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<mu2>0.5</mu2>
</contrast>
<sample>
<step>4</step>
</sample>
</ecm>
<klt>
<mask_border>5</mask_border>
<max_features>300</max_features>
<window_size>5</window_size>
<quality>0.015</quality>
<min_distance>8</min_distance>
<harris>0.04</harris>
<size_block>3</size_block>
<pyramid_lvl>3</pyramid_lvl>
</klt>
<camera>
<u0>315.385037</u0>
<v0>238.464158</v0>
<px>536.389905</px>
<py>537.240938</py>
</camera>
<face>
<angle_appear>70</angle_appear>
<angle_disappear>80</angle_disappear>
<near_clipping>0.1</near_clipping>
<far_clipping>100</far_clipping>
<fov_clipping>1</fov_clipping>
</face>
</conf>

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V1
# 3D Points
8 # Number of points
0 0 0 # Point 0: X Y Z
0 0 -0.017
0.232 0 -0.017
0.232 0 0
0.232 0.155 0
0.232 0.155 -0.017
0 0.155 -0.017
0 0.155 0 # Point 7
# 3D Lines
0 # Number of lines
# Faces from 3D lines
0 # Number of faces
# Faces from 3D points
6 # Number of faces
4 0 1 2 3 # Face 0: [number of points] [index of the 3D points]...
4 1 6 5 2
4 4 5 6 7
4 0 3 4 7
4 5 4 3 2
4 0 7 6 1 # Face 5
# 3D cylinders
0 # Number of cylinders
# 3D circles
0 # Number of circles

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<?xml version="1.0"?>
<conf>
<ecm>
<mask>
<size>5</size>
<nb_mask>180</nb_mask>
</mask>
<range>
<tracking>8</tracking>
</range>
<contrast>
<edge_threshold>10000</edge_threshold>
<mu1>0.5</mu1>
<mu2>0.5</mu2>
</contrast>
<sample>
<step>4</step>
</sample>
</ecm>
<klt>
<mask_border>5</mask_border>
<max_features>300</max_features>
<window_size>5</window_size>
<quality>0.015</quality>
<min_distance>8</min_distance>
<harris>0.01</harris>
<size_block>3</size_block>
<pyramid_lvl>3</pyramid_lvl>
</klt>
<camera>
<u0>315.385037</u0>
<v0>238.464158</v0>
<px>536.389905</px>
<py>537.240938</py>
</camera>
<face>
<angle_appear>70</angle_appear>
<angle_disappear>80</angle_disappear>
<near_clipping>0.1</near_clipping>
<far_clipping>100</far_clipping>
<fov_clipping>1</fov_clipping>
</face>
</conf>

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ongoing/Visp_tests/learn_tests/book_training/train_image_000.yml Normal file
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Book cMo for training image 000
rows: 4
cols: 4
data:
- [0.5665910387, -0.8235039121, 0.02856399138, -0.004725561405]
- [-0.612495359, -0.444094245, -0.6539340461, 0.05318802529]
- [0.5512023495, 0.3530178583, -0.7560121439, 0.398670845]
- [0, 0, 0, 1]

8
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Book cMo for training image 001
rows: 4
cols: 4
data:
- [-0.6442421126, -0.7648185729, 0.00215661327, 0.1005236514]
- [-0.5078681912, 0.4256891629, -0.7489049586, -0.03211180135]
- [0.5718583747, -0.4835713879, -0.6626739108, 0.4744785946]
- [0, 0, 0, 1]

8
ongoing/Visp_tests/learn_tests/book_training/train_image_002.yml Normal file
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Book cMo for training image 002
rows: 4
cols: 4
data:
- [0.5931860286, 0.8050635925, -0.001716825525, -0.1530651462]
- [0.5488091651, -0.4059313336, -0.7307723672, -0.09305480426]
- [-0.5890151405, 0.4325417487, -0.6826190738, 0.5348952909]
- [0, 0, 0, 1]

8
ongoing/Visp_tests/learn_tests/book_training/train_image_003.yml Normal file
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Book cMo for training image 003
rows: 4
cols: 4
data:
- [-0.4033631815, 0.9148985892, -0.01608462887, -0.04515121073]
- [0.6287289807, 0.2643387569, -0.7313172297, -0.168069378]
- [-0.6648293109, -0.3050993168, -0.6818477794, 0.5964581417]
- [0, 0, 0, 1]

8
ongoing/Visp_tests/learn_tests/book_training/train_image_004.yml Normal file
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Book cMo for training image 004
rows: 4
cols: 4
data:
- [-0.9985837435, 0.05141800958, -0.0136636575, 0.09960909748]
- [0.04448102767, 0.6659832775, -0.7446393169, -0.1074251457]
- [-0.02918810413, -0.7441924902, -0.6673272002, 0.5639220715]
- [0, 0, 0, 1]

8
ongoing/Visp_tests/learn_tests/book_training/train_image_005.yml Normal file
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Book cMo for training image 005
rows: 4
cols: 4
data:
- [-0.2314201876, -0.9583649151, 0.1672763771, 0.09835545579]
- [0.7484075924, -0.06552319445, 0.6599945353, -0.0974700766]
- [-0.6215551242, 0.2779269699, 0.7324109687, 0.5499983612]
- [0, 0, 0, 1]

8
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Book cMo for training image 005
rows: 4
cols: 4
data:
- [-0.2314201876, -0.9583649151, 0.1672763771, 0.09835545579]
- [0.7484075924, -0.06552319445, 0.6599945353, -0.0974700766]
- [-0.6215551242, 0.2779269699, 0.7324109687, 0.5499983612]
- [0; 0; 0; 1]

8
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Book cMo for training image 006
rows: 4
cols: 4
data:
- [-0.9945932359, 0.06416141072, 0.0816554258, 0.1218390655]
- [-0.03089262028, -0.933506626, 0.357226854, 0.06690798951]
- [0.09914605993, 0.3527728626, 0.93044149, 0.2360881003]
- [0, 0, 0, 1]

8
ongoing/Visp_tests/learn_tests/book_training/train_image_006.yml~ Normal file
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Book cMo for training image 008
rows: 4
cols: 4
data:
- [0.02063568325, -0.5653102458, -0.8246202123, 0.0403687505]
- [0.8210674394, 0.4801939642, -0.3086454546, -0.1745029756]
- [0.5704580865, -0.6706996964, 0.4740669666, 0.4630312508]
- [0, 0, 0, 1]

8
ongoing/Visp_tests/learn_tests/book_training/train_image_007.yml Normal file
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Book cMo for training image 007
rows: 4
cols: 4
data:
- [-0.03028468063, -0.4076156195, 0.9126512723, 0.05370078927]
- [-0.6648925807, 0.6899748614, 0.2860988409, 0.00863761359]
- [-0.7463247914, -0.5981506477, -0.2919162695, 0.5950621033]
- [0, 0, 0, 1]

9
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Book cMo for training image 007
rows: 4
cols: 4
data:
- [-0.03609085509, -0.3148440768, 0.9484569877, 0.04713881051]
- [-0.8006242946, 0.5771011583, 0.1611055304, 0.02971868344]
- [-0.5980787482, -0.7535432704, -0.2728998912, 0.6240615433]
- [0, 0, 0, 1]

8
ongoing/Visp_tests/learn_tests/book_training/train_image_008.yml Normal file
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Book cMo for training image 006
rows: 4
cols: 4
data:
- [0.02063568325, -0.5653102458, -0.8246202123, 0.0403687505]
- [0.8210674394, 0.4801939642, -0.3086454546, -0.1745029756]
- [0.5704580865, -0.6706996964, 0.4740669666, 0.4630312508]
- [0, 0, 0, 1]

8
ongoing/Visp_tests/learn_tests/book_training/train_image_009.yml Normal file
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Book cMo for training image 009
rows: 4
cols: 4
data:
- [-0.9994394457, 0.006264701471, -0.03288689451, 0.1238992921]
- [0.03112213929, -0.1881441717, -0.9816481972, 0.01594317069]
- [-0.01233721043, -0.9821214406, 0.1878437358, 0.4121166041]
- [0, 0, 0, 1]

8
ongoing/Visp_tests/learn_tests/book_training/train_image_010.yml Normal file
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Book cMo for training image 010
rows: 4
cols: 4
data:
- [-0.4708977952, -0.8758554817, 0.1055103862, 0.1637869872]
- [0.5260415703, -0.1827654425, 0.8305883814, -0.07876409183]
- [-0.7081917345, 0.4466250868, 0.5468002369, 0.3485744882]
- [0, 0, 0, 1]

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V1
# 3D Points
8 # Number of points
0 0 0 # Point 0: X Y Z
0 0 -0.080
0.200 0 -0.080
0.200 0 0
0.200 0.120 0
0.200 0.120 -0.080
0 0.120 -0.080
0 0.120 0 # Point 7
# 3D Lines
0 # Number of lines
# Faces from 3D lines
0 # Number of faces
# Faces from 3D points
6 # Number of faces
4 0 1 2 3 # Face 0: [number of points] [index of the 3D points]...
4 1 6 5 2
4 4 5 6 7
4 0 3 4 7
4 5 4 3 2
4 0 7 6 1 # Face 5
# 3D cylinders
0 # Number of cylinders
# 3D circles
0 # Number of circles

