public Inception3(int classes = 1000, string prefix = "", ParameterDict @params = null) : base(prefix,
@params)
{
Features = new HybridSequential("");
Features.Add(Inception.MakeBasicConv(32, (3, 3), (2, 2)));
Features.Add(Inception.MakeBasicConv(32, (3, 3)));
Features.Add(Inception.MakeBasicConv(64, (3, 3), padding: (1, 1)));
Features.Add(new MaxPool2D((3, 3), (2, 2)));
Features.Add(Inception.MakeBasicConv(80, (1, 1)));
Features.Add(Inception.MakeBasicConv(192, (3, 3)));
Features.Add(new MaxPool2D((3, 3), (2, 2)));
Features.Add(Inception.MakeA(32, "A1_"));
Features.Add(Inception.MakeA(64, "A2_"));
Features.Add(Inception.MakeA(64, "A3_"));
Features.Add(Inception.MakeB("B_"));
Features.Add(Inception.MakeC(128, "C1_"));
Features.Add(Inception.MakeC(160, "C2_"));
Features.Add(Inception.MakeC(160, "C3_"));
Features.Add(Inception.MakeC(192, "C4_"));
Features.Add(Inception.MakeD("D_"));
Features.Add(Inception.MakeE("E1_"));
Features.Add(Inception.MakeE("E2_"));
Features.Add(new AvgPool2D((8, 8)));
Features.Add(new Dropout(0.5f));
RegisterChild(Features, "features");
Output = new Dense(classes);
RegisterChild(Output, "output");
}
// Use this for initialization
void Start()
{
bool loaded = TfInvoke.CheckLibraryLoaded();
_inceptionGraph = new Inception();
_liveCameraView = false;
/*
* WebCamDevice[] devices = WebCamTexture.devices;
* cameraCount = devices.Length;
*
* if (cameraCount == 0)
* {
* _liveCameraView = false;
* }
* else
* {
* _liveCameraView = true;
* webcamTexture = new WebCamTexture(devices[0].name);
*
* baseRotation = transform.rotation;
* webcamTexture.Play();
* //data = new Color32[webcamTexture.width * webcamTexture.height];
* }*/
StartCoroutine(_inceptionGraph.Init());
}
public InceptionPage()
: base()
{
_inceptionGraph = new Inception();
_inceptionGraph.OnDownloadProgressChanged += onDownloadProgressChanged;
_inceptionGraph.OnDownloadCompleted += onDownloadCompleted;
OnImagesLoaded += (sender, image) =>
{
try
{
SetMessage("Please wait...");
SetImage();
Tensor imageTensor = Emgu.TF.Models.ImageIO.ReadTensorFromImageFile(image[0], 224, 224, 128.0f, 1.0f / 128.0f);
Stopwatch watch = Stopwatch.StartNew();
Inception.RecognitionResult result = _inceptionGraph.MostLikely(imageTensor);
SetMessage(String.Format("Object is {0} with {1}% probability. Recognized in {2} milliseconds.", result.Label, result.Probability * 100, watch.ElapsedMilliseconds));
SetImage(image[0]);
}
catch (Exception excpt)
{
String msg = excpt.Message.Replace(System.Environment.NewLine, " ");
SetMessage(msg);
}
};
}
public IActionResult ReTrainInception()
{
Inception.Model = Inception.LoadAndScoreModel(tagsTsv, inceptionTrainImagesFolder, inceptionPb, imageClassifierZip);
Console.WriteLine("inception re-trained");
return(Ok("inception re-trained"));
}
public void TestMultiSession()
{
Tensor imageTensor = ImageIO.ReadTensorFromImageFile <float>("grace_hopper.jpg", 224, 224, 128.0f, 1.0f);
int sessionCount = 10;
Inception[] graphs = new Inception[sessionCount];
bool[] processCompleted = new bool[sessionCount];
for (int i = 0; i < sessionCount; i++)
{
graphs[i] = new Inception();
processCompleted[i] = false;
graphs[i].OnDownloadCompleted += (sender, e) =>
{
Inception.RecognitionResult[] results = graphs[i].Recognize(imageTensor);
processCompleted[i] = true;
};
graphs[i].Init();
Trace.WriteLine(System.String.Format("Reading graph {0}", i));
Thread.Sleep(1000);
}
while (!processCompleted.All((v) => v))
{
Thread.Sleep(1000);
}
}
partial void inceptionClicked(NSObject sender)
{
messageLabel.StringValue = "Inception Clicked";
mainImageView.Image = null;
Inception inceptionGraph = new Inception();
String fileName = "space_shuttle.jpg";
Tensor imageTensor = Emgu.TF.Models.ImageIO.ReadTensorFromImageFile(fileName, 224, 224, 128.0f, 1.0f / 128.0f);
