public void TestBOWKmeansTrainer2()
{
Image <Gray, byte> box = EmguAssert.LoadImage <Gray, byte>("box.png");
Brisk detector = new Brisk(30, 3, 1.0f);
VectorOfKeyPoint kpts = new VectorOfKeyPoint();
Mat descriptors = new Mat();
detector.DetectAndCompute(box, null, kpts, descriptors, false);
Mat descriptorsF = new Mat();
descriptors.ConvertTo(descriptorsF, CvEnum.DepthType.Cv32F);
//Matrix<float> descriptorsF = descriptors.Convert<float>();
BOWKMeansTrainer trainer = new BOWKMeansTrainer(100, new MCvTermCriteria(), 3, CvEnum.KMeansInitType.PPCenters);
trainer.Add(descriptorsF);
Mat vocabulary = new Mat();
trainer.Cluster(vocabulary);
BFMatcher matcher = new BFMatcher(DistanceType.L2);
BOWImgDescriptorExtractor extractor = new BOWImgDescriptorExtractor(detector, matcher);
Mat vocabularyByte = new Mat();
vocabulary.ConvertTo(vocabularyByte, CvEnum.DepthType.Cv8U);
extractor.SetVocabulary(vocabularyByte);
Mat descriptors2 = new Mat();
extractor.Compute(box, kpts, descriptors2);
}
public void TestBOWKmeansTrainer()
{
Image <Gray, byte> box = EmguAssert.LoadImage <Gray, byte>("box.png");
SURF detector = new SURF(500);
VectorOfKeyPoint kpts = new VectorOfKeyPoint();
Mat descriptors = new Mat();
detector.DetectAndCompute(box, null, kpts, descriptors, false);
BOWKMeansTrainer trainer = new BOWKMeansTrainer(100, new MCvTermCriteria(), 3, CvEnum.KMeansInitType.PPCenters);
trainer.Add(descriptors);
Mat vocabulary = new Mat();
trainer.Cluster(vocabulary);
BFMatcher matcher = new BFMatcher(DistanceType.L2);
BOWImgDescriptorExtractor extractor = new BOWImgDescriptorExtractor(detector, matcher);
extractor.SetVocabulary(vocabulary);
Mat descriptors2 = new Mat();
extractor.Compute(box, kpts, descriptors2);
}
/// <summary>
/// Utilizado para carregar as imagens de treinamento e gerar o arquivo de descritores
/// </summary>
private void LoadTrain()
{
Image <Bgr, Byte> image;
for (int i = 0; i < banknotes.Count; i++)
{
var banknote = banknotes[i];
//J = 1 pois é o indice que está no nome das imagens de treinamento
for (int j = 1; j <= banknoteSizes[banknote]; j++)
{
var nameFile = $@"{Path}\Train\{banknote} ({j}).jpg";
image = new Image <Bgr, Byte>(nameFile);
listaImagensTreino.Add(nameFile, image);
MKeyPoint[] keypoints;
keypoints = extractor.Detect(image);
listaKeyPointsImagensTreino.Add(nameFile, keypoints);
Mat features = new Mat();
extractor.Compute(image, new VectorOfKeyPoint(keypoints), features);
featureUnclustered.PushBack(features);
}
}
//Armazenando os descritores processados da etapa de cima em um arquivo train_descriptors.yml
FileStorage fs = new FileStorage($@"{Path}\SVM Datasets\train_descriptors.yml", FileStorage.Mode.Write);
//Adicionando Label train_descriptors ao arquivo train_descriptors.yml
fs.Write(featureUnclustered, "train_descriptors");
fs.ReleaseAndGetString();
//Adicionando descritores não processados no BOW
bowTrainer.Add(featureUnclustered);
}
private static void BowTest()
{
DescriptorMatcher matcher = new BFMatcher();
Feature2D extractor = AKAZE.Create();
Feature2D detector = AKAZE.Create();
TermCriteria criteria = new TermCriteria(CriteriaType.Count | CriteriaType.Eps, 10, 0.001);
BOWKMeansTrainer bowTrainer = new BOWKMeansTrainer(200, criteria, 1);
BOWImgDescriptorExtractor bowDescriptorExtractor = new BOWImgDescriptorExtractor(extractor, matcher);
Mat img = null;
KeyPoint[] keypoint = detector.Detect(img);
Mat features = new Mat();
extractor.Compute(img, ref keypoint, features);
bowTrainer.Add(features);
throw new NotImplementedException();
}
private static void BowTest()
{
DescriptorMatcher matcher = new BFMatcher();
Feature2D extractor = AKAZE.Create();
Feature2D detector = AKAZE.Create();
TermCriteria criteria = new TermCriteria(CriteriaType.Count | CriteriaType.Eps, 10, 0.001);
BOWKMeansTrainer bowTrainer = new BOWKMeansTrainer(200, criteria, 1);
