Arg

Scott Klum
1 parent af97fc18
Showing 6 changed files with 66 additions and 12 deletions
openbr/janus.cpp
openbr/plugins/landmarks.cpp
openbr/plugins/misc.cpp
openbr/plugins/nn.cpp
openbr/plugins/stream.cpp
openbr/plugins/svm.cpp
-#include "janus.h"
-#include "janus_io.h"
+#include "iarpa_janus.h"
+#include "iarpa_janus_io.h"
 #include "openbr_plugin.h"
  
 using namespace br;
@@ -12,6 +12,21 @@ size_t janus_max_template_size()
     return 33554432; // 32 MB
 }
  
+janus_error janus_flatten_gallery(const janus_gallery gallery, janus_flat_gallery flat_gallery, size_t *bytes)
+{
+
+}
+
+janus_error janus_compare(const janus_flat_gallery target, const size_t target_bytes, const janus_flat_gallery query, const size_t query_bytes, float *similarity_matrix, janus_template_id *target_ids, janus_template_id *query_ids)
+{
+
+}
+
+janus_error janus_flatten_template(const janus_template template_, janus_flat_template flat_template, size_t *bytes)
+{
+
+}
+
 janus_error janus_initialize(const char *sdk_path, const char *model_file)
 {
     int argc = 1;
@@ -465,7 +465,7 @@ class NormalizePointsTransform : public UntrainableTransform
  
     Q_PROPERTY(int index READ get_index WRITE set_index RESET reset_index STORED false)
     BR_PROPERTY(int, index, 0)
-;
+
     void project(const Template &src, Template &dst) const
     {
         dst = src;
@@ -474,7 +474,9 @@ class NormalizePointsTransform : public UntrainableTransform
         QPointF normPoint = points.at(index);
  
         QList<QPointF> normalizedPoints;
-        normalizedPoints.append(normPoint);
+        // We have nose and two eyes and I want a feature vector like:
+        // (nose.x-right_eye.x,nose.y-right_eye.y),(nose.x-left_eye.x,nose.y-left_eye.y),(0,0) because we're centering on the nose
+
         for (int i=0; i<points.size(); i++)
             if (i!=index)
                 normalizedPoints.append(normPoint-points[i]);
@@ -485,6 +487,32 @@ class NormalizePointsTransform : public UntrainableTransform
  
 BR_REGISTER(Transform, NormalizePointsTransform)
  
+class PointDisplacementTransform : public UntrainableTransform
+{
+    Q_OBJECT
+
+    void project(const Template &src, Template &dst) const
+    {
+        dst = src;
+
+        QList<QPointF> points = dst.file.points();
+        QList<QPointF> normalizedPoints;
+
+        for (int i=0; i<points.size(); i++)
+            for (int j=0; j<points.size(); j++)
+                if (j!=i) {
+                    QPointF normalizedPoint = points[i]-points[j];
+                    normalizedPoint.setX(pow(normalizedPoint.x(),2));
+                    normalizedPoint.setY(pow(normalizedPoint.y(),2));
+                    normalizedPoints.append(normalizedPoint);
+                }
+
+        dst.file.setPoints(normalizedPoints);
+    }
+};
+
+BR_REGISTER(Transform, PointDisplacementTransform)
+
 } // namespace br
  
 #include "landmarks.moc"
@@ -38,8 +38,6 @@ class OpenTransform : public UntrainableMetaTransform
  
     void project(const Template &src, Template &dst) const
     {
-        qDebug() << "here";
-
         dst.file = src.file;
         if (src.empty()) {
             if (Globals->verbose)
@@ -485,7 +483,6 @@ class SaveMatTransform : public UntrainableMetaTransform
  
     void project(const Template &src, Template &dst) const
     {
-                                    qDebug() << "saving mat";
         dst = src;
         dst.file.set(propName, QVariant::fromValue(dst.m()));
     }
@@ -67,10 +67,10 @@ class MLPTransform : public MetaTransform
     void init()
     {
         Mat layers = Mat(neuronsPerLayer.size(), 1, CV_32SC1);
-        for (int i=0; i<neuronsPerLayer.size(); i++) {
+        for (int i=0; i<neuronsPerLayer.size(); i++)
             layers.row(i) = Scalar(neuronsPerLayer.at(i));
-        }
-        mlp.create(layers,CvANN_MLP::SIGMOID_SYM, 1, 1);
+
+        mlp.create(layers,CvANN_MLP::SIGMOID_SYM, 1, 1);        
     }
  
     void train(const TemplateList &data)
@@ -1531,6 +1531,11 @@ public:
         processingStages.clear();
     }
  
+    DirectStreamTransform()
+    {
+        readStage = NULL;
+    }
+
 protected:
     QList<bool> stage_variance;
  
@@ -1594,6 +1599,14 @@ public:
  
     bool timeVarying() const { return true; }
  
+                                   // Stream acts as a shallow interface to DirectStream, so it's fine to remove ourselves here
+                                      Transform *simplify(bool &newTransform)
+                                      {
+                                          newTransform = false;
+
+                                          return basis->simplify(newTransform);
+                                      }
+
     void project(const Template &src, Template &dst) const
     {
         basis->project(src,dst);
@@ -74,7 +74,7 @@ static void trainSVM(SVM &amp;svm, Mat data, Mat lab, int kernel, int type, float C,
             svm.train_auto(data, lab, Mat(), Mat(), params, 5);
         } catch (...) {
             qWarning("Some classes do not contain sufficient examples or are not discriminative enough for accurate SVM classification.");
-            svm.train(data, lab);
+            svm.train(data, lab, Mat(), Mat(), params);
         }
     } else {
         params.C = C;
@@ -161,7 +161,8 @@ private:
             dst.m().at<float>(0, 0) = prediction;
             // positive values ==> first class
             // negative values ==> second class
-            prediction = prediction > 0 ? 0 : 1;
+            if (type != EPS_SVR && type != NU_SVR)
+                prediction = prediction > 0 ? 0 : 1;
         }
         if (type == EPS_SVR || type == NU_SVR) {
             dst.file.set(outputVariable, prediction);