//MultiboxGraph.Result detectResult = graph.Detect(imageTensor);
float[] probability = inceptionGraph.Recognize(imageTensor);
String resStr = String.Empty;
if (probability != null)
{
String[] labels = inceptionGraph.Labels;
float maxVal = 0;
int maxIdx = 0;
for (int i = 0; i < probability.Length; i++)
{
if (probability[i] > maxVal)
{
maxVal = probability[i];
maxIdx = i;
}
}
resStr = String.Format("Object is {0} with {1}% probability.", labels[maxIdx], maxVal * 100);
}
messageLabel.StringValue = resStr;
mainImageView.Image = new NSImage(fileName);
}
async void inceptionClicked(NSObject sender)
{
SetMessage("Please wait while we download Inception model from internet...");
SetImage(null);
if (_inceptionGraph == null)
{
_inceptionGraph = new Inception();
_inceptionGraph.OnDownloadProgressChanged += OnDownloadProgressChanged;
//_inceptionGraph.OnDownloadCompleted += inceptionGraph_OnDownloadCompleted;
}
await _inceptionGraph.Init();
String fileName = "space_shuttle.jpg";
//Tensor imageTensor = Emgu.TF.Models.ImageIO.ReadTensorFromImageFile<float>(fileName, 224, 224, 128.0f, 1.0f / 128.0f);
Tensor imageTensor = Emgu.TF.Models.ImageIO.ReadTensorFromImageFile <float>(fileName, 224, 224, 128.0f, 1.0f);
var recognitionResults = _inceptionGraph.Recognize(imageTensor);
String resStr = String.Empty;
if (recognitionResults != null && recognitionResults.Length > 0)
{
resStr = String.Format("Object is {0} with {1}% probability.", recognitionResults[0].Label, recognitionResults[0].Probability * 100);
}
SetMessage(resStr);
SetImage(new NSImage(fileName));
}
/// <summary>
/// Ovládání hry v jednotlivých tabech
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void TabChanged(object sender, SelectionChangedEventArgs e)
{
if (inc.IsSelected) // při spuštění hry
{
incB = new Inception(p);
}
if (story.IsSelected) //příběh
{
storyB = new Story();
}
/*
* if (equip.IsSelected) //výměna oblečených itemů
* {
* equipB = new Equip();
* }
*/
if (stats.IsSelected) //editace statů
{
statsB = new Stats();
}
if (fight.IsSelected) //soubojová meachanika a spuštění
{
fightB = new Fight(enemy, p);
}
}
// Use this for initialization
void Start()
{
bool loaded = TfInvoke.Init();
_inceptionGraph = new Inception();
//Change the following flag to set default detection based on image / live camera view
_liveCameraView = false;
if (_liveCameraView)
{
_devices = WebCamTexture.devices;
_cameraCount = _devices.Length;
if (_cameraCount == 0)
{
_liveCameraView = false;
}
else
{
_liveCameraView = true;
_webcamTexture = new WebCamTexture(_devices[0].name);
baseRotation = transform.rotation;
_webcamTexture.Play();
//data = new Color32[webcamTexture.width * webcamTexture.height];
}
}
StartCoroutine(_inceptionGraph.Init());
}
public void Recognise(string fileName)
{
FileSource = BitmapFrame.Create(new Uri(fileName));
ImageFileName = fileName;
try
{
using (_inceptionGraph = new Inception())
{
_fileName = fileName;
_inceptionGraph.OnDownloadCompleted += OnDownloadComplete;
if (!string.IsNullOrEmpty(DownloadURL) &&
!string.IsNullOrEmpty(InceptionGraphFileName) &&
!string.IsNullOrEmpty(OutputLabelsFileName))
{
_inceptionGraph.Init(new string[] { InceptionGraphFileName, OutputLabelsFileName }, DownloadURL);
}
else
{
_inceptionGraph.Init();
}
}
}
catch (Exception ex)
{
_exceptionLogDataAccess.LogException(ex.ToString());
IsModalVisible = false;
}
}
public InceptionPage()
: base()
{
var button = this.TopButton;
button.Text = "Perform Image Classification";
button.Clicked += OnButtonClicked;
_inception = new Inception();
_inception.OnDownloadProgressChanged += onDownloadProgressChanged;
}
/// <summary>
/// Two DnsResourceDataTransactionKey are equal iff their algorithm, inception, expiration, mode, error, key and other fields are equal.
