template <class T> void testFeatTable(const T & table, const char * testName)
{
FeatureMap testFeatureMap;
dummyFace.replace_table(TtfUtil::Tag::Feat, &table, sizeof(T));
gr_face * face = gr_make_face_with_ops(&dummyFace, &face_handle::ops, gr_face_dumbRendering);
if (!face) throw std::runtime_error("failed to load font");
bool readStatus = testFeatureMap.readFeats(*face);
testAssert("readFeats", readStatus);
fprintf(stderr, testName, NULL);
testAssertEqual("test num features %hu,%hu\n", testFeatureMap.numFeats(), table.m_header.m_numFeat);
for (size_t i = 0; i < sizeof(table.m_defs) / sizeof(FeatDefn); i++)
{
const FeatureRef * ref = testFeatureMap.findFeatureRef(table.m_defs[i].m_featId);
testAssert("test feat\n", ref);
testAssertEqual("test feat settings %hu %hu\n", ref->getNumSettings(), table.m_defs[i].m_numFeatSettings);
testAssertEqual("test feat label %hu %hu\n", ref->getNameId(), table.m_defs[i].m_label);
size_t settingsIndex = (table.m_defs[i].m_settingsOffset - sizeof(FeatHeader)
- (sizeof(FeatDefn) * table.m_header.m_numFeat)) / sizeof(FeatSetting);
for (size_t j = 0; j < table.m_defs[i].m_numFeatSettings; j++)
{
testAssertEqual("setting label %hu %hu\n", ref->getSettingName(j),
table.m_settings[settingsIndex+j].m_label);
}
}
gr_face_destroy(face);
}
int main(int argc, char * argv[])
{
gr_face * face = 0;
try
{
if (argc != 2) throw std::length_error("not enough arguments: need a backing font");
dummyFace = face_handle(argv[1]);
testFeatTable<FeatTableTestA>(testDataA, "A\n");
testFeatTable<FeatTableTestB>(testDataB, "B\n");
testFeatTable<FeatTableTestB>(testDataBunsorted, "Bu\n");
testFeatTable<FeatTableTestC>(testDataCunsorted, "C\n");
testFeatTable<FeatTableTestD>(testDataDunsorted, "D\n");
testFeatTable<FeatTableTestE>(testDataE, "E\n");
// test a bad settings offset stradling the end of the table
FeatureMap testFeatureMap;
dummyFace.replace_table(TtfUtil::Tag::Feat, &testBadOffset, sizeof testBadOffset);
face = gr_make_face_with_ops(&dummyFace, &face_handle::ops, gr_face_dumbRendering);
bool readStatus = testFeatureMap.readFeats(*face);
testAssert("fail gracefully on bad table", !readStatus);
}
catch (std::exception & e)
{
fprintf(stderr, "%s: %s\n", argv[0], e.what());
gr_face_destroy(face);
return 1;
}
gr_face_destroy(face);
return 0;
}
void Test::RunLocalizationTest(RGBImage* image) {
DLLExecution * executor = new DLLExecution(image);
executor->executePreProcessingStep1(false);
executor->executePreProcessingStep2(false);
executor->executePreProcessingStep3(false);
executor->executePreProcessingStep4(false);
executor->prepareLocalization();
IntensityImage* img = executor->resultPreProcessingStep4;
HistogramLocalization localizer = HistogramLocalization();
FeatureMap features = FeatureMap();
FeatureMap detectedItems = localizer.locateHead(img, features);
RGB colorRed(244, 67, 54);
RGBImage * debug = ImageFactory::newRGBImage(executor->resultPreProcessingStep1->getWidth(), executor->resultPreProcessingStep1->getHeight());
ImageIO::intensityToRGB(*executor->resultPreProcessingStep1, *debug);
for (int i = 0; i < 3; ++i)
HereBeDragons::TriumphInLoveFleshStaysNoFatherReason(*debug, Point2D<double>(detectedItems.getFeature(i).getX(), detectedItems.getFeature(i).getY()), colorRed);
ImageIO::saveRGBImage(*debug, ImageIO::getDebugFileName("localisation.png"));
ImageIO::showImage(*debug);
}
bool CAddonButtonMap::Load(void)
{
FeatureMap features;
DriverMap driverMap;
PrimitiveVector ignoredPrimitives;
bool bSuccess = false;
if (auto addon = m_addon.lock())
{
bSuccess |= addon->GetFeatures(m_device, m_strControllerId, features);
bSuccess |= addon->GetIgnoredPrimitives(m_device, ignoredPrimitives);
}
// GetFeatures() was changed to always return false if no features were
// retrieved. Check here, just in case its contract is changed or violated in
// the future.
