changed interface of feature detector. now it returns directly a list of features instead of only corners

This commit is contained in:
Christian Forster
2014-04-28 16:43:00 +02:00
parent 5e48a22f4d
commit 42a7222b43
13 changed files with 96 additions and 121 deletions
+1 -1
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@@ -46,7 +46,7 @@ struct Seed
float z_range; //!< Max range of the possible depth. float z_range; //!< Max range of the possible depth.
float sigma2; //!< Variance of normal distribution. float sigma2; //!< Variance of normal distribution.
Matrix2d patch_cov; //!< Patch covariance in reference image. Matrix2d patch_cov; //!< Patch covariance in reference image.
Seed(Feature* ftr, float angle, float depth_mean, float depth_min); Seed(Feature* ftr, float depth_mean, float depth_min);
}; };
/// Depth filter implements the Bayesian Update proposed in: /// Depth filter implements the Bayesian Update proposed in:
+9 -8
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@@ -52,15 +52,19 @@ public:
virtual ~AbstractDetector() {}; virtual ~AbstractDetector() {};
virtual void detect( virtual void detect(
Frame* frame,
const ImgPyr& img_pyr, const ImgPyr& img_pyr,
const Features& fts,
const double detection_threshold, const double detection_threshold,
Corners* corners) = 0; Features& fts) = 0;
/// Notify the detector that a cell already has a feature before detection. /// Flag the grid cell as occupied
void setGridOccpuancy(const Vector2d& px); void setGridOccpuancy(const Vector2d& px);
/// Set grid cells of existing features as occupied
void setExistingFeatures(const Features& fts);
protected: protected:
static const int border_ = 8; //!< no feature should be within 8px of border. static const int border_ = 8; //!< no feature should be within 8px of border.
const int cell_size_; const int cell_size_;
const int n_pyr_levels_; const int n_pyr_levels_;
@@ -70,14 +74,11 @@ protected:
void resetGrid(); void resetGrid();
void setExistingFeatures(const Features& fts);
inline int getCellIndex(int x, int y, int level) inline int getCellIndex(int x, int y, int level)
{ {
const int scale = (1<<level); const int scale = (1<<level);
return (scale*y)/cell_size_*grid_n_cols_ + (scale*x)/cell_size_; return (scale*y)/cell_size_*grid_n_cols_ + (scale*x)/cell_size_;
} }
}; };
typedef boost::shared_ptr<AbstractDetector> DetectorPtr; typedef boost::shared_ptr<AbstractDetector> DetectorPtr;
@@ -94,10 +95,10 @@ public:
virtual ~FastDetector() {} virtual ~FastDetector() {}
virtual void detect( virtual void detect(
Frame* frame,
const ImgPyr& img_pyr, const ImgPyr& img_pyr,
const Features& fts,
const double detection_threshold, const double detection_threshold,
Corners* corners); Features& fts);
}; };
} // namespace feature_detection } // namespace feature_detection
+1 -1
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@@ -79,7 +79,7 @@ public:
void removeKeyPoint(Feature* ftr); void removeKeyPoint(Feature* ftr);
/// Return number of point observations. /// Return number of point observations.
size_t nObs() const; inline size_t nObs() const { return fts_.size(); }
/// Check if a point in (w)orld coordinate frame is visible in the image. /// Check if a point in (w)orld coordinate frame is visible in the image.
bool isVisible(const Vector3d& xyz_w) const; bool isVisible(const Vector3d& xyz_w) const;
+2 -1
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@@ -56,7 +56,8 @@ public:
int n_succeeded_reproj_; //!< Number of succeeded reprojections. Used to assess the quality of the point. int n_succeeded_reproj_; //!< Number of succeeded reprojections. Used to assess the quality of the point.
int last_structure_optim_; //!< Timestamp of last point optimization int last_structure_optim_; //!< Timestamp of last point optimization
Point(Vector3d pos); Point(const Vector3d& pos);
Point(const Vector3d& pos, Feature* ftr);
~Point(); ~Point();
/// Add a reference to a frame. /// Add a reference to a frame.