/// </summary>
public bool Equals(DnsResourceDataTransactionKey other)
{
return(other != null &&
Algorithm.Equals(other.Algorithm) &&
Inception.Equals(other.Inception) &&
Expiration.Equals(other.Expiration) &&
Mode.Equals(other.Mode) &&
Error.Equals(other.Error) &&
Key.Equals(other.Key) &&
Other.Equals(other.Other));
}
partial void inceptionClicked(NSObject sender)
{
SetMessage("Please wait while we download Inception model from internet...");
SetImage(null);
if (_inceptionGraph == null)
{
_inceptionGraph = new Inception();
_inceptionGraph.OnDownloadProgressChanged += OnDownloadProgressChanged;
_inceptionGraph.OnDownloadCompleted += inceptionGraph_OnDownloadCompleted;
}
_inceptionGraph.Init();
}
public override bool IsValid(params ILegalPerson[] persons)
{
var lessor = this.Lessor(persons);
var lessee = this.Lessee(persons);
if (lessor == null || lessee == null)
{
return(false);
}
var orTitle = lessor.GetLegalPersonTypeName();
var eeTitle = lessee.GetLegalPersonTypeName();
if (Inception == DateTime.MinValue)
{
AddReasonEntry($"{orTitle} {lessor.Name} and {eeTitle} {lessee.Name}, {nameof(Inception)} is unassigned");
return(false);
}
if (Terminus != null && Terminus.Value <= Inception)
{
AddReasonEntry($"{orTitle} {lessor.Name} and {eeTitle} {lessee.Name}, {nameof(Terminus)} " +
$"date of {Terminus.Value.ToShortDateString()} is less-than-equal-to " +
$"the {nameof(Inception)} date of {Inception.ToShortDateString()}");
return(false);
}
if (Assent != null && !Assent.IsValid(persons))
{
AddReasonEntryRange(Assent.GetReasonEntries());
return(false);
}
if (SubjectProperty == null || Offer == null)
{
AddReasonEntry($"{orTitle} {lessor.Name} and {eeTitle} {lessee.Name}, " +
$"{nameof(SubjectProperty)} '{SubjectProperty?.Name}' " +
$"or {nameof(Offer)} '{Offer?.Name}' is unassigned");
return(false);
}
if (Acceptance?.Invoke(Offer) == null)
{
AddReasonEntry($"{orTitle} {lessor.Name} and {eeTitle} {lessee.Name}, {nameof(Acceptance)} " +
$"using {nameof(Offer)} '{Offer.Name}' returned nothing");
return(false);
}
return(true);
}
public MainForm()
{
InitializeComponent();
TfInvoke.CheckLibraryLoaded();
messageLabel.Text = String.Empty;
cameraButton.Text = "Start Camera";
DisableUI();
//Use the following code for the full inception model
inceptionGraph = new Inception();
inceptionGraph.OnDownloadProgressChanged += OnDownloadProgressChangedEventHandler;
inceptionGraph.OnDownloadCompleted += onDownloadCompleted;
inceptionGraph.Init();
}
// Use this for initialization
void Start()
{
//Warning: The following code is used to get around a https certification issue for downloading tesseract language files from Github
