int main(int argc, char** argv){
int wordVecDim = 50;
int paragraphVecDim = 50;
int contextSize = 5;
double learningRate = 0.025;
int numNegative = 5;
int minFreq = 10;
int iteration = 1;
int numThreads = 1;
double shrink = 0.0;
std::string input = "INPUT.txt";
std::string output = "OUTPUT";
Utils::procArg(argc, argv,
wordVecDim, paragraphVecDim, contextSize, learningRate, numNegative, minFreq, iteration, numThreads,
input, output);
Vocabulary voc(wordVecDim, contextSize, paragraphVecDim);
voc.read(input, minFreq);
shrink = learningRate/iteration;
for (int i = 0; i < iteration; ++i){
printf("Iteration %2d (current learning rate: %f)\n", i+1, learningRate);
voc.train(input, learningRate, shrink, numNegative, numThreads);
learningRate -= shrink;
}
voc.save(output+".bin");
//voc.wordKnn(10);
voc.outputParagraphVector(output+".pv");
voc.outputWordVector(output+".wv");
return 0;
}
void RunObjectness(CStr &resName, double base, int W, int NSS, int numPerSz)
{
srand((unsigned int)time(NULL));
DataSetVOC voc("../VOC2007/");
voc.loadAnnotations();
//voc2007.loadDataGenericOverCls();
printf("Dataset:`%s' with %d training and %d testing\n", _S(voc.wkDir), voc.trainNum, voc.testNum);
printf("%s Base = %g, W = %d, NSS = %d, perSz = %d\n", _S(resName), base, W, NSS, numPerSz);
Objectness objNess(voc, base, W, NSS);
vector<vector<Vec4i> > boxesTests;
//objNess.getObjBndBoxesForTests(boxesTests, 250);
//If running this for the first time, set preloadModel to false.
//Although it is not tested, ensure that the model is generated by
//the getObjBndBoxesForTestsFast function and not
//the getObjBndBoxesForTests function which uses colour information too.
//To avoid running out of memory, you can load images only during
//prediction by setting preloadImages to false.
bool preloadModel = true, preloadImages = false;
objNess.getObjBndBoxesForTestsFast(boxesTests, numPerSz, preloadModel, preloadImages);
objNess.getRandomBoxes(boxesTests);
objNess.evaluatePerClassRecall(boxesTests, resName, numPerSz);
objNess.illuTestReults(boxesTests);
}
SuperSample::SuperSample():width(2),height(2)
{
scanline=false;
alpha_aware=true;
Layer::Vocab voc(get_param_vocab());
Layer::fill_static(voc);
}
void
OrbLocationRecognition::setup(const std::string& vocFilename)
{
m_frameTags.clear();
m_frames.clear();
OrbVocabulary voc(vocFilename);
m_db.setVocabulary(voc);
}
BofSearchManager::BofSearchManager(QString folderPath) : folderPath(folderPath)
{
std::cout << "Building / loading vocabulary..\n";
// Read vocabulary from desk
imdb::vocabulary voc(folderPath, 3000);
vocab = voc.centers;
std::cout << "Building / loading index..\n";
// Indexing
std::string tf = "video_google", idf = "video_google";
_tf = imdb::make_tf(tf);
_idf = imdb::make_idf(idf);
index.prepare(folderPath, voc);
}
void SearchUtil::load_vocabulary() {
Configs* config = Configs::createInstance();
Inverted inv;
stringstream filename;
filename << config->VOCABULARY_DIRECTORY << config->VOCABULARY_FILENAME;
ifstream voc(filename.str(), ios::in);
cout << filename.str() << endl;
if(voc.is_open()) {
string word;
unsigned long key;
while (voc >> word >> key)
config->vocabulary_p[word] = key;
voc.close();
