Merge branch 'master' of https://github.com/biometrics/openbr

Josh Klontz
2 parents c08b5850 b0b6e5cd
Showing 2 changed files with 175 additions and 6 deletions
openbr/plugins/svm.cpp
openbr/plugins/tree.cpp
@@ -59,7 +59,7 @@ static void loadSVM(SVM &amp;svm, QDataStream &amp;stream)
     svm.load(qPrintable(tempFile.fileName()));
 }
-static void trainSVM(SVM &svm, Mat data, Mat lab, int kernel, int type, float C, float gamma)
+static void trainSVM(SVM &svm, Mat data, Mat lab, int kernel, int type, float C, float gamma, int folds, bool balanceFolds, int termCriteria)
 {
     if (data.type() != CV_32FC1)
         qFatal("Expected single channel floating point training data.");
@@ -69,9 +69,18 @@ static void trainSVM(SVM &amp;svm, Mat data, Mat lab, int kernel, int type, float C,
     params.svm_type = type;
     params.p = 0.1;
     params.nu = 0.5;
+    params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, termCriteria, FLT_EPSILON);
+
     if ((C == -1) || ((gamma == -1) && (kernel == CvSVM::RBF))) {
         try {
-            svm.train_auto(data, lab, Mat(), Mat(), params, 5);
+            svm.train_auto(data, lab, Mat(), Mat(), params, folds,
+                           CvSVM::get_default_grid(CvSVM::C),
+                           CvSVM::get_default_grid(CvSVM::GAMMA),
+                           CvSVM::get_default_grid(CvSVM::P),
+                           CvSVM::get_default_grid(CvSVM::NU),
+                           CvSVM::get_default_grid(CvSVM::COEF),
+                           CvSVM::get_default_grid(CvSVM::DEGREE),
+                           balanceFolds);
         } catch (...) {
             qWarning("Some classes do not contain sufficient examples or are not discriminative enough for accurate SVM classification.");
             svm.train(data, lab, Mat(), Mat(), params);
@@ -104,6 +113,9 @@ class SVMTransform : public Transform
     Q_PROPERTY(QString inputVariable READ get_inputVariable WRITE set_inputVariable RESET reset_inputVariable STORED false)
     Q_PROPERTY(QString outputVariable READ get_outputVariable WRITE set_outputVariable RESET reset_outputVariable STORED false)
     Q_PROPERTY(bool returnDFVal READ get_returnDFVal WRITE set_returnDFVal RESET reset_returnDFVal STORED false)
+    Q_PROPERTY(int termCriteria READ get_termCriteria WRITE set_termCriteria RESET reset_termCriteria STORED false)
+    Q_PROPERTY(int folds READ get_folds WRITE set_folds RESET reset_folds STORED false)
+    Q_PROPERTY(bool balanceFolds READ get_balanceFolds WRITE set_balanceFolds RESET reset_balanceFolds STORED false)
 public:
     enum Kernel { Linear = CvSVM::LINEAR,
@@ -125,7 +137,9 @@ private:
     BR_PROPERTY(QString, inputVariable, "Label")
     BR_PROPERTY(QString, outputVariable, "")
     BR_PROPERTY(bool, returnDFVal, false)
-
+    BR_PROPERTY(int, termCriteria, 1000)
+    BR_PROPERTY(int, folds, 5)
+    BR_PROPERTY(bool, balanceFolds, false)
     SVM svm;
     QHash<QString, int> labelMap;
@@ -146,7 +160,8 @@ private:
             QList<int> dataLabels = _data.indexProperty(inputVariable, labelMap, reverseLookup);
             lab = OpenCVUtils::toMat(dataLabels);
         }
-        trainSVM(svm, data, lab, kernel, type, C, gamma);
+
+        trainSVM(svm, data, lab, kernel, type, C, gamma, folds, balanceFolds, termCriteria);
     }
     void project(const Template &src, Template &dst) const
@@ -207,7 +222,9 @@ class SVMDistance : public Distance
     Q_PROPERTY(Kernel kernel READ get_kernel WRITE set_kernel RESET reset_kernel STORED false)
     Q_PROPERTY(Type type READ get_type WRITE set_type RESET reset_type STORED false)
     Q_PROPERTY(QString inputVariable READ get_inputVariable WRITE set_inputVariable RESET reset_inputVariable STORED false)
-
+    Q_PROPERTY(int termCriteria READ get_termCriteria WRITE set_termCriteria RESET reset_termCriteria STORED false)
+    Q_PROPERTY(int folds READ get_folds WRITE set_folds RESET reset_folds STORED false)
+    Q_PROPERTY(bool balanceFolds READ get_balanceFolds WRITE set_balanceFolds RESET reset_balanceFolds STORED false)
 public:
     enum Kernel { Linear = CvSVM::LINEAR,
@@ -225,6 +242,9 @@ private:
     BR_PROPERTY(Kernel, kernel, Linear)
     BR_PROPERTY(Type, type, EPS_SVR)
     BR_PROPERTY(QString, inputVariable, "Label")
+    BR_PROPERTY(int, termCriteria, 1000)
+    BR_PROPERTY(int, folds, 5)
+    BR_PROPERTY(bool, balanceFolds, false)
     SVM svm;
@@ -249,7 +269,7 @@ private:
         deltaData = deltaData.rowRange(0, index);
         deltaLab = deltaLab.rowRange(0, index);
-        trainSVM(svm, deltaData, deltaLab, kernel, type, -1, -1);
+        trainSVM(svm, deltaData, deltaLab, kernel, type, -1, -1, folds, balanceFolds, termCriteria);
     }
     float compare(const Mat &a, const Mat &b) const
+#include <opencv2/ml/ml.hpp>
+
+#include "openbr_internal.h"
+#include "openbr/core/opencvutils.h"
+#include <QString>
+#include <QTemporaryFile>
+
+using namespace std;
+using namespace cv;
+
+namespace br
+{
+
+static void storeForest(const CvRTrees &forest, QDataStream &stream)
+{
+    // Create local file
+    QTemporaryFile tempFile;
+    tempFile.open();
+    tempFile.close();
+
+    // Save MLP to local file
+    forest.save(qPrintable(tempFile.fileName()));
+
+    // Copy local file contents to stream
+    tempFile.open();
+    QByteArray data = tempFile.readAll();
+    tempFile.close();
+    stream << data;
+}
+
+static void loadForest(CvRTrees &forest, QDataStream &stream)
+{
+    // Copy local file contents from stream
+    QByteArray data;
+    stream >> data;
+
+    // Create local file
+    QTemporaryFile tempFile(QDir::tempPath()+"/forest");
+    tempFile.open();
+    tempFile.write(data);
+    tempFile.close();
+
+    // Load MLP from local file
+    forest.load(qPrintable(tempFile.fileName()));
+}
+
+/*!
