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delete uncorrect fetures

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This commit is contained in:
VahdPourAmin
2021-09-30 15:02:46 +02:00
parent 461d5b3896
commit 44ab99ccc2
18 changed files with 1057 additions and 1590 deletions
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GPU_version/vio/CMakeLists.txt
-2
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@@ -85,5 +85,3 @@ ADD_DEFINITIONS(-DKERNEL_DIR=\"${PROJECT_SOURCE_DIR}/kernel\")
ADD_DEFINITIONS(-DPROJECT_DIR=\"${PROJECT_SOURCE_DIR}\")
ADD_DEFINITIONS(-DVIO_DEBUG=true)
ADD_DEFINITIONS(-DG2O_USE_VENDORED_CERES=ON)
GPU_version/vio/frame_handler_log.txt
+966 -212
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File diff suppressed because it is too large Load Diff
GPU_version/vio/src/frame_handler_mono.cpp
+3 -3
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@@ -166,8 +166,8 @@ FrameHandlerBase::UpdateResult FrameHandlerMono::processSecondFrame()
fprintf(log_,"[%s] Init: Selected Second frame. \t The number of features: %d depth mean:%f min:%f\n",
vio::time_in_HH_MM_SS_MMM().c_str(),new_frame_->fts_.size(),depth_mean,depth_min);
#endif
std::cerr<<"run BA\n";
ba_glob_->new_key_frame();
//std::cerr<<"run BA\n";
//ba_glob_->new_key_frame();
ROS_INFO("VIO initialized :)");
ROS_INFO("Running ...");
return RESULT_IS_KEYFRAME;
@@ -261,7 +261,7 @@ FrameHandlerBase::UpdateResult FrameHandlerMono::processFrame()
// add keyframe to map
map_.addKeyframe(new_frame_);
//std::cerr<<"BA run\n";
ba_glob_->new_key_frame();
//ba_glob_->new_key_frame();
return RESULT_IS_KEYFRAME;
}
GPU_version/vio/src/point.cpp
+11
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@@ -184,6 +184,17 @@ void Point::optimize(const size_t n_iter)
break;
}
for(auto it=obs_.begin(); it!=obs_.end(); ++it) {
Vector2d e = vk::project2d((*it)->f) - vk::project2d(Vector3d((*it)->frame->se3() * (*it)->point->pos_));
//e /= (1 << (*it)->level);
// std::cerr<<"error - point.h : "<<e.norm()<<std::endl;
if (e.norm() > 1.0) //{
// we don't need to delete a reference in the point since it was not created yet
(*it)->point->n_failed_reproj_++;
// } else
// it++;
}
}
} // namespace vio
GPU_version/vio/src/pose_optimizer.cpp
+25 -5
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@@ -47,7 +47,7 @@ void optimizeGaussNewton(
// compute the scale of the error for robust estimation
std::vector<float> errors;
for(auto it=frame->fts_.begin(); it!=frame->fts_.end(); ++it)
for(auto it=frame->fts_.begin(); it!=frame->fts_.end(); /*++it*/)
{
if((*it)->point == NULL)
continue;
@@ -55,8 +55,14 @@ void optimizeGaussNewton(
Vector2d e = vk::project2d((*it)->f)
- vk::project2d(Vector3d(frame->se3()*(*it)->point->pos_));
e *= 1.0 / (1<<(*it)->level);
//std::cerr<<"pose optimizer - error : "<<e.norm()<<std::endl;
if(e.norm() > 0.01){
it = frame->fts_.erase(it);
continue;
}
chi2_vec_init.push_back(e.norm()); // just for debug
errors.push_back(e.norm());
it++;
}
if(errors.empty())
@@ -118,20 +124,34 @@ void optimizeGaussNewton(
}
size_t n_deleted_refs = 0;
for(Features::iterator it=frame->fts_.begin(); it!=frame->fts_.end(); ++it)
//for(Features::iterator it=frame->fts_.begin(); it!=frame->fts_.end(); /*++it*/)
for(auto it=frame->fts_.begin(); it!=frame->fts_.end(); /*++it*/)
{
if((*it)->point == NULL)
continue;
//std::cerr<<"123 - pose optimizer - frame->fts_.size() : "<<frame->fts_.size()<<std::endl;
//std::cerr<<"124 - pose optimizer - (*it)->point : "<<(*it)->point<<std::endl;
if((*it)->point == NULL) {
it++;
//std::cerr<<"126 - pose optimizer - (*it)->point == NULL : "<<std::endl;
continue;
}
//std::cerr<<"129 - pose optimizer "<<(*it)->f.x()<<" "<<(*it)->f.y()<<" "<<(*it)->f.z()<<"\t"<<(*it)->point->pos_.x()<<" "<<(*it)->point->pos_.y()<<" "<<(*it)->point->pos_.z()<<std::endl;
Vector2d e = vk::project2d((*it)->f) - vk::project2d(Vector3d(frame->se3()*(*it)->point->pos_));
//std::cerr<<"131 - pose optimizer"<<std::endl;
e /= (1<<(*it)->level);
//std::cerr<<"133 - pose optimizer"<<std::endl;
chi2_vec_final.push_back(e.norm());
//std::cerr<<"135 - pose optimizer"<<std::endl;
if(e.norm() > 2.0 / frame->cam_->errorMultiplier2())
{
// we don't need to delete a reference in the point since it was not created yet
(*it)->point.reset();
//std::cerr<<"befor erase 140 pose optimizer"<<std::endl;
it = frame->fts_.erase(it);
//std::cerr<<"after erase 142 pose optimizer"<<std::endl;
++n_deleted_refs;
}
else
it++;
}
num_obs -= n_deleted_refs;
#if VIO_DEBUG
GPU_version/vio/src/reprojector.cpp
+52 -2
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@@ -81,11 +81,37 @@ namespace vio {
std::vector<cv::KeyPoint> keypoints_cur;
list<std::shared_ptr<Feature>>::iterator it_cur=keypoints.begin();
cv::Mat cur_des=cv::Mat(keypoints.size(),64,CV_8UC1);
// std::cerr<<"84"<<std::endl;
//cv::Mat maskup = cv::Mat(keypoints.size(),64,CV_8UC1);
//cv::Mat maskdown = cv::Mat(keypoints.size(),64,CV_8UC1);
