public void LoadData(ref List <InputDataType> inputData, ref List <OutputDataType> outputData)
{
String folderPath = "TRAINING_DATA2";
foreach (string file in Directory.EnumerateFiles(folderPath, "*.TD"))
{
var readFile = File.ReadAllBytes(file);
var skeletonList = ReadWriteObjectFile.FromByteArray <List <Skeleton> >(readFile);
foreach (Skeleton skel in skeletonList)
{
var output = Regex.Replace(file.ToUpper(), @"[\d-]\.TD", string.Empty).ToUpper().Split(new string[] { "\\" }, StringSplitOptions.None)[1];
outputData.Add(DataTypeResolver <OutputDataType> .CastObject(output));
inputData.Add(DataTypeResolver <InputDataType> .CastObject(skel));
}
}
}
private static void NameOfMethodToCall()
{
foreach (var potentialSensor in KinectSensor.KinectSensors)
{
if (potentialSensor.Status == KinectStatus.Connected)
{
KinectController.sensor = potentialSensor;
break;
}
}
if (null != KinectController.sensor)
{
TransformSmoothParameters smoothingParam = new TransformSmoothParameters();
//smoothingParam.Smoothing = 0.7f;
//smoothingParam.Correction = 0.3f;
//smoothingParam.Prediction = 1.0f;
//smoothingParam.JitterRadius = 1.0f;
//smoothingParam.MaxDeviationRadius = 1.0f;
//// Turn on the skeleton stream to receive skeleton frames
KinectController.sensor.SkeletonStream.Enable(smoothingParam);
// Add an event handler to be called whenever there is new color frame data
KinectController.sensor.SkeletonFrameReady += SensorSkeletonFrameReady;
//KinectController.postureRecognition = new PostureRecognition<Skeleton, string>(PatternType.AnglePatternElbowKnee, DataTrainingType.DataTrainingFile, 100000);
//KinectController.postureRecognition.training();
KinectController.postureRecognition = ReadWriteObjectFile.FromByteArray <PostureRecognition <Skeleton, string> >(Convert.FromBase64String(DbServices.GetTrainingRed("seven", "posture")));
// Start the sensor!
try
{
KinectController.sensor.Start();
}
catch (IOException)
{
KinectController.sensor = null;
}
}
//while (true) ;
// This will be executed on another thread
}
static void Main(string[] args)
{
PostureRecognition <Skeleton, string> postureRecognition = new PostureRecognition <Skeleton, string>(PatternType.AnglePatternElbowKnee, DataTrainingType.DataTrainingFile, 100000);
//1000000
//Console.WriteLine("MOSTRANDO DATOS BRUTOS" + "---------------------------------------------------");
//List<Pose> poselist = LoadData("TRAINING_DATA3");
//printPoses(poselist);
//Console.WriteLine("ITERACION : " + postureRecognition.training() + "-------------------------------------------------------\n");
Console.WriteLine("TESTING" + "-------------------------------------------------------");
postureRecognition = ReadWriteObjectFile.FromByteArray <PostureRecognition <Skeleton, string> >(Convert.FromBase64String(DbServices.GetTrainingRed("octavo", "posture")));
String folderPath = "TESTING";
int counter;
foreach (string file in Directory.EnumerateFiles(folderPath, "*.TD"))
{
var readFile = File.ReadAllBytes(file);
var skeletonList = ReadWriteObjectFile.FromByteArray <List <Skeleton> >(readFile);
counter = 0;
foreach (Skeleton skel in skeletonList)
{
if (counter == 7)
{
break;
}
var x = postureRecognition.Predict(skel);
if (x != null && x.Length > 0)
{
Console.Write(x + "\n");
}
counter++;
}
}
}
public static List <Pose> LoadData(string folderPath)
{
List <Pose> poseslist = new List <Pose>();
//String folderPath = "TRAINING_DATA3";
foreach (string file in Directory.EnumerateFiles(folderPath, "*.TD"))
{
var readFile = File.ReadAllBytes(file);
var skeletonList = ReadWriteObjectFile.FromByteArray <List <Skeleton> >(readFile);
//Console.WriteLine("NRO: " + skeletonList.Count );
foreach (Skeleton skel in skeletonList)
{
var output = Regex.Replace(file.ToUpper(), @"[\d]*?\.TD", string.Empty).ToUpper().Split(new string[] { "\\" }, StringSplitOptions.None)[1];
//outputData.Add(DataTypeResolver<OutputDataType>.CastObject(output));
//inputData.Add(DataTypeResolver<InputDataType>.CastObject(skel));
//Console.WriteLine("FILE: " + output);
Pose posetemp = new Pose();
posetemp.InputData = skel;
posetemp.OutputData = output;
poseslist.Add(posetemp);
}
}
return(poseslist);
}
private void WindowLoaded(object sender, RoutedEventArgs e)
{
// Create the drawing group we'll use for drawing
this.drawingGroup = new DrawingGroup();
// Create an image source that we can use in our image control
this.imageSource = new DrawingImage(this.drawingGroup);
// Display the drawing using our image control
Image.Source = this.imageSource;
// Look through all sensors and start the first connected one.