if (bSuccess && features.empty())
bSuccess = false;
if (bSuccess)
driverMap = CreateLookupTable(features);
else
CLog::Log(LOGDEBUG, "Failed to load button map for \"%s\"", m_device->DeviceName().c_str());
{
CSingleLock lock(m_mutex);
m_features = std::move(features);
m_driverMap = std::move(driverMap);
m_ignoredPrimitives = std::move(CPeripheralAddonTranslator::TranslatePrimitives(ignoredPrimitives));
}
return true;
}
int main()
{
// Insert of two features into a map and iterate over the features.
FeatureMap map;
Feature feature;
feature.setRT(15.0);
feature.setMZ(571.3);
map.push_back(feature); //append feature 1
feature.setRT(23.3);
feature.setMZ(1311.3);
map.push_back(feature); //append feature 2
// Iteration over FeatureMap
for (auto it = map.begin(); it != map.end(); ++it)
{
cout << it->getRT() << " - " << it->getMZ() << endl;
}
// Calculate and output the ranges
map.updateRanges();
cout << "Int: " << map.getMinInt() << " - " << map.getMaxInt() << endl;
cout << "RT: " << map.getMin()[0] << " - " << map.getMax()[0] << endl;
cout << "m/z: " << map.getMin()[1] << " - " << map.getMax()[1] << endl;
// ... and many more
return 0;
} //end of main
void InternalCalibration::calibrateMapGlobally(const FeatureMap<> & feature_map, FeatureMap<> & calibrated_feature_map, std::vector<PeptideIdentification> & ref_ids, String trafo_file_name)
{
checkReferenceIds_(ref_ids);
calibrated_feature_map = feature_map;
// clear the ids
for (Size f = 0; f < calibrated_feature_map.size(); ++f)
{
calibrated_feature_map[f].getPeptideIdentifications().clear();
}
// map the reference ids onto the features
IDMapper mapper;
Param param;
param.setValue("rt_tolerance", (DoubleReal)param_.getValue("rt_tolerance"));
param.setValue("mz_tolerance", param_.getValue("mz_tolerance"));
param.setValue("mz_measure", param_.getValue("mz_tolerance_unit"));
mapper.setParameters(param);
std::vector<ProteinIdentification> vec;
mapper.annotate(calibrated_feature_map, ref_ids, vec);
// calibrate
calibrateMapGlobally(calibrated_feature_map, calibrated_feature_map, trafo_file_name);
// copy the old ids
calibrated_feature_map.setUnassignedPeptideIdentifications(feature_map.getUnassignedPeptideIdentifications());
for (Size f = 0; f < feature_map.size(); ++f)
{
calibrated_feature_map[f].getPeptideIdentifications().clear();
if (!feature_map[f].getPeptideIdentifications().empty())
{
calibrated_feature_map[f].setPeptideIdentifications(feature_map[f].getPeptideIdentifications());
}
}
}
ExitCodes main_(int, const char **)
{
String in = getStringOption_("in");
String out = getStringOption_("out");
String tr_file = getStringOption_("tr");
bool force = getFlag_("force");
boost::shared_ptr<PeakMap > exp ( new PeakMap );
MzMLFile mzmlfile;
mzmlfile.setLogType(log_type_);
mzmlfile.load(in, *exp);
TargetedExpType transition_exp;
TraMLFile().load(tr_file, transition_exp);
FeatureMap output;
OpenSwath::SpectrumAccessPtr input = SimpleOpenMSSpectraFactory::getSpectrumAccessOpenMSPtr(exp);
run_(input, output, transition_exp, force);