cv::Mat maskup = cv::Mat_<uchar>(1, keypoints.size());
cv::Mat maskdown = cv::Mat_<uchar>(1, keypoints.size());
// std::cerr<<"88"<<std::endl;
//// uint8_t one[64] = {255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,
//// 255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,
//// 255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,
//// 255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255};
//// uint8_t zero[64] = {0};
// std::cerr<<"94"<<std::endl;
for (int i=0;i<keypoints.size() && it_cur !=keypoints.end();++i) {
keypoints_cur.push_back(cv::KeyPoint((*it_cur)->px.x(), (*it_cur)->px.y(), 7.f,(*it_cur)->score));
keypoints_cur.push_back(cv::KeyPoint((*it_cur)->px.x(), (*it_cur)->px.y(), 7.f, (*it_cur)->score));
memcpy(cur_des.data+(i*64),(*it_cur)->descriptor,sizeof(uint8_t)*64);
if ((*it_cur)->px.y() < frame->img().rows/2) {
maskup.at<uchar>(0, i) = 1;
maskdown.at<uchar>(0, i) = 0;
//memcpy(maskup.data + (i * 64), one, sizeof(uint8_t) * 64);
//memcpy(maskdown.data + (i * 64), zero, sizeof(uint8_t) * 64);
}
else{
maskup.at<uchar>(0, i) = 0;
maskdown.at<uchar>(0, i) = 1;
//memcpy(maskup.data + (i * 64), zero, sizeof(uint8_t) * 64);
//memcpy(maskdown.data + (i * 64), one, sizeof(uint8_t) * 64);
}
++it_cur;
}
// std::cerr<<"114"<<std::endl;
list<pair<FramePtr, double> > close_kfs;
map_.getCloseKeyframes(frame, close_kfs);
if (!last_frame->fts_.empty())
@@ -103,8 +129,32 @@ namespace vio {
std::vector<cv::KeyPoint> keypoints_kfs;
std::vector<std::vector<cv::DMatch>> matches;
keypoints_kfs.push_back(cv::KeyPoint((*it_ref)->px.x(), (*it_ref)->px.y(), 7.f,(*it_ref)->score));
// std::cerr<<"135"<<std::endl;
cv::Mat ref_des=cv::Mat(1,64,CV_8UC1,(*it_ref)->descriptor);
matcher->knnMatch(ref_des, cur_des, matches,1);
// std::cerr<<"136"<<std::endl;
//std::cerr<<maskup.size<<" "<<maskdown.size<<" "<<cur_des.size<<std::endl;
// cv::Mat mup, mdown;
// cv::cvtColor(maskup, mup, cv::COLOR_GRAY2RGB);
// cv::imwrite("/root/Projects/ROS_p_33_dev/src/p_33_vio/GPU_version/vio/maskup.png", mup);
// cv::cvtColor(maskdown, mdown, cv::COLOR_GRAY2RGB);
// cv::imwrite("/root/Projects/ROS_p_33_dev/src/p_33_vio/GPU_version/vio/maskdown.png", mdown);
// std::cerr<<"137"<<std::endl;
if ((*it_ref)->px.y() < frame->img().rows/2) {
std::cerr<<"if"<<std::endl;
matcher->knnMatch(ref_des, cur_des, matches, 1, cv::InputArray(maskup));
}
else{
std::cerr<<"else"<<std::endl;
matcher->knnMatch(ref_des, cur_des, matches, 1, cv::InputArray(maskdown));
}
// std::cerr<<"144"<<std::endl;
//matcher->knnMatch(ref_des, cur_des, matches, 1);
for(auto&& match:matches.back()){
it_cur=keypoints.begin();
std::advance(it_cur,match.trainIdx);
GPU_version/vio/test/README.md
-24
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@@ -1,24 +0,0 @@
#### Download Test Data
Create a folder where you will store all datasets, e.g.:
mkdir Datasets
In your `.bashrc` script, create a new environment variable pointing to the dataset folder:
export SVO_DATASET_DIR=${HOME}/Datasets
Source your new `.bashrc` script, go to the new dataset folder and download the test data:
source ~/.bashrc
cd ${SVO_DATASET_DIR}
wget http://rpg.ifi.uzh.ch/datasets/sin2_tex2_h1_v8_d.tar.gz -O - | tar -xz
You can then run the tests, e.g.:
rosrun svo test_pipeline
or if you are not using ROS:
cd svo/bin
./test_pipeline
GPU_version/vio/test/benchmark.csv
-307
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@@ -1,307 +0,0 @@
local_ba,point_optimizer,pose_optimizer,pyramid_creation,reproject,sparse_img_align,tot_time,dropout,img_align_n_tracked,loba_err_fin,loba_err_init,loba_n_erredges_fin,loba_n_erredges_init,n_candidates,repr_n_mps,repr_n_new_references,sfba_error_final,sfba_error_init,sfba_n_edges_final,sfba_thresh
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1 local_ba point_optimizer pose_optimizer pyramid_creation reproject sparse_img_align tot_time dropout img_align_n_tracked loba_err_fin loba_err_init loba_n_erredges_fin loba_n_erredges_init n_candidates repr_n_mps repr_n_new_references sfba_error_final sfba_error_init sfba_n_edges_final sfba_thresh
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GPU_version/vio/test/data/.gitignore
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GPU_version/vio/test/results/.gitignore
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GPU_version/vio/test/test_depth_filter.cpp
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// This file is part of SVO - Semi-direct Visual Odometry.
//
// Copyright (C) 2014 Christian Forster <forster at ifi dot uzh dot ch>
// (Robotics and Perception Group, University of Zurich, Switzerland).
//
// SVO is free software: you can redistribute it and/or modify it under the
// terms of the GNU General Public License as published by the Free Software
// Foundation, either version 3 of the License, or any later version.
//
// SVO is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program. If not, see <http://www.gnu.org/licenses/>.
