int main(int argc, char** argv)
{
// initialize variables
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_original (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_unaltered (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_objects (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_planeInliers (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_planeOutliers (new pcl::PointCloud<pcl::PointXYZ>);
pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients());
pcl::PointIndices::Ptr indices (new pcl::PointIndices());
pcl::PCDReader reader;
pcl::PCDWriter writer;
int result = 0; // catch function return
// enable variables for time logging
boost::posix_time::ptime time_before_execution;
boost::posix_time::ptime time_after_execution;
boost::posix_time::time_duration difference;
// read point cloud through command line
if (argc != 2)
{
std::cout << "usage: " << argv[0] << " <filename>\n";
return 0;
}
else
{ // read cloud and display its size
std::cout << "Reading Point Cloud" << std::endl;
reader.read(argv[1], *cloud_original);
#ifdef DEBUG
std::cout << "size of original cloud: "
<< cloud_original->points.size() << " points" << std::endl;
#endif
}
//**************************************************************************
// test passthroughFilter() function
#ifdef PASSTHROUGH_FILTER_TEST
std::cout << "RUNNING PASSTHROUGH FILTER TESTS" << std::endl;
log_passthroughFilter(cloud_original, cloud_filtered, "../pictures/T01_passthrough_01.pcd",
"passthrough filter, custom 1: ","z", -2.5, 0);
log_passthroughFilter(cloud_original, cloud_filtered, "../pictures/T01_passthrough_02.pcd",
"passthrough filter, custom 2: ","y", -3.0, 3.0);
log_passthroughFilter(cloud_original, cloud_filtered, "../pictures/T01_passthrough_03.pcd",
"passthrough filter, custom 3: ","x", -3.0, 3.0);
#endif
//**************************************************************************
// test voxelFilter() function
#ifdef VOXEL_FILTER_TEST
std::cout << "RUNNING VOXEL FILTER LEAF SIZE TEST" << std::endl;
test_voxelFilter_leafSize(cloud_original);
#endif
//**************************************************************************
// test removeNoise() function
#ifdef NOISE_REMOVAL_TEST
std::cout << "RUNNING NOISE REMOVAL NEAREST NEIGHBORS TEST" << std::endl;
test_removeNoise_neighbors(cloud_original);
std::cout << "RUNNING NOISE REMOVAL THRESHOLD TEST" << std::endl;
test_removeNoise_stdDeviation(cloud_original);
#endif
//**************************************************************************
// test getPrism() function
#ifdef EXTRACT_PRISM_DATA_TEST
std::cout << "RUNNING EXTRACT PRISM ITERATIONS TEST" << std::endl;
test_getPrism_iterations(cloud_original);
std::cout << "RUNNING EXTRACT PRISM THRESHOLD TEST" << std::endl;
test_getPrism_threshold(cloud_original);
#endif
//**************************************************************************
// test objectSegmentation() function
#ifdef SEGMENT_OBJECTS_TEST
std::cout << "RUNNING OBJECT SEGMENTATION TEST" << std::endl;
test_segmentObjects(cloud_original);
#endif
//**************************************************************************
// test objectSegmentation() function
#ifdef EUCLIDEAN_CLUSTER_TEST
std::cout << "RUNNING EUCLIDEAN CLUSTER TEST" << std::endl;
test_getClusters(cloud_original);
#endif
return 0;
}
int main(int argc, char** argv)
{
// initialize variables
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_original (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_unaltered (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_objects (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_planeInliers (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_planeOutliers (new pcl::PointCloud<pcl::PointXYZ>);
pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients());
pcl::PointIndices::Ptr indices (new pcl::PointIndices());
pcl::PCDReader reader;
pcl::PCDWriter writer;
int result = 0; // catch function return
// enable variables for time logging
boost::posix_time::ptime time_before_execution;
boost::posix_time::ptime time_after_execution;
boost::posix_time::time_duration difference;
// read point cloud through command line
if (argc != 2)
{
std::cout << "usage: " << argv[0] << " <filename>\n";
return 0;
}
else
{ // read cloud and display its size
std::cout << "Reading Point Cloud" << std::endl;
reader.read(argv[1], *cloud_original);
#ifdef DEBUG
std::cout << "size of original cloud: "
<< cloud_original->points.size() << " points" << std::endl;
#endif
}
//**************************************************************************
// test passthroughFilter() function
std::cout << "RUNNING PASSTHROUGH FILTER TESTS" << std::endl;
log_passthroughFilter(cloud_original, cloud_filtered, "../pictures/T01_passthrough_01.pcd",
"passthrough filter, custom 1: ","z", -2.5, 0);
log_passthroughFilter(cloud_original, cloud_filtered, "../pictures/T01_passthrough_02.pcd",
"passthrough filter, custom 2: ","y", -3.0, 3.0);
log_passthroughFilter(cloud_original, cloud_filtered, "../pictures/T01_passthrough_03.pcd",
"passthrough filter, custom 3: ","x", -3.0, 3.0);
