diff --git a/include/vision/processor/active_search.h b/include/vision/processor/active_search.h
index cd2afc405c58d6300370c8e424919d241193a016..bdfca97a02f83ed9ee6b7055b2247a63a51330a1 100644
--- a/include/vision/processor/active_search.h
+++ b/include/vision/processor/active_search.h
@@ -40,96 +40,96 @@
namespace wolf{
- /**
- * \brief Active search tesselation grid.
- *
- * \author jsola, dinesh
- *
- * This class implements a tesselation grid for achieving active search
- * behavior in landmark initialization.
- *
- * The grid defines a set of cells in the image.
- * The idea is to count the number of projected landmarks, or active tracks, per grid cell,
- * and use one randomly chosen cell that is empty
- * for feature detection and landmark or track initialization.
- * This guarantees that the system will automatically populate all the
- * regions of the image.
- *
- * The feature density can be controlled by adjusting the grid's number of cells. Important notes:
- * - Typically, use grids of 5x5 to 18x12 cells.
- * - Try to make reasonably square cells.
- * - The final cell sizes are always integers, even if the H and V number of cells are not an exact divisors of the image size.
- *
- * This class implements a few interesting features:
- * - The grid can be randomly re-positioned at each frame to avoid dead zones at the cell edges.
- * - Only the inner cells are activated for feature detection to avoid reaching the image edges.
- * - The region of interest (ROI) associated with a particular cell is shrunk with a parameterizable amount
- * to guarantee a minimum 'separation' between existing and new features.
- * - The region of interest is ensured to lie at a distance from the image boundaries, defined by the parameter 'margin'.
- *
- * The blue and green grids in the figure below represent the grid
- * at two different offsets, corresponding to two different frames.
- *
- * \image html tesselationGrid.png "The tesselation grid used for active feature detection and initialization"
- *
- * This second figure shows a typical situation that we use to explain the basic mechanism.
- *
- * \image html tesselationExample.png "A typical configuration of the tesselation grid"
- *
- * Observe the figure and use the following facts as an operation guide:
- * - The grid is offset by a fraction of a cell size.
- * - use renew() at each frame to clear the grid and set a new offset.
- * - Projected landmarks are represented by red dots.
- * - After projection, use hitCell() to add a new dot to the grid.
- * - Cells with projected landmarks inside are 'occupied'.
- * - Only the inner cells (thick blue rectangle) are considered for Region of Interest (ROI) extraction.
- * - One cell is chosen randomly among those that are empty.
- * - Use pickRoi() to obtain an empty ROI for initialization.
- * - The ROI is smaller than the cell to guarantee a minimum feature separation.
- * - Use the optional 'separation' parameter at construction time to control this separation.
- * - A new feature is to be be searched inside this ROI.
- * - If there is no feature found in this ROI, call blockCell() function to avoid searching in this area again.
- * - If you need to search more than one feature per frame, proceed like this:
- * - Call pickRoi().
- * - Try to detect a Feature in ROI.
- * - If successful detection
- * - add the detected pixel with hitCell().
- * - Else
- * - block the cell with blockCell().
- * - Repeat these steps for each feature to be searched.
- *
- * We include here a schematic active-search pseudo-code algorithm to illustrate its operation:
- * \code
- * // Init necessary objects
- * ActiveSearch activeSearch;
- * ActiveSearchGrid grid(640, 480, 4, 4, 10); // Construction with 10 pixels separation.
- *
- * // We start projecting everybody
- * for (obs = begin(); obs != end(); obs++) // loop observations
- * {
- * obs->project();
- * if (obs->isVisible())
- * grid.hiCell(obs->expectation.x()); // add only visible landmarks
- * }
- *
- * // Then we process the selected observations
- * activeSearch.selectObs(); // select interesting features
- * for (activeSearch.selectedObs); // loop selected obs
- * obs.process(); // process observation
- *
- * // Now we go to initialization
- * num_new_detections = 0;
- * while(num_new_detections < max_detections)
- * grid.pickRoi(roi); // roi is now region of interest
- * if (detectFeature(roi)) // detect inside ROI
- * initLandmark(); // initialize only if successful detection
- * num_new_detections++;
- * else
- * blockCell(roi)
- *
- * \endcode
- *
- */
+/**
+ * \brief Active search tesselation grid.