#include <list>
#include <vector>
#include <string>
#include <boost/bind.hpp>
#include <boost/make_shared.hpp>
#include <boost/shared_ptr.hpp>
#include <vikit/pinhole_camera.h>
#include <vikit/abstract_camera.h>
#include <vikit/atan_camera.h>
#include <vikit/math_utils.h>
#include <vikit/file_reader.h>
#include <vikit/blender_utils.h>
#include <vikit/timer.h>
#include <iostream>
#include <svo/feature_detection.h>
#include <svo/depth_filter.h>
#include <svo/frame.h>
#include <svo/feature.h>
#include <svo/point.h>
#include <svo/config.h>
#include "test_utils.h"
namespace {
struct ConvergedSeed {
int x_, y_;
double depth_, error_;
ConvergedSeed(int x, int y, double depth, double error) :
x_(x), y_(y), depth_(depth), error_(error)
{}
};
/// DepthFilter Test-Fixture
class DepthFilterTest {
public:
DepthFilterTest();
virtual ~DepthFilterTest();
void depthFilterCb(svo::Point* point, double depth_sigma2);
void testReconstruction(std::string dataset_dir, std::string experiment_name);
std::list<ConvergedSeed> results_;
std::vector<double> errors_;
size_t n_converged_seeds_;
vk::PinholeCamera* cam_;
svo::DepthFilter* depth_filter_;
svo::FramePtr frame_ref_;
svo::FramePtr frame_cur_;
cv::Mat depth_ref_;
};
DepthFilterTest::DepthFilterTest() :
n_converged_seeds_(0),
cam_(new vk::PinholeCamera(752, 480, 315.5, 315.5, 376.0, 240.0)),
depth_filter_(NULL)
{
errors_.reserve(1000);
}
DepthFilterTest::~DepthFilterTest()
{
delete cam_;
}
void DepthFilterTest::depthFilterCb(svo::Point* point, double depth_sigma2)
{
double depth = (frame_ref_->pos() - point->pos_).norm();
double error = fabs(depth - depth_ref_.at<float>(point->obs_.front()->px[1],
point->obs_.front()->px[0]));
results_.push_back(ConvergedSeed(
point->obs_.front()->px[0], point->obs_.front()->px[1], depth, error));
errors_.push_back(error);
delete point->obs_.front();
delete point;
++n_converged_seeds_;
}
void DepthFilterTest::testReconstruction(
std::string dataset_dir,
std::string experiment_name)
{
vk::FileReader<vk::blender_utils::file_format::ImageNameAndPose> sequence_file_reader(dataset_dir+"/trajectory.txt");
std::vector<vk::blender_utils::file_format::ImageNameAndPose> sequence;
sequence_file_reader.skipComments();
if(!sequence_file_reader.next())
std::runtime_error("Failed to open sequence file");
sequence_file_reader.readAllEntries(sequence);
std::cout << "RUN EXPERIMENT: read " << sequence.size() << " dataset entries." << std::endl;
std::vector<vk::blender_utils::file_format::ImageNameAndPose>::iterator it = sequence.begin();
vk::Timer t;
std::list<size_t> n_converged_per_iteration;
svo::feature_detection::DetectorPtr feature_detector(
new svo::feature_detection::FastDetector(
cam_->width(), cam_->height(), svo::Config::gridSize(), svo::Config::nPyrLevels()));
svo::DepthFilter::callback_t depth_filter_cb = boost::bind(&DepthFilterTest::depthFilterCb, this, _1, _2);
depth_filter_ = new svo::DepthFilter(feature_detector, depth_filter_cb);
depth_filter_->options_.verbose = true;
for(int i=0; it != sequence.end() && i<20; ++it, ++i)
{
std::string img_name(dataset_dir+"/img/" + (*it).image_name_ + "_0.png");
printf("reading image: '%s'\n", img_name.c_str());
cv::Mat img(cv::imread(img_name, 0));
assert(!img.empty());
Sophus::SE3 T_w_f(it->q_, it->t_);
if(i == 0)
{
// create reference frame and load ground truth depthmap
frame_ref_ = boost::make_shared<svo::Frame>(cam_, img, 0.0);
frame_ref_->T_f_w_ = T_w_f.inverse();
depth_filter_->addKeyframe(frame_ref_, 2, 0.5);
vk::blender_utils::loadBlenderDepthmap(dataset_dir+"/depth/" + (*it).image_name_ + "_0.depth", *cam_, depth_ref_);
continue;
}
n_converged_seeds_ = 0;
frame_cur_ = boost::make_shared<svo::Frame>(cam_, img, 0.0);
frame_cur_->T_f_w_ = T_w_f.inverse();
depth_filter_->addFrame(frame_cur_);
n_converged_per_iteration.push_back(n_converged_seeds_);
}
printf("Experiment '%s' took %f ms\n", experiment_name.c_str(), t.stop()*1000);
// compute mean, median and variance of error in converged area
{
printf("# converged: \t %zu (ref: 287)\n", errors_.size());
double sum_error = 0;
std::for_each(errors_.begin(), errors_.end(), [&](double& e){sum_error+=e;});
printf("mean error: \t %f cm (ref: 0.080357)\n", sum_error*100/errors_.size());
std::vector<double>::iterator it = errors_.begin()+0.5*errors_.size();
std::nth_element(errors_.begin(), it, errors_.end());
printf("50-percentile: \t %f cm (ref: 0.062042)\n", *it*100);
it = errors_.begin()+0.8*errors_.size();
std::nth_element(errors_.begin(), it, errors_.end());
printf("80-percentile: \t %f cm (ref: 0.124526)\n", *it*100);
it = errors_.begin()+0.95*errors_.size();
std::nth_element(errors_.begin(), it, errors_.end());
printf("95-percentile: \t %f cm (ref: 0.200417)\n", *it*100);
}
// trace error
std::string trace_dir(svo::test_utils::getTraceDir());
std::string trace_name(trace_dir + "/depth_filter_" + experiment_name + ".txt");
std::ofstream ofs(trace_name.c_str());
for(std::list<ConvergedSeed>::iterator i=results_.begin(); i!=results_.end(); ++i)
ofs << i->x_ << ", " << i->y_ << ", " << fabs(i->error_) << std::endl;
ofs.close();
// trace convergence rate
trace_name = trace_dir + "/depth_filter_" + experiment_name + "_convergence.txt";
ofs.open(trace_name.c_str());
for(std::list<size_t>::iterator it=n_converged_per_iteration.begin();
it!=n_converged_per_iteration.end(); ++it)
ofs << *it << std::endl;
ofs.close();
// write ply file for pointcloud visualization in Meshlab
trace_name = trace_dir + "/depth_filter_" + experiment_name + ".ply";
ofs.open(trace_name.c_str());
ofs << "ply" << std::endl
<< "format ascii 1.0" << std::endl
<< "element vertex " << results_.size() << std::endl
<< "property float x" << std::endl
<< "property float y" << std::endl
<< "property float z" << std::endl
<< "property uchar blue" << std::endl
<< "property uchar green" << std::endl
<< "property uchar red" << std::endl
<< "end_header" << std::endl;
for(std::list<ConvergedSeed>::iterator i=results_.begin(); i!=results_.end(); ++i)
{
cv::Vec3b c = frame_ref_->img_pyr_[0].at<cv::Vec3b>(i->y_, i->x_);
Eigen::Vector3d p = cam_->cam2world(i->x_, i->y_)*i->depth_;
ofs << p[0] << " " << p[1] << " " << p[2] << " "
<< (int) c[0] << " " << (int) c[1] << " " << (int) c[2] << std::endl;
}
}
} // namespace
int main(int argc, char** argv)
{
DepthFilterTest test;
std::string dataset_dir(svo::test_utils::getDatasetDir() + "/sin2_tex2_h1_v8_d");
std::string experiment_name("sin2_tex2_h1_v8_d");
test.testReconstruction(dataset_dir, experiment_name);
return 0;
}
GPU_version/vio/test/test_feature_alignment.cpp
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// This file is part of SVO - Semi-direct Visual Odometry.
