#include <iostream>
#include <visp3/core/vpConfig.h>
#if defined(VISP_HAVE_OCCIPITAL_STRUCTURE) && defined(VISP_HAVE_OPENCV)
#include <visp3/core/vpDisplay.h>
#include <visp3/core/vpIoTools.h>
#include <visp3/core/vpXmlParserCamera.h>
#include <visp3/gui/vpDisplayX.h>
#include <visp3/gui/vpDisplayGDI.h>
#include <visp3/gui/vpDisplayOpenCV.h>
#include <visp3/mbt/vpMbGenericTracker.h>
#include <visp3/sensor/vpOccipitalStructure.h>
#include <visp3/vision/vpKeyPoint.h>
int main(int argc, char *argv[])
{
std::string config_color = "", config_depth = "";
std::string model_color = "", model_depth = "";
std::string init_file = "";
bool use_ogre = false;
bool use_scanline = false;
bool use_edges = true;
bool use_klt = true;
bool use_depth = true;
bool learn = false;
bool auto_init = false;
double proj_error_threshold = 25;
std::string learning_data = "learning/data-learned.bin";
bool display_projection_error = false;
for (int i = 1; i < argc; i++) {
if (std::string(argv[i]) == "--config_color" && i+1 < argc) {
config_color = std::string(argv[i+1]);
} else if (std::string(argv[i]) == "--config_depth" && i+1 < argc) {
config_depth = std::string(argv[i+1]);
} else if (std::string(argv[i]) == "--model_color" && i+1 < argc) {
model_color = std::string(argv[i+1]);
} else if (std::string(argv[i]) == "--model_depth" && i+1 < argc) {
model_depth = std::string(argv[i+1]);
} else if (std::string(argv[i]) == "--init_file" && i+1 < argc) {
init_file = std::string(argv[i+1]);
} else if (std::string(argv[i]) == "--proj_error_threshold" && i+1 < argc) {
proj_error_threshold = std::atof(argv[i+1]);
} else if (std::string(argv[i]) == "--use_ogre") {
use_ogre = true;
} else if (std::string(argv[i]) == "--use_scanline") {
use_scanline = true;
} else if (std::string(argv[i]) == "--use_edges" && i+1 < argc) {
use_edges = (std::atoi(argv[i+1]) == 0 ? false : true);
} else if (std::string(argv[i]) == "--use_klt" && i+1 < argc) {
use_klt = (std::atoi(argv[i+1]) == 0 ? false : true);
} else if (std::string(argv[i]) == "--use_depth" && i+1 < argc) {
use_depth = (std::atoi(argv[i+1]) == 0 ? false : true);
} else if (std::string(argv[i]) == "--learn") {
learn = true;
} else if (std::string(argv[i]) == "--learning_data" && i+1 < argc) {
learning_data = argv[i+1];
} else if (std::string(argv[i]) == "--auto_init") {
auto_init = true;
} else if (std::string(argv[i]) == "--display_proj_error") {
display_projection_error = true;
} else if (std::string(argv[i]) == "--help" || std::string(argv[i]) == "-h") {
std::cout << "Usage: \n" << argv[0]
<< " [--model_color <object.cao>] [--model_depth <object.cao>]"
" [--config_color <object.xml>] [--config_depth <object.xml>]"
" [--init_file <object.init>] [--use_ogre] [--use_scanline]"
" [--proj_error_threshold <threshold between 0 and 90> (default: "<< proj_error_threshold << ")]"
" [--use_edges <0|1> (default: 1)] [--use_klt <0|1> (default: 1)] [--use_depth <0|1> (default: 1)]"
" [--learn] [--auto_init] [--learning_data <path to .bin> (default: learning/data-learned.bin)]"
" [--display_proj_error]" << std::endl;
std::cout << "\n** How to track a 4.2 cm width cube with manual initialization:\n"
<< argv[0]
<< " --model_color model/cube/cube.cao --use_edges 1 --use_klt 1 --use_depth 1"
<< std::endl;
std::cout << "\n** How to learn the cube and create a learning database:\n" << argv[0]
<< " --model_color model/cube/cube.cao --use_edges 1 --use_klt 1 --use_depth 1 --learn"
<< std::endl;
std::cout << "\n** How to track the cube with initialization from learning database:\n" << argv[0]
<< " --model_color model/cube/cube.cao --use_edges 1 --use_klt 1 --use_depth 1 --auto_init"
<< std::endl;
return 0;
}
}
if (model_depth.empty()) {
model_depth = model_color;
}
if (config_color.empty()) {
config_color = (parentname.empty() ?
"" : (parentname +
"/")) +
vpIoTools::getNameWE(model_color) +
".xml";
}
if (config_depth.empty()) {
config_depth = (parentname.empty() ?
"" : (parentname +
"/")) +
vpIoTools::getNameWE(model_color) +
"_depth.xml";
}
if (init_file.empty()) {
init_file = (parentname.empty() ?