output.ensureUniqueId();
StringList ms_runs;
exp->getPrimaryMSRunPath(ms_runs);
output.setPrimaryMSRunPath(ms_runs);
FeatureXMLFile().store(out, output);
return EXECUTION_OK;
}
ExitCodes main_(int, const char **)
{
String in = getStringOption_("in"), out = getStringOption_("out");
FileTypes::Type in_type = FileHandler::getType(in);
if (in_type == FileTypes::FEATUREXML)
{
FeatureMap<> features;
FeatureXMLFile().load(in, features);
for (FeatureMap<>::Iterator feat_it = features.begin();
feat_it != features.end(); ++feat_it)
{
resolveConflict_(feat_it->getPeptideIdentifications());
}
addDataProcessing_(features,
getProcessingInfo_(DataProcessing::FILTERING));
FeatureXMLFile().store(out, features);
}
else // consensusXML
{
ConsensusMap consensus;
ConsensusXMLFile().load(in, consensus);
for (ConsensusMap::Iterator cons_it = consensus.begin();
cons_it != consensus.end(); ++cons_it)
{
resolveConflict_(cons_it->getPeptideIdentifications());
}
addDataProcessing_(consensus,
getProcessingInfo_(DataProcessing::FILTERING));
ConsensusXMLFile().store(out, consensus);
}
return EXECUTION_OK;
}
CAddonButtonMap::DriverMap CAddonButtonMap::CreateLookupTable(const FeatureMap& features)
{
using namespace JOYSTICK;
DriverMap driverMap;
for (FeatureMap::const_iterator it = features.begin(); it != features.end(); ++it)
{
const ADDON::JoystickFeature& feature = it->second;
switch (feature.Type())
{
case JOYSTICK_FEATURE_TYPE_SCALAR:
{
driverMap[CPeripheralAddonTranslator::TranslatePrimitive(feature.Primitive(JOYSTICK_SCALAR_PRIMITIVE))] = it->first;
break;
}
case JOYSTICK_FEATURE_TYPE_ANALOG_STICK:
{
std::vector<JOYSTICK_FEATURE_PRIMITIVE> primitives = {
JOYSTICK_ANALOG_STICK_UP,
JOYSTICK_ANALOG_STICK_DOWN,
JOYSTICK_ANALOG_STICK_RIGHT,
JOYSTICK_ANALOG_STICK_LEFT,
};
for (auto primitive : primitives)
driverMap[CPeripheralAddonTranslator::TranslatePrimitive(feature.Primitive(primitive))] = it->first;
break;
}
case JOYSTICK_FEATURE_TYPE_ACCELEROMETER:
{
std::vector<JOYSTICK_FEATURE_PRIMITIVE> primitives = {
JOYSTICK_ACCELEROMETER_POSITIVE_X,
JOYSTICK_ACCELEROMETER_POSITIVE_Y,
JOYSTICK_ACCELEROMETER_POSITIVE_Z,
};
for (auto primitive : primitives)
{
CDriverPrimitive translatedPrimitive = CPeripheralAddonTranslator::TranslatePrimitive(feature.Primitive(primitive));
driverMap[translatedPrimitive] = it->first;
// Map opposite semiaxis
CDriverPrimitive oppositePrimitive = CDriverPrimitive(translatedPrimitive.Index(), 0, translatedPrimitive.SemiAxisDirection() * -1, 1);
driverMap[oppositePrimitive] = it->first;
}
break;
}
default:
break;
}
}
return driverMap;
}
bool StudentLocalization::stepFindChinContours(const IntensityImage &image, FeatureMap &features) const {
const Feature & mouthCenter = features.getFeature(Feature::FEATURE_MOUTH_CENTER);
const Feature & chin = features.getFeature(Feature::FEATURE_CHIN);
Feature & chinContour = features.getFeature(Feature::FEATURE_CHIN_CONTOUR);
// The mouth could interfere with the localization of the chin contours.
