static void
get_histogram (IplImage * src, CvPoint point, CvSize size,
CvHistogram * histogram)
{
IplImage *srcAux =
cvCreateImage (cvSize (size.width, size.height), IPL_DEPTH_8U, 3);
cvSetImageROI (src, cvRect (point.x, point.y, size.width, size.height));
cvCopy (src, srcAux, 0);
calc_histogram (srcAux, histogram);
cvReleaseImage (&srcAux);
}
/**
* @brief encapsulation of cv::caclHist, all of the meaning of the parameters are same as the cv::caclHist, but these api
* are easier to use. ex : cv::Mat hist = calc_histogram({hsv}, {0, 1}, {180, 256}, { {0, 180}, {0, 256} });
*
* @return histogram
*/
inline
cv::Mat calc_histogram(std::initializer_list<cv::Mat> images, std::initializer_list<int> channels,
std::initializer_list<int> hist_sizes, std::initializer_list<float[2]> ranges,
cv::InputArray mask = cv::Mat(), bool uniform = true,
bool accumulate = false)
{
cv::Mat output;
calc_histogram(images, output, channels, hist_sizes, ranges, mask, uniform, accumulate);
return output;
}
/**
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++;
}
}
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);
}
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;
}
static GstFlowReturn
kms_pointer_detector_transform_frame_ip (GstVideoFilter * filter,
GstVideoFrame * frame)
{
KmsPointerDetector *pointerdetector = KMS_POINTER_DETECTOR (filter);
GstMapInfo info;
double min_Bhattacharyya = 1.0, bhattacharyya = 1, bhattacharyya2 =
1, bhattacharyya3 = 1;
int i = 0;
pointerdetector->frameSize = cvSize (frame->info.width, frame->info.height);
kms_pointer_detector_initialize_images (pointerdetector, frame);
gst_buffer_map (frame->buffer, &info, GST_MAP_READ);
pointerdetector->cvImage->imageData = (char *) info.data;
if ((pointerdetector->iteration > FRAMES_TO_RESET)
&& (pointerdetector->state != CAPTURING_SECOND_HIST)) {
get_histogram (pointerdetector->cvImage, pointerdetector->upCornerRect1,
pointerdetector->trackinRectSize, pointerdetector->histSetUpRef);
pointerdetector->histRefCapturesCounter = 0;
pointerdetector->secondHistCapturesCounter = 0;
pointerdetector->state = CAPTURING_REF_HIST;
pointerdetector->colorRect1 = WHITE;
pointerdetector->colorRect2 = WHITE;
pointerdetector->iteration = 6;
}
if (pointerdetector->iteration == 5) {
get_histogram (pointerdetector->cvImage, pointerdetector->upCornerRect1,
pointerdetector->trackinRectSize, pointerdetector->histSetUpRef);
pointerdetector->state = CAPTURING_REF_HIST;
goto end;
}
if (pointerdetector->iteration < 6)
goto end;
get_histogram (pointerdetector->cvImage, pointerdetector->upCornerRect1,
pointerdetector->trackinRectSize, pointerdetector->histSetUp1);
bhattacharyya2 =
cvCompareHist (pointerdetector->histSetUp1,
pointerdetector->histSetUpRef, CV_COMP_BHATTACHARYYA);
if ((bhattacharyya2 >= COMPARE_THRESH_SECOND_HIST)
&& (pointerdetector->state == CAPTURING_REF_HIST)) {
pointerdetector->histRefCapturesCounter++;
if (pointerdetector->histRefCapturesCounter > 20) {
pointerdetector->histRefCapturesCounter = 0;
pointerdetector->colorRect1 = CV_RGB (0, 255, 0);
pointerdetector->state = CAPTURING_SECOND_HIST;
}
}
if (pointerdetector->state == CAPTURING_SECOND_HIST) {
get_histogram (pointerdetector->cvImage, pointerdetector->upCornerRect2,
pointerdetector->trackinRectSize, pointerdetector->histSetUp2);
bhattacharyya3 =
cvCompareHist (pointerdetector->histSetUp1,
pointerdetector->histSetUp2, CV_COMP_BHATTACHARYYA);
if (bhattacharyya3 < COMPARE_THRESH_2_RECT) {
pointerdetector->secondHistCapturesCounter++;
if (pointerdetector->secondHistCapturesCounter > 15) {
pointerdetector->secondHistCapturesCounter = 0;
pointerdetector->state = BOTH_HIST_SIMILAR;
pointerdetector->colorRect2 = CV_RGB (0, 255, 0);
