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added instructions on how to download test data. updated all tests for new dataset.

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This commit is contained in:
Christian Forster
2014-06-11 17:41:10 +02:00
parent 49da07745d
commit ab867a3047
9 changed files with 84 additions and 59 deletions
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svo/src/depth_filter.cpp
+5 -2
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@@ -126,7 +126,9 @@ void DepthFilter::initializeSeeds(FramePtr frame)
seeds_.push_back(Seed(ftr, new_keyframe_mean_depth_, new_keyframe_min_depth_));
});
SVO_DEBUG_STREAM("DepthFilter: Initialized "<<new_features.size()<<" new seeds");
if(options_.verbose)
SVO_INFO_STREAM("DepthFilter: Initialized "<<new_features.size()<<" new seeds");
seeds_updating_halt_ = false;
}
void DepthFilter::removeKeyframe(FramePtr frame)
@@ -160,7 +162,8 @@ void DepthFilter::reset()
frame_queue_.pop();
seeds_updating_halt_ = false;
SVO_DEBUG_STREAM("DepthFilter: RESET.");
if(options_.verbose)
SVO_INFO_STREAM("DepthFilter: RESET.");
}
void DepthFilter::updateSeedsLoop()
svo/test/README.md
+23 -3
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@@ -1,4 +1,24 @@
Download Synthetic Test Data:
-----------------------------
#### Download Test Data
TODO
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
svo/test/test_depth_filter.cpp
+16 -18
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@@ -54,7 +54,6 @@ class DepthFilterTest {
std::list<ConvergedSeed> results_;
std::vector<double> errors_;
double error_sum_;
size_t n_converged_seeds_;
vk::PinholeCamera* cam_;
svo::DepthFilter* depth_filter_;
@@ -65,7 +64,7 @@ class DepthFilterTest {
DepthFilterTest::DepthFilterTest() :
n_converged_seeds_(0),
cam_(new vk::PinholeCamera(752, 480, 217.083701215, 217.083701215, 376, 240)),
cam_(new vk::PinholeCamera(752, 480, 315.5, 315.5, 376.0, 240.0)),
depth_filter_(NULL)
{
errors_.reserve(1000);
@@ -91,7 +90,6 @@ void DepthFilterTest::depthFilterCb(svo::Point* point, double depth_sigma2)
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");
@@ -110,28 +108,28 @@ void DepthFilterTest::testReconstruction(
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<50; ++it, ++i)
for(int i=0; it != sequence.end() && i<20; ++it, ++i)
{
std::string img_name(dataset_dir+"/img/" + (*it).image_name_ + "_0.png");
if(i==0)
printf("reading image: '%s'\n", img_name.c_str());
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 = Sophus::SE3(it->q_.toRotationMatrix(), it->t_);
Sophus::SE3 T_w_f(it->q_, it->t_);
if(i == 0)
{
// create reference frame and load ground truth depthmap
frame_ref_.reset(new svo::Frame(cam_, img, 0.0));
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_, 3, 1);
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_ = svo::FramePtr(new svo::Frame(cam_, img, 0.0));
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_);
@@ -140,19 +138,19 @@ void DepthFilterTest::testReconstruction(
// compute mean, median and variance of error in converged area
{
printf("# converged: \t %zu (ref: 349)\n", errors_.size());
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: 1.510507)\n", sum_error*100/errors_.size());
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: 0.809308)\n", *it*100);
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: 1.580725)\n", *it*100);
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: 2.557531)\n", *it*100);
printf("95-percentile: \t %f cm (ref: 0.200417)\n", *it*100);
}
// trace error
@@ -199,8 +197,8 @@ void DepthFilterTest::testReconstruction(
int main(int argc, char** argv)
{
DepthFilterTest test;
std::string dataset_dir(svo::test_utils::getDatasetDir() + "/flying_room_1_rig_1");
std::string experiment_name("synth_fast_2d");
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;
}
svo/test/test_feature_alignment.cpp
+4 -4
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@@ -54,7 +54,7 @@ void generateRefPatchNoWarpInterpolate(const cv::Mat& img, const Vector2d& px,
int main(int argc, char **argv)
{
std::string img_name(svo::test_utils::getDatasetDir() + "/flying_room_1_rig_1/img/frame_000001_0.png");
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);
@@ -86,7 +86,7 @@ int main(int argc, char **argv)
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: 1.637000ms, Odroid-U2: ?, 0.008788px) \n", t.stop()*1000, e.norm());
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)
@@ -95,7 +95,7 @@ int main(int argc, char **argv)
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: 2.147000ms, Odroid-U2: ?, 0.009632px)\n", t.stop()*1000, e.norm());
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!