//
// Copyright (C) 2014 Christian Forster <forster at ifi dot uzh dot ch>
// (Robotics and Perception Group, University of Zurich, Switzerland).
//
// SVO is free software: you can redistribute it and/or modify it under the
// terms of the GNU General Public License as published by the Free Software
// Foundation, either version 3 of the License, or any later version.
//
// SVO is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program. If not, see <http://www.gnu.org/licenses/>.
#include <string.h>
#include <svo/global.h>
#include <svo/feature_alignment.h>
#include <vikit/timer.h>
#include <vikit/aligned_mem.h>
#include "test_utils.h"
#if __ARM_NEON__
#include <arm_neon.h>
#endif
using namespace Eigen;
void generateRefPatchNoWarpInterpolate(const cv::Mat& img, const Vector2d& px,
cv::Mat& ref_patch_with_border)
{
// compute interpolation weights
const int u_r = floor(px[0]);
const int v_r = floor(px[1]);
const float subpix_u = px[0]-u_r;
const float subpix_v = px[1]-v_r;
const float wTL = (1.0-subpix_u)*(1.0-subpix_v);
const float wTR = subpix_u * (1.0-subpix_v);
const float wBL = (1.0-subpix_u)*subpix_v;
const float wBR = subpix_u * subpix_v;
// loop through search_patch, interpolate
ref_patch_with_border = cv::Mat(10, 10, CV_8UC1);
uint8_t* patch_ptr = ref_patch_with_border.data;
const int stride = img.step.p[0];
for(int y=0; y<10; ++y)
{
uint8_t* img_ptr = (uint8_t*) img.data + (v_r+y-4-1)*stride + u_r-4-1;
for(int x=0; x<10; ++x, ++patch_ptr, ++img_ptr)
*patch_ptr = wTL*img_ptr[0] + wTR*img_ptr[1] + wBL*img_ptr[stride] + wBR*img_ptr[stride+1];
}
}
int main(int argc, char **argv)
{
std::string img_name(svo::test_utils::getDatasetDir() + "/sin2_tex2_h1_v8_d/img/frame_000002_0.png");
printf("Loading image '%s'\n", img_name.c_str());
cv::Mat img(cv::imread(img_name, 0));
assert(img.type() == CV_8UC1);
// TODO: test on corner/gradient features!
Vector2d px_true(130.2,120.3);
// create reference patch with border
cv::Mat ref_patch_with_border;
generateRefPatchNoWarpInterpolate(img, px_true, ref_patch_with_border);
// create reference patch, aligned
uint8_t* ref_patch = vk::aligned_mem::aligned_alloc<uint8_t>(64, 16);
uint8_t* ref_patch_ptr = ref_patch;
for(int y=1; y<9; ++y)
{
uint8_t* ref_patch_border_ptr = ref_patch_with_border.data + y*10 + 1;
for(int x=0; x<8; ++x, ++ref_patch_border_ptr, ++ref_patch_ptr)
*ref_patch_ptr = *ref_patch_border_ptr;
}
Vector2d px_est, px_error(-1.1, -0.8);
double h_inv;
vk::Timer t;
for(int i=0; i<1000; ++i)
{
px_est = px_true-px_error;
Vector2f dir = px_error.normalized().cast<float>();
svo::feature_alignment::align1D(img, dir, ref_patch_with_border.data, ref_patch, 3, px_est, h_inv);
}
Vector2d e = px_est-px_true;
printf("1000Xalign 1D took %fms, error = %fpx \t (ref i7-W520: 1.982000ms, 0.000033px) \n", t.stop()*1000, e.norm());
t.start();
for(int i=0; i<1000; ++i)
{
px_est = px_true-px_error;
svo::feature_alignment::align2D(img, ref_patch_with_border.data, ref_patch, 3, px_est, true);
}
e = px_est-px_true;
printf("1000Xalign 2D took %fms, error = %fpx \t (ref i7-W520: 2.306000ms, 0.015102px)\n", t.stop()*1000, e.norm());
#ifdef __SSE2__
// Note, this KLT implementation is not invariant to illuminatino changes!
t.start();
for(int i=0; i<1000; ++i)
{
px_est = px_true-px_error;
svo::feature_alignment::align2D_SSE2(img, ref_patch_with_border.data, ref_patch, 3, px_est);
}
e = px_est-px_true;
printf("1000Xalign 2D SSE2 %fms, error = %fpx \t (ref i7-W520: 0.460000ms, 0.021881px)\n", t.stop()*1000, e.norm());
#endif
#ifdef __ARM_NEON__
t.start();
for(int i=0; i<1000; ++i)
{
px_est = px_true-px_error;
svo::feature_alignment::align2D_NEON(img, ref_patch_with_border.data, ref_patch, 3, px_est);
}
e = px_est-px_true;
printf("1000Xalign 2D NEON %fms, error = %fpx \t (ref Odroid-U2: ?, ?)\n", t.stop()*1000, e.norm());
#endif
return 0;
}
GPU_version/vio/test/test_feature_detection.cpp
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// This file is part of SVO - Semi-direct Visual Odometry.
//
// Copyright (C) 2014 Christian Forster <forster at ifi dot uzh dot ch>
// (Robotics and Perception Group, University of Zurich, Switzerland).