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# Install script for directory: /home/harle/catkin_ws/src/BaxterInterface/Visp_tests/learn_tests
# Set the install prefix
IF(NOT DEFINED CMAKE_INSTALL_PREFIX)
SET(CMAKE_INSTALL_PREFIX "/usr/local")
ENDIF(NOT DEFINED CMAKE_INSTALL_PREFIX)
STRING(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}")
# Set the install configuration name.
IF(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME)
IF(BUILD_TYPE)
STRING(REGEX REPLACE "^[^A-Za-z0-9_]+" ""
CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}")
ELSE(BUILD_TYPE)
SET(CMAKE_INSTALL_CONFIG_NAME "")
ENDIF(BUILD_TYPE)
MESSAGE(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"")
ENDIF(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME)
# Set the component getting installed.
IF(NOT CMAKE_INSTALL_COMPONENT)
IF(COMPONENT)
MESSAGE(STATUS "Install component: \"${COMPONENT}\"")
SET(CMAKE_INSTALL_COMPONENT "${COMPONENT}")
ELSE(COMPONENT)
SET(CMAKE_INSTALL_COMPONENT)
ENDIF(COMPONENT)
ENDIF(NOT CMAKE_INSTALL_COMPONENT)
# Install shared libraries without execute permission?
IF(NOT DEFINED CMAKE_INSTALL_SO_NO_EXE)
SET(CMAKE_INSTALL_SO_NO_EXE "1")
ENDIF(NOT DEFINED CMAKE_INSTALL_SO_NO_EXE)
IF(CMAKE_INSTALL_COMPONENT)
SET(CMAKE_INSTALL_MANIFEST "install_manifest_${CMAKE_INSTALL_COMPONENT}.txt")
ELSE(CMAKE_INSTALL_COMPONENT)
SET(CMAKE_INSTALL_MANIFEST "install_manifest.txt")
ENDIF(CMAKE_INSTALL_COMPONENT)
FILE(WRITE "/home/harle/catkin_ws/src/BaxterInterface/Visp_tests/learn_tests/${CMAKE_INSTALL_MANIFEST}" "")
FOREACH(file ${CMAKE_INSTALL_MANIFEST_FILES})
FILE(APPEND "/home/harle/catkin_ws/src/BaxterInterface/Visp_tests/learn_tests/${CMAKE_INSTALL_MANIFEST}" "${file}\n")
ENDFOREACH(file)