/* for (auto i = config->vocabulary_p.begin(); i != config->vocabulary_p.end(); ++i)
cout << i->first << ", " << i->second << endl;*/
}
void RunObjectness(CStr &resName, double base, double intUionThr, int W, int NSS, int numPerSz)
{
srand((unsigned int)time(NULL));
//DataSetVOC voc("../../DataSet/VOC2007/"); // BING Training
DataSetVOC voc("../../DataSet/VOC2007/", true, true); // Personness Training
//DataSetVOC voc("../../DataSet/VOC2012/", true, true); // Personness Test
voc.loadAnnotations();
//voc.loadDataGenericOverCls();
printf("Dataset:`%s' with %d training and %d testing\n", _S(voc.wkDir), voc.trainNum, voc.testNum);
printf("%s Base = %g, intUionThr = %g, W = %d, NSS = %d, perSz = %d\n", _S(resName), base, intUionThr, W, NSS, numPerSz);
Objectness objNess(voc, base, intUionThr, W, NSS);
vector<vector<Vec4i>> boxesTests;
//objNess.getObjBndBoxesForTests(boxesTests, 250);
objNess.getObjBndBoxesForTestsFast(boxesTests, numPerSz);
//objNess.getRandomBoxes(boxesTests);
//objNess.evaluatePerClassRecall(boxesTests, resName, 1000);
objNess.illuTestReults(boxesTests);
//objNess.chammoru_test();
}
void loopClosing3d(const BoWFeatures &features)
{
int OFFSET = I_OFFSET;
double SANITY_THRESHOLD = SANITY;
double MATCH_THRESHOLD = SOGLIA_3D;
int SIZE_BUCKET = 7;
int TEMP_CONS = 5;
bool loop_found = false;
if (!DEBUG) { wait(); }
vector<int> bucket;
vector<double> xs, ys;
readPoseFile((dataset_sub+"syncPCDGT.txt").c_str(), xs, ys);
cout << "3D: Acquisizione Ground Truth" << endl;
ofstream report("report_3d.rep");
DUtilsCV::GUI::tWinHandler winplot = "Traiettoria3D";
DUtilsCV::Drawing::Plot::Style normal_style(2); // thickness
DUtilsCV::Drawing::Plot::Style loop_style('r', 2); // color, thickness
DUtilsCV::Drawing::Plot implot(240, 320,
- *std::max_element(xs.begin(), xs.end()),
- *std::min_element(xs.begin(), xs.end()),
*std::min_element(ys.begin(), ys.end()),
*std::max_element(ys.begin(), ys.end()), 25);
NarfVocabulary voc(filename_voc_3d);
NarfDatabase db(voc, false);
for (int i=0;i<files_list_3d.size();i++){
loop_found = false;
if ((i+1)>OFFSET){
BowVector v1, v2;
voc.transform(features[i], v1);
voc.transform(features[i-1], v2);
double san = voc.score(v1,v2);
cout << "S:" << san << endl;
if (san >= SANITY_THRESHOLD){
QueryResults ret;
db.query(features[i], ret,db.size());
for (int j = 0; j < ret.size(); j++){ //scansiono risultati
if (ret[j].Score > MATCH_THRESHOLD){ //sanity check
if ((i - OFFSET) >= ret[j].Id){ //scarto la coda
if (bucket.size() < SIZE_BUCKET){
bucket.push_back(ret[j].Id);
cout << i <<"-> bucket: " << ret[j].Id<<endl;
}
else
{
break;
}
}
}
}
ret.clear();
if (bucket.size()>0){
cout << "---- TEMPORAL CONSISTENCY {"<<i<< "} ----" << endl;
vector<int> i_back; //contiene le precedenti
for(int yyy = 0; yyy < TEMP_CONS; yyy++){
i_back.push_back((i-(yyy+1)));
}
BowVector v1, v2;
for (int aa = 0 ; aa < bucket.size(); aa++){//per ogni elemento del bucket
double max_score = 0;
int max_id = -1;
int centro = -1;
for (int cc = 0; cc<i_back.size();cc++){
voc.transform(features[i - (cc+1)],v1);
if (centro == -1){ centro = bucket[aa]; }
for(int bb = (centro - (TEMP_CONS/2) - 1) ; bb < centro + (TEMP_CONS/2); bb++){
int cursore = bb + 1;