//
// SVO is free software: you can redistribute it and/or modify it under the
// terms of the GNU General Public License as published by the Free Software
// Foundation, either version 3 of the License, or any later version.
//
// SVO is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program. If not, see <http://www.gnu.org/licenses/>.
#include <string.h>
#include <svo/global.h>
#include <svo/config.h>
#include <svo/frame.h>
#include <svo/feature_detection.h>
#include <svo/depth_filter.h>
#include <svo/feature.h>
#include <vikit/timer.h>
#include <vikit/vision.h>
#include <vikit/abstract_camera.h>
#include <vikit/atan_camera.h>
#include "test_utils.h"
namespace {
using namespace Eigen;
using namespace std;
void testCornerDetector()
{
std::string img_name(svo::test_utils::getDatasetDir() + "/sin2_tex2_h1_v8_d/img/frame_000002_0.png");
printf("Loading image '%s'\n", img_name.c_str());
cv::Mat img(cv::imread(img_name, 0));
assert(img.type() == CV_8UC1 && !img.empty());
vk::AbstractCamera* cam = new vk::ATANCamera(752, 480, 0.511496, 0.802603, 0.530199, 0.496011, 0.934092);
svo::FramePtr frame(new svo::Frame(cam, img, 0.0));
// Corner detection
vk::Timer t;
svo::Features fts;
svo::feature_detection::FastDetector fast_detector(
img.cols, img.rows, svo::Config::gridSize(), svo::Config::nPyrLevels());
for(int i=0; i<100; ++i)
{
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: 7.166360ms, 40000)\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);
std::for_each(fts.begin(), fts.end(), [&](svo::Feature* i){ delete i; });
}
} // namespace
int main(int argc, char **argv)
{
testCornerDetector();
return 0;
}
GPU_version/vio/test/test_matcher.cpp
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// This file is part of SVO - Semi-direct Visual Odometry.
//
// Copyright (C) 2014 Christian Forster <forster at ifi dot uzh dot ch>
// (Robotics and Perception Group, University of Zurich, Switzerland).
//
// SVO is free software: you can redistribute it and/or modify it under the
// terms of the GNU General Public License as published by the Free Software
// Foundation, either version 3 of the License, or any later version.
//
// SVO is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program. If not, see <http://www.gnu.org/licenses/>.
#include <algorithm>
#include <vikit/pinhole_camera.h>
#include <vikit/math_utils.h>
#include <vikit/blender_utils.h>
#include <svo/matcher.h>
#include <svo/frame.h>
#include <svo/config.h>
#include <svo/feature.h>
#include "test_utils.h"
namespace {
/// Matcher Test-Fixture
class MatcherTest {
public:
MatcherTest()
{
cam_ = new vk::PinholeCamera(752, 480, 315.5, 315.5, 376.0, 240.0);
// load images
std::string dataset_dir(svo::test_utils::getDatasetDir() + "/sin2_tex2_h1_v8_d");
std::string img_name(dataset_dir+"/img/frame_000002_0.png");
printf("Loading image '%s'\n", img_name.c_str());
cv::Mat img_ref(cv::imread(img_name, 0));
img_name = std::string(dataset_dir+"/img/frame_000006_0.png");
printf("Loading image '%s'\n", img_name.c_str());
cv::Mat img_cur(cv::imread(img_name, 0));
assert(!img_ref.empty() && !img_cur.empty());
// create frame
frame_ref_ = new svo::Frame(cam_, img_ref, 1.0);
frame_cur_ = new svo::Frame(cam_, img_cur, 2.0);
ref_ftr_ = new svo::Feature(frame_ref_, Eigen::Vector2d(300, 260), 0);
// set poses
Eigen::Vector3d t_w_ref(0.1131, 0.1131, 2.0000);
Eigen::Vector3d t_w_cur(0.5673, 0.5641, 2.0000);
Eigen::Quaterniond q_w_ref(0.0, 0.8227, 0.2149, 0.0);
Eigen::Quaterniond q_w_cur(0.0, 0.8235, 0.2130, 0.0);
frame_ref_->T_f_w_ = Sophus::SE3(q_w_ref, t_w_ref).inverse();
frame_cur_->T_f_w_ = Sophus::SE3(q_w_cur, t_w_cur).inverse();
// load ground-truth depth
vk::blender_utils::loadBlenderDepthmap(dataset_dir+"/depth/frame_000002_0.depth", *cam_, depth_ref_);
}
virtual ~MatcherTest()
{
delete cam_;
delete frame_ref_;
delete frame_cur_;
delete ref_ftr_;
}
// Individual tests
void testEpipolarSearchFullImg();
void testWarpAffine();
// Objects declared here can be used by all tests
vk::PinholeCamera* cam_;
svo::Frame* frame_ref_;
svo::Frame* frame_cur_;
svo::Feature* ref_ftr_;
cv::Mat depth_ref_;
};
void MatcherTest::testEpipolarSearchFullImg()
{
svo::Matcher matcher;
cv::Mat depthmap(frame_ref_->cam_->height(), frame_ref_->cam_->width(), CV_32FC1, cv::Scalar(0));
cv::Mat error_map(depthmap.rows, depthmap.cols, CV_32FC1, cv::Scalar(0));
cv::Mat mask(depthmap.rows, depthmap.cols, CV_8UC1, cv::Scalar(1));
double depth_range = 0.8;
size_t n_converged = 0;
double sum_error = 0;
std::vector<double> errors; errors.reserve(depthmap.cols*depthmap.rows);
for(int y=4; y<depthmap.rows-4; ++y)
{
for(int x=4; x<depthmap.cols-4; ++x)
{
svo::Feature ftr(frame_ref_, Eigen::Vector2d(x,y), 0);
double depth_gt = depth_ref_.at<float>(y, x);
double depth_estimate;
int res = matcher.findEpipolarMatchDirect(
*frame_ref_, *frame_cur_, ftr, depth_gt, fmax(depth_gt-depth_range,0.0),
depth_gt+depth_range, depth_estimate);
if(res)
{
depthmap.at<float>(y,x) = depth_estimate;
error_map.at<float>(y,x) = fabsf(depth_estimate-depth_gt);
++n_converged;
sum_error += error_map.at<float>(y,x);
errors.push_back(error_map.at<float>(y,x));
}
else
mask.at<uint8_t>(y,x) = 0;
}
}
// compute mean, median and variance of error in converged area
printf("n converged: \t %zu mm (ref: 216114)\n", n_converged);
printf("mean error: \t %f mm (ref: 0.410084)\n", sum_error*100/n_converged);
