extern "C" JNIEXPORT void JNICALL
Java_budny_moneykeeper_cv_Recognizer_nativeRecognize(
JNIEnv*, jobject,
jlong j_recognizer, jlong j_src_mat, jlong j_dst_mat) {
Recognizer* recognizer = reinterpret_cast<Recognizer*>(j_recognizer);
cv::Mat& src_mat = *(reinterpret_cast<cv::Mat*>(j_src_mat));
cv::Mat& dst_mat = *(reinterpret_cast<cv::Mat*>(j_dst_mat));
recognizer->recognize(src_mat, dst_mat);
}
int main(int argc, char *argv[])
{
QApplication a(argc, argv);
Recognizer w;
w.show();
return a.exec();
}
int main(int argc, char** argv)
{
printf("Recognizer began to work\n");
Recognizer* rec = new Recognizer();
bool flag = rec->Init(argc, argv);
if (flag)
{
rec->Run();
};
delete rec;
printf("Bye!\n");
return 0;
}
int main(int argc, const char** argv)
{
//----------------------------------- variables initialisation ---------------------------------------------------------
std::thread t[2];
// Video is the class for use video and detection
Video myVideo(0); //0 is the defaut camera, use -1 for any cammera or a number between 1 to 99
Recognizer myRecognizer;
std::vector<cv::Mat> lastFacesDetected;
int i = 0;
std::string namePersonRecognized, goodName;
time_t timer;
time(&timer); //set timer to the current date in ms in
cv::Mat imgNull;
//Launch video in a thread
t[0] = std::thread([&] {myVideo.start(); });
myVideo.startFaceDetect();
while (true)
{
//dont recognize if the model is not trained or if no face never get Recognize
if (abs(time(NULL) - timer) > 1 && !myVideo.getLastFacesDetected().empty() && !myRecognizer.equalTest(myVideo.getLastFacesDetected(), lastFacesDetected))//Maybe add myVideo.getLastFacesDetected() != oldLastFacesDetected for not recognize the same image again and again
{
lastFacesDetected = myVideo.getLastFacesDetected(); // get all the current face in video stream
i = 0;
for (std::vector<cv::Mat>::iterator face = lastFacesDetected.begin(); face != lastFacesDetected.end(); face++, i++)//for each face detected
{
myVideo.setImgToPrint(*face); // display in another windows in the video thread the region of interest corresponding to the face detected
myRecognizer.askForAddImageToCurrentTrainingAndSave(*face, "customDatabase/"); //Ask if you want to add the face
myVideo.setImgToPrint(imgNull); //remove the second windows
}
//std::cout << "face analyzed = " << i << std::endl;
time(&timer);
}
cv::waitKey(1);
}
return 0;
}
int main(int argc, char *argv[])
{
QCoreApplication a(argc, argv);
QStringList args = a.arguments();
QString inFile;
QString outFile;
if (argc > 1)
inFile = args[1];
if (argc > 2)
outFile = args[2];
if (inFile.isEmpty()) {
std::cout << "run: story_1 \"input file\" [\"output file\"] " << std::endl;
return 0;
}
Recognizer r;
r.parseFile(inFile, outFile);
}
void *classify(void *threadid)
{
Detector detector;
long tid;
tid = (long)threadid;
char img[100];
char buf[1024];
sprintf(img, "data/%d.jpg", tid);
int* ids = new int[3];
int* sims = new int[3];
string* paths = new string[3];
for (int j = 0; j < 10; j++){
Mat landmarks;
Mat face = detector.detectNorm(img, rec.getWidth(), rec.getHeight(), rec.getPatchSize(), landmarks, rec.getNumLandmarks(), true, false);
int num = rec.classify(face, landmarks, 3, ids, sims, paths);
//int num = rec.classify(img, 3, ids, sims, paths);
for (int i = 0; i < num; i++){
sprintf(buf, "thread %d, prediction: %d %d, similarity: %d\n", tid, i, ids[i], sims[i]);
}
cout<<buf;
}
pthread_exit(NULL);
}
void recognizing(string filename, vector<uint> ids) {
Mat image = imread(filename);
Recognizer* rcg = new Recognizer(0.70f, 0.065f);
Preprocessor* pr = new Preprocessor(image);
pr->preprocess(10);
