fix floating point precision

apps/sft/include/sft/octave.hpp

@@ -147,7 +147,7 @@ public:
     virtual void setRejectThresholds(cv::Mat& thresholds);
     virtual void write( CvFileStorage* fs, string name) const;
 
-    virtual void write( cv::FileStorage &fs, const FeaturePool& pool, const Mat& thresholds = Mat()) const;
+    virtual void write( cv::FileStorage &fs, const FeaturePool& pool, const Mat& thresholds) const;
 
     int logScale;
 
@@ -160,7 +160,7 @@ protected:
 
     float predict( const Mat& _sample, const cv::Range range) const;
 private:
-    void traverse(const CvBoostTree* tree, cv::FileStorage& fs, int& nfeatures, int* used, const float* th = 0) const;
+    void traverse(const CvBoostTree* tree, cv::FileStorage& fs, int& nfeatures, int* used, const double* th) const;
     virtual void initial_weights(double (&p)[2]);
 
     cv::Rect boundingBox;

apps/sft/octave.cpp

@@ -116,7 +116,7 @@ void sft::Octave::setRejectThresholds(cv::Mat& thresholds)
     for (int si = 0; si < nsamples; ++si)
     {
         float decision = dptr[si] = predict(trainData.col(si), stab, false, false);
-        mptr[si] = cv::saturate_cast<uchar>((uint)(responses.ptr<float>(si)[0] == 1.f && decision == 1.f));
+        mptr[si] = cv::saturate_cast<uchar>((uint)( (responses.ptr<float>(si)[0] == 1.f) && (decision == 1.f)));
     }
 
     // std::cout << "WARNING: responses " << responses << std::endl;
@@ -144,8 +144,10 @@ void sft::Octave::setRejectThresholds(cv::Mat& thresholds)
         double mintrace = 0.;
         cv::minMaxLoc(traces.row(w), &mintrace);
         thptr[w] = mintrace;
-        // std::cout << "mintrace " << mintrace << std::endl << traces.colRange(0, npositives) << std::endl;
+        // std::cout << "mintrace " << mintrace << std::endl << traces.colRange(0, npositives).rowRange(w, w + 1) << std::endl  << std::endl << std::endl << std::endl;
     }
+
+    std::cout << "WARNING: thresholds "    << thresholds << std::endl;
 }
 
 namespace {
@@ -300,7 +302,7 @@ template <typename T> int sgn(T val) {
     return (T(0) < val) - (val < T(0));
 }
 
-void sft::Octave::traverse(const CvBoostTree* tree, cv::FileStorage& fs, int& nfeatures, int* used, const float* th) const
+void sft::Octave::traverse(const CvBoostTree* tree, cv::FileStorage& fs, int& nfeatures, int* used, const double* th) const
 {
     std::queue<const CvDTreeNode*> nodes;
     nodes.push( tree->get_root());
@@ -310,6 +312,7 @@ void sft::Octave::traverse(const CvBoostTree* tree, cv::FileStorage& fs, int& nf
     float* leafs = new float[(int)pow(2.f, get_params().max_depth)];
 
     fs << "{";
+    fs << "treeThreshold" << *th;
     fs << "internalNodes" << "[";
     while (!nodes.empty())
     {
@@ -349,14 +352,16 @@ void sft::Octave::traverse(const CvBoostTree* tree, cv::FileStorage& fs, int& nf
 
     fs << "leafValues" << "[";
     for (int ni = 0; ni < -leafValIdx; ni++)
-        fs << ( (!th) ? leafs[ni] : (sgn(leafs[ni]) * *th));
+        fs << leafs[ni];//( (!th) ? leafs[ni] : (sgn(leafs[ni]) * *th));
     fs << "]";
 