//**************************************************************************
// test voxelFilter() function
std::cout << "RUNNING VOXEL FILTER TEST" << std::endl;
log_voxelFilter(cloud_original, cloud_filtered, "../pictures/T02_voxel_01.pcd",
"voxel filter, custom 1:", 0.01);
log_voxelFilter(cloud_original, cloud_filtered, "../pictures/T02_voxel_02.pcd",
"voxel filter, custom 2:", 0.02);
log_voxelFilter(cloud_original, cloud_filtered, "../pictures/T02_voxel_03.pcd",
"voxel filter, custom 3:", 0.04);
log_voxelFilter(cloud_original, cloud_filtered, "../pictures/T02_voxel_04.pcd",
"voxel filter, custom 4:", 0.08);
//**************************************************************************
// test removeNoise() function
std::cout << "RUNNING NOISE REMOVAL TEST" << std::endl;
log_removeNoise(cloud_original, cloud_filtered, "../pictures/T03_noise_01.pcd",
"noise removal, custom 1: ", 50, 1.0);
log_removeNoise(cloud_original, cloud_filtered, "../pictures/T03_noise_01.pcd",
"noise removal, custom 1: ", 100, 1.0);
log_removeNoise(cloud_original, cloud_filtered, "../pictures/T03_noise_01.pcd",
"noise removal, custom 1: ", 10, 1.0);
log_removeNoise(cloud_original, cloud_filtered, "../pictures/T03_noise_01.pcd",
"noise removal, custom 1: ", 50, 1.9);
log_removeNoise(cloud_original, cloud_filtered, "../pictures/T03_noise_01.pcd",
"noise removal, custom 1: ", 50, 0.1);
//**************************************************************************
// test getPlane() function
std::cout << "RUNNING PLANE SEGMENTATION TEST" << std::endl;
log_getPlane(cloud_original, cloud_planeInliers, cloud_planeOutliers,
coefficients, indices, "../pictures/T04_planeInliers_01.pcd",
"plane segmentation, custom 1: ", 0, 1.57, 1000, 0.01);
log_getPlane(cloud_original, cloud_planeInliers, cloud_planeOutliers,
coefficients, indices, "../pictures/T04_planeInliers_02.pcd",
"plane segmentation, custom 2: ", 0, 1.57, 1, 0.01);
log_getPlane(cloud_original, cloud_planeInliers, cloud_planeOutliers,
coefficients, indices, "../pictures/T04_planeInliers_03.pcd",
"plane segmentation, custom 3: ", 0, 1.57, 2000, 0.01);
log_getPlane(cloud_original, cloud_planeInliers, cloud_planeOutliers,
coefficients, indices, "../pictures/T04_planeInliers_04.pcd",
"plane segmentation, custom 4: ", 0, 0.76, 1000, 0.01);
log_getPlane(cloud_original, cloud_planeInliers, cloud_planeOutliers,
coefficients, indices, "../pictures/T04_planeInliers_05.pcd",
"plane segmentation, custom 5: ", 0, 2.09, 1000, 0.01);
log_getPlane(cloud_original, cloud_planeInliers, cloud_planeOutliers,
coefficients, indices, "../pictures/T04_planeInliers_06.pcd",
"plane segmentation, custom 6: ", 0.35, 1.57, 1000, 0.01);
log_getPlane(cloud_original, cloud_planeInliers, cloud_planeOutliers,
coefficients, indices, "../pictures/T04_planeInliers_07.pcd",
"plane segmentation, custom 7: ", 0.79, 1.57, 1000, 0.01);
log_getPlane(cloud_original, cloud_planeInliers, cloud_planeOutliers,
coefficients, indices, "../pictures/T04_planeInliers_08.pcd",
"plane segmentation, custom 8: ", 0, 1.57, 1000, 0.001);
log_getPlane(cloud_original, cloud_planeInliers, cloud_planeOutliers,
coefficients, indices, "../pictures/T04_planeInliers_09.pcd",
"plane segmentation, custom 9: ", 0, 1.57, 1000, 0.01);
//**************************************************************************
// test getPrism() function
std::cout << "RUNNING EXTRACT PRISM TEST" << std::endl;
log_getPrism(cloud_original, cloud_objects, "../pictures/T05_objects_01.pcd",
"polygonal prism, custom 1: ", 0, 1.57, 1000, 0.01, 0.02, 0.2);
// test getPrism() function
std::cout << "RUNNING EXTRACT PRISM TEST" << std::endl;
// get output from default plane
time_before_execution = boost::posix_time::microsec_clock::local_time(); // time before
result = c44::getPrism(cloud_original, cloud_objects, 0, 1.57, 1000, 0.01, 0.02, 0.2);
time_after_execution = boost::posix_time::microsec_clock::local_time(); // time after
if(result < 0)
{
std::cout << "ERROR: could not find polygonal prism data" << std::endl;
}
else
{
writer.write<pcl::PointXYZ> ("../pictures/T05_objects_01.pcd", *cloud_objects); // write out cloud
#ifdef DEBUG
difference = time_after_execution - time_before_execution; // get execution time
std::cout << std::setw(5) << difference.total_milliseconds() << ": "
<< "polygonal prism, default: "
<< cloud_objects->points.size() << " points" << std::endl;
#endif
}
return 0;
}
int
main (int argc, char** argv)
{
// ros::init(argc, argv, "extract_sec");
// ros::NodeHandle node_handle;
// pcl::visualization::PCLVisualizer result_viewer("planar_segmentation");
boost::shared_ptr<pcl::visualization::PCLVisualizer> result_viewer (new pcl::visualization::PCLVisualizer ("planar_segmentation"));
result_viewer->addCoordinateSystem(0.3, "reference", 0);
result_viewer->setCameraPosition(-0.499437, 0.111597, -0.758007, -0.443141, 0.0788583, -0.502855, -0.034703, -0.992209, -0.119654);
result_viewer->setCameraClipDistances(0.739005, 2.81526);
// result_viewer->setCameraPosition(Position, Focal point, View up);
// result_viewer->setCameraClipDistances(Clipping plane);
/***************************************
* parse arguments
