From 19236b6e4316be27fe6da8f534d36c4c3ed8c85c Mon Sep 17 00:00:00 2001
From: "marina.kolpakova" <marina.kolpakova@itseez.com>
Date: Mon, 24 Dec 2012 09:33:25 +0400
Subject: [PATCH] remove dead code

---
 apps/sft/octave.cpp | 36 +-----------------------------------
 1 file changed, 1 insertion(+), 35 deletions(-)

diff --git a/apps/sft/octave.cpp b/apps/sft/octave.cpp
index 4e26ad9..14f3016 100644
--- a/apps/sft/octave.cpp
+++ b/apps/sft/octave.cpp
@@ -85,13 +85,6 @@ sft::Octave::~Octave(){}
 bool sft::Octave::train( const cv::Mat& _trainData, const cv::Mat& _responses, const cv::Mat& varIdx,
        const cv::Mat& sampleIdx, const cv::Mat& varType, const cv::Mat& missingDataMask)
 {
-
-    // std::cout << "WARNING: sampleIdx " << sampleIdx << std::endl;
-    // std::cout << "WARNING: trainData " << _trainData << std::endl;
-    // std::cout << "WARNING: _responses " << _responses << std::endl;
-    // std::cout << "WARNING: varIdx" << varIdx << std::endl;
-    // std::cout << "WARNING: varType" << varType << std::endl;
-
     bool update = false;
     return cv::Boost::train(_trainData, CV_COL_SAMPLE, _responses, varIdx, sampleIdx, varType, missingDataMask, params,
     update);
@@ -119,10 +112,6 @@ void sft::Octave::setRejectThresholds(cv::Mat& thresholds)
         mptr[si] = cv::saturate_cast<uchar>((uint)( (responses.ptr<float>(si)[0] == 1.f) && (decision == 1.f)));
     }
 
-    // std::cout << "WARNING: responses " << responses << std::endl;
-    // std::cout << "WARNING: desisions " << desisions << std::endl;
-    // std::cout << "WARNING: ppmask "    << ppmask    << std::endl;
-
     int weaks = weak->total;
     thresholds.create(1, weaks, CV_64FC1);
     double* thptr = thresholds.ptr<double>(0);
@@ -144,10 +133,7 @@ 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).rowRange(w, w + 1) << std::endl  << std::endl << std::endl << std::endl;
     }
-
-    std::cout << "WARNING: thresholds "    << thresholds << std::endl;
 }
 
 namespace {
@@ -211,8 +197,6 @@ public:
 };
 }
 
-// ToDo: parallelize it, fix curring
-// ToDo: sunch model size and shrinced model size usage/ Now model size mean already shrinked model
 void sft::Octave::processPositives(const Dataset& dataset, const FeaturePool& pool)
 {
     Preprocessor prepocessor(shrinkage);
@@ -227,8 +211,6 @@ void sft::Octave::processPositives(const Dataset& dataset, const FeaturePool& po
     {
         const string& curr = *it;
 
-        // dprintf("Process candidate positive image %s\n", curr.c_str());
-
         cv::Mat sample = cv::imread(curr);
 
         cv::Mat channels = integrals.row(total).reshape(0, h / shrinkage * 10 + 1);
@@ -266,9 +248,6 @@ void sft::Octave::generateNegatives(const Dataset& dataset)
     {
         int curr = iRand(idxEng);
 
-        // dprintf("View %d-th sample\n", curr);
-        // dprintf("Process %s\n", dataset.neg[curr].c_str());
-
         Mat frame = cv::imread(dataset.neg[curr]);
 
         int maxW = frame.cols - 2 * boundingBox.x - boundingBox.width;
@@ -352,7 +331,7 @@ void sft::Octave::traverse(const CvBoostTree* tree, cv::FileStorage& fs, int& nf
 
     fs << "leafValues" << "[";
     for (int ni = 0; ni < -leafValIdx; ni++)
-        fs << leafs[ni];//( (!th) ? leafs[ni] : (sgn(leafs[ni]) * *th));
+        fs << leafs[ni];
     fs << "]";
 
 
@@ -447,19 +426,6 @@ bool sft::Octave::train(const Dataset& dataset, const FeaturePool& pool, int wea
     bool ok = train(trainData, responses, varIdx, sampleIdx, varType, missingMask);
     if (!ok)
         std::cout << "ERROR: tree can not be trained " << std::endl;
-
-#if defined SELF_TEST
-    cv::Mat a(1, nfeatures, CV_32FC1);
-    cv::Mat votes(1, cvSliceLength( CV_WHOLE_SEQ, weak ), CV_32FC1, cv::Scalar::all(0));
-
-    // std::cout << a.cols << " " << a.rows << " !!!!!!!!!!! " << data->var_all << std::endl;
-    for (int si = 0; si < nsamples; ++si)
-    {
-        // trainData.col(si).copyTo(a.reshape(0,trainData.rows));
-        float desision = predict(trainData.col(si), votes, false, true);
-        // std::cout << "desision " << desision << " class " << responses.at<float>(si, 0) << votes <<std::endl;
-    }
-#endif
     return ok;
 
 }
-- 
2.7.4