+10 -17
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@@ -34,7 +34,7 @@ namespace svo {
int Seed::batch_counter = 0; int Seed::batch_counter = 0;
int Seed::seed_counter = 0; int Seed::seed_counter = 0;
Seed::Seed(Feature* ftr, float angle, float depth_mean, float depth_min) : Seed::Seed(Feature* ftr, float depth_mean, float depth_min) :
batch_id(batch_counter), batch_id(batch_counter),
id(seed_counter++), id(seed_counter++),
ftr(ftr), ftr(ftr),
@@ -113,29 +113,22 @@ void DepthFilter::addKeyframe(FramePtr frame, double depth_mean, double depth_mi
void DepthFilter::initializeSeeds(FramePtr frame) void DepthFilter::initializeSeeds(FramePtr frame)
{ {
feature_detection::Corners corners; Features new_features;
feature_detector_->detect(frame->img_pyr_, frame->fts_, Config::triangMinCornerScore(), &corners); feature_detector_->setExistingFeatures(frame->fts_);
feature_detector_->detect(frame.get(), frame->img_pyr_,
Config::triangMinCornerScore(), new_features);
seeds_updating_halt_ = true; seeds_updating_halt_ = true;
lock_t lock(seeds_mut_); // by locking the updateSeeds function stops lock_t lock(seeds_mut_); // by locking the updateSeeds function stops
size_t new_seeds = 0;
++Seed::batch_counter; ++Seed::batch_counter;
// now for all maximum corners, initialize a new seed // initialize a seed for every new feature
for(feature_detection::Corners::iterator it=corners.begin(); it!=corners.end(); ++it) std::for_each(new_features.begin(), new_features.end(), [&](Feature* ftr){
{ seeds_.push_back(Seed(ftr, new_keyframe_mean_depth_, new_keyframe_min_depth_));
// we must check again if the score is above the threshold because the Corners });
// vector is initialized with dummy corners
if(it->score > Config::triangMinCornerScore())
{
Feature* new_feature(new Feature(frame.get(), Vector2d(it->x, it->y), it->level));
seeds_.push_back(Seed(new_feature, it->angle, new_keyframe_mean_depth_, new_keyframe_min_depth_));
++new_seeds;
}
}
if(options_.verbose) if(options_.verbose)
SVO_INFO_STREAM("DepthFilter: Initialized "<<new_seeds<<" new seeds"); SVO_INFO_STREAM("DepthFilter: Initialized "<<new_features.size()<<" new seeds");
seeds_updating_halt_ = false; seeds_updating_halt_ = false;
} }
+12 -8
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@@ -64,15 +64,12 @@ FastDetector::FastDetector(
{} {}
void FastDetector::detect( void FastDetector::detect(
Frame* frame,
const ImgPyr& img_pyr, const ImgPyr& img_pyr,
const Features& existing_fts,
const double detection_threshold, const double detection_threshold,
Corners* corners) Features& fts)
{ {
setExistingFeatures(existing_fts); Corners corners(grid_n_cols_*grid_n_rows_, Corner(0,0,detection_threshold,0,0.0f));
// now, detect features and perform nonmax suppression
corners->resize(grid_n_cols_*grid_n_rows_, Corner(0,0,detection_threshold,0,0.0f));
for(int L=0; L<n_pyr_levels_; ++L) for(int L=0; L<n_pyr_levels_; ++L)
{ {
const int scale = (1<<L); const int scale = (1<<L);
@@ -102,10 +99,17 @@ void FastDetector::detect(
if(grid_occupancy_[k]) if(grid_occupancy_[k])
continue; continue;
const float score = vk::shiTomasiScore(img_pyr[L], xy.x, xy.y); const float score = vk::shiTomasiScore(img_pyr[L], xy.x, xy.y);
if(score > corners->at(k).score) if(score > corners.at(k).score)
corners->at(k) = Corner(xy.x*scale, xy.y*scale, score, L, 0.0f); corners.at(k) = Corner(xy.x*scale, xy.y*scale, score, L, 0.0f);
} }
} }
// Create feature for every corner that has high enough corner score
std::for_each(corners.begin(), corners.end(), [&](Corner& c) {
if(c.score > detection_threshold)
fts.push_back(new Feature(frame, Vector2d(c.x, c.y), c.level));
});
resetGrid(); resetGrid();
