void ImageSaliencyDetector::compute() {
if (mSrcImage.empty()) {
throw std::logic_error("ImageSaliencyDetector: Source image is empty!");
}
srand(static_cast<unsigned int>(time(NULL)));
// Get edge information
EdgeDetector canny;
canny.compute(mSrcImage);
setMagnitudes(canny.getGradMagnitudes());
setOrientations(canny.getGradOrientations());
// Quantize magnitudes
quantizeMagnitudes();
// Perform iterative saliency detection mechanism
int squaredNHood = neighborhoodSize * neighborhoodSize;
int halfNHood = neighborhoodSize / 2;
int reqNumSamples = static_cast<int>(samplingPercentage * (mSrcImage.cols * mSrcImage.rows * squaredNHood));
int imageHeight = mSrcImage.rows;
int imageWidth = mSrcImage.cols;
int counter = 0;
while (counter < reqNumSamples) {
KernelDensityInfo kernelSum;
BoundingBox2D bounds;
std::vector<Location2D> samples(4);
// Randomly select the location of the first sample
samples[0].y = rand() % imageHeight;
samples[0].x = rand() % imageWidth;
// The other 3 samples MUST be selected in the neighborhood of the first
samples[1].y = (rand() % neighborhoodSize) + (samples[0].y - halfNHood);
samples[1].x = (rand() % neighborhoodSize) + (samples[0].x - halfNHood);
samples[2].y = (rand() % neighborhoodSize) + (samples[0].y - halfNHood);
samples[2].x = (rand() % neighborhoodSize) + (samples[0].x - halfNHood);
samples[3].y = (rand() % neighborhoodSize) + (samples[0].y - halfNHood);
samples[3].x = (rand() % neighborhoodSize) + (samples[0].x - halfNHood);
kernelSum = calculateKernelSum(samples);
bounds = getApplicableBounds(samples);
updateApplicableRegion(bounds, kernelSum);
++counter;
}
updateSaliencyMap();
}
void Matrix44F::setFrontOrientation(const Vector3F& front)
{
Vector3F side = front.perpendicular();
Vector3F up = front.cross(side);
setOrientations(side, up, front);
}