+ *
+ * \author jsola, dinesh
+ *
+ * This class implements a tesselation grid for achieving active search
+ * behavior in landmark initialization.
+ *
+ * The grid defines a set of cells in the image.
+ * The idea is to count the number of projected landmarks, or active tracks, per grid cell,
+ * and use one randomly chosen cell that is empty
+ * for feature detection and landmark or track initialization.
+ * This guarantees that the system will automatically populate all the
+ * regions of the image.
+ *
+ * The feature density can be controlled by adjusting the grid's number of cells. Important notes:
+ * - Typically, use grids of 5x5 to 18x12 cells.
+ * - Try to make reasonably square cells.
+ * - The final cell sizes are always integers, even if the H and V number of cells are not an exact divisors of the image size.
+ *
+ * This class implements a few interesting features:
+ * - The grid can be randomly re-positioned at each frame to avoid dead zones at the cell edges.
+ * - Only the inner cells are activated for feature detection to avoid reaching the image edges.
+ * - The region of interest (ROI) associated with a particular cell is shrunk with a parameterizable amount
+ * to guarantee a minimum 'separation' between existing and new features.
+ * - The region of interest is ensured to lie at a distance from the image boundaries, defined by the parameter 'margin'.
+ *
+ * The blue and green grids in the figure below represent the grid
+ * at two different offsets, corresponding to two different frames.
+ *
+ * \image html tesselationGrid.png "The tesselation grid used for active feature detection and initialization"
+ *
+ * This second figure shows a typical situation that we use to explain the basic mechanism.
+ *
+ * \image html tesselationExample.png "A typical configuration of the tesselation grid"
+ *
+ * Observe the figure and use the following facts as an operation guide:
+ * - The grid is offset by a fraction of a cell size.
+ * - use renew() at each frame to clear the grid and set a new offset.
+ * - Projected landmarks are represented by red dots.
+ * - After projection, use hitCell() to add a new dot to the grid.
+ * - Cells with projected landmarks inside are 'occupied'.
+ * - Only the inner cells (thick blue rectangle) are considered for Region of Interest (ROI) extraction.
+ * - One cell is chosen randomly among those that are empty.
+ * - Use pickRoi() to obtain an empty ROI for initialization.
+ * - The ROI is smaller than the cell to guarantee a minimum feature separation.
+ * - Use the optional 'separation' parameter at construction time to control this separation.
+ * - A new feature is to be be searched inside this ROI.
+ * - If there is no feature found in this ROI, call blockCell() function to avoid searching in this area again.
+ * - If you need to search more than one feature per frame, proceed like this:
+ * - Call pickRoi().
+ * - Try to detect a Feature in ROI.
+ * - If successful detection
+ * - add the detected pixel with hitCell().
+ * - Else
+ * - block the cell with blockCell().
+ * - Repeat these steps for each feature to be searched.
+ *
+ * We include here a schematic active-search pseudo-code algorithm to illustrate its operation:
+ * \code
+ * // Init necessary objects
+ * ActiveSearch activeSearch;
+ * ActiveSearchGrid grid(640, 480, 4, 4, 10); // Construction with 10 pixels separation.