// This requires that a Kinect is connected at the time of app startup.
// To make your app robust against plug/unplug,
// it is recommended to use KinectSensorChooser provided in Microsoft.Kinect.Toolkit (See components in Toolkit Browser).
foreach (var potentialSensor in KinectSensor.KinectSensors)
{
if (potentialSensor.Status == KinectStatus.Connected)
{
this.sensor = potentialSensor;
break;
}
}
if (null != this.sensor)
{
TransformSmoothParameters smoothingParam = new TransformSmoothParameters();
smoothingParam.Smoothing = 0.7f;
smoothingParam.Correction = 0.3f;
smoothingParam.Prediction = 1.0f;
smoothingParam.JitterRadius = 1.0f;
smoothingParam.MaxDeviationRadius = 1.0f;
// Turn on the skeleton stream to receive skeleton frames
this.sensor.SkeletonStream.Enable(smoothingParam);
// Add an event handler to be called whenever there is new color frame data
this.sensor.SkeletonFrameReady += this.SensorSkeletonFrameReady;
//------------------------------------------------ CREATE AND SAVE TRAINING RED----------------------------------------
postureRecognition = new PostureRecognition <Skeleton, string>(PatternType.AnglePatternElbowKnee, DataTrainingType.DataTrainingFile, 100000);
postureRecognition.training();
string code = Convert.ToBase64String(ReadWriteObjectFile.ObjectToByteArray(postureRecognition));
DbServices.AddTrainingRed("noveno", code, "posture");
//---------------------------------------------------------------------------------------------------------------------
//------------------------------------------------ LOAD TRAINIG RED FROM DATABASE -------------------------------------
//postureRecognition = ReadWriteObjectFile.FromByteArray<PostureRecognition<Skeleton, string>>(Convert.FromBase64String(DbServices.GetTrainingRed("seven", "posture")));
//---------------------------------------------------------------------------------------------------------------------
//ipAdress = System.Net.IPAddress.Parse("192.168.0.6");
//ipEndpoint = new IPEndPoint(ipAdress, 19834);
//client = new Socket(ipAdress.AddressFamily, SocketType.Stream, ProtocolType.Tcp);
//client.Connect(ipEndpoint);
//ExecuteThreadServiceAsync();
//StartSocketListenner();
// Start the sensor!
try
{
this.sensor.Start();
}
catch (IOException)
{
this.sensor = null;
}
}
/*
* // Turn on the skeleton stream to receive skeleton frames
* this.sensor.SkeletonStream.Enable();
*
* // Add an event handler to be called whenever there is new color frame data
* this.sensor.SkeletonFrameReady += this.SensorSkeletonFrameReady;
*
* // Start the sensor!
* try
* {
* this.sensor.Start();
* }
* catch (IOException)
* {
* this.sensor = null;
* }
* }
*/
if (null == this.sensor)
{
this.statusBarText.Text = Properties.Resources.NoKinectReady;
}
}