Mat cover;
if(!pr->warpObject()) {
cout << "#WARP-UNSUCCESSFUL" << endl;
cover = image.clone();
image.release();
Size newSize = (cover.size().width > cover.size().height) ? Size(1024, 768) : Size(768, 1024);
resize(cover, cover, newSize);
cout << "using the whole image..." << endl;
} else {
cover = pr->WarpedCover();
}
Mat trainingImage;
for(size_t i = 0; i < ids.size(); ++i) {
cout << "training " << i << endl;
rcg->train(ids.at(i));
}
if(rcg->recognize(Preprocessor::kuwaharaNagaoFilter(cover)))
{
Book result = rcg->LastResult();
cout << "#BEGIN-RESULT " << result.Id() << " #END-RESULT";
} else {
cout << "#BEGIN-RESULT null #END-RESULT";
}
cout << endl;
}
int main(int argc, char** argv){
struct timeval begin, end;
gettimeofday(&begin, NULL);
pthread_t threads[NUM_THREADS];
//rec.cleanSHM();
//rec.loadModel(datpath);
rec.loadModelFromSHM();
gettimeofday(&end, NULL);
double elapsed = (end.tv_sec - begin.tv_sec) +
((end.tv_usec - begin.tv_usec)/1000000.0);
//rec.evaluate();
cout<<"Model loaded in "<<elapsed<<" seconds"<<endl;
int* ids = new int[3];
int* sims = new int[3];
string* paths = new string[3];
int count = 0;
/*while(true){
cout<<"please input test img"<<endl;
string filename, simpath;
cin>>filename;
//filename = "data/test.jpg";
int num = rec.classify(filename.data(), 3, ids, sims, paths);
cout<<count<<" "<<num<<endl;
count++;
for (int i = 0; i < num; i++){
cout<<"Prediction "<<i<<" "<<ids[i]<<" similarity: "<<sims[i]<<endl;;
cout<<"Most similar image: "<<paths[i]<<endl;
}
}*/
for(int i=0; i < NUM_THREADS; i++ ){
cout << "main() : creating thread, " << i << endl;
int rc = pthread_create(&threads[i], NULL, classify, (void *)i);
if (rc){
cout << "Error:unable to create thread," << rc << endl;
exit(-1);
}
}
pthread_exit(NULL);
return 0;
}
int main (int argc, char *argv[])
{
QCoreApplication app (argc, argv);
bool generate_dot = false;
bool generate_report = false;
bool no_lines = false;
bool debug_info = true;
bool troll_copyright = false;
QString file_name = 0;
QStringList args = app.arguments ();
args.removeFirst ();
foreach (QString arg, args)
{
if (arg == QLatin1String ("-h") || arg == QLatin1String ("--help"))
help_me ();
else if (arg == QLatin1String ("-v") || arg == QLatin1String ("--verbose"))
generate_report = true;
else if (arg == QLatin1String ("--dot"))
generate_dot = true;
else if (arg == QLatin1String ("--no-lines"))
no_lines = true;
else if (arg == QLatin1String ("--no-debug"))
debug_info = false;
else if (arg == QLatin1String ("--troll"))
troll_copyright = true;
else if (file_name.isEmpty ())
file_name = arg;
else
qerr << "*** Warning. Ignore argument `" << arg << "'" << endl;
}
if (file_name.isEmpty ())
{
help_me ();
exit (EXIT_SUCCESS);
}
Grammar grammar;
Recognizer p (&grammar, no_lines);
if (! p.parse (file_name))
exit (EXIT_FAILURE);
if (grammar.rules.isEmpty ())
{
qerr << "*** Fatal. No rules!" << endl;
exit (EXIT_FAILURE);
}
else if (grammar.start == grammar.names.end ())
{
qerr << "*** Fatal. No start symbol!" << endl;
exit (EXIT_FAILURE);
}
grammar.buildExtendedGrammar ();
grammar.buildRuleMap ();
Automaton aut (&grammar);
aut.build ();
CppGenerator gen (p, grammar, aut, generate_report);
gen.setDebugInfo (debug_info);
gen.setTrollCopyright (troll_copyright);
gen ();
if (generate_dot)
{
DotGraph genDotFile (qout);
genDotFile (&aut);
}
else if (generate_report)
{
ParseTable genParseTable (qout);
genParseTable(&aut);
}
return EXIT_SUCCESS;
}
void StrokesRecognizer::onRecognizeClicked() {
Recognizer rec;
QString recognizedChar = rec.recognize(mpCanvas->strokes());
mpCharacterResult->setText(recognizedChar);
}