***************************************/
if(argc<5)
{
pcl::console::print_info("Usage: extract_sec DATA_PATH/PCD_FILE_FORMAT START_INDEX END_INDEX DEMO_NAME (opt)STEP_SIZE(1)");
exit(1);
}
int view_id=0;
int step=1;
std::string basename_cloud=argv[1];
unsigned int index_start = std::atoi(argv[2]);
unsigned int index_end = std::atoi(argv[3]);
std::string demo_name=argv[4];
if(argc>5) step=std::atoi(argv[5]);
/***************************************
* set up result directory
***************************************/
mkdir("/home/zhen/Documents/Dataset/human_result", S_IRWXU | S_IRWXG | S_IROTH | S_IXOTH);
char result_folder[50];
std::snprintf(result_folder, sizeof(result_folder), "/home/zhen/Documents/Dataset/human_result/%s", demo_name.c_str());
mkdir(result_folder, S_IRWXU | S_IRWXG | S_IROTH | S_IXOTH);
std::string basename_pcd = (basename_cloud.find(".pcd") == std::string::npos) ? (basename_cloud + ".pcd") : basename_cloud;
std::string filename_pcd;
std::string mainGraph_file;
mainGraph_file = std::string(result_folder) + "/mainGraph.txt";
// write video config
char video_file[100];
std::snprintf(video_file, sizeof(video_file), "%s/video.txt", result_folder);
std::ofstream video_config(video_file);
if (video_config.is_open())
{
video_config << index_start << " " << index_end << " " << demo_name << " " << step;
video_config.close();
}
/***************************************
* set up cloud, segmentation, tracker, detectors, graph, features
***************************************/
TableObject::Segmentation tableObjSeg;
TableObject::Segmentation initialSeg;
TableObject::track3D tracker(false);
TableObject::colorDetector finger1Detector(0,100,0,100,100,200); //right
TableObject::colorDetector finger2Detector(150,250,0,100,0,100); //left
TableObject::touchDetector touchDetector(0.01);
TableObject::bottleDetector bottleDetector;
TableObject::mainGraph mainGraph((int)index_start);
std::vector<manipulation_features> record_features;
manipulation_features cur_features;
TableObject::pcdCloud pcdSceneCloud;
CloudPtr sceneCloud;
CloudPtr planeCloud(new Cloud);
CloudPtr cloud_objects(new Cloud);
CloudPtr cloud_finger1(new Cloud);
CloudPtr cloud_finger2(new Cloud);
CloudPtr cloud_hull(new Cloud);
CloudPtr track_target(new Cloud);
CloudPtr tracked_cloud(new Cloud);
std::vector<pcl::PointIndices> clusters;
pcl::ModelCoefficients coefficients;
pcl::PointIndices f1_indices;
pcl::PointIndices f2_indices;
Eigen::Affine3f toBottleCoordinate;
Eigen::Affine3f transformation;
Eigen::Vector3f bottle_init_ori;
// set threshold of size of clustered cloud
tableObjSeg.setThreshold(30);
initialSeg.setThreshold(500);
// downsampler
pcl::ApproximateVoxelGrid<pcl::PointXYZRGBA> grid;
float leaf_size=0.005;
grid.setLeafSize (leaf_size, leaf_size, leaf_size);
/***************************************
* start processing
***************************************/
unsigned int idx = index_start;
int video_id=0;
bool change = false;
while( idx <= index_end && !result_viewer->wasStopped())
{
std::cout << std::endl;
std::cout << "frame id=" << idx << std::endl;
filename_pcd = cv::format(basename_cloud.c_str(), idx);
if(idx==index_start) {
/***************************************
* Intialization:
* -plane localization
* -object cluster extraction
* -bottle cluster localization
***************************************/
initialSeg.resetCloud(filename_pcd, false);
initialSeg.seg(false);
initialSeg.getObjects(cloud_objects, clusters);
initialSeg.getCloudHull(cloud_hull);
initialSeg.getPlaneCoefficients(coefficients);
initialSeg.getsceneCloud(pcdSceneCloud);
initialSeg.getTableTopCloud(planeCloud);
sceneCloud=pcdSceneCloud.getCloud();
/***************************************
* fingertip, hand_arm removal
***************************************/
//opencv color filtering for fingertip_1
{
pcl::ScopeTime t_finger1("Finger 1(blue) detection");
finger1Detector.setInputCloud(cloud_objects, clusters);
finger1Detector.filter(f1_indices,cloud_finger1);
}
finger1Detector.showDetectedCloud(result_viewer, "finger1");
//opencv color filtering for fingertip_2
{
pcl::ScopeTime t_finger2("Finger 2(orange) detection");
finger2Detector.setInputCloud(cloud_objects, clusters);
finger2Detector.filter(f2_indices,cloud_finger2);
}
finger2Detector.showDetectedCloud(result_viewer, "finger2");
// remove hand (include cluster that contains the detected fingertips and also the other clusters that are touching the cluster)
std::vector<int> hand_arm1=TableObject::findHand(cloud_objects, clusters, f1_indices);
for(int i=hand_arm1.size()-1; i>=0; i--)
{
clusters.erase(clusters.begin()+hand_arm1[i]);
std::cout << "removing hand_arm : cluster index = " << hand_arm1[i] << std::endl;
}
std::vector<int> hand_arm2=TableObject::findHand(cloud_objects, clusters, f2_indices);
for(int i=hand_arm2.size()-1; i>=0; i--)
{
clusters.erase(clusters.begin()+hand_arm2[i]);
std::cout << "removing hand_arm : cluster index = " << hand_arm2[i] << std::endl;
}
// DEBUG
// pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGBA> plane(planeCloud);
// result_viewer->addPointCloud<RefPointType>(planeCloud, plane, "tabletop");