//Do not use this code in a production environment. Please make sure you understand the security implication from the following code before using it
ServicePointManager.ServerCertificateValidationCallback += delegate(object sender, X509Certificate cert, X509Chain chain, SslPolicyErrors sslPolicyErrors) {
HttpWebRequest webRequest = sender as HttpWebRequest;
if (webRequest != null)
{
String requestStr = webRequest.Address.AbsoluteUri;
if (requestStr.StartsWith(@"https://github.com/") || requestStr.StartsWith(@"https://raw.githubusercontent.com/"))
{
return(true);
}
}
return(false);
};
TfInvoke.CheckLibraryLoaded();
WebCamDevice[] devices = WebCamTexture.devices;
cameraCount = devices.Length;
_inceptionGraph = new Inception();
_inceptionLabels = _inceptionGraph.Labels;
if (cameraCount == 0)
{
_liveCameraView = false;
Texture2D texture = Resources.Load <Texture2D>("space_shuttle");
RecognizeAndUpdateText(texture);
this.GetComponent <GUITexture>().texture = texture;
this.GetComponent <GUITexture>().pixelInset = new Rect(-texture.width / 2, -texture.height / 2, texture.width, texture.height);
//this.GetComponent<GUIText>().pixelOffset = new Vector2();
}
else
{
_liveCameraView = true;
webcamTexture = new WebCamTexture(devices[0].name);
baseRotation = transform.rotation;
webcamTexture.Play();
//data = new Color32[webcamTexture.width * webcamTexture.height];
}
}
public async Task TestInceptionBatch()
{
//using (Tensor imageTensor = ImageIO.ReadTensorFromImageFile<float>("grace_hopper.jpg", 224, 224, 128.0f, 1.0f))
using (Tensor imageTensor = ImageIO.ReadTensorFromImageFiles <float>(
new String[] { "grace_hopper.jpg", "grace_hopper.jpg" },
224,
224,
128.0f,
1.0f))
using (Inception inceptionGraph = new Inception())
{
await inceptionGraph.Init();
Inception.RecognitionResult[][] results = inceptionGraph.Recognize(imageTensor);
Trace.WriteLine(String.Format("Object is {0} with {1}% probability", results[0][0].Label, results[0][0].Probability * 100));
}
}
private void PrivateBoot()
{
try
{
Application app = (Application)Inception.CreateInstanceAndUnwrap("Framework", "Dover.Framework.Application");
SAPServiceFactory.PrepareForInception(Inception); // need to be after Application creation because of assembly resolving from embedded resources.
Inception.SetData("assemblyName", "Framework"); // Used to get current AssemblyName for logging and reflection
InceptionAddinManager = app.Resolve <AddinManager>();
Sponsor <Application> appSponsor = new Sponsor <Application>(app);
Sponsor <AddinManager> addInManagerSponsor = new Sponsor <AddinManager>(InceptionAddinManager);
app.RunInception();
AppDomain.Unload(Inception); // release AppDomain on shutdown.