if (cursore < 0) {continue;}
voc.transform(features[cursore],v2);
double score = voc.score(v1,v2);
cout << i - (cc+1) << " vs " << cursore << " score: " << score << endl;
if (score > MATCH_THRESHOLD && score > max_score){
max_id = cursore;
max_score = score;
}
}
if (max_id == -1){
bucket[aa] = -1;
centro = -1;
max_score = 0;
}else{
centro = max_id;
max_score = 0;
max_id = -1;
}
}
}
}
bucket.erase(std::remove( bucket.begin(), bucket.end(),-1), bucket.end());
double maxInliers = numeric_limits<double>::max();
double maxIdInliers = -1;
double tyu = 0;
if(bucket.size() == 1){
maxIdInliers = bucket[0];
}else{
#pragma omp parallel for
for(int yy = 0; yy < bucket.size(); yy++){
BowVector v1,v2;
tyu = reg_3D->getCentroidDiff(i,bucket[yy]);
cout << "score: " << tyu <<endl;
if (maxInliers > tyu){
maxInliers = tyu;
maxIdInliers = bucket[yy];
}
}
}
if(maxIdInliers != -1){
loop_found = true;
cout << "LOOP TROVATO : " << i <<" per immagine: " << maxIdInliers << endl;
implot.line(-xs[maxIdInliers], ys[maxIdInliers], -xs[i], ys[i], loop_style);
}
bucket.clear();
if(loop_found)
{
string ii,maxid;
ii = boost::lexical_cast<string>(i);
maxid = boost::lexical_cast<string>(maxIdInliers);
report << i <<":"<< maxIdInliers<<endl;
report.flush();
}
}
}
db.add(features[i]);
implot.line(-xs[i-1], ys[i-1], -xs[i], ys[i], normal_style);
DUtilsCV::GUI::showImage(implot.getImage(), true, &winplot, 10);
}
report.close();
stats* owl = new stats("report_3d.rep",registro_interno,OFFSET,"3D");
cout << owl->toString();
}
void bingQdpmRocTest(vector<string> &dirs,
int windowLimit = -1, double timeLimitMs = -1, float ratioThreshold = -1)
{
size_t imageCount = 0;
size_t personCount = 0;
size_t matchCount = 0;
vector<ScoreTp> pScores;
TickMeter tm;
vector<std::string>::const_iterator it = dirs.begin();
char buf[512];
for (; it != dirs.end(); it++) {
string dir = *it;
DataSetVOC voc(dir, true, true);
voc.loadAnnotations();
const size_t testNum = voc.testSet.size();
const char *imgPath =_S(voc.imgPathW);
// Objectness
double base = 2;
double intUionThr = 0.5;
int W = 8;
int NSS = 2;
#ifdef WINDOW_GUESS
Objectness objNess(voc, base, intUionThr, W, NSS);
objNess.loadTrainedModel(TRAIN_MODEL);
#endif
// LSVM DPM
string dpmPersonModel = "../ExtraData/latentsvmXml/person.xml";
vector<string> models;
models.push_back(dpmPersonModel);
QUniLsvmDetector detector(models);
float overlapThreshold = 0.2f;
if (ratioThreshold > 0)
detector.setRatioThreshold(ratioThreshold);
printf("%d: \n", testNum);
for (int i = 0; i < testNum; i++) {
const vector<Vec4i> &boxesGT = voc.gtTestBoxes[i];
const size_t gtNumCrnt = boxesGT.size();
if (gtNumCrnt <= 0)
continue;
imageCount++;
personCount += gtNumCrnt;
Mat image = imread(format(imgPath, _S(voc.testSet[i])));
if (image.ptr() == NULL) {
fprintf(stderr, "No JPG Image !\n");
exit(1);
}
int numPerSz = 130;
ValStructVec<float, Vec4i> boxes;
double preObj = tm.getTimeMilli();
double objTime = 0.;
#ifdef WINDOW_GUESS // window guess
tm.start();
objNess.getObjBndBoxes(image, boxes, numPerSz);
tm.stop();
objTime = tm.getTimeMilli() - preObj;
#endif
double localTimeLimitMs = timeLimitMs;
if (timeLimitMs > 0) {
localTimeLimitMs -= objTime;
if (localTimeLimitMs < 0.)