} }
-7
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@@ -137,13 +137,6 @@ void Frame::removeKeyPoint(Feature* ftr)
setKeyPoints(); setKeyPoints();
} }
size_t Frame::nObs() const
{
size_t n=0;
std::for_each(fts_.begin(), fts_.end(), [&](Feature* i){if(i->point != NULL) ++n; });
return n;
}
bool Frame::isVisible(const Vector3d& xyz_w) const bool Frame::isVisible(const Vector3d& xyz_w) const
{ {
Vector3d xyz_f = T_f_w_*xyz_w; Vector3d xyz_f = T_f_w_*xyz_w;
+9 -14
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@@ -109,24 +109,19 @@ void detectFeatures(
vector<cv::Point2f>& px_vec, vector<cv::Point2f>& px_vec,
vector<Vector3d>& f_vec) vector<Vector3d>& f_vec)
{ {
feature_detection::Corners corners; Features new_features;
feature_detection::FastDetector detector( feature_detection::FastDetector detector(
frame->img().cols, frame->img().rows, Config::gridSize(), Config::nPyrLevels()); frame->img().cols, frame->img().rows, Config::gridSize(), Config::nPyrLevels());
detector.detect(frame->img_pyr_, frame->fts_, Config::triangMinCornerScore(), &corners); detector.detect(frame.get(), frame->img_pyr_, Config::triangMinCornerScore(), new_features);
// now for all maximum corners, initialize a new seed // now for all maximum corners, initialize a new seed
px_vec.clear(); px_vec.reserve(corners.size()); px_vec.clear(); px_vec.reserve(new_features.size());
f_vec.clear(); f_vec.reserve(corners.size()); f_vec.clear(); f_vec.reserve(new_features.size());
for(feature_detection::Corners::iterator it=corners.begin(); it!=corners.end(); ++it) std::for_each(new_features.begin(), new_features.end(), [&](Feature* ftr){
{ px_vec.push_back(cv::Point2f(ftr->px[0], ftr->px[1]));
// we must check again if the score is above the threshold because the Corners f_vec.push_back(ftr->f);
// vector is initialized with dummy corners delete ftr;
if(it->score > Config::triangMinCornerScore()) });
{
px_vec.push_back(cv::Point2f(it->x, it->y));
f_vec.push_back(frame->c2f(it->x, it->y));
}
}
} }
void trackKlt( void trackKlt(
+17 -1
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@@ -24,7 +24,7 @@ namespace svo {
int Point::point_counter_ = 0; int Point::point_counter_ = 0;
Point::Point(Vector3d pos) : Point::Point(const Vector3d& pos) :
id_(point_counter_++), id_(point_counter_++),
pos_(pos), pos_(pos),
normal_(Vector3d::Zero()), normal_(Vector3d::Zero()),
@@ -38,6 +38,22 @@ Point::Point(Vector3d pos) :
last_structure_optim_(0) last_structure_optim_(0)
{} {}
Point::Point(const Vector3d& pos, Feature* ftr) :
id_(point_counter_++),
pos_(pos),
normal_(Vector3d::Zero()),
n_obs_(1),
v_pt_(NULL),
last_published_ts_(0),
last_projected_kf_id_(-1),
type_(TYPE_UNKNOWN),
n_failed_reproj_(0),
n_succeeded_reproj_(0),
last_structure_optim_(0)
{
obs_.push_front(ftr);
}
Point::~Point() Point::~Point()
{} {}
+16 -23
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@@ -17,8 +17,10 @@
#include <string.h> #include <string.h>
#include <svo/global.h> #include <svo/global.h>
#include <svo/config.h> #include <svo/config.h>
#include <svo/frame.h>
#include <svo/feature_detection.h> #include <svo/feature_detection.h>
#include <svo/depth_filter.h> #include <svo/depth_filter.h>
#include <svo/feature.h>
#include <vikit/timer.h> #include <vikit/timer.h>
#include <vikit/vision.h> #include <vikit/vision.h>
#include <vikit/abstract_camera.h> #include <vikit/abstract_camera.h>
@@ -37,38 +39,29 @@ void testCornerDetector()
cv::Mat img(cv::imread(img_name, 0)); cv::Mat img(cv::imread(img_name, 0));
assert(img.type() == CV_8UC1 && !img.empty()); assert(img.type() == CV_8UC1 && !img.empty());
svo::ImgPyr img_pyr; vk::AbstractCamera* cam = new vk::ATANCamera(752, 480, 0.511496, 0.802603, 0.530199, 0.496011, 0.934092);