@@ -106,7 +106,7 @@ int main(int argc, char **argv)
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: 0.548000ms, 0.023032px)\n", t.stop()*1000, e.norm());
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__
svo/test/test_feature_detection.cpp
+2 -2
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@@ -34,7 +34,7 @@ using namespace std;
void testCornerDetector()
{
std::string img_name(svo::test_utils::getDatasetDir() + "/flying_room_1_rig_1/img/frame_000001_0.png");
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());
@@ -51,7 +51,7 @@ void testCornerDetector()
{
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, fts.size());
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);
svo/test/test_matcher.cpp
+16 -16
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@@ -31,14 +31,14 @@ class MatcherTest {
public:
MatcherTest()
{
cam_ = new vk::PinholeCamera(752, 480, 217.083701215, 217.083701215, 376, 240);
cam_ = new vk::PinholeCamera(752, 480, 315.5, 315.5, 376.0, 240.0);
// load images
std::string dataset_dir(svo::test_utils::getDatasetDir() + "/flying_room_1_rig_1");
std::string img_name(dataset_dir+"/img/frame_000071_0.png");
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_000097_0.png");
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());
@@ -49,15 +49,15 @@ class MatcherTest {
ref_ftr_ = new svo::Feature(frame_ref_, Eigen::Vector2d(300, 260), 0);
// set poses
Eigen::Vector3d t_w_ref(1.0054399967193604, 1.75655996799469, 4.610799789428711);
Eigen::Vector3d t_w_cur(-0.046640001237392426, 2.163439989089966, 4.6279802322387695);
Eigen::Quaterniond q_w_ref(0.015279541723430157, 0.7095794081687927, 0.7044594287872314, 0.0006494877743534744);
Eigen::Quaterniond q_w_cur(0.004395846277475357, -0.7776520252227783, -0.6286740899085999, -0.0026246386114507914);
frame_ref_->T_f_w_ = Sophus::SE3(q_w_ref.toRotationMatrix(), t_w_ref).inverse();
frame_cur_->T_f_w_ = Sophus::SE3(q_w_cur.toRotationMatrix(), t_w_cur).inverse();
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_000071_0.depth", *cam_, depth_ref_);
vk::blender_utils::loadBlenderDepthmap(dataset_dir+"/depth/frame_000002_0.depth", *cam_, depth_ref_);
}
virtual ~MatcherTest()
@@ -114,17 +114,17 @@ void MatcherTest::testEpipolarSearchFullImg()
}
// compute mean, median and variance of error in converged area
printf("n converged: \t %zu mm (ref: 169936)\n", n_converged);
printf("mean error: \t %f mm (ref: 1.912410)\n", sum_error*100/n_converged);
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: 1.462241)\n", *it*100);
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: 2.620957)\n", *it*100);
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: 4.154051)\n", *it*100);
printf("95-percentile: \t %f mm (ref: 0.263539)\n", *it*100);
// save results to file
std::ofstream output_stream;
svo/test/test_pipeline.cpp
+8 -4
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@@ -45,7 +45,7 @@ public:
BenchmarkNode::BenchmarkNode()
{
cam_ = new vk::PinholeCamera(752, 480, 217.083701215, 217.083701215, 376, 240);
cam_ = new vk::PinholeCamera(752, 480, 315.5, 315.5, 376.0, 240.0);
vo_ = new svo::FrameHandlerMono(cam_);
vo_->start();
}
@@ -58,13 +58,13 @@ BenchmarkNode::~BenchmarkNode()
void BenchmarkNode::runFromFolder()
{
for(int img_id = 1; img_id < 310; ++img_id)
for(int img_id = 2; img_id < 188; ++img_id)
{
// load image
std::stringstream ss;
ss << svo::test_utils::getDatasetDir() << "/flying_room_1_rig_1/img/frame_"
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 == 1)
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());
@@ -74,9 +74,13 @@ void BenchmarkNode::runFromFolder()
// 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_.
}
}
}
svo/test/test_pose_optimizer.cpp
+7 -7
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@@ -37,11 +37,11 @@ class PoseOptimizerTest {
public:
PoseOptimizerTest()
{
cam_ = new vk::PinholeCamera(752, 480, 217.083701215, 217.083701215, 376, 240);
cam_ = new vk::PinholeCamera(752, 480, 315.5, 315.5, 376.0, 240.0);
// load image
std::string dataset_dir(svo::test_utils::getDatasetDir() + "/flying_room_1_rig_1");
std::string img_name(dataset_dir+"/img/frame_000001_0.png");
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());
@@ -50,12 +50,12 @@ class PoseOptimizerTest {
frame_.reset(new svo::Frame(cam_, img, 1.0));
// set pose
Eigen::Vector3d t_w_ref(2.139359951019287, -1.9544399976730347, 2.8595199584960938 );
Eigen::Quaterniond q_w_ref(0.0027939670253545046, 0.8382523059844971, 0.5443645715713501, -0.03150530532002449);
frame_->T_f_w_ = Sophus::SE3(q_w_ref.toRotationMatrix(), t_w_ref).inverse();
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_000001_0.depth", *cam_, depthmap_);
vk::blender_utils::loadBlenderDepthmap(dataset_dir + "/depth/frame_000002_0.depth", *cam_, depthmap_);
// detect features
feature_detection::FastDetector detector(
svo/test/test_sparse_img_align.cpp
+3 -3
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@@ -47,7 +47,7 @@ class SparseImgAlignTest {
};
SparseImgAlignTest::SparseImgAlignTest() :
cam_(new vk::PinholeCamera(752, 480, 217.083701215, 217.083701215, 376, 240))
cam_(new vk::PinholeCamera(752, 480, 315.5, 315.5, 376.0, 240.0))
{}
SparseImgAlignTest::~SparseImgAlignTest()
@@ -82,7 +82,7 @@ void SparseImgAlignTest::testSequence(
assert(!img.empty());
// load pose
Sophus::SE3 T_w_gt = Sophus::SE3(iter->q_.toRotationMatrix(), iter->t_);
Sophus::SE3 T_w_gt(iter->q_, iter->t_);
Sophus::SE3 T_gt_w = T_w_gt.inverse(); // ground-truth
if(i==0)
@@ -152,7 +152,7 @@ void SparseImgAlignTest::testSequence(
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() + "/flying_room_1_rig_1");
std::string dataset_dir(svo::test_utils::getDatasetDir() + "/sin2_tex2_h1_v8_d");
svo::Config::triangMinCornerScore() = 20;
svo::Config::kltMinLevel() = 0;
SparseImgAlignTest test;