/* Normalizes a histogram.
* sum over all elements of the histogram will be equal to 1.0
*/
inline std::vector<double> normalize_histogram( const std::vector<size_t>& in )
{
std::vector<double> result;
if ( in.empty() )
return result;
size_t count = std::accumulate( in.begin(), in.end(), size_t() );
return normalize_histogram( in, count );
}
/**
gets observations (histograms) for sampled particles and writes them into an array without receiving a new frame
D E B U G F U N C T I O N
@param sampled_particles: pointer to array with sampled particles
@param number_of_particles: number of particles
@param observations: pointer to array of observations (histograms)
*/
void get_observations_without_new_frame ( particle * sampled_particles, int number_of_particles, histogram * observations) {
int i,x1,x2,y1,y2;
histogram * tmp = observations;
// 1) read frame
read_frame();
// 2) create hsv image
convert_bgr2hsv();
// 3) for every particle: calculate histogram
for (i=0; i<number_of_particles; i++) {
// calculate start and end position of region
x1 = (sampled_particles->x / PF_GRANULARITY) - ((sampled_particles->s * (( sampled_particles->width - 1) / 2)) / PF_GRANULARITY);
x2 = (sampled_particles->x / PF_GRANULARITY) + ((sampled_particles->s * (( sampled_particles->width - 1) / 2)) / PF_GRANULARITY);
y1 = (sampled_particles->y / PF_GRANULARITY) - ((sampled_particles->s * (( sampled_particles->height - 1) / 2)) / PF_GRANULARITY);
y2 = (sampled_particles->y / PF_GRANULARITY) + ((sampled_particles->s * (( sampled_particles->height - 1) / 2)) / PF_GRANULARITY);
// correct positions, if needed
if (x1<0) {
x1 = 0;
}
if (y1<0) {
y1 = 0;
}
if (x2>SIZE_X-1) {
x2 = SIZE_X - 1;
}
if (y2>SIZE_Y-1) {
y2 = SIZE_Y - 1;
}
// calculate histogram
calc_histogram(hsvImage, x1, y1, x2, y2, tmp);
//calc_histogram(x1, y1, x2, y2, tmp);
//4) normalize histogram
normalize_histogram (tmp);
// next histogram
tmp++;
sampled_particles++;
}
}
histogram* calc_histogram( PointCloudT::Ptr& cloud )
{
histogram* histo;
float* hist;
int bin;
float h = 0, s = 0, v = 0;
histo = (histogram*)malloc( sizeof(histogram) );
histo->n = NH*NS + NV;
hist = histo->histo;
memset( hist, 0, histo->n * sizeof(float) );
for( int i = 0; i < cloud->points.size(); i++ )
{
rgb2hsv( (int)cloud->points[i].r, (int)cloud->points[i].g, (int)cloud->points[i].b, h, s, v );
bin = histo_bin( h, s, v );
hist[bin] += 1;
}
normalize_histogram( histo );
return histo;
}
int main( int argc, char** argv )
{
IplImage* hsv_img;
IplImage** hsv_ref_imgs;
IplImage* l32f, * l;
histogram* ref_histo;
double max;
int i;
arg_parse( argc, argv );
/* compute HSV histogram over all reference image */
hsv_img = bgr2hsv( in_img );
hsv_ref_imgs = (IplImage**)malloc( num_ref_imgs * sizeof( IplImage* ) );
for( i = 0; i < num_ref_imgs; i++ )
hsv_ref_imgs[i] = bgr2hsv( ref_imgs[i] );
ref_histo = calc_histogram( hsv_ref_imgs, num_ref_imgs );
normalize_histogram( ref_histo );
/* compute likelihood at every pixel in input image */
fprintf( stderr, "Computing likelihood... " );
fflush( stderr );
l32f = likelihood_image( hsv_img, ref_imgs[0]->width,
ref_imgs[0]->height, ref_histo );
fprintf( stderr, "done\n");
/* convert likelihood image to uchar and display */
cvMinMaxLoc( l32f, NULL, &max, NULL, NULL, NULL );
l = cvCreateImage( cvGetSize( l32f ), IPL_DEPTH_8U, 1 );
cvConvertScale( l32f, l, 255.0 / max, 0 );
cvNamedWindow( "likelihood", 1 );
cvShowImage( "likelihood", l );
cvNamedWindow( "image", 1 );
cvShowImage( "image", in_img );
cvWaitKey(0);
}
/** @brief MATLAB Driver.