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//! \example tutorial-detection-object-mbt2.cpp
#include <visp3/core/vpConfig.h>
#include <visp3/core/vpIoTools.h>
#include <visp3/gui/vpDisplayGDI.h>
#include <visp3/gui/vpDisplayOpenCV.h>
#include <visp3/gui/vpDisplayX.h>
#include <visp3/io/vpVideoReader.h>
#include <visp3/mbt/vpMbGenericTracker.h>
#include <visp3/vision/vpKeyPoint.h>
//Options
#define IMATCHING
// #define HYBRID_MATCHING
// #define VAR_TRESH
#ifdef HYBRID_MATCHING
enum State
{
WAITING_FOR_INITIALIZATION,
TRACKING,
LOST,
UNCERTAIN //=Tracking OK / Matching FAIL
};
#endif
#if (VISP_HAVE_OPENCV_VERSION >= 0x020400)
void learn(const vpImage<unsigned char> &I, vpMbGenericTracker &tracker, vpKeyPoint &keypoint_learning, int id)
{
//! [Keypoints reference detection]
std::vector<cv::KeyPoint> trainKeyPoints;
double elapsedTime;
keypoint_learning.detect(I, trainKeyPoints, elapsedTime);
//! [Keypoints reference detection]
//! [Keypoints selection on faces]
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;
vpHomogeneousMatrix cMo;
tracker.getPose(cMo);
vpCameraParameters cam;
tracker.getCameraParameters(cam);
vpKeyPoint::compute3DForPointsInPolygons(cMo, cam, trainKeyPoints, polygons, roisPt, points3f);
//! [Keypoints selection on faces]
//! [Keypoints build reference]
keypoint_learning.buildReference(I, trainKeyPoints, points3f, true, id);
//! [Keypoints build reference]
//! [Display reference keypoints]
for (std::vector<cv::KeyPoint>::const_iterator it = trainKeyPoints.begin(); it != trainKeyPoints.end(); ++it) {
vpDisplay::displayCross(I, (int)it->pt.y, (int)it->pt.x, 4, vpColor::red);
}
//! [Display reference keypoints]
}
#endif
#ifdef VAR_TRESH
//Compute cummulative variance of a list of vector.
double varVector(std::list< vpArray2D<double> > vector_list)
{
//Attention aux vecteurs vides
double var[3], mean[3];
//Compute means
for (std::list< vpArray2D<double> >::iterator it=vector_list.begin(); it != vector_list.end(); ++it)
{
if((*it).size()!=3)
{
std::cout<<"WARNING varVector : invalid array size"<<std::endl;
return -1;
}
else
{
for(unsigned int j=0; j< 3; j++)
{
mean[j]+=*(*it)[j];
}
}
}
for(unsigned int j=0; j<3; j++)
{
mean[j] /= vector_list.size();
}
//Compute variances
for (std::list< vpArray2D<double> >::iterator it=vector_list.begin(); it != vector_list.end(); ++it)
{
for(unsigned int j=0; j< 3; j++)
{
var[j]+=(*(*it)[j]-mean[j])*(*(*it)[j]-mean[j]);
}
}
for(unsigned int j=0; j<3; j++)
{
var[j] /= vector_list.size();
}
return var[0]+var[1]+var[2];
}
#endif
#ifdef HYBRID_MATCHING
float computeConfidenceLevel(std::list<State> states)
{
float res = 0;
for (std::list<State>::iterator it=states.begin(); it != states.end(); ++it)
{
switch(*it)
{
case TRACKING:
res += 1;
break;
case UNCERTAIN:
res += 0.5;
break;
case LOST:
res -=1;
case WAITING_FOR_INITIALIZATION:
break;
}
}
res = res/states.size();
return res;
}
#endif
int main(int argc, char **argv)
{
#if (VISP_HAVE_OPENCV_VERSION >= 0x020400)
//! [MBT code]
try {
std::string videoname = "book.mpeg";
for (int i = 0; i < argc; i++) {
if (std::string(argv[i]) == "--name")
videoname = std::string(argv[i + 1]);
else if (std::string(argv[i]) == "--help") {
std::cout << "\nUsage: " << argv[0] << " [--name <video name>] [--help]\n" << std::endl;
return 0;
}
}
std::string parentname = vpIoTools::getParent(videoname);
std::string objectname = vpIoTools::getNameWE(videoname);
if (!parentname.empty())
objectname = parentname + "/" + objectname;
std::cout << "Video name: " << videoname << std::endl;
std::cout << "Tracker requested config files: " << objectname << ".[init,"
#ifdef VISP_HAVE_XML2
<< "xml,"
#endif
<< "cao or wrl]" << std::endl;
std::cout << "Tracker optional config files: " << objectname << ".[ppm]" << std::endl;
vpImage<unsigned char> I;
vpHomogeneousMatrix cMo;
vpCameraParameters cam;
vpMbGenericTracker tracker; //Bug lors de la config de type KLT de tracker dans le constructeur.
tracker.setTrackerType(vpMbGenericTracker::EDGE_TRACKER | vpMbGenericTracker::KLT_TRACKER);
bool usexml = false;
#ifdef VISP_HAVE_XML2
if (vpIoTools::checkFilename(objectname + ".xml")) {
tracker.loadConfigFile(objectname + ".xml");
tracker.getCameraParameters(cam);
usexml = true;
}
#endif
if (!usexml) {
vpMe me;
me.setMaskSize(5);
me.setMaskNumber(180);
me.setRange(7);
me.setThreshold(5000);
me.setMu1(0.5);
me.setMu2(0.5);
me.setSampleStep(4);
me.setNbTotalSample(250);
tracker.setMovingEdge(me);
cam.initPersProjWithoutDistortion(547, 542, 339, 235);
tracker.setCameraParameters(cam);
tracker.setAngleAppear(vpMath::rad(89));
tracker.setAngleDisappear(vpMath::rad(89));
tracker.setNearClippingDistance(0.01);
tracker.setFarClippingDistance(10.0);
tracker.setClipping(tracker.getClipping() | vpMbtPolygon::FOV_CLIPPING);
vpKltOpencv klt_settings;
klt_settings.setMaxFeatures(300);
klt_settings.setWindowSize(5);
klt_settings.setQuality(0.015);
klt_settings.setMinDistance(8);
klt_settings.setHarrisFreeParameter(0.01);
klt_settings.setBlockSize(3);
klt_settings.setPyramidLevels(3);
tracker.setKltOpencv(klt_settings);
tracker.setKltMaskBorder(5);
}
tracker.setOgreVisibilityTest(false);
if (vpIoTools::checkFilename(objectname + ".cao"))
tracker.loadModel(objectname + ".cao");
else if (vpIoTools::checkFilename(objectname + ".wrl"))
tracker.loadModel(objectname + ".wrl");
tracker.setDisplayFeatures(true);
//! [MBT code]
//! [Keypoint declaration]
vpKeyPoint keypoint_learning("ORB", "ORB", "BruteForce-Hamming");
#if (VISP_HAVE_OPENCV_VERSION < 0x030000)
keypoint_learning.setDetectorParameter("ORB", "nLevels", 1);
#else
cv::Ptr<cv::ORB> orb_learning = keypoint_learning.getDetector("ORB").dynamicCast<cv::ORB>();
if (orb_learning != NULL) {
orb_learning->setNLevels(1);
}
#endif
//! [Keypoint declaration]
#if defined(VISP_HAVE_X11)
vpDisplayX display;
#elif defined(VISP_HAVE_GDI)
vpDisplayGDI display;
#elif defined(VISP_HAVE_OPENCV)
vpDisplayOpenCV display;
#else
std::cout << "No image viewer is available..." << std::endl;
return 0;
#endif
videoname = "data_test/%03d.png";
/*
* Start the part of the code dedicated to object learning from 3 images
*/
unsigned int init_data_nb = 11;
// vpVideoReader g;
// g.setFileName("book_training/train_image_%03d.png");
std::string trainData[] = {
"book_training/train_image_000",
"book_training/train_image_001",
"book_training/train_image_002",
"book_training/train_image_003",
"book_training/train_image_004",
"book_training/train_image_005",
"book_training/train_image_006",
"book_training/train_image_007",
"book_training/train_image_008",
"book_training/train_image_009",
"book_training/train_image_010"};
// std::string trainData[] = {
// "book2_training/train_image_000",
// "book2_training/train_image_001",
// "book2_training/train_image_002",
// "book2_training/train_image_003"};
// std::string trainData[] = {
// "box_training/train_image_000",
// "box_training/train_image_001",
// "box_training/train_image_002",
// "box_training/train_image_003",
// "box_training/train_image_004",
// "box_training/train_image_005"};
vpHomogeneousMatrix initPoseTab[init_data_nb];
// std::cout<<"Load init cMo"<<std::endl;
// vpArray2D<double>::loadYAML(cMoName[0],initPoseTab[0]);
// initPoseTab[0].print();
//Acquisition des cMo d'intialisation
for (int i = 0; i < init_data_nb; i++)
{
// std::cout<<i<<std::endl;
vpArray2D<double>::loadYAML(trainData[i]+".yml",initPoseTab[i]);
}
for (int i = 0; i < init_data_nb; i++) {
//Acquisition des images d'initialisation
vpImageIo::read(I, trainData[i]+".png");
// if(i==0 || !g.end())
// g.acquire(I);
// else
// std::cout<<"Missing training image !"<<std::endl;
// vpArray2D<double>::loadYAML(cMoName[i],initPoseTab[i]);
if (i == 0) {
display.init(I, 10, 10);
}
std::stringstream title;
title << "Learning cube on image: " << trainData[i];
// title << "Learning cube on image: " << g.getFrameIndex();
vpDisplay::setTitle(I, title.str().c_str());
vpDisplay::display(I);
//! [Set tracker pose]
// tracker.setPose(I, initPoseTab[i]);
// tracker.initFromPose(I,initPoseTab[i]);
tracker.init(I);
//! [Set tracker pose]
// std::cout<<"Refine"<<std::endl;
//! [Refine pose]
// tracker.track(I);
//! [Refine pose]
// std::cout<<"Display"<<std::endl;
//! [Display tracker pose]
tracker.getPose(cMo);
tracker.display(I, cMo, cam, vpColor::red);
//! [Display tracker pose]
// std::cout<<"Learn"<<std::endl;
//! [Learn cube call]
learn(I, tracker, keypoint_learning, i);
//! [Learn cube call]
vpDisplay::displayText(I, 10, 10, "Learning step: keypoints are detected on visible cube faces", vpColor::red);
if (i < 2) {
vpDisplay::displayText(I, 30, 10, "Click to continue the learning...", vpColor::red);
} else {
vpDisplay::displayText(I, 30, 10, "Click to continue with the detection...", vpColor::red);
}
vpDisplay::flush(I);
// vpDisplay::getClick(I, true);
std::cout<<trainData[i]<<" learned !"<<std::endl;
}
// g.close();
//! [Save learning data]
#ifdef IMATCHING
keypoint_learning.saveLearningData("book_learning_data.bin", true, true); //+sauvegarde des images d'entrainement pour l'affichage
#else
keypoint_learning.saveLearningData("book_learning_data.bin", true, false);
#endif
//! [Save learning data]
/*
* Start the part of the code dedicated to detection and localization
*/
//! [Init keypoint detection]
vpKeyPoint::vpFilterMatchingType filterType = vpKeyPoint::noFilterMatching;
vpKeyPoint keypoint_detection("ORB", "ORB", "BruteForce-Hamming",filterType);
#if (VISP_HAVE_OPENCV_VERSION < 0x030000)
keypoint_detection.setDetectorParameter("ORB", "nLevels", 1);
#else
cv::Ptr<cv::ORB> orb_detector = keypoint_detection.getDetector("ORB").dynamicCast<cv::ORB>();