localTimeLimitMs = 0.;
}
vector<QRect> searchBoxes;
if (windowLimit > 0) {
for (int j = 0; j < (int)boxes.size() && j < windowLimit; j++) {
const Vec4i &bb = boxes[j];
QRect rt(bb[0], bb[1], bb[2], bb[3]);
searchBoxes.push_back(rt);
}
} else {
for (int j = 0; j < (int)boxes.size(); j++) {
const Vec4i &bb = boxes[j];
QRect rt(bb[0], bb[1], bb[2], bb[3]);
searchBoxes.push_back(rt);
}
}
tm.start();
detector.setup(image, overlapThreshold, localTimeLimitMs);
tm.stop();
vector<FeatureMapCoord> ftrMapCoords;
#ifdef WINDOW_GUESS
detector.cvtBox2FtrMapCoord(&searchBoxes, &ftrMapCoords);
#else
detector.genFullFtrMapCoord(&ftrMapCoords);
preObj = tm.getTimeMilli();
tm.start();
#ifdef SHUFFLE_WINDOW
random_shuffle(ftrMapCoords.begin(), ftrMapCoords.end());
#endif
tm.stop();
double randGenTime = tm.getTimeMilli() - preObj;
if (localTimeLimitMs > 0 && localTimeLimitMs - preObj >= 0.)
detector.setTimeLimit(localTimeLimitMs - preObj);
#endif
vector<QUniLsvmDetector::ObjectDetection> detections;
vector<vector<FeatureMapCoord> *> fmcss;
fmcss.push_back(&ftrMapCoords);
tm.start();
detector.detect(detections, fmcss);
tm.stop();
vector<DetectedInfo> di(detections.size());
vector<int> gtIdx(gtNumCrnt, -1);
int detectCount = 0;
for (size_t j = 0; j < detections.size(); j++) {
const QUniLsvmDetector::ObjectDetection& od = detections[j];
if (od.score < RECOMMENDABLE_THRESHOLD)
continue;
detectCount++;
Vec4i bb(od.rect.x + 1, od.rect.y + 1, od.rect.x + od.rect.width, od.rect.y + od.rect.height);
// score matchScore for the ROC curve
double maxMatchScore = 0;
int maxMatchId = -1;
for (int k = 0; k < gtNumCrnt; k++) {
double matchScore = DataSetVOC::interUnio(bb, boxesGT[k]);
if (matchScore > maxMatchScore) {
maxMatchScore = matchScore;
maxMatchId = k;
}
}
uchar match = maxMatchScore > 0.5 ? 1 : 0;
if (match) {
int preDetectedIdx = gtIdx[maxMatchId];
if (preDetectedIdx >= 0) {
if (maxMatchScore > di[preDetectedIdx].matchScore) {
di[preDetectedIdx].matched = false;
gtIdx[maxMatchId] = int(j);
di[j].matchScore = maxMatchScore;
di[j].matched = true;
}
} else {
gtIdx[maxMatchId] = int(j);
di[j].matchScore = maxMatchScore;
di[j].matched = true;
}
}
#ifdef SAVE_IMAGE_RESULT
// save the result image
char buf[256];
sprintf(buf, "%2f", od.score);
Point pt(max((bb[2] + bb[0] - 85) / 2, 0), (bb[1] + bb[3]) / 2);
putText(image, buf, pt, FONT_HERSHEY_SIMPLEX, 0.5, Scalar::all(255), 3, CV_AA);
putText(image, buf, pt, FONT_HERSHEY_SIMPLEX, 0.5, Scalar::all(0), 1, CV_AA);
rectangle(image, od.rect, cv::Scalar(0, 255, 0), 2);
#endif
}
for (size_t j = 0; j < detectCount; j++) { // detections are sorted in descending order
const QUniLsvmDetector::ObjectDetection& od = detections[j];
if (di[j].matched)
matchCount++;
pScores.push_back(ScoreTp(od.score, di[j].matched));
}
#ifdef SAVE_IMAGE_RESULT
imwrite((voc.testSet[i] + "_DpmResult.png").c_str(), image);
#endif
printf("%d ", i + 1);
}
printf("\n");
}
printf("BingQdpmRocTest time = %f sec\n", tm.getTimeSec());
printf("GT %d, Matched %d/%d \n", personCount, matchCount, pScores.size());
// Calculate log-average miss rate
