290 lines
10 KiB
C++
290 lines
10 KiB
C++
/*
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This file was part of GSoC Project: Facemark API for OpenCV
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Final report: https://gist.github.com/kurnianggoro/74de9121e122ad0bd825176751d47ecc
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Student: Laksono Kurnianggoro
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Mentor: Delia Passalacqua
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*/
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/*----------------------------------------------
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* Usage:
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* facemark_demo_aam <face_cascade_model> <eyes_cascade_model> <training_images> <annotation_files> [test_files]
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*
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* Example:
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* facemark_demo_aam ../face_cascade.xml ../eyes_cascade.xml ../images_train.txt ../points_train.txt ../test.txt
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*
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* Notes:
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* the user should provides the list of training images_train
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* accompanied by their corresponding landmarks location in separated files.
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* example of contents for images_train.txt:
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* ../trainset/image_0001.png
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* ../trainset/image_0002.png
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* example of contents for points_train.txt:
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* ../trainset/image_0001.pts
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* ../trainset/image_0002.pts
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* where the image_xxxx.pts contains the position of each face landmark.
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* example of the contents:
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* version: 1
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* n_points: 68
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* {
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* 115.167660 220.807529
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* 116.164839 245.721357
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* 120.208690 270.389841
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* ...
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* }
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* example of the dataset is available at https://ibug.doc.ic.ac.uk/download/annotations/lfpw.zip
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*--------------------------------------------------*/
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#include <stdio.h>
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#include <fstream>
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#include <sstream>
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#include "opencv2/core.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/imgproc.hpp"
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#include "opencv2/face.hpp"
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#include <iostream>
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#include <string>
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#include <ctime>
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using namespace std;
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using namespace cv;
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using namespace cv::face;
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bool myDetector( InputArray image, OutputArray ROIs, CascadeClassifier *face_cascade);
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bool getInitialFitting(Mat image, Rect face, std::vector<Point2f> s0,
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CascadeClassifier eyes_cascade, Mat & R, Point2f & Trans, float & scale);
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bool parseArguments(int argc, char** argv, String & cascade,
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String & model, String & images, String & annotations, String & testImages
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);
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int main(int argc, char** argv )
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{
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String cascade_path,eyes_cascade_path,images_path, annotations_path, test_images_path;
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if(!parseArguments(argc, argv, cascade_path,eyes_cascade_path,images_path, annotations_path, test_images_path))
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return -1;
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//! [instance_creation]
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/*create the facemark instance*/
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FacemarkAAM::Params params;
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params.scales.push_back(2.0);
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params.scales.push_back(4.0);
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params.model_filename = "AAM.yaml";
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Ptr<FacemarkAAM> facemark = FacemarkAAM::create(params);
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//! [instance_creation]
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//! [load_dataset]
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/*Loads the dataset*/
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std::vector<String> images_train;
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std::vector<String> landmarks_train;
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loadDatasetList(images_path,annotations_path,images_train,landmarks_train);
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//! [load_dataset]
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//! [add_samples]
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Mat image;
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std::vector<Point2f> facial_points;
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for(size_t i=0;i<images_train.size();i++){
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image = imread(images_train[i].c_str());
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loadFacePoints(landmarks_train[i],facial_points);
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facemark->addTrainingSample(image, facial_points);
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}
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//! [add_samples]
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//! [training]
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/* trained model will be saved to AAM.yml */
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facemark->training();
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//! [training]
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//! [load_test_images]
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/*test using some images*/
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String testFiles(images_path), testPts(annotations_path);
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if(!test_images_path.empty()){
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testFiles = test_images_path;
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testPts = test_images_path; //unused
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}
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std::vector<String> images;
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std::vector<String> facePoints;
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loadDatasetList(testFiles, testPts, images, facePoints);
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//! [load_test_images]
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//! [trainsformation_variables]
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float scale ;
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Point2f T;
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Mat R;
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//! [trainsformation_variables]
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//! [base_shape]
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FacemarkAAM::Data data;
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facemark->getData(&data);