svo::frame_utils::createImgPyramid( svo::FramePtr frame(new svo::Frame(cam, img, 0.0));
img, max(svo::Config::nPyrLevels(), svo::Config::kltMaxLevel()+1), img_pyr);
svo::Features fts;
// Corner detection // Corner detection
vk::Timer t; vk::Timer t;
svo::feature_detection::Corners corners; svo::Features fts;
svo::feature_detection::FastDetector fast_detector( svo::feature_detection::FastDetector fast_detector(
img.cols, img.rows, svo::Config::gridSize(), svo::Config::nPyrLevels()); img.cols, img.rows, svo::Config::gridSize(), svo::Config::nPyrLevels());
for(int i=0; i<100; ++i) for(int i=0; i<100; ++i)
{ {
fast_detector.detect(img_pyr, fts, svo::Config::triangMinCornerScore(), &corners); fast_detector.detect(frame.get(), frame->img_pyr_, svo::Config::triangMinCornerScore(), fts);
} }
printf("Fast corner detection took %f ms, %zu corners detected (ref i7-W520: 8.06878ms, 416)\n", t.stop()*10, corners.size()); printf("Fast corner detection took %f ms, %zu corners detected (ref i7-W520: 8.06878ms, 416)\n", t.stop()*10, fts.size());
printf("Note, in this case, feature detection also contains the cam2world projection of the feature.\n");
cv::Mat img_rgb = cv::Mat(img.size(), CV_8UC3);
cv::cvtColor(img, img_rgb, CV_GRAY2RGB);
std::for_each(fts.begin(), fts.end(), [&](svo::Feature* i){
cv::circle(img_rgb, cv::Point2f(i->px[0], i->px[1]), 4*(i->level+1), cv::Scalar(0,255,0), 1);
});
cv::imshow("ref_img", img_rgb);
cv::waitKey(0);
if(false) std::for_each(fts.begin(), fts.end(), [&](svo::Feature* i){ delete i; });
{
cv::Mat img_rgb = cv::Mat(img.size(), CV_8UC3);
cv::cvtColor(img, img_rgb, CV_GRAY2RGB);
for(svo::feature_detection::Corners::iterator it=corners.begin(); it!=corners.end(); ++it)
{
// we must check again if the score is above the threshold because the Corners
// vector is initialized with dummy corners
if(it->score > svo::Config::triangMinCornerScore())
{
cv::circle(img_rgb, cv::Point2f(it->x, it->y), 4*(it->level+1), cv::Scalar(0,255,0), 1);
}
}
cv::imshow("ref_img", img_rgb);
cv::waitKey(0);
}
} }
} // namespace } // namespace
+6 -16
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@@ -58,25 +58,15 @@ class PoseOptimizerTest {
vk::blender_utils::loadBlenderDepthmap(dataset_dir + "/depth/frame_000001_0.depth", *cam_, depthmap_); vk::blender_utils::loadBlenderDepthmap(dataset_dir + "/depth/frame_000001_0.depth", *cam_, depthmap_);
// detect features // detect features
feature_detection::Corners corners;
feature_detection::FastDetector detector( feature_detection::FastDetector detector(
cam_->width(), cam_->height(), Config::gridSize(), Config::nPyrLevels()); cam_->width(), cam_->height(), Config::gridSize(), Config::nPyrLevels());
detector.detect(frame_->img_pyr_, frame_->fts_, Config::triangMinCornerScore(), &corners); detector.detect(frame_.get(), frame_->img_pyr_, Config::triangMinCornerScore(), frame_->fts_);
size_t n_fts = 0; size_t n_fts = 0;
for(feature_detection::Corners::iterator it=corners.begin(); it!=corners.end(); ++it) std::for_each(frame_->fts_.begin(), frame_->fts_.end(), [&](Feature* i){
{ Point* point(new Point(frame_->f2w(i->f*depthmap_.at<float>(i->px[1], i->px[0])), i));
// we must check again if the score is above the threshold because the Corners i->point = point;
// vector is initialized with dummy corners ++n_fts;
if(it->score > Config::triangMinCornerScore()) });
{
Feature* ftr(new Feature(frame_.get(), Vector2d(it->x, it->y), it->level));
Point* point(new Point(frame_->f2w(ftr->f*depthmap_.at<float>(it->y, it->x))));
ftr->point = point;
ftr->frame = frame_.get();
frame_->addFeature(ftr);
++n_fts;
}
}
