generic-library/opencv
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commented debug out

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marina.kolpakova
2012-12-12 00:31:03 +04:00
parent 2e4b8d07cc
commit 69304611db
2 changed files with 21 additions and 17 deletions
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28
apps/sft/octave.cpp
View File

@@ -84,11 +84,11 @@ bool sft::Octave::train( const cv::Mat& _trainData, const cv::Mat& _responses, c
const cv::Mat& sampleIdx, const cv::Mat& varType, const cv::Mat& missingDataMask) const cv::Mat& sampleIdx, const cv::Mat& varType, const cv::Mat& missingDataMask)
{ {
std::cout << "WARNING: sampleIdx " << sampleIdx << std::endl; // std::cout << "WARNING: sampleIdx " << sampleIdx << std::endl;
std::cout << "WARNING: trainData " << _trainData << std::endl; // std::cout << "WARNING: trainData " << _trainData << std::endl;
std::cout << "WARNING: _responses " << _responses << std::endl; // std::cout << "WARNING: _responses " << _responses << std::endl;
std::cout << "WARNING: varIdx" << varIdx << std::endl; // std::cout << "WARNING: varIdx" << varIdx << std::endl;
std::cout << "WARNING: varType" << varType << std::endl; // std::cout << "WARNING: varType" << varType << std::endl;
bool update = false; bool update = false;
return cv::Boost::train(_trainData, CV_COL_SAMPLE, _responses, varIdx, sampleIdx, varType, missingDataMask, params, return cv::Boost::train(_trainData, CV_COL_SAMPLE, _responses, varIdx, sampleIdx, varType, missingDataMask, params,
@@ -117,9 +117,9 @@ void sft::Octave::setRejectThresholds(cv::Mat& thresholds)
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; // std::cout << "WARNING: responses " << responses << std::endl;
std::cout << "WARNING: desisions " << desisions << std::endl; // std::cout << "WARNING: desisions " << desisions << std::endl;
std::cout << "WARNING: ppmask " << ppmask << std::endl; // std::cout << "WARNING: ppmask " << ppmask << std::endl;
int weaks = weak->total; int weaks = weak->total;
thresholds.create(1, weaks, CV_64FC1); thresholds.create(1, weaks, CV_64FC1);
@@ -142,7 +142,7 @@ void sft::Octave::setRejectThresholds(cv::Mat& thresholds)
double mintrace = 0.; double mintrace = 0.;
cv::minMaxLoc(traces.row(w), &mintrace); cv::minMaxLoc(traces.row(w), &mintrace);
thptr[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) << std::endl;
} }
} }
@@ -223,7 +223,7 @@ void sft::Octave::processPositives(const Dataset& dataset, const FeaturePool& po
{ {
const string& curr = *it; const string& curr = *it;
dprintf("Process candidate positive image %s\n", curr.c_str()); // dprintf("Process candidate positive image %s\n", curr.c_str());
cv::Mat sample = cv::imread(curr); cv::Mat sample = cv::imread(curr);
@@ -262,8 +262,8 @@ void sft::Octave::generateNegatives(const Dataset& dataset)
{ {
int curr = iRand(idxEng); int curr = iRand(idxEng);
dprintf("View %d-th sample\n", curr); // dprintf("View %d-th sample\n", curr);
dprintf("Process %s\n", dataset.neg[curr].c_str()); // dprintf("Process %s\n", dataset.neg[curr].c_str());
Mat frame = cv::imread(dataset.neg[curr]); Mat frame = cv::imread(dataset.neg[curr]);
@@ -349,12 +349,12 @@ bool sft::Octave::train(const Dataset& dataset, const FeaturePool& pool, int wea
cv::Mat a(1, nfeatures, CV_32FC1); cv::Mat a(1, nfeatures, CV_32FC1);
cv::Mat votes(1, cvSliceLength( CV_WHOLE_SEQ, weak ), CV_32FC1, cv::Scalar::all(0)); 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; // std::cout << a.cols << " " << a.rows << " !!!!!!!!!!! " << data->var_all << std::endl;
for (int si = 0; si < nsamples; ++si) for (int si = 0; si < nsamples; ++si)
{ {
// trainData.col(si).copyTo(a.reshape(0,trainData.rows)); // trainData.col(si).copyTo(a.reshape(0,trainData.rows));
float desision = predict(trainData.col(si), votes, false, true); float desision = predict(trainData.col(si), votes, false, true);
std::cout << "desision " << desision << " class " << responses.at<float>(si, 0) << votes <<std::endl; // std::cout << "desision " << desision << " class " << responses.at<float>(si, 0) << votes <<std::endl;
} }
#endif #endif
return ok; return ok;

10
apps/sft/sft.cpp
View File

@@ -129,16 +129,20 @@ int main(int argc, char** argv)
if (boost.train(dataset, pool, cfg.weaks, cfg.treeDepth)) if (boost.train(dataset, pool, cfg.weaks, cfg.treeDepth))
{ {
std::cout << "Octave " << *it << " was successfully trained..." << std::endl;
CvFileStorage* fout = cvOpenFileStorage(cfg.resPath(it).c_str(), 0, CV_STORAGE_WRITE); CvFileStorage* fout = cvOpenFileStorage(cfg.resPath(it).c_str(), 0, CV_STORAGE_WRITE);
boost.write(fout, cfg.cascadeName); boost.write(fout, cfg.cascadeName);
// strong.push_back(octave);
cvReleaseFileStorage( &fout); cvReleaseFileStorage( &fout);
cv::Mat thresholds; cv::Mat thresholds;
boost.setRejectThresholds(thresholds); boost.setRejectThresholds(thresholds);
std::cout << "thresholds " << thresholds << std::endl; // std::cout << "thresholds " << thresholds << std::endl;
cv::FileStorage tfs(("thresholds." + cfg.resPath(it)).c_str(), cv::FileStorage::WRITE);
tfs << "thresholds" << thresholds;
std::cout << "Octave " << *it << " was successfully trained..." << std::endl;
} }
} }