/// <summary>
/// Finds the terminal region (=leaf node) for each sample in X.
/// </summary>
/// <param name="x"></param>
/// <returns></returns>
public MathNet.Numerics.LinearAlgebra.Generic.Vector <double> apply(
MathNet.Numerics.LinearAlgebra.Generic.Matrix <double> x)
{
//cdef double* threshold = this.threshold
uint nSamples = (uint)x.RowCount;
MathNet.Numerics.LinearAlgebra.Generic.Vector <double> @out = DenseVector.Create((int)nSamples, i => 0.0);
for (int i = 0; i < nSamples; i++)
{
uint nodeId = 0;
// While node_id not a leaf
while (ChildrenLeft[nodeId] != _TREE_LEAF)
{
// ... and children_right[node_id] != _TREE_LEAF:
if (x[i, (int)Feature[nodeId]] <= Threshold[nodeId])
{
nodeId = ChildrenLeft[nodeId];
}
else
{
nodeId = ChildrenRight[nodeId];
}
}
@out[i] = nodeId;
}
return(@out);
}
/// <summary>
/// Predict target for X.
/// </summary>
public MathNet.Numerics.LinearAlgebra.Generic.Matrix <double>[] predict(
MathNet.Numerics.LinearAlgebra.Generic.Matrix <double> x)
{
uint nSamples = (uint)x.RowCount;
if (nOutputs == 1)
{
MathNet.Numerics.LinearAlgebra.Generic.Matrix <double> @out =
DenseMatrix.Create((int)nSamples, (int)maxNClasses, (i, j) => 0.0);
for (int i = 0; i < nSamples; i++)
{
uint nodeId = 0;
// While node_id not a leaf
while (ChildrenLeft[nodeId] != _TREE_LEAF)
{
// ... and children_right[node_id] != _TREE_LEAF:
if (x[i, (int)Feature[nodeId]] <= Threshold[nodeId])
{
nodeId = ChildrenLeft[nodeId];
}
else
{
nodeId = ChildrenRight[nodeId];
}
}
uint offset = nodeId * valueStride;
for (int c = 0; c < NClasses[0]; c++)
{
@out[i, c] = Value[offset + c];
}
}
return(new[] { @out });
}
else // n_outputs > 1
{
// out_multi = np.zeros((n_samples,
// n_outputs,
// max_n_classes), dtype=np.float64)
// Note: I've changed order
var outMulti = new MathNet.Numerics.LinearAlgebra.Generic.Matrix <double> [nOutputs];
for (int i = 0; i < outMulti.Length; i++)
{
outMulti[i] = DenseMatrix.Create((int)nSamples, (int)maxNClasses, (c, r) => 0.0);
}
for (int i = 0; i < nSamples; i++)
{
uint nodeId = 0;
// While node_id not a leaf
while (ChildrenLeft[nodeId] != _TREE_LEAF)
{
// ... and children_right[node_id] != _TREE_LEAF:
if (x[i, (int)Feature[nodeId]] <= Threshold[nodeId])
{
nodeId = ChildrenLeft[nodeId];
}
else
{
nodeId = ChildrenRight[nodeId];
}
}
uint offset = nodeId * valueStride;
for (int k = 0; k < nOutputs; k++)
{
for (int c = 0; c < NClasses[k]; c++)
{
//out_multi[i, k, c] = value[offset + c];
//Note: I've changed order
outMulti[k][i, c] = Value[offset + c];
}
offset += maxNClasses;
}
}
return(outMulti);
}
}
/// <summary>
/// Build a decision tree from the training set (X, y).
