private UnifiedCameraNeuralNetwork[] _neuralNets; // A network for each pair of camaras (main camera and camera i)
/// <summary>
/// Creates a new calibration object.
/// Calibration will occur for the session that was recorded at sessionTimestamp using the numOfCameras defined.
/// </summary>
/// <param name="sessionTimestamp"> Timestamp of the session recorded </param>
/// <param name="numOfCameras"> Number of cameras in the recording </param>
public Calibration(DateTime sessionTimestamp, int numOfCameras, SkelDisplay display)
{
// Non configurable ANN parameters (each network maps a pair of cameras)
// The input is one of the cameras, the output is the absolute camera.
_neuralNets = new UnifiedCameraNeuralNetwork[numOfCameras];
int inputLayerSize = SkelJointsData.numOfJoints * 3;
int outputLayerSize = inputLayerSize;
// Configurable ANN parameters
int hiddenLayerSize = inputLayerSize * inputLayerSize;
float learningRate = 0.15f;
bool isStochastic = true;
float momentum = 0.1f;
// Create an ANN for each pair of cameras
for (int i = 0; i < numOfCameras - 1; i++)
{
_neuralNets[i] = new UnifiedCameraNeuralNetwork(inputLayerSize, hiddenLayerSize, outputLayerSize,
learningRate, isStochastic, momentum);
}
_numOfCameras = numOfCameras;
_display = display;
_replay = new SkelReplay(sessionTimestamp, numOfCameras);
}
public static void initNeuralNetworkTest()
{
initApproxFunc();
ann = new UnifiedCameraNeuralNetwork(inputLayerSize, hiddenLayerSize, outputLayerSize,
learningRate, isStochastic, momentum);
Random random = new Random(123);
for (int i = 0; i < numOfSamples; i++)
{
samples[i] = (float)random.NextDouble() * (maxRange - minRange) + minRange;
}
for (int i = 0; i < numOfTests; i++)
{
tests[i] = (float)random.NextDouble() * (maxRange - minRange) + minRange;
}
}
private static float performEpoch(UnifiedCameraNeuralNetwork ann,
int numOfSamples, float[] samples, int numOfTests, float[] tests,
DrawingGroup drawingGroup)
{
int failedTests = 0;
using (DrawingContext dc = drawingGroup.Open())
{
// Draw a transparent background to set the render size
float RenderWidth = 640f;
float RenderHeight = 480f;
dc.DrawRectangle(Brushes.Black, null, new System.Windows.Rect(0.0, 0.0, RenderWidth, RenderHeight));
drawingGroup.ClipGeometry = new RectangleGeometry(new System.Windows.Rect(0.0, 0.0, RenderWidth, RenderHeight));
for (int i = 0; i < numOfSamples; i++)
{
float x = samples[i];
float[] input = new float[1];
float[] expected = new float[1];
input[0] = (float)x;
expected[0] = normalizeFunc(x); // Normalize
ann.train(input, expected);
if (i % 10000 == 9999)
{
Console.WriteLine((i + 1) + " samples trained..");
}
}
if (ann.Stochastic)
{
ann.forceWeightUpdates();
}
for (int i = 0; i < numOfTests; i++)
{
float x = tests[i];
float[] input = new float[1];
input[0] = (float)x;
float result = approximationFunc(x);
float prediction = ann.feedForward(input)[0, 0];
if (float.IsNaN(prediction))
{
throw new InvalidOperationException("ANN returned a NaN output.");
}
prediction = denormalizeFunc(prediction); // Denormalize
// Draw
int brushSize = 2;
float xPlot = (x / (maxRange - minRange)) * RenderWidth;
float funcPlot = RenderHeight - normalizeFunc(x) * RenderHeight;
float predictPlot = RenderHeight - normalize(prediction) * RenderHeight;
dc.DrawEllipse(groundTruthBrush, null, new System.Windows.Point(xPlot, funcPlot), brushSize, brushSize);
dc.DrawEllipse(predictionBrush, null, new System.Windows.Point(xPlot, predictPlot), brushSize, brushSize);
float diff = Math.Abs(result - prediction);
if (diff > 0.0001)
{
failedTests++;
}
}
}
Console.WriteLine("Total failed tests: " + failedTests);
float successRate = 1.0f - (float)((float)failedTests / (float)numOfTests);
Console.WriteLine("Success rate: " + successRate);
return(successRate);
}