void ODUFinderModule::configure(tue::Configuration config) {
if (!config.value("database_path", database_path_, tue::OPTIONAL))
std::cout << "[" << module_name_ << "] " << "Parameter 'database_path' not found. Using default: " << database_path_ << std::endl;
if (!config.value("debug_mode", debug_mode_, tue::OPTIONAL))
std::cout << "[" << module_name_ << "] " << "Parameter 'debug_mode' not found. Using default: " << debug_mode_ << std::endl;
if (!config.value("score_factor", score_factor_, tue::OPTIONAL))
std::cout << "[" << module_name_ << "] " << "Parameter 'score_factor' not found. Using default: " << score_factor_ << std::endl;
if (!config.value("debug_folder", debug_folder_, tue::OPTIONAL))
std::cout << "[" << module_name_ << "] " << "Parameter 'debug_folder' not found. Using default: " << debug_folder_ << std::endl;
database_path_ = module_path_ + database_path_;
if (debug_mode_){
std::cout << "[" << module_name_ << "] " << "Debug mode enabled. Debug folder: " << debug_folder_ << std::endl;
ed::perception::cleanDebugFolder(debug_folder_);
}
// creat odu finder instance
odu_finder_ = new odu_finder::ODUFinder(database_path_, debug_mode_);
if (odu_finder_->get_n_models_loaded() == 0)
std::cout << "[" << module_name_ << "] " << "No models were loaded!" << std::endl;
else
std::cout << "[" << module_name_ << "] " << "Loaded information for " << odu_finder_->get_n_models_loaded() << " models" << std::endl;
initialized_ = true;
std::cout << "[" << module_name_ << "] " << "Ready!" << std::endl;
}
void TFPublisherPlugin::configure(tue::Configuration config)
{
config.value("root_frame_id", root_frame_id_);
config.value("exclude", exclude_, tue::config::OPTIONAL);
// Remove possible beginning slash
if (!exclude_.empty() && exclude_[0] == '/')
exclude_ = exclude_.substr(1);
}
bool Associator::configure(tue::Configuration config)
{
if ( config.readGroup("association") )
{
if ( config.readArray("association_modules",tue::REQUIRED) )
{
while ( config.nextArrayItem() )
{
std::string module_type;
config.value("type", module_type );
if ( module_type == "nearest_neighbor" )
{
boost::shared_ptr<NearestNeighborCC> costCalculator(new NearestNeighborCC);
costCalculators_.push_back(costCalculator);
double max_no_std_devs;
if ( !config.value("max_no_std_devs", max_no_std_devs) )
std::cout << "[ASSOCIATOR] Configure: Warning! No max_no_std_devs defined for " << module_type << " cost calculator module" << std::endl;
max_assoc_dists_.push_back(max_no_std_devs);
}
else if ( module_type == "edge_tension" )
{
boost::shared_ptr<EdgeTensionCC> costCalculator(new EdgeTensionCC);
costCalculators_.push_back(costCalculator);
double max_no_std_devs;
if ( !config.value("max_no_std_devs", max_no_std_devs) )
std::cout << "[ASSOCIATOR] Configure: Warning! No max_no_std_devs defined for " << module_type << " cost calculator module" << std::endl;
max_assoc_dists_.push_back(max_no_std_devs);
}
else
{
std::cout << "[ASSOCIATOR] Configure: Warning! Unknown association cost module type given in config" << std::endl;
}
}
config.endArray();
if ( !costCalculators_.size() )
{
std::cout << "\033[31m" << "[ASSOCIATOR] Configure: No cost calculator modules available!" << "\033[0m" << std::endl;
return false;
}
}
config.endGroup();
}
else
{
std::cout << "\033[31m" << "[ASSOCIATOR] Configure: No configuration for association found!" << "\033[0m" << std::endl;
return false;
}
return true;
}
void LocalizationPlugin::configure(tue::Configuration config)
{
std::string laser_topic;
config.value("laser_topic", laser_topic);
if (config.hasError())
return;
ros::NodeHandle nh;
// Subscribe to laser topic
ros::SubscribeOptions sub_options =
ros::SubscribeOptions::create<sensor_msgs::LaserScan>(
laser_topic, 1, boost::bind(&LocalizationPlugin::laserCallback, this, _1), ros::VoidPtr(), &cb_queue_);
sub_laser_ = nh.subscribe(sub_options);