+ *
+ * // We start projecting everybody
+ * for (obs = begin(); obs != end(); obs++) // loop observations
+ * {
+ * obs->project();
+ * if (obs->isVisible())
+ * grid.hiCell(obs->expectation.x()); // add only visible landmarks
+ * }
+ *
+ * // Then we process the selected observations
+ * activeSearch.selectObs(); // select interesting features
+ * for (activeSearch.selectedObs); // loop selected obs
+ * obs.process(); // process observation
+ *
+ * // Now we go to initialization
+ * num_new_detections = 0;
+ * while(num_new_detections < max_detections)
+ * grid.pickRoi(roi); // roi is now region of interest
+ * if (detectFeature(roi)) // detect inside ROI
+ * initLandmark(); // initialize only if successful detection
+ * num_new_detections++;
+ * else
+ * blockCell(roi)
+ *
+ * \endcode
+ *
+ */
class ActiveSearchGrid {
private:
diff --git a/src/processor/active_search.cpp b/src/processor/active_search.cpp
index 9a9ffe22e0c7615530966cd2547c2cbdd8f55bfc..76abe9f868d315edc31cc09e953053dfe15027fd 100644
--- a/src/processor/active_search.cpp
+++ b/src/processor/active_search.cpp
@@ -31,7 +31,6 @@
namespace wolf{
-// CLASS ActiveSearchGrid
ActiveSearchGrid::ActiveSearchGrid() : separation_(0), margin_(0) {}
ActiveSearchGrid::ActiveSearchGrid(const int & _img_size_h, const int & _img_size_v, const int & _n_cells_h,
@@ -70,7 +69,6 @@ void ActiveSearchGrid::resizeImage(unsigned int _img_size_h, unsigned int _img_s
}
-// Functions to fill in cells
bool ActiveSearchGrid::pickEmptyCell(Eigen::Vector2i & _cell) {
int k = 0;
Eigen::Vector2i cell0;
@@ -95,9 +93,6 @@ bool ActiveSearchGrid::pickEmptyCell(Eigen::Vector2i & _cell) {
return false;
}
-/*
- * Get cell origin (exact pixel)
- */
Eigen::Vector2i ActiveSearchGrid::cellOrigin(const Eigen::Vector2i & _cell) {
Eigen::Vector2i cell0;
cell0(0) = offset_(0) + cell_size_(0) * _cell(0);
@@ -139,43 +134,5 @@ void ActiveSearchGrid::blockCell(const cv::Rect & _roi)
projections_count_(cell(0), cell(1)) = -1;
}
-/*
-#if 0
- ////////////////////////////////////////////////////////
- // ACTIVE SEARCH ALGORITHMS
- ////////////////////////////////////////////////////////
-
- map<double, observation_ptr_t> ActiveSearch::projectAll(const sensor_ptr_t & senPtr, Size & numVis) {
- map<double, observation_ptr_t> visObs;
- for (SensorAbstract::DataManagerList::iterator dmaIter = senPtr->dataManagerList().begin(); dmaIter!=senPtr->dataManagerList().end(); dmaIter++ )
- {
- data_manager_ptr_t dmaPtr = *dmaIter;
- for (DataManagerAbstract::ObservationList::iterator obsIter = dmaPtr->observationList().begin(); obsIter
- != dmaPtr->observationList().end(); obsIter++) {
- observation_ptr_t obsPtr = *obsIter;
- obsPtr->project();
- obsPtr->predictVisibility();
- if (obsPtr->isVisible()) {
- obsPtr->predictInfoGain();
- visObs[obsPtr->expectation.infoGain] = obsPtr; // this automatically sorts the observations ! ;-)
- }
- }
- }
- return visObs;
- }
-
- void ActiveSearch::predictApp(const observation_ptr_t & obsPtr) {
-
- // Get landmark descriptor
- landmark_ptr_t lmkPtr = obsPtr->landmarkPtr();
-
- // Get the sensor's current global pose
- vec7 senPose = obsPtr->sensorPtr()->globalPose();
- }
-
- void ActiveSearch::scanObs(const observation_ptr_t & obsPtr, const image::ConvexRoi & roi) {
- }
-#endif
-*/
}