public void TestRTreesLetterRecognition()
{
Matrix<float> data, response;
ReadLetterRecognitionData(out data, out response);
int trainingSampleCount = (int) (data.Rows * 0.8);
Matrix<Byte> varType = new Matrix<byte>(data.Cols + 1, 1);
varType.SetValue((byte) MlEnum.VarType.Numerical); //the data is numerical
varType[data.Cols, 0] = (byte) MlEnum.VarType.Categorical; //the response is catagorical
Matrix<byte> sampleIdx = new Matrix<byte>(data.Rows, 1);
using (Matrix<byte> sampleRows = sampleIdx.GetRows(0, trainingSampleCount, 1))
sampleRows.SetValue(255);
using (RTrees forest = new RTrees())
using (TrainData td = new TrainData(data, MlEnum.DataLayoutType.RowSample, response, null, sampleIdx, null, varType))
{
forest.MaxDepth = 10;
forest.MinSampleCount = 10;
forest.RegressionAccuracy = 0.0f;
forest.UseSurrogates = false;
forest.MaxCategories = 15;
forest.CalculateVarImportance = true;
forest.ActiveVarCount = 4;
forest.TermCriteria = new MCvTermCriteria(100, 0.01f);
bool success = forest.Train(td);
if (!success)
return;
double trainDataCorrectRatio = 0;
double testDataCorrectRatio = 0;
for (int i = 0; i < data.Rows; i++)
{
using (Matrix<float> sample = data.GetRow(i))
{
double r = forest.Predict(sample, null);
r = Math.Abs(r - response[i, 0]);
if (r < 1.0e-5)
{
if (i < trainingSampleCount)
trainDataCorrectRatio++;
else
testDataCorrectRatio++;
}
}
}
trainDataCorrectRatio /= trainingSampleCount;
testDataCorrectRatio /= (data.Rows - trainingSampleCount);
StringBuilder builder = new StringBuilder("Variable Importance: ");
/*
using (Matrix<float> varImportance = forest.VarImportance)
{
for (int i = 0; i < varImportance.Cols; i++)
{
builder.AppendFormat("{0} ", varImportance[0, i]);
}
}*/
EmguAssert.WriteLine(String.Format("Prediction accuracy for training data :{0}%", trainDataCorrectRatio * 100));
EmguAssert.WriteLine(String.Format("Prediction accuracy for test data :{0}%", testDataCorrectRatio * 100));
EmguAssert.WriteLine(builder.ToString());
}
}
private void btnTest_Click(object sender, EventArgs e)
{
Emgu.CV.ML.RTrees forest = new Emgu.CV.ML.RTrees();
FileStorage fsr = new FileStorage(@"C:\Users\welah\Desktop\testing\forest.xml", FileStorage.Mode.Read);
forest.Read(fsr.GetRoot());
int total_pos = 0;
int total_neg = 0;
int ann = 0;
/*string[] images_paths_pos = Directory.GetFiles(@"D:\uOttawa\Winter 2018\INRIAPerson\INRIAPerson\test_64x128_H96\pos");
* List<float> xspos = new List<float>();
* foreach (var image_path in images_paths_pos)
* {
* Image<Bgr, byte> img_original = new Image<Bgr, byte>(image_path);
* Image<Bgr, byte> ped = img_original;
* ped.ROI = new Rectangle(6,6,64,128);
* List<Image<Gray, UInt16>> channels = functionsGets.GetACF(ped);
* Matrix<float> s = functionsGets.getMatFromImageList(channels);
* float x = Emgu.CV.ML.StatModelExtensions.Predict(tree, s);
* xspos.Add(x);
* Console.WriteLine(x.ToString());
* if (Math.Round(x) == 1)
* total_pos++;
* }
* string[] images_paths_neg = Directory.GetFiles(@"D:\uOttawa\Winter 2018\INRIAPerson\INRIAPerson\Test\neg");
* List<float> xsneg = new List<float>();
* foreach (var image_path in images_paths_neg)
* {
* Image<Bgr, byte> img_original = new Image<Bgr, byte>(image_path);
* List<Rectangle> list_bounding_boxes = coreFunctions.getBoundingBoxes(image_path, tbAnnotations.Text);
* foreach (var rectangle in list_bounding_boxes)
* {
* Image<Bgr, byte> neg = img_original;
* neg.ROI = rectangle;
* if (neg.Width > 64 || neg.Height > 128)
* {
* neg = neg.Resize(64, 128, Inter.Linear);
* }
* List<Image<Gray, UInt16>> channels = functionsGets.GetACF(neg);