std::vector<double>::iterator it = errors.begin()+0.5*errors.size();
std::nth_element(errors.begin(), it, errors.end());
printf("50-percentile: \t %f mm (ref: 0.083203)\n", *it*100);
it = errors.begin()+0.8*errors.size();
std::nth_element(errors.begin(), it, errors.end());
printf("80-percentile: \t %f mm (ref: 0.161824)\n", *it*100);
it = errors.begin()+0.95*errors.size();
std::nth_element(errors.begin(), it, errors.end());
printf("95-percentile: \t %f mm (ref: 0.263539)\n", *it*100);
// save results to file
std::ofstream output_stream;
std::string trace_dir(svo::test_utils::getTraceDir());
std::string output_filename(trace_dir + "/depthmap.bin");
output_stream.open(output_filename.c_str(), std::ios::out | std::ios::binary | std::ios::trunc);
output_stream.write((const char *)depthmap.data, depthmap.rows*depthmap.cols*sizeof(float));
output_stream.close();
output_filename.assign(trace_dir + "/depthmap_error.bin");
output_stream.open(output_filename.c_str(), std::ios::out | std::ios::binary | std::ios::trunc);
output_stream.write((const char *)error_map.data, error_map.rows*error_map.cols*sizeof(float));
output_stream.close();
output_filename.assign(trace_dir + "/depthmap_mask.bin");
output_stream.open(output_filename.c_str(), std::ios::out | std::ios::binary | std::ios::trunc);
output_stream.write((const char *)mask.data, mask.rows*mask.cols*sizeof(uint8_t));
output_stream.close();
}
void MatcherTest::testWarpAffine()
{
const int halfpatch_size = 15;
const int patch_size=halfpatch_size*2;
svo::Matcher matcher;
double depth = 1.0;
Sophus::SE3 T_cur_ref(frame_cur_->T_f_w_*frame_ref_->T_f_w_.inverse());
Eigen::Vector2d px_cur(frame_cur_->cam_->world2cam(T_cur_ref*(ref_ftr_->f*depth)));
// compute reference patch
cv::Mat ref_patch(matcher.halfpatch_size_+1, matcher.halfpatch_size_+1,
CV_8U, matcher.patch_with_border_);
Eigen::Matrix2d A_cur_ref;
svo::warp::getWarpMatrixAffine(
*cam_, *cam_, ref_ftr_->px, ref_ftr_->f, 1.0, T_cur_ref, ref_ftr_->level, A_cur_ref);
int level_cur = svo::warp::getBestSearchLevel(A_cur_ref, svo::Config::nPyrLevels()-1);
svo::warp::warpAffine(
A_cur_ref, ref_ftr_->frame->img_pyr_[ref_ftr_->level], ref_ftr_->px,
ref_ftr_->level, level_cur, matcher.halfpatch_size_+1, matcher.patch_with_border_);
// copy current patch
Eigen::Vector2i pxi(px_cur[0]/(1<<level_cur)+0.5, px_cur[1]/(1<<level_cur)+0.5);
cv::Mat cur_patch(patch_size, patch_size, CV_8U);
frame_cur_->img_pyr_.at(level_cur)(cv::Range(pxi[1]-halfpatch_size,
pxi[1]+halfpatch_size),
cv::Range(pxi[0]-halfpatch_size,
pxi[0]+halfpatch_size)).copyTo(cur_patch);
// display results
cv::circle(frame_cur_->img_pyr_[level_cur],
cv::Point2f(pxi[0], pxi[1]),
patch_size, cv::Scalar(255), 2);
cv::circle(frame_ref_->img_pyr_[ref_ftr_->level],
cv::Point2f(ref_ftr_->px[0]/(1<<ref_ftr_->level), ref_ftr_->px[1]/(1<<ref_ftr_->level)),
patch_size, cv::Scalar(255), 2);
cv::imshow("cur_patch", cur_patch);
cv::imshow("ref_patch", ref_patch);
cv::imshow("ref_img", frame_ref_->img_pyr_[ref_ftr_->level]);
cv::imshow("cur_img", frame_cur_->img_pyr_[level_cur]);
cv::waitKey(0);
}
} // namespace
int main(int argc, char** argv)
{
MatcherTest test;
test.testEpipolarSearchFullImg();
// test.testWarpAffine();
return 0;
}
GPU_version/vio/test/test_pipeline.cpp
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@@ -1,98 +0,0 @@
// This file is part of SVO - Semi-direct Visual Odometry.
//
// Copyright (C) 2014 Christian Forster <forster at ifi dot uzh dot ch>
// (Robotics and Perception Group, University of Zurich, Switzerland).
//
// SVO is free software: you can redistribute it and/or modify it under the
// terms of the GNU General Public License as published by the Free Software
// Foundation, either version 3 of the License, or any later version.
//
// SVO is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program. If not, see <http://www.gnu.org/licenses/>.
#include <svo/config.h>
#include <svo/frame_handler_mono.h>
#include <svo/map.h>
#include <svo/frame.h>
#include <vector>
#include <string>
#include <vikit/math_utils.h>
#include <vikit/vision.h>
#include <vikit/abstract_camera.h>
#include <vikit/atan_camera.h>
#include <vikit/pinhole_camera.h>
#include <opencv2/opencv.hpp>
#include <sophus/se3.h>
#include <iostream>
#include "test_utils.h"
namespace vio {
class BenchmarkNode
{
vk::AbstractCamera* cam_;
svo::FrameHandlerMono* vo_;
public:
BenchmarkNode();
~BenchmarkNode();
void runFromFolder();
};
BenchmarkNode::BenchmarkNode()
{
cam_ = new vk::PinholeCamera(752, 480, 315.5, 315.5, 376.0, 240.0);
vo_ = new svo::FrameHandlerMono(cam_);
vo_->start();
}
BenchmarkNode::~BenchmarkNode()
{
delete vo_;
delete cam_;
}
void BenchmarkNode::runFromFolder()
{
for(int img_id = 2; img_id < 188; ++img_id)
{
// load image
std::stringstream ss;
ss << svo::test_utils::getDatasetDir() << "/sin2_tex2_h1_v8_d/img/frame_"
<< std::setw( 6 ) << std::setfill( '0' ) << img_id << "_0.png";
if(img_id == 2)
std::cout << "reading image " << ss.str() << std::endl;
cv::Mat img(cv::imread(ss.str().c_str(), 0));
assert(!img.empty());
// process frame
vo_->addImage(img, 0.01*img_id);
// display tracking quality
if(vo_->lastFrame() != NULL)
{
std::cout << "Frame-Id: " << vo_->lastFrame()->id_ << " \t"
<< "#Features: " << vo_->lastNumObservations() << " \t"
<< "Proc. Time: " << vo_->lastProcessingTime()*1000 << "ms \n";
// access the pose of the camera via vo_->lastFrame()->T_f_w_.