"" : (parentname +
"/")) +
vpIoTools::getNameWE(model_color) +
".init";
}
std::cout << "Tracked features: " << std::endl;
std::cout << " Use edges : " << use_edges << std::endl;
std::cout << " Use klt : " << use_klt << std::endl;
std::cout << " Use depth : " << use_depth << std::endl;
std::cout << "Tracker options: " << std::endl;
std::cout << " Use ogre : " << use_ogre << std::endl;
std::cout << " Use scanline: " << use_scanline << std::endl;
std::cout << " Proj. error : " << proj_error_threshold << std::endl;
std::cout << " Display proj. error: " << display_projection_error << std::endl;
std::cout << "Config files: " << std::endl;
std::cout << " Config color: " << "\"" << config_color << "\"" << std::endl;
std::cout << " Config depth: " << "\"" << config_depth << "\"" << std::endl;
std::cout << " Model color : " << "\"" << model_color << "\"" << std::endl;
std::cout << " Model depth : " << "\"" << model_depth << "\"" << std::endl;
std::cout << " Init file : " << "\"" << init_file << "\"" << std::endl;
std::cout << "Learning options : " << std::endl;
std::cout << " Learn : " << learn << std::endl;
std::cout << " Auto init : " << auto_init << std::endl;
std::cout << " Learning data: " << learning_data << std::endl;
if (!use_edges && !use_klt && !use_depth) {
std::cout << "You must choose at least one visual features between edge, KLT and depth." << std::endl;
return EXIT_FAILURE;
}
if (config_color.empty() || config_depth.empty() || model_color.empty() || model_depth.empty() || init_file.empty()) {
std::cout << "config_color.empty() || config_depth.empty() || model_color.empty() || model_depth.empty() || init_file.empty()" << std::endl;
return EXIT_FAILURE;
}
ST::CaptureSessionSettings settings;
settings.source = ST::CaptureSessionSourceId::StructureCore;
settings.structureCore.visibleEnabled = true;
settings.applyExpensiveCorrection = true;
try {
}
std::cout <<
"Catch an exception: " << e.
what() << std::endl;
std::cout << "Check if the Structure Core camera is connected..." << std::endl;
return EXIT_SUCCESS;
}
std::cout << "Sensor internal camera parameters for color camera: " << cam_color << std::endl;
std::cout << "Sensor internal camera parameters for depth camera: " << cam_depth << std::endl;
unsigned int _posx = 100, _posy = 50;
#ifdef VISP_HAVE_X11
#elif defined(VISP_HAVE_GDI)
#elif defined(VISP_HAVE_OPENCV)
#endif
if (use_edges || use_klt)
d1.
init(I_gray, _posx, _posy,
"Color stream");
if (use_depth)
d2.
init(I_depth, _posx + I_gray.
getWidth()+10, _posy,
"Depth stream");
while (true) {
sc.
acquire((
unsigned char *)I_color.
bitmap, (
unsigned char *)I_depth_raw.
bitmap, NULL, NULL, NULL);
if (use_edges || use_klt) {
break;
}
}
if (use_depth) {
break;
}
}
}
std::vector<int> trackerTypes;
if (use_edges && use_klt)
else if (use_edges)
else if (use_klt)
if (use_depth)
std::map<std::string, vpHomogeneousMatrix> mapOfCameraTransformations;
std::map<std::string, const vpImage<unsigned char> *> mapOfImages;
std::map<std::string, std::string> mapOfInitFiles;
std::map<std::string, const std::vector<vpColVector> *> mapOfPointclouds;
std::map<std::string, unsigned int> mapOfWidths, mapOfHeights;
std::map<std::string, vpHomogeneousMatrix> mapOfCameraPoses;
std::vector<vpColVector> pointcloud;
if ((use_edges || use_klt) && use_depth) {
std::cout << "Sensor internal depth_M_color: \n" << depth_M_color << std::endl;
mapOfCameraTransformations["Camera2"] = depth_M_color;
mapOfImages["Camera1"] = &I_gray;
mapOfImages["Camera2"] = &I_depth;
mapOfInitFiles["Camera1"] = init_file;
}
else if (use_edges || use_klt) {
}
else if (use_depth) {
}
#if (defined(VISP_HAVE_OPENCV_NONFREE) || defined(VISP_HAVE_OPENCV_XFEATURES2D)) || \
(VISP_HAVE_OPENCV_VERSION >= 0x030411 && CV_MAJOR_VERSION < 4) || (VISP_HAVE_OPENCV_VERSION >= 0x040400)
std::string detectorName = "SIFT";
std::string extractorName = "SIFT";
std::string matcherName = "BruteForce";
#else
std::string detectorName = "FAST";
std::string extractorName = "ORB";
std::string matcherName = "BruteForce-Hamming";
#endif
if (learn || auto_init) {
#if !(defined(VISP_HAVE_OPENCV_NONFREE) || defined(VISP_HAVE_OPENCV_XFEATURES2D))
# if (VISP_HAVE_OPENCV_VERSION < 0x030000)
keypoint.setDetectorParameter("ORB", "nLevels", 1);
# else
cv::Ptr<cv::ORB> orb_detector = keypoint.
