|
using System.Collections.Generic; |
|
using System.Configuration; |
|
using System.IO; |
|
using System.IO.Compression; |
|
using System.Linq; |
|
using System.Net; |
|
using System.Net.Http; |
|
using System.Text; |
|
using System.Threading.Tasks; |
|
using TensorFlow; |
|
|
|
namespace TensorflowServerlessAzureLib |
|
{ |
|
public class TensorflowImageClassification |
|
{ |
|
const string modelsDir = @"D:\home\site\wwwroot\models"; |
|
|
|
private static IDictionary<string, TFGraph> graphCache = new ConcurrentDictionary<string, TFGraph>(); |
|
|
|
private static IDictionary<string, string[]> labelsCache = new ConcurrentDictionary<string, string[]>(); |
|
|
|
public static async Task<HttpResponseMessage> Run(HttpRequestMessage req, TextWriter log) |
|
{ |
|
var queryParameters = PrepareQueryParameters(req); |
|
log.WriteLine("start"); |
|
var imageUrl = queryParameters["q"]; |
|
var result = await TensorflowImageClassification.ExecuteIntercaption(imageUrl); |
|
log.WriteLine(result); |
|
var response = new HttpResponseMessage(HttpStatusCode.OK); |
|
|
|
response.Content = new StringContent(result, Encoding.UTF8, "application/text"); |
|
return response; |
|
} |
|
|
|
public static async Task<HttpResponseMessage> RunIntercaption(HttpRequestMessage req) |
|
{ |
|
var input = PrepareQueryParameters(req) ["q"]; |
|
var res = await ExecuteIntercaption(input); |
|
return new HttpResponseMessage() { Content = new StringContent(res) }; |
|
} |
|
|
|
private static Dictionary<string, string> PrepareQueryParameters(HttpRequestMessage req) |
|
{ |
|
return req.RequestUri.PathAndQuery.Split('?') [1].Split('&').Select(x => { var v = x.Split('='); return new { Key = v[0], Value = v[1] }; }).ToDictionary(x => x.Key, x => x.Value); |
|
} |
|
|
|
public static async Task<string> ExecuteIntercaption(string input) |
|
{ |
|
var tensor = await PrepareImageTensor(input); |
|
Console.WriteLine($"Image tensor prepared"); |
|
tensor = CreateTensorFromImageFile(tensor); |
|
Console.WriteLine($"Image processed"); |
|
return ExecuteModel(tensor, "input", "output", "interception"); |
|
} |
|
|
|
private static async Task<TFTensor> PrepareImageTensor(string input) |
|
{ |
|
var contents = await PrepareImage(input); |
|
var tensor = TFTensor.CreateString(contents); |
|
return tensor; |
|
} |
|
|
|
private static string ExecuteModel(TFTensor tensor, string inputName, string outputName, string modelKey) |
|
{ |
|
ModelFiles(modelsDir); |
|
var graph = PrepareGraph(modelKey); |
|
Console.WriteLine($"Graph {modelKey} prepared"); |
|
using(var session = new TFSession(graph)) |
|
{ |
|
var runner = session.GetRunner(); |
|
runner.AddInput(graph[inputName][0], tensor).Fetch(graph[outputName][0]); |
|
var output = runner.Run(); |
|
Console.WriteLine($"Classification finished"); |
|
return ReadResult(output, modelKey); |
|
} |
|
} |
|
|
|
private static async Task<byte[]> PrepareImage(string input) |
|
{ |
|
byte[] contents; |
|
using(HttpClient client = new HttpClient()) |
|
{ |
|
contents = await client.GetByteArrayAsync(input); |
|
} |
|
|
|
return contents; |
|
} |
|
|
|
private static string ReadResult(TFTensor[] output, string modelKey) |
|
{ |
|
var result = output[0]; |
|
var rshape = result.Shape; |
|
if (result.NumDims != 2 || rshape[0] != 1) |
|
{ |
|
var shape = ""; |
|
foreach (var d in rshape) |
|
{ |
|
shape += $"{d} "; |
|
} |
|
shape = shape.Trim(); |
|
Console.WriteLine($"Error: expected to produce a [1 N] shaped tensor where N is the number of labels, instead it produced one with shape [{shape}]"); |
|
Environment.Exit(1); |
|
} |
|
// You can get the data in two ways, as a multi-dimensional array, or arrays of arrays, |