}
}
}
} // namespace vio
int main(int argc, char** argv)
{
{
svo::BenchmarkNode benchmark;
benchmark.runFromFolder();
}
printf("BenchmarkNode finished.\n");
return 0;
}
GPU_version/vio/test/test_pose_optimizer.cpp
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@@ -1,110 +0,0 @@
// This file is part of SVO - Semi-direct Visual Odometry.
//
// Copyright (C) 2014 Christian Forster <forster at ifi dot uzh dot ch>
// (Robotics and Perception Group, University of Zurich, Switzerland).
//
// SVO is free software: you can redistribute it and/or modify it under the
// terms of the GNU General Public License as published by the Free Software
// Foundation, either version 3 of the License, or any later version.
//
// SVO is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program. If not, see <http://www.gnu.org/licenses/>.
#include <algorithm>
#include <vikit/pinhole_camera.h>
#include <vikit/math_utils.h>
#include <vikit/sample.h>
#include <vikit/blender_utils.h>
#include <svo/frame.h>
#include <svo/point.h>
#include <svo/feature.h>
#include <svo/config.h>
#include <svo/feature_detection.h>
#include <svo/pose_optimizer.h>
#include "test_utils.h"
namespace {
using namespace vio;
using namespace Eigen;
/// Pose Optimizer Test-Fixture
class PoseOptimizerTest {
public:
PoseOptimizerTest()
{
cam_ = new vk::PinholeCamera(752, 480, 315.5, 315.5, 376.0, 240.0);
// load image
std::string dataset_dir(svo::test_utils::getDatasetDir() + "/sin2_tex2_h1_v8_d");
std::string img_name(dataset_dir+"/img/frame_000002_0.png");
printf("Loading image '%s'\n", img_name.c_str());
cv::Mat img(cv::imread(img_name, 0));
assert(!img.empty());
// create frame
frame_.reset(new svo::Frame(cam_, img, 1.0));
// set pose
Eigen::Vector3d t_w_ref(0.1131, 0.1131, 2.0000);
Eigen::Quaterniond q_w_ref(0.0, 0.8227, 0.2149, 0.0);
frame_->T_f_w_ = Sophus::SE3(q_w_ref, t_w_ref).inverse();
// load ground-truth depth
vk::blender_utils::loadBlenderDepthmap(dataset_dir + "/depth/frame_000002_0.depth", *cam_, depthmap_);
// detect features
feature_detection::FastDetector detector(
cam_->width(), cam_->height(), Config::gridSize(), Config::nPyrLevels());
detector.detect(frame_.get(), frame_->img_pyr_, Config::triangMinCornerScore(), frame_->fts_);
size_t n_fts = 0;
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));
i->point = point;
++n_fts;
});
printf("Added %zu features to frame.\n", n_fts);
}
virtual ~PoseOptimizerTest()
{
delete cam_;
}
// Individual tests
void test(const Vector3d& pose_disturbance, double pixel_sigma2);
// Objects declared here can be used by all tests
vk::PinholeCamera* cam_;
FramePtr frame_;
cv::Mat depthmap_;
};
void PoseOptimizerTest::test(const Vector3d& pose_disturbance, double pixel_sigma2)
{
printf("Add %f px noise to each observation\n", pixel_sigma2);
for(Features::iterator it=frame_->fts_.begin(); it!=frame_->fts_.end(); ++it)
{
(*it)->px += Vector2d(vk::Sample::gaussian(pixel_sigma2), vk::Sample::gaussian(pixel_sigma2));
(*it)->f = frame_->c2f((*it)->px);
}
frame_->T_f_w_ = frame_->T_f_w_*SE3(Matrix3d::Identity(), pose_disturbance);
double estimated_scale, error_init, error_final;
size_t num_obs;
pose_optimizer::optimizeGaussNewton(
Config::reprojThresh(), 10, true, frame_,
estimated_scale, error_init, error_final, num_obs);
}
} // namespace
int main(int argc, char** argv)
{
PoseOptimizerTest test;
test.test(Eigen::Vector3d(0.2, 0.2, 0.2), 1.0);
return 0;
}
GPU_version/vio/test/test_sparse_img_align.cpp
-162
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@@ -1,162 +0,0 @@
// This file is part of SVO - Semi-direct Visual Odometry.
//
// Copyright (C) 2014 Christian Forster <forster at ifi dot uzh dot ch>
// (Robotics and Perception Group, University of Zurich, Switzerland).
//
// SVO is free software: you can redistribute it and/or modify it under the
// terms of the GNU General Public License as published by the Free Software
// Foundation, either version 3 of the License, or any later version.
//
// SVO is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program. If not, see <http://www.gnu.org/licenses/>.