a_current_ = 0;
laser_pose_ = geo::Pose3D::identity();
laser_pose_.t.z = 0.3;
}
std::shared_ptr<SupervisedController> ControllerFactory::createController(tue::Configuration& config, double dt) const
{
std::shared_ptr<SupervisedController> supervised_controller;
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
// Get controller name and type
std::string name, type;
if (!config.value("name", name) | !config.value("type", type))
return supervised_controller;
config.setShortErrorContext(name);
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
// Create controller core from given type
std::map<std::string, t_controller_creator>::const_iterator it = controller_types_.find(type);
if (it == controller_types_.end())
{
config.addError("Unknown controller type: '" + type + "'");
return supervised_controller;
}
std::shared_ptr<Controller> c = it->second();
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
// Configure controller core
c->configure(config, dt);
c->setName(name);
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
// Wrap controller in supervised controller, and configure it
supervised_controller.reset(new SupervisedController);
supervised_controller->setController(c);
supervised_controller->configure(config, dt);
return supervised_controller;
}
void FaceDetector::configureClassification(tue::Configuration config)
{
// default values in case configure(...) is not called!
debug_mode_ = false;
classifier_front_scale_factor_ = 1.2;
classifier_front_min_neighbours_ = 3;
classif_front_min_size_ = cv::Size(20,20);
classifier_profile_scale_factor_= 1.2;
classifier_profile_min_neighbours_ = 3;
classif_profile_min_size_ = cv::Size(20,20);
debug_folder_ = "/tmp/face_detector/";
// load training files for frontal classifier
std::string cascade_front_files_path_;
if (config.value("cascade_front_files_path", cascade_front_files_path_))
{
if (!classifier_front_.load(cascade_front_files_path_))
config.addError("Unable to load front haar cascade files (" + cascade_front_files_path_ + ")");
}
// load training files for profile classifier
std::string cascade_profile_files_path_;
if (config.value("cascade_profile_front_path", cascade_profile_files_path_))
{
if (!classifier_profile_.load(cascade_profile_files_path_))
config.addError("Unable to load profile haar cascade files (" + cascade_profile_files_path_ + ")");
}
if (config.hasError())
return;
if (!config.value("debug_mode", debug_mode_, tue::OPTIONAL))
ed::log::info() << "Parameter 'debug_mode' not found. Using default: " << debug_mode_ << std::endl;
if (!config.value("debug_folder", debug_folder_, tue::OPTIONAL))
ed::log::info() << "Parameter 'debug_folder' not found. Using default: " << debug_folder_ << std::endl;
if (!config.value("classifier_front_scale_factor", classifier_front_scale_factor_, tue::OPTIONAL))
ed::log::info() << "Parameter 'classifier_front_scale_factor' not found. Using default: " << classifier_front_scale_factor_ << std::endl;
if (!config.value("classifier_front_min_neighbours", classifier_front_min_neighbours_, tue::OPTIONAL))
ed::log::info() << "Parameter 'classifier_front_min_neighbours' not found. Using default: " << classifier_front_min_neighbours_ << std::endl;
if (!config.value("classifier_profile_scale_factor", classifier_profile_scale_factor_, tue::OPTIONAL))
ed::log::info() << "Parameter 'classifier_profile_scale_factor' not found. Using default: " << classifier_profile_scale_factor_ << std::endl;
if (!config.value("classifier_profile_min_neighbours", classifier_profile_min_neighbours_, tue::OPTIONAL))
ed::log::info() << "Parameter 'classifier_profile_min_neighbours' not found. Using default: " << classifier_profile_min_neighbours_ << std::endl;
if (debug_mode_)
{
// clean the debug folder if debugging is active
try {
boost::filesystem::path dir(debug_folder_);
boost::filesystem::remove_all(dir);
