inline void quality_test(cv::Ptr<quality::QualityBase> ptr, const TMat& cmp, const Scalar& expected, const std::size_t quality_maps_expected = 1, const bool empty_expected = false )
{
std::vector<cv::Mat> qMats = {};
ptr->getQualityMaps(qMats);
EXPECT_TRUE( qMats.empty());
quality_expect_near( expected, ptr->compute(cmp));
if (empty_expected)
EXPECT_TRUE(ptr->empty());
else
EXPECT_FALSE(ptr->empty());
ptr->getQualityMaps(qMats);
EXPECT_EQ( qMats.size(), quality_maps_expected);
for (auto& qm : qMats)
{
EXPECT_GT(qm.rows, 0);
EXPECT_GT(qm.cols, 0);
}
ptr->clear();
EXPECT_TRUE(ptr->empty());
}
void calculatePointCloud(cv::Ptr<cv::StereoSGBM> sgbm, Mat & imgU1, Mat & imgU2)
{
Mat normalized_depth_map, depth_map;
sgbm->compute(imgU1, imgU2, depth_map);
normalize(depth_map, normalized_depth_map, 0, 255, CV_MINMAX, CV_8U);
imshow("depth_map", normalized_depth_map);
waitKey(0);
}
Mat StereoCamera::getDisparityMatrix(const Mat &left, const Mat &right) {
Mat magic(left.rows, left.cols, CV_8UC1);
Mat imgLeftGray, imgRightGray;
cvtColor(left, imgLeftGray, COLOR_BGR2GRAY);
cvtColor(right, imgRightGray, COLOR_BGR2GRAY);
sgbm->compute(imgLeftGray, imgRightGray, magic);
return magic;
}
void extract(const cv::Mat &color, const cv::Rect &roi, const cv::Mat &mask, std::vector<cv::KeyPoint> &keypoints, cv::Mat &descriptors)
{
detector->detect(color(roi), keypoints, mask);
for(size_t i = 0; i < keypoints.size(); ++i)
{
cv::KeyPoint &p = keypoints[i];
p.pt.x += roi.x;
p.pt.y += roi.y;
}
extractor->compute(color, keypoints, descriptors);
}
Node::Node(ros::NodeHandle* nh,
const cv::Mat& visual, const cv::Mat& depth,
image_geometry::PinholeCameraModel cam_model,
cv::Ptr<cv::FeatureDetector> detector,
cv::Ptr<cv::DescriptorExtractor> extractor,
cv::Ptr<cv::DescriptorMatcher> matcher,
const sensor_msgs::PointCloud2ConstPtr& point_cloud,
unsigned int msg_id,
unsigned int id,
const cv::Mat& detection_mask):
nh_(nh),
msg_id_(msg_id),
id_(id),
cloudMessage_(*point_cloud),
cam_model_(cam_model),
matcher_(matcher)
{
std::clock_t starttime=std::clock();
ROS_FATAL_COND(detector.empty(), "No valid detector!");
detector->detect( visual, feature_locations_2d_, detection_mask);// fill 2d locations
ROS_INFO_STREAM_COND_NAMED(( (std::clock()-starttime) / (double)CLOCKS_PER_SEC) > 0.01, "timings", "Feature detection runtime: " << ( std::clock() - starttime ) / (double)CLOCKS_PER_SEC );
ROS_INFO("Found %d Keypoints", (int)feature_locations_2d_.size());
cloud_pub = nh_->advertise<sensor_msgs::PointCloud2>("/rgbdslam/batch_clouds",20);
cloud_pub2 = nh_->advertise<sensor_msgs::PointCloud2>("/rgbdslam/my_clouds",20);
// get pcl::Pointcloud to extract depthValues a pixel positions
std::clock_t starttime5=std::clock();
// TODO: This takes 0.1 seconds and is not strictly necessary
//pcl::fromROSMsg(*point_cloud,pc);
pcl::fromROSMsg(*point_cloud,pc_col);
ROS_INFO_STREAM_COND_NAMED(( (std::clock()-starttime5) / (double)CLOCKS_PER_SEC) > 0.01, "timings", "projection runtime: " << ( std::clock() - starttime5 ) / (double)CLOCKS_PER_SEC );
// project pixels to 3dPositions and create search structures for the gicp
projectTo3D(depth, feature_locations_2d_, feature_locations_3d_,pc_col); //takes less than 0.01 sec
std::clock_t starttime4=std::clock();
