void XmlLoader::readGraphics(QDomElement const &graphic)
{
QDomNodeList graphicAttributes = graphic.childNodes();
int sizePictureX = 0;
int sizePictureY = 0;
for (unsigned i = 0; i < graphicAttributes.length(); ++i) {
QDomElement type = graphicAttributes.at(i).toElement();
if (type.tagName() == "picture") {
sizePictureX = (type.attribute("sizex", "")).toInt();
sizePictureY = (type.attribute("sizey", "")).toInt();
if (mReadFile) {
if (mStrX + distanceFigure + sizePictureX >= sizeEmrtyRectX) {
mStrY = mFloorY;
mStrX = 0;
}
mDrift = QPoint(mStrX + distanceFigure, mStrY + distanceFigure);
} else
mDrift = QPoint(mScene->centerEmpty().x() - sizePictureX / 2, mScene->centerEmpty().y() - sizePictureY / 2);
readPicture(type);
}
else if (type.tagName() == "labels")
readLabels(type);
else if (type.tagName() == "ports")
readPorts(type);
else
qDebug() << "Incorrect graphics tag";
}
if (mReadFile) {
mFloorY = qMax(mFloorY, mDrift.y() + sizePictureY);
mStrX = mDrift.x() + sizePictureX;
}
}
int GFFFile::init(int n)
{
if(!reading()) return errcode = 6;
if(n == -1) n = initstages;
if((n > 0) && !inited[0] && readHeader()) return errcode;
if((n > 1) && !inited[1] && readLabels()) return errcode;
if((n > 2) && !inited[2] && getStruct(0, topstruct)) return errcode;
return errcode = 0;
}
static void processGenomic(struct sqlConnection *conn, struct customPp *cpp,
int colCount, char *formatType, boolean firstLineLabels,
struct labeledFile *fileList, boolean report)
/* Process three column file into chromGraph. Abort if
* there's a problem. */
{
char **row;
struct chromGraph *cg;
struct hash *chromHash = chromInfoHash(conn);
struct labeledFile *fileEl;
struct chromInfo *ci;
int i;
int rowCount = 0;
Chopper chopper = getChopper(formatType);
char *line;
AllocArray(row, colCount);
if (firstLineLabels)
readLabels(cpp, 2, chopper, row, colCount, fileList);
while ((line = customFactoryNextRealTilTrack(cpp)) != NULL)
{
chopper(line, row, colCount);
char *chrom = cloneString(row[0]);
int start = cppNeedNum(cpp, row, 1);
ci = hashFindVal(chromHash, chrom);
if (ci == NULL)
cppAbort(cpp, "Chromosome %s not found in this assembly (%s).",
chrom, sqlGetDatabase(conn));
if (start >= ci->size)
{
cppAbort(cpp, "Chromosome %s is %d bases long, but got coordinate %d.",
chrom, ci->size, start);
}
else if (start < 0)
{
cppAbort(cpp, "Negative base position %d on chromosome %s.",
start, chrom);
}
for (i=2, fileEl = fileList; i<colCount; ++i, fileEl = fileEl->next)
{
char *val = row[i];
if (val[0] != 0)
{
AllocVar(cg);
cg->chrom = chrom;
cg->chromStart = start;
cg->val = cppNeedDouble(cpp, row, i);
slAddHead(&fileEl->cgList, cg);
}
}
++rowCount;
}
if (report)
printf("Read in %d markers and values in <i>chromosome base</i> format.<BR>\n",
rowCount);
}
bool RTABMapDBAdapter::readData(const std::vector<std::string> &dbPaths,
Words &words, Labels &labels) {
// Read data from databases
std::map<int, std::map<int, std::unique_ptr<rtabmap::Signature>>>
allSignatures;
std::list<std::unique_ptr<Label>> allLabels;
int dbId = 0;
for (const auto &dbPath : dbPaths) {