boost::filesystem::create_directories(dir);
}
catch(const boost::filesystem::filesystem_error& e)
{
if(e.code() == boost::system::errc::permission_denied)
ed::log::error() << "boost::filesystem permission denied" << std::endl;
else
ed::log::error() << "boost::filesystem failed with error: " << e.code().message() << std::endl;
}
// create debug window
cv::namedWindow("Face Detector Output", CV_WINDOW_AUTOSIZE);
}
}
void FaceDetector::writeFaceDetectionResult(const ed::Measurement& msr, const cv::Rect& rgb_roi,
const std::vector<cv::Rect>& rgb_face_rois,
int& face_counter, tue::Configuration& result) const
{
// get color image
const cv::Mat& color_image = msr.image()->getRGBImage();
// get color image
const cv::Mat& depth_image = msr.image()->getDepthImage();
// Calculate size factor between depth and rgb images
double f_depth_rgb = (double)depth_image.cols / color_image.cols;
// Create depth view
rgbd::View depth_view(*msr.image(), depth_image.cols);
for (uint j = 0; j < rgb_face_rois.size(); j++)
{
cv::Rect rgb_face_roi = rgb_face_rois[j];
rgb_face_roi.x += rgb_roi.x;
rgb_face_roi.y += rgb_roi.y;
if (debug_mode_)
ed::perception::saveDebugImage("face_detector-rgb", color_image(rgb_face_roi));
result.addArrayItem();
result.setValue("index", face_counter);
// add 2D location of the face
result.setValue("x", rgb_face_roi.x);
result.setValue("y", rgb_face_roi.y);
result.setValue("width", rgb_face_roi.width);
result.setValue("height", rgb_face_roi.height);
// Compute face roi for depth image
cv::Rect depth_face_roi(f_depth_rgb * rgb_face_roi.x, f_depth_rgb * rgb_face_roi.y,
f_depth_rgb * rgb_face_roi.width, f_depth_rgb * rgb_face_roi.height);
if (debug_mode_)
ed::perception::saveDebugImage("face_detector-depth", depth_image(depth_face_roi));
cv::Mat face_area = depth_image(depth_face_roi);
float avg_depth = ed::perception::getMedianDepth(face_area);
if (avg_depth > 0)
{
// calculate the center point of the face
cv::Point2i p_2d(depth_face_roi.x + depth_face_roi.width/2,
depth_face_roi.y + depth_face_roi.height/2);
geo::Vector3 projection = depth_view.getRasterizer().project2Dto3D(p_2d.x, p_2d.y) * avg_depth;
geo::Vector3 point_map = msr.sensorPose() * projection;
// add 3D location of the face
result.setValue("map_x", point_map.x);
result.setValue("map_y", point_map.y);
result.setValue("map_z", point_map.z);
}
else
{
std::cout << "[ED FACE DETECTOR] Could not calculate face's average depth. Map coordinates might be incorrect!" << std::endl;
}
result.endArrayItem();
face_counter++;
}
}
void GUIPlugin::configure(tue::Configuration config)
{
std::string srv_get_measurements, srv_set_label, srv_raise_event, srv_get_command, map_image_topic;
config.value("srv_get_measurements", srv_get_measurements);
config.value("srv_set_label", srv_set_label);
config.value("srv_raise_event", srv_raise_event);
config.value("srv_get_command", srv_get_command);
config.value("map_image_topic", map_image_topic);
// TODO: make configurable
trigger_map_publish_ = ed::EventClock(10);
int image_width, image_height;
config.value("map_image_width", image_width);
config.value("map_image_height", image_height);
double world_width, world_height;
config.value("world_width", world_width);
config.value("world_height", world_height);
double cam_x, cam_y, cam_z;
config.value("cam_x", cam_x);
config.value("cam_y", cam_y);
config.value("cam_z", cam_z);
if (config.hasError())
return;
ros::NodeHandle nh;
if (srv_get_measurements_.getService() != srv_get_measurements)
{
ros::AdvertiseServiceOptions opt_get_measurements =
ros::AdvertiseServiceOptions::create<ed_msgs::GetMeasurements>(
srv_get_measurements, boost::bind(&GUIPlugin::srvGetMeasurements, this, _1, _2), ros::VoidPtr(), &cb_queue_);
srv_get_measurements_ = nh.advertiseService(opt_get_measurements);
}
if (srv_set_label_.getService() != srv_set_label)
{
