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 override void OnButtonClicked(Object sender, EventArgs args)
{
base.OnButtonClicked(sender, args);
if (_buttonMode == ButtonMode.WaitingModelDownload)
{
if (_model == Model.Flower)
{
//use a retrained model to recognize followers
_inceptionGraph.Init(
new string[] { "optimized_graph.pb", "output_labels.txt" },
"https://github.com/emgucv/models/raw/master/inception_flower_retrain/",
"Placeholder",
"final_result");
}
else
{
//The inception model
_inceptionGraph.Init();
}
}
else
{
if (_model == Model.Flower)
{
LoadImages(new string[] { "tulips.jpg" });
}
else
{
LoadImages(new string[] { "space_shuttle.jpg" });
}
}
}
// 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());
}
// 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());
}
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));
}
private async void OnButtonClicked(Object sender, EventArgs args)
{
SetMessage("Please wait while the Inception Model is being downloaded...");
await _inception.Init();
if (!_inception.Imported)
{
SetMessage("Failed to initialize Inception Model.");
return;
}
SetImage();
String[] imageFiles = await LoadImages(new string[] { "tulips.jpg" });
//handle user cancel
if (imageFiles == null || (imageFiles.Length > 0 && imageFiles[0] == null))
{
SetMessage("");
return;
}
Stopwatch watch = Stopwatch.StartNew();
var result = _inception.Recognize(imageFiles[0]);
watch.Stop();
String resStr = String.Format("Object is {0} with {1}% probability. Recognition completed in {2} milliseconds.", result[0].Label, result[0].Probability * 100, watch.ElapsedMilliseconds);
SetImage(imageFiles[0]);
SetMessage(resStr);
}
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 void OnButtonClicked(Object sender, EventArgs args)
{
base.OnButtonClicked(sender, args);
if (_buttonMode == ButtonMode.WaitingModelDownload)
{
_inceptionGraph.Init();
}
else
{
LoadImages(new string[] { "space_shuttle.jpg" });
}
}
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();
}
private async void OnButtonClicked(Object sender, EventArgs args)
{
SetMessage("Please wait while the Inception Model is being downloaded...");
await _inception.Init();
SetImage();
String[] imageFiles = await LoadImages(new string[] { "tulips.jpg" });
Stopwatch watch = Stopwatch.StartNew();
var result = _inception.Recognize(imageFiles[0]);
watch.Stop();
String resStr = String.Format("Object is {0} with {1}% probability. Recognition completed in {2} milliseconds.", result[0].Label, result[0].Probability * 100, watch.ElapsedMilliseconds);
SetImage(imageFiles[0]);
SetMessage(resStr);
}
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));
}
}
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 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);
}
}
}
