// Computes an Lidfaces model with images in src and corresponding labels
// in labels.
void Lidfaces::train(cv::InputArrayOfArrays src, cv::InputArray labels)
{
std::vector<std::vector<cv::KeyPoint> > allKeyPoints;
cv::Mat descriptors;
// Get SIFT keypoints and LID descriptors
detectKeypointsAndDescriptors(src, allKeyPoints, descriptors);
// kmeans function requires points to be CV_32F
descriptors.convertTo(descriptors, CV_32FC1);
// Do k-means clustering
const int CLUSTER_COUNT = params::lidFace::clustersAsPercentageOfKeypoints*descriptors.rows;
cv::Mat histogramLabels;
// This function populates histogram bin labels
// The nth element of histogramLabels is an integer which represents the cluster that the
// nth element of allKeyPoints is a member of.
kmeans(
descriptors, // The points we are clustering are the descriptors
CLUSTER_COUNT, // The number of clusters (K)
histogramLabels, // The label of the corresponding keypoint
params::kmeans::termCriteria,
params::kmeans::attempts,
params::kmeans::flags,
mCenters);
// Convert to single channel 32 bit float as the matrix needs to be in a form supported
// by calcHist
histogramLabels.convertTo(histogramLabels, CV_32FC1);
// We end up with a histogram for each image
const size_t NUM_IMAGES = getSize(src);
std::vector<cv::Mat> hists(NUM_IMAGES);
// mCodebook.resize(NUM_IMAGES);
// The histogramLabels vector contains ALL the points from EVERY image. We need to split
// it up into groups of points for each image.
// Because there are the same number of points in each image, and the points were put
// into the labels vector in order, we can simply divide the labels vector evenly to get
// the individual image's points.
std::vector<cv::Mat> separatedLabels;
for (unsigned int i = 0, startRow = 0; i < NUM_IMAGES; ++i)
{
separatedLabels.push_back(
histogramLabels.rowRange(
startRow,
startRow + allKeyPoints[i].size()));
startRow += allKeyPoints[i].size();
}
// Populate the hists vector
generateHistograms(hists, separatedLabels, CLUSTER_COUNT);
// Make the magnitude of each histogram equal to 1
normalizeHistograms(hists);
mCodebook = hists;
mLabels = labels.getMat();
}
RandomForestImage::RandomForestImage(const std::vector<std::string>& treeFiles,
const std::vector<int>& deviceIds,
const AccelerationMode accelerationMode,
const double histogramBias)
: configuration(), ensemble(treeFiles.size()),
m_predictionAllocator(boost::make_shared<cuv::pooled_cuda_allocator>())
{
if (treeFiles.empty()) {
throw std::runtime_error("cannot construct empty forest");
}
std::vector<TrainingConfiguration> configurations(treeFiles.size());
tbb::parallel_for(tbb::blocked_range<size_t>(0, treeFiles.size(), 1),
[&](const tbb::blocked_range<size_t>& range) {
for(size_t tree = range.begin(); tree != range.end(); tree++) {
CURFIL_INFO("reading tree " << tree << " from " << treeFiles[tree]);
boost::shared_ptr<RandomTreeImage> randomTree;
std::string hostname;
boost::filesystem::path folderTraining;
boost::posix_time::ptime date;
TrainingConfiguration configuration = RandomTreeImport::readJSON(treeFiles[tree], randomTree, hostname,
folderTraining, date);
CURFIL_INFO("trained " << date << " on " << hostname);
CURFIL_INFO("training folder: " << folderTraining);
assert(randomTree);
ensemble[tree] = randomTree;
configurations[tree] = configuration;
CURFIL_INFO(*randomTree);
}
});
for (size_t i = 1; i < treeFiles.size(); i++) {
bool strict = false;
if (!configurations[0].equals(configurations[i], strict)) {
CURFIL_ERROR("configuration of tree 0: " << configurations[0]);
CURFIL_ERROR("configuration of tree " << i << ": " << configurations[i]);
throw std::runtime_error("different configurations");
}
if (ensemble[0]->getTree()->getNumClasses() != ensemble[i]->getTree()->getNumClasses()) {
CURFIL_ERROR("number of classes of tree 0: " << ensemble[0]->getTree()->getNumClasses());
CURFIL_ERROR("number of classes of tree " << i << ": " << ensemble[i]->getTree()->getNumClasses());
throw std::runtime_error("different number of classes in trees");
}
}
CURFIL_INFO("training configuration " << configurations[0]);
this->configuration = configurations[0];
this->configuration.setDeviceIds(deviceIds);
this->configuration.setAccelerationMode(accelerationMode);
normalizeHistograms(histogramBias);
}
// Predicts the label and confidence for a given sample.