orb_detector = keypoint_detection.getDetector("ORB").dynamicCast<cv::ORB>();
if (orb_detector != NULL) {
orb_detector->setNLevels(1);
}
#endif
//! [Init keypoint detection]
//! [Load teabox learning data]
keypoint_detection.loadLearningData("book_learning_data.bin", true);
//! [Load teabox learning data]
#ifdef IMATCHING
//! [Create image matching]
vpImage<unsigned char> IMatching;
keypoint_detection.createImageMatching(I, IMatching);
//! [Create image matching]
#endif
// std::cout<<"Reading : "<<videoname<<std::endl;
vpVideoReader g;
g.setFileName(videoname);
g.open(I);
#if defined VISP_HAVE_X11
vpDisplayX display2;
#elif defined VISP_HAVE_GTK
vpDisplayGTK display2;
#elif defined VISP_HAVE_GDI
vpDisplayGDI display2;
#else
vpDisplayOpenCV display2;
#endif
#ifdef IMATCHING
display2.init(IMatching, 50, 50, "Display matching between learned and current images");
#endif
vpDisplay::setTitle(I, "Cube detection and localization");
///// TEST ////
// if(rect_roi==NULL)
// rect_roi = new vpRect();
// double time=0, nb=0;
// display.init(I, 10, 10);
#ifdef VAR_TRESH
//Var Treshold
std::list< vpArray2D<double> > translation_mem;
std::list< vpArray2D<double> > rotation_mem;
unsigned int var_mem_length = 15; // + = stabilité / - = rapidité
double tvar_treshold = 0.3;
double rvar_treshold = 0.5;
double tvar, rvar;
//Var Treshold
#endif
#ifdef HYBRID_MATCHING
vpRect* rect_roi = new vpRect();
State track_state = WAITING_FOR_INITIALIZATION;
std::list<State> state_mem;
unsigned int state_mem_length = 10;
#endif
///////////////
double error;
bool click_done = false;
while (!g.end()) {
g.acquire(I);
vpDisplay::display(I);
#ifdef IMATCHING
//! [Insert image matching]
keypoint_detection.insertImageMatching(I, IMatching);
//! [Insert image matching]
vpDisplay::display(IMatching);
#endif
// vpDisplay::displayText(I, 10, 10, "Detection and localization in process...", vpColor::red);
double elapsedTime;
unsigned int nbMatch;
unsigned int match_tresh = 50;
vpRect boundingBox;
vpImagePoint center;
std::list<vpImagePoint> mem;
tracker.init(I);
// ! [Matching and pose estimation]
// if (keypoint_detection.matchPoint(I, cam, cMo, error, elapsedTime,NULL,*rect_roi)) {
// if (keypoint_detection.matchPoint(I, cam, cMo, error, elapsedTime)) {
// nbMatch = keypoint_detection.matchPoint(I);
keypoint_detection.matchPointAndDetect(I, boundingBox, center);
nbMatch = keypoint_detection.getMatches().size();
std::cout<<"Nb Match : "<<nbMatch<<std::endl;
if(nbMatch>match_tresh){
vpDisplay::displayPoint(I,center,vpColor::blue,10);
vpDisplay::displayRectangle(I, boundingBox, vpColor::blue,false,2);
//! [Matching and pose estimation]
try
{
tracker.track(I);
// tracker.testTracking();
}
catch(...)
{
}
tracker.getPose(cMo);
#ifdef VAR_TRESH
//Var Treshold
translation_mem.push_front(cMo.getTranslationVector());
rotation_mem.push_front(cMo.getThetaUVector());
if(translation_mem.size()==var_mem_length)
{
// tvar = varVector(translation_mem);
// std::cout<<"Var trans : "<<tvar<<std::endl;
translation_mem.pop_back();
}
if(rotation_mem.size()==var_mem_length)
{
// rvar=varVector(rotation_mem);
// std::cout<<"Var rot : "<<rvar<<std::endl;
rotation_mem.pop_back();
}
tvar = varVector(translation_mem);
rvar=varVector(rotation_mem);
if(tvar < tvar_treshold && rvar < rvar_treshold)
{
//Var Treshold
#endif
#ifdef HYBRID_MATCHING
//Init ou Reinit du tracker après perte ou après incertitude
if(track_state != TRACKING || (tvar<0.1 && rvar<0.1))
{
tracker.initFromPose(I,cMo);
track_state = TRACKING;
}
#endif
//! [Tracker set pose]
// tracker.setPose(I, cMo);
//! [Tracker set pose]
/////TEST/////
// time+=elapsedTime; nb++;
// std::vector<vpImagePoint> roi;
// for(unsigned int i=0; i<tracker.getNbPolygon();i++)
// {
// std::vector<vpImagePoint> temp = tracker.getPolygon(i)->getRoi(cam);
// roi.insert(roi.end(), temp.begin(), temp.end());
// }
// delete rect_roi;
// rect_roi = new vpRect(roi);
//Affichage de l'origine pointée par cMo
// vpPoint origine;
// origine.setWorldCoordinates(0,0,0) ;
// // std::cout <<"Origne (o):"<< origine.oP << std::endl ;
// origine.changeFrame(cMo) ;
// // std::cout << "Origine (c) : "<< origine.cP << std::endl ;
// origine.project(cMo);
// // std::cout << "Origine (2D) : "<< origine.p << std::endl ;
// vpImagePoint ImRes;
// vpMeterPixelConversion::convertPoint(cam,origine.p[0],origine.p[1],ImRes);
// // std::cout<<"Point : ";
// // std::cout<<ImRes.get_u()<<" / "<<ImRes.get_v();
// // std::cout<<std::endl;
// vpDisplay::displayPoint(I,ImRes,vpColor::blue,10);
/////////////
//! [Display]
// tracker.display(I, cMo, cam, vpColor::red, 2);
// vpDisplay::displayFrame(I, cMo, cam, 0.05, vpColor::none, 3);
#ifdef VAR_TRESH
}
else
{
std::cout<<"Object detection uncertain in frame : "<<g.getFrameIndex()<<std::endl;
#ifdef HYBRID_MATCHING
//Tentative d'améliorationde la détédction avec ROI
if(track_state == TRACKING || track_state == UNCERTAIN) //Tracker initialisé
{
std::vector<vpImagePoint> roi;
for(unsigned int i=0; i<tracker.getNbPolygon();i++)
{
std::vector<vpImagePoint> temp = tracker.getPolygon(i)->getRoi(cam);
roi.insert(roi.end(), temp.begin(), temp.end());
}
delete rect_roi;
rect_roi = new vpRect(roi);
if (keypoint_detection.matchPoint(I, cam, cMo, error, elapsedTime,NULL,*rect_roi))
{
//Remplace previous result
translation_mem.pop_front();
rotation_mem.pop_front();
translation_mem.push_front(cMo.getTranslationVector());
rotation_mem.push_front(cMo.getThetaUVector());
tvar = varVector(translation_mem);
rvar=varVector(rotation_mem);
if(tvar < tvar_treshold && rvar < rvar_treshold)
{
std::cout<<"Object detection improved : "<<g.getFrameIndex()<<std::endl;
vpDisplay::displayFrame(I, cMo, cam, 0.05, vpColor::none, 3);
track_state = TRACKING;
}
else
{
track_state = UNCERTAIN;
}
}
//BoF BOF
// else if( track_state = WAITING_FOR_INITIALIZATION )
// {
// track_state = UNCERTAIN;
// }
}
#endif
}
#endif
//! [Display]
#ifdef IMATCHING
keypoint_detection.displayMatching(I, IMatching);
#endif
//! [Get RANSAC inliers outliers]
std::vector<vpImagePoint> ransacInliers = keypoint_detection.getRansacInliers();
std::vector<vpImagePoint> ransacOutliers = keypoint_detection.getRansacOutliers();
//! [Get RANSAC inliers outliers]
//! [Display RANSAC inliers]
for (std::vector<vpImagePoint>::const_iterator it = ransacInliers.begin(); it != ransacInliers.end(); ++it) {
vpDisplay::displayCircle(I, *it, 4, vpColor::green);
#ifdef IMATCHING
vpImagePoint imPt(*it);
imPt.set_u(imPt.get_u() + I.getWidth());
imPt.set_v(imPt.get_v() + I.getHeight());
vpDisplay::displayCircle(IMatching, imPt, 4, vpColor::green);
vpDisplay::displayCircle(I, imPt, 4, vpColor::green);
#endif
}
//! [Display RANSAC inliers]
//! [Display RANSAC outliers]
for (std::vector<vpImagePoint>::const_iterator it = ransacOutliers.begin(); it != ransacOutliers.end(); ++it) {
vpDisplay::displayCircle(I, *it, 4, vpColor::red);
#ifdef IMATCHING
vpImagePoint imPt(*it);
imPt.set_u(imPt.get_u() + I.getWidth());
imPt.set_v(imPt.get_v() + I.getHeight());
vpDisplay::displayCircle(IMatching, imPt, 4, vpColor::red);
vpDisplay::displayCircle(I, imPt, 4, vpColor::red);
#endif
}
//! [Display RANSAC outliers]
#ifdef IMATCHING
//! [Display image matching]
keypoint_detection.displayMatching(I, IMatching);
//! [Display image matching]
//! [Display model image matching]
// vpCameraParameters cam2;
// cam2.initPersProjWithoutDistortion(cam.get_px(), cam.get_py(), cam.get_u0() + I.getWidth(),
// cam.get_v0() + I.getHeight());
// tracker.setCameraParameters(cam2);
// tracker.setPose(IMatching, cMo);
// tracker.display(IMatching, cMo, cam2, vpColor::red, 2);
// vpDisplay::displayFrame(IMatching, cMo, cam2, 0.05, vpColor::none, 3);
//! [Display model image matching]
#endif
}
else
{
std::cout<<"Object Lost in frame : "<<g.getFrameIndex()<<std::endl;
#ifdef HYBRID_MATCHING
track_state = UNCERTAIN;
#endif
}
#ifdef HYBRID_MATCHING
if(track_state == TRACKING || track_state == UNCERTAIN) //Tracker intialisé
{
try
{
tracker.track(I);
// tracker.testTracking();
}
catch(...)
{
track_state = LOST;
std::cout<<"Tracker : Object Lost in frame : "<<g.getFrameIndex()<<std::endl;
}
if(track_state !=LOST)
{
tracker.getPose(cMo);
tracker.display(I, cMo, cam, vpColor::blue, 4);
}
}
state_mem.push_front(track_state);
if(state_mem.size()==state_mem_length)
{
std::stringstream title2;
title2 << "Confidence level :"<<computeConfidenceLevel(state_mem);
// vpDisplay::displayText(I, 10, 10, "Confidence Level : "+std::to_string(computeConfidenceLevel(state_mem)), vpColor::red); //C++11
vpDisplay::displayText(I, 10, 10, title2.str().c_str(), vpColor::red);
// std::cout<<"Confidence level :"<<computeConfidenceLevel(state_mem)<<std::endl;
state_mem.pop_back();
}
#endif
vpDisplay::flush(I);
if (vpDisplay::getClick(I, false)) {
click_done = true;
break;
}
// vpDisplay::getClick(I, true);
#ifdef IMATCHING
vpDisplay::displayText(IMatching, 30, 10, "A click to exit.", vpColor::red);
vpDisplay::flush(IMatching);
if (vpDisplay::getClick(IMatching, false)) {
click_done = true;
break;
}
// vpDisplay::getClick(IMatching, true);
#endif
}
// std::cout<<"Moyenne temps execution : "<<time/nb<<std::endl;
// delete rect_roi;
#if defined IMATCHING
if (!click_done)
vpDisplay::getClick(IMatching);
#endif
#ifdef VISP_HAVE_XML2
vpXmlParser::cleanup();
#endif
#if defined(VISP_HAVE_COIN3D) && (COIN_MAJOR_VERSION == 3)
SoDB::finish();
#endif
} catch (const vpException &e) {
std::cout << "Catch an exception: " << e << std::endl;
}
#else
(void)argc;
(void)argv;
std::cout << "Install OpenCV and rebuild ViSP to use this example." << std::endl;
#endif
return 0;
}

