/// <summary> /// Отделяет изображение от фона /// </summary> /// <param name="source">Исходное изображение</param> /// <param name="destinatation">Результат разделения</param> private void separateBackground(IplImage source, IplImage destinatation) { // Преобразуем иходное изображение в HSV source.CvtColor(hsvImg, ColorConversion.RgbToHsv); // Разбиваем изображение на отельные каналы hsvImg.CvtPixToPlane(hImg, sImg, vImg, null); // Если диапазон Hue состоит из 2х частей if (BackgroundRange.HMin > BackgroundRange.HMax) { hImg.InRangeS(CvScalar.RealScalar(BackgroundRange.HMin), CvScalar.RealScalar(HsvRange.MAX_H), tmpImg); hImg.InRangeS(CvScalar.RealScalar(HsvRange.MIN_H), CvScalar.RealScalar(BackgroundRange.HMax), hImg); Cv.Or(tmpImg, hImg, hImg); } // Если диапазон Hue состоит из 1 части else { hImg.InRangeS(CvScalar.RealScalar(BackgroundRange.HMin), CvScalar.RealScalar(BackgroundRange.HMax), hImg); } // Ограничиваем значение остальных цветовых компонент sImg.InRangeS(CvScalar.RealScalar(BackgroundRange.SMin), CvScalar.RealScalar(BackgroundRange.SMax), sImg); vImg.InRangeS(CvScalar.RealScalar(BackgroundRange.VMin), CvScalar.RealScalar(BackgroundRange.VMax), vImg); // Формируем окончательный результат Cv.And(hImg, sImg, destinatation); Cv.And(destinatation, vImg, destinatation); Cv.Not(destinatation, destinatation); }
/// <summary> /// SVM /// </summary> /// <param name="dataFilename"></param> /// <param name="filenameToSave"></param> /// <param name="filenameToLoad"></param> private void BuildSvmClassifier(string dataFilename, string filenameToSave, string filenameToLoad) { //C_SVCのパラメータ const float SvmC = 1000; //RBFカーネルのパラメータ const float SvmGamma = 0.1f; CvMat data = null; CvMat responses = null; CvMat sampleIdx = null; int nsamplesAll = 0, ntrainSamples = 0; double trainHr = 0, testHr = 0; CvSVM svm = new CvSVM(); CvTermCriteria criteria = new CvTermCriteria(100, 0.001); try { ReadNumClassData(dataFilename, 16, out data, out responses); } catch { Console.WriteLine("Could not read the database {0}", dataFilename); return; } Console.WriteLine("The database {0} is loaded.", dataFilename); nsamplesAll = data.Rows; ntrainSamples = (int)(nsamplesAll * 0.2); // Create or load Random Trees classifier if (filenameToLoad != null) { // load classifier from the specified file svm.Load(filenameToLoad); ntrainSamples = 0; if (svm.GetSupportVectorCount() == 0) { Console.WriteLine("Could not read the classifier {0}", filenameToLoad); return; } Console.WriteLine("The classifier {0} is loaded.", filenameToLoad); } else { // create classifier by using <data> and <responses> Console.Write("Training the classifier ..."); // 2. create sample_idx sampleIdx = new CvMat(1, nsamplesAll, MatrixType.U8C1); { CvMat mat; Cv.GetCols(sampleIdx, out mat, 0, ntrainSamples); mat.Set(CvScalar.RealScalar(1)); Cv.GetCols(sampleIdx, out mat, ntrainSamples, nsamplesAll); mat.SetZero(); } // 3. train classifier // 方法、カーネルにより使わないパラメータは0で良く、 // 重みについてもNULLで良い svm.Train(data, responses, null, sampleIdx, new CvSVMParams(CvSVM.C_SVC, CvSVM.RBF, 0, SvmGamma, 0, SvmC, 0, 0, null, criteria)); Console.WriteLine(); } // compute prediction error on train and test data for (int i = 0; i < nsamplesAll; i++) { double r; CvMat sample; Cv.GetRow(data, out sample, i); r = svm.Predict(sample); // compare results Console.WriteLine( "predict: {0}, responses: {1}, {2}", (char)r, (char)responses.DataArraySingle[i], Math.Abs((double)r - responses.DataArraySingle[i]) <= float.Epsilon ? "Good!" : "Bad!" ); r = Math.Abs((double)r - responses.DataArraySingle[i]) <= float.Epsilon ? 1 : 0; if (i < ntrainSamples) { trainHr += r; } else { testHr += r; } } testHr /= (double)(nsamplesAll - ntrainSamples); trainHr /= (double)ntrainSamples; Console.WriteLine("Gamma={0:F5}, C={1:F5}", SvmGamma, SvmC); if (filenameToLoad != null) { Console.WriteLine("Recognition rate: test = {0:F1}%", testHr * 100.0); } else { Console.WriteLine("Recognition rate: train = {0:F1}%, test = {1:F1}%", trainHr * 100.0, testHr * 100.0); } Console.WriteLine("Number of Support Vector: {0}", svm.GetSupportVectorCount()); // Save SVM classifier to file if needed if (filenameToSave != null) { svm.Save(filenameToSave); } Console.Read(); if (sampleIdx != null) { sampleIdx.Dispose(); } data.Dispose(); responses.Dispose(); svm.Dispose(); }