// CloudPtr debug(new Cloud);
// initialSeg.getOutPlaneCloud(debug);
// pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGBA> out_plane(debug);
// result_viewer->addPointCloud<RefPointType>(debug, out_plane, "out_plane");
// choose bottle_id at 1st frame & confirm fitted model is correct
TableObject::view3D::drawClusters(result_viewer, cloud_objects, clusters, true);
while (!result_viewer->wasStopped ())
{
result_viewer->spinOnce (100);
boost::this_thread::sleep (boost::posix_time::microseconds (100000));
}
std::cout << "cluster size = " << clusters.size() << std::endl;
/***************************************
* Localizing cylinder
***************************************/
CloudPtr cluster_bottle (new Cloud);
int bottle_id = 0;
pcl::copyPointCloud (*cloud_objects, clusters[bottle_id], *cluster_bottle);
bottleDetector.setInputCloud(cluster_bottle);
bottleDetector.fit();
bottleDetector.getTransformation(toBottleCoordinate);
bottle_init_ori= bottleDetector.getOrientation();
float x, y, z, roll, pitch, yaw;
pcl::getTranslationAndEulerAngles(toBottleCoordinate.inverse(), x, y, z, roll, pitch, yaw);
result_viewer->removeCoordinateSystem("reference", 0);
result_viewer->addCoordinateSystem(0.3, toBottleCoordinate.inverse(), "reference", 0);
bottleDetector.drawOrientation(result_viewer);
/***************************************
* Record features
***************************************/
bottle_features cur_bottle_features;
cur_bottle_features.loc[0] = x;
cur_bottle_features.loc[1] = y;
cur_bottle_features.loc[2] = z;
cur_bottle_features.ori[0] = roll;
cur_bottle_features.ori[1] = pitch;
cur_bottle_features.ori[2] = yaw;
cur_bottle_features.color[0] = bottleDetector.getCenter().r;
cur_bottle_features.color[1] = bottleDetector.getCenter().g;
cur_bottle_features.color[2] = bottleDetector.getCenter().b;
cur_bottle_features.size[0] = bottleDetector.getHeight();
cur_bottle_features.size[1] = bottleDetector.getRadius();
cur_features.bottle = cur_bottle_features;
pcl::PointXYZ center_finger1 = TableObject::computeObjCentroid(cloud_finger1);
pcl::PointXYZ center_finger2 = TableObject::computeObjCentroid(cloud_finger2);
center_finger1 = pcl::transformPoint<pcl::PointXYZ>(center_finger1, toBottleCoordinate);
center_finger2 = pcl::transformPoint<pcl::PointXYZ>(center_finger2, toBottleCoordinate);
cur_features.gripper_1.loc[0] = center_finger1.x;
cur_features.gripper_1.loc[1] = center_finger1.y;
cur_features.gripper_1.loc[2] = center_finger1.z;
cur_features.gripper_2.loc[0] = center_finger2.x;
cur_features.gripper_2.loc[1] = center_finger2.y;
cur_features.gripper_2.loc[2] = center_finger2.z;
record_features.push_back(cur_features);
/***************************************
* Tracking initialization
***************************************/
{
pcl::ScopeTime t_track("Tracker initialization");
tracker.setTarget(cluster_bottle, bottleDetector.getCenter());
tracker.initialize();
}
/***************************************
* Touch detection
***************************************/
std::vector<CloudPtr> touch_clouds;
touch_clouds.push_back(cluster_bottle);
touch_clouds.push_back(cloud_finger1);
touch_clouds.push_back(cloud_finger2);
// touch detection between each pair of objects (including fingertips, tabletop objects and tabletop)
for(int i=0; i<touch_clouds.size(); i++)
{
int j;
bool touch;
for(j=i+1; j<touch_clouds.size(); j++)
{
// touch detection between object_i and object_j
char relation [50];
std::sprintf(relation, "object%d_object%d", i, j);
std::cout << relation << std::endl;
{
pcl::ScopeTime t("Touch detection");
touch=touchDetector.detect(touch_clouds[i], touch_clouds[j]);
}
// touchDetector.showTouch(result_viewer, relation, 100+250*(j-i-1), 40+20*i);
// relational scene graph -> main graph
if(touch) {
mainGraph.addInitialRelationalGraph(2);
} else {
mainGraph.addInitialRelationalGraph(0);
}
}
// touch detection between each objects and tabletop
char relation [50];
std::sprintf (relation, "object%d_object%d", i, (int)touch_clouds.size());
std::cout << relation << std::endl;
{
pcl::ScopeTime t("Touch detection");
touch=touchDetector.detectTableTouch(touch_clouds[i], coefficients);
}
// touchDetector.showTouch(result_viewer, relation, 100+250*(j-i-1), 40+20*i);
// relational scene graph -> main graph
if(touch) {
mainGraph.addInitialRelationalGraph(2);
} else {
mainGraph.addInitialRelationalGraph(0);
}
}
/***************************************
* Visualization
***************************************/
// draw extracted object clusters
// TableObject::view3D::drawClusters(result_viewer, cloud_objects, touch_clusters);
// draw extracted plane points
// pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGBA> plane(planeCloud);
// result_viewer->addPointCloud<RefPointType>(planeCloud, plane, "tabletop");
// std::stringstream ss;
// ss << (int)touch_clusters.size();
// result_viewer->addText3D(ss.str(), planeCloud->points.at(334*640+78),0.1);
// draw extracted plane contour polygon
result_viewer->addPolygon<RefPointType>(cloud_hull, 0, 255, 0, "polygon");
change = true;
} else
{
/***************************************