int main(int argc,char** argv){
if (argc < 2){
std::cout<<"Please enter <input.pcd> file"<<std::endl;
return 0;
}
if (pcl::io::loadPCDFile (argv[1], *model) < 0)
{
std::cout << "Error loading model cloud." << std::endl;
return (-1);
}
model_name = argv[1];
model_name = model_name.substr(0,model_name.size()-4);
if(pcl::console::find_switch(argc,argv,"-vfh")){
vfh = true;
}
else if(pcl::console::find_switch(argc,argv,"-rv")){
std::cout<<"performing Resultant vector feature calculation"<<std::endl;
rv = true;
}else{
std::cout<<"no algorithm specified using default algorithm vfh"<<std::endl;
vfh = true;
}
if (pcl::console::find_switch (argc, argv, "-ds"))
{
_downsample = true;
std::cout<<"performing downsampling on the input file"<<std::endl;
}
if (pcl::console::find_switch (argc, argv, "-sn"))
{
show_normals = true;
std::cout<<"showing calclated normals"<<std::endl;
}
if(_downsample){
rec.setInputCloud(model,_leaf_size);
std::cout<<"saving downsampled file to model_down.pcd"<<std::endl;
pcl::io::savePCDFileASCII ("model_down.pcd", *(rec.getCloud()));
}
else{
rec.setInputCloud(model);
std::cout<<"setting input model without further downsampling"<<std::endl;
}
if(pcl::console::find_switch(argc,argv,"--showaxis")){
_show_axis = true;
}
if(vfh){
std::cout<<"estimating VFH features"<<std::endl;
rec.Estimate_VFH_Features();
}
else if(rv){
std::cout<<"estimating features using the resultant vector"<<std::endl;
rec.Estimate_RV_Features();
PointNormalCloudT::Ptr cloud (new PointNormalCloudT);
pcl::PointCloud<pcl::Normal>::Ptr norm_cloud (new pcl::PointCloud<pcl::Normal>);
cloud = rec.getPointNormalCloud();
norm_cloud = rec.getNormalCloud();
pcl::PointCloud<pcl::PointXYZ>::Ptr plaincloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::copyPointCloud(*cloud,*plaincloud);
//pcl::PointXYZ mass_center(rec.rv_centroid.x,rec.rv_centroid.y,rec.rv_centroid.z);
pcl::PointXYZ mass_center(0,0,0);
pcl::PointXYZ kinectZ(0,0,-1);
pcl::PointXYZ res_vec (rec.rv_resultant.normal_x,rec.rv_resultant.normal_y,rec.rv_resultant.normal_z);
//pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZ> rgb(plaincloud);
//viewer.addPointCloud<pcl::PointXYZ> (plaincloud, rgb, "model_cloud");
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointNormal> single_color(cloud, double(0), double(255), double(0));
viewer.addPointCloud(cloud,single_color,"sample cloud");
if(_show_axis){
viewer.addLine(mass_center,res_vec,1.0f,0.0f,0.0f,"resultantvector");
viewer.addLine(mass_center,kinectZ,1.0f,1.0f,0.0f,"KinectZ");
}
std::cout<<"resultant vector :"<<res_vec.x<<" i"<<" + "<<res_vec.y<<" j"<<" + "<<res_vec.z<<" k"<<std::endl;
if(show_normals){
std::cout<<"showing 1 in "<<normalsratio<<" normals"<<std::endl;
viewer.addPointCloudNormals<pcl::PointNormal,pcl::Normal>(cloud, norm_cloud,normalsratio, 0.05, "normalscloud");
}
while(!viewer.wasStopped())
viewer.spinOnce();
}
std::cout<<"feature calculation complete"<<std::endl;
ofstream myfile;
if (vfh){
std::stringstream ss;
ss<<"vfh_"<<model_name<<".txt";
myfile.open(ss.str().c_str());
for(size_t k =0 ;k<308;k++){
if(k != 307)
myfile << rec._vfh_features->points[0].histogram[k]<<",";
else
myfile << rec._vfh_features->points[0].histogram[k];
}
std::cout<<"wrote the histogram to file :" <<ss.str()<<std::endl;
myfile.close();
}else if(rv){
std::stringstream ss;
ss<<"rv_"<<model_name<<".txt";
myfile.open(ss.str().c_str());
for(int k = 0;k<181;k++){
if(k != 180)
myfile << rec._rv_features_181[k]<<",";
else
myfile << rec._rv_features_181[k];
}
std::cout<<"wrote the histogram to file: "<< ss.str()<<std::endl;
//myfile.open(ss.c_str());
}
}