// projectTo3d need a dense cloud to use the points.at(px.x,px.y)-Call
ROS_INFO_STREAM_COND_NAMED(( (std::clock()-starttime4) / (double)CLOCKS_PER_SEC) > 0.01, "timings", "projection runtime: " << ( std::clock() - starttime4 ) / (double)CLOCKS_PER_SEC );
std::clock_t starttime2=std::clock();
extractor->compute(visual, feature_locations_2d_, feature_descriptors_); //fill feature_descriptors_ with information
assert(feature_locations_2d_.size() == feature_locations_3d_.size());
ROS_INFO_STREAM_COND_NAMED(( (std::clock()-starttime2) / (double)CLOCKS_PER_SEC) > 0.01, "timings", "Feature extraction runtime: " << ( std::clock() - starttime2 ) / (double)CLOCKS_PER_SEC );
flannIndex = NULL;
ROS_INFO_STREAM_COND_NAMED(( (std::clock()-starttime) / (double)CLOCKS_PER_SEC) > 0.01, "timings", "constructor runtime: "<< ( std::clock() - starttime ) / (double)CLOCKS_PER_SEC <<"sec");
}
Node::Node(ros::NodeHandle& nh, const cv::Mat& visual,
cv::Ptr<cv::FeatureDetector> detector,
cv::Ptr<cv::DescriptorExtractor> extractor,
cv::Ptr<cv::DescriptorMatcher> matcher,
const sensor_msgs::PointCloud2ConstPtr point_cloud,
const cv::Mat& detection_mask)
: id_(0),
flannIndex(NULL),
matcher_(matcher)
{
#ifdef USE_ICP_CODE
gicp_initialized = false;
#endif
std::clock_t starttime=std::clock();
#ifdef USE_SIFT_GPU
SiftGPUFeatureDetector* siftgpu = SiftGPUFeatureDetector::GetInstance();
float* descriptors = siftgpu->detect(visual, feature_locations_2d_);
if (descriptors == NULL) {
ROS_FATAL("Can't run SiftGPU");
}
#else
ROS_FATAL_COND(detector.empty(), "No valid detector!");
detector->detect( visual, feature_locations_2d_, detection_mask);// fill 2d locations
#endif
ROS_INFO("Feature detection and descriptor extraction runtime: %f", ( std::clock() - starttime ) / (double)CLOCKS_PER_SEC);
ROS_INFO_STREAM_COND_NAMED(( (std::clock()-starttime) / (double)CLOCKS_PER_SEC) > global_min_time_reported, "timings", "Feature detection runtime: " << ( std::clock() - starttime ) / (double)CLOCKS_PER_SEC );
/*
if (id_ == 0)
cloud_pub_ = nh_->advertise<sensor_msgs::PointCloud2>("clouds_from_node_base",10);
else{
*/
cloud_pub_ = nh.advertise<sensor_msgs::PointCloud2>("/rgbdslam/batch_clouds",20);
// cloud_pub_ransac = nh_->advertise<sensor_msgs::PointCloud2>("clouds_from_node_current_ransac",10);
//} */
// get pcl::Pointcloud to extract depthValues a pixel positions
std::clock_t starttime5=std::clock();
// TODO: If batch sending/saving of clouds would be removed, the pointcloud wouldn't have to be saved
// which would slim down the Memory requirements
pcl::fromROSMsg(*point_cloud,pc_col);
ROS_INFO_STREAM_COND_NAMED(( (std::clock()-starttime5) / (double)CLOCKS_PER_SEC) > global_min_time_reported, "timings", "pc2->pcl conversion runtime: " << ( std::clock() - starttime5 ) / (double)CLOCKS_PER_SEC );
// project pixels to 3dPositions and create search structures for the gicp
#ifdef USE_SIFT_GPU
// removes also unused descriptors from the descriptors matrix
// build descriptor matrix
projectTo3DSiftGPU(feature_locations_2d_, feature_locations_3d_, pc_col, descriptors, feature_descriptors_); //takes less than 0.01 sec
if (descriptors != NULL) delete descriptors;
#else
projectTo3D(feature_locations_2d_, feature_locations_3d_, pc_col); //takes less than 0.01 sec
