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#include "opencv2/face.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/objdetect.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>
#include <vector>
#include <string>
using namespace std;
using namespace cv;
using namespace cv::face;
static bool myDetector(InputArray image, OutputArray faces, CascadeClassifier *face_cascade)
{
Mat gray;
if (image.channels() > 1)
cvtColor(image, gray, COLOR_BGR2GRAY);
else
gray = image.getMat().clone();
equalizeHist(gray, gray);
std::vector<Rect> faces_;
face_cascade->detectMultiScale(gray, faces_, 1.4, 2, CASCADE_SCALE_IMAGE, Size(30, 30));
Mat(faces_).copyTo(faces);
return true;
}
int main(int argc,char** argv){
//Give the path to the directory containing all the files containing data
CommandLineParser parser(argc, argv,
"{ help h usage ? | | give the following arguments in following format }"
"{ annotations a |. | (required) path to annotations txt file [example - /data/annotations.txt] }"
"{ config c | | (required) path to configuration xml file containing parameters for training.[ example - /data/config.xml] }"
"{ model m | | (required) path to configuration xml file containing parameters for training.[ example - /data/model.dat] }"
"{ width w | 460 | The width which you want all images to get to scale the annotations. large images are slow to process [default = 460] }"
"{ height h | 460 | The height which you want all images to get to scale the annotations. large images are slow to process [default = 460] }"
"{ face_cascade f | | Path to the face cascade xml file which you want to use as a detector}"
);
//Read in the input arguments
if (parser.has("help")){
parser.printMessage();
cerr << "TIP: Use absolute paths to avoid any problems with the software!" << endl;
return 0;
}
string directory(parser.get<string>("annotations"));
//default initialisation
Size scale(460,460);
scale = Size(parser.get<int>("width"),parser.get<int>("height"));
if (directory.empty()){
parser.printMessage();
cerr << "The name of the directory from which annotations have to be found is empty" << endl;
return -1;
}
string configfile_name(parser.get<string>("config"));
if (configfile_name.empty()){
parser.printMessage();
cerr << "No configuration file name found which contains the parameters for training" << endl;
return -1;
}
string modelfile_name(parser.get<string>("model"));
if (modelfile_name.empty()){
parser.printMessage();
cerr << "No name for the model_file found in which the trained model has to be saved" << endl;
return -1;
}
string cascade_name(parser.get<string>("face_cascade"));
if (cascade_name.empty()){
parser.printMessage();
cerr << "The name of the cascade classifier to be loaded to detect faces is not found" << endl;
return -1;
}
//create a vector to store names of files in which annotations
//and image names are found
/*The format of the file containing annotations should be of following format
/data/abc/abc.jpg
123.45,345.65
321.67,543.89
The above format is similar to HELEN dataset which is used for training model
*/
vector<String> filenames;
//reading the files from the given directory
glob(directory + "*.txt",filenames);
//create a pointer to call the base class
//pass the face cascade xml file which you want to pass as a detector
CascadeClassifier face_cascade;
face_cascade.load(cascade_name);
FacemarkKazemi::Params params;
params.configfile = configfile_name;
Ptr<FacemarkKazemi> facemark = FacemarkKazemi::create(params);
facemark->setFaceDetector((FN_FaceDetector)myDetector, &face_cascade);
//create a vector to store image names
vector<String> imagenames;
//create object to get landmarks
vector< vector<Point2f> > trainlandmarks,Trainlandmarks;
//gets landmarks and corresponding image names in both the vectors
//vector to store images
vector<Mat> trainimages;
loadTrainingData(filenames,trainlandmarks,imagenames);
for(unsigned long i=0;i<300;i++){
string imgname = imagenames[i].substr(0, imagenames[i].size()-1);
string img = directory + string(imgname) + ".jpg";
Mat src = imread(img);
if(src.empty()){
cerr<<string("Image "+img+" not found\n.")<<endl;
continue;
}
trainimages.push_back(src);
Trainlandmarks.push_back(trainlandmarks[i]);
}
cout<<"Got data"<<endl;
facemark->training(trainimages,Trainlandmarks,configfile_name,scale,modelfile_name);
cout<<"Training complete"<<endl;
return 0;
}
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