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/*----------------------------------------------
* the user should provide the list of training images_train,
* accompanied by their corresponding landmarks location in separated files.
* example of contents for images.txt:
* ../trainset/image_0001.png
* ../trainset/image_0002.png
* example of contents for annotation.txt:
* ../trainset/image_0001.pts
* ../trainset/image_0002.pts
* where the image_xxxx.pts contains the position of each face landmark.
* example of the contents:
* version: 1
* n_points: 68
* {
* 115.167660 220.807529
* 116.164839 245.721357
* 120.208690 270.389841
* ...
* }
* example of the dataset is available at https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/
*--------------------------------------------------*/
#include "opencv2/face.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/objdetect.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 }"
"{ images i | | (required) path to images txt file [example - /data/images.txt] }"
"{ 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 file containing trained model for face landmark detection[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 annotations(parser.get<string>("annotations"));
string imagesList(parser.get<string>("images"));
//default initialisation
Size scale(460,460);
scale = Size(parser.get<int>("width"),parser.get<int>("height"));
if (annotations.empty()){
parser.printMessage();
cerr << "Name for annotations file not found. Aborting...." << endl;
return -1;
}
if (imagesList.empty()){
parser.printMessage();
cerr << "Name for file containing image list not found. Aborting....." << 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 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);
std::vector<String> images;
std::vector<std::vector<Point2f> > facePoints;
loadTrainingData(imagesList, annotations, images, facePoints, 0.0);
//gets landmarks and corresponding image names in both the vectors
vector<Mat> Trainimages;
std::vector<std::vector<Point2f> > Trainlandmarks;
//vector to store images
Mat src;
for(unsigned long i=0;i<images.size();i++){
src = imread(images[i]);
if(src.empty()){
cout<<images[i]<<endl;
cerr<<string("Image not found\n.Aborting...")<<endl;
continue;
}
Trainimages.push_back(src);
Trainlandmarks.push_back(facePoints[i]);
}
cout<<"Got data"<<endl;
facemark->training(Trainimages,Trainlandmarks,configfile_name,scale,modelfile_name);
cout<<"Training complete"<<endl;
return 0;
}
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