**/
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
int M,N,S=0,smin=0,K,num_levels=0 ;
const int* dimensions ;
const double* P_pt ;
const double* G_pt ;
float* descr_pt ;
float* buffer_pt ;
float sigma0 ;
float magnif = 3.0f ; /* Spatial bin extension factor. */
int NBP = 4 ; /* Number of bins for one spatial direction (even). */
int NBO = 8 ; /* Number of bins for the ortientation. */
int mode = NOSCALESPACE ;
int buffer_size=0;
enum {IN_G=0,IN_P,IN_SIGMA0,IN_S,IN_SMIN} ;
enum {OUT_L=0} ;
/* ------------------------------------------------------------------
** Check the arguments
** --------------------------------------------------------------- */
if (nin < 3) {
mexErrMsgTxt("At least three arguments are required") ;
} else if (nout > 1) {
mexErrMsgTxt("Too many output arguments.");
}
if( !uIsRealScalar(in[IN_SIGMA0]) ) {
mexErrMsgTxt("SIGMA0 should be a real scalar") ;
}
if(!mxIsDouble(in[IN_G]) ||
mxGetNumberOfDimensions(in[IN_G]) > 3) {
mexErrMsgTxt("G should be a real matrix or 3-D array") ;
}
sigma0 = (float) *mxGetPr(in[IN_SIGMA0]) ;
dimensions = mxGetDimensions(in[IN_G]) ;
M = dimensions[0] ;
N = dimensions[1] ;
G_pt = mxGetPr(in[IN_G]) ;
P_pt = mxGetPr(in[IN_P]) ;
K = mxGetN(in[IN_P]) ;
if( !uIsRealMatrix(in[IN_P],-1,-1)) {
mexErrMsgTxt("P should be a real matrix") ;
}
if ( mxGetM(in[IN_P]) == 4) {
/* Standard (scale space) mode */
mode = SCALESPACE ;
num_levels = dimensions[2] ;
if(nin < 5) {
mexErrMsgTxt("Five arguments are required in standard mode") ;
}
if( !uIsRealScalar(in[IN_S]) ) {
mexErrMsgTxt("S should be a real scalar") ;
}
if( !uIsRealScalar(in[IN_SMIN]) ) {
mexErrMsgTxt("SMIN should be a real scalar") ;
}
if( !uIsRealMatrix(in[IN_P],4,-1)) {
mexErrMsgTxt("When the e mode P should be a 4xK matrix.") ;
}
S = (int)(*mxGetPr(in[IN_S])) ;
smin = (int)(*mxGetPr(in[IN_SMIN])) ;
} else if ( mxGetM(in[IN_P]) == 3 ) {
mode = NOSCALESPACE ;
num_levels = 1 ;
S = 1 ;
smin = 0 ;
} else {
mexErrMsgTxt("P should be either a 3xK or a 4xK matrix.") ;
}
/* Parse the property-value pairs */
{
char str [80] ;
int arg = (mode == SCALESPACE) ? IN_SMIN + 1 : IN_SIGMA0 + 1 ;
while(arg < nin) {
int k ;
if( !uIsString(in[arg],-1) ) {
mexErrMsgTxt("The first argument in a property-value pair"
" should be a string") ;
}
mxGetString(in[arg], str, 80) ;
#ifdef WINDOWS
for(k = 0 ; properties[k] && strcmpi(str, properties[k]) ; ++k) ;
#else
for(k = 0 ; properties[k] && strcasecmp(str, properties[k]) ; ++k) ;
#endif
switch (k) {
case PROP_NBP:
if( !uIsRealScalar(in[arg+1]) ) {
mexErrMsgTxt("'NumSpatialBins' should be real scalar") ;
}
NBP = (int) *mxGetPr(in[arg+1]) ;
if( NBP <= 0 || (NBP & 0x1) ) {
mexErrMsgTxt("'NumSpatialBins' must be positive and even") ;
}
break ;
case PROP_NBO:
if( !uIsRealScalar(in[arg+1]) ) {
mexErrMsgTxt("'NumOrientBins' should be a real scalar") ;
}
NBO = (int) *mxGetPr(in[arg+1]) ;
if( NBO <= 0 ) {
mexErrMsgTxt("'NumOrientlBins' must be positive") ;
}
break ;
case PROP_MAGNIF:
if( !uIsRealScalar(in[arg+1]) ) {
mexErrMsgTxt("'Magnif' should be a real scalar") ;
}
magnif = (float) *mxGetPr(in[arg+1]) ;
if( magnif <= 0 ) {
mexErrMsgTxt("'Magnif' must be positive") ;
}
break ;
case PROP_UNKNOWN:
mexErrMsgTxt("Property unknown.") ;
break ;
}
arg += 2 ;
}
}
/* -----------------------------------------------------------------
* Pre-compute gradient and angles
* -------------------------------------------------------------- */
/* Alloc two buffers and make sure their size is multiple of 128 for
* better alignment (used also by the Altivec code below.)