/// </summary>
public void build(MathNet.Numerics.LinearAlgebra.Generic.Matrix <double> x,
MathNet.Numerics.LinearAlgebra.Generic.Matrix <double> y,
MathNet.Numerics.LinearAlgebra.Generic.Vector <double> sampleWeight = null)
{
// Prepare data before recursive partitioning
// Initial capacity
int initCapacity;
if (this.maxDepth <= 10)
{
initCapacity = (int)Math.Pow(2, (this.maxDepth + 1)) - 1;
}
else
{
initCapacity = 2047;
}
this.Resize(initCapacity);
// Recursive partition (without actual recursion)
SplitterBase splitter = this.splitter;
splitter.init(x, y, sampleWeight == null ? null : sampleWeight.ToArray());
uint stackNValues = 5;
uint stackCapacity = 50;
uint[] stack = new uint[stackCapacity];
stack[0] = 0; // start
stack[1] = splitter.n_samples; // end
stack[2] = 0; // depth
stack[3] = _TREE_UNDEFINED; // parent
stack[4] = 0; // is_left
uint pos = 0;
uint feature = 0;
double threshold = 0;
double impurity = 0;
while (stackNValues > 0)
{
stackNValues -= 5;
uint start = stack[stackNValues];
uint end = stack[stackNValues + 1];
uint depth = stack[stackNValues + 2];
uint parent = stack[stackNValues + 3];
bool isLeft = stack[stackNValues + 4] != 0;
uint nNodeSamples = end - start;
bool isLeaf = ((depth >= this.maxDepth) ||
(nNodeSamples < this.minSamplesSplit) ||
(nNodeSamples < 2 * this.minSamplesLeaf));
splitter.node_reset(start, end, ref impurity);
isLeaf = isLeaf || (impurity < 1e-7);
if (!isLeaf)
{
splitter.node_split(ref pos, ref feature, ref threshold);
isLeaf = isLeaf || (pos >= end);
}
uint nodeId = this.AddNode(parent, isLeft, isLeaf, feature,
threshold, impurity, nNodeSamples);
if (isLeaf)
{
// Don't store value for internal nodes
splitter.node_value(this.Value, nodeId * this.valueStride);
}
else
{
if (stackNValues + 10 > stackCapacity)
{
stackCapacity *= 2;
var newStack = new uint[stackCapacity];
Array.Copy(stack, newStack, stack.Length);
stack = newStack;
}
// Stack right child
stack[stackNValues] = pos;
stack[stackNValues + 1] = end;
stack[stackNValues + 2] = depth + 1;
stack[stackNValues + 3] = nodeId;
stack[stackNValues + 4] = 0;
stackNValues += 5;
// Stack left child
stack[stackNValues] = start;
stack[stackNValues + 1] = pos;
stack[stackNValues + 2] = depth + 1;
stack[stackNValues + 3] = nodeId;
stack[stackNValues + 4] = 1;
stackNValues += 5;
}
}
this.Resize((int)this.NodeCount);
this.splitter = null; // Release memory
}
public static void writeMatrixToMatLabFile(MathNet.Numerics.LinearAlgebra.Generic.Matrix <double> matrix, string file, string name)
{
MathNet.Numerics.LinearAlgebra.IO.MatlabMatrixWriter wr = new MathNet.Numerics.LinearAlgebra.IO.MatlabMatrixWriter(file);
wr.WriteMatrix(matrix, name);
wr.Close();
}
static void Main(string[] args)
{
if (args.Length < 3 || !(File.Exists(Path.GetFullPath(args[0]))) || !(File.Exists(Path.GetFullPath(args[1]))))
{
Help();
Environment.Exit(0);
}
Console.WriteLine("Running Pre Step.");
Bitmap srcBitmap = null;
DenseMatrix srcMat = new DenseMatrix(256, 256);
try
{
srcBitmap = new Bitmap(Path.GetFullPath(args[0]));
}
catch (FileNotFoundException e)
{
Help();