ros::AdvertiseServiceOptions opt_set_label =
ros::AdvertiseServiceOptions::create<ed_msgs::SetLabel>(
srv_set_label, boost::bind(&GUIPlugin::srvSetLabel, this, _1, _2), ros::VoidPtr(), &cb_queue_);
srv_set_label_ = nh.advertiseService(opt_set_label);
}
if (srv_raise_event_.getService() != srv_raise_event)
{
ros::AdvertiseServiceOptions opt_raise_event =
ros::AdvertiseServiceOptions::create<ed_msgs::RaiseEvent>(
srv_raise_event, boost::bind(&GUIPlugin::srvRaiseEvent, this, _1, _2), ros::VoidPtr(), &cb_queue_);
srv_raise_event_ = nh.advertiseService(opt_raise_event);
}
if (srv_get_command_.getService() != srv_get_command)
{
ros::AdvertiseServiceOptions opt_get_command =
ros::AdvertiseServiceOptions::create<ed_msgs::GetGUICommand>(
srv_get_command, boost::bind(&GUIPlugin::srvGetCommand, this, _1, _2), ros::VoidPtr(), &cb_queue_);
srv_get_command_ = nh.advertiseService(opt_get_command);
}
if (pub_image_map_.getTopic() != map_image_topic)
pub_image_map_ = nh.advertise<tue_serialization::Binary>("/ed/gui/map_image", 1);
map_image_ = cv::Mat(image_width, image_height, CV_8UC3, cv::Scalar(50, 50, 50));
projector_pose_.setOrigin(geo::Vector3(cam_x, cam_y, cam_z));
projector_pose_.setBasis(geo::Matrix3::identity());
projector_.setFocalLengths(image_width / (world_width / cam_z),
image_height / (world_height / cam_z));
projector_.setOpticalCenter(image_width / 2, image_height / 2);
projector_.setOpticalTranslation(0, 0);
}
void HumanContourMatcher::process(ed::EntityConstPtr e, tue::Configuration& result) const
{
// if initialization failed, return
if (!init_success_)
return;
// Get the best measurement from the entity
ed::MeasurementConstPtr msr = e->lastMeasurement();
if (!msr)
return;
float depth_sum = 0;
float avg_depht;
float classification_error = 0;
float classification_deviation = 0;
std::string classification_stance;
uint point_counter = 0;
bool is_human = false;
// Get the depth image from the measurement
const cv::Mat& depth_image = msr->image()->getDepthImage();
const cv::Mat& color_image = msr->image()->getRGBImage();
cv::Mat mask_cv = cv::Mat::zeros(depth_image.rows, depth_image.cols, CV_8UC1);
// Iterate over all points in the mask. You must specify the width of the image on which you
// want to apply the mask (see the begin(...) method).
for(ed::ImageMask::const_iterator it = msr->imageMask().begin(depth_image.cols); it != msr->imageMask().end(); ++it)
{
// mask's (x, y) coordinate in the depth image
const cv::Point2i& p_2d = *it;
// TODO dont creat a Mat mask, just give human_classifier_.Classify a vector of 2D points!
// paint a mask
mask_cv.at<unsigned char>(p_2d) = 255;
// calculate measurement average depth
depth_sum += depth_image.at<float>(p_2d);
point_counter++;
}
avg_depht = depth_sum/(float)point_counter;
is_human = human_classifier_.Classify(depth_image, color_image, mask_cv, avg_depht, classification_error, classification_deviation, classification_stance);
// ----------------------- assert results -----------------------
// create group if it doesnt exist
if (!result.readGroup("perception_result", tue::OPTIONAL))
{
result.writeGroup("perception_result");
}
// assert the results to the entity
result.writeGroup(kModuleName);
result.setValue("label", "Human Shape");
if (classification_error > 0){
result.setValue("stance", classification_stance);
result.setValue("error", classification_error);
result.setValue("deviation", classification_deviation);
}
if(is_human)
{
result.setValue("score", 1.0);
}else{
result.setValue("score", 0.0);
}
result.endGroup(); // close human_contour_matcher group
result.endGroup(); // close perception_result group
}
void TFPublisherPlugin::configure(tue::Configuration config)
{
config.value("root_frame_id", root_frame_id_);
}
void ColorMatcher::configureClassification(tue::Configuration config)
{
config.value("color_margin", color_margin_);
initialize();
}