int main( int argc, char **argv ) {
daw::rfb::RFBServer server{640, 480, daw::rfb::BitDepth::eight};
auto th = std::thread( [&]( ) {
std::random_device rd;
std::mt19937 gen{rd( )};
std::uniform_int_distribution<uint16_t> dist_x{0, server.max_x( )};
std::uniform_int_distribution<uint16_t> dist_y{0, server.max_y( )};
std::uniform_int_distribution<uint8_t> dist_c{0, 255};
while( true ) {
auto x = dist_x( gen );
auto y = dist_y( gen );
uint16_t width = 20;
uint16_t height = 20;
if( x + width >= server.width( ) ) {
width = ( server.width( ) - x ) - 1;
}
if( y + height >= server.height( ) ) {
height = ( server.height( ) - y ) - 1;
}
draw_rectagle( server, x, y, x + width, y + height, {dist_c( gen ), dist_c( gen ), dist_c( gen ), 0} );
std::this_thread::sleep_for( std::chrono::seconds( 2 ) );
}
} );
server.listen( 1234, daw::nodepp::lib::net::ip_version::ipv4 );
return EXIT_SUCCESS;
}
// -----------------------------------------------------------------------------
//
// Purpose and Method:
// Inputs:
// Outputs:
// Dependencies:
// Restrictions and Caveats:
//
// -----------------------------------------------------------------------------
void
trainTree
(
ForestParam mp_param,
std::vector< std::vector<FaceAnnotation> > &ann,
int idx_forest,
int idx_tree
)
{
// Try to read the facial feature conditional regression tree
char tree_path[200];
sprintf(tree_path, "%s/forest_%d/tree_%03d.txt", mp_param.tree_path.c_str(), idx_forest, idx_tree);
PRINT("Read facial feature regression tree: " << tree_path);
Tree<MPSample> *tree;
bool is_tree_load = Tree<MPSample>::load(&tree, tree_path);
// Estimate the number of images available for each class
int imgs_per_class = mp_param.nimages;
for (unsigned int i=0; i < ann.size(); i++)
imgs_per_class = std::min(imgs_per_class, static_cast<int>(ann[i].size()));
PRINT("Number of images per class: " << imgs_per_class);
// Random annotations for this head pose
srand(idx_tree+1);
std::vector<FaceAnnotation> annotations;
std::random_shuffle(ann[idx_forest].begin(), ann[idx_forest].end());
annotations.insert(annotations.end(), ann[idx_forest].begin(), ann[idx_forest].begin()+imgs_per_class);
std::vector<MPSample*> mp_samples;
mp_samples.reserve(annotations.size()*mp_param.npatches);
PRINT("Total number of images: " << annotations.size());
PRINT("Reserved patches: " << annotations.size()*mp_param.npatches);
boost::mt19937 rng;
rng.seed(idx_tree+1);
boost::progress_display show_progress(annotations.size());
for (int i=0; i < annotations.size(); i++, ++show_progress)
{
// Load image
TRACE("Evaluate image: " << annotations[i].url);
cv::Mat img = loadImage(mp_param.image_path, annotations[i].url);
if (img.empty())
{
ERROR("Could not load: " << annotations[i].url);
continue;
}
// Convert image to gray scale
cv::Mat img_gray;
cv::cvtColor(img, img_gray, cv::COLOR_BGR2GRAY);
// Scale image and annotations
float scale = static_cast<float>(mp_param.face_size)/static_cast<float>(annotations[i].bbox.width);
cv::Mat img_scaled;
cv::resize(img_gray, img_scaled, cv::Size(img_gray.cols*scale, img_gray.rows*scale), 0, 0);
annotations[i].bbox.x *= scale;
annotations[i].bbox.y *= scale;
annotations[i].bbox.width *= scale;
annotations[i].bbox.height *= scale;
for (unsigned int j=0; j < annotations[i].parts.size(); j++)
annotations[i].parts[j] *= scale;
// Extract face image and enlarge to make sure that all facial features are enclosed
cv::Rect enlarge_bbox;
enlargeFace(img_scaled, enlarge_bbox, annotations[i]);
cv::Mat img_roi = img_scaled(enlarge_bbox);
// Extract patches from this image sample
ImageSample *sample = new ImageSample(img_roi, mp_param.features, false);
// Extract positive patches
int patch_size = mp_param.getPatchSize();
boost::uniform_int<> dist_x(1, img_roi.cols-patch_size-2);
boost::uniform_int<> dist_y(1, img_roi.rows-patch_size-2);
boost::variate_generator< boost::mt19937&, boost::uniform_int<> > rand_x(rng, dist_x);
boost::variate_generator< boost::mt19937&, boost::uniform_int<> > rand_y(rng, dist_y);
for (int j=0; j < mp_param.npatches; j++)
{
cv::Rect bbox = cv::Rect(rand_x(), rand_y(), patch_size, patch_size);
MPSample *mps = new MPSample(sample, bbox, annotations[i].parts, mp_param.face_size, true);
mp_samples.push_back(mps);
}
}
PRINT("Used patches: " << mp_samples.size());
if (is_tree_load && !tree->isFinished())
tree->update(mp_samples, &rng);
else
tree = new Tree<MPSample>(mp_samples, mp_param, &rng, tree_path);
};