* object cloud extraction
***************************************/
tableObjSeg.resetCloud(filename_pcd, false);
tableObjSeg.seg(cloud_hull,false);
tableObjSeg.getObjects(cloud_objects, clusters);
tableObjSeg.getsceneCloud(pcdSceneCloud);
sceneCloud=pcdSceneCloud.getCloud();
/***************************************
* fingertip extraction
***************************************/
//opencv color filtering for fingertip_1
{
pcl::ScopeTime t_finger1("Finger 1(blue) detection");
finger1Detector.setInputCloud(cloud_objects, clusters);
finger1Detector.filter(f1_indices,cloud_finger1);
}
finger1Detector.showDetectedCloud(result_viewer, "finger1");
//opencv color filtering for fingertip_2
{
pcl::ScopeTime t_finger1("Finger 2(orange) detection");
finger2Detector.setInputCloud(cloud_objects, clusters);
finger2Detector.filter(f2_indices,cloud_finger2);
}
finger2Detector.showDetectedCloud(result_viewer, "finger2");
/***************************************
* filter out black glove cluster & gray sleeve, also update cloud_objects with removed cluster indices
***************************************/
for(int i=0; i<clusters.size(); i++)
{
pcl::CentroidPoint<RefPointType> color_points;
for(int j=0; j<clusters[i].indices.size(); j++)
{
color_points.add(cloud_objects->at(clusters[i].indices[j]));
}
RefPointType mean_color;
color_points.get(mean_color);
if(mean_color.r>30 & mean_color.r<70 & mean_color.g>30 & mean_color.g<70 & mean_color.b>30 & mean_color.b<70)
{
clusters.erase(clusters.begin()+ i);
i=i-1;
}
}
/***************************************
* Tracking objects
***************************************/
{
pcl::ScopeTime t_track("Tracking");
grid.setInputCloud (sceneCloud);
grid.filter (*track_target);
tracker.track(track_target, transformation);
tracker.getTrackedCloud(tracked_cloud);
}
tracker.viewTrackedCloud(result_viewer);
// tracker.drawParticles(result_viewer);
/***************************************
* compute tracked <center, orientation>
***************************************/
pcl::PointXYZ bottle_loc_point(0,0,0);
bottle_loc_point = pcl::transformPoint<pcl::PointXYZ>(bottle_loc_point, transformation);
result_viewer->removeShape("bottle_center");
// result_viewer->addSphere<pcl::PointXYZ>(bottle_loc_point, 0.05, "bottle_center");
Eigen::Vector3f bottle_ori;
pcl::transformVector(bottle_init_ori,bottle_ori,transformation);
TableObject::view3D::drawArrow(result_viewer, bottle_loc_point, bottle_ori, "bottle_arrow");
/***************************************
* calculate toTrackedBottleCoordinate
***************************************/
Eigen::Affine3f toTrackedBottleCoordinate;
Eigen::Vector3f p( bottle_loc_point.x, bottle_loc_point.y, bottle_loc_point.z ); // position
// get a vector that is orthogonal to _orientation ( yc = _orientation x [1,0,0]' )
Eigen::Vector3f yc( 0, bottle_ori[2], -bottle_ori[1] );
yc.normalize();
// get a transform that rotates _orientation into z and moves cloud into origin.
pcl::getTransformationFromTwoUnitVectorsAndOrigin(yc, bottle_ori, p, toTrackedBottleCoordinate);
result_viewer->removeCoordinateSystem("reference");
result_viewer->addCoordinateSystem(0.3, toTrackedBottleCoordinate.inverse(), "reference", 0);
float x, y, z, roll, pitch, yaw;
pcl::getTranslationAndEulerAngles(toTrackedBottleCoordinate.inverse(), x, y, z, roll, pitch, yaw);
/***************************************
* setup bottle feature
***************************************/
cur_features = record_features[video_id-1];
cur_features.bottle.loc[0] = x;
cur_features.bottle.loc[1] = y;
cur_features.bottle.loc[2] = z;
cur_features.bottle.ori[0] = roll;
cur_features.bottle.ori[1] = pitch;
cur_features.bottle.ori[2] = yaw;
pcl::PointXYZ center_finger1 = TableObject::computeObjCentroid(cloud_finger1);
pcl::PointXYZ center_finger2 = TableObject::computeObjCentroid(cloud_finger2);
center_finger1 = pcl::transformPoint<pcl::PointXYZ>(center_finger1, toTrackedBottleCoordinate);
center_finger2 = pcl::transformPoint<pcl::PointXYZ>(center_finger2, toTrackedBottleCoordinate);
cur_features.gripper_1.loc[0] = center_finger1.x;
cur_features.gripper_1.loc[1] = center_finger1.y;
cur_features.gripper_1.loc[2] = center_finger1.z;
cur_features.gripper_2.loc[0] = center_finger2.x;
cur_features.gripper_2.loc[1] = center_finger2.y;
cur_features.gripper_2.loc[2] = center_finger2.z;
record_features.push_back(cur_features);
/***************************************
* Touch detection
***************************************/
std::vector<CloudPtr> touch_clouds;
touch_clouds.push_back(tracked_cloud);
touch_clouds.push_back(cloud_finger1);
touch_clouds.push_back(cloud_finger2);
// touch detection between each pair of objects (including fingertips, tabletop objects and tabletop)
for(int i=0; i<touch_clouds.size(); i++)
{
int j;
bool touch;
for(j=i+1; j<touch_clouds.size(); j++)
{
// touch detection between object_i and object_j
char relation [50];
std::sprintf(relation, "object%d_object%d", i, j);
std::cout << relation << std::endl;
{
pcl::ScopeTime t("Touch detection");
touch=touchDetector.detect(touch_clouds[i], touch_clouds[j]);
}
// touchDetector.showTouch(result_viewer, relation, 100+250*(j-i-1), 40+20*i);