// Therefore use an offset based on which mouthcorner is the furthest away.
// This however could still prove troublesome, so add a slight margin;
// represented by the 'magic' number.
const double radius = chin.getY() - mouthCenter.getY();
const double smallestOffsetRadiusFactor = 0.75;
const double biggestOffsetRadiusFactor = 2;
//
// 180° matches half a circle.
// 0° points to the east in goniometrics, where increasing angles are counter-clockwise.
// Since the chin is usually on the bottompart of the image, the points either need to be
// found clockwise (-180), or start looking at an offset.
//
const int halfCircle = 180;
const int angleStepSize = 10;
// roughly 19 points need to be found.
// 0 through 18 does just that.
for (int i = 0; i <= halfCircle / angleStepSize; ++i)
{
// Determine the angle of the line among which we try to look for a point on the chin's contour.
int angle = halfCircle + i * angleStepSize;
// The size of a step in both the horizontal and verticical direction along the line with the current angle.
double xDir = std::cos(angle * M_PI / 180.0);
double yDir = -std::sin(angle * M_PI / 180.0);
// Normalized size of a step that is made alongside the current angle.
const double increment = 1;
// Make multiple steps alongside the current angle within bounds where it is likely a contour for the chin.
for (double distance = radius * smallestOffsetRadiusFactor; distance < radius * biggestOffsetRadiusFactor; distance += increment)
{
// Determine the location to check for a contour of the chin.
double xCheck = mouthCenter.getX() + (xDir * distance);
double yCheck = mouthCenter.getY() + (yDir * distance);
// Image should be thresholded grayscale, where the pixels are eiher 0 or 255; check in the middle of it.
const unsigned char treshhold = 128;
if (image.getPixel(xCheck, yCheck) <= treshhold)
{
chinContour.addPoint(Point2D<double>(xCheck, yCheck));
std::cout << "Found contour at: " << xCheck << "; " << yCheck << "\n";
break;
}
}
}
return (chinContour.getPoints().size() > 0);
}
double length_sq() {
double l = 0.0;
FeatureIterator fit;
for (fit = feat_map.begin(); fit != feat_map.end(); fit++) {
Feature &f = fit->second;
l += f.value * f.value;
}
return l;
}
void DeepPyramid::constructFeatureMapPyramid(const Mat& img, vector<FeatureMap>& maps) const {
vector<Mat> imgPyramid;
constructImagePyramid(img, imgPyramid);
for (int i = 0; i < levelCount; i++) {
FeatureMap map;
net->processImage(imgPyramid[i], map);
map.normalize();
maps.push_back(map);
}
}
void SeedListGenerator::convertSeedList(const FeatureMap& features,
SeedList& seeds)
{
seeds.clear();
for (FeatureMap::ConstIterator feat_it = features.begin();
feat_it != features.end(); ++feat_it)
{
DPosition<2> point(feat_it->getRT(), feat_it->getMZ());
seeds.push_back(point);
}
}
int main(int argc, char * argv[])
{
FeatureMap fm;
Feature feature;
fm.push_back(feature);
std::string s = ExampleLibraryFile::printSomething();
std::cout << "From external lib: " << s << "\n";
std::cout << "All good and well!\n";
return 0;
}
CAddonButtonMap::DriverMap CAddonButtonMap::CreateLookupTable(const FeatureMap& features)
{
DriverMap driverMap;
for (FeatureMap::const_iterator it = features.begin(); it != features.end(); ++it)
{
const ADDON::JoystickFeature& feature = it->second;
switch (feature.Type())
{
case JOYSTICK_FEATURE_TYPE_SCALAR:
{