auto dbSignatures = readSignatures(dbPath);
allSignatures.emplace(dbId, std::move(dbSignatures));
auto dbLabels = readLabels(dbPath, dbId, allSignatures);
std::move(dbLabels.begin(), dbLabels.end(), std::back_inserter(allLabels));
dbId++;
}
std::cout << "Building Index for Words" << std::endl;
std::list<std::unique_ptr<Word>> allWords = createWords(allSignatures);
std::cout << "Total Number of words: " << allWords.size() << std::endl;
long count = 0;
for (const auto &word : allWords) {
for (const auto &desc : word->getDescriptorsByDb()) {
count += desc.second.rows;
}
}
std::cout << "Total Number of descriptors: " << count << std::endl;
allWords = clusterPointsInWords(allWords);
count = 0;
for (const auto &word : allWords) {
for (const auto &desc : word->getDescriptorsByDb()) {
count += desc.second.rows;
}
}
std::cout << "Total Number of points: " << count << std::endl;
words.putWords(std::move(allWords));
std::cout << "Building Index for Labels" << std::endl;
labels.putLabels(std::move(allLabels));
return true;
}
int main(int argc, char *argv[])
{
if (argc != 4)
{
cout << "Usage: " << endl;
cout << "ACCV2009.exe Folder imfn1 imfn2" << endl;
return -1;
}
string folder = argv[1];
string imfn1 = argv[2];
string imfn2 = argv[3];
string outfolder = "../output";
_mkdir(outfolder.c_str());
Mat im1 = imread(folder + imfn1 + ".png", 1);
Mat im2 = imread(folder + imfn2 + ".png", 1);
int width = im1.cols;
int height = im1.rows;
//颜色空间转换
Mat im1_H, im1_S, im1_I;
Mat im2_H, im2_S, im2_I;
//CvtColorBGR2HSI(im1, im1_H, im1_S, im1_I);
//CvtColorBGR2HSI(im2, im2_H, im2_S, im2_I);
cv_CvtColorBGR2HSI(im1, im1_H, im1_S, im1_I);
cv_CvtColorBGR2HSI(im2, im2_H, im2_S, im2_I);
int sigmaS = 5;
float sigmaR = 10;
int minR = 800;
vector<int> labels1(0), labels2(0);
//#define RQ_DEBUG
#ifdef RQ_DEBUG
int regionum1, regionum2;
DoMeanShift(im1, sigmaS, sigmaR, minR, labels1, regionum1);
DoMeanShift(im2, sigmaS, sigmaR, minR, labels2, regionum2);
cout << endl;
saveLabels(labels1, width, height, outfolder+"/labels_" + imfn1 + ".txt");
saveLabels(labels2, width, height, outfolder+"/labels_" + imfn2 + ".txt");
#else
int regionum1, regionum2;
readLabels(outfolder+"/labels_" + imfn1 + ".txt", width, height, labels1, regionum1);
readLabels(outfolder+"/labels_" + imfn2 + ".txt", width, height, labels2, regionum2);
#endif
string siftmatchfn = folder + "matches_sift.txt";
vector<Point2f> features1(0), features2(0);
ReadSiftMatches(siftmatchfn, features1, features2);
vector<vector<Point2f>> matches1(0), matches2(0);
vector<vector<Point2f>> pixelTable2(0);
ComputeRegionMatches(labels2, regionum2, width, features1, features2, matches1, matches2);
cout << "all regions have matches." << endl << endl;
//显示每个区域的matches : 显示一次就好
vector<float> DeltaH(0), DeltaS(0), DeltaI(0);
RegionDeltaColor(im1_H, im2_H, matches1, matches2, DeltaH);
RegionDeltaColor(im1_S, im2_S, matches1, matches2, DeltaS);
RegionDeltaColor(im1_I, im2_I, matches1, matches2, DeltaI);
Mat new_im2_H, new_im2_S, new_im2_I;
CorrectColor(im2_H, labels2, DeltaH, new_im2_H);