void Lidfaces::predict(cv::InputArray src, int& label, double& dist) const
{
label = -1;
dist = DBL_MAX;
std::vector<std::vector<cv::KeyPoint> > keyPoints;
cv::Mat descriptors;
std::vector<cv::Mat> imageVector; // A vector containing just one image (this is so we can use the same detectKeypointsAndDescriptors function
imageVector.push_back(src.getMat());
// Get SIFT keypoints and LID descriptors
detectKeypointsAndDescriptors(imageVector, keyPoints, descriptors);
// Cluster the image using the training centres
int closestCentroidIndex = 0;
mCenters.convertTo(mCenters, CV_32FC1);
descriptors.convertTo(descriptors, CV_32FC1);
cv::Mat histogramLabels(descriptors.rows, 1, CV_32F);
// For each descriptor
for (int descriptorIndex = 0; descriptorIndex < descriptors.rows; ++descriptorIndex)
{
// (Give it a classification)
double smallestDist = DBL_MAX;
// For each centroid
for (int centroidIndex = 0; centroidIndex < mCenters.rows; ++centroidIndex)
{
// Calculate the distance from the descriptor to the centroid
double currentDist = cv::norm(
descriptors.row(descriptorIndex) - mCenters.row(centroidIndex));
// If it is the smallest distance, remember it and the centroid
if (currentDist < smallestDist)
{
smallestDist = currentDist;
closestCentroidIndex = centroidIndex;
}
}
histogramLabels.at<float>(descriptorIndex) = closestCentroidIndex;
}
assert(histogramLabels.rows == descriptors.rows);
std::vector<cv::Mat> separatedLabels;
std::vector<cv::Mat> hists(1);
separatedLabels.push_back(histogramLabels);
generateHistograms(hists, separatedLabels, mCenters.rows);
normalizeHistograms(hists);
dist = DBL_MAX;
std::multimap<int, double> distances; // Maps label to distance
// Compare this histogram against all the other histograms
for (size_t codebookIndex = 0; codebookIndex < mCodebook.size(); ++codebookIndex)
{
// Get dist hist
double currentDist = cv::compareHist(hists[0], mCodebook[codebookIndex], CV_COMP_CHISQR);
distances.insert(std::pair<int, double>(mLabels.at<int>(codebookIndex), currentDist));
}
// Calculate the smallest average distance
double smallestAverageDist = DBL_MAX;
int closestLabel = -1;
double curDist = 0;
for (int curLabel = 0; curLabel < mCenters.rows; ++curLabel) // For each curLabel
{
if (distances.count(curLabel) == 0) // If this histogram has none of this label
continue; // Don't bother calculating it
double totalDist = 0;
std::pair<std::multimap<int, double>::const_iterator, std::multimap<int, double>::const_iterator> itRange = distances.equal_range(curLabel);
for (std::multimap<int, double>::const_iterator it = itRange.first; it != itRange.second; ++it)
{
totalDist += it->second;
}
curDist = totalDist/distances.count(curLabel);
if (curDist < smallestAverageDist)
{
smallestAverageDist = curDist;
closestLabel = curLabel;
}
}
label = closestLabel;
dist = smallestAverageDist;
}