#endif
// projectTo3d need a dense cloud to use the points.at(px.x,px.y)-Call
#ifdef USE_ICP_CODE
std::clock_t starttime4=std::clock();
createGICPStructures();
ROS_INFO_STREAM_COND_NAMED(( (std::clock()-starttime4) / (double)CLOCKS_PER_SEC) > global_min_time_reported, "timings", "gicp runtime: " << ( std::clock() - starttime4 ) / (double)CLOCKS_PER_SEC );
#endif
std::clock_t starttime2=std::clock();
#ifndef USE_SIFT_GPU
// ROS_INFO("Use extractor");
//cv::Mat topleft, topright;
//topleft = visual.colRange(0,visual.cols/2+50);
//topright= visual.colRange(visual.cols/2+50, visual.cols-1);
//std::vector<cv::KeyPoint> kp1, kp2;
//extractor->compute(topleft, kp1, feature_descriptors_); //fill feature_descriptors_ with information
extractor->compute(visual, feature_locations_2d_, feature_descriptors_); //fill feature_descriptors_ with information
#endif
assert(feature_locations_2d_.size() == feature_locations_3d_.size());
ROS_INFO_STREAM_COND_NAMED(( (std::clock()-starttime2) / (double)CLOCKS_PER_SEC) > global_min_time_reported, "timings", "Feature extraction runtime: " << ( std::clock() - starttime2 ) / (double)CLOCKS_PER_SEC );
ROS_INFO_STREAM_COND_NAMED(( (std::clock()-starttime) / (double)CLOCKS_PER_SEC) > global_min_time_reported, "timings", "constructor runtime: "<< ( std::clock() - starttime ) / (double)CLOCKS_PER_SEC <<"sec");
}
/*
generate the data needed to train a codebook/vocabulary for bag-of-words methods
*/
int generateVocabTrainData(std::string trainPath,
std::string vocabTrainDataPath,
cv::Ptr<cv::FeatureDetector> &detector,
cv::Ptr<cv::DescriptorExtractor> &extractor)
{
//Do not overwrite any files
std::ifstream checker;
checker.open(vocabTrainDataPath.c_str());
if(checker.is_open()) {
std::cerr << vocabTrainDataPath << ": Training Data already present" <<
std::endl;
checker.close();
return -1;
}
//load training movie
cv::VideoCapture movie;
movie.open(trainPath);
if (!movie.isOpened()) {
std::cerr << trainPath << ": training movie not found" << std::endl;
return -1;
}
//extract data
std::cout << "Extracting Descriptors" << std::endl;
cv::Mat vocabTrainData;
cv::Mat frame, descs, feats;
std::vector<cv::KeyPoint> kpts;
std::cout.setf(std::ios_base::fixed);
std::cout.precision(0);
while(movie.read(frame)) {
//detect & extract features
detector->detect(frame, kpts);
extractor->compute(frame, kpts, descs);
//add all descriptors to the training data
vocabTrainData.push_back(descs);
//show progress
cv::drawKeypoints(frame, kpts, feats);
cv::imshow("Training Data", feats);
std::cout << 100.0*(movie.get(CV_CAP_PROP_POS_FRAMES) /
movie.get(CV_CAP_PROP_FRAME_COUNT)) << "%. " <<
vocabTrainData.rows << " descriptors \r";
fflush(stdout);
if(cv::waitKey(5) == 27) {
cv::destroyWindow("Training Data");
std::cout << std::endl;
return -1;
}
}
cv::destroyWindow("Training Data");
std::cout << "Done: " << vocabTrainData.rows << " Descriptors" << std::endl;
//save the training data
cv::FileStorage fs;
fs.open(vocabTrainDataPath, cv::FileStorage::WRITE);
fs << "VocabTrainData" << vocabTrainData;
fs.release();
return 0;
}
/*****************************************************************************
// The knn matching with k = 2
// This code performs the matching and the refinement.