// relational scene graph -> main graph
if(touch) {
mainGraph.addRelationalGraph(2);
} else {
mainGraph.addRelationalGraph(0);
}
}
// touch detection between each objects and tabletop
char relation [50];
std::sprintf (relation, "object%d_object%d", i, (int)touch_clouds.size());
std::cout << relation << std::endl;
{
pcl::ScopeTime t("Touch detection");
touch=touchDetector.detectTableTouch(touch_clouds[i], coefficients);
}
// touchDetector.showTouch(result_viewer, relation, 100+250*(j-i-1), 40+20*i);
// relational scene graph -> main graph
if(touch) {
mainGraph.addRelationalGraph(2);
} else {
mainGraph.addRelationalGraph(0);
}
}
/***************************************
* Visualization
***************************************/
// draw extracted point clusters
// TableObject::view3D::drawText(result_viewer, cloud_objects, touch_clusters);
/***************************************
* Main Graph
***************************************/
change = mainGraph.compareRelationGraph((int)idx);
}
// darw original cloud
pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGBA> rgb(sceneCloud);
if(!result_viewer->updatePointCloud<RefPointType>(sceneCloud, rgb, "new frame"))
result_viewer->addPointCloud<RefPointType>(sceneCloud, rgb, "new frame");
result_viewer->spinOnce (100);
boost::this_thread::sleep (boost::posix_time::microseconds (100000));
//debug
std::cout << cur_features.bottle.loc[0] << " " << cur_features.bottle.loc[1] << " " << cur_features.bottle.loc[2]
<< " " << cur_features.bottle.ori[0] << " " << cur_features.bottle.ori[1] << " " << cur_features.bottle.ori[2]
<< " " << cur_features.bottle.color[0] << " " << cur_features.bottle.color[1] << " " << cur_features.bottle.color[2]
<< " " << cur_features.bottle.size[0] << " " << cur_features.bottle.size[1]
<< " " << cur_features.gripper_1.loc[0] << " " << cur_features.gripper_1.loc[1] << " " << cur_features.gripper_1.loc[2]
<< " " << cur_features.gripper_2.loc[0] << " " << cur_features.gripper_2.loc[1] << " " << cur_features.gripper_2.loc[2]
<< std::endl;
if(change)
{
char screenshot[100]; // make sure it's big enough
std::snprintf(screenshot, sizeof(screenshot), "%s/sec_%d.png", result_folder, (int)idx);
std::cout << screenshot << std::endl;
result_viewer->saveScreenshot(screenshot);
//record features
char feature_file[100]; // make sure it's big enough
std::snprintf(feature_file, sizeof(feature_file), "%s/features_original.txt", result_folder);
std::ofstream feature_writer(feature_file, std::ofstream::out | std::ofstream::app);
feature_writer << cur_features.bottle.loc[0] << " " << cur_features.bottle.loc[1] << " " << cur_features.bottle.loc[2]
<< " " << cur_features.bottle.ori[0] << " " << cur_features.bottle.ori[1] << " " << cur_features.bottle.ori[2]
<< " " << cur_features.bottle.color[0] << " " << cur_features.bottle.color[1] << " " << cur_features.bottle.color[2]
<< " " << cur_features.bottle.size[0] << " " << cur_features.bottle.size[1]
<< " " << cur_features.gripper_1.loc[0] << " " << cur_features.gripper_1.loc[1] << " " << cur_features.gripper_1.loc[2]
<< " " << cur_features.gripper_2.loc[0] << " " << cur_features.gripper_2.loc[1] << " " << cur_features.gripper_2.loc[2]
<< std::endl;
feature_writer.close();
std::cout << "features saved at " << feature_file << std::endl;
}
char screenshot[200]; // make sure it's big enough
std::snprintf(screenshot, sizeof(screenshot), "%s/video/sec_%d.png", result_folder, (int)video_id);
std::cout << screenshot << std::endl;
result_viewer->saveScreenshot(screenshot);
idx=idx+step;
video_id=video_id+1;
}
mainGraph.displayMainGraph();
mainGraph.recordMainGraph(mainGraph_file);
while (!result_viewer->wasStopped ())
{
result_viewer->spinOnce (100);
boost::this_thread::sleep (boost::posix_time::microseconds (100000));
}
}
void
cloud_cb (const sensor_msgs::PointCloud2ConstPtr& input)
{
// std::cout << "\n\n----------------Received point cloud!-----------------\n";
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr downsampled (new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_planar (new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_objects (new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_red (new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_green (new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_blue (new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_yellow (new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_concat_clusters (new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_concat_hulls (new pcl::PointCloud<pcl::PointXYZRGB>);
sensor_msgs::PointCloud2 downsampled2, planar2, objects2, filtered2, red2, green2, blue2, yellow2, concat_clusters2, concat_hulls2;
std::vector<pcl::PointCloud<pcl::PointXYZRGB>::Ptr> color_clouds, cluster_clouds, hull_clouds;
std::vector<pcl::PointXYZRGB> labels;
// fromROSMsg(*input, *cloud);
// pub_input.publish(*input);
// Downsample the input PointCloud
pcl::VoxelGrid<sensor_msgs::PointCloud2> sor;
sor.setInputCloud (input);
// sor.setLeafSize (0.01, 0.01, 0.01); //play around with leafsize (more samples, better resolution?)