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//! \example tutorial-detection-object-mbt2.cpp
#include <visp3/core/vpConfig.h>
#include <visp3/core/vpIoTools.h>
#include <visp3/gui/vpDisplayGDI.h>
#include <visp3/gui/vpDisplayOpenCV.h>
#include <visp3/gui/vpDisplayX.h>
#include <visp3/io/vpVideoReader.h>
#include <visp3/mbt/vpMbGenericTracker.h>
#include <visp3/vision/vpKeyPoint.h>
#if (VISP_HAVE_OPENCV_VERSION >= 0x020400)
void learnCube(const vpImage<unsigned char> &I, vpMbGenericTracker &tracker, vpKeyPoint &keypoint_learning, int id)
{
//! [Keypoints reference detection]
std::vector<cv::KeyPoint> trainKeyPoints;
double elapsedTime;
keypoint_learning.detect(I, trainKeyPoints, elapsedTime);
//! [Keypoints reference detection]
//! [Keypoints selection on faces]
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;
vpHomogeneousMatrix cMo;
tracker.getPose(cMo);
vpCameraParameters cam;
tracker.getCameraParameters(cam);
vpKeyPoint::compute3DForPointsInPolygons(cMo, cam, trainKeyPoints, polygons, roisPt, points3f);
//! [Keypoints selection on faces]
//! [Keypoints build reference]
keypoint_learning.buildReference(I, trainKeyPoints, points3f, true, id);
//! [Keypoints build reference]
//! [Display reference keypoints]
for (std::vector<cv::KeyPoint>::const_iterator it = trainKeyPoints.begin(); it != trainKeyPoints.end(); ++it) {
vpDisplay::displayCross(I, (int)it->pt.y, (int)it->pt.x, 4, vpColor::red);
}
//! [Display reference keypoints]
}
#endif
int main(int argc, char **argv)
{
#if (VISP_HAVE_OPENCV_VERSION >= 0x020400)
//! [MBT code]
try {
std::string videoname = "book.mpeg";
std::string videoname2 = "book2.mpeg";
for (int i = 0; i < argc; i++) {
if (std::string(argv[i]) == "--name")
videoname = std::string(argv[i + 1]);
else if (std::string(argv[i]) == "--help") {
std::cout << "\nUsage: " << argv[0] << " [--name <video name>] [--help]\n" << std::endl;
return 0;
}
}
std::string parentname = vpIoTools::getParent(videoname);
std::string objectname = vpIoTools::getNameWE(videoname);
std::string parentname2 = vpIoTools::getParent(videoname2);
std::string objectname2 = vpIoTools::getNameWE(videoname2);
if (!parentname.empty())
objectname = parentname + "/" + objectname;
if (!parentname2.empty())
objectname2 = parentname2 + "/" + objectname2;
std::cout << "Video name: " << videoname << std::endl;
std::cout << "Tracker requested config files: " << objectname << ".[init,"
#ifdef VISP_HAVE_XML2
<< "xml,"
#endif
<< "cao or wrl]" << std::endl;
std::cout << "Tracker optional config files: " << objectname << ".[ppm]" << std::endl;
vpImage<unsigned char> I;
vpHomogeneousMatrix cMo;
vpCameraParameters cam;
vpImage<unsigned char> I2;
vpHomogeneousMatrix cMo2;
vpMbGenericTracker tracker(vpMbGenericTracker::EDGE_TRACKER);
vpMbGenericTracker tracker2(vpMbGenericTracker::EDGE_TRACKER);
bool usexml = false;
#ifdef VISP_HAVE_XML2
if (vpIoTools::checkFilename(objectname + ".xml")) {
tracker.loadConfigFile(objectname + ".xml");
tracker2.loadConfigFile(objectname + ".xml");
tracker.getCameraParameters(cam);
usexml = true;
}
#endif
tracker.setOgreVisibilityTest(false);
tracker2.setOgreVisibilityTest(false);
if (vpIoTools::checkFilename(objectname + ".cao"))
tracker.loadModel(objectname + ".cao");
else if (vpIoTools::checkFilename(objectname + ".wrl"))
tracker.loadModel(objectname + ".wrl");
tracker.setDisplayFeatures(true);
if (vpIoTools::checkFilename(objectname2 + ".cao"))
tracker2.loadModel(objectname2 + ".cao");
tracker2.setDisplayFeatures(true);
//! [MBT code]
//! [Keypoint declaration]
vpKeyPoint keypoint_learning("ORB", "ORB", "BruteForce-Hamming");
keypoint_learning.setDetectorParameter("ORB", "nLevels", 1);
vpKeyPoint keypoint_learning2("ORB", "ORB", "BruteForce-Hamming");
keypoint_learning2.setDetectorParameter("ORB", "nLevels", 1);
//! [Keypoint declaration]
#if defined(VISP_HAVE_X11)
vpDisplayX display;
#elif defined(VISP_HAVE_GDI)
vpDisplayGDI display;
#elif defined(VISP_HAVE_OPENCV)
vpDisplayOpenCV display;
#else
std::cout << "No image viewer is available..." << std::endl;
return 0;
#endif
/*
* Start the part of the code dedicated to object learning from 3 images
*/
//Acquisition des images d'initialisation
std::string imageName[] = {
"book_training/train_image_000.png",
"book_training/train_image_001.png",
"book_training/train_image_002.png",
"book_training/train_image_003.png",
"book_training/train_image_004.png",
"book_training/train_image_005.png",
"book_training/train_image_007.png",
"book_training/train_image_008.png"};
// vpVideoReader g;
// g.setFileName("book_training/train_image_%03d.png");
std::string imageName2[] = {
"book2_training/train_image_000.png",
"book2_training/train_image_001.png",
"book2_training/train_image_002.png",
"book2_training/train_image_003.png"};
//Acquisition des cMo d'intialisation
std::string cMoName[] = {
"book_training/train_image_000.yml",
"book_training/train_image_001.yml",
"book_training/train_image_002.yml",
"book_training/train_image_003.yml",
"book_training/train_image_004.yml",
"book_training/train_image_005.yml",
"book_training/train_image_007.yml",
"book_training/train_image_008.yml"};
std::string cMoName2[] = {
"book2_training/train_image_000.yml",
"book2_training/train_image_001.yml",
"book2_training/train_image_002.yml",
"book2_training/train_image_003.yml"};
vpHomogeneousMatrix initPoseTab[8];
vpHomogeneousMatrix initPoseTab2[4];
// std::cout<<"Load init cMo"<<std::endl;
// vpArray2D<double>::loadYAML(cMoName[0],initPoseTab[0]);
// initPoseTab[0].print();
// std::fstream file;
// file.open(cMoName2[1].c_str(), std::fstream::in);
// if (!file) {
// file.close();
// return false;
// }
for (int i = 0; i < 8; i++)
{
vpArray2D<double>::loadYAML(cMoName[i],initPoseTab[i]);
std::cout<<cMoName[i]<<" loaded !"<<std::endl;
}
for (int i = 0; i < 4; i++)
{
vpMatrix::loadMatrixYAML(cMoName2[i],initPoseTab2[i]);
std::cout<<cMoName2[i]<<" loaded !"<<std::endl;
}
//Tracker 1
for (int i = 0; i < 8; i++) {
vpImageIo::read(I, imageName[i]);
// if(i==0 || !g.end())
// g.acquire(I);
// else
// std::cout<<"Missing training image !"<<std::endl;
// vpArray2D<double>::loadYAML(cMoName[i],initPoseTab[i]);
if (i == 0) {
display.init(I, 10, 10);
}
std::stringstream title;
title << "Learning cube on image: " << imageName[i];
// title << "Learning cube on image: " << g.getFrameIndex();
vpDisplay::setTitle(I, title.str().c_str());
vpDisplay::display(I);
//! [Set tracker pose]
tracker.setPose(I, initPoseTab[i]);
//! [Set tracker pose]
// std::cout<<"Refine"<<std::endl;
//! [Refine pose]
tracker.track(I);
//! [Refine pose]
// std::cout<<"Display"<<std::endl;
//! [Display tracker pose]
tracker.getPose(cMo);
tracker.display(I, cMo, cam, vpColor::red);
//! [Display tracker pose]
// std::cout<<"Learn"<<std::endl;
//! [Learn cube call]
learnCube(I, tracker, keypoint_learning, i);
//! [Learn cube call]
vpDisplay::displayText(I, 10, 10, "Learning step: keypoints are detected on visible cube faces", vpColor::red);
if (i < 2) {
vpDisplay::displayText(I, 30, 10, "Click to continue the learning...", vpColor::red);
} else {
vpDisplay::displayText(I, 30, 10, "Click to continue with the detection...", vpColor::red);
}
vpDisplay::flush(I);
// vpDisplay::getClick(I, true);
std::cout<<imageName[i]<<" learned !"<<std::endl;
}
// g.close();
//Tracker 2
for (int i = 0; i < 4; i++) {
vpImageIo::read(I, imageName2[i]);
// if(i==0 || !g.end())
// g.acquire(I);
// else
// std::cout<<"Missing training image !"<<std::endl;
// vpArray2D<double>::loadYAML(cMoName[i],initPoseTab[i]);
if (i == 0) {
display.init(I, 10, 10);
}
std::stringstream title;
title << "Learning cube on image: " << imageName2[i];
// title << "Learning cube on image: " << g.getFrameIndex();
vpDisplay::setTitle(I, title.str().c_str());