// @paraam query_image: the input image
// @param matches_out: a pointer that stores the output matches. It is necessary for
// pose estimation.
*/
int knn_match(cv::Mat& query_image, std::vector< cv::DMatch> * matches_out)
{
// variabels that keep the query keypoints and query descriptors
std::vector<cv::KeyPoint> keypointsQuery;
cv::Mat descriptorQuery;
// Temporary variables for the matching results
std::vector< std::vector< cv::DMatch> > matches1;
std::vector< std::vector< cv::DMatch> > matches2;
std::vector< std::vector< cv::DMatch> > matches_opt1;
//////////////////////////////////////////////////////////////////////
// 1. Detect the keypoints
// This line detects keypoints in the query image
_detector->detect(query_image, keypointsQuery);
// If keypoints were found, descriptors are extracted.
if(keypointsQuery.size() > 0)
{
// extract descriptors
_extractor->compute( query_image, keypointsQuery, descriptorQuery);
}
//////////////////////////////////////////////////////////////////////////////
// 2. Here we match the descriptors with the database descriptors.
// with k-nearest neighbors with k=2
_matcher.knnMatch(descriptorQuery , matches1, 2);
#ifdef DEBUG_OUT
std::cout << "Found " << matches1.size() << " matching feature descriptors out of " << _matcher.getTrainDescriptors().size() << " database descriptors." << std::endl;
#endif
//////////////////////////////////////////////////////////////////////////////
// 3 Filter the matches.
// Accept only matches (knn with k=2) which belong ot one images
// The database tree within _matcher contains descriptors of all input images.
// We expect that both nearest neighbors must belong to one image.
// Otherwise we can remove the result.
// Along with this, we count which reference image has the highest number of matches.
// At this time, we consinder this image as the searched image.
// we init the variable hit with 0
std::vector<int> hits(_num_ref_images);
for (int i=0; i<_num_ref_images; i++)
{
hits[i] = 0;
}
// the loop runs through all matches and comparees the image indes
// imgIdx. The must be equal otherwise the matches belong to two
// different reference images.
for (int i=0; i<matches1.size(); i++)
{
// The comparison.
if(matches1[i].at(0).imgIdx == matches1[i].at(1).imgIdx)
{
// we keep it
matches_opt1.push_back(matches1[i]);
// and count a hit
hits[matches1[i].at(0).imgIdx]++;
}
}
#ifdef DEBUG_OUT
std::cout << "Optimized " << matches_opt1.size() << " feature descriptors." << std::endl;
#endif
// Now we search for the highest number of hits in our hit array
// The variable max_idx keeps the image id.
// The variable max_value the amount of hits.
int max_idx = -1;
int max_value = 0;
for (int i=0; i<_num_ref_images; i++)
{
#ifdef DEBUG_OUT
std::cout << "for " << i << " : " << hits[i] << std::endl;
#endif
if(hits[i] > max_value)
{
max_value = hits[i];
max_idx = i;
}
}
///////////////////////////////////////////////////////
// 4. The cross-match
// At this time, we test the database agains the query descriptors.
// The variable max_id stores the reference image id. Thus, we test only
// the descriptors that belong to max_idx agains the query descriptors.
_matcher.knnMatch(_descriptorsRefDB[max_idx], descriptorQuery, matches2, 2);
///////////////////////////////////////////////////////
// 5. Refinement; Ratio test
// The ratio test only accept matches which are clear without ambiguity.
// The best hit must be closer to the query descriptors than the second hit.
int removed = ratioTest(matches_opt1);
#ifdef DEBUG_OUT
std::cout << "Removed " << removed << " matched." << std::endl;
#endif
removed = ratioTest(matches2);
#ifdef DEBUG_OUT
std::cout << "Removed " << removed << " matched." << std::endl;
#endif
///////////////////////////////////////////////////////
// 6. Refinement; Symmetry test
// We only accept matches which appear in both knn-matches.
