double Classifier::getFilterError(string trainingDirectory, Annotations::Tag tag,
ErrorType errorType) {
Annotations annotations;
Filter* filter = getFilter(tag);
// read annotations
string locationsFileName = trainingDirectory + "/" + Globals::annotationsFileName;
annotations.readAnnotations(locationsFileName);
// get the frames directory
string framesDirectory = annotations.getFramesDirectory();
// reset total
double totalError = 0;
// iterate over the set of all annotations
vector<FrameAnnotation*>& frameAnnotations = annotations.getFrameAnnotations();
for (unsigned int i = 0; i < frameAnnotations.size(); i++) {
FrameAnnotation* fa = frameAnnotations[i];
// get LOI
CvPoint& location = fa->getLOI(tag);
if (!location.x && !location.y)
continue;
// compose filename
char buffer[256];
sprintf(buffer, "frame_%d.png", fa->getFrameNumber());
string fileName = framesDirectory + "/" + buffer;
// load image
IplImage* inputImg = cvLoadImage((const char*)fileName.c_str());
if (!inputImg) {
string err = "Filter::update. Cannot load file " + fileName + ".";
throw (err);
}
IplImage* image = cvCreateImage(cvGetSize(inputImg), IPL_DEPTH_8U, 1);
cvCvtColor(inputImg, image, CV_BGR2GRAY);
// get the location of the left eye
CvPoint offset;
offset.x = offset.y = 0;
IplImage* roi = (roiFunction)? roiFunction(image, *fa, offset, Annotations::Face) : 0;
location.x -= offset.x;
location.y -= offset.y;
// apply filter
fftw_complex* imageFFT = filter->preprocessImage((roi)? roi : image);
IplImage* postFilterImg = filter->apply(imageFFT);
// compute location
double min;
double max;
CvPoint minLoc;
CvPoint maxLoc;
cvMinMaxLoc(postFilterImg, &min, &max, &minLoc, &maxLoc);
// compute squared error as the distance between the location
// found and the location as annotated
double xdiff = abs(maxLoc.x - location.x);
double ydiff = abs(maxLoc.y - location.y);
switch (errorType) {
case OneNorm:
totalError += (xdiff + ydiff);
break;
case TwoNorm:
totalError += sqrt(xdiff * xdiff + ydiff * ydiff);
break;
case MSE:
totalError += (xdiff * xdiff + ydiff * ydiff);
break;
default:
totalError += ((xdiff > ydiff)? xdiff : ydiff);
break;
}
if (roi)
cvReleaseImage(&roi);
cvReleaseImage(&image);
cvReleaseImage(&inputImg);
}
return totalError / frameAnnotations.size();
}
int Classifier::getZone(IplImage* frame, double& confidence, FrameAnnotation& fa) {
if (!leftEye || !rightEye || !nose) {
string err = "Classifier::getZone. Location extractors malformed.";
throw (err);
}
// the roi offset
CvPoint offset;
// LOIs
CvPoint leftEyeLocation;
CvPoint rightEyeLocation;
CvPoint noseLocation;
// computing the confidence of the location identification
double leftPSR;
double rightPSR;
double nosePSR;
CvPoint center = fa.getLOI(Annotations::Face);
if (!center.x || !center.y) {
center.x = Globals::imgWidth / 2;
center.y = Globals::imgHeight / 2;
fa.setFace(center);
}
offset.x = offset.y = 0;
IplImage* roi = (roiFunction)? roiFunction(frame, fa, offset, Annotations::Face) : 0;
// all location extractors do identical preprocessing. Therefore, preprocess
// once using say the left eye extractor and re-use it for all three extractors
fftw_complex* preprocessedImage = leftEye->getPreprocessedImage((roi)? roi : frame);
#pragma omp parallel sections num_threads(2)
{
#pragma omp section
{
leftEye->setImage(preprocessedImage);
leftEye->apply();
leftEye->getMaxLocation(leftEyeLocation, leftPSR);
leftEyeLocation.x += offset.x;
leftEyeLocation.y += offset.y;
}
#pragma omp section
{
// get the location of the right eye
rightEye->setImage(preprocessedImage);
rightEye->apply();
rightEye->getMaxLocation(rightEyeLocation, rightPSR);
rightEyeLocation.x += offset.x;
rightEyeLocation.y += offset.y;
}
}
if (roi)
cvReleaseImage(&roi);
center.x = (leftEyeLocation.x + rightEyeLocation.x) / 2;
center.y = leftEyeLocation.y + Globals::noseDrop;
fa.setNose(center);
offset.x = offset.y = 0;
roi = (roiFunction)? roiFunction(frame, fa, offset, Annotations::Nose) : 0;
// free the preprocessed image
fftw_free(preprocessedImage);
// all location extractors do identical preprocessing. Therefore, preprocess
// once using say the left eye extractor and re-use it for all three extractors
preprocessedImage = nose->getPreprocessedImage((roi)? roi : frame);
// get the location of the nose
nose->setImage(preprocessedImage);
nose->apply();
nose->getMaxLocation(noseLocation, nosePSR);
noseLocation.x += offset.x;
noseLocation.y += offset.y;
// free the preprocessed image
fftw_free(preprocessedImage);
fa.setLeftIris(leftEyeLocation);
fa.setRightIris(rightEyeLocation);
fa.setNose(noseLocation);
// we are done with the images at this point. Free roi if not zero
if (roi)
cvReleaseImage(&roi);
// cout << "Confidence (L, R, N) = (" << leftPSR << ", " <<
// rightPSR << ")" << endl;
// extract features vector
vector<double> data;
for (int i = 0; i < nFeatures; i++) {
double value = featureExtractors[i]->extract(&fa);
data.push_back(value);
}
// normalize
normalize(data);
// create SVM Light objects to classify
DOC* doc;
WORD* words = (WORD*)malloc(sizeof(WORD) * (nFeatures + 1));
for (int i = 0; i < nFeatures; i++) {
words[i].wnum = featureExtractors[i]->getId();
words[i].weight = data[i];
}
// SVM Light expects that the features vector has a zero element
// to indicate termination and hence
words[nFeatures].wnum = 0;
words[nFeatures].weight = 0.0;
// create doc
string comment = "Gaze SVM";
doc = create_example(-1, 0, 0, 0.0, create_svector(words, (char*)comment.c_str(), 1.0));
int maxIndex = 0;
confidence = -FLT_MAX;
double dists[Globals::numZones];
// classify using each zone model
#pragma omp parallel for num_threads(Globals::numZones)
for (unsigned int i = 0; i < Globals::numZones; i++) {
if (kernelType == Trainer::Linear)
dists[i] = classify_example_linear(models[i], doc);
else
dists[i] = classify_example(models[i], doc);
}
for (unsigned int i = 0; i < Globals::numZones; i++) {
if (confidence < dists[i]) {
confidence = dists[i];
maxIndex = i + 1;
}
}
free_example(doc, 1);
free(words);
return maxIndex;
}