}
catch (Exception e)
{
System.Windows.Forms.MessageBox.Show(string.Format("{0}\n{1}", e.Message, e.StackTrace));
throw e;
}
}
public MainForm()
{
InitializeComponent();
TfInvoke.CheckLibraryLoaded();
messageLabel.Text = String.Empty;
DisableUI();
inceptionGraph = new Inception();
inceptionGraph.OnDownloadProgressChanged += OnDownloadProgressChangedEventHandler;
inceptionGraph.OnDownloadCompleted += onDownloadCompleted;
//Use the following code for the full inception model
inceptionGraph.Init();
//Uncomment the following code to use a retrained model to recognize followers, downloaded from the internet
//inceptionGraph.Init(new string[] {"optimized_graph.pb", "output_labels.txt"}, "https://github.com/emgucv/models/raw/master/inception_flower_retrain/", "Mul", "final_result");
}
public InceptionPage()
: base()
{
var button = this.GetButton();
button.Text = "Perform Image Classification";
button.Clicked += OnButtonClicked;
_inception = new Inception();
OnImagesLoaded += (sender, imageFiles) =>
{
SetMessage("Please wait...");
SetImage();
_imageFiles = imageFiles;
#if !DEBUG
try
#endif
{
if (_inception.Imported)
{
onDownloadCompleted(this, new System.ComponentModel.AsyncCompletedEventArgs(null, false, null));
}
else
{
SetMessage("Please wait while the Inception Model is being downloaded...");
_inception.OnDownloadProgressChanged += onDownloadProgressChanged;
_inception.OnDownloadCompleted += onDownloadCompleted;
_inception.Init();
}
}
#if !DEBUG
catch (Exception e)
{
String msg = e.Message.Replace(System.Environment.NewLine, " ");
SetMessage(msg);
}
#endif
};
}
public void Recognize(String fileName)
{
fileNameTextBox.Text = fileName;
pictureBox.ImageLocation = fileName;
//Use the following code for the full inception model
Inception inceptionGraph = new Inception();
Tensor imageTensor = ImageIO.ReadTensorFromImageFile(fileName, 224, 224, 128.0f, 1.0f / 128.0f);
//Uncomment the following code to use a retrained model to recognize followers, downloaded from the internet
//Inception inceptionGraph = new Inception(null, new string[] {"optimized_graph.pb", "output_labels.txt"}, "https://github.com/emgucv/models/raw/master/inception_flower_retrain/", "Mul", "final_result");
//Tensor imageTensor = ImageIO.ReadTensorFromImageFile(fileName, 299, 299, 128.0f, 1.0f / 128.0f);
//Uncomment the following code to use a retrained model to recognize followers, if you deployed the models with the application
//For ".pb" and ".txt" bundled with the application, set the url to null
//Inception inceptionGraph = new Inception(null, new string[] {"optimized_graph.pb", "output_labels.txt"}, null, "Mul", "final_result");
//Tensor imageTensor = ImageIO.ReadTensorFromImageFile(fileName, 299, 299, 128.0f, 1.0f / 128.0f);
float[] probability = inceptionGraph.Recognize(imageTensor);
String resStr = String.Empty;
if (probability != null)
{
String[] labels = inceptionGraph.Labels;
float maxVal = 0;
int maxIdx = 0;
for (int i = 0; i < probability.Length; i++)
{
if (probability[i] > maxVal)
{
maxVal = probability[i];
maxIdx = i;
}
}
resStr = String.Format("Object is {0} with {1}% probability.", labels[maxIdx], maxVal * 100);
}
messageLabel.Text = resStr;
}
public void TestInception()
{
Tensor imageTensor = ImageIO.ReadTensorFromImageFile("grace_hopper.jpg", 224, 224, 128.0f, 1.0f);
Inception inceptionGraph = new Inception();
float[] probability = inceptionGraph.Recognize(imageTensor);
if (probability != null)
{
String[] labels = inceptionGraph.Labels;
float maxVal = 0;
int maxIdx = 0;
for (int i = 0; i < probability.Length; i++)
{
if (probability[i] > maxVal)