// It should not matter whether we test the database against the query desriptors
// or the query descriptors against the database.
// If we do not find the same solution in both directions, we toss the match.
std::vector<cv::DMatch> symMatches;
symmetryTest( matches_opt1, matches2, symMatches);
#ifdef DEBUG_OUT
std::cout << "Kept " << symMatches.size() << " matches after symetry test test." << std::endl;
#endif
///////////////////////////////////////////////////////
// 7. Refinement; Epipolar constraint
// We perform a Epipolar test using the RANSAC method.
if(symMatches.size() > 25)
{
matches_out->clear();
ransacTest( symMatches, _keypointsRefDB[max_idx], keypointsQuery, *matches_out);
}
#ifdef DEBUG_OUT
std::cout << "Kept " << matches_out->size() << " matches after RANSAC test." << std::endl;
#endif
///////////////////////////////////////////////////////
// 8. Draw this image on screen.
cv::Mat out;
cv::drawMatches(feature_map_database[max_idx]._ref_image , _keypointsRefDB[max_idx], query_image, keypointsQuery, *matches_out, out, cv::Scalar(255,255,255), cv::Scalar(0,0,255));
std::string num_matches_str;
std::strstream conv;
conv << matches_out->size();
conv >> num_matches_str;
std::string text;
text.append( num_matches_str);
text.append("( " + _num_ref_features_in_db_str + " total)");
text.append(" matches were found in reference image ");
text.append( feature_map_database[max_idx]._ref_image_str);
putText(out, text, cvPoint(20,20),
cv::FONT_HERSHEY_COMPLEX_SMALL, 1.0, cvScalar(0,255,255), 1, CV_AA);
cv::imshow("result", out);
if (run_video) cv::waitKey(1);
else cv::waitKey();
// Delete the images
query_image.release();
out.release();
return max_idx;
}
/*****************************************************************************
// This applies a brute force match without a trained datastructure.
// It also calculates the two nearest neigbors.
// @paraam query_image: the input image
// @param matches_out: a pointer that stores the output matches. It is necessary for
// pose estimation.
*/
int brute_force_match(cv::Mat& query_image, std::vector< cv::DMatch> * matches_out)
{
// variabels that keep the query keypoints and query descriptors
std::vector<cv::KeyPoint> keypointsQuery;
cv::Mat descriptorQuery;
// Temporary variables for the matching results
std::vector< std::vector< cv::DMatch> > matches1;
std::vector< std::vector< cv::DMatch> > matches2;
std::vector< std::vector< cv::DMatch> > matches_opt1;
//////////////////////////////////////////////////////////////////////
// 1. Detect the keypoints
// This line detects keypoints in the query image
_detector->detect(query_image, keypointsQuery);
// If keypoints were found, descriptors are extracted.
if(keypointsQuery.size() > 0)
{
// extract descriptors
_extractor->compute( query_image, keypointsQuery, descriptorQuery);
}
#ifdef DEBUG_OUT
std::cout << "Found " << descriptorQuery.size() << " feature descriptors in the image." << std::endl;
#endif
//////////////////////////////////////////////////////////////////////////////
// 2. Here we match the descriptors with all descriptors in the database
// with k-nearest neighbors with k=2
int max_removed = INT_MAX;
int max_id = -1;
for(int i=0; i<_descriptorsRefDB.size(); i++)
{
std::vector< std::vector< cv::DMatch> > matches_temp1;
// Here we match all query descriptors agains all db descriptors and try to find
// matching descriptors
_brute_force_matcher.knnMatch( descriptorQuery, _descriptorsRefDB[i], matches_temp1, 2);
///////////////////////////////////////////////////////
// 3. Refinement; Ratio test
// The ratio test only accept matches which are clear without ambiguity.