stable_sort(begin(pScores), end(pScores),
[](const ScoreTp &p1, const ScoreTp &p2) { return p1.first > p2.first; });
vector<float> fp(pScores.size());
for (size_t i = 0; i < fp.size(); i++)
fp[i] = !pScores[i].second;
vector<float> tp(pScores.size());
tp[0] = pScores[0].second;
for (size_t i = 1; i < tp.size(); i++)
tp[i] = tp[i - 1] + pScores[i].second;
for (size_t i = 0; i < tp.size(); i++)
tp[i] /= personCount;
for (size_t i = 1; i < fp.size(); i++)
fp[i] += fp[i - 1];
for (size_t i = 0; i < fp.size(); i++)
fp[i] /= imageCount;
sprintf(buf, "%s%03d_%03.fms_6137gt_%04dtp_%04ddt_%.0fs.m",
METHOD_NAME, max(windowLimit, 0), timeLimitMs, matchCount, pScores.size(), tm.getTimeSec());
FILE *matlabFile = fopen(buf, "w");
printVector(matlabFile, tp, "tp");
printVector(matlabFile, fp, "fp");
char *matlabContent = "tp = tp'; fp = fp';\n"
"addpath(genpath('d:/81_dataset/03_Caltech/piotr_toolbox/')); savepath;\n"
"xs1=[-inf; fp];\n"
"ys1=[0; tp];\n"
"ref=10.^(-2:.25:0);\n"
"lims=[3.1e-4 1e1 .5 1];\n"
"m=length(ref);\n"
"for i=1:m\n"
"\tj=find(xs1<=ref(i));\n"
"\tmiss(i)=ys1(j(end));\n"
"end\n"
"miss=exp(mean(log(max(1e-10,1-miss))));\n"
"show=figure();\n"
"plotRoc([fp tp],'logx',1,'logy',1,'xLbl','fppi',...\n"
"\t'lims',lims,'color','g','smooth',1,'fpTarget',ref);\n"
"title(sprintf('log-average miss rate = %.2f%%',miss*100));\n"
"savefig(['MORU' 'Roc'],show,'png');\n";
fwrite(matlabContent, strlen(matlabContent), 1, matlabFile);
fclose(matlabFile);
}
void
OrbLocationRecognition::setup(const std::string& vocFilename,
const SparseGraphConstPtr& graph,
const cv::Mat& matchingMask)
{
m_frameTags.clear();
m_frames.clear();
std::vector<bool> cameraFlags(matchingMask.rows);
for (int i = 0; i < matchingMask.rows; ++i)
{
cameraFlags.at(i) = (cv::countNonZero(matchingMask.row(i)) > 0);
}
OrbVocabulary voc(vocFilename);
m_db.setVocabulary(voc);
// build vocabulary tree
std::vector<std::vector<DVision::ORB::bitset> > features;
for (size_t segmentId = 0; segmentId < graph->frameSetSegments().size(); ++segmentId)
{
const FrameSetSegment& segment = graph->frameSetSegment(segmentId);
for (size_t frameSetId = 0; frameSetId < segment.size(); ++frameSetId)
{
const FrameSetPtr& frameSet = segment.at(frameSetId);
for (size_t frameId = 0; frameId < frameSet->frames().size(); ++frameId)
{
const FramePtr& frame = frameSet->frames().at(frameId);
if (frame.get() == 0)
{
continue;
}
if (!cameraFlags.at(frame->cameraId()))
{
continue;
}
FrameTag tag;
tag.frameSetSegmentId = segmentId;
tag.frameSetId = frameSetId;
tag.frameId = frameId;
m_frameTagMap.insert(std::make_pair(tag, m_frameTagMap.size()));
m_frameTags.push_back(tag);
m_frames.push_back(frame);
features.push_back(frameToFeatures(frame));
}
}
}
for (size_t i = 0; i < features.size(); ++i)
{
m_db.add(features.at(i));
}
}
int main()
{
// Dataset Voc
string dir = "../DataSet/VOC2012/";
// string dir = "../DataSet/VOC2007/";
DataSetVOC voc(dir, false, true);
voc.loadAnnotations();
const size_t testNum = voc.testSet.size();
const char* imgPath =_S(voc.imgPathW);
// LSVM DPM