cvCopyHist (pointerdetector->histSetUp2, &pointerdetector->histModel);
pointerdetector->upCornerFinalRect.x = 10;
pointerdetector->upCornerFinalRect.y = 10;
pointerdetector->histRefCapturesCounter = 0;
pointerdetector->secondHistCapturesCounter = 0;
}
}
}
for (i = 0; i < pointerdetector->numOfRegions; i++) {
int horizOffset =
pointerdetector->upCornerFinalRect.x +
pointerdetector->windowScale * (rand () %
pointerdetector->trackinRectSize.width -
pointerdetector->trackinRectSize.width / 2);
int vertOffset =
pointerdetector->upCornerFinalRect.y +
pointerdetector->windowScale * (rand () %
pointerdetector->trackinRectSize.height -
pointerdetector->trackinRectSize.height / 2);
pointerdetector->trackingPoint1Aux.x = horizOffset;
pointerdetector->trackingPoint1Aux.y = vertOffset;
pointerdetector->trackingPoint2Aux.x =
horizOffset + pointerdetector->trackinRectSize.width;
pointerdetector->trackingPoint2Aux.y =
vertOffset + pointerdetector->trackinRectSize.height;
if ((horizOffset > 0)
&& (pointerdetector->trackingPoint2Aux.x <
pointerdetector->cvImage->width)
&& (vertOffset > 0)
&& (pointerdetector->trackingPoint2Aux.y <
pointerdetector->cvImage->height)) {
if (pointerdetector->show_debug_info)
cvRectangle (pointerdetector->cvImage,
pointerdetector->trackingPoint1Aux,
pointerdetector->trackingPoint2Aux, CV_RGB (0, 255, 0), 1, 8, 0);
cvSetImageROI (pointerdetector->cvImage,
cvRect (pointerdetector->trackingPoint1Aux.x,
pointerdetector->trackingPoint1Aux.y,
pointerdetector->trackinRectSize.width,
pointerdetector->trackinRectSize.height));
cvCopy (pointerdetector->cvImage, pointerdetector->cvImageAux1, 0);
cvResetImageROI (pointerdetector->cvImage);
calc_histogram (pointerdetector->cvImageAux1,
pointerdetector->histCompare);
bhattacharyya =
cvCompareHist (pointerdetector->histModel,
pointerdetector->histCompare, CV_COMP_BHATTACHARYYA);
if ((bhattacharyya < min_Bhattacharyya)
&& (bhattacharyya < COMPARE_THRESH_HIST_REF)) {
min_Bhattacharyya = bhattacharyya;
pointerdetector->trackingPoint1 = pointerdetector->trackingPoint1Aux;
pointerdetector->trackingPoint2 = pointerdetector->trackingPoint2Aux;
}
}
}
cvRectangle (pointerdetector->cvImage, pointerdetector->upCornerRect1,
pointerdetector->downCornerRect1, pointerdetector->colorRect1, 1, 8, 0);
cvRectangle (pointerdetector->cvImage, pointerdetector->upCornerRect2,
pointerdetector->downCornerRect2, pointerdetector->colorRect2, 1, 8, 0);
if (min_Bhattacharyya < 0.95) {
pointerdetector->windowScale = pointerdetector->windowScaleRef;
} else {
pointerdetector->windowScale = pointerdetector->cvImage->width / 8;
}
CvPoint finalPointerPositionAux;
finalPointerPositionAux.x = pointerdetector->upCornerFinalRect.x +
pointerdetector->trackinRectSize.width / 2;
finalPointerPositionAux.y = pointerdetector->upCornerFinalRect.y +
pointerdetector->trackinRectSize.height / 2;
if (abs (pointerdetector->finalPointerPosition.x -
finalPointerPositionAux.x) < 55 ||
abs (pointerdetector->finalPointerPosition.y -
finalPointerPositionAux.y) < 55) {
finalPointerPositionAux.x =
(finalPointerPositionAux.x +
pointerdetector->finalPointerPosition.x) / 2;
finalPointerPositionAux.y =
(finalPointerPositionAux.y +
pointerdetector->finalPointerPosition.y) / 2;
}
pointerdetector->upCornerFinalRect = pointerdetector->trackingPoint1;
pointerdetector->downCornerFinalRect = pointerdetector->trackingPoint2;
pointerdetector->finalPointerPosition.x = finalPointerPositionAux.x;
pointerdetector->finalPointerPosition.y = finalPointerPositionAux.y;
cvCircle (pointerdetector->cvImage, pointerdetector->finalPointerPosition,
10.0, WHITE, -1, 8, 0);
kms_pointer_detector_check_pointer_position (pointerdetector);
end:
pointerdetector->iteration++;
gst_buffer_unmap (frame->buffer, &info);
return GST_FLOW_OK;
}