mx_real_t getShimmer(fextract_series_t *pulses, mx_real_t *signal, int n_samples, mx_real_t maxPitch, mx_real_t minPitch) {
mx_real_t pmin =0.8/maxPitch, pmax=1.25/minPitch;
mx_real_t maximumPeriodFactor=1.3, maximumAmplitudeFactor=1.6;
mx_real_t result;
fextract_series_t *peaks=NULL;
peaks = getPeaks(pulses,signal,n_samples,pmin,pmax,maximumPeriodFactor);
if (peaks == NULL)
return -1;
result = getShimmer_local (peaks, pmin, pmax, maximumAmplitudeFactor);
series_destroy(peaks);
return result;
}
void PoldiPeakSearch::exec() {
g_log.information() << "PoldiPeakSearch:" << std::endl;
Workspace2D_sptr correlationWorkspace = getProperty("InputWorkspace");
MantidVec correlationQValues = correlationWorkspace->readX(0);
MantidVec correlatedCounts = correlationWorkspace->readY(0);
g_log.information() << " Auto-correlation data read." << std::endl;
Unit_sptr xUnit = correlationWorkspace->getAxis(0)->unit();
if (xUnit->caption() == "") {
g_log.information()
<< " Workspace does not have unit, defaulting to MomentumTransfer."
<< std::endl;
xUnit = UnitFactory::Instance().create("MomentumTransfer");
} else {
g_log.information() << " Unit of workspace is " << xUnit->caption() << "."
<< std::endl;
}
setMinimumDistance(getProperty("MinimumPeakSeparation"));
setMinimumPeakHeight(getProperty("MinimumPeakHeight"));
setMaximumPeakNumber(getProperty("MaximumPeakNumber"));
if (m_doubleMinimumDistance > static_cast<int>(correlatedCounts.size())) {
throw(std::runtime_error("MinimumPeakSeparation is smaller than number of "
"spectrum points - no peaks possible."));
}
g_log.information() << " Parameters set." << std::endl;
MantidVec summedNeighborCounts = getNeighborSums(correlatedCounts);
g_log.information() << " Neighboring counts summed, contains "
<< summedNeighborCounts.size() << " data points."
<< std::endl;
std::list<MantidVec::const_iterator> peakPositionsSummed =
findPeaks(summedNeighborCounts.begin(), summedNeighborCounts.end());
g_log.information() << " Peaks detected in summed spectrum: "
<< peakPositionsSummed.size() << std::endl;
/* This step is required because peaks are actually searched in the
* "sum-of-neighbors"-spectrum.
* The mapping removes the offset from the peak position which results from
* different beginning
* of this vector compared to the original correlation counts.
*/
std::list<MantidVec::const_iterator> peakPositionsCorrelation =
mapPeakPositionsToCorrelationData(peakPositionsSummed,
summedNeighborCounts.begin(),
correlatedCounts.begin());
g_log.information() << " Peak positions transformed to original spectrum."
<< std::endl;
/* Since intensities are required for filtering, they are extracted from the
* original count data,
* along with the Q-values.
*/
std::vector<PoldiPeak_sptr> peakCoordinates =
getPeaks(correlatedCounts.begin(), correlatedCounts.end(),
peakPositionsCorrelation, correlationQValues, xUnit);
g_log.information()
<< " Extracted peak positions in Q and intensity guesses." << std::endl;
UncertainValue backgroundWithSigma =
getBackgroundWithSigma(peakPositionsCorrelation, correlatedCounts);
g_log.information() << " Calculated average background and deviation: "
<< UncertainValueIO::toString(backgroundWithSigma)
<< std::endl;
if ((*getProperty("MinimumPeakHeight")).isDefault()) {
setMinimumPeakHeight(minimumPeakHeightFromBackground(backgroundWithSigma));
}
std::vector<PoldiPeak_sptr> intensityFilteredPeaks(peakCoordinates.size());
auto newEnd = std::remove_copy_if(
peakCoordinates.begin(), peakCoordinates.end(),
intensityFilteredPeaks.begin(),
boost::bind(&PoldiPeakSearch::isLessThanMinimum, this, _1));
intensityFilteredPeaks.resize(
std::distance(intensityFilteredPeaks.begin(), newEnd));
g_log.information() << " Peaks above minimum intensity ("
<< m_minimumPeakHeight
<< "): " << intensityFilteredPeaks.size() << std::endl;
std::sort(intensityFilteredPeaks.begin(), intensityFilteredPeaks.end(),
boost::bind<bool>(&PoldiPeak::greaterThan, _1, _2,
&PoldiPeak::intensity));
for (std::vector<PoldiPeak_sptr>::const_iterator peak =
intensityFilteredPeaks.begin();
peak != intensityFilteredPeaks.end(); ++peak) {
m_peaks->addPeak(*peak);
}
/* The derived background error is set as error in the workspace containing
* correlation data, so it may be used as weights for peak fitting later on.