driverMap[CPeripheralAddonTranslator::TranslatePrimitive(feature.Primitive())] = it->first;
break;
}
case JOYSTICK_FEATURE_TYPE_ANALOG_STICK:
{
driverMap[CPeripheralAddonTranslator::TranslatePrimitive(feature.Up())] = it->first;
driverMap[CPeripheralAddonTranslator::TranslatePrimitive(feature.Down())] = it->first;
driverMap[CPeripheralAddonTranslator::TranslatePrimitive(feature.Right())] = it->first;
driverMap[CPeripheralAddonTranslator::TranslatePrimitive(feature.Left())] = it->first;
break;
}
case JOYSTICK_FEATURE_TYPE_ACCELEROMETER:
{
CDriverPrimitive x_axis(CPeripheralAddonTranslator::TranslatePrimitive(feature.PositiveX()));
CDriverPrimitive y_axis(CPeripheralAddonTranslator::TranslatePrimitive(feature.PositiveY()));
CDriverPrimitive z_axis(CPeripheralAddonTranslator::TranslatePrimitive(feature.PositiveZ()));
driverMap[x_axis] = it->first;
driverMap[y_axis] = it->first;
driverMap[z_axis] = it->first;
CDriverPrimitive x_axis_opposite(x_axis.Index(), x_axis.SemiAxisDirection() * -1);
CDriverPrimitive y_axis_opposite(y_axis.Index(), y_axis.SemiAxisDirection() * -1);
CDriverPrimitive z_axis_opposite(z_axis.Index(), z_axis.SemiAxisDirection() * -1);
driverMap[x_axis_opposite] = it->first;
driverMap[y_axis_opposite] = it->first;
driverMap[z_axis_opposite] = it->first;
break;
}
default:
break;
}
}
return driverMap;
}
bool StudentLocalization::stepFindHead(const IntensityImage &image, FeatureMap &features) const {
int yTop = getTopOfHead(image);
int alpha = 13;
int* histogram;
int left, right;
int diffFirstLast = -1, maxDiffFirstLast = -1;
int ySides = -1;
//number of slices the image will be 'cut' into for the histograms
int count = ((image.getHeight() - yTop - 1) / alpha) + 1;
for (int i = 0; i < count; i++) {
//get histogram of current slice
histogram = getHistogramX(image, alpha, yTop + i * alpha);
findSidesInHistogram(histogram, image.getWidth(), &left, &right);
//if no two sides have been found continue to the next slice
if (left == -1 || right == -1) {
continue;
}
//find the first peak gap between the left and right side to detect the position of the ears
diffFirstLast = right - left;
if (diffFirstLast > maxDiffFirstLast) {
maxDiffFirstLast = diffFirstLast;
}
else {
//if the gap is lower than the max, a peak has been found
//and the y position of the ears will be set to the middle of the slice
//where the peak is detected
ySides = yTop + (i + 1) * alpha;
break;
}
}
//if no peak is detected return false
if (ySides == -1) {
return false;
}
//set the results in the feature map
Point2D<double> * pointLeftSideHead = new Point2D<double>(left, ySides);
Feature * leftSideHead = new Feature(Feature::FEATURE_HEAD_LEFT_SIDE, *pointLeftSideHead);
features.putFeature(*leftSideHead);
Point2D<double> * pointRightSideHead = new Point2D<double>(right, ySides);
Feature * rightSideHead = new Feature(Feature::FEATURE_HEAD_RIGHT_SIDE, *pointRightSideHead);
features.putFeature(*rightSideHead);
Point2D<double> * pointTopOfHead = new Point2D<double>((left + right) / 2, yTop);
Feature * topOfHead = new Feature(Feature::FEATURE_HEAD_TOP, *pointTopOfHead);
features.putFeature(*topOfHead);
return true;
}
void markFeatureLocations_(FeatureMap & feature_map, MSExperiment<> & exp, QImage & image, bool transpose, QColor color)
{
double xcoef = image.width(), ycoef = image.height();
if (transpose)
{