CorrectColor(im2_S, labels2, DeltaS, new_im2_S);
CorrectColor(im2_I, labels2, DeltaI, new_im2_I);
Mat new_im2;
//CvtColorHSI2BGR(new_im2_H, new_im2_S, new_im2_I, new_im2);
cv_CVtColorHSI2BGR(new_im2_H, new_im2_S, new_im2_I, new_im2);
cout << "done." << endl;
imshow("new im2", new_im2);
waitKey(0);
destroyAllWindows();
string savefn = outfolder + "/accv2009_" + imfn2 + ".png";
cout << "save " << savefn << endl;
imwrite(savefn, new_im2);
}
void processDb(struct sqlConnection *conn,
struct customPp *cpp, int colCount, char *formatType,
boolean firstLineLabels, struct labeledFile *fileList,
char *table, char *query, char *aliasTable, char *aliasQuery,
boolean report, boolean isSnpTable)
/* Process two column input file into chromGraph. Treat first
* column as a name to look up in bed-format table, which should
* not be split. Return TRUE on success. */
{
struct hash *hash = NULL;
if (!isSnpTable)
hash = tableToChromPosHash(conn, table, query);
struct hash *aliasHash = NULL;
char **row;
int match = 0, total = 0;
struct chromGraph *cg;
struct chromPos *pos;
if (!isSnpTable && report)
printf("Loaded %d elements from %s table for mapping.<BR>\n",
hash->elCount, table);
if (aliasTable != NULL)
{
aliasHash = tableToAliasHash(conn, aliasTable, aliasQuery);
if (report)
printf("Loaded %d aliases from %s table as well.<BR>\n",
aliasHash->elCount, aliasTable);
}
fflush(stdout); // trying to keep browser from timing out
AllocArray(row, colCount);
Chopper chopper = getChopper(formatType);
char *line;
AllocArray(row, colCount);
if (firstLineLabels)
readLabels(cpp, 1, chopper, row, colCount, fileList);
while ((line = customFactoryNextRealTilTrack(cpp)) != NULL)
{
chopper(line, row, colCount);
char *name = row[0];
char *marker = cloneString(name);
touppers(name);
++total;
if (isSnpTable)
pos = snpIndexLookup(conn, table, query, name);
else
{
pos = hashFindVal(hash, name);
if (pos == NULL && aliasHash != NULL)
{
name = hashFindVal(aliasHash, name);
if (name != NULL)
pos = hashFindVal(hash, name);
}
}
if (pos != NULL)
{
int i;
struct labeledFile *fileEl;
++match;
for (i=1, fileEl=fileList; i < colCount; ++i, fileEl = fileEl->next)
{
char *val = row[i];
if (val[0] != 0)
{
AllocVar(cg);
cg->chrom = pos->chrom;
cg->chromStart = pos->pos;
cg->val = cppNeedDouble(cpp, row, i);
cg->marker = cloneString(marker);
slAddHead(&fileEl->cgList, cg);
}
}
}
freeMem(marker);
}
if (report)
printf("Mapped %d of %d (%3.1f%%) of markers<BR>\n", match, total,
100.0*match/total);
}
void LDAModel::train()
{
if (descriptor_matrix_.cols() == 0)
{
throw Exception::InconsistentUsage(__FILE__, __LINE__, "Data must be read into the model before training!");
}
readLabels();
// map values of Y to their index
map<int, unsigned int> label_to_pos;
for (unsigned int i = 0; i < labels_.size(); i++)
{
label_to_pos.insert(make_pair(labels_[i], i));
}
// calculate sigma_ = covariance matrix of descriptors
sigma_.resize(descriptor_matrix_.cols(), descriptor_matrix_.cols());
for (int i = 0; i < descriptor_matrix_.cols(); i++)
{
double mi = Statistics::getMean(descriptor_matrix_, i);