{
maxVal = probability[i];
maxIdx = i;
}
}
Trace.WriteLine(String.Format("Object is {0} with {1}% probability", labels[maxIdx], maxVal * 100));
}
}
public async Task <IActionResult> ImagePredictAsync()
{
using var reader = new StreamReader(Request.Body, Encoding.UTF8);
string body = await reader.ReadToEndAsync();
byte[] imageBytes = Convert.FromBase64String(body);
string result = "";
try
{
var testImage = Path.Combine(inceptionTrainImagesFolder, "capture.jpg");
Image image = Image.FromStream(new MemoryStream(imageBytes));
image.Save(testImage, ImageFormat.Jpeg);
var imageData = new ImageNetData()
{
ImagePath = testImage,
Label = Path.GetFileNameWithoutExtension(testImage)
};
if (Inception.Model is null)
{
Inception.Model = Inception.LoadModel(tagsTsv, inceptionTrainImagesFolder, inceptionPb, imageClassifierZip);
}
var prediction = Inception.Model.Predict(imageData);
result = prediction.PredictedLabelValue;
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
}
return(Ok(result));
}
// Update is called once per frame
void Update()
{
if (_inceptionGraph == null)
{
if (_loadingModel)
{
return;
}
_loadingModel = true;
DisplayText.text = String.Format("Loading Inception Model, please wait...");
System.Threading.ThreadPool.QueueUserWorkItem(
(o) =>
{
try
{
_inceptionGraph = new Inception();
_inceptionLabels = _inceptionGraph.Labels;
}
catch (Exception e)
{
//DisplayText.text = e.Message;
Console.WriteLine(e);
return;
//throw;
}
_loadingModel = false;
});
return;
}
if (_liveCameraView)
{
if (webcamTexture != null && webcamTexture.didUpdateThisFrame)
{
#region convert the webcam texture to RGBA bytes
if (data == null || (data.Length != webcamTexture.width * webcamTexture.height))
{
data = new Color32[webcamTexture.width * webcamTexture.height];
}
webcamTexture.GetPixels32(data);
if (bytes == null || bytes.Length != data.Length * 4)
{
bytes = new byte[data.Length * 4];
}
GCHandle handle = GCHandle.Alloc(data, GCHandleType.Pinned);
Marshal.Copy(handle.AddrOfPinnedObject(), bytes, 0, bytes.Length);
handle.Free();
#endregion
#region convert the RGBA bytes to texture2D
if (resultTexture == null || resultTexture.width != webcamTexture.width ||
resultTexture.height != webcamTexture.height)
{
resultTexture = new Texture2D(webcamTexture.width, webcamTexture.height, TextureFormat.RGBA32,
false);
}
resultTexture.LoadRawTextureData(bytes);
resultTexture.Apply();
#endregion
if (!_textureResized)
{
this.GetComponent <GUITexture>().pixelInset = new Rect(-webcamTexture.width / 2,
-webcamTexture.height / 2, webcamTexture.width, webcamTexture.height);
_textureResized = true;
}
transform.rotation = baseRotation * Quaternion.AngleAxis(webcamTexture.videoRotationAngle, Vector3.up);
RecognizeAndUpdateText(resultTexture);
this.GetComponent <GUITexture>().texture = resultTexture;
//count++;
}
}
else
{
if (!_staticViewRendered)
{
if (_inceptionGraph == null)
{
return;
}
Texture2D texture = Resources.Load <Texture2D>("space_shuttle");
RecognizeAndUpdateText(texture);
this.GetComponent <GUITexture>().texture = texture;
this.GetComponent <GUITexture>().pixelInset = new Rect(-texture.width / 2, -texture.height / 2, texture.width, texture.height);
_staticViewRendered = true;
}
}
}
public void TestInception()
{
using (Tensor imageTensor = ImageIO.ReadTensorFromImageFile <float>("grace_hopper.jpg", 224, 224, 128.0f, 1.0f))
using (Inception inceptionGraph = new Inception())
{
bool processCompleted = false;
inceptionGraph.OnDownloadCompleted += (sender, e) =>
{
HashSet <string> opNames = new HashSet <string>();
HashSet <string> couldBeInputs = new HashSet <string>();