Environment.Exit(0);
}
Console.WriteLine("Running Step1.");
for (int j = 0; j < 256; j++)
{
for (int i = 0; i < 256; i++)
{
srcMat.At(j, i, srcBitmap.GetPixel(i, j).R);
}
}
Console.WriteLine(" Load target image.");
FDT data = FDT.DeSerializeObject(Path.GetFullPath(args[1]));
Console.WriteLine(" Load data file.");
DenseMatrix output = new DenseMatrix(256, 256);
Console.WriteLine("Running Step2.");
foreach (XYSO xyso in data.DataList)
{
MathNet.Numerics.LinearAlgebra.Generic.Matrix <double> highMat = srcMat.SubMatrix(xyso.highy * 4, 16, xyso.highx * 4, 16);
DenseMatrix high8Mat = new DenseMatrix(8, 8);
for (int j = 0; j < 8; j++)
{
for (int i = 0; i < 8; i++)
{
double z = xyso.s * 0.25 * (highMat[2 * j, 2 * i] + highMat[2 * j, 2 * i + 1] + highMat[2 * j + 1, 2 * i] + highMat[2 * j, 2 * i + 1]) + xyso.o;
high8Mat.At(j, i, z);
}
}
output.SetSubMatrix(xyso.lowy * 8, 8, xyso.lowx * 8, 8, high8Mat);
}
Bitmap outBitmap = new Bitmap(256, 256);
for (int j = 0; j < 256; j++)
{
for (int i = 0; i < 256; i++)
{
if (output[j, i] > 255)
{
outBitmap.SetPixel(i, j, Color.FromArgb(255, 255, 255));
}
else if (output[j, i] < 0)
{
outBitmap.SetPixel(i, j, Color.FromArgb(0, 0, 0));
}
else
{
outBitmap.SetPixel(i, j, Color.FromArgb(Convert.ToInt32(output[j, i]), Convert.ToInt32(output[j, i]), Convert.ToInt32(output[j, i])));
}
}
}
outBitmap.Save(Path.GetFullPath(args[2]));
Console.WriteLine("Done.");
}
static void Main(string[] args)
{
if (args.Length < 2 || !(File.Exists(Path.GetFullPath(args[0]))))
{
//System.Diagnostics.Trace.WriteLine("Args[0] target file => " + Path.GetFullPath(args[0]));
Help();
Environment.Exit(0);
}
Bitmap srcBitmap = null;
try
{
srcBitmap = new Bitmap(Path.GetFullPath(args[0]));
}
catch (FileNotFoundException e)
{
Help();
Environment.Exit(0);
}
//for (int x = 0; x < srcBitmap.Width; x++)
//{
// for (int y = 0; y < srcBitmap.Height; y++)
// {
// }
//}
// Pre Step.
Console.WriteLine("Running Pre Step.");
DenseMatrix imgMat = new DenseMatrix(srcBitmap.Height, srcBitmap.Width);
for (int j = 0; j < srcBitmap.Height; j++)
{
for (int i = 0; i < srcBitmap.Width; i++)
{
imgMat.At(j, i, srcBitmap.GetPixel(i, j).R);
}
}
// Step 1.
Console.WriteLine("Running Step 1.");
int lowBlkWidthCnt = srcBitmap.Width / 8;
int lowBlkHeightCnt = srcBitmap.Height / 8;
List <DenseMatrix> lowBlkList = new List <DenseMatrix>(lowBlkWidthCnt * lowBlkHeightCnt);
List <double> lowIntwList = new List <double>(lowBlkWidthCnt * lowBlkHeightCnt);
for (int j = 0; j < lowBlkHeightCnt; j++)
{
for (int i = 0; i < lowBlkWidthCnt; i++)
{
DenseMatrix mat = null;
double intw = 0;
try
{
MathNet.Numerics.LinearAlgebra.Generic.Matrix <double> tmpMatrix = imgMat.SubMatrix(j * 8, 8, i * 8, 8);
mat = new DenseMatrix(tmpMatrix.RowCount, tmpMatrix.ColumnCount);
mat.SetSubMatrix(0, 8, 0, 8, tmpMatrix);
IEnumerable <Tuple <int, int, double> > e = mat.IndexedEnumerator();
intw = e.Sum(element => element.Item3);
}
catch (ArgumentOutOfRangeException e)
{
}
catch (ArgumentException e)
{
}
lowBlkList.Add(mat);
lowIntwList.Add(intw);
Console.Write(".");
}
}
Console.WriteLine();
// Step 2.