xcoef /= exp.getMaxRT() - exp.getMinRT();
ycoef /= exp.getMaxMZ() - exp.getMinMZ();
}
else
{
xcoef /= exp.getMaxMZ() - exp.getMinMZ();
ycoef /= exp.getMaxRT() - exp.getMinRT();
}
for (FeatureMap::Iterator feat_iter = feature_map.begin();
feat_iter != feature_map.end(); ++feat_iter)
{
const ConvexHull2D convex_hull = feat_iter->getConvexHull();
DBoundingBox<2> box = convex_hull.getBoundingBox();
double rt = feat_iter->getRT();
double mz = feat_iter->getMZ();
double lower_mz = box.minY();
double lower_rt = box.minX();
double upper_mz = box.maxY();
double upper_rt = box.maxX();
int lx, ly, ux, uy, cx, cy;
if (transpose)
{
lx = int(xcoef * (lower_rt - exp.getMinRT()));
ly = int(ycoef * (exp.getMaxMZ() - lower_mz));
ux = int(xcoef * (upper_rt - exp.getMinRT()));
uy = int(ycoef * (exp.getMaxMZ() - upper_mz));
cx = int(xcoef * (rt - exp.getMinRT()));
cy = int(ycoef * (mz - lower_mz));
}
else
{
lx = int(xcoef * (lower_mz - exp.getMinMZ()));
ly = int(ycoef * (exp.getMaxRT() - lower_rt));
ux = int(xcoef * (upper_mz - exp.getMinMZ()));
uy = int(ycoef * (exp.getMaxRT() - upper_rt));
cx = int(xcoef * (mz - exp.getMinMZ()));
cy = int(ycoef * (exp.getMaxRT() - rt));
}
addFeatureBox_(ly, lx, uy, ux, image, color);
addPoint_(cx, cy, image, Qt::black); // mark center
}
}
void copy(FeatureMap& dest, const FeatureMap& src)
{
assert(dest.input_length == src.input_length);
assert(dest.feature_count == src.feature_count);
const auto input_length = dest.input_length;
const auto feature_count = dest.feature_count;
memcpy(dest.biases(), src.biases(), sizeof(float) * feature_count);
for(uint32_t k = 0; k < feature_count; k++)
{
memcpy(dest.feature(k), src.feature(k), sizeof(float) * input_length);
}
}
void MapAlignmentTransformer::transformSingleFeatureMap(FeatureMap<> & fmap,
const TransformationDescription & trafo)
{
for (vector<Feature>::iterator fmit = fmap.begin(); fmit != fmap.end(); ++fmit)
{
applyToFeature_(*fmit, trafo);
}
// adapt RT values of unassigned peptides:
if (!fmap.getUnassignedPeptideIdentifications().empty())
{
transformSinglePeptideIdentification(
fmap.getUnassignedPeptideIdentifications(), trafo);
}
}
void InternalCalibration::applyTransformation_(const FeatureMap<> & feature_map, FeatureMap<> & calibrated_feature_map)
{
calibrated_feature_map = feature_map;
for (Size f = 0; f < feature_map.size(); ++f)
{
DoubleReal mz = feature_map[f].getMZ();
mz = trafo_.apply(mz);
calibrated_feature_map[f].setMZ(mz);
// apply transformation to convex hulls and subordinates
for (Size s = 0; s < calibrated_feature_map[f].getSubordinates().size(); ++s)
{
// subordinates
DoubleReal mz = calibrated_feature_map[f].getSubordinates()[s].getMZ();
mz = trafo_.apply(mz);
calibrated_feature_map[f].getSubordinates()[s].setMZ(mz);
}
for (Size s = 0; s < calibrated_feature_map[f].getConvexHulls().size(); ++s)
{
// convex hulls
std::vector<DPosition<2> > point_vec = calibrated_feature_map[f].getConvexHulls()[s].getHullPoints();
calibrated_feature_map[f].getConvexHulls()[s].clear();
for (Size p = 0; p < point_vec.size(); ++p)
{
DoubleReal mz = point_vec[p][1];
mz = trafo_.apply(mz);
point_vec[p][1] = mz;
}
calibrated_feature_map[f].getConvexHulls()[s].setHullPoints(point_vec);
}
}
}
void SeedListGenerator::convertSeedList(const SeedList& seeds,
FeatureMap& features)
{
features.clear(true); // "true" should really be a default value here...
