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enable training test. refactor globbing

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pull/643/head
marina.kolpakova 12 years ago
parent 9f3ce0dd97
commit cf66942505
2 changed files with 12 additions and 185 deletions
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  1. 96
      apps/sft/dataset.cpp
  2. 101
      modules/softcascade/test/test_training.cpp

96
apps/sft/dataset.cpp
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@ -46,116 +46,32 @@
#include <iostream>
#include <queue>
inline std::string itoa(long i) { return cv::format("%ld", i); }
#if !defined (_WIN32) && ! defined(__MINGW32__)
# include <glob.h>
namespace {
using namespace sft;
void glob(const string& path, svector& ret)
{
glob_t glob_result;
glob(path.c_str(), GLOB_TILDE, 0, &glob_result);
ret.clear();
ret.reserve(glob_result.gl_pathc);
for(unsigned int i = 0; i < glob_result.gl_pathc; ++i)
{
ret.push_back(std::string(glob_result.gl_pathv[i]));
dprintf("%s\n", ret[i].c_str());
}
globfree(&glob_result);
}
}
#else
# include <windows.h>
namespace {
using namespace sft;
void glob(const string& refRoot, const string& refExt, svector &refvecFiles)
{
std::string strFilePath; // File path
std::string strExtension; // Extension
std::string strPattern = refRoot + "\\*.*";
WIN32_FIND_DATA FileInformation; // File information
HANDLE hFile = ::FindFirstFile(strPattern.c_str(), &FileInformation);
if(hFile == INVALID_HANDLE_VALUE)
CV_Error(CV_StsBadArg, "Your dataset search path is incorrect");
do
{
if(FileInformation.cFileName[0] != '.')
{
strFilePath.erase();
strFilePath = refRoot + "\\" + FileInformation.cFileName;
if( !(FileInformation.dwFileAttributes & FILE_ATTRIBUTE_DIRECTORY) )
{
// Check extension
strExtension = FileInformation.cFileName;
strExtension = strExtension.substr(strExtension.rfind(".") + 1);
if(strExtension == refExt)
// Save filename
refvecFiles.push_back(strFilePath);
}
}
}
while(::FindNextFile(hFile, &FileInformation) == TRUE);
// Close handle
::FindClose(hFile);
DWORD dwError = ::GetLastError();
if(dwError != ERROR_NO_MORE_FILES)
CV_Error(CV_StsBadArg, "Your dataset search path is incorrect");
}
}
#endif
// in the default case data folders should be aligned as following:
// 1. positives: <train or test path>/octave_<octave number>/pos/*.png
// 2. negatives: <train or test path>/octave_<octave number>/neg/*.png
ScaledDataset::ScaledDataset(const string& path, const int oct)
sft::ScaledDataset::ScaledDataset(const string& path, const int oct)
{
dprintf("%s\n", "get dataset file names...");
dprintf("%s\n", "Positives globing...");
#if !defined (_WIN32) && ! defined(__MINGW32__)
glob(path + "/pos/octave_" + itoa(oct) + "/*.png", pos);
#else
glob(path + "/pos/octave_" + itoa(oct), "png", pos);
#endif
cv::glob(path + "/pos/octave_" + cv::format("%d", oct) + "/*.png", pos);
dprintf("%s\n", "Negatives globing...");
#if !defined (_WIN32) && ! defined(__MINGW32__)
glob(path + "/neg/octave_" + itoa(oct) + "/*.png", neg);
#else
glob(path + "/neg/octave_" + itoa(oct), "png", neg);
#endif
cv::glob(path + "/neg/octave_" + cv::format("%d", oct) + "/*.png", neg);
// Check: files not empty
CV_Assert(pos.size() != size_t(0));
CV_Assert(neg.size() != size_t(0));
}
cv::Mat ScaledDataset::get(SampleType type, int idx) const
cv::Mat sft::ScaledDataset::get(SampleType type, int idx) const
{
const std::string& src = (type == POSITIVE)? pos[idx]: neg[idx];
return cv::imread(src);
}
int ScaledDataset::available(SampleType type) const
int sft::ScaledDataset::available(SampleType type) const
{
return (int)((type == POSITIVE)? pos.size():neg.size());
}
ScaledDataset::~ScaledDataset(){}
sft::ScaledDataset::~ScaledDataset(){}