vpDisplay::display(I);
//! [Set tracker pose]
tracker2.setPose(I, initPoseTab2[i]);
//! [Set tracker pose]
// std::cout<<"Refine"<<std::endl;
//! [Refine pose]
tracker2.track(I);
//! [Refine pose]
// std::cout<<"Display"<<std::endl;
//! [Display tracker pose]
tracker2.getPose(cMo2);
tracker2.display(I, cMo2, cam, vpColor::red);
//! [Display tracker pose]
// std::cout<<"Learn"<<std::endl;
//! [Learn cube call]
learnCube(I, tracker2, keypoint_learning2, i);
//! [Learn cube call]
vpDisplay::displayText(I, 10, 10, "Learning step: keypoints are detected on visible cube faces", vpColor::red);
if (i < 2) {
vpDisplay::displayText(I, 30, 10, "Click to continue the learning...", vpColor::red);
} else {
vpDisplay::displayText(I, 30, 10, "Click to continue with the detection...", vpColor::red);
}
vpDisplay::flush(I);
// vpDisplay::getClick(I, true);
std::cout<<imageName2[i]<<" learned !"<<std::endl;
}
//! [Save learning data]
keypoint_learning.saveLearningData("book_learning_data.bin", true); //BUG ? chargement des data bloqué si on enregistre pas les images
keypoint_learning2.saveLearningData("book2_learning_data.bin", true);
//! [Save learning data]
// std::cout<<"Saved"<<std::endl;
/*
* Start the part of the code dedicated to detection and localization
*/
//! [Init keypoint detection]
vpKeyPoint keypoint_detection("ORB", "ORB", "BruteForce-Hamming");
#if (VISP_HAVE_OPENCV_VERSION < 0x030000)
keypoint_detection.setDetectorParameter("ORB", "nLevels", 1);
#else
cv::Ptr<cv::ORB> orb_detector = keypoint_detection.getDetector("ORB").dynamicCast<cv::ORB>();
orb_detector = keypoint_detection.getDetector("ORB").dynamicCast<cv::ORB>();
if (orb_detector != NULL) {
orb_detector->setNLevels(1);
}
#endif
vpKeyPoint keypoint_detection2("ORB", "ORB", "BruteForce-Hamming");
keypoint_detection2.setDetectorParameter("ORB", "nLevels", 1);
//! [Init keypoint detection]
// std::cout<<"Load"<<std::endl;
//! [Load teabox learning data]
keypoint_detection.loadLearningData("book_learning_data.bin", true);
keypoint_detection2.loadLearningData("book2_learning_data.bin", true);
//! [Load teabox learning data]
// std::cout<<"Loaded"<<std::endl;
//! [Create image matching]
vpImage<unsigned char> IMatching;
keypoint_detection.createImageMatching(I, IMatching);
//! [Create image matching]
videoname = "data_test_duo2/%03d.jpg";
// std::cout<<"Reading : "<<videoname<<std::endl;
vpVideoReader g;
g.setFileName(videoname);
g.open(I);
// std::cout<<"Reading : "<<videoname<<std::endl;
// std::string testName[] = {"train_image_000.png", "train_image_002.png", "train_image_003.png", "train_image_004.png", "train_image_005.png"};
// vpImageIo::read(I, testName[0]);
#if defined VISP_HAVE_X11
vpDisplayX display2;
#elif defined VISP_HAVE_GTK
vpDisplayGTK display2;
#elif defined VISP_HAVE_GDI
vpDisplayGDI display2;
#else
vpDisplayOpenCV display2;
#endif
display2.init(IMatching, 50, 50, "Display matching between learned and current images");
vpDisplay::setTitle(I, "Cube detection and localization");
///// TEST ////
// vpRect* rect_roi = NULL;
// if(rect_roi==NULL)
// rect_roi = new vpRect();
// double time=0, nb=0;
display.init(I, 10, 10);
///////////////
double error;
bool click_done = false;
while (!g.end()) {
// for(unsigned int i = 1; i< 5; i++) {
// vpImageIo::read(I, testName[i]);
// for(unsigned int i=0; i<20;i++)
// {
// if(!g.end())
// g.acquire(I);
// else
// break;
// }
g.acquire(I);
vpDisplay::display(I);
//! [Insert image matching]
keypoint_detection2.insertImageMatching(I, IMatching);
//! [Insert image matching]
vpDisplay::display(IMatching);
vpDisplay::displayText(I, 10, 10, "Detection and localization in process...", vpColor::red);
double elapsedTime;
// ! [Matching and pose estimation]
//Tracker 1
// if (keypoint_detection.matchPoint(I, cam, cMo, error, elapsedTime,NULL,*rect_roi)) {
if (keypoint_detection.matchPoint(I, cam, cMo, error, elapsedTime)) {
//! [Matching and pose estimation]
// std::cout<<"Match !"<<std::endl;
//! [Display]
// tracker.display(I, cMo, cam, vpColor::red, 2);
vpDisplay::displayFrame(I, cMo, cam, 0.05, vpColor::none, 3);
//! [Display]
// keypoint_detection.displayMatching(I, IMatching);
//! [Get RANSAC inliers outliers]
std::vector<vpImagePoint> ransacInliers = keypoint_detection.getRansacInliers();
std::vector<vpImagePoint> ransacOutliers = keypoint_detection.getRansacOutliers();
//! [Get RANSAC inliers outliers]
//! [Display RANSAC inliers]
for (std::vector<vpImagePoint>::const_iterator it = ransacInliers.begin(); it != ransacInliers.end(); ++it) {
vpDisplay::displayCircle(I, *it, 4, vpColor::green);
vpImagePoint imPt(*it);
imPt.set_u(imPt.get_u() + I.getWidth());
imPt.set_v(imPt.get_v() + I.getHeight());
vpDisplay::displayCircle(IMatching, imPt, 4, vpColor::green);
}
//! [Display RANSAC inliers]
//! [Display RANSAC outliers]
for (std::vector<vpImagePoint>::const_iterator it = ransacOutliers.begin(); it != ransacOutliers.end(); ++it) {
vpDisplay::displayCircle(I, *it, 4, vpColor::red);
vpImagePoint imPt(*it);
imPt.set_u(imPt.get_u() + I.getWidth());
imPt.set_v(imPt.get_v() + I.getHeight());
vpDisplay::displayCircle(IMatching, imPt, 4, vpColor::red);
}
//! [Display RANSAC outliers]
//! [Display image matching]
// keypoint_detection.displayMatching(I, IMatching);
//! [Display image matching]
}
else
{
std::cout<<"Object Lost in frame : "<<g.getFrameIndex()<<std::endl;
}
//Tracker 2
if (keypoint_detection2.matchPoint(I, cam, cMo2, error, elapsedTime)) {
//! [Matching and pose estimation]
// std::cout<<"Match !"<<std::endl;
//! [Display]
// tracker.display(I, cMo, cam, vpColor::red, 2);
vpDisplay::displayFrame(I, cMo, cam, 0.05, vpColor::none, 3);
//! [Display]
keypoint_detection2.displayMatching(I, IMatching);
//! [Get RANSAC inliers outliers]
std::vector<vpImagePoint> ransacInliers = keypoint_detection2.getRansacInliers();
std::vector<vpImagePoint> ransacOutliers = keypoint_detection2.getRansacOutliers();
//! [Get RANSAC inliers outliers]
//! [Display RANSAC inliers]
for (std::vector<vpImagePoint>::const_iterator it = ransacInliers.begin(); it != ransacInliers.end(); ++it) {
vpDisplay::displayCircle(I, *it, 4, vpColor::green);
vpImagePoint imPt(*it);
imPt.set_u(imPt.get_u() + I.getWidth());
imPt.set_v(imPt.get_v() + I.getHeight());
vpDisplay::displayCircle(IMatching, imPt, 4, vpColor::green);
}
//! [Display RANSAC inliers]
//! [Display RANSAC outliers]
for (std::vector<vpImagePoint>::const_iterator it = ransacOutliers.begin(); it != ransacOutliers.end(); ++it) {
vpDisplay::displayCircle(I, *it, 4, vpColor::red);
vpImagePoint imPt(*it);
imPt.set_u(imPt.get_u() + I.getWidth());
imPt.set_v(imPt.get_v() + I.getHeight());
vpDisplay::displayCircle(IMatching, imPt, 4, vpColor::red);
}
//! [Display RANSAC outliers]
//! [Display image matching]
keypoint_detection.displayMatching(I, IMatching);
//! [Display image matching]
}
else
{
std::cout<<"Object 2 Lost in frame : "<<g.getFrameIndex()<<std::endl;
}
vpDisplay::flush(I);
vpDisplay::displayText(IMatching, 30, 10, "A click to exit.", vpColor::red);
vpDisplay::flush(IMatching);
if (vpDisplay::getClick(I, false)) {
click_done = true;
break;
}
if (vpDisplay::getClick(IMatching, false)) {
click_done = true;
break;
}
// vpDisplay::getClick(I, true);
// vpDisplay::getClick(IMatching, true);
}
// std::cout<<"Moyenne temps execution : "<<time/nb<<std::endl;
// delete rect_roi;
if (!click_done)
vpDisplay::getClick(IMatching);
#ifdef VISP_HAVE_XML2
vpXmlParser::cleanup();
#endif
#if defined(VISP_HAVE_COIN3D) && (COIN_MAJOR_VERSION == 3)
SoDB::finish();
#endif
} catch (const vpException &e) {
std::cout << "Catch an exception: " << e << std::endl;
}
#else
(void)argc;
(void)argv;
std::cout << "Install OpenCV and rebuild ViSP to use this example." << std::endl;
#endif
return 0;
}