sor.setLeafSize (0.001, 0.001, 0.001);
sor.filter (downsampled2);
fromROSMsg(downsampled2,*downsampled);
pub_downsampled.publish (downsampled2);
// Segment the largest planar component from the downsampled cloud
pcl::SACSegmentation<pcl::PointXYZRGB> seg;
pcl::ModelCoefficients::Ptr coeffs (new pcl::ModelCoefficients ());
pcl::PointIndices::Ptr inliers (new pcl::PointIndices ());
seg.setOptimizeCoefficients (true);
seg.setModelType (pcl::SACMODEL_PLANE);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setMaxIterations (100);
seg.setDistanceThreshold (0.0085);
seg.setInputCloud (downsampled);
seg.segment (*inliers, *coeffs);
// Extract the planar inliers from the input cloud
pcl::ExtractIndices<pcl::PointXYZRGB> extract;
extract.setInputCloud (downsampled);
extract.setIndices (inliers);
extract.setNegative (false);
extract.filter (*cloud_planar);
// toROSMsg(*cloud_planar,planar2);
// pub_planar.publish (planar2);
// Remove the planar inliers, extract the rest
extract.setNegative (true);
extract.filter (*cloud_objects);
// toROSMsg(*cloud_objects,objects2);
// pub_objects.publish (objects2);
// PassThrough Filter
pcl::PassThrough<pcl::PointXYZRGB> pass;
pass.setInputCloud (cloud_objects);
pass.setFilterFieldName ("z"); //all duplos in pcd1
pass.setFilterLimits (0.8, 1.0);
pass.filter (*cloud_filtered);
toROSMsg(*cloud_filtered,filtered2);
pub_filtered.publish (filtered2);
//don't passthrough filter, does color filter work too? (cloud_red has many points in top right off the table)
// Segment filtered PointCloud by color (red, green, blue, yellow)
for (size_t i = 0 ; i < cloud_filtered->points.size () ; i++)
{
if ( (int(cloud_filtered->points[i].r) > 2*int(cloud_filtered->points[i].g)) && (cloud_filtered->points[i].r > cloud_filtered->points[i].b) )
cloud_red->points.push_back(cloud_filtered->points[i]);
if ( (cloud_filtered->points[i].g > cloud_filtered->points[i].r) && (cloud_filtered->points[i].g > cloud_filtered->points[i].b) )
cloud_green->points.push_back(cloud_filtered->points[i]);
if ( (cloud_filtered->points[i].b > cloud_filtered->points[i].r) && (cloud_filtered->points[i].b > cloud_filtered->points[i].g) )
cloud_blue->points.push_back(cloud_filtered->points[i]);
if ( (cloud_filtered->points[i].r > cloud_filtered->points[i].g) && (int(cloud_filtered->points[i].g) - int(cloud_filtered->points[i].b) > 30) )
cloud_yellow->points.push_back(cloud_filtered->points[i]);
}
cloud_red->header.frame_id = "base_link";
cloud_red->width = cloud_red->points.size ();
cloud_red->height = 1;
color_clouds.push_back(cloud_red);
toROSMsg(*cloud_red,red2);
pub_red.publish (red2);
cloud_green->header.frame_id = "base_link";
cloud_green->width = cloud_green->points.size ();
cloud_green->height = 1;
color_clouds.push_back(cloud_green);
toROSMsg(*cloud_green,green2);
pub_green.publish (green2);
cloud_blue->header.frame_id = "base_link";
cloud_blue->width = cloud_blue->points.size ();
cloud_blue->height = 1;
color_clouds.push_back(cloud_blue);
toROSMsg(*cloud_blue,blue2);
pub_blue.publish (blue2);
cloud_yellow->header.frame_id = "base_link";
cloud_yellow->width = cloud_yellow->points.size ();
cloud_yellow->height = 1;
color_clouds.push_back(cloud_yellow);
toROSMsg(*cloud_yellow,yellow2);
pub_yellow.publish (yellow2);
// Extract Euclidian clusters from color-segmented PointClouds
int j(0), num_red (0), num_green(0), num_blue(0), num_yellow(0);
for (size_t cit = 0 ; cit < color_clouds.size() ; cit++)
{
pcl::KdTree<pcl::PointXYZRGB>::Ptr tree (new pcl::KdTreeFLANN<pcl::PointXYZRGB>);
tree->setInputCloud (color_clouds[cit]);
std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<pcl::PointXYZRGB> ec;
ec.setClusterTolerance (0.0075); // 0.01
// ec.setMinClusterSize (12);
// ec.setMaxClusterSize (75);
ec.setMinClusterSize (100);
ec.setMaxClusterSize (4000);
ec.setSearchMethod (tree);
ec.setInputCloud (color_clouds[cit]);
ec.extract (cluster_indices);
for (std::vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin (); it != cluster_indices.end (); ++it)
{
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_cluster (new pcl::PointCloud<pcl::PointXYZRGB>);
for (std::vector<int>::const_iterator pit = it->indices.begin () ; pit != it->indices.end () ; pit++)
cloud_cluster->points.push_back (color_clouds[cit]->points[*pit]);
cloud_cluster->width = cloud_cluster->points.size ();
cloud_cluster->height = 1;
cloud_cluster->is_dense = true;
cloud_cluster->header.frame_id = "base_link";
cluster_clouds.push_back(cloud_cluster);
labels.push_back(cloud_cluster->points[0]);
if (cit == 0) num_red++;
if (cit == 1) num_green++;
if (cit == 2) num_blue++;
if (cit == 3) num_yellow++;
// Create ConvexHull for cluster (keep points on perimeter of cluster)
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_hull (new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::ConvexHull<pcl::PointXYZRGB> chull;
chull.setInputCloud (cloud_cluster);
chull.reconstruct (*cloud_hull);
cloud_hull->width = cloud_hull->points.size ();
cloud_hull->height = 1;