// The best hit must be closer to the query descriptors than the second hit.
int removed = ratioTest(matches_temp1);
// We only accept the match with the highest number of hits / the vector with the minimum revmoved features
int num_matches = matches_temp1.size();
if(removed < max_removed)
{
max_removed = removed;
max_id = i;
matches1.clear();
matches1 = matches_temp1;
}
}
#ifdef DEBUG_OUT
std::cout << "Feature map number " << max_id << " has the highest hit with "<< matches1.size() - max_removed << " descriptors." << std::endl;
#endif
std::vector< std::vector< cv::DMatch> > matches_temp2;
// Here we match all query descriptors agains all db descriptors and try to find
// matching descriptors
_brute_force_matcher.knnMatch(_descriptorsRefDB[max_id], descriptorQuery, matches_temp2, 2);
// The ratio test only accept matches which are clear without ambiguity.
// The best hit must be closer to the query descriptors than the second hit.
int removed = ratioTest(matches_temp2);
///////////////////////////////////////////////////////
// 6. Refinement; Symmetry test
// We only accept matches which appear in both knn-matches.
// It should not matter whether we test the database against the query desriptors
// or the query descriptors against the database.
// If we do not find the same solution in both directions, we toss the match.
std::vector<cv::DMatch> symMatches;
symmetryTest( matches1, matches_temp2, symMatches);
#ifdef DEBUG_OUT
std::cout << "Kept " << symMatches.size() << " matches after symetry test test." << std::endl;
#endif
///////////////////////////////////////////////////////
// 7. Refinement; Epipolar constraint
// We perform a Epipolar test using the RANSAC method.
if(symMatches.size() > 25)
{
matches_out->clear();
ransacTest( symMatches, _keypointsRefDB[max_id], keypointsQuery, *matches_out);
}
#ifdef DEBUG_OUT
std::cout << "Kept " << matches_out->size() << " matches after RANSAC test." << std::endl;
#endif
///////////////////////////////////////////////////////
// 8. Draw this image on screen.
cv::Mat out;
cv::drawMatches(feature_map_database[max_id]._ref_image , _keypointsRefDB[max_id], query_image, keypointsQuery, *matches_out, out, cv::Scalar(255,255,255), cv::Scalar(0,0,255));
std::string num_matches_str;
std::strstream conv;
conv << matches_out->size();
conv >> num_matches_str;
std::string text;
text.append( num_matches_str);
text.append("( " + _num_ref_features_in_db_str + " total)");
text.append(" matches were found in reference image ");
text.append( feature_map_database[max_id]._ref_image_str);
putText(out, text, cvPoint(20,20),
cv::FONT_HERSHEY_COMPLEX_SMALL, 1.0, cvScalar(0,255,255), 1, CV_AA);
cv::imshow("result", out);
if (run_video) cv::waitKey(1);
else cv::waitKey();
// Delete the images
query_image.release();
out.release();
return max_id;
}
/*****************************************************************************
Inits the database by loading all images from a certain directory, extracts the feature
keypoints and descriptors and saves them in the database
-keypointsRefDB and _descriptorsRefDB
@param directory_path: the path of all input images for query, e.g., C:/my_database
@param files: a std::vector object with a list of filenames.
*/
void init_database(std::string directory_path, std::vector<std::string> files)
{
// This code reads the image names from the argv variable, loads the image
// detect keypoints, extract the descriptors and push everything into
// two database objects, indicate by the ending DB.
for (int i = 0; i<files.size(); i++)
{
FeatureMap fm;
// Fetch the ref. image name from the input array
fm._ref_image_str = directory_path;
fm._ref_image_str.append("/");
fm._ref_image_str.append( files[i]);
bool ret = exists_test(fm._ref_image_str);
if (ret == false) continue;
// Load the image
fm._ref_image = cv::imread(fm._ref_image_str);
cvtColor( fm._ref_image , fm._ref_image , CV_BGR2GRAY);
// Detect the keypoints
_detector->detect(fm._ref_image,fm._keypointsRef);
std::cout << "For referemce image " << fm._ref_image_str << " - found " << fm._keypointsRef.size() << " keypoints" << std::endl;
// If keypoints found
if(fm._keypointsRef.size() > 0)
{
// extract descriptors
_extractor->compute( fm._ref_image, fm._keypointsRef, fm._descriptorRef);
// push descriptors and keypoints into database
_keypointsRefDB.push_back(fm._keypointsRef);
_descriptorsRefDB.push_back(fm._descriptorRef);
// count the total number of feature descriptors and keypoints in the database.
