bool Connectome::read(const QString &fileName, bool quiet)
{
if (!this->isEmpty())
clear();
QFile file(fileName);
if (!file.open(QIODevice::ReadOnly)) {
if (!quiet)
QMessageBox::critical(0, "Warning!", QString("Cannot read file %1:\n%2.")
.arg(file.fileName()).arg(file.errorString()));
return false;
}
qint64 sz = sizeof(header);
if ( file.read( (char *) &header, sz ) != sz ) {
file.close();
return false;
}
// test it is a Connectome file
QString test = QString(header.fileTag);
test.resize(4);
if ( test != "Cnct" ) {
QMessageBox::critical(0, "Warning!", "This is not a Connectome file.");
file.close();
return false;
}
// move file pointer 64, where data starts
file.seek(64);
NW = mat(header.nRegions,header.nRegions);
sz = sizeof(double)*header.nRegions*header.nRegions;
if (file.read( (char *) NW.memptr(), sz ) != sz) {
file.close();
return false;
}
centroids = fmat(header.nRegions,3);
sz = sizeof(float)*header.nRegions*3;
if (file.read( (char *) centroids.memptr(), sz ) != sz) {
file.close();
return false;
}
sz = header.nR*header.nC*header.nS;
maskImage = uchar_cube(header.nR,header.nC,header.nS);
if (file.read( (char *) maskImage.memptr(), sz ) != sz) {
file.close();
return false;
}
// assuming the rest of the file is a ; separated string list
sz = file.size() - file.pos();
char *tmp = new char[sz];
if (file.read( (char *) tmp, sz ) != sz) {
file.close();
return false;
}
file.close();
names = QString(tmp).split(";");
delete tmp;
if (names.count() != header.nRegions)
return false;
computeMetrics();
return true;
}
double RideItem::timeInHrZone(int zone)
{
computeMetrics();
if (!ride())
return 0.0;
assert(zone < numHrZones());
return time_in_hr_zone[zone];
}
std::vector<float> SegmentationImpl::computeMetrics() const
{
if (!solutionAvailable()) {
LERROR << "Solution not available to compute metrics";
return std::vector<float>();
}
if (!inputImageGroundTruthAvailable()) {
LERROR << "No ground truth available to compute metrics";
return std::vector<float>();
}
return computeMetrics(solution(), inputImageGroundTruth(), settings()->numLabels());
}
void
postProcessResults(int option)
{
int i, k, randomExcursionSampleSize, generalSampleSize;
int passRate, case1, case2, numOfFiles = 2;
double p_hat;
char s[200];
for ( i=1; i<=NUMOFTESTS; i++ ) { // FOR EACH TEST
if ( testVector[i] ) {
// SPECIAL CASES -- HANDLING MULTIPLE FILES FOR A SINGLE TEST
if ( ((i == TEST_CUSUM) && testVector[TEST_CUSUM] ) ||
((i == TEST_NONPERIODIC) && testVector[TEST_NONPERIODIC] ) ||
((i == TEST_RND_EXCURSION) && testVector[TEST_RND_EXCURSION]) ||
((i == TEST_RND_EXCURSION_VAR) && testVector[TEST_RND_EXCURSION_VAR]) ||
((i == TEST_SERIAL) && testVector[TEST_SERIAL]) ) {
if ( (i == TEST_NONPERIODIC) && testVector[TEST_NONPERIODIC] )
numOfFiles = MAXNUMOFTEMPLATES;
else if ( (i == TEST_RND_EXCURSION) && testVector[TEST_RND_EXCURSION] )
numOfFiles = 8;
else if ( (i == TEST_RND_EXCURSION_VAR) && testVector[TEST_RND_EXCURSION_VAR] )
numOfFiles = 18;
else
numOfFiles = 2;
for ( k=0; k<numOfFiles; k++ ) {
if ( k < 10 )
sprintf(s, "experiments/%s/%s/data%1d.txt", generatorDir[option], testNames[i], k+1);
else if ( k < 100 )
sprintf(s, "experiments/%s/%s/data%2d.txt", generatorDir[option], testNames[i], k+1);
else
sprintf(s, "experiments/%s/%s/data%3d.txt", generatorDir[option], testNames[i], k+1);
if ( (i == TEST_RND_EXCURSION) || (i == TEST_RND_EXCURSION_VAR) )
randomExcursionSampleSize = computeMetrics(s,i);
else
generalSampleSize = computeMetrics(s,i);
}
}
else {
sprintf(s, "experiments/%s/%s/results.txt", generatorDir[option], testNames[i]);
generalSampleSize = computeMetrics(s,i);
}
}
}
fprintf(summary, "\n\n- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n");
case1 = 0;
case2 = 0;
if ( testVector[TEST_RND_EXCURSION] || testVector[TEST_RND_EXCURSION_VAR] )
case2 = 1;
for ( i=1; i<=NUMOFTESTS; i++ ) {
if ( testVector[i] && (i != TEST_RND_EXCURSION) && (i != TEST_RND_EXCURSION_VAR) ) {
case1 = 1;
break;
}
}
p_hat = 1.0 - ALPHA;
if ( case1 ) {
if ( generalSampleSize == 0 ) {
fprintf(summary, "The minimum pass rate for each statistical test with the exception of the\n");
fprintf(summary, "random excursion (variant) test is undefined.\n\n");
}
else {
passRate = (p_hat - 3.0 * sqrt((p_hat*ALPHA)/generalSampleSize)) * generalSampleSize;
fprintf(summary, "The minimum pass rate for each statistical test with the exception of the\n");
fprintf(summary, "random excursion (variant) test is approximately = %d for a\n", generalSampleSize ? passRate : 0);
fprintf(summary, "sample size = %d binary sequences.\n\n", generalSampleSize);
}
}
if ( case2 ) {
if ( randomExcursionSampleSize == 0 )
fprintf(summary, "The minimum pass rate for the random excursion (variant) test is undefined.\n\n");
else {
passRate = (p_hat - 3.0 * sqrt((p_hat*ALPHA)/randomExcursionSampleSize)) * randomExcursionSampleSize;