HashSet <string> couldBeOutputs = new HashSet <string>();
foreach (Operation op in inceptionGraph.Graph)
{
String name = op.Name;
opNames.Add(name);
if (op.NumInputs == 0 && op.OpType.Equals("Placeholder"))
{
couldBeInputs.Add(op.Name);
AttrMetadata dtypeMeta = op.GetAttrMetadata("dtype");
AttrMetadata shapeMeta = op.GetAttrMetadata("shape");
DataType type = op.GetAttrType("dtype");
Int64[] shape = op.GetAttrShape("shape");
Buffer valueBuffer = op.GetAttrValueProto("shape");
Buffer shapeBuffer = op.GetAttrTensorShapeProto("shape");
Tensorflow.TensorShapeProto shapeProto =
Tensorflow.TensorShapeProto.Parser.ParseFrom(shapeBuffer.Data);
}
if (op.OpType.Equals("Const"))
{
AttrMetadata dtypeMeta = op.GetAttrMetadata("dtype");
AttrMetadata valueMeta = op.GetAttrMetadata("value");
using (Tensor valueTensor = op.GetAttrTensor("value"))
{
var dim = valueTensor.Dim;
}
}
if (op.OpType.Equals("Conv2D"))
{
AttrMetadata stridesMeta = op.GetAttrMetadata("strides");
AttrMetadata paddingMeta = op.GetAttrMetadata("padding");
AttrMetadata boolMeta = op.GetAttrMetadata("use_cudnn_on_gpu");
Int64[] strides = op.GetAttrIntList("strides");
bool useCudnn = op.GetAttrBool("use_cudnn_on_gpu");
String padding = op.GetAttrString("padding");
}
foreach (Output output in op.Outputs)
{
int[] shape = inceptionGraph.Graph.GetTensorShape(output);
if (output.NumConsumers == 0)
{
couldBeOutputs.Add(name);
}
}
Buffer buffer = inceptionGraph.Graph.GetOpDef(op.OpType);
Tensorflow.OpDef opDef = Tensorflow.OpDef.Parser.ParseFrom(buffer.Data);
}
using (Buffer versionDef = inceptionGraph.Graph.Versions())
{
int l = versionDef.Length;
}
Inception.RecognitionResult[] results = inceptionGraph.Recognize(imageTensor);
Trace.WriteLine(String.Format("Object is {0} with {1}% probability", results[0].Label, results[0].Probability * 100));
processCompleted = true;
};
inceptionGraph.Init();
while (!processCompleted)
{
Thread.Sleep(1000);
}
}
}
public void TestInception()
{
using (Tensor imageTensor = ImageIO.ReadTensorFromImageFile("grace_hopper.jpg", 224, 224, 128.0f, 1.0f))
using (Inception inceptionGraph = new Inception())
{
bool processCompleted = false;
inceptionGraph.OnDownloadCompleted += (sender, e) =>
{
HashSet <string> opNames = new HashSet <string>();
HashSet <string> couldBeInputs = new HashSet <string>();
HashSet <string> couldBeOutputs = new HashSet <string>();
foreach (Operation op in inceptionGraph.Graph)
{
String name = op.Name;
opNames.Add(name);
if (op.NumInputs == 0 && op.OpType.Equals("Placeholder"))
{
couldBeInputs.Add(op.Name);
}
foreach (Output output in op.Outputs)
{
int[] shape = inceptionGraph.Graph.GetTensorShape(output);
if (output.NumConsumers == 0)
{
couldBeOutputs.Add(name);
}
}
}
using (Buffer versionDef = inceptionGraph.Graph.Versions())
{
int l = versionDef.Length;
}
float[] probability = inceptionGraph.Recognize(imageTensor);
if (probability != null)
{
String[] labels = inceptionGraph.Labels;
float maxVal = 0;
int maxIdx = 0;
for (int i = 0; i < probability.Length; i++)
{
if (probability[i] > maxVal)
{
maxVal = probability[i];
maxIdx = i;
}
}
Trace.WriteLine(String.Format("Object is {0} with {1}% probability", labels[maxIdx], maxVal * 100));
}
processCompleted = true;
};
inceptionGraph.Init();
while (!processCompleted)
{
Thread.Sleep(1000);
}
}
}
public InceptionPage()
: base()
{
var button = this.GetButton();
button.Text = "Perform Image Recognition";
button.Clicked += OnButtonClicked;
OnImagesLoaded += async(sender, image) =>
{
SetMessage("Please wait...");
SetImage();
Task <Tuple <string, string, long> > t = new Task <Tuple <string, string, long> >(