*/
buffer_size = (M*N*num_levels + 0x7f) & (~ 0x7f) ;
buffer_pt = (float*) mxMalloc( sizeof(float) * 2 * buffer_size ) ;
descr_pt = (float*) mxCalloc( NBP*NBP*NBO*K, sizeof(float) ) ;
{
/* Offsets to move in the scale space. */
const int yo = 1 ;
const int xo = M ;
const int so = M*N ;
int x,y,s ;
#define at(x,y) (*(pt + (x)*xo + (y)*yo))
/* Compute the gradient */
for(s = 0 ; s < num_levels ; ++s) {
const double* pt = G_pt + s*so ;
for(x = 1 ; x < N-1 ; ++x) {
for(y = 1 ; y < M-1 ; ++y) {
float Dx = 0.5 * ( at(x+1,y) - at(x-1,y) ) ;
float Dy = 0.5 * ( at(x,y+1) - at(x,y-1) ) ;
buffer_pt[(x*xo+y*yo+s*so) + 0 ] = Dx ;
buffer_pt[(x*xo+y*yo+s*so) + buffer_size] = Dy ;
}
}
}
/* Compute angles and modules */
{
float* pt = buffer_pt ;
int j = 0 ;
while (j < N*M*num_levels) {
#if defined( MACOSX ) && defined( __ALTIVEC__ )
if( ((unsigned int)pt & 0x7) == 0 && j+3 < N*M*num_levels ) {
/* If aligned to 128 bit and there are at least 4 pixels left */
float4 a, b, c, d ;
a.vec = vec_ld(0,(vector float*)(pt )) ;
b.vec = vec_ld(0,(vector float*)(pt + buffer_size)) ;
c.vec = vatan2f(b.vec,a.vec) ;
a.x[0] = a.x[0]*a.x[0]+b.x[0]*b.x[0] ;
a.x[1] = a.x[1]*a.x[1]+b.x[1]*b.x[1] ;
a.x[2] = a.x[2]*a.x[2]+b.x[2]*b.x[2] ;
a.x[3] = a.x[3]*a.x[3]+b.x[3]*b.x[3] ;
d.vec = vsqrtf(a.vec) ;
vec_st(c.vec,0,(vector float*)(pt + buffer_size)) ;
vec_st(d.vec,0,(vector float*)(pt )) ;
j += 4 ;
pt += 4 ;
} else {
#endif
float Dx = *(pt ) ;
float Dy = *(pt + buffer_size) ;
*(pt ) = sqrtf(Dx*Dx + Dy*Dy) ;
if (*pt > 0)
*(pt + buffer_size) = atan2f(Dy, Dx) ;
else
*(pt + buffer_size) = 0 ;
j += 1 ;
pt += 1 ;
#if defined( MACOSX ) && defined( __ALTIVEC__ )
}
#endif
}
}
}
/* -----------------------------------------------------------------
* Do the job
* -------------------------------------------------------------- */
if(K > 0) {
int p ;
/* Offsets to move in the buffer */
const int yo = 1 ;
const int xo = M ;
const int so = M*N ;
/* Offsets to move in the descriptor. */
/* Use Lowe's convention. */
const int binto = 1 ;
const int binyo = NBO * NBP ;
const int binxo = NBO ;
const int bino = NBO * NBP * NBP ;
for(p = 0 ; p < K ; ++p, descr_pt += bino) {
/* The SIFT descriptor is a three dimensional histogram of the position
* and orientation of the gradient. There are NBP bins for each spatial
* dimesions and NBO bins for the orientation dimesion, for a total of
* NBP x NBP x NBO bins.