+ * \ingroup transforms
+ * \brief Wraps OpenCV's random trees framework
+ * \author Scott Klum \cite sklum
+ * \brief http://docs.opencv.org/modules/ml/doc/random_trees.html
+ */
+class ForestTransform : public MetaTransform
+{
+    Q_OBJECT
+    Q_PROPERTY(bool classification READ get_classification WRITE set_classification RESET reset_classification STORED true)
+    Q_PROPERTY(float splitPercentage READ get_splitPercentage WRITE set_splitPercentage RESET reset_splitPercentage STORED true)
+    Q_PROPERTY(int maxDepth READ get_maxDepth WRITE set_maxDepth RESET reset_maxDepth STORED true)
+    Q_PROPERTY(int maxTrees READ get_maxTrees WRITE set_maxTrees RESET reset_maxTrees STORED true)
+    Q_PROPERTY(float forestAccuracy READ get_forestAccuracy WRITE set_forestAccuracy RESET reset_forestAccuracy STORED true)
+    Q_PROPERTY(bool returnConfidence READ get_returnConfidence WRITE set_returnConfidence RESET reset_returnConfidence STORED true)
+    Q_PROPERTY(bool overwriteMat READ get_overwriteMat WRITE set_overwriteMat RESET reset_overwriteMat STORED true)
+    Q_PROPERTY(QString inputVariable READ get_inputVariable WRITE set_inputVariable RESET reset_inputVariable STORED false)
+    Q_PROPERTY(QString outputVariable READ get_outputVariable WRITE set_outputVariable RESET reset_outputVariable STORED false)
+    BR_PROPERTY(bool, classification, true)
+    BR_PROPERTY(float, splitPercentage, .01)
+    BR_PROPERTY(int, maxDepth, std::numeric_limits<int>::max())
+    BR_PROPERTY(int, maxTrees, 10)
+    BR_PROPERTY(float, forestAccuracy, .1)
+    BR_PROPERTY(bool, returnConfidence, true)
+    BR_PROPERTY(bool, overwriteMat, true)
+    BR_PROPERTY(QString, inputVariable, "Label")
+    BR_PROPERTY(QString, outputVariable, "")
+
+    CvRTrees forest;
+
+    void train(const TemplateList &data)
+    {
+        Mat samples = OpenCVUtils::toMat(data.data());
+        Mat labels = OpenCVUtils::toMat(File::get<float>(data, inputVariable));
+
+        Mat types = Mat(samples.cols + 1, 1, CV_8U);
+        types.setTo(Scalar(CV_VAR_NUMERICAL));
+
+        if (classification) {
+            types.at<char>(samples.cols, 0) = CV_VAR_CATEGORICAL;
+        } else {
+            types.at<char>(samples.cols, 0) = CV_VAR_NUMERICAL;
+        }
+
+        int minSamplesForSplit = data.size()*splitPercentage;
+        forest.train( samples, CV_ROW_SAMPLE, labels, Mat(), Mat(), types, Mat(),
+                    CvRTParams(maxDepth,
+                               minSamplesForSplit,
+                               0,
+                               false,
+                               2,
+                               0, // priors
+                               false,
+                               0,
+                               maxTrees,
+                               forestAccuracy,
+                               CV_TERMCRIT_EPS));
+
+        qDebug() << "Number of trees:" << forest.get_tree_count();
+    }
+
+    void project(const Template &src, Template &dst) const
+    {
+        dst = src;
+
+        float response;
+        if (classification && returnConfidence) {
+            // Fuzzy class label
+            response = forest.predict_prob(src.m().reshape(1,1));
+        } else {
+            response = forest.predict(src.m().reshape(1,1));
+        }
+
+        if (overwriteMat) {
+            dst.m() = Mat(1, 1, CV_32F);
+            dst.m().at<float>(0, 0) = response;
+        } else {
+            dst.file.set(outputVariable, response);
+        }
+    }
+
+    void load(QDataStream &stream)
+    {
+        loadForest(forest,stream);
+    }
+
+    void store(QDataStream &stream) const
+    {
+        storeForest(forest,stream);
+    }
+
+    void init()
+    {
+        if (outputVariable.isEmpty())
+            outputVariable = inputVariable;
+    }
+};
+
+BR_REGISTER(Transform, ForestTransform)
+
+} // namespace br
+
+#include "tree.moc"