_num_ref_features_in_db += fm._keypointsRef.size();
}
// Draw the keypoints
/*
cv::Mat out;
cv::drawKeypoints(fm._ref_image , fm._keypointsRef, out);
cv::imshow("out", out );
cv::waitKey();
out.release();
*/
feature_map_database.push_back(fm);
}
// Converting the total number of features to a string.
std::strstream conv;
conv << _num_ref_features_in_db;
conv >> _num_ref_features_in_db_str;
}
void callback(const sensor_msgs::ImageConstPtr& msg)
{
if (image_0_ == NULL)
{
// Take first image:
try
{
image_0_ = cv_bridge::toCvCopy(msg,
sensor_msgs::image_encodings::isColor(msg->encoding) ?
sensor_msgs::image_encodings::BGR8 :
sensor_msgs::image_encodings::MONO8);
}
catch (cv_bridge::Exception& e)
{
ROS_ERROR_STREAM("Failed to take first image: " << e.what());
return;
}
ROS_INFO("First image taken");
// Detect keypoints:
detector_->detect(image_0_->image, keypoints_0_);
ROS_INFO_STREAM(keypoints_0_.size() << " points found.");
// Extract keypoints' descriptors:
extractor_->compute(image_0_->image, keypoints_0_, descriptors_0_);
}
else
{
// Take second image:
try
{
image_1_ = cv_bridge::toCvShare(msg,
sensor_msgs::image_encodings::isColor(msg->encoding) ?
sensor_msgs::image_encodings::BGR8 :
sensor_msgs::image_encodings::MONO8);
}
catch (cv_bridge::Exception& e)
{
ROS_ERROR_STREAM("Failed to take image: " << e.what());
return;
}
// Detect keypoints:
std::vector<cv::KeyPoint> keypoints_1;
detector_->detect(image_1_->image, keypoints_1);
ROS_INFO_STREAM(keypoints_1.size() << " points found on the new image.");
// Extract keypoints' descriptors:
cv::Mat descriptors_1;
extractor_->compute(image_1_->image, keypoints_1, descriptors_1);
// Compute matches:
std::vector<cv::DMatch> matches;
match(descriptors_0_, descriptors_1, matches);
// Compute homography:
cv::Mat H;
homography(keypoints_0_, keypoints_1, matches, H);
// Draw matches:
const int s = std::max(image_0_->image.rows, image_0_->image.cols);
cv::Size size(s, s);
cv::Mat draw_image;
warped_image_ = boost::make_shared<cv_bridge::CvImage>(
image_0_->header, image_0_->encoding,
cv::Mat(size, image_0_->image.type()));
if (!H.empty()) // filter outliers
{
std::vector<char> matchesMask(matches.size(), 0);
const size_t N = matches.size();
std::vector<int> queryIdxs(N), trainIdxs(N);
for (size_t i = 0; i < N; ++i)
{
queryIdxs[i] = matches[i].queryIdx;
trainIdxs[i] = matches[i].trainIdx;
}
std::vector<cv::Point2f> points1, points2;
cv::KeyPoint::convert(keypoints_0_, points1, queryIdxs);
cv::KeyPoint::convert(keypoints_1, points2, trainIdxs);
cv::Mat points1t;
cv::perspectiveTransform(cv::Mat(points1), points1t, H);
double maxInlierDist = threshold_ < 0 ? 3 : threshold_;
for (size_t i1 = 0; i1 < points1.size(); ++i1)
{
if (cv::norm(points2[i1] - points1t.at<cv::Point2f>((int)i1,0)) <= maxInlierDist ) // inlier
matchesMask[i1] = 1;
}
// draw inliers
cv::drawMatches(
image_0_->image, keypoints_0_,
image_1_->image, keypoints_1, matches,
draw_image, cv::Scalar(0, 255, 0), cv::Scalar(0, 0, 255),