101
modules/softcascade/test/test_training.cpp
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@ -46,13 +46,6 @@
#include <fstream>
#include <vector>
#include "test_precomp.hpp"
#if !defined (_WIN32) && ! defined(__MINGW32__)
# include <glob.h>
#else
# include <windows.h>
#endif
using namespace std;
namespace {
@ -74,92 +67,10 @@ private:
svector neg;
};
string itoa(long i)
{
char s[65];
sprintf(s, "%ld", i);
return std::string(s);
}
#if !defined (_WIN32) && ! defined(__MINGW32__)
void glob(const string& path, svector& ret)
{
glob_t glob_result;
glob(path.c_str(), GLOB_TILDE, 0, &glob_result);
ret.clear();
ret.reserve(glob_result.gl_pathc);
for(unsigned int i = 0; i < glob_result.gl_pathc; ++i)
{
ret.push_back(std::string(glob_result.gl_pathv[i]));
}
globfree(&glob_result);
}
#else
void glob(const string& refRoot, const string& refExt, svector &refvecFiles)
{
std::string strFilePath; // File path
std::string strExtension; // Extension
std::string strPattern = refRoot + "\\*.*";
WIN32_FIND_DATA FileInformation; // File information
HANDLE hFile = ::FindFirstFile(strPattern.c_str(), &FileInformation);
if(hFile == INVALID_HANDLE_VALUE)
CV_Error(CV_StsBadArg, "Your dataset search path is incorrect");
do
{
if(FileInformation.cFileName[0] != '.')
{
strFilePath.erase();
strFilePath = refRoot + "\\" + FileInformation.cFileName;
if( !(FileInformation.dwFileAttributes & FILE_ATTRIBUTE_DIRECTORY) )
{
// Check extension
strExtension = FileInformation.cFileName;
strExtension = strExtension.substr(strExtension.rfind(".") + 1);
if(strExtension == refExt)
// Save filename
refvecFiles.push_back(strFilePath);
}
}
}
while(::FindNextFile(hFile, &FileInformation) == TRUE);
// Close handle
::FindClose(hFile);
DWORD dwError = ::GetLastError();
if(dwError != ERROR_NO_MORE_FILES)
CV_Error(CV_StsBadArg, "Your dataset search path is incorrect");
}
#endif
ScaledDataset::ScaledDataset(const string& path, const int oct)
{
#if !defined (_WIN32) && ! defined(__MINGW32__)
glob(path + "/pos/octave_" + itoa(oct) + "/*.png", pos);
#else
glob(path + "/pos/octave_" + itoa(oct), "png", pos);
#endif
#if !defined (_WIN32) && ! defined(__MINGW32__)
glob(path + "/neg/octave_" + itoa(oct) + "/*.png", neg);
#else
glob(path + "/neg/octave_" + itoa(oct), "png", neg);
#endif
cv::glob(path + cv::format("/octave_%d/*.png", oct), pos);
cv::glob(path + "/*.png", neg);
// Check: files not empty
CV_Assert(pos.size() != size_t(0));
@ -181,7 +92,7 @@ ScaledDataset::~ScaledDataset(){}
}
TEST(DISABLED_SoftCascade, training)
TEST(SoftCascade, training)
{
// // 2. check and open output file
string outXmlPath = cv::tempfile(".xml");
@ -214,8 +125,8 @@ TEST(DISABLED_SoftCascade, training)
cv::Ptr<FeaturePool> pool = FeaturePool::create(model, nfeatures, 10);
nfeatures = pool->size();
int npositives = 20;
int nnegatives = 40;
int npositives = 10;
int nnegatives = 20;
cv::Rect boundingBox = cv::Rect( cvRound(20 * octave), cvRound(20 * octave),
cvRound(64 * octave), cvRound(128 * octave));
@ -223,7 +134,7 @@ TEST(DISABLED_SoftCascade, training)
cv::Ptr<ChannelFeatureBuilder> builder = ChannelFeatureBuilder::create("HOG6MagLuv");
cv::Ptr<Octave> boost = Octave::create(boundingBox, npositives, nnegatives, *it, shrinkage, builder);
std::string path = cvtest::TS::ptr()->get_data_path() + "softcascade/sample_training_set";
std::string path = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/sample_training_set";
ScaledDataset dataset(path, *it);
if (boost->train(&dataset, pool, 3, 2))

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