printf("Added %zu features to frame.\n", n_fts); printf("Added %zu features to frame.\n", n_fts);
} }
+7 -11
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@@ -97,20 +97,16 @@ void SparseImgAlignTest::testSequence(
dataset_dir+"/depth/"+iter->image_name_+"_0.depth", *cam_, depthmap); dataset_dir+"/depth/"+iter->image_name_+"_0.depth", *cam_, depthmap);
// extract features, generate features with 3D points // extract features, generate features with 3D points
svo::feature_detection::Corners corners;
feature_detector->detect( feature_detector->detect(
frame_ref_->img_pyr_, frame_ref_->fts_, svo::Config::triangMinCornerScore(), &corners); frame_ref_.get(), frame_ref_->img_pyr_, svo::Config::triangMinCornerScore(), frame_ref_->fts_);
for(svo::feature_detection::Corners::iterator corner=corners.begin(); corner!=corners.end(); ++corner) std::for_each(frame_ref_->fts_.begin(), frame_ref_->fts_.end(), [&](svo::Feature* i) {
{ Eigen::Vector3d pt_pos_cur = i->f*depthmap.at<float>(i->px[1], i->px[0]);
svo::Feature* ftr = new svo::Feature(frame_ref_.get(), Eigen::Vector2d(corner->x, corner->y), corner->level);
Eigen::Vector3d pt_pos_cur = ftr->f*depthmap.at<float>(corner->y, corner->x);
Eigen::Vector3d pt_pos_w = frame_ref_->T_f_w_.inverse()*pt_pos_cur; Eigen::Vector3d pt_pos_w = frame_ref_->T_f_w_.inverse()*pt_pos_cur;
svo::Point* pt = new svo::Point(pt_pos_w); svo::Point* pt = new svo::Point(pt_pos_w, i);
ftr->point = pt; i->point = pt;
frame_ref_->addFeature(ftr); });
}
printf("Added %zu 3d pts to the reference frame.\n", corners.size()); printf("Added %zu 3d pts to the reference frame.\n", frame_ref_->nObs());
T_prev_w = frame_ref_->T_f_w_; T_prev_w = frame_ref_->T_f_w_;
T_prevgt_w = T_gt_w; T_prevgt_w = T_gt_w;
continue; continue;
+6 -13
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@@ -190,21 +190,14 @@ void BenchmarkNode::runBlenderBenchmark(const std::string& dataset_dir)
// extract features, generate features with 3D points // extract features, generate features with 3D points
svo::feature_detection::FastDetector detector( svo::feature_detection::FastDetector detector(
cam_->width(), cam_->height(), svo::Config::gridSize(), svo::Config::nPyrLevels()); cam_->width(), cam_->height(), svo::Config::gridSize(), svo::Config::nPyrLevels());
svo::feature_detection::Corners corners; detector.detect(frame_ref.get(), frame_ref->img_pyr_, svo::Config::triangMinCornerScore(), frame_ref->fts_);
detector.detect(frame_ref->img_pyr_, frame_ref->fts_, svo::Config::triangMinCornerScore(), &corners); std::for_each(frame_ref->fts_.begin(), frame_ref->fts_.end(), [&](Feature* ftr) {
for(auto corner=corners.begin(); corner!=corners.end(); ++corner) Eigen::Vector3d pt_pos_cur = ftr->f*depthmap.at<float>(ftr->px[1], ftr->px[0]);
{
if(corner->score < Config::triangMinCornerScore())
continue;
svo::Feature* ftr = new svo::Feature(frame_ref.get(), Eigen::Vector2d(corner->x, corner->y), corner->level);
Eigen::Vector3d pt_pos_cur = ftr->f*depthmap.at<float>(corner->y, corner->x);
Eigen::Vector3d pt_pos_world = frame_ref->T_f_w_.inverse()*pt_pos_cur; Eigen::Vector3d pt_pos_world = frame_ref->T_f_w_.inverse()*pt_pos_cur;
svo::Point* point = new svo::Point(pt_pos_world); svo::Point* point = new svo::Point(pt_pos_world, ftr);
ftr->point = point; ftr->point = point;
ftr->point->addFrameRef(ftr); });
frame_ref->addFeature(ftr); SVO_INFO_STREAM("Added "<<frame_ref->nObs()<<" 3d pts to the reference frame.");
}
SVO_INFO_STREAM("Added "<<corners.size()<<" 3d pts to the reference frame.");
vo_->setFirstFrame(frame_ref); vo_->setFirstFrame(frame_ref);
SVO_INFO_STREAM("Set reference frame."); SVO_INFO_STREAM("Set reference frame.");
} }