Size counter = 0;
for (SeedList::const_iterator seed_it = seeds.begin();
seed_it != seeds.end(); ++seed_it, ++counter)
{
Feature feature;
feature.setRT(seed_it->getX());
feature.setMZ(seed_it->getY());
feature.setUniqueId(counter);
features.push_back(feature);
}
// // assign unique ids:
// features.applyMemberFunction(&UniqueIdInterface::setUniqueId);
}
void MapAlignmentTransformer::transformRetentionTimes(
FeatureMap& fmap, const TransformationDescription& trafo,
bool store_original_rt)
{
for (vector<Feature>::iterator fmit = fmap.begin(); fmit != fmap.end();
++fmit)
{
applyToFeature_(*fmit, trafo, store_original_rt);
}
// adapt RT values of unassigned peptides:
if (!fmap.getUnassignedPeptideIdentifications().empty())
{
transformRetentionTimes(fmap.getUnassignedPeptideIdentifications(), trafo,
store_original_rt);
}
}
void QuantitativeExperimentalDesign::mergeFeatureMaps_(FeatureMap<> & out, const String & experiment, StringList & file_paths)
{
FeatureMap<> map;
LOG_INFO << "Merge feature maps: " << endl;
UInt counter = 1;
for (StringList::Iterator file_it = file_paths.begin(); file_it != file_paths.end(); ++file_it, ++counter)
{
//load should clear the map
FeatureXMLFile().load(*file_it, map);
for (FeatureMap<>::iterator it = map.begin(); it != map.end(); ++it)
{
it->setMetaValue("experiment", DataValue(experiment));
}
out += map;
}
}
bool StudentLocalization::stepFindChinContours(const IntensityImage &image, FeatureMap &features) const {
//Get the position of the mouth
Feature Mouth = features.getFeature(Feature::FEATURE_MOUTH_TOP);
Point2D<double> MouthPosition;
//Initialise the chin feature
std::vector<Point2D<double>> ChinPositions;
Feature Chin = Feature(Feature::FEATURE_CHIN);
Feature ChinContour = Feature(Feature::FEATURE_CHIN_CONTOUR);
int y = 0;
int m = 0;
//Draw a half circle starting from the center of the mouth
for (int j = HALF_CIRCLE; j > 0; j -= DEGREE_STEP){
//reset the position of the mouth
MouthPosition.set(Mouth.getX(), Mouth.getY());
MouthPosition.set(drawLine(j, START_POSITION, MouthPosition));
m = m + 1;
for (int i = 0; i < MEASURE_RANGE; i++){
MouthPosition.set(drawLine(j, MEASURE_STEP, MouthPosition));
Intensity pixel = image.getPixel(MouthPosition.getX(), MouthPosition.getY());
// If the intensity of the current pixel is lower than 2 (which means it is black)
if (pixel < 2){
// If the current angle is within the bounds of the half circle
if (j < RIGHT_HALF && j > LEFT_HALF){
ChinContour.addPoint(drawLine(j, 2, MouthPosition));
}
else{
//Draw a point on the mouth position, to indicate the detection failed.