() =>
{
try
{
SetMessage("Please wait while we download the Inception Model from internet.");
Inception inceptionGraph = new Inception();
SetMessage("Please wait...");
Tensor imageTensor = Emgu.TF.Models.ImageIO.ReadTensorFromImageFile(image[0], 224, 224, 128.0f, 1.0f / 128.0f);
//MultiboxGraph.Result detectResult = graph.Detect(imageTensor);
float[] probability = inceptionGraph.Recognize(imageTensor);
String resStr = String.Empty;
if (probability != null)
{
String[] labels = inceptionGraph.Labels;
float maxVal = 0;
int maxIdx = 0;
for (int i = 0; i < probability.Length; i++)
{
if (probability[i] > maxVal)
{
maxVal = probability[i];
maxIdx = i;
}
}
resStr = String.Format("Object is {0} with {1}% probability.", labels[maxIdx], maxVal * 100);
}
return(new Tuple <string, string, long>(image[0], resStr, 0));
//SetImage(t.Result.Item1);
//GetLabel().Text = String.Format("Detected {0} in {1} milliseconds.", t.Result.Item2, t.Result.Item3);
}
catch (Exception e)
{
String msg = e.Message.Replace(System.Environment.NewLine, " ");
SetMessage(msg);
return(new Tuple <string, string, long>(null, msg, 0));
}
});
t.Start();
#if !(__UNIFIED__)
var result = await t;
SetImage(t.Result.Item1);
GetLabel().Text = t.Result.Item2;
#endif
};
}
public InceptionPage(Model model)
: base()
{
Title = model == Model.Flower ? "Flower Recognition" : "Object recognition (Inception)";
_model = model;
if (_inceptionGraph == null)
{
SessionOptions so = new SessionOptions();
if (TfInvoke.IsGoogleCudaEnabled)
{
Tensorflow.ConfigProto config = new Tensorflow.ConfigProto();
config.GpuOptions = new Tensorflow.GPUOptions();
config.GpuOptions.AllowGrowth = true;
so.SetConfig(config.ToProtobuf());
}
_inceptionGraph = new Inception(null, so);
_inceptionGraph.OnDownloadProgressChanged += onDownloadProgressChanged;
_inceptionGraph.OnDownloadCompleted += onDownloadCompleted;
_inceptionGraph.OnDownloadCompleted += (sender, e) =>
{
OnButtonClicked(sender, e);
};
}
OnImagesLoaded += (sender, image) =>
{
#if !DEBUG
try
#endif
{
SetMessage("Please wait...");
SetImage();
Tensor imageTensor;
if (_model == Model.Flower)
{
imageTensor = Emgu.TF.Models.ImageIO.ReadTensorFromImageFile <float>(image[0], 299, 299, 0.0f, 1.0f / 255.0f, false, false);
}
else
{
imageTensor =
Emgu.TF.Models.ImageIO.ReadTensorFromImageFile <float>(image[0], 224, 224, 128.0f, 1.0f);
}
Inception.RecognitionResult result;
if (_coldSession)
{
//First run of the recognition graph, here we will compile the graph and initialize the session
//This is expected to take much longer time than consecutive runs.
result = _inceptionGraph.Recognize(imageTensor)[0];
_coldSession = false;
}
//Here we are trying to time the execution of the graph after it is loaded
//If we are not interest in the performance, we can skip the following 3 lines
Stopwatch sw = Stopwatch.StartNew();
result = _inceptionGraph.Recognize(imageTensor)[0];
sw.Stop();
String msg = String.Format("Object is {0} with {1}% probability. Recognized in {2} milliseconds.", result.Label, result.Probability * 100, sw.ElapsedMilliseconds);
SetMessage(msg);
var jpeg = Emgu.Models.NativeImageIO.ImageFileToJpeg(image[0]);
SetImage(jpeg.Raw, jpeg.Width, jpeg.Height);
}
#if !DEBUG
catch (Exception excpt)
{
String msg = excpt.Message.Replace(System.Environment.NewLine, " ");
SetMessage(msg);
}
#endif
};
}
public void TestLoadLargeGraph()
{
Inception inceptionGraph = new Inception(null, new string[] { "optimized_graph.pb", "output_labels.txt" }, "https://github.com/emgucv/models/raw/master/inception_flower_retrain/", "Mul", "final_result");
}