*/
setErrorsOnWorkspace(correlationWorkspace, backgroundWithSigma.error());
setProperty("OutputWorkspace", m_peaks->asTableWorkspace());
}
int main(int argc, char **argv) {
//void main() {
FILE *output; //keypoint output (text)
Image *sourceImage, *sourceImage2;
Image *doubledImage;
Image *currentImage;
Octave *octavesList;
Octave *temp, *current;
KeyPoint *peaks;
KeyPoint *counter, *prevPoint;
KeyPoint *keypoints, *keyCounter;
int candidates = 0;
double scale;
double ***mags, ***directions;
int octaveCount;
int tempHeight, tempWidth, tempDepth;
int totalKeypoints = 0;
double imageSigma;
char fileName[32] = {'\0'};
FILE *candidateOutput;
FILE *trashOutput;
FILE *filteredOutput;
/*
if(argc < 2) {
printf("Usage: sift dat_file\n");
return 0;
}
*/
/* read input */
//sourceImage = readPGMFile(argv[1]);
sourceImage = readDATFile("./image.dat");
if(sourceImage == NULL) {
printf("file was not read\n");
return 1;
}
scale = START_SCALE;
/* print constants used */
printf("Constants:\n");
printf("START_SIGMA=%f\n", START_SIGMA);
printf("OCTAVE_LIMIT=%d\n", OCTAVE_LIMIT);
printf("IMAGES_PER_OCTAVE=%d\n", IMAGES_PER_OCTAVE);
printf("SKIP_OCTAVES=%d\n", SKIP_OCTAVES);
printf("MIN_SIZE=%d\n", MIN_SIZE);
printf("MAX_ADJ_STEPS=%d\n", MAX_ADJ_STEPS);
printf("EDGE_RATIO=%f\n", EDGE_RATIO);
printf("CONTRAST_THRESH=%f\n", CONTRAST_THRESH);
printf("CAP=%f\n", CAP);
/* double image to save data */
doubledImage = doubleImage(sourceImage);
if(doubledImage == NULL) {
return 1;
}
scale /= 2.0;
printf("\nImage doubled\n");
/* preprocessing smoothing*/
printf("Preprocessing smoothing\n");
doubledImage = gaussianConvolution2DFast(doubledImage, 1.0);
// doubledImage = gaussianConvolution2DFast(sourceImage, 1.0);
/* build octaves of scalespace */
printf("Octave pyramid construction\n");
currentImage = doubledImage;
octavesList = NULL;
octaveCount = 0;
while(octaveCount < OCTAVE_LIMIT && currentImage->width >= MIN_SIZE && currentImage->height >= MIN_SIZE) {
if(currentImage == NULL) {
printf(" ***Current image null. Exiting.\n");
return 1;
}
/* create octave pointer wrapper */
printf(" Octave for pic %d %d (%d)\n", currentImage->width, currentImage->height, octaveCount);
temp = (Octave *) malloc(sizeof(Octave));
if(temp == NULL) {
printf(" ***Out of memory. Exiting.\n");
return 1;
}
/* build scale space L(x,y,sigma) */
printf(" Gaussian Scale Space\n");
if(octaveCount == 0)
imageSigma = 1.0; //blur of the first image is 1.0
else
imageSigma = START_SIGMA;
temp->gaussianSpace = buildScaleSpaceOctave(currentImage, scale, imageSigma);
/* build diff space D(x,y,sigma) */
printf(" Diff Gaussian Space\n");
temp->diffGaussianSpace = buildDifferences(temp->gaussianSpace);
/* store and go on */
octavesList = link(octavesList, temp);
currentImage = halveImage(getLastImage(temp->gaussianSpace));
scale *= 2.0;
++octaveCount;
printf(" Image halved\n");
}
--octaveCount; /* one extra */
/* generate keypoints from each octave of scalespace */
printf("Keypoint generation\n");
candidateOutput = fopen("output_candidates.txt", "w"); //helpful output file
trashOutput = fopen("output_trash.txt", "w"); //helpful output file
filteredOutput = fopen("output_filtered.txt", "w"); //helpful output file
histOutput = fopen("output_histogram.txt", "w"); //outputs histograms
histOutput2 = fopen("output_histogram2.txt", "w"); //turn on/off in scalespace.c
keypoints = NULL;
sourceImage2 = cloneImage(sourceImage);
for(current = octavesList; current != NULL && octaveCount >= SKIP_OCTAVES; current = current->next, --octaveCount) {