ChinContour.addPoint(MouthPosition);
}
break;
}
}
}
features.putFeature(ChinContour);
return true;
}
void DeepPyramid::processFeatureMap(int filterIdx, const FeatureMap &map, vector<BoundingBox> &detectedObjects) const {
Size mapSize = map.size();
Size filterSize = rootFilter[filterIdx]->getMapSize();
cout << "size: "<<map.size()<<endl;
for (int width = 0; width < mapSize.width-filterSize.width; width+=stride) {
for (int height = 0; height < mapSize.height-filterSize.height; height+=stride) {
FeatureMap extractedMap;
map.extractFeatureMap(Rect(Point(width, height), filterSize), extractedMap);
if (rootFilter[filterIdx]->predict(extractedMap) == OBJECT) {
BoundingBox box;
box.norm5Box = Rect(Point(width, height), filterSize);
box.confidence = std::fabs(rootFilter[filterIdx]->predict(extractedMap, true));
box.map = extractedMap;
detectedObjects.push_back(box);
}
}
}
}
void QuantitativeExperimentalDesign::applyDesign2Quantifier(PeptideAndProteinQuant & quantifier, TextFile & file, StringList & file_paths)
{
// vector< pair<PeptideAndProteinQuant::PeptideData,PeptideAndProteinQuant::ProteinQuant> >& result)
//create mapping from experimental setting to all respective file names
map<String, StringList> design2FileBaseName;
mapFiles2Design_(design2FileBaseName, file);
//filter out all non-existing files
map<String, StringList> design2FilePath;
findRelevantFilePaths_(design2FileBaseName, design2FilePath, file_paths);
//determine wether we deal with idXML or featureXML
FileTypes::Type in_type = FileHandler::getType(file_paths.front());
if (in_type == FileTypes::FEATUREXML)
{
FeatureMap<> features;
for (map<String, StringList>::iterator iter = design2FilePath.begin(); iter != design2FilePath.end(); ++iter)
{
mergeFeatureMaps_(features, iter->first, iter->second);
}
LOG_INFO << "Number of proteinIdentifications: " << features.getProteinIdentifications().size() << endl;
ProteinIdentification & proteins = features.getProteinIdentifications()[0];
quantifier.quantifyPeptides(features);
quantifier.quantifyProteins(proteins);
}
else
{
ConsensusMap consensus;
for (map<String, StringList>::iterator iter = design2FilePath.begin(); iter != design2FilePath.end(); ++iter)
{
mergeConsensusMaps_(consensus, iter->first, iter->second);
}
LOG_INFO << "Number of proteinIdentifications: " << consensus.getProteinIdentifications().size() << endl;
ProteinIdentification & proteins = consensus.getProteinIdentifications()[0];
quantifier.quantifyPeptides(consensus);
quantifier.quantifyProteins(proteins);
}
}
Int main()
{
FeatureMap map;
Feature feature;
feature.setRT(15.0);
feature.setMZ(571.3);
map.push_back(feature); //append feature 1
feature.setRT(23.3);
feature.setMZ(1311.3);
map.push_back(feature); //append feature 2
for (FeatureMap::Iterator it = map.begin(); it != map.end(); ++it)
{
cout << it->getRT() << " - " << it->getMZ() << endl;
}
return 0;
} //end of main
/// Detailed Constructor
MSQuantifications::MSQuantifications(FeatureMap<> fm, ExperimentalSettings& es, std::vector<DataProcessing>& dps, std::vector<std::vector<std::pair<String, DoubleReal> > > label) :
ExperimentalSettings()
{
MSQuantifications::QUANT_TYPES quant_type = MSQuantifications::LABELFREE;
this->setAnalysisSummaryQuantType(quant_type);
//~ AssayList,InputFiles,SoftwareList
//~ aus exp.
this->registerExperiment(es,dps,label);
this->setDataProcessingList(fm.getDataProcessing()); //TODO add dp from experiment (i.e. mzml) ?
feature_maps_ = std::vector<FeatureMap<> > (1,fm);
}
void applyFDRcutoff(FeatureMap & feature_map, double cutoff, String fdr_name)
{
FeatureMap out_feature_map = feature_map;
out_feature_map.clear(false);
for (Size i = 0; i < feature_map.size(); i++)
{
if ((double)feature_map[i].getMetaValue(fdr_name) < cutoff)
{
out_feature_map.push_back(feature_map[i]);
}
}
feature_map = out_feature_map;
}
void EDTAFile::store(const String& filename, const FeatureMap& map) const
{
TextFile tf;
tf.addLine("RT\tm/z\tintensity\tcharge");
for (Size i = 0; i < map.size(); ++i)
{
const Feature& f = map[i];
tf.addLine(String(f.getRT()) + "\t" + f.getMZ() + "\t" + f.getIntensity() + "\t" + f.getCharge());
}
tf.store(filename);
}