*
* The support of each spatial bin has an extension of SBP = 3sigma
* pixels, where sigma is the scale of the keypoint. Thus all the bins
* together have a support SBP x NBP pixels wide . Since weighting and
* interpolation of pixel is used, another half bin is needed at both
* ends of the extension. Therefore, we need a square window of SBP x
* (NBP + 1) pixels. Finally, since the patch can be arbitrarly rotated,
* we need to consider a window 2W += sqrt(2) x SBP x (NBP + 1) pixels
* wide.
*/
const float x = (float) *P_pt++ ;
const float y = (float) *P_pt++ ;
const float s = (float) (mode == SCALESPACE) ? (*P_pt++) : 0.0 ;
const float theta0 = (float) *P_pt++ ;
const float st0 = sinf(theta0) ;
const float ct0 = cosf(theta0) ;
const int xi = (int) floor(x+0.5) ; /* Round-off */
const int yi = (int) floor(y+0.5) ;
const int si = (int) floor(s+0.5) - smin ;
const float sigma = sigma0 * powf(2, s / S) ;
const float SBP = magnif * sigma ;
const int W = (int) floor( sqrt(2.0) * SBP * (NBP + 1) / 2.0 + 0.5) ;
int bin ;
int dxi ;
int dyi ;
const float* pt ;
float* dpt ;
/* Check that keypoints are within bounds . */
if(xi < 0 ||
xi > N-1 ||
yi < 0 ||
yi > M-1 ||
((mode==SCALESPACE) &&
(si < 0 ||
si > dimensions[2]-1) ) )
continue ;
/* Center the scale space and the descriptor on the current keypoint.
* Note that dpt is pointing to the bin of center (SBP/2,SBP/2,0).
*/
pt = buffer_pt + xi*xo + yi*yo + si*so ;
dpt = descr_pt + (NBP/2) * binyo + (NBP/2) * binxo ;
#define atd(dbinx,dbiny,dbint) (*(dpt + (dbint)*binto + (dbiny)*binyo + (dbinx)*binxo))
/*
* Process each pixel in the window and in the (1,1)-(M-1,N-1)
* rectangle.
*/
for(dxi = max(-W, 1-xi) ; dxi <= min(+W, N-2-xi) ; ++dxi) {
for(dyi = max(-W, 1-yi) ; dyi <= min(+W, M-2-yi) ; ++dyi) {
/* Compute the gradient. */
float mod = *(pt + dxi*xo + dyi*yo + 0 ) ;
float angle = *(pt + dxi*xo + dyi*yo + buffer_size ) ;
#ifdef LOWE_COMPATIBLE
float theta = fast_mod(-angle + theta0) ;
#else
float theta = fast_mod(angle - theta0) ;
#endif
/* Get the fractional displacement. */
float dx = ((float)(xi+dxi)) - x;
float dy = ((float)(yi+dyi)) - y;
/* Get the displacement normalized w.r.t. the keypoint orientation
* and extension. */
float nx = ( ct0 * dx + st0 * dy) / SBP ;
float ny = (-st0 * dx + ct0 * dy) / SBP ;
float nt = NBO * theta / (2*M_PI) ;
/* Get the gaussian weight of the sample. The gaussian window
* has a standard deviation of NBP/2. Note that dx and dy are in
* the normalized frame, so that -NBP/2 <= dx <= NBP/2. */
const float wsigma = NBP/2 ;
float win = expf(-(nx*nx + ny*ny)/(2.0 * wsigma * wsigma)) ;
/* The sample will be distributed in 8 adijacient bins.