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ongoing/Visp_tests/learn_tests/train_image_001.yml~ Normal file
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Book cMo for training image 001
rows: 4
cols: 4
data:
- [0.9994924983, -0.03180660616, 0.00175657469, -0.1363227211]
- [-0.02176505783, -0.7221308743, -0.6914139735, 0.03040660551]
- [0.02326000876, 0.6910248478, -0.7224566643, 0.4120091688]
- [0, 0, 0, 1]

BIN
ongoing/Visp_tests/learn_tests/tutorial-detection-object-mbt2 Executable file
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ongoing/Visp_tests/learn_tests/tutorial-detection-object-mbt2.cpp Normal file
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//! \example tutorial-detection-object-mbt2.cpp
#include <visp3/core/vpConfig.h>
#include <visp3/core/vpIoTools.h>
#include <visp3/gui/vpDisplayGDI.h>
#include <visp3/gui/vpDisplayOpenCV.h>
#include <visp3/gui/vpDisplayX.h>
#include <visp3/io/vpVideoReader.h>
#include <visp3/mbt/vpMbGenericTracker.h>
#include <visp3/vision/vpKeyPoint.h>
//Options
// #define IMATCHING
#define HYBRID_MATCHING
#define VAR_TRESH
#ifdef HYBRID_MATCHING
enum State
{
WAITING_FOR_INITIALIZATION,
TRACKING,
LOST,
UNCERTAIN //=Tracking OK / Matching FAIL
};
#endif
#if (VISP_HAVE_OPENCV_VERSION >= 0x020400)
void learnCube(const vpImage<unsigned char> &I, vpMbGenericTracker &tracker, vpKeyPoint &keypoint_learning, int id)
{
//! [Keypoints reference detection]
std::vector<cv::KeyPoint> trainKeyPoints;
double elapsedTime;
keypoint_learning.detect(I, trainKeyPoints, elapsedTime);
//! [Keypoints reference detection]
//! [Keypoints selection on faces]
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;
vpHomogeneousMatrix cMo;
tracker.getPose(cMo);
vpCameraParameters cam;
tracker.getCameraParameters(cam);
vpKeyPoint::compute3DForPointsInPolygons(cMo, cam, trainKeyPoints, polygons, roisPt, points3f);
//! [Keypoints selection on faces]
//! [Keypoints build reference]
keypoint_learning.buildReference(I, trainKeyPoints, points3f, true, id);
//! [Keypoints build reference]
//! [Display reference keypoints]
for (std::vector<cv::KeyPoint>::const_iterator it = trainKeyPoints.begin(); it != trainKeyPoints.end(); ++it) {
vpDisplay::displayCross(I, (int)it->pt.y, (int)it->pt.x, 4, vpColor::red);
}
//! [Display reference keypoints]
}
#endif
#ifdef VAR_TRESH
//Compute cummulative variance of a list of vector.
double varVector(std::list< vpArray2D<double> > vector_list)
{
//Attention aux vecteurs vides
double var[3], mean[3];
//Compute means
for (std::list< vpArray2D<double> >::iterator it=vector_list.begin(); it != vector_list.end(); ++it)
{
if((*it).size()!=3)
{
std::cout<<"WARNING varVector : invalid array size"<<std::endl;
return -1;
}
else
{
for(unsigned int j=0; j< 3; j++)
{
mean[j]+=*(*it)[j];
}
}
}
for(unsigned int j=0; j<3; j++)
{
mean[j] /= vector_list.size();
}
//Compute variances
for (std::list< vpArray2D<double> >::iterator it=vector_list.begin(); it != vector_list.end(); ++it)
{
for(unsigned int j=0; j< 3; j++)
{
var[j]+=(*(*it)[j]-mean[j])*(*(*it)[j]-mean[j]);
}
}
for(unsigned int j=0; j<3; j++)
{
var[j] /= vector_list.size();
}
return var[0]+var[1]+var[2];
}
#endif
#ifdef HYBRID_MATCHING
float computeConfidenceLevel(std::list<State> states)
{
float res = 0;
for (std::list<State>::iterator it=states.begin(); it != states.end(); ++it)
{
switch(*it)
{
case TRACKING:
res += 1;
break;
case UNCERTAIN:
res += 0.5;
break;
case LOST:
res -=1;
case WAITING_FOR_INITIALIZATION:
break;
}
}
res = res/states.size();
return res;
}
#endif
int main(int argc, char **argv)
{
#if (VISP_HAVE_OPENCV_VERSION >= 0x020400)
//! [MBT code]
try {
std::string videoname = "book.mpeg";
for (int i = 0; i < argc; i++) {
if (std::string(argv[i]) == "--name")
videoname = std::string(argv[i + 1]);
else if (std::string(argv[i]) == "--help") {
std::cout << "\nUsage: " << argv[0] << " [--name <video name>] [--help]\n" << std::endl;
return 0;
}
}
std::string parentname = vpIoTools::getParent(videoname);
std::string objectname = vpIoTools::getNameWE(videoname);
if (!parentname.empty())
objectname = parentname + "/" + objectname;
std::cout << "Video name: " << videoname << std::endl;
std::cout << "Tracker requested config files: " << objectname << ".[init,"
#ifdef VISP_HAVE_XML2
<< "xml,"
#endif
<< "cao or wrl]" << std::endl;
std::cout << "Tracker optional config files: " << objectname << ".[ppm]" << std::endl;
vpImage<unsigned char> I;
vpHomogeneousMatrix cMo;
vpCameraParameters cam;
vpMbGenericTracker tracker; //Bug lors de la config de type KLT de tracker dans le constructeur.
tracker.setTrackerType(vpMbGenericTracker::EDGE_TRACKER | vpMbGenericTracker::KLT_TRACKER);
bool usexml = false;
#ifdef VISP_HAVE_XML2
if (vpIoTools::checkFilename(objectname + ".xml")) {
tracker.loadConfigFile(objectname + ".xml");
tracker.getCameraParameters(cam);
usexml = true;
}
#endif
if (!usexml) {
vpMe me;
me.setMaskSize(5);
me.setMaskNumber(180);
me.setRange(7);
me.setThreshold(5000);
me.setMu1(0.5);
me.setMu2(0.5);
me.setSampleStep(4);
me.setNbTotalSample(250);
tracker.setMovingEdge(me);
cam.initPersProjWithoutDistortion(547, 542, 339, 235);
tracker.setCameraParameters(cam);
tracker.setAngleAppear(vpMath::rad(89));
tracker.setAngleDisappear(vpMath::rad(89));
tracker.setNearClippingDistance(0.01);
tracker.setFarClippingDistance(10.0);
tracker.setClipping(tracker.getClipping() | vpMbtPolygon::FOV_CLIPPING);
vpKltOpencv klt_settings;
klt_settings.setMaxFeatures(300);
klt_settings.setWindowSize(5);
klt_settings.setQuality(0.015);
klt_settings.setMinDistance(8);
klt_settings.setHarrisFreeParameter(0.01);
klt_settings.setBlockSize(3);
klt_settings.setPyramidLevels(3);
tracker.setKltOpencv(klt_settings);
tracker.setKltMaskBorder(5);
}
tracker.setOgreVisibilityTest(false);
if (vpIoTools::checkFilename(objectname + ".cao"))
tracker.loadModel(objectname + ".cao");
else if (vpIoTools::checkFilename(objectname + ".wrl"))
tracker.loadModel(objectname + ".wrl");
tracker.setDisplayFeatures(true);
//! [MBT code]
//! [Keypoint declaration]
vpKeyPoint keypoint_learning("ORB", "ORB", "BruteForce-Hamming");
#if (VISP_HAVE_OPENCV_VERSION < 0x030000)
keypoint_learning.setDetectorParameter("ORB", "nLevels", 1);
#else
cv::Ptr<cv::ORB> orb_learning = keypoint_learning.getDetector("ORB").dynamicCast<cv::ORB>();
if (orb_learning != NULL) {
orb_learning->setNLevels(1);
}
#endif
//! [Keypoint declaration]
#if defined(VISP_HAVE_X11)
vpDisplayX display;
#elif defined(VISP_HAVE_GDI)
vpDisplayGDI display;
#elif defined(VISP_HAVE_OPENCV)
vpDisplayOpenCV display;
#else
std::cout << "No image viewer is available..." << std::endl;
return 0;
#endif
videoname = "data_test5/%03d.jpg";
/*
* Start the part of the code dedicated to object learning from 3 images
*/
unsigned int init_data_nb = 11;
// vpVideoReader g;
// g.setFileName("book_training/train_image_%03d.png");
std::string trainData[] = {
"book_training/train_image_000",
"book_training/train_image_001",
"book_training/train_image_002",
"book_training/train_image_003",
"book_training/train_image_004",
"book_training/train_image_005",
"book_training/train_image_006",
"book_training/train_image_007",
"book_training/train_image_008",
"book_training/train_image_009",
"book_training/train_image_010"};
// std::string trainData[] = {
// "book2_training/train_image_000",
// "book2_training/train_image_001",
// "book2_training/train_image_002",
// "book2_training/train_image_003"};
// std::string trainData[] = {
// "box_training/train_image_000",
// "box_training/train_image_001",
// "box_training/train_image_002",
// "box_training/train_image_003",
// "box_training/train_image_004",
// "box_training/train_image_005"};
vpHomogeneousMatrix initPoseTab[init_data_nb];
// std::cout<<"Load init cMo"<<std::endl;
// vpArray2D<double>::loadYAML(cMoName[0],initPoseTab[0]);
// initPoseTab[0].print();
//Acquisition des cMo d'intialisation
for (int i = 0; i < init_data_nb; i++)
{
// std::cout<<i<<std::endl;
vpArray2D<double>::loadYAML(trainData[i]+".yml",initPoseTab[i]);
}
for (int i = 0; i < init_data_nb; i++) {
//Acquisition des images d'initialisation
vpImageIo::read(I, trainData[i]+".png");
// if(i==0 || !g.end())
// g.acquire(I);
// else
// std::cout<<"Missing training image !"<<std::endl;
// vpArray2D<double>::loadYAML(cMoName[i],initPoseTab[i]);
if (i == 0) {
display.init(I, 10, 10);
}
std::stringstream title;
title << "Learning cube on image: " << trainData[i];
// title << "Learning cube on image: " << g.getFrameIndex();
vpDisplay::setTitle(I, title.str().c_str());
vpDisplay::display(I);
//! [Set tracker pose]
// tracker.setPose(I, initPoseTab[i]);
tracker.initFromPose(I,initPoseTab[i]);
//! [Set tracker pose]
// std::cout<<"Refine"<<std::endl;
//! [Refine pose]
tracker.track(I);
//! [Refine pose]
// std::cout<<"Display"<<std::endl;
//! [Display tracker pose]
tracker.getPose(cMo);
tracker.display(I, cMo, cam, vpColor::red);
//! [Display tracker pose]
// std::cout<<"Learn"<<std::endl;
//! [Learn cube call]
learnCube(I, tracker, keypoint_learning, i);
//! [Learn cube call]
vpDisplay::displayText(I, 10, 10, "Learning step: keypoints are detected on visible cube faces", vpColor::red);
if (i < 2) {
vpDisplay::displayText(I, 30, 10, "Click to continue the learning...", vpColor::red);
} else {
vpDisplay::displayText(I, 30, 10, "Click to continue with the detection...", vpColor::red);
}
vpDisplay::flush(I);
// vpDisplay::getClick(I, true);
std::cout<<trainData[i]<<" learned !"<<std::endl;
}
// g.close();
//! [Save learning data]
#ifdef IMATCHING
keypoint_learning.saveLearningData("book_learning_data.bin", true, true); //+sauvegarde des images d'entrainement pour l'affichage