+
     fs << "}";
 }
 
 void sft::Octave::write( cv::FileStorage &fso, const FeaturePool& pool, const Mat& thresholds) const
 {
+    CV_Assert(!thresholds.empty());
     cv::Mat used( 1, weak->total * (pow(2, params.max_depth) - 1), CV_32SC1);
     int* usedPtr = used.ptr<int>(0);
     int nfeatures = 0;
@@ -373,19 +378,13 @@ void sft::Octave::write( cv::FileStorage &fso, const FeaturePool& pool, const Ma
             CvBoostTree* tree;
             CV_READ_SEQ_ELEM( tree, reader );
 
-            if (!thresholds.empty())
-                traverse(tree, fso, nfeatures, usedPtr, thresholds.ptr<float>(0)+ i);
-            else
-                traverse(tree, fso, nfeatures, usedPtr);
+            traverse(tree, fso, nfeatures, usedPtr, thresholds.ptr<double>(0) + i);
         }
-        //
-
     fso << "]";
     // features
 
     fso << "features" << "[";
     for (int i = 0; i < nfeatures; ++i)
-        // fso << usedPtr[i];
         pool.write(fso, usedPtr[i]);
     fso << "]"
         << "}";
@@ -409,7 +408,6 @@ bool sft::Octave::train(const Dataset& dataset, const FeaturePool& pool, int wea
     // 1. fill integrals and classes
     processPositives(dataset, pool);
     generateNegatives(dataset);
-    // exit(0);
 
     // 2. only sumple case (all features used)
     int nfeatures = pool.size();
@@ -550,7 +548,6 @@ void sft::FeaturePool::fill(int desired)
 
         if (std::find(pool.begin(), pool.end(),f) == pool.end())
         {
-            // std::cout << f << std::endl;
             pool.push_back(f);
         }
     }

apps/sft/sft.cpp

@@ -100,16 +100,6 @@ int main(int argc, char** argv)
         return 1;
     }
 
-    cv::FileStorage fsr(cfg.outXmlPath + ".raw.xml" , cv::FileStorage::WRITE);
-    if(!fsr.isOpened())
-    {
-        std::cout << "Training stopped. Output classifier Xml file " <<cfg.outXmlPath + ".raw.xml" << " can't be opened." << std::endl << std::flush;
-        return 1;
-    }
-
-    // ovector strong;
-    // strong.reserve(cfg.octaves.size());
-
     fso << cfg.cascadeName
         << "{"
         << "stageType"   << "BOOST"
@@ -120,16 +110,6 @@ int main(int argc, char** argv)
         << "shrinkage"   << cfg.shrinkage
         << "octaves"     << "[";
 
-    fsr << cfg.cascadeName
-        << "{"
-        << "stageType"   << "BOOST"
-        << "featureType" << "ICF"
-        << "octavesNum"  << (int)cfg.octaves.size()
-        << "width"       << cfg.modelWinSize.width
-        << "height"      << cfg.modelWinSize.height
-        << "shrinkage"   << cfg.shrinkage
-        << "octaves"     << "[";
-
     // 3. Train all octaves
     for (ivector::const_iterator it = cfg.octaves.begin(); it != cfg.octaves.end(); ++it)
     {
@@ -163,8 +143,6 @@ int main(int argc, char** argv)
             boost.setRejectThresholds(thresholds);
 
             boost.write(fso, pool, thresholds);
-            boost.write(fsr, pool);
-            // std::cout << "thresholds " << thresholds << std::endl;
 
             cv::FileStorage tfs(("thresholds." + cfg.resPath(it)).c_str(), cv::FileStorage::WRITE);
             tfs << "thresholds" << thresholds;
@@ -174,18 +152,6 @@ int main(int argc, char** argv)
     }
 
     fso << "]" << "}";
-    fsr << "]" << "}";
-
-//     // // 6. Set thresolds
-//     // cascade.prune();
-
-//     // // 7. Postprocess
-//     // cascade.normolize();
-
-//     // // 8. Write result xml
-//     // cv::FileStorage ofs(cfg.outXmlPath, cv::FileStorage::WRITE);
-//     // ofs << cfg.cascadeName << cascade;
-
     std::cout << "Training complete..." << std::endl;
     return 0;
 }
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