fprintf(summary, "The minimum pass rate for the random excursion (variant) test\n");
fprintf(summary, "is approximately = %d for a sample size = %d binary sequences.\n\n", passRate, randomExcursionSampleSize);
}
}
fprintf(summary, "For further guidelines construct a probability table using the MAPLE program\n");
fprintf(summary, "provided in the addendum section of the documentation.\n");
fprintf(summary, "- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n");
}
void SegmentationImpl::saveResult()
{
try {
fs::path outputFolder(outputSettings()->resultPath());
if (outputSettings()->subFolderByDate()) {
outputFolder /= currentDateString();
}
fs::create_directories(outputFolder);
// compute filename (remove possible exisiting datetime prefix when loading an image from the results folder)
std::string colorFilenameStr = fs::path(inputSettings()->color()).filename().string();
boost::regex re("^\\d{8}_\\d{6}_");
colorFilenameStr = boost::regex_replace(colorFilenameStr, re, "");
if (outputSettings()->prefixDateTime()) {
colorFilenameStr = currentDateTimeString() + "_" + colorFilenameStr;
}
// Note: we don't save groundTruthLabelMapping, since it will already be applied to the save ground truth image.
fs::path colorFilename = colorFilenameStr;
fs::path depthFilename = replaceString(colorFilenameStr, inputSettings()->colorMatch(), inputSettings()->depthReplace());
fs::path depthFilenameNormalized = depthFilename.stem().string() + "_norm" + depthFilename.extension().string();
fs::path groundTruthFilename = replaceString(colorFilenameStr, inputSettings()->colorMatch(), inputSettings()->groundTruthReplace());
fs::path groundTruthFilenameNormalized = groundTruthFilename.stem().string() + "_norm" + groundTruthFilename.extension().string();
fs::path scribbleFilename = replaceString(colorFilenameStr, inputSettings()->colorMatch(), inputSettings()->scribblesReplace());
fs::path settingsFilename = replaceString(colorFilenameStr, inputSettings()->colorMatch(), inputSettings()->settingsReplace());
fs::path solutionFilename = replaceString(colorFilenameStr, inputSettings()->colorMatch(), outputSettings()->solutionReplace());
fs::path solutionFilenameNormalized = solutionFilename.stem().string() + "_norm" + solutionFilename.extension().string();
fs::path weightFilename = replaceString(colorFilenameStr, inputSettings()->colorMatch(), outputSettings()->weightReplace());
fs::path datatermFilename = replaceString(colorFilenameStr, inputSettings()->colorMatch(), outputSettings()->datatermReplace());
fs::path datatermFilenameEqual = datatermFilename.stem().string() + "_equalized" + datatermFilename.extension().string();
fs::path visualizationFilename = replaceString(colorFilenameStr, inputSettings()->colorMatch(), outputSettings()->visualizationReplace());
fs::path metricsFilename = replaceString(colorFilenameStr, inputSettings()->colorMatch(), outputSettings()->metricsReplace());
std::string colorPath = (outputFolder / colorFilename).string();
if (inputImageColorAvailable()) {
saveImage(colorPath, inputImageColor());
}
if (inputImageDepthAvailable()) {
saveDepthImage((outputFolder / depthFilename).string(), inputImageDepth());
saveDepthImage((outputFolder / depthFilenameNormalized).string(), inputImageDepth(), true);
}
if (inputImageGroundTruthAvailable()) {
saveImage((outputFolder / groundTruthFilename).string(), inputImageGroundTruth(), false);
saveImage((outputFolder / groundTruthFilenameNormalized).string(), inputImageGroundTruth(), true);
}
saveScribbleImage((outputFolder / scribbleFilename).string(), scribbles_);
if (solutionAvailable()) {
saveImage((outputFolder / solutionFilename).string(), solution(), false);
saveImage((outputFolder / solutionFilenameNormalized).string(), solution(), true);
}
if (weightAvailable()) {
saveImage((outputFolder / weightFilename).string(), weight(), true);
}
if (datatermAvailable()) {
saveImage((outputFolder / datatermFilename).string(), dataterm(), true);
saveImage((outputFolder / datatermFilenameEqual).string(), dataterm(), true, true);
}
if (tvvisualizer_->visualizationAvailable()) {
saveImage((outputFolder / visualizationFilename).string(), tvvisualizer_->visualization());
}
settings::BackendPtr backend(new settings::Backend());
copyAlgorithmicSettings(settingsBackend_, backend);
InputSettings inputSettings(backend);
inputSettings.set_color(colorPath);
inputSettings.set_overrideGroundTruthLabelMapping(false);
settings::SerializerQt serializer(backend, (outputFolder / settingsFilename).string(), false);
serializer.save();
if (solutionAvailable() && inputImageGroundTruthAvailable()) {
saveMetrics((outputFolder / metricsFilename).string(), computeMetrics());
}
} catch (const fs::filesystem_error& ex) {
LERROR << "Failed to save result with filesystem error: " << ex.what();
}
}