#else
keypoint_learning.saveLearningData("book_learning_data.bin", true, false);
#endif
//! [Save learning data]
/*
* Start the part of the code dedicated to detection and localization
*/
//! [Init keypoint detection]
vpKeyPoint keypoint_detection("ORB", "ORB", "BruteForce-Hamming");
#if (VISP_HAVE_OPENCV_VERSION < 0x030000)
keypoint_detection.setDetectorParameter("ORB", "nLevels", 1);
#else
cv::Ptr<cv::ORB> orb_detector = keypoint_detection.getDetector("ORB").dynamicCast<cv::ORB>();
orb_detector = keypoint_detection.getDetector("ORB").dynamicCast<cv::ORB>();
if (orb_detector != NULL) {
orb_detector->setNLevels(1);
}
#endif
//! [Init keypoint detection]
//! [Load teabox learning data]
keypoint_detection.loadLearningData("book_learning_data.bin", true);
//! [Load teabox learning data]
#ifdef IMATCHING
//! [Create image matching]
vpImage<unsigned char> IMatching;
keypoint_detection.createImageMatching(I, IMatching);
//! [Create image matching]
#endif
// std::cout<<"Reading : "<<videoname<<std::endl;
vpVideoReader g;
g.setFileName(videoname);
g.open(I);
#if defined VISP_HAVE_X11
vpDisplayX display2;
#elif defined VISP_HAVE_GTK
vpDisplayGTK display2;
#elif defined VISP_HAVE_GDI
vpDisplayGDI display2;
#else
vpDisplayOpenCV display2;
#endif
#ifdef IMATCHING
display2.init(IMatching, 50, 50, "Display matching between learned and current images");
#endif
vpDisplay::setTitle(I, "Cube detection and localization");
///// TEST ////
// if(rect_roi==NULL)
// rect_roi = new vpRect();
// double time=0, nb=0;
// display.init(I, 10, 10);
#ifdef VAR_TRESH
//Var Treshold
std::list< vpArray2D<double> > translation_mem;
std::list< vpArray2D<double> > rotation_mem;
unsigned int var_mem_length = 15; // + = stabilité / - = rapidité
double tvar_treshold = 0.3;
double rvar_treshold = 0.5;
double tvar, rvar;
//Var Treshold
#endif
#ifdef HYBRID_MATCHING
vpRect* rect_roi = new vpRect();
State track_state = WAITING_FOR_INITIALIZATION;
std::list<State> state_mem;
unsigned int state_mem_length = 10;
#endif
///////////////
double error;
bool click_done = false;
while (!g.end()) {
g.acquire(I);
vpDisplay::display(I);
#ifdef IMATCHING
//! [Insert image matching]
keypoint_detection.insertImageMatching(I, IMatching);
//! [Insert image matching]
vpDisplay::display(IMatching);
#endif
// vpDisplay::displayText(I, 10, 10, "Detection and localization in process...", vpColor::red);
double elapsedTime;
// ! [Matching and pose estimation]
// if (keypoint_detection.matchPoint(I, cam, cMo, error, elapsedTime,NULL,*rect_roi)) {
if (keypoint_detection.matchPoint(I, cam, cMo, error, elapsedTime)) {
//! [Matching and pose estimation]
// std::cout<<"Match !"<<std::endl;
// std::cout<<"T :"<<std::endl<<cMo.getTranslationVector()<<std::endl<<"R :"<<std::endl<<cMo.getThetaUVector()<<std::endl;
#ifdef VAR_TRESH
//Var Treshold
translation_mem.push_front(cMo.getTranslationVector());
rotation_mem.push_front(cMo.getThetaUVector());
if(translation_mem.size()==var_mem_length)
{
// tvar = varVector(translation_mem);
// std::cout<<"Var trans : "<<tvar<<std::endl;
translation_mem.pop_back();
}
if(rotation_mem.size()==var_mem_length)
{
// rvar=varVector(rotation_mem);
// std::cout<<"Var rot : "<<rvar<<std::endl;
rotation_mem.pop_back();
}
tvar = varVector(translation_mem);
rvar=varVector(rotation_mem);
if(tvar < tvar_treshold && rvar < rvar_treshold)
{
//Var Treshold
#endif
#ifdef HYBRID_MATCHING
//Init ou Reinit du tracker après perte ou après incertitude
if(track_state != TRACKING || (tvar<0.1 && rvar<0.1))
{
tracker.initFromPose(I,cMo);
track_state = TRACKING;
}
#endif
//! [Tracker set pose]
// tracker.setPose(I, cMo);
//! [Tracker set pose]
/////TEST/////
// time+=elapsedTime; nb++;
// std::vector<vpImagePoint> roi;
// for(unsigned int i=0; i<tracker.getNbPolygon();i++)
// {
// std::vector<vpImagePoint> temp = tracker.getPolygon(i)->getRoi(cam);
// roi.insert(roi.end(), temp.begin(), temp.end());
// }
// delete rect_roi;
// rect_roi = new vpRect(roi);
//Affichage de l'origine pointée par cMo
// vpPoint origine;
// origine.setWorldCoordinates(0,0,0) ;
// // std::cout <<"Origne (o):"<< origine.oP << std::endl ;
// origine.changeFrame(cMo) ;
// // std::cout << "Origine (c) : "<< origine.cP << std::endl ;
// origine.project(cMo);
// // std::cout << "Origine (2D) : "<< origine.p << std::endl ;
// vpImagePoint ImRes;
// vpMeterPixelConversion::convertPoint(cam,origine.p[0],origine.p[1],ImRes);
// // std::cout<<"Point : ";
// // std::cout<<ImRes.get_u()<<" / "<<ImRes.get_v();
// // std::cout<<std::endl;
// vpDisplay::displayPoint(I,ImRes,vpColor::blue,10);
/////////////
//! [Display]
// tracker.display(I, cMo, cam, vpColor::red, 2);
vpDisplay::displayFrame(I, cMo, cam, 0.05, vpColor::none, 3);
#ifdef VAR_TRESH
}
else
{
std::cout<<"Object detection uncertain in frame : "<<g.getFrameIndex()<<std::endl;
#ifdef HYBRID_MATCHING
//Tentative d'améliorationde la détédction avec ROI
if(track_state == TRACKING || track_state == UNCERTAIN) //Tracker initialisé
{
std::vector<vpImagePoint> roi;
for(unsigned int i=0; i<tracker.getNbPolygon();i++)
{
std::vector<vpImagePoint> temp = tracker.getPolygon(i)->getRoi(cam);
roi.insert(roi.end(), temp.begin(), temp.end());
}
delete rect_roi;
rect_roi = new vpRect(roi);
if (keypoint_detection.matchPoint(I, cam, cMo, error, elapsedTime,NULL,*rect_roi))
{
//Remplace previous result
translation_mem.pop_front();
rotation_mem.pop_front();
translation_mem.push_front(cMo.getTranslationVector());
rotation_mem.push_front(cMo.getThetaUVector());
tvar = varVector(translation_mem);
rvar=varVector(rotation_mem);
if(tvar < tvar_treshold && rvar < rvar_treshold)
{
std::cout<<"Object detection improved : "<<g.getFrameIndex()<<std::endl;
vpDisplay::displayFrame(I, cMo, cam, 0.05, vpColor::none, 3);
track_state = TRACKING;
}
else
{
track_state = UNCERTAIN;
}
}
//BoF BOF
// else if( track_state = WAITING_FOR_INITIALIZATION )
// {
// track_state = UNCERTAIN;
// }
}
#endif
}
#endif
//! [Display]
// unsigned int nbMatch = keypoint_detection.matchPoint(I);
// std::cout<<"Nb KeyPoint : "<<nbMatch<<std::endl;
// if(nbMatch > 20)
// {
#ifdef IMATCHING
keypoint_detection.displayMatching(I, IMatching);
#endif
//! [Get RANSAC inliers outliers]
std::vector<vpImagePoint> ransacInliers = keypoint_detection.getRansacInliers();
std::vector<vpImagePoint> ransacOutliers = keypoint_detection.getRansacOutliers();
//! [Get RANSAC inliers outliers]
//! [Display RANSAC inliers]
for (std::vector<vpImagePoint>::const_iterator it = ransacInliers.begin(); it != ransacInliers.end(); ++it) {
vpDisplay::displayCircle(I, *it, 4, vpColor::green);
#ifdef IMATCHING
vpImagePoint imPt(*it);
imPt.set_u(imPt.get_u() + I.getWidth());
imPt.set_v(imPt.get_v() + I.getHeight());
vpDisplay::displayCircle(IMatching, imPt, 4, vpColor::green);
#endif
}
//! [Display RANSAC inliers]
//! [Display RANSAC outliers]
for (std::vector<vpImagePoint>::const_iterator it = ransacOutliers.begin(); it != ransacOutliers.end(); ++it) {
vpDisplay::displayCircle(I, *it, 4, vpColor::red);
#ifdef IMATCHING
vpImagePoint imPt(*it);
imPt.set_u(imPt.get_u() + I.getWidth());
imPt.set_v(imPt.get_v() + I.getHeight());
vpDisplay::displayCircle(IMatching, imPt, 4, vpColor::red);
#endif
}
//! [Display RANSAC outliers]
#ifdef IMATCHING
//! [Display image matching]
keypoint_detection.displayMatching(I, IMatching);
//! [Display image matching]
//! [Display model image matching]
// vpCameraParameters cam2;
// cam2.initPersProjWithoutDistortion(cam.get_px(), cam.get_py(), cam.get_u0() + I.getWidth(),
// cam.get_v0() + I.getHeight());
// tracker.setCameraParameters(cam2);
// tracker.setPose(IMatching, cMo);
// tracker.display(IMatching, cMo, cam2, vpColor::red, 2);
// vpDisplay::displayFrame(IMatching, cMo, cam2, 0.05, vpColor::none, 3);
//! [Display model image matching]
#endif
}
else
{
std::cout<<"Object Lost in frame : "<<g.getFrameIndex()<<std::endl;
#ifdef HYBRID_MATCHING
track_state = UNCERTAIN;
#endif
}
#ifdef HYBRID_MATCHING
if(track_state == TRACKING || track_state == UNCERTAIN) //Tracker intialisé
{
try
{
tracker.track(I);
// tracker.testTracking();
}
catch(...)
{
track_state = LOST;
std::cout<<"Tracker : Object Lost in frame : "<<g.getFrameIndex()<<std::endl;
}
if(track_state !=LOST)
{
tracker.getPose(cMo);
tracker.display(I, cMo, cam, vpColor::blue, 4);
}
}
state_mem.push_front(track_state);
if(state_mem.size()==state_mem_length)
{
std::stringstream title2;
title2 << "Confidence level :"<<computeConfidenceLevel(state_mem);
// vpDisplay::displayText(I, 10, 10, "Confidence Level : "+std::to_string(computeConfidenceLevel(state_mem)), vpColor::red); //C++11
vpDisplay::displayText(I, 10, 10, title2.str().c_str(), vpColor::red);
// std::cout<<"Confidence level :"<<computeConfidenceLevel(state_mem)<<std::endl;
state_mem.pop_back();
}
#endif
vpDisplay::flush(I);
if (vpDisplay::getClick(I, false)) {
click_done = true;
break;
}
// vpDisplay::getClick(I, true);
#ifdef IMATCHING
vpDisplay::displayText(IMatching, 30, 10, "A click to exit.", vpColor::red);
vpDisplay::flush(IMatching);
if (vpDisplay::getClick(IMatching, false)) {
click_done = true;
break;
}
// vpDisplay::getClick(IMatching, true);
#endif
}
// std::cout<<"Moyenne temps execution : "<<time/nb<<std::endl;
// delete rect_roi;
#if defined IMATCHING
if (!click_done)
vpDisplay::getClick(IMatching);
#endif
#ifdef VISP_HAVE_XML2
vpXmlParser::cleanup();
#endif
#if defined(VISP_HAVE_COIN3D) && (COIN_MAJOR_VERSION == 3)
SoDB::finish();
#endif
} catch (const vpException &e) {
std::cout << "Catch an exception: " << e << std::endl;
}
#else
(void)argc;
(void)argv;
std::cout << "Install OpenCV and rebuild ViSP to use this example." << std::endl;
#endif
return 0;
}