|
// code can be nicer to read with one or the other, pick it based on how you want to process |
|
// it |
|
bool jagged = true; |
|
var bestIdx = 0; |
|
float best = 0; |
|
if (jagged) |
|
{ |
|
var probabilities = ((float[][]) result.GetValue(jagged: true)) [0]; |
|
for (int i = 0; i < probabilities.Length; i++) |
|
{ |
|
if (probabilities[i] > best) |
|
{ |
|
bestIdx = i; |
|
best = probabilities[i]; |
|
} |
|
} |
|
} |
|
else |
|
{ |
|
var val = (float[, ]) result.GetValue(jagged: false); |
|
// Result is [1,N], flatten array |
|
for (int i = 0; i < val.GetLength(1); i++) |
|
{ |
|
if (val[0, i] > best) |
|
{ |
|
bestIdx = i; |
|
best = val[0, i]; |
|
} |
|
} |
|
} |
|
var res = $"Image best match: [{bestIdx}] {best * 100.0}% {PrepareLabels(modelKey)[bestIdx]}"; |
|
Console.WriteLine(res); |
|
return res; |
|
} |
|
|
|
static TFTensor CreateTensorFromImageFile(TFTensor tensor) |
|
{ |
|
TFOutput input, output; |
|
// Construct a graph to normalize the image |
|
using(var graph = ConstructGraphToNormalizeImage(out input, out output)) |
|
{ |
|
// Execute that graph to normalize this one image |
|
using(var session = new TFSession(graph)) |
|
{ |
|
var normalized = session.Run( |
|
inputs: new [] { input }, |
|
inputValues: new [] { tensor }, |
|
outputs: new [] { output }); |
|
return normalized[0]; |
|
} |
|
} |
|
} |
|
|
|
static TFGraph ConstructGraphToNormalizeImage(out TFOutput input, out TFOutput output) |
|
{ |
|
// Some constants specific to the pre-trained model at: |
|
// https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip |
|
// |
|
// - The model was trained after with images scaled to 224x224 pixels. |
|
// - The colors, represented as R, G, B in 1-byte each were converted to |
|
// float using (value - Mean)/Scale. |
|
const int W = 224; |
|
const int H = 224; |
|
const float Mean = 117; |
|
const float Scale = 1; |
|
var graph = new TFGraph(); |
|
input = graph.Placeholder(TFDataType.String); |
|
output = graph.Div( |
|
x: graph.Sub( |
|
x : graph.ResizeBilinear( |
|
images: graph.ExpandDims( |
|
input : graph.Cast( |
|
graph.DecodeJpeg(contents: input, channels: 3), DstT: TFDataType.Float), |
|
dim: graph.Const(0, "make_batch")), |
|
size : graph.Const(new int[] { W, H }, "size")), |
|
y : graph.Const(Mean, "mean")), |
|
y : graph.Const(Scale, "scale")); |
|
|
|
return graph; |
|
} |
|
|
|
private static TFGraph PrepareGraph(string modelKey) |
|
{ |
|
TFGraph graph = new TFGraph(); |
|
if (!graphCache.TryGetValue(modelKey, out graph)) |
|
{ |
|
graph = new TFGraph(); |
|
var model = File.ReadAllBytes($"{modelsDir}/tensorflow_inception_graph.pb"); |
|
graph.Import(model, ""); |
|
graphCache[modelKey] = graph; |
|
} |
|
|
|
return graph; |
|
} |
|
|
|
private static string[] PrepareLabels(string modelKey) |
|
{ |
|
string[] labels; |
|
if (!labelsCache.TryGetValue(modelKey, out labels)) |
|
{ |
|
labels = File.ReadAllLines($"{modelsDir}/imagenet_comp_graph_label_strings.txt"); |
|
labelsCache[modelKey] = labels; |
|
} |
|
|
|
return labels; |
|
} |
|
|
|
private static void ModelFiles(string dir) |
|
{ |
|
string url = "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip"; |
|
|
|
var modelFile = Path.Combine(dir, "tensorflow_inception_graph.pb"); |
|
var labelsFile = Path.Combine(dir, "imagenet_comp_graph_label_strings.txt"); |
|
var zipfile = Path.Combine(dir, "inception5h.zip"); |
|
|
|
if (File.Exists(modelFile) && File.Exists(labelsFile)) |
|
return; |
|
|
|
Directory.CreateDirectory(dir); |
|
var wc = new WebClient(); |
|
wc.DownloadFile(url, zipfile); |
|
ZipFile.ExtractToDirectory(zipfile, dir); |
|
File.Delete(zipfile); |
|
} |
|
} |
|
} |
/.gitignore
Last active 
