Head Pose Estimation on Random Forest in G Fanelli's paper - c++

Question

I have been working on head pose estimation on depth data. And I have read G Fanelli's paper-"Real Time Head Pose Estimation from Consumer Depth Cameras" "Real Time Head Pose Estimation with Random Regression Forests". I test the data and the code Fanelli published on the website(http://www.vision.ee.ethz.ch/~gfanelli/head_pose/head_forest.html). However when I run the code, there is a problem. The error information is "usage: ./head_pose_estimation config_file depth_image". I think it is about file reading but I don't how to fix it.
and the code is like this:
int main(int argc, char* argv[])
{
if( argc != 3 )
{
cout << "usage: ./head_pose_estimation config_file depth_image" << endl;
exit(-1);
}
loadConfig(argv[1]);
CRForestEstimator estimator;
if( !estimator.loadForest(g_treepath.c_str(), g_ntrees) ){
cerr << "could not read forest!" << endl;
exit(-1);
}
string depth_fname(argv[2]);
//read calibration file (should be in the same directory as the depth image!)
string cal_filename = depth_fname.substr(0,depth_fname.find_last_of("/")+1);
cal_filename += "depth.cal";
ifstream is(cal_filename.c_str());
if (!is){
cerr << "depth.cal file not found in the same folder as the depth image! " << endl;
return -1;
}
//read intrinsics only
float depth_intrinsic[9]; for(int i =0; i<9; ++i) is >> depth_intrinsic[i];
is.close();
Mat depthImg;
//read depth image (compressed!)
if (!loadDepthImageCompressed( depthImg, depth_fname.c_str() ))
return -1;
Mat img3D;
img3D.create( depthImg.rows, depthImg.cols, CV_32FC3 );
//get 3D from depth
for(int y = 0; y < img3D.rows; y++)
{
Vec3f* img3Di = img3D.ptr<Vec3f>(y);
const int16_t* depthImgi = depthImg.ptr<int16_t>(y);
for(int x = 0; x < img3D.cols; x++){
float d = (float)depthImgi[x];
if ( d < g_max_z && d > 0 ){
img3Di[x][0] = d * (float(x) - depth_intrinsic[2])/depth_intrinsic[0];
img3Di[x][1] = d * (float(y) - depth_intrinsic[5])/depth_intrinsic[4];
img3Di[x][2] = d;
}
else{
img3Di[x] = 0;
}
}
}
g_means.clear();
g_votes.clear();
g_clusters.clear();
string pose_filename(depth_fname.substr(0,depth_fname.find_last_of('_')));
pose_filename += "_pose.bin";
cv::Vec<float,POSE_SIZE> gt;
bool have_gt = false;
//try to read in the ground truth from a binary file
FILE* pFile = fopen(pose_filename.c_str(), "rb");
if(pFile){
have_gt = true;
have_gt &= ( fread( &gt[0], sizeof(float),POSE_SIZE, pFile) == POSE_SIZE );
fclose(pFile);
}
//do the actual estimate
estimator.estimate( img3D,
g_means,
g_clusters,
g_votes,
g_stride,
g_maxv,
g_prob_th,
g_larger_radius_ratio,
g_smaller_radius_ratio,
false,
g_th
);
cout << "Heads found : " << g_means.size() << endl;
//assuming there's only one head in the image!
if(g_means.size()>0){
cout << "Estimated: " << g_means[0][0] << " " << g_means[0][1] << " " << g_means[0][2] << " " << g_means[0][3] << " " << g_means[0][4] << " " << g_means[0][5] <<endl;
float pt2d_est[2];
float pt2d_gt[2];
if(have_gt){
cout << "Ground T.: " << gt[0] << " " << gt[1] << " " << gt[2] << " " << gt[3] << " " << gt[4] << " " << gt[5] <<endl;
cv::Vec<float,POSE_SIZE> err = (gt-g_means[0]);
//multiply(err,err,err);
for(int n=0;n<POSE_SIZE;++n)
err[n] = err[n]*err[n];
float h_err = sqrt(err[0]+err[1]+err[2]);
float a_err = sqrt(err[3]+err[4]+err[5]);
cout << "Head error : " << h_err << " mm " << endl;
cout << "Angle error : " << a_err <<" degrees " << endl;
pt2d_gt[0] = depth_intrinsic[0]*gt[0]/gt[2] + depth_intrinsic[2];
pt2d_gt[1] = depth_intrinsic[4]*gt[1]/gt[2] + depth_intrinsic[5];
}
pt2d_est[0] = depth_intrinsic[0]*g_means[0][0]/g_means[0][2] + depth_intrinsic[2];
pt2d_est[1] = depth_intrinsic[4]*g_means[0][1]/g_means[0][2] + depth_intrinsic[5];
}
return 0;
}
can anyone could tell me how to fix the problem?Thanks so much!

Answer 1

You should always read the readme.txt (here attached in head_pose_estimation.tgz) before testing an application:
To run the example code, type ./head_pose_estimation config.txt
data/frame_XXXX_depth.bin. The config.txt file contains all parameters
needed for the head pose estimation, e.g., the path to the forest, the
stride, and z threshold used to segment the person from the
background.