getDetector(
"ORB").dynamicCast<cv::ORB>();
if (orb_detector) {
orb_detector->setNLevels(1);
}
# endif
#endif
}
if (auto_init) {
std::cout << "Cannot enable auto detection. Learning file \"" << learning_data << "\" doesn't exist" << std::endl;
return EXIT_FAILURE;
}
} else {
if ((use_edges || use_klt) && use_depth)
tracker.
initClick(mapOfImages, mapOfInitFiles,
true);
else if (use_edges || use_klt)
else if (use_depth)
if (learn)
}
bool run_auto_init = false;
if (auto_init) {
run_auto_init = true;
}
std::vector<double> times_vec;
try {
int learn_id = 1;
bool quit = false;
bool learn_position = false;
double loop_t = 0;
while (!quit) {
bool tracking_failed = false;
if (use_edges || use_klt || run_auto_init) {
}
if (use_depth) {
}
if ((use_edges || use_klt) && use_depth) {
mapOfImages["Camera1"] = &I_gray;
mapOfPointclouds["Camera2"] = &pointcloud;
mapOfWidths["Camera2"] = width;
mapOfHeights["Camera2"] = height;
} else if (use_edges || use_klt) {
mapOfImages["Camera"] = &I_gray;
} else if (use_depth) {
mapOfPointclouds["Camera"] = &pointcloud;
mapOfWidths["Camera"] = width;
mapOfHeights["Camera"] = height;
}
if (run_auto_init) {
if (keypoint.
matchPoint(I_gray, cam_color, cMo)) {
std::cout << "Auto init succeed" << std::endl;
if ((use_edges || use_klt) && use_depth) {
mapOfCameraPoses["Camera1"] = cMo;
mapOfCameraPoses["Camera2"] = depth_M_color *cMo;
} else if (use_edges || use_klt) {
} else if (use_depth) {
}
} else {
if (use_edges || use_klt) {
}
if (use_depth) {
}
continue;
}
}
try {
if (run_auto_init) {
run_auto_init = false;
}
if ((use_edges || use_klt) && use_depth) {
tracker.
track(mapOfImages, mapOfPointclouds, mapOfWidths, mapOfHeights);
} else if (use_edges || use_klt) {
} else if (use_depth) {
tracker.
track(mapOfImages, mapOfPointclouds, mapOfWidths, mapOfHeights);
}
tracking_failed = true;
if (auto_init) {
std::cout << "Tracker needs to restart (tracking exception)" << std::endl;
run_auto_init = true;
}
}
double proj_error = 0;
}
else {
}
if (auto_init && proj_error > proj_error_threshold) {
std::cout << "Tracker needs to restart (projection error detected: " << proj_error << ")" << std::endl;
run_auto_init = true;
tracking_failed = true;
}
if (!tracking_failed) {
if ((use_edges || use_klt) && use_depth) {
tracker.
display(I_gray, I_depth, cMo, depth_M_color*cMo, cam_color, cam_depth,
vpColor::red, 3);
} else if (use_edges || use_klt) {
} else if (use_depth) {
}
{
std::stringstream ss;
}
{
std::stringstream ss;
}
}
std::stringstream ss;
ss << "Loop time: " << loop_t << " ms";
if (use_edges || use_klt) {
if (learn)
else if (auto_init)
else
quit = true;
learn_position = true;
run_auto_init = true;
}
}
}
if (use_depth) {
quit = true;
}
}
if (learn_position) {
std::vector<cv::KeyPoint> trainKeyPoints;
keypoint.
detect(I_gray, trainKeyPoints);
std::vector<vpPolygon> polygons;
std::vector<std::vector<vpPoint> > roisPt;
std::pair<std::vector<vpPolygon>, std::vector<std::vector<vpPoint> > > pair = tracker.
getPolygonFaces();
polygons = pair.first;
roisPt = pair.second;
std::vector<cv::Point3f> points3f;
keypoint.
buildReference(I_gray, trainKeyPoints, points3f,
true, learn_id++);
for (std::vector<cv::KeyPoint>::const_iterator it = trainKeyPoints.begin(); it != trainKeyPoints.end(); ++it) {
}
learn_position = false;
std::cout << "Data learned" << std::endl;
}
times_vec.push_back(loop_t);
}
if (learn) {
std::cout << "Save learning file: " << learning_data << std::endl;
}
std::cout <<
"Catch an exception: " << e.
what() << std::endl;
}
if (!times_vec.empty()) {
}
return EXIT_SUCCESS;
}
#elif defined(VISP_HAVE_OCCIPITAL_STRUCTURE)
int main() {
std::cout << "Install OpenCV 3rd party, configure and build ViSP again to use this example" << std::endl;
return 0;
}
#else
int main() {
std::cout << "Install libStructure 3rd party, configure and build ViSP again to use this example" << std::endl;
return 0;
}
#endif