* Now we get the ``lower-left'' bin. */
int binx = fast_floor( nx - 0.5 ) ;
int biny = fast_floor( ny - 0.5 ) ;
int bint = fast_floor( nt ) ;
float rbinx = nx - (binx+0.5) ;
float rbiny = ny - (biny+0.5) ;
float rbint = nt - bint ;
int dbinx ;
int dbiny ;
int dbint ;
/* Distribute the current sample into the 8 adijacient bins. */
for(dbinx = 0 ; dbinx < 2 ; ++dbinx) {
for(dbiny = 0 ; dbiny < 2 ; ++dbiny) {
for(dbint = 0 ; dbint < 2 ; ++dbint) {
if( binx+dbinx >= -(NBP/2) &&
binx+dbinx < (NBP/2) &&
biny+dbiny >= -(NBP/2) &&
biny+dbiny < (NBP/2) ) {
float weight = win
* mod
* fabsf(1 - dbinx - rbinx)
* fabsf(1 - dbiny - rbiny)
* fabsf(1 - dbint - rbint) ;
atd(binx+dbinx, biny+dbiny, (bint+dbint) % NBO) += weight ;
}
}
}
}
}
}
{
/* Normalize the histogram to L2 unit length. */
normalize_histogram(descr_pt, descr_pt + NBO*NBP*NBP) ;
/* Truncate at 0.2. */
for(bin = 0; bin < NBO*NBP*NBP ; ++bin) {
if (descr_pt[bin] > 0.2) descr_pt[bin] = 0.2;
}
/* Normalize again. */
normalize_histogram(descr_pt, descr_pt + NBO*NBP*NBP) ;
}
}
}
/* Restore pointer to the beginning of the descriptors. */
descr_pt -= NBO*NBP*NBP*K ;
{
int k ;
double* L_pt ;
out[OUT_L] = mxCreateDoubleMatrix(NBP*NBP*NBO, K, mxREAL) ;
L_pt = mxGetPr(out[OUT_L]) ;
for(k = 0 ; k < NBP*NBP*NBO*K ; ++k) {
L_pt[k] = descr_pt[k] ;
}
}
mxFree(descr_pt) ;
mxFree(buffer_pt) ;
}
int main (int argc, char** argv)
{
//ROS Initialization
ros::init(argc, argv, "detecting_people");
ros::NodeHandle nh;
ros::Rate rate(13);
ros::Subscriber state_sub = nh.subscribe("followme_state", 5, &stateCallback);
ros::Publisher people_pub = nh.advertise<frmsg::people>("followme_people", 5);
frmsg::people pub_people_;
CloudConverter* cc_ = new CloudConverter();
while (!cc_->ready_xyzrgb_)
{
ros::spinOnce();
rate.sleep();
if (!ros::ok())
{
printf("Terminated by Control-c.\n");
return -1;
}
}
// Input parameter from the .yaml
std::string package_path_ = ros::package::getPath("detecting_people") + "/";
cv::FileStorage* fs_ = new cv::FileStorage(package_path_ + "parameters.yml", cv::FileStorage::READ);
// Algorithm parameters:
std::string svm_filename = package_path_ + "trainedLinearSVMForPeopleDetectionWithHOG.yaml";
std::cout << svm_filename << std::endl;
float min_confidence = -1.5;
float min_height = 1.3;
float max_height = 2.3;
float voxel_size = 0.06;
Eigen::Matrix3f rgb_intrinsics_matrix;
rgb_intrinsics_matrix << 525, 0.0, 319.5, 0.0, 525, 239.5, 0.0, 0.0, 1.0; // Kinect RGB camera intrinsics
// Read if some parameters are passed from command line:
pcl::console::parse_argument (argc, argv, "--svm", svm_filename);
pcl::console::parse_argument (argc, argv, "--conf", min_confidence);
pcl::console::parse_argument (argc, argv, "--min_h", min_height);
pcl::console::parse_argument (argc, argv, "--max_h", max_height);
// Read Kinect live stream:
PointCloudT::Ptr cloud_people (new PointCloudT);
cc_->ready_xyzrgb_ = false;
while ( !cc_->ready_xyzrgb_ )
{
ros::spinOnce();
rate.sleep();
}
pcl::PointCloud<pcl::PointXYZRGB>::ConstPtr cloud = cc_->msg_xyzrgb_;
// Display pointcloud:
pcl::visualization::PointCloudColorHandlerRGBField<PointT> rgb(cloud);
viewer.addPointCloud<PointT> (cloud, rgb, "input_cloud");
viewer.setCameraPosition(0,0,-2,0,-1,0,0);
// Add point picking callback to viewer:
struct callback_args cb_args;
PointCloudT::Ptr clicked_points_3d (new PointCloudT);
cb_args.clicked_points_3d = clicked_points_3d;