printf(" Octave %d\n", octaveCount);
fprintf(histOutput, "Octave %d\n", octaveCount);
fprintf(histOutput, "---------\n");
fprintf(histOutput2, "Octave %d\n", octaveCount);
fprintf(histOutput2, "---------\n");
/* find keypoint candidates */
printf(" Looking for peaks\n");
peaks = getPeaks(current);
/* temporary output */
scale = current->diffGaussianSpace->imageScale;
printPoints(candidateOutput, peaks, scale, octaveCount);
markImageSPoint(peaks, sourceImage2, scale); //output candidates
if(peaks != NULL)
printf(" found %d candidates\n", peaks->count);
else
printf(" found 0 candidates\n");
/* filter peaks for contrast and edge-iness and localize them */
printf(" Filtering and localizing\n");
trash = NULL;
peaks = filterAndLocalizePeaks(current->diffGaussianSpace, current->gaussianSpace, peaks);
/* temporary output */
printPoints(filteredOutput, peaks, scale, octaveCount);
printTrashed(trashOutput, trash, scale, octaveCount);
if(peaks != NULL)
printf(" have %d candidates remaining\n", peaks->count);
else
printf(" have 0 candidates remaining\n");
/* precompute magnitudes and orientations */
printf(" Generating magnitudes and orientations\n");
tempHeight = current->gaussianSpace->imgHeight;
tempWidth = current->gaussianSpace->imgWidth;
tempDepth = current->gaussianSpace->imageCount - 2;
current->gaussianSpace->magnitudes = allocate3D(tempHeight, tempWidth, tempDepth);
current->gaussianSpace->directions = allocate3D(tempHeight, tempWidth, tempDepth);
getMagnitudesAndOrientations(current->gaussianSpace);
/* assign orientation(s) */
printf(" Assigning orientation(s)\n");
generateKeypoints(current->gaussianSpace, peaks);
/* generate descriptors*/
printf(" Calculating descriptors\n");
createDescriptors(current->gaussianSpace, peaks);
/* multiply by imgScale to get real values */
for(counter = peaks; counter != NULL; counter = counter->next) {
counter->finalX *= counter->imgScale;
counter->finalY *= counter->imgScale;
//also multiply x,y?
counter->scale *= counter->imgScale;
}
/* merge lists */
if(peaks != NULL) {
if(keypoints != NULL) {
keypoints->listRear->next = peaks;
keypoints->listRear = peaks->listRear;
}
else
keypoints = peaks;
}
/* deallocate memory */
deallocate3D(current->gaussianSpace->magnitudes, tempHeight, tempWidth);
deallocate3D(current->gaussianSpace->directions, tempHeight, tempWidth);
/* deallocate trashed points */
counter = trash;
prevPoint = NULL;
while(counter != NULL) {
prevPoint = counter;
counter = counter->next;
free(prevPoint);
}
}
fclose(histOutput);
fclose(histOutput2);
/* write footer to each file */
printFooter(candidateOutput);
fclose(candidateOutput);
printFooter(trashOutput);
fclose(trashOutput);
printFooter(filteredOutput);
fclose(filteredOutput);
/* output keypoint descriptors */
printf("Output\n");
output = fopen("output.txt", "w");
for(keyCounter = keypoints; keyCounter != NULL; keyCounter=keyCounter->next) {
++totalKeypoints;
}
fprintf(output, "%d %d\n", totalKeypoints, 128);
for(keyCounter = keypoints; keyCounter != NULL; keyCounter=keyCounter->next) {
printKeyPoint(keyCounter, output);
}
fclose(output);
/* output keypoints on image*/
markImage(keypoints, sourceImage);
writeDATFile(sourceImage, "./output/keypoints.dat");
/* output initial candidates on image */
writeDATFile(sourceImage2, "./output/keypoints_all.dat");
printf("Done. %d keypoints found.\n", totalKeypoints);
free(sourceImage);
free(sourceImage2);
free(doubledImage);
//free octaves - should put it here
return 0;
}