std::string dpmPersonModel = "../ExtraData/latentsvmXml/person.xml";
std::vector<std::string> models;
models.push_back(dpmPersonModel);
QUniLsvmDetector detector(models);
float overlapThreshold = 0.2f;
size_t imageCount = 0;
size_t personCount = 0;
size_t matchCount = 0;
vector<ScoreTp> pScores;
TickMeter tm;
for (int i = 0; i < testNum; i++){
const vector<Vec4i> &boxesGT = voc.gtTestBoxes[i];
const size_t gtNumCrnt = boxesGT.size();
if (gtNumCrnt <= 0)
continue;
imageCount++;
personCount += gtNumCrnt;
Mat image = imread(format(imgPath, _S(voc.testSet[i])));
tm.start();
vector<QUniLsvmDetector::ObjectDetection> detections;
detector.setup(image, overlapThreshold);
// No threading, nearly original LSVM
vector<FeatureMapCoord> ftrMapCoords;
vector<vector<FeatureMapCoord> *> fmcss;
fmcss.push_back(&ftrMapCoords);
detector.genFullFtrMapCoord(&ftrMapCoords);
detector.detect(detections, fmcss);
tm.stop();
vector<DetectedInfo> di(detections.size());
vector<int> gtIdx(gtNumCrnt, -1);
int detectCount = 0;
for (size_t j = 0; j < detections.size(); j++) {
const QUniLsvmDetector::ObjectDetection& od = detections[j];
if (od.score < RECOMMENDABLE_THRESHOLD)
continue;
detectCount++;
Vec4i bb(od.rect.x + 1, od.rect.y + 1, od.rect.x + od.rect.width, od.rect.y + od.rect.height);
// score matchScore for the ROC curve
double maxMatchScore = 0;
int maxMatchId = -1;
for (int k = 0; k < gtNumCrnt; k++) {
double matchScore = DataSetVOC::interUnio(bb, boxesGT[k]);
if (matchScore > maxMatchScore) {
maxMatchScore = matchScore;
maxMatchId = k;
}
}
uchar match = maxMatchScore > 0.5 ? 1 : 0;
if (match) {
int preDetectedIdx = gtIdx[maxMatchId];
if (preDetectedIdx >= 0) {
if (maxMatchScore > di[preDetectedIdx].matchScore) {
di[preDetectedIdx].matched = false;
gtIdx[maxMatchId] = int(j);
di[j].matchScore = maxMatchScore;
di[j].matched = true;
}
} else {
gtIdx[maxMatchId] = int(j);
di[j].matchScore = maxMatchScore;
di[j].matched = true;
}
}
}
for (size_t j = 0; j < detectCount; j++) {
const QUniLsvmDetector::ObjectDetection& od = detections[j];
if (di[j].matched)
matchCount++;
pScores.push_back(ScoreTp(od.score, di[j].matched));
}
#ifdef SAVE_YML
vector<Vec4i> newBoxesGT;
for (size_t j = 0; j < gtIdx.size(); j++) {
if (gtIdx[j] < 0)
continue;
newBoxesGT.push_back(boxesGT[j]);
}
if (newBoxesGT.size() > 0) {
// name
// h, w
string name = _S(voc.testSet[i]);
writePersonYml(name, image.rows, image.cols, newBoxesGT);
}
#endif
}
printf("QdpmRocTest time = %f sec\n", tm.getTimeSec());
printf("GT %d, Matched %d/%d \n", personCount, matchCount, pScores.size());
// Calculate log-average miss rate
stable_sort(begin(pScores), end(pScores),
[](const ScoreTp &p1, const ScoreTp &p2) { return p1.first > p2.first; });
vector<float> fp(pScores.size());
for (size_t i = 0; i < fp.size(); i++)
fp[i] = !pScores[i].second;
vector<float> tp(pScores.size());
tp[0] = pScores[0].second;
for (size_t i = 1; i < tp.size(); i++)
tp[i] = tp[i - 1] + pScores[i].second;
for (size_t i = 0; i < tp.size(); i++)
tp[i] /= personCount;
for (size_t i = 1; i < fp.size(); i++)
fp[i] += fp[i - 1];
for (size_t i = 0; i < fp.size(); i++)