cb_args.viewerPtr = pcl::visualization::PCLVisualizer::Ptr(&viewer);
viewer.registerPointPickingCallback (pp_callback, (void*)&cb_args);
std::cout << "Shift+click on three floor points, then press 'Q'..." << std::endl;
// Spin until 'Q' is pressed:
viewer.spin();
std::cout << "done." << std::endl;
//cloud_mutex.unlock ();
// Ground plane estimation:
Eigen::VectorXf ground_coeffs;
ground_coeffs.resize(4);
std::vector<int> clicked_points_indices;
for (unsigned int i = 0; i < clicked_points_3d->points.size(); i++)
clicked_points_indices.push_back(i);
pcl::SampleConsensusModelPlane<PointT> model_plane(clicked_points_3d);
model_plane.computeModelCoefficients(clicked_points_indices,ground_coeffs);
std::cout << "Ground plane: " << ground_coeffs(0) << " " << ground_coeffs(1) << " " << ground_coeffs(2) << " " << ground_coeffs(3) << std::endl;
// Initialize new viewer:
pcl::visualization::PCLVisualizer viewer("PCL Viewer"); // viewer initialization
viewer.setCameraPosition(0,0,-2,0,-1,0,0);
// Create classifier for people detection:
pcl::people::PersonClassifier<pcl::RGB> person_classifier;
person_classifier.loadSVMFromFile(svm_filename); // load trained SVM
// People detection app initialization:
pcl::people::GroundBasedPeopleDetectionApp<PointT> people_detector; // people detection object
people_detector.setVoxelSize(voxel_size); // set the voxel size
people_detector.setIntrinsics(rgb_intrinsics_matrix); // set RGB camera intrinsic parameters
people_detector.setClassifier(person_classifier); // set person classifier
people_detector.setHeightLimits(min_height, max_height); // set person classifier
// people_detector.setSensorPortraitOrientation(true); // set sensor orientation to vertical
// For timing:
static unsigned count = 0;
static double last = pcl::getTime ();
int people_count = 0;
histogram* first_hist;
int max_people_num = (int)fs_->getFirstTopLevelNode()["max_people_num"];
// Main loop:
while (!viewer.wasStopped() && ros::ok() )
{
if ( current_state == 1 )
{
if ( cc_->ready_xyzrgb_ ) // if a new cloud is available
{
// std::cout << "In state 1!!!!!!!!!!" << std::endl;
std::vector<float> x;
std::vector<float> y;
std::vector<float> depth;
cloud = cc_->msg_xyzrgb_;
PointCloudT::Ptr cloud_new(new PointCloudT(*cloud));
cc_->ready_xyzrgb_ = false;
// Perform people detection on the new cloud:
std::vector<pcl::people::PersonCluster<PointT> > clusters; // vector containing persons clusters
people_detector.setInputCloud(cloud_new);
people_detector.setGround(ground_coeffs); // set floor coefficients
people_detector.compute(clusters); // perform people detection
ground_coeffs = people_detector.getGround(); // get updated floor coefficients
// Draw cloud and people bounding boxes in the viewer:
viewer.removeAllPointClouds();
viewer.removeAllShapes();
pcl::visualization::PointCloudColorHandlerRGBField<PointT> rgb(cloud);
viewer.addPointCloud<PointT> (cloud, rgb, "input_cloud");
unsigned int k = 0;
std::vector<pcl::people::PersonCluster<PointT> >::iterator it;
std::vector<pcl::people::PersonCluster<PointT> >::iterator it_min;
float min_z = 10.0;
float histo_dist_min = 2.0;
int id = -1;
for(it = clusters.begin(); it != clusters.end(); ++it)
{
if(it->getPersonConfidence() > min_confidence) // draw only people with confidence above a threshold
{
x.push_back((it->getTCenter())[0]);
y.push_back((it->getTCenter())[1]);
depth.push_back(it->getDistance());
// draw theoretical person bounding box in the PCL viewer:
/*pcl::copyPointCloud( *cloud, it->getIndices(), *cloud_people);
if ( people_count == 0 )
{
first_hist = calc_histogram_a( cloud_people );
people_count++;
it->drawTBoundingBox(viewer, k);
}
else if ( people_count <= 10 )
{
histogram* hist_tmp = calc_histogram_a( cloud_people );
add_hist( first_hist, hist_tmp );
it->drawTBoundingBox(viewer, k);
people_count++;
}*/
pcl::copyPointCloud( *cloud, it->getIndices(), *cloud_people);
if ( people_count < 11 )
{
if ( it->getDistance() < min_z )
{
it_min = it;
min_z = it->getDistance();
}
}
else if ( people_count == 11 )
{
normalize_histogram( first_hist );
people_count++;
histogram* hist_tmp = calc_histogram( cloud_people );
float tmp = histo_dist_sq( first_hist, hist_tmp );
std::cout << "The histogram distance is " << tmp << std::endl;
histo_dist_min = tmp;
it_min = it;
id = k;
}
else
{
histogram* hist_tmp = calc_histogram( cloud_people );
float tmp = histo_dist_sq( first_hist, hist_tmp );
std::cout << "The histogram distance is " << tmp << std::endl;
if ( tmp < histo_dist_min )
{
histo_dist_min = tmp;
it_min = it;
id = k;
}
}
k++;
//std::cout << "The data of the center is " << cloud->points [(cloud->width >> 1) * (cloud->height + 1)].x << " " << cloud->points [(cloud->width >> 1) * (cloud->height + 1)].y << " " << cloud->points [(cloud->width >> 1) * (cloud->height + 1)].z << std::endl;
//std::cout << "The size of the people cloud is " << cloud_people->points.size() << std::endl;
std::cout << "The " << k << " person's distance is " << it->getDistance() << std::endl;
}
}
pub_people_.x = x;
pub_people_.y = y;
pub_people_.depth = depth;
if ( k > 0 )
{
if ( people_count <= 11 )
{
pcl::copyPointCloud( *cloud, it_min->getIndices(), *cloud_people);
if ( people_count == 0)
{
first_hist = calc_histogram_a( cloud_people );
people_count++;
it_min->drawTBoundingBox(viewer, 1);
}
else if ( people_count < 11 )
{
histogram* hist_tmp = calc_histogram_a( cloud_people );
add_hist( first_hist, hist_tmp );
it_min->drawTBoundingBox(viewer, 1);
people_count++;
}
}
else
{
pub_people_.id = k-1;
if ( histo_dist_min < 1.3 )
{
it_min->drawTBoundingBox(viewer, 1);
std::cout << "The minimum distance of the histogram is " << histo_dist_min << std::endl;
std::cout << "The vector is " << it_min->getTCenter() << std::endl << "while the elements are " << (it_min->getTCenter())[0] << " " << (it_min->getTCenter())[1] << std::endl;
}
else
{
pub_people_.id = -1;
}
}
}
else
{
pub_people_.id = -1;
}
pub_people_.header.stamp = ros::Time::now();
people_pub.publish(pub_people_);
std::cout << k << " people found" << std::endl;
viewer.spinOnce();
ros::spinOnce();
// Display average framerate:
if (++count == 30)
{
double now = pcl::getTime ();
std::cout << "Average framerate: " << double(count)/double(now - last) << " Hz" << std::endl;
count = 0;
last = now;
}
//cloud_mutex.unlock ();
}
}
else
{
viewer.spinOnce();
ros::spinOnce();
// std::cout << "In state 0!!!!!!!!!" << std::endl;
}
}
return 0;
}
std::vector<double> create_normalized_histogram( iterator begin, iterator end, size_t num_buckets )
{
return normalize_histogram( create_histogram( begin, end, num_buckets ) );
}
std::vector<double> create_normalized_histogram( iterator begin, iterator end, size_t num_buckets, typename std::iterator_traits<iterator>::value_type min, typename std::iterator_traits<iterator>::value_type max )
{
return normalize_histogram( create_histogram( begin, end, num_buckets, min, max ) );
}
