int main(int argc, char** argv) {
// check arguments
if (argc != 3 && argc != 4) {
printf("Usage: PartsBasedDetector model_file image_file [depth_file]\n");
exit(-1);
}
// determine the type of model to read
boost::scoped_ptr<Model> model;
string ext = boost::filesystem::path(argv[1]).extension().string();
if (ext.compare(".xml") == 0 || ext.compare(".yaml") == 0) {
model.reset(new FileStorageModel);
} else if (ext.compare(".mat") != 0) {
model.reset(new MatlabIOModel);
} else {
printf("Unsupported model format: %s\n", ext.c_str());
exit(-2);
}
bool ok = model->deserialize(argv[1]);
if (!ok) {
printf("Error deserializing file\n");
exit(-3);
}
// create the PartsBasedDetector and distribute the model parameters
PartsBasedDetector<float> pbd;
pbd.distributeModel(*model);
// load the image from file
Mat_<float> depth;
Mat im = imread(argv[2]);
if (im.empty()) {
printf("Image not found or invalid image format\n");
exit(-4);
}
// detect potential candidates in the image
double t = (double)getTickCount();
vector<Candidate> candidates;
pbd.detect(im, depth, candidates);
printf("Detection time: %f\n", ((double)getTickCount() - t)/getTickFrequency());
printf("Number of candidates: %ld\n", candidates.size());
// display the best candidates
Visualize visualize(model->name());
SearchSpacePruning<float> ssp;
Mat canvas;
if (candidates.size() > 0) {
Candidate::sort(candidates);
//Candidate::nonMaximaSuppression(im, candidates, 0.2);
visualize.candidates(im, candidates, canvas, true);
visualize.image(canvas);
waitKey();
}
return 0;
}
int main(int argc, char** argv) {
/*int padding = 12;
int interval = 10;
// check arguments
if (argc != 3 && argc != 4) {
printf("Usage: PartsBasedDetector model_file image_file [depth_file]\n");
exit(-1);
}
vector<FFLD::HOGPyramid::Matrix> scores;
vector<FFLD::Mixture::Indices> argmaxes;
vector<vector<vector<FFLD::Model::Positions> > > positions;
const string file("../tmp.jpg");
cout << file << endl;
FFLD::JPEGImage image(file);
FFLD::HOGPyramid pyramidFFLD(image, padding, padding, interval);
if (!FFLD::Patchwork::Init((pyramidFFLD.levels()[0].rows() - padding + 15) & ~15,
(pyramidFFLD.levels()[0].cols() - padding + 15) & ~15)) {
cerr << "\nCould not initialize the Patchwork class" << endl;
return -1;
}
cout << "Initialized FFTW in " << stop() << " ms" << endl;
FFLD::Mixture mixture;
string modelffld("../model_car_final-1.2.4.txt");
cout << modelffld << endl;
ifstream in(modelffld.c_str(), ios::binary);
if (!in.is_open()) {
//showUsage();
cerr << "\nInvalid model file " << modelffld << endl;
return -1;
}
in >> mixture;
//mixture.convolve(pyramidFFLD, scores, argmaxes, &positions);//full ffld run
cout << "Convolution :p " << endl;
const int nbModels = mixture.models().size();
const int nbLevels = pyramidFFLD.levels().size();
// Convolve with all the models
vector<vector<FFLD::HOGPyramid::Matrix> > tmp(nbModels);
//convolve(pyramid, tmp, positions);//going inside
// Transform the filters if needed
#pragma omp critical
//if (mixture.filterCache_.empty())
mixture.cacheFilters();
// Create a patchwork
const FFLD::Patchwork patchwork(pyramidFFLD);
// Convolve the patchwork with the filters
std::vector<FFLD::Patchwork::Filter> filterCache_;
filterCache_ = mixture.filterCacheObj();
vector<vector<FFLD::HOGPyramid::Matrix> > convolutions(filterCache_.size());
patchwork.convolve(filterCache_, convolutions);///convolve patch with filters,
const int tmpnbFilters = 54, tmpnbPlanes = 12, tmpnbLevels = 41;
//nb filters is number of filters (Read from model.txt)
// nblevels is number of HOGPyramid levels
// nbplanes comes from converting pyramid to patchwork, rectangle logic ?
/* Mat C;
for (int i = 0; i < tmpnbFilters * tmpnbPlanes; ++i) {
const int k = i / tmpnbPlanes; // Filter index
const int l = i % tmpnbPlanes; // Plane index
for (int j = 0; j < tmpnbLevels; ++j) {
FFLD::HOGPyramid::Matrix ffldResponse = convolutions[k][j];
FFLD::HOGPyramid::Matrix tempffldResponse;
eigen2cv(ffldResponse,C);
cv2eigen(C,tempffldResponse);
cout << "ffldResponse dim " << ffldResponse.rows() << " " << ffldResponse.cols() << " C dim " << C.rows << " " << C.cols << " ffld::Eigen dim " << tempffldResponse.rows() << " " << tempffldResponse.cols() <<endl;
}
}*/
//cout << " convolution size " << convolutions.size() << "rows " << convolutions[1].rows() << endl;
// convert the convolusions Eigen matrix to cvMat obj
//cout << " Mixture model size " << nbModels << endl;
// determine the type of model to read
cout << " Now starting Bristow " << endl;
boost::scoped_ptr<Model> model;
string ext = boost::filesystem::path(argv[1]).extension().string();
if (ext.compare(".xml") == 0 || ext.compare(".yaml") == 0) {
model.reset(new FileStorageModel);
}
#ifdef WITH_MATLABIO
else if (ext.compare(".mat") == 0) {
model.reset(new MatlabIOModel);
}
#endif
else {
printf("Unsupported model format: %s\n", ext.c_str());
exit(-2);
}
bool ok = model->deserialize(argv[1]);
if (!ok) {
printf("Error deserializing file\n");
exit(-3);
}
cout << " Calling Distribute Model " << endl;
// create the PartsBasedDetector and distribute the model parameters
PartsBasedDetector<float> pbd;
pbd.distributeModel(*model);
cout << " After Distribute Model " << endl;
// load the image from file
Mat_<float> depth;
Mat im = imread(argv[2]);
if (im.empty()) {
printf("Image not found or invalid image format\n");
exit(-4);
}
if (argc == 4) {
depth = imread(argv[3], CV_LOAD_IMAGE_ANYDEPTH);
// convert the depth image from mm to m
depth = depth / 1000.0f;
}
// detect potential candidates in the image
double t = (double)getTickCount();
vector<Candidate> candidates;
pbd.detect(im, depth, candidates);
cout << " image dim " << im.rows << " " << im.cols << endl;
printf("Detection time: %f\n", ((double)getTickCount() - t)/getTickFrequency());
printf("Number of candidates: %ld\n", candidates.size());
// display the best candidates
Visualize visualize(model->name());
SearchSpacePruning<float> ssp;
Mat canvas;
if (candidates.size() > 0) {
Candidate::sort(candidates);
Candidate::nonMaximaSuppression(im, candidates, 0.2);
visualize.candidates(im, candidates, canvas, true);
visualize.image(canvas);
waitKey();
}
return 0;
}