public void Evaluate(IEvolutionState state, Individual ind, int subpop, int threadnum)
{
if (!ind.Evaluated)
{
int imageIndex;
var SVMTrainData = new StreamWriter(@"F:\Gesty\features\traindata" + threadnum + ".txt");
var SVMTestData = new StreamWriter(@"F:\Gesty\features\testdata" + threadnum + ".txt");
for (imageIndex = 0; imageIndex < imageList.Length; imageIndex++)
{
var image = new Image <Gray, Byte>(imageList[imageIndex]);
var line = new StringBuilder();
image.CopyTo(currentImage[threadnum]);
image.CopyTo(originalImage[threadnum]);
image.Dispose();
((GPIndividual)ind).Trees[0].Child.Eval(state, threadnum, Input, Stack, ((GPIndividual)ind), this);
//int[] features = imageTransformer.GetSuperpixelFeatures(currentImage[threadnum]);
int[] features = ImageTransformer.GetSquareSuperpixelFeatures(currentImage[threadnum], 25);
line.Append((imageIndex / 213) + 1 + " ");
for (int i = 1; i <= features.Length; i++)
{
line.Append(i + ":" + features[i - 1] + " ");
}
var lineString = line.ToString().Trim();
if (imageIndex % 2 == 0)
{
SVMTrainData.WriteLine(lineString);
}
else
{
SVMTestData.WriteLine(lineString);
}
}
SVMTrainData.Close();
SVMTestData.Close();
var problem = SVMProblemHelper.Load(@"F:\Gesty\features\traindata" + threadnum + ".txt");
var testProblem = SVMProblemHelper.Load(@"F:\Gesty\features\testdata" + threadnum + ".txt");
var model = problem.Train(Parameter);
double[] target = testProblem.Predict(model);
double accuracy = testProblem.EvaluateClassificationProblem(target);
var f = ((KozaFitness)ind.Fitness);
f.SetStandardizedFitness(state, (float)(100 - accuracy));
ind.Evaluated = true;
}
}
public void Evaluate(IEvolutionState state, Individual ind, int subpop, int threadnum)
{
if (!ind.Evaluated)
{
int counter = 0;
var features = new int[5, NumOfImagesPerCategory *NumOfClasses, NumOfFeatures];
var labels = new int[5, NumOfImagesPerCategory *NumOfClasses];
for (int i = 0; i < 5; i++)
{
counter = 0;
var categoryDir = CategoryDirs[i];
var subcategoryDirs = Directory.EnumerateDirectories(categoryDir).ToArray();
for (int j = 0; j < NumOfClasses; j++)
{
var subcategoryDir = subcategoryDirs[j];
var images = Directory.GetFiles(subcategoryDir);
for (int k = 0; k < NumOfImagesPerCategory; k++)
{
var tempImage = new Image <Gray, Byte>(images[k]);
tempImage.CopyTo(currentImage[threadnum]);
tempImage.CopyTo(originalImage[threadnum]);
tempImage.Dispose();
((GPIndividual)ind).Trees[0].Child.Eval(state, threadnum, Input, Stack, ((GPIndividual)ind), this);
int[] imageFeatures = ImageTransformer.GetSquareSuperpixelFeatures(currentImage[threadnum], SuperpixelSize);
for (int x = 0; x < imageFeatures.Length; x++)
{
features[i, counter, x] = imageFeatures[x];
labels[i, counter] = j + 1;
}
counter++;
}
}
}
/*
* var trainDataFile = new StreamWriter(@"F:\Gesty\problem2\features\traindata" + threadnum + ".txt");
* var testDataFile = new StreamWriter(@"F:\Gesty\problem2\features\testdata" + threadnum + ".txt");
*
* for(int i=0; i<4; i++)
* {
* for(int j=0; j<1000; j++)
* {
* var line = new StringBuilder();
* line.Append(labels[i, j].ToString() + " ");