for (int j = 0; j < descriptor_matrix_.cols(); j++)
{
sigma_(i, j) = Statistics::getCovariance(descriptor_matrix_, i, j, mi);
}
}
Eigen::MatrixXd I(sigma_.cols(), sigma_.cols()); I.setIdentity();
I *= lambda_;
sigma_ += I;
sigma_ = sigma_.inverse();
mean_vectors_.resize(Y_.cols());
no_substances_.clear();
no_substances_.resize(labels_.size(), 0);
for (int c = 0; c < Y_.cols(); c++)
{
vector < int > no_substances_c(labels_.size(), 0); // no of substances in each class for activitiy c
mean_vectors_[c].resize(labels_.size(), descriptor_matrix_.cols());
mean_vectors_[c].setZero();
for (int i = 0; i < descriptor_matrix_.rows(); i++) // calculate sum vector of each class
{
int yi = static_cast < int > (Y_(i, c)); // Y_ will contains only ints for classifications
int pos = label_to_pos.find(yi)->second;
for (int j = 0; j < descriptor_matrix_.cols(); j++)
{
mean_vectors_[c](pos, j) += descriptor_matrix_(i, j);
}
if (c == 0) no_substances_c[pos]++;
}
for (int i = 0; i < mean_vectors_[c].rows(); i++) // calculate mean vector of each class
{
if (no_substances_c[i] == 0)
{
mean_vectors_[c].row(i).setConstant(std::numeric_limits<double>::infinity()); // set mean of classes NOT occurring for activitiy c to inf
}
for (int j = 0; j < descriptor_matrix_.cols(); j++)
{
mean_vectors_[c](i, j) = mean_vectors_[c](i, j)/no_substances_c[i];
}
}
for (unsigned int i = 0; i < no_substances_.size(); i++) // update overall number of substances per class
{
no_substances_[i] += no_substances_c[i];
}
}
}
int main(int argc, char** argv)
{
/////////////////////////////////////////////////////
///usage:
///AnormalyDetection -t [0 1] [ [-f] or [-d [0 1 2 3...] [-l lable] location] ]
//AnormalyDetection -t [0 1] -f
//AnormalyDetection -t [0 1] -d [0 1 2 3...] [-l lable] location
///////////////////////////////////////////////////////
/*printf("argc = %d\n", argc);
for(int i = 1; i < argc; i ++)
printf("%d %s\n", i, argv[i]);*/
char **output = new char*[4];
for(int i = 0; i < 4; i ++)
{
output[i] = new char[MaxStringLength];
memcpy(output[i], "\0", MaxStringLength);
}
int n = 0;
if(!options(argc, argv, output, n))
{
printf("error usage!\n");
printf("usage:program options location\n");
printf("options:-t, -f, -d , -l\n");
return 1;
}
/*for(int i = 0; i < 4; i ++)
{
printf("%d: %s\n", i+1, output[i]);
}*/
int type = atoi(output[0]);
if(n == 1)
{
if(type == 0)
{
trainSVMModel();
}
if(type == 1)
{
//printf("entering detection...\n");
predictSVM();
}
return 0;
}
int dataset = atoi(output[1]);
switch(dataset)
{
case 1://UMN
frameWidth = 320;
frameHeight = 240;
break;
case 2://BEHAVE
frameWidth = 640;
frameHeight = 480;
break;
case 3://UCSD_PED1
frameWidth = 238;
frameHeight = 158;
break;
case 4://UCSD_PED2
frameWidth = 238;
frameHeight = 158;
break;
default:
break;
}
char* location = output[2];
vector<string> files;
readDataset(location, files);
if(type == 0)
{
trainSVMModel(files);
}
if(type == 1)
{
//printf("entering detection...\n");
vector<int> labels;
readLabels(output[3], labels);
predictSVM(files, labels);
}
return 0;
}