/// <summary> /// RTrees /// </summary> /// <param name="dataFilename"></param> /// <param name="filenameToSave"></param> /// <param name="filenameToLoad"></param> private void BuildRtreesClassifier(string dataFilename, string filenameToSave, string filenameToLoad) { CvMat data = null; CvMat responses = null; CvMat varType = null; CvMat sampleIdx = null; int nsamplesAll = 0, ntrainSamples = 0; double trainHr = 0, testHr = 0; CvRTrees forest = new CvRTrees(); try { ReadNumClassData(dataFilename, 16, out data, out responses); } catch { Console.WriteLine("Could not read the database {0}", dataFilename); return; } Console.WriteLine("The database {0} is loaded.", dataFilename); nsamplesAll = data.Rows; ntrainSamples = (int)(nsamplesAll * 0.8); // Create or load Random Trees classifier if (filenameToLoad != null) { // load classifier from the specified file forest.Load(filenameToLoad); ntrainSamples = 0; if (forest.GetTreeCount() == 0) { Console.WriteLine("Could not read the classifier {0}", filenameToLoad); return; } Console.WriteLine("The classifier {0} is loaded.", filenameToLoad); } else { // create classifier by using <data> and <responses> Console.Write("Training the classifier ..."); // 1. create type mask varType = new CvMat(data.Cols + 1, 1, MatrixType.U8C1); varType.Set(CvScalar.ScalarAll(CvStatModel.CV_VAR_ORDERED)); varType.SetReal1D(data.Cols, CvStatModel.CV_VAR_CATEGORICAL); // 2. create sample_idx sampleIdx = new CvMat(1, nsamplesAll, MatrixType.U8C1); { CvMat mat; Cv.GetCols(sampleIdx, out mat, 0, ntrainSamples); mat.Set(CvScalar.RealScalar(1)); Cv.GetCols(sampleIdx, out mat, ntrainSamples, nsamplesAll); mat.SetZero(); } // 3. train classifier forest.Train( data, DTreeDataLayout.RowSample, responses, null, sampleIdx, varType, null, new CvRTParams(10, 10, 0, false, 15, null, true, 4, new CvTermCriteria(100, 0.01f)) ); Console.WriteLine(); } // compute prediction error on train and test data for (int i = 0; i < nsamplesAll; i++) { double r; CvMat sample; Cv.GetRow(data, out sample, i); r = forest.Predict(sample); r = Math.Abs((double)r - responses.DataArraySingle[i]) <= float.Epsilon ? 1 : 0; if (i < ntrainSamples) { trainHr += r; } else { testHr += r; } } testHr /= (double)(nsamplesAll - ntrainSamples); trainHr /= (double)ntrainSamples; Console.WriteLine("Recognition rate: train = {0:F1}%, test = {1:F1}%", trainHr * 100.0, testHr * 100.0); Console.WriteLine("Number of trees: {0}", forest.GetTreeCount()); // Print variable importance Mat varImportance0 = forest.GetVarImportance(); CvMat varImportance = varImportance0.ToCvMat(); if (varImportance != null) { double rtImpSum = Cv.Sum(varImportance).Val0; Console.WriteLine("var#\timportance (in %):"); for (int i = 0; i < varImportance.Cols; i++) { Console.WriteLine("{0}\t{1:F1}", i, 100.0f * varImportance.DataArraySingle[i] / rtImpSum); } } // Print some proximitites Console.WriteLine("Proximities between some samples corresponding to the letter 'T':"); { CvMat sample1, sample2; int[,] pairs = new int[, ] { { 0, 103 }, { 0, 106 }, { 106, 103 }, { -1, -1 } }; for (int i = 0; pairs[i, 0] >= 0; i++) { Cv.GetRow(data, out sample1, pairs[i, 0]); Cv.GetRow(data, out sample2, pairs[i, 1]); Console.WriteLine("proximity({0},{1}) = {2:F1}%", pairs[i, 0], pairs[i, 1], forest.GetProximity(sample1, sample2) * 100.0); } } // Save Random Trees classifier to file if needed if (filenameToSave != null) { forest.Save(filenameToSave); } Console.Read(); if (sampleIdx != null) { sampleIdx.Dispose(); } if (varType != null) { varType.Dispose(); } data.Dispose(); responses.Dispose(); forest.Dispose(); }