* for (int k=0; k<NumOfFeatures; k++)
* {
* line.Append((k + 1).ToString() + ":" + features[i, j, k].ToString() + " ");
* }
* trainDataFile.WriteLine(line.ToString().Trim());
* }
* }
* for (int j = 0; j < 1000; j++)
* {
* var line = new StringBuilder();
* line.Append(labels[4, j].ToString() + " ");
* for (int k = 0; k < NumOfFeatures; k++)
* {
* line.Append((k + 1).ToString() + ":" + features[4, j, k].ToString() + " ");
* }
* testDataFile.WriteLine(line.ToString().Trim());
* }
* trainDataFile.Close();
* testDataFile.Close();
*/
var confMatI = new double[10, 10];
double accuracy = 0;
for (int x = 0; x < 5; x++)
{
var trainData = new Matrix <float>(NumOfImagesPerCategory * NumOfClasses * 4, NumOfFeatures);
var trainClasses = new Matrix <int>(NumOfImagesPerCategory * NumOfClasses * 4, 1);
var testData = new Matrix <float>(NumOfImagesPerCategory * NumOfClasses, NumOfFeatures);
var testClasses = new Matrix <int>(NumOfImagesPerCategory * NumOfClasses, 1);
//trainData
int imageCount = 0;
for (int i = 0; i < 5; i++)
{
if (i != x)
{
for (int j = 0; j < NumOfImagesPerCategory * NumOfClasses; j++)
{
for (int k = 0; k < NumOfFeatures; k++)
{
trainData[imageCount, k] = features[i, j, k];
trainClasses[imageCount, 0] = labels[i, j];
}
imageCount++;
}
}
else
{
for (int j = 0; j < NumOfImagesPerCategory * NumOfClasses; j++)
{
for (int k = 0; k < NumOfFeatures; k++)
{
testData[j, k] = features[i, j, k];
testClasses[j, 0] = labels[i, j];
}
}
}
}
Emgu.CV.ML.SVM model = new Emgu.CV.ML.SVM();
var predictions = new Matrix <float>(NumOfImagesPerCategory * NumOfClasses, 1);
var trainData2 = new TrainData(trainData, Emgu.CV.ML.MlEnum.DataLayoutType.RowSample, trainClasses);
model.Type = Emgu.CV.ML.SVM.SvmType.CSvc;
model.SetKernel(Emgu.CV.ML.SVM.SvmKernelType.Poly);
model.TermCriteria = new MCvTermCriteria(10000, 0.001);
model.Degree = 3;
model.Gamma = 0.001;
model.Coef0 = 0;
model.C = 1000;
model.Nu = 0.5;
model.P = 0.1;
model.Train(trainData2);
model.Predict(testData, predictions);
// var predictionsArray = (float[,])predictions.GetData();
int correctPredictions = 0;
for (int i = 0; i < predictions.Rows; i++)
{
if ((int)predictions[i, 0] == testClasses[i, 0])
{
correctPredictions++;
}
var predictedLabel = (int)predictions[i, 0];
var trueLabel = testClasses[i, 0];
confMatI[predictedLabel - 1, trueLabel - 1]++;
}
for (int i = 0; i < 10; i++)
{
for (int j = 0; j < 10; j++)
{
confMat[i, j] = (confMatI[i, j] / 500) * 100;
}
}
if (correctPredictions > 0)
{
accuracy += 100 * ((double)correctPredictions / (double)predictions.Rows);
}
}
/*
* for(int i=0; i<NumOfImagesPerCategory*NumOfClasses*4; i++)
* {
* for(int j=0; j<NumOfFeatures; j++)
* {
* //trainData[i, j] = ((trainData[i, j] - 0) / (255 - 0)) * (1 + 1) - 1;
* }
* trainClasses[i, 0] = ((trainClasses[i, 0] - 1) / (NumOfClasses - 1)) * (1 + 1) - 1;
* }
*/
//testData
/*
* for (int j = 0; j < NumOfImagesPerCategory * NumOfClasses; j++)
* {
* var line = new StringBuilder();
* line.Append(labels[4, j] + " ");
* for (int k = 0; k < NumOfFeatures; k++)
* {
* line.Append(k + 1 + ":" + features[4, j, k] + " ");