fp[i] /= imageCount;
FILE *matlabFile = fopen("drawLAMR.m", "w");
printVector(matlabFile, tp, "tp");
printVector(matlabFile, fp, "fp");
char *matlabContent = "tp = tp'; fp = fp';\n"
"addpath(genpath('d:/81_dataset/03_Caltech/piotr_toolbox/')); savepath;\n"
"xs1=[-inf; fp];\n"
"ys1=[0; tp];\n"
"ref=10.^(-2:.25:0);\n"
"lims=[3.1e-4 1e1 0. 1];\n"
"m=length(ref);\n"
"for i=1:m\n"
"\tj=find(xs1<=ref(i));\n"
"\tmiss(i)=ys1(j(end));\n"
"end\n"
"miss=exp(mean(log(max(1e-10,1-miss))));\n"
"show=figure();\n"
"plotRoc([fp tp],'logx',1,'logy',1,'xLbl','fppi',...\n"
"\t'lims',lims,'color','g','smooth',1,'fpTarget',ref);\n"
"title(sprintf('log-average miss rate = %.2f%%',miss*100));\n"
"savefig(['MORU' 'Roc'],show,'png');\n";
fwrite(matlabContent, strlen(matlabContent), 1, matlabFile);
fclose(matlabFile);
return 0;
}
void Town::depositWithdrawl(int choice) {
Party &party = *_vm->_party;
Screen &screen = *_vm->_screen;
Sound &sound = *_vm->_sound;
int gold, gems;
if (choice) {
gold = party._bankGold;
gems = party._bankGems;
} else {
gold = party._gold;
gems = party._gems;
}
for (uint idx = 0; idx < _buttons.size(); ++idx)
_buttons[idx]._sprites = &_icons2;
_buttons[0]._value = Common::KEYCODE_o;
_buttons[1]._value = Common::KEYCODE_e;
_buttons[2]._value = Common::KEYCODE_ESCAPE;
Common::String msg = Common::String::format(Res.GOLD_GEMS,
Res.DEPOSIT_WITHDRAWL[choice],
XeenEngine::printMil(gold).c_str(),
XeenEngine::printMil(gems).c_str());
screen._windows[35].open();
screen._windows[35].writeString(msg);
drawButtons(&screen._windows[35]);
screen._windows[35].update();
sound.stopSound();
File voc("coina.voc");
bool flag = false;
do {
switch (townWait()) {
case Common::KEYCODE_o:
flag = false;
break;
case Common::KEYCODE_e:
flag = true;
break;
case Common::KEYCODE_ESCAPE:
break;
default:
continue;
}
if ((choice && !party._bankGems && flag) ||
(choice && !party._bankGold && !flag) ||
(!choice && !party._gems && flag) ||
(!choice && !party._gold && !flag)) {
party.notEnough(flag, choice, 1, WT_2);
} else {
screen._windows[35].writeString(Res.AMOUNT);
int amount = NumericInput::show(_vm, 35, 10, 77);
if (amount) {
if (flag) {
if (party.subtract(true, amount, choice, WT_2)) {
if (choice) {
party._gems += amount;
} else {
party._bankGems += amount;
}
}
} else {
if (party.subtract(false, amount, choice, WT_2)) {
if (choice) {
party._gold += amount;
} else {
party._bankGold += amount;
}
}
}
}
if (choice) {
gold = party._bankGold;
gems = party._bankGems;
} else {
gold = party._gold;
gems = party._gems;
}
sound.playSound(voc);
msg = Common::String::format(Res.GOLD_GEMS_2, Res.DEPOSIT_WITHDRAWL[choice],
XeenEngine::printMil(gold).c_str(), XeenEngine::printMil(gems).c_str());
screen._windows[35].writeString(msg);
screen._windows[35].update();
}
// TODO
} while (!_vm->shouldQuit() && _buttonValue != Common::KEYCODE_ESCAPE);
for (uint idx = 0; idx < _buttons.size(); ++idx)
_buttons[idx]._sprites = &_icons1;
_buttons[0]._value = Common::KEYCODE_d;
_buttons[1]._value = Common::KEYCODE_w;
_buttons[2]._value = Common::KEYCODE_ESCAPE;
}