Console.WriteLine("Running Step 2.");
int highBlkWidthCnt = (srcBitmap.Width - 16) / 4;
int highBlkHeightCnt = (srcBitmap.Height - 16) / 4;
List <DenseMatrix> highBlkList = new List <DenseMatrix>(highBlkWidthCnt * highBlkHeightCnt);
List <double> highggAList = new List <double>(highBlkWidthCnt * highBlkHeightCnt);
List <double> highgAList = new List <double>(highBlkWidthCnt * highBlkHeightCnt);
List <DenseMatrix> highInvMatList = new List <DenseMatrix>(highBlkWidthCnt * highBlkHeightCnt);
for (int j = 0; j < highBlkHeightCnt; j++)
{
for (int i = 0; i < highBlkWidthCnt; i++)
{
DenseMatrix mat = new DenseMatrix(8, 8);
for (int l = 0; l < 8; l++)
{
for (int k = 0; k < 8; k++)
{
mat.At(l, k, 0.25 * (double)(srcBitmap.GetPixel((i * 4) + (k * 2), (j * 4) + (l * 2)).R + srcBitmap.GetPixel((i * 4) + (k * 2), (j * 4) + (l * 2 + 1)).R + srcBitmap.GetPixel((i * 4) + (k * 2 + 1), (j * 4) + (l * 2)).R + srcBitmap.GetPixel((i * 4) + (k * 2), (j * 4) + (l * 2 + 1)).R));
}
}
double intggA = mat.IndexedEnumerator().Sum(element => element.Item3 * element.Item3);
double intgA = mat.IndexedEnumerator().Sum(element => element.Item3);
highBlkList.Add(mat);
highggAList.Add(intggA);
highgAList.Add(intgA);
DenseMatrix multMat = new DenseMatrix(2, 2);
multMat.At(0, 0, intggA);
multMat.At(0, 1, intgA);
multMat.At(1, 0, intgA);
multMat.At(1, 1, 64);
DenseMatrix invMat = new DenseMatrix(2, 2);
invMat.SetSubMatrix(0, 2, 0, 2, multMat.Inverse());
highInvMatList.Add(invMat);
Console.Write(".");
}
}
Console.WriteLine();
// Step 3.
Console.WriteLine("Running Step 3.");
FDT result = new FDT();
for (int j = 0; j < lowBlkHeightCnt; j++)
{
for (int i = 0; i < lowBlkWidthCnt; i++)
{
XYSO minxyso;
minxyso.lowx = i;
minxyso.lowy = j;
minxyso.highx = 0;
minxyso.highy = 0;
minxyso.s = 0;
minxyso.o = 0;
minxyso.e = double.MaxValue;
for (int l = 0; l < highBlkHeightCnt; l++)
{
for (int k = 0; k < highBlkWidthCnt; k++)
{
DenseMatrix targetHighMat = highBlkList[l * highBlkWidthCnt + k];
DenseMatrix targetLowMat = lowBlkList[j * lowBlkWidthCnt + i];
double[] targetHighMatData = targetHighMat.ToRowWiseArray();
double[] targetLowMatData = targetLowMat.ToRowWiseArray();
//IEnumerable<Tuple<int, int, double>> targetHighMatEnum = targetHighMat.IndexedEnumerator();
//IEnumerable<Tuple<int, int, double>> targetLowMatEnum = targetLowMat.IndexedEnumerator();
double targetHnLElementSum = 0;
for (int m = 0; m < 64; m++)
{
targetHnLElementSum += targetHighMatData[m] * targetLowMatData[m];
}
MathNet.Numerics.LinearAlgebra.Generic.Matrix <double> so = highInvMatList[l * highBlkWidthCnt + k].Multiply(new DenseMatrix(2, 1, new double[] { targetHnLElementSum, lowIntwList[j * lowBlkWidthCnt + i] }));
double e = (lowBlkList[j * lowBlkWidthCnt + i] - (so[0, 0] * highBlkList[l * highBlkWidthCnt + k]) - new DenseMatrix(8, 8, so[1, 0])).IndexedEnumerator().Sum(element => element.Item3 * element.Item3);
if (e < minxyso.e)
{
minxyso.highx = k;
minxyso.highy = l;
minxyso.s = so[0, 0];
minxyso.o = so[1, 0];
minxyso.e = e;