* }
* testData.WriteLine(line.ToString().Trim());
* }
*
* trainData.Close();
* testData.Close();
*/
//predictions.Dispose();
/*
* var netData = new SharpLearning.Containers.Matrices.F64Matrix(NumOfImagesPerCategory * NumOfClasses * 4, NumOfFeatures);
* var netTargets = new double[NumOfImagesPerCategory * NumOfClasses * 4];
* int imageCount = 0;
* for (int i = 0; i < 4; i++)
* {
* for (int j = 0; j < NumOfImagesPerCategory * NumOfClasses; j++)
* {
* for (int k = 0; k < NumOfFeatures; k++)
* {
* netData[imageCount, k] = features[i, j, k];
* netTargets[imageCount] = labels[i, j];
* }
* imageCount++;
* }
* }
*/
/*
* var CVNeuralNet = new Emgu.CV.ML.ANN_MLP();
*
* CVNeuralNet.TermCriteria = new MCvTermCriteria(10000, 0.001);
* var layerSizes = new Matrix<int>(new int[4] { NumOfFeatures, NumOfFeatures * 10, NumOfFeatures*5, 1 });
* CVNeuralNet.SetLayerSizes(layerSizes);
* CVNeuralNet.SetTrainMethod(ANN_MLP.AnnMlpTrainMethod.Rprop);
* CVNeuralNet.SetActivationFunction(ANN_MLP.AnnMlpActivationFunction.SigmoidSym);
* CVNeuralNet.BackpropMomentumScale = 0.01;
* CVNeuralNet.BackpropWeightScale = 0.2;
* var trainData2 = new TrainData(trainData, Emgu.CV.ML.MlEnum.DataLayoutType.RowSample, trainClasses);
* var predictions = new Matrix<float>(NumOfImagesPerCategory * NumOfClasses*4, 1);
* CVNeuralNet.Train(trainData2);
* CVNeuralNet.Predict(trainData, predictions);
*/
/*
* var net = new NeuralNet();
* net.Add(new InputLayer(NumOfFeatures));
* net.Add(new DropoutLayer(0.5));
* net.Add(new DenseLayer(NumOfFeatures * 4, SharpLearning.Neural.Activations.Activation.Sigmoid));
* net.Add(new DenseLayer(NumOfFeatures * 4, SharpLearning.Neural.Activations.Activation.Sigmoid));
* net.Add(new DropoutLayer(0.5));
* net.Add(new SoftMaxLayer(NumOfClasses));
* var learner = new ClassificationNeuralNetLearner(net, new SquareLoss());
* var model = learner.Learn(netData, netTargets);
* var predictions = model.Predict(netData);
* int correctPredictions = 0;
* for (int i = 0; i < predictions.Length; i++)
* {
* if ((int)predictions[i] == netTargets[i])
* correctPredictions++;
* }
* if (correctPredictions > 0)
* accuracy = 100 * ((double)correctPredictions / (double)predictions.Length);
*/
/*
* var problem = SVMProblemHelper.Load(@"F:\Gesty\problem2\features\traindata" + threadnum + ".txt");
* var testProblem = SVMProblemHelper.Load(@"F:\Gesty\problem2\features\testdata" + threadnum + ".txt");
* var model = problem.Train(Parameter);
* double[] target = testProblem.Predict(model);
* double accuracy = testProblem.EvaluateClassificationProblem(target);
*/
var f = ((KozaFitness)ind.Fitness);
f.SetStandardizedFitness(state, (float)(100 - (accuracy / 5)));
ind.Evaluated = true;
var transFeatures = new StreamWriter(@"F:\Gesty\testy\transFeatures.csv");
for (int i1 = 0; i1 < 1000; i1++)
{
for (int i2 = 0; i2 < 5; i2++)
{
var line = new StringBuilder();
line.Append(labels[i2, i1].ToString() + ',');
for (int i3 = 0; i3 < 64; i3++)
{
line.Append(features[i2, i1, i3].ToString() + ',');
}
transFeatures.WriteLine(line.ToString().Trim(','));
}
}
transFeatures.Close();
}
}