cloud_hull->header.frame_id = "base_link";
hull_clouds.push_back(cloud_hull);
j++;
}
}
std::cout << "Number of RED clusters: " << num_red << std::endl;
std::cout << "Number of GREEN clusters: " << num_green << std::endl;
std::cout << "Number of BLUE clusters: " << num_blue << std::endl;
std::cout << "Number of YELLOW clusters: " << num_yellow << std::endl;
std::cout << "TOTAL number of clusters: " << j << std::endl;
// Concatenate PointCloud clusters and convex hulls
for (size_t k = 0 ; k < cluster_clouds.size() ; k++)
{
for (size_t l = 0 ; l < cluster_clouds[k]->size() ; l++)
{
cloud_concat_clusters->points.push_back(cluster_clouds[k]->points[l]);
cloud_concat_hulls->points.push_back(hull_clouds[k]->points[l]);
}
}
cloud_concat_clusters->header.frame_id = "base_link";
cloud_concat_clusters->width = cloud_concat_clusters->points.size ();
cloud_concat_clusters->height = 1;
toROSMsg(*cloud_concat_clusters,concat_clusters2);
pub_concat_clusters.publish (concat_clusters2);
cloud_concat_hulls->header.frame_id = "base_link";
cloud_concat_hulls->width = cloud_concat_hulls->points.size ();
cloud_concat_hulls->height = 1;
toROSMsg(*cloud_concat_hulls,concat_hulls2);
pub_concat_hulls.publish (concat_hulls2);
// Estimate the volume of each cluster
double height, width;
std::vector <double> heights, widths;
std::vector <int> height_ids, width_ids;
for (size_t k = 0 ; k < cluster_clouds.size() ; k++)
{
// Calculate cluster height
double tallest(0), shortest(1000), widest(0) ;
for (size_t l = 0 ; l < cluster_clouds[k]->size() ; l++)
{
double point_to_plane_dist;
point_to_plane_dist = coeffs->values[0] * cluster_clouds[k]->points[l].x +
coeffs->values[1] * cluster_clouds[k]->points[l].y +
coeffs->values[2] * cluster_clouds[k]->points[l].z + coeffs->values[3] /
sqrt( pow(coeffs->values[0], 2) + pow(coeffs->values[1], 2)+ pow(coeffs->values[2], 2) );
if (point_to_plane_dist < 0) point_to_plane_dist = 0;
if (point_to_plane_dist > tallest) tallest = point_to_plane_dist;
if (point_to_plane_dist < shortest) shortest = point_to_plane_dist;
}
// Calculate cluster width
for (size_t m = 0 ; m < hull_clouds[k]->size() ; m++)
{
double parallel_vector_dist;
for (size_t n = m ; n < hull_clouds[k]->size()-1 ; n++)
{
parallel_vector_dist = sqrt( pow(hull_clouds[k]->points[m].x - hull_clouds[k]->points[n+1].x,2) +
pow(hull_clouds[k]->points[m].y - hull_clouds[k]->points[n+1].y,2) +
pow(hull_clouds[k]->points[m].z - hull_clouds[k]->points[n+1].z,2) );
if (parallel_vector_dist > widest) widest = parallel_vector_dist;
}
}
// Classify block heights (error +/- .005m)
height = (shortest < .01) ? tallest : tallest - shortest; //check for stacked blocks
heights.push_back(height);
if (height > .020 && height < .032) height_ids.push_back(0); //0: standing flat
else if (height > .036 && height < .043) height_ids.push_back(1); //1: standing side
else if (height > .064) height_ids.push_back(2); //2: standing long
else height_ids.push_back(-1); //height not classified
// Classify block widths (error +/- .005m)
width = widest;
widths.push_back(widest);
if (width > .032-.005 && width < .0515+.005) width_ids.push_back(1); //1: short
else if (width > .065-.005 && width < .0763+.005) width_ids.push_back(2); //2: medium
else if (width > .1275-.005 && width < .1554+.005) width_ids.push_back(4); //4: long
else width_ids.push_back(-1); //width not classified
}
// Classify block size using width information
std::vector<int> block_ids, idx_1x1, idx_1x2, idx_1x4, idx_unclassified;
int num_1x1(0), num_1x2(0), num_1x4(0), num_unclassified(0);
for (size_t p = 0 ; p < width_ids.size() ; p++)
{
if (width_ids[p] == 1)
{
block_ids.push_back(1); //block is 1x1
idx_1x1.push_back(p);
num_1x1++;
}
else if (width_ids[p] == 2)
{
block_ids.push_back(2); //block is 1x2
idx_1x2.push_back(p);
num_1x2++;
}
else if (width_ids[p] == 4)
{
block_ids.push_back(4); //block is 1x4
idx_1x4.push_back(p);
num_1x4++;
}
else
{
block_ids.push_back(-1); //block not classified
idx_unclassified.push_back(p);
num_unclassified++;
}
}
// Determine Duplos of the same size
std::cout << "\nThere are " << num_1x1 << " blocks of size 1x1 ";
if (num_1x1>0) std::cout << "(cluster index: ";
for (size_t q = 0 ; q < idx_1x1.size() ; q++)
std::cout << idx_1x1[q] << ", ";
if (num_1x1>0) std::cout << ")";
std::cout << "\nThere are " << num_1x2 << " blocks of size 1x2 ";
if (num_1x2>0) std::cout << "(cluster index: ";
for (size_t q = 0 ; q < idx_1x2.size() ; q++)
std::cout << idx_1x2[q] << ", ";
if (num_1x2>0) std::cout << ")";
std::cout << "\nThere are " << num_1x4 << " blocks of size 1x4 ";
if (num_1x4>0) std::cout << "(cluster index: ";
for (size_t q = 0 ; q < idx_1x4.size() ; q++)
std::cout << idx_1x4[q] << ", ";
if (num_1x4>0) std::cout << ")";
std::cout << "\nThere are " << num_unclassified << " unclassified blocks ";
if (num_unclassified>0) std::cout << "(cluster index: ";
for (size_t q = 0 ; q < idx_unclassified.size() ; q++)
std::cout << idx_unclassified[q] << ", ";
if (num_unclassified>0) std::cout << ")";
std::cout << "\n\n\n";
return;
}