}
}
}
//Console.WriteLine("Low Block (x,y)=(" + minxyso.lowx + "," + minxyso.lowy + "), High Block (x,y)=(" + minxyso.highx + "," + minxyso.highy + "), so= (" + minxyso.s + "," + minxyso.o + "), e=" + minxyso.e);
result.DataList.Add(minxyso);
Console.Write(".");
}
}
Console.WriteLine();
Console.WriteLine("Running Post Step.");
FDT.SerializeObject(args[1] + ".fdt", result);
Console.WriteLine("Done.");
}
public MainWindow()
{
//this must ALWAYS be first
InitializeComponent();
//// one sensor is currently supported
this.kinectsensor = KinectSensor.GetDefault();
// get the depth (display) extents
FrameDescription frameDescription = this.kinectsensor.DepthFrameSource.FrameDescription;
// open the reader for the body frames
this.bodyFrameReader = this.kinectsensor.BodyFrameSource.OpenReader();
this.bodyFrameReader.FrameArrived += this.Reader_FrameArrived;
// open the sensor
kinectsensor.Open();
recordNumber = 0;
//initialization Buttons for visibility
initButtons();
this.SetThreshold(HARD_THRESHOLD);
currentUser = -1; //bugs out?!
currentState = ScreenStates.HOME;
//initialize global skeleton buffer
bStorage = new Body[6]; //"hard"-coded limit of 6 (more like 2)
//setup timer
timer = new System.Windows.Threading.DispatcherTimer();
timer.Tick += new EventHandler(timerTick);
timer.Interval = new TimeSpan(0, 0, 0, 0, 100); //trigger every 50 ms (faster updates in progress bar)
timerCount = 0;
//initialize dataStore -- 4 user limit
dataStore = new MathNet.Numerics.LinearAlgebra.Generic.Matrix<float>[4][][];
for (int i = 0; i < dataStore.Length; i++)
{
//for each user give space for "1" gesture
dataStore[i] = new MathNet.Numerics.LinearAlgebra.Generic.Matrix<float>[1][];
for (int j = 0; j < dataStore[i].Length; j++)
{
//each gesture give space for "1" sample of the gesture
dataStore[i][j] = new MathNet.Numerics.LinearAlgebra.Generic.Matrix<float>[numRecords];
}
}
userNames = new string[]{"User 1", "User 2", "User 3", "User 4"};
userHasData = new bool[4] { false, false, false, false };
dataEnrolled = false;
//set up recordingBuffer
rb = new RecordingBuffer(5 * 30, 25); //full joints 5 seconds;
rb.clearBuffer();
//set state to welcome
setProgramState(ScreenStates.HOME);
this.kinectManager = KinectManager.Default;
//setup the cursor with images
Image[] cursorImages = new Image[3];
Image cursorImage0 = new Image();
cursorImage0.Source = ImageExtensions.ToBitmapSource(KinectLogin.Properties.Resources.hand1);
cursorImages[0] = cursorImage0;
Image cursorImage1 = new Image();
cursorImage1.Source = ImageExtensions.ToBitmapSource(KinectLogin.Properties.Resources.hand2);
cursorImages[1] = cursorImage1;
Image cursorImage2 = new Image();
cursorImage2.Source = ImageExtensions.ToBitmapSource(KinectLogin.Properties.Resources.hand3);
cursorImages[2] = cursorImage2;
this.cursorView.SetCursorImages(cursorImages);
this.kinectManager.HandManager.Cursor = this.cursorView;
this.kinectManager.AddStateListener(this);
//MessageBox.Show("Success!");
//Loaded += OnLoaded;
}