* Matrix<float> s = functionsGets.getMatFromImageList(channels);
* float x = Emgu.CV.ML.StatModelExtensions.Predict(tree, s);
* xsneg.Add(x);
* if (Math.Round(x) == 2)
* total_neg++;
* }
* }
* float avpos = xspos.Average();
* float maxpos = xspos.Max();
* float minpos = xspos.Min();
* float avneg = xsneg.Average();
* float maxneg = xsneg.Max();
* float minneg = xsneg.Min();
*
* TextWriter tw = new StreamWriter("xpos.txt");
*
* foreach (var x in xspos)
* tw.WriteLine(x.ToString());
*
* tw.Close();
*
* TextWriter tw1 = new StreamWriter("xneg.txt");
*
* foreach (var x in xsneg)
* tw1.WriteLine(x.ToString());
*
* tw1.Close();*/
string[] image_paths = Directory.GetFiles(tbImagesTest.Text);
foreach (var image_path in image_paths)
{
List <Rectangle> list_rectangles_detected = new List <Rectangle>();
Image <Bgr, byte> image_original = new Image <Bgr, byte>(image_path);
//image_original = image_original.SmoothGaussian(3);
Image <Bgr, byte> image_temp = image_original.Copy();
//List<Image<Gray, UInt16>> list_channels_original = functionsGets.GetACF(image_temp);
float factor = 1;
int total_windows = 0;
while (true)
{
if (factor * image_original.Width > 64 && factor * image_original.Height > 128)
{
image_temp = image_original.Copy().Resize(factor, Inter.Linear);
//List<Image<Gray, UInt16>> list_channels_original = functionsGets.GetACF(image_temp);
//image_temp.Save(tbOutputTest.Text + @"\" + Path.GetFileNameWithoutExtension(image_path) + " " + factor.ToString() + ".jpg");
for (int m = 0; m < image_temp.Height - 128; m += 8)
{
for (int n = 0; n < image_temp.Width - 64; n += 8)
{
total_windows++;
List <Image <Gray, UInt16> > list_channels_window = new List <Image <Gray, UInt16> >();
Rectangle rect = new Rectangle(n, m, 64, 128);
Image <Bgr, byte> win = image_temp.Copy();
win.ROI = rect;
win = win.SmoothGaussian(3);
list_channels_window = functionsGets.GetACF(win);
int counter = 0;
//image_temp.Draw(rect, new Bgr(Color.Blue));
/*for (int i = 0; i < list_channels_original.Count(); i++)
* {
* Image<Gray, UInt16> channel = list_channels_original[i];
* channel.ROI = rect;
* list_channels_window.Add(channel);
* }*/
Matrix <float> s = functionsGets.getMatFromImageList(list_channels_window);
float x = forest.Predict(s);
double rounded = Math.Round(x, 2);
if (rounded <= 1.05)
{
list_rectangles_detected.Add(functionsGets.ScaleRectangleToOriginalImage(rect, factor));
//image_temp.Draw(rect, new Bgr(Color.Red));
total_pos++;
}
else//if (Math.Round(x) == 2)
{
//list_rectangles_detected.Add(functionsGets.ScaleRectangleToOriginalImage(rect, factor));
total_neg++;
}
}
}
//image_temp.Save(tbOutputTest.Text + @"\" + Path.GetFileNameWithoutExtension(image_path) + " " + factor.ToString() + ".png");
//image_temp = image_original.Resize(factor, Inter.Linear);
//image_temp.Save(tbOutputTest.Text + @"\" + Path.GetFileNameWithoutExtension(image_path) + " " + (factor - 0.2).ToString() + ".jpg");
//image_temp = image_original.Resize(factor, Inter.Linear);
//image_temp = image_temp.Resize(factor, Inter.Linear);
//image_temp.Save(tbOutputTest.Text + @"\" + Path.GetFileNameWithoutExtension(image_path) + " " + (factor - 0.2).ToString() + ".jpg");
}
else
{
break;
}
factor = factor - (float)(0.2);
}
Emgu.CV.Util.VectorOfRect vec = new VectorOfRect();
vec.Push(list_rectangles_detected.ToArray());
CvInvoke.GroupRectangles(vec, 1, 0.2);
vec = functionsGets.RemoveContainedRects(vec);
for (int i = 0; i < vec.Size; i++)
{
image_original.Draw(vec[i], new Bgr(Color.Red));
}
image_original.Save(tbOutputTest.Text + @"\" + Path.GetFileName(image_path));
}
}