#include <vio/pinhole_camera.h>
#include <vio/math_utils.h>
#include <vio/file_reader.h>
#include <vio/timer.h>
#include <iostream>
#include <vio/feature_detection.h>
#include <vio/sparse_img_align.h>
#include <vio/frame.h>
#include <vio/point.h>
#include <vio/feature.h>
#include <vio/config.h>
#include "test_utils.h"
namespace {
/// SparseImgAlign Test-Fixture
class SparseImgAlignTest {
public:
SparseImgAlignTest();
virtual ~SparseImgAlignTest();
void testSequence(
const std::string& dataset_dir,
const std::string& experiment_name,
svo::feature_detection::AbstractDetector* feature_detector);
vk::PinholeCamera* cam_;
svo::FramePtr frame_ref_;
svo::FramePtr frame_cur_;
};
SparseImgAlignTest::SparseImgAlignTest() :
cam_(new vk::PinholeCamera(752, 480, 315.5, 315.5, 376.0, 240.0))
{}
SparseImgAlignTest::~SparseImgAlignTest()
{}
void SparseImgAlignTest::testSequence(
const std::string& dataset_dir,
const std::string& experiment_name,
svo::feature_detection::AbstractDetector* feature_detector)
{
vk::FileReader<vk::blender_utils::file_format::ImageNameAndPose> sequence_file_reader(dataset_dir+"/trajectory.txt");
std::vector<vk::blender_utils::file_format::ImageNameAndPose> sequence;
sequence_file_reader.skipComments();
if(!sequence_file_reader.next())
std::runtime_error("Failed to open sequence file");
sequence_file_reader.readAllEntries(sequence);
printf("RUN EXPERIMENT: read %zu dataset entries.\n", sequence.size());
std::vector<vk::blender_utils::file_format::ImageNameAndPose>::iterator iter = sequence.begin();
std::list<double> translation_error;
Sophus::SE3 T_prev_w, T_prevgt_w;
std::string trace_dir(svo::test_utils::getTraceDir());
std::string trace_name(trace_dir + "/sparse_img_align_" + experiment_name + "_trans_estimate.txt");
std::ofstream ofs(trace_name.c_str());
for(int i=0; iter != sequence.end() && i<30; ++iter, ++i)
{
// load img
// IMPORTANT: We need to flip the image because the Povray dataset has a
// negative focal length in y direction which we didn't set.
std::string img_name(dataset_dir+"/img/" + iter->image_name_ + "_0.png");
cv::Mat img(cv::imread(img_name, 0));
assert(!img.empty());
// load pose
Sophus::SE3 T_w_gt(iter->q_, iter->t_);
Sophus::SE3 T_gt_w = T_w_gt.inverse(); // ground-truth
if(i==0)
{
// set reference frame
frame_ref_.reset(new svo::Frame(cam_, img, 0.0));
frame_ref_->T_f_w_ = T_gt_w;
// load ground-truth depth
cv::Mat depthmap;
vk::blender_utils::loadBlenderDepthmap(
dataset_dir+"/depth/"+iter->image_name_+"_0.depth", *cam_, depthmap);
// extract features, generate features with 3D points
feature_detector->detect(
frame_ref_.get(), frame_ref_->img_pyr_, svo::Config::triangMinCornerScore(), frame_ref_->fts_);
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]);
Eigen::Vector3d pt_pos_w = frame_ref_->T_f_w_.inverse()*pt_pos_cur;
svo::Point* pt = new svo::Point(pt_pos_w, i);
i->point = pt;
});
printf("Added %zu 3d pts to the reference frame.\n", frame_ref_->nObs());
T_prev_w = frame_ref_->T_f_w_;
T_prevgt_w = T_gt_w;
continue;
}
frame_cur_.reset(new svo::Frame(cam_, img, 0.0));
//frame_cur_->T_f_w_ = frame_ref_->T_f_w_; // start at reference frame
frame_cur_->T_f_w_ = T_prev_w; // start at last frame
// run image align
vk::Timer t;
svo::SparseImgAlign img_align(svo::Config::kltMaxLevel(), svo::Config::kltMinLevel(),
30, svo::SparseImgAlign::GaussNewton, false, false);
img_align.run(frame_ref_, frame_cur_);
// compute error
Sophus::SE3 T_f_gt = frame_cur_->T_f_w_ * T_gt_w.inverse();
translation_error.push_back(T_f_gt.translation().norm());
printf("[%3.i] time = %f ms \t |t| = %f \t translation error = %f\n",
i, t.stop()*1000, (frame_ref_->T_f_w_*T_gt_w.inverse()).translation().norm(),
translation_error.back());
// save old pose for next iteration
T_prev_w = frame_cur_->T_f_w_;
T_prevgt_w = T_gt_w;
ofs << frame_cur_->T_f_w_.inverse().translation().transpose() << " "
<< T_gt_w.inverse().translation().transpose() << std::endl;
}
ofs.close();
// trace error
trace_name = trace_dir + "/sparse_img_align_" + experiment_name + "_trans_error.txt";
ofs.open(trace_name.c_str());
for(std::list<double>::iterator it=translation_error.begin(); it!=translation_error.end(); ++it)
ofs << *it << std::endl;
ofs.close();
}
} // namespace
int main(int argc, char** argv)
{
std::string experiment_name("flying_room_1_rig_1_fast_minlev0");
std::string dataset_dir(svo::test_utils::getDatasetDir() + "/sin2_tex2_h1_v8_d");
svo::Config::triangMinCornerScore() = 20;
svo::Config::kltMinLevel() = 0;
SparseImgAlignTest test;
svo::feature_detection::FastDetector detector(
test.cam_->width(), test.cam_->height(), svo::Config::gridSize(), svo::Config::nPyrLevels());
test.testSequence(dataset_dir, experiment_name, &detector);
return 0;
}
GPU_version/vio/test/test_utils.h
-57
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@@ -1,57 +0,0 @@
// This file is part of SVO - Semi-direct Visual Odometry.
//
// Copyright (C) 2014 Christian Forster <forster at ifi dot uzh dot ch>
// (Robotics and Perception Group, University of Zurich, Switzerland).
//
// SVO is free software: you can redistribute it and/or modify it under the
// terms of the GNU General Public License as published by the Free Software
// Foundation, either version 3 of the License, or any later version.
//
// SVO is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program. If not, see <http://www.gnu.org/licenses/>.
#ifndef TEST_UTILS_H_
#define TEST_UTILS_H_
#include <string.h>
#include <cstdlib> // for getenv
#ifdef SVO_USE_ROS
# include <ros/package.h>
# include <vikit/params_helper.h>
#endif
namespace vio {
namespace test_utils {
std::string getDatasetDir()
{
const char* env_dir = std::getenv("SVO_DATASET_DIR");
#ifdef SVO_USE_ROS
std::string dataset_dir(ros::package::getPath("svo")+"/test/data");
if(env_dir != NULL)
dataset_dir = std::string(env_dir);
return dataset_dir;
#else
return std::string(env_dir);
#endif
}
std::string getTraceDir()
{
#ifdef SVO_USE_ROS
std::string default_dir(ros::package::getPath("svo")+"/test/results");
return vk::getParam<std::string>("svo/trace_dir", default_dir);
#else
return "/tmp";
#endif
}
} // namespace test_utils
} // namespace vio
#endif // TEST_UTILS_H_