//For training images, each images will be clipped by 1/3 part.
vector<cv::Mat> WordModel::imgTranslator(string imgFolder, string docFilename, string wordFolder)
{
FileManager fm;
ImageManager im;
vector<string> v_wd = fm.FileReader(wordFolder, "\\*.wd");
cv::Mat words;
cv::flann::KMeansIndexParams params(64);
cv::flann::Index index; //= generateFlannIndex(words);
if(v_wd.size() == 0)
{
CubeMat cm = fm.CubeMatSampling(imgFolder, 5);
cv::Mat allFeatureSamples;
for (int i=0; i<cm.size(); i++)
{
vector<string> cube = cm[i];
for (int j=0; j<cube.size(); j++)
{
cv::Mat imat = cv::imread(cube[++j]);
imat = im.ImageClipper(imat);
cv::Mat descriptor = im.ImgSiftCollector(imat, false, 0.9);
allFeatureSamples.push_back(descriptor);
}
cout << "\rCube_" << i << "is done.";
}
fm.SaveMat2Disk<float>(allFeatureSamples, "model\\feature.sift");
words = generateWordDictionary(allFeatureSamples, 200000);
fm.SaveMat2Disk<float>(words, "model\\word.wd");
index.build(words, params);
index.save("model\\index.flann");
}
else
{
for (int i=0; i < v_wd.size(); i++)
{
string wd_file = v_wd[i];
cv::Mat _words;
fm.ReadMatFromDisk<float>(wd_file, &_words);
words.push_back(_words);
}
index.load(words, wordFolder+"\\index.flann");
}
//Transfer image set into GibbsLDA++ document
vector<cv::Mat> v_docs;
CubeMat cm = fm.CubeMatSampling(imgFolder, 5);
//CubeMat cm = fm.CubeMatBuilder(imgFolder);
cv::Mat allFeatureSamples;
for (int i=0; i < cm.size(); i++)
{
vector<string> cube = cm[i];
for (int j=0; j < cube.size(); j++)
{
cv::Mat imat = cv::imread(cube[++j]);
imat = im.ImageClipper(imat);
cv::Mat descriptor = im.ImgSiftCollector(imat, false, 0.9);
//cv::Mat centers = generateClusterMembershipForFeatures(index, descriptor, 1);
cv::Mat distance, indices;
index.knnSearch(descriptor, indices, distance, 1);
v_docs.push_back(indices);
}
cout << "\rCube_" << i << "is done.";
}
saveImgClusterToTrnFile<unsigned>("model\\trndocs.data", v_docs);
return v_docs;
}
/*!
\fn clusterImageBasedonTopicDist
Params:
@param1: string thetaFilename
return:
cv::Mat center
center topic distribution
cluster all image topic distribution into 20 groups by kmeans, the center topic distribution is returned
*/
cv::Mat WordModel::clusterImageBasedonTopicDist(string thetaFilename, string imgfolder)
{
cv::Mat thetaMat;
FileManager fm;
ImageManager im;
fm.ThetaDataReader(thetaFilename, &thetaMat);
//fm.ReadMatFromDisk<float>(thetaFilename, &thetaMat);
CV_Assert(thetaMat.data != 0);
cv::Mat labels, centers;
cv::kmeans(thetaMat, 32, labels, cv::TermCriteria(), 5, 0, centers);
//fm.SaveMat2Disk<float>(centers, "model\\flann\\center.theta");
vector<vector<int>> v_groups(32);
for (int i=0; i<thetaMat.rows; i++)
{
int idx = labels.at<int>(i, 0);
v_groups[idx].push_back(i);
}
cv::Mat gps_mat;
fm.ReadMatFromDisk<double>("model\\gps.dat", &gps_mat);
CubeMat cm = fm.CubeMatSampling(imgfolder, 5);
//organize sampled images into one vector, only double side views will be inserted
vector<string> imglist;
for (int i=0; i<cm.size(); i++)
{
imglist.push_back(cm[i][1]);
imglist.push_back(cm[i][3]);
}
#if 0
for (int i=0; i<v_groups.size(); i++)
{
vector<int> _v = v_groups[i];
cv::Mat sift_features, gps_features;
for (int j=0; j < _v.size(); j++)
{
int idx = _v[j];
double lat = gps_mat.at<double>(idx, 0);
double lng = gps_mat.at<double>(idx, 1);
cv::Mat gps_row(1,2, CV_64F);
gps_row.at<double>(0,0) = lat;
gps_row.at<double>(0,1) = lng;
string imgname = imglist[idx];
cv::Mat imat = cv::imread(imgname);
imat = im.ImageClipper(imat);
cv::Mat descriptor;
descriptor = im.ImgSiftCollector(imat, false, 0.9);
sift_features.push_back(descriptor);
for (int k=0; k<descriptor.rows; k++)
{
gps_features.push_back(gps_row);
}
}
char buffer[256];
sprintf(buffer, "model\\flann\\%d.sift", i);
fm.SaveMat2Disk<float>(sift_features, static_cast<string>(buffer));
sprintf(buffer, "model\\flann\\%d.gps", i);
fm.SaveMat2Disk<double>(gps_features, static_cast<string>(buffer));
}
#else
string trainingimgclusterfolder = "imgs\\pitts";
for (int i=0; i<v_groups.size(); i++)
{
vector<int> _v=v_groups[i];
for (int j=0; j<_v.size(); j++)
{
int idx = _v[j];
string imgname = imglist[idx];
char buffer[256];
sprintf(buffer, "%s\\%d\\", trainingimgclusterfolder.c_str(), i);
string dstfolder = fm.DirectoryBuilder(static_cast<string>(buffer));
vector<string> _v;
fm.file_name_splitter(imgname, &_v, "\\");
dstfolder = dstfolder + _v[_v.size() - 1];
fm.copyFile(imgname, dstfolder);
}
}
#endif
/*
cv::flann::Index index(centers, cv::flann::KMeansIndexParams());
cv::Mat thetaMat_test, indices, distance;
string thetafilename_test = "model\\test_docs.dat.theta";
fm.ThetaDataReader(thetafilename_test, &thetaMat_test);
index.knnSearch(thetaMat_test, indices, distance, 1);
for (int i=0; i < indices.rows; i++)
{
cout << indices.row(i) << endl;
}
getchar();
*/
return centers;
}