matchesMask,
cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
// draw outliers
for (size_t i1 = 0; i1 < matchesMask.size(); ++i1)
matchesMask[i1] = !matchesMask[i1];
cv::drawMatches(
image_0_->image, keypoints_0_,
image_1_->image, keypoints_1, matches,
draw_image, cv::Scalar(0, 0, 255), cv::Scalar(255, 0, 0),
matchesMask,
cv::DrawMatchesFlags::DRAW_OVER_OUTIMG | cv::DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
ROS_INFO_STREAM("Number of inliers: " << cv::countNonZero(matchesMask));
// Wrap the new image using the homography,
// so we should have something similar to the original image:
warpPerspective(
image_1_->image, warped_image_->image, H, size,
cv::INTER_LINEAR | cv::WARP_INVERSE_MAP);
// Print the homography found:
ROS_INFO_STREAM("Homography = " << H);
}
else
{
cv::drawMatches(
image_0_->image, keypoints_0_,
image_1_->image, keypoints_1, matches,
draw_image);
image_1_->image.copyTo(warped_image_->image);
ROS_WARN_STREAM("No homography transformation found!");
}
// Publish warped image (using homography):
warped_image_->header = image_1_->header;
pub_.publish(warped_image_->toImageMsg());
// Show images:
cv::imshow("correspondences", draw_image);
cv::imshow("homography", warped_image_->image);
cv::waitKey(3);
}
}
void reduce_measurement_g2o::compute_features(const cv::Mat & rgb,
const cv::Mat & depth, const Eigen::Vector3f & intrinsics,
cv::Ptr<cv::FeatureDetector> & fd,
cv::Ptr<cv::DescriptorExtractor> & de,
std::vector<cv::KeyPoint> & filtered_keypoints,
pcl::PointCloud<pcl::PointXYZ> & keypoints3d, cv::Mat & descriptors) {
cv::Mat gray;
if (rgb.channels() != 1) {
cv::cvtColor(rgb, gray, cv::COLOR_BGR2GRAY);
} else {
gray = rgb;
}
cv::GaussianBlur(gray, gray, cv::Size(3, 3), 3);
int threshold = 400;
fd->setInt("hessianThreshold", threshold);
//int threshold = 100;
//fd->setInt("thres", threshold);
std::vector<cv::KeyPoint> keypoints;
cv::Mat mask(depth.size(), CV_8UC1);
depth.convertTo(mask, CV_8U);
fd->detect(gray, keypoints, mask);
for (int i = 0; i < 5; i++) {
if (keypoints.size() < 300) {
threshold = threshold / 2;
fd->setInt("hessianThreshold", threshold);
//fd->setInt("thres", threshold);
keypoints.clear();
fd->detect(gray, keypoints, mask);
} else {
break;
}
}
if (keypoints.size() > 400)
keypoints.resize(400);
filtered_keypoints.clear();
keypoints3d.clear();
for (size_t i = 0; i < keypoints.size(); i++) {
if (depth.at<unsigned short>(keypoints[i].pt) != 0) {
filtered_keypoints.push_back(keypoints[i]);
pcl::PointXYZ p;
p.z = depth.at<unsigned short>(keypoints[i].pt) / 1000.0f;
p.x = (keypoints[i].pt.x - intrinsics[1]) * p.z / intrinsics[0];
p.y = (keypoints[i].pt.y - intrinsics[2]) * p.z / intrinsics[0];
//ROS_INFO("Point %f %f %f from %f %f ", p.x, p.y, p.z, keypoints[i].pt.x, keypoints[i].pt.y);
keypoints3d.push_back(p);
}
}
de->compute(gray, filtered_keypoints, descriptors);
}
void keyframe::extractDescriptors(cv::Ptr<cv::DescriptorExtractor>& extractor) {
extractor->compute(im, keypoints, descriptors);
}