BOWImgDescriptorExtractor bowDescriptorExtractor = new BOWImgDescriptorExtractor(extractor, matcher);
Mat img = null;
KeyPoint[] keypoint = detector.Detect(img);
Mat features = new Mat();
extractor.Compute(img, ref keypoint, features);
bowTrainer.Add(features);
throw new NotImplementedException();
}
static void train_svm()
{
int n_samples = 0;
SURF surf = new SURF(400);
List <Bitmap> samples = new List <Bitmap>();
List <Tuple <Bitmap, int> > data = new List <Tuple <Bitmap, int> >();
/*
* foreach (string s in System.IO.Directory.GetFiles("mail_samples"))
* {
* Bitmap f1 = new Bitmap(s);//ImageDecoder.DecodeFromFile(s);
* data.Add(new Tuple<Bitmap, int>(f1, +1));
* }
* foreach (string s in System.IO.Directory.GetFiles("phone_icons"))
* {
* Bitmap f1 = new Bitmap(s); //ImageDecoder.DecodeFromFile(s);
* data.Add(new Tuple<Bitmap, int>(f1, -1));
* }
*/
foreach (string s in System.IO.Directory.GetFiles(@"C:\test\iphone_icon"))
{
Bitmap f1 = new Bitmap(s);//ImageDecoder.DecodeFromFile(s);
if (string.Compare(System.IO.Path.GetFileNameWithoutExtension(s), "temp_1") == 0 ||
string.Compare(System.IO.Path.GetFileNameWithoutExtension(s), "scoll_selected_icon") == 0
)
{
data.Add(new Tuple <Bitmap, int>(f1, +1));
}
else
{
data.Add(new Tuple <Bitmap, int>(f1, 0));
}
}
n_samples = data.Count;
// computr bow
Mat m = new Mat();
foreach (Tuple <Bitmap, int> v in data)
{
Image <Bgr, Byte> i = new Image <Bgr, byte>(v.Item1);
Mat ii = new Mat();
CvInvoke.CvtColor(i, ii, ColorConversion.Bgr2Gray);
MKeyPoint[] kp = surf.Detect(ii);
Mat desc = new Mat();
surf.Compute(ii, new VectorOfKeyPoint(kp), desc);
m.PushBack(desc);
}
// Create the vocabulary with KMeans.
MCvTermCriteria tc = new MCvTermCriteria(100, 0.00001);
BOWKMeansTrainer bowTrainer = new BOWKMeansTrainer(16, tc, 3, KMeansInitType.PPCenters);
bowTrainer.Add(m);
Mat voca = new Mat();
bowTrainer.Cluster(voca);
//
BFMatcher matcher = new BFMatcher(DistanceType.L2);
BOWImgDescriptorExtractor bowDex = new BOWImgDescriptorExtractor(surf, matcher);
bowDex.SetVocabulary(voca);
//
Mat tDesc = new Mat();
//Matrix<int> tLabel = new Matrix<int>(1, n_samples);
Matrix <int> tLabel = new Matrix <int>(n_samples, 1);
//foreach (Tuple<Bitmap, int> v in data)
for (int j = 0; j < data.Count; j++)
{
Image <Bgr, Byte> i = new Image <Bgr, byte>(data[j].Item1);
Mat ii = new Mat();
CvInvoke.CvtColor(i, ii, ColorConversion.Bgr2Gray);
MKeyPoint[] kp = surf.Detect(ii);
Mat desc = new Mat();
bowDex.Compute(ii, new VectorOfKeyPoint(kp), desc);
tDesc.PushBack(desc);
//tLabel[0, j] = data[j].Item2;
tLabel[j, 0] = data[j].Item2;
}
//
//SVM model = new SVM();
//model.SetKernel(Emgu.CV.ML.SVM.SvmKernelType.Linear);
//model.Type = SVM.SvmType.CSvc;
//model.C = 1;
//model.TermCriteria = new MCvTermCriteria(100, 0.00001);
SVM svm = new SVM();
svm.C = 312.5;
svm.Gamma = 0.50625000000000009;
svm.SetKernel(SVM.SvmKernelType.Rbf);
svm.Type = SVM.SvmType.CSvc;
svm.Nu = 0.5;
TrainData td = new TrainData(tDesc, Emgu.CV.ML.MlEnum.DataLayoutType.RowSample, tLabel);
bool tained = svm.TrainAuto(td);
using (FileStorage fs = new FileStorage("voca.yaml", FileStorage.Mode.Write))
{
svm.Write(fs);
fs.Write(voca, "voca");
}
//using (FileStorage fs = new FileStorage("svm.yaml", FileStorage.Mode.Write))
//{
// svm.Write(fs);
//}
//svm.Save("svm.yaml");
// test
{
//Image<Bgr, Byte> test_img = new Image<Bgr, byte>(@"C:\test\scroll_left.jpg");
Image <Bgr, Byte> test_img = new Image <Bgr, byte>(@"C:\test\iphone_icon\temp_1.jpg");
//Image<Bgr, Byte> test_img = new Image<Bgr, byte>(@"C:\projects\local\testMQ\testMQ\bin\Debug\phone_icons\icon_2.jpg");
//Image<Bgr, Byte> test_img = new Image<Bgr, byte>(@"C:\test\35928233-email-icon-on-blue-background-clean-vector.jpg");
Mat ii = new Mat();
CvInvoke.CvtColor(test_img, ii, ColorConversion.Bgr2Gray);
MKeyPoint[] kp = surf.Detect(ii);
Mat desc = new Mat();
bowDex.Compute(ii, new VectorOfKeyPoint(kp), desc);
float r = svm.Predict(desc);
}
}