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std::vector<Point2f> s0 = data.s0;
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//! [base_shape]
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//! [fitting]
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/*fitting process*/
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std::vector<Rect> faces;
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//! [load_cascade_models]
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CascadeClassifier face_cascade(cascade_path);
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CascadeClassifier eyes_cascade(eyes_cascade_path);
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//! [load_cascade_models]
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for(int i=0;i<(int)images.size();i++){
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printf("image #%i ", i);
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//! [detect_face]
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image = imread(images[i]);
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myDetector(image, faces, &face_cascade);
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//! [detect_face]
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if(faces.size()>0){
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//! [get_initialization]
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std::vector<FacemarkAAM::Config> conf;
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std::vector<Rect> faces_eyes;
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for(unsigned j=0;j<faces.size();j++){
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if(getInitialFitting(image,faces[j],s0,eyes_cascade, R,T,scale)){
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conf.push_back(FacemarkAAM::Config(R,T,scale,(int)params.scales.size()-1));
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faces_eyes.push_back(faces[j]);
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}
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}
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//! [get_initialization]
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//! [fitting_process]
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if(conf.size()>0){
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printf(" - face with eyes found %i ", (int)conf.size());
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std::vector<std::vector<Point2f> > landmarks;
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double newtime = (double)getTickCount();
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facemark->fitConfig(image, faces_eyes, landmarks, conf);
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double fittime = ((getTickCount() - newtime)/getTickFrequency());
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for(unsigned j=0;j<landmarks.size();j++){
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drawFacemarks(image, landmarks[j],Scalar(0,255,0));
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}
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printf("%f ms\n",fittime*1000);
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imshow("fitting", image);
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waitKey(0);
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}else{
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printf("initialization cannot be computed - skipping\n");
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}
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//! [fitting_process]
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}
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} //for
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//! [fitting]
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}
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bool myDetector(InputArray image, OutputArray faces, CascadeClassifier *face_cascade)
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{
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Mat gray;
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if (image.channels() > 1)
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cvtColor(image, gray, COLOR_BGR2GRAY);
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else
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gray = image.getMat().clone();
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equalizeHist(gray, gray);
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std::vector<Rect> faces_;
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face_cascade->detectMultiScale(gray, faces_, 1.4, 2, CASCADE_SCALE_IMAGE, Size(30, 30));
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Mat(faces_).copyTo(faces);
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return true;
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}
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bool getInitialFitting(Mat image, Rect face, std::vector<Point2f> s0 ,CascadeClassifier eyes_cascade, Mat & R, Point2f & Trans, float & scale){
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std::vector<Point2f> mybase;
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std::vector<Point2f> T;
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std::vector<Point2f> base = Mat(Mat(s0)+Scalar(image.cols/2,image.rows/2)).reshape(2);
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std::vector<Point2f> base_shape,base_shape2 ;
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Point2f e1 = Point2f((float)((base[39].x+base[36].x)/2.0),(float)((base[39].y+base[36].y)/2.0)); //eye1
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Point2f e2 = Point2f((float)((base[45].x+base[42].x)/2.0),(float)((base[45].y+base[42].y)/2.0)); //eye2
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if(face.width==0 || face.height==0) return false;
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std::vector<Point2f> eye;
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bool found=false;
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Mat faceROI = image( face);
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std::vector<Rect> eyes;
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//-- In each face, detect eyes
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eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, CASCADE_SCALE_IMAGE, Size(20, 20) );
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if(eyes.size()==2){
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found = true;
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int j=0;
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Point2f c1( (float)(face.x + eyes[j].x + eyes[j].width*0.5), (float)(face.y + eyes[j].y + eyes[j].height*0.5));
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j=1;
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Point2f c2( (float)(face.x + eyes[j].x + eyes[j].width*0.5), (float)(face.y + eyes[j].y + eyes[j].height*0.5));
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Point2f pivot;
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double a0,a1;
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if(c1.x<c2.x){
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pivot = c1;
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a0 = atan2(c2.y-c1.y, c2.x-c1.x);
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}else{
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pivot = c2;
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a0 = atan2(c1.y-c2.y, c1.x-c2.x);
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}
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scale = (float)(norm(Mat(c1)-Mat(c2))/norm(Mat(e1)-Mat(e2)));
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mybase= Mat(Mat(s0)*scale).reshape(2);
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Point2f ey1 = Point2f((float)((mybase[39].x+mybase[36].x)/2.0),(float)((mybase[39].y+mybase[36].y)/2.0));
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Point2f ey2 = Point2f((float)((mybase[45].x+mybase[42].x)/2.0),(float)((mybase[45].y+mybase[42].y)/2.0));
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#define TO_DEGREE 180.0/3.14159265
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a1 = atan2(ey2.y-ey1.y, ey2.x-ey1.x);
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Mat rot = getRotationMatrix2D(Point2f(0,0), (a1-a0)*TO_DEGREE, 1.0);
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rot(Rect(0,0,2,2)).convertTo(R, CV_32F);
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base_shape = Mat(Mat(R*scale*Mat(Mat(s0).reshape(1)).t()).t()).reshape(2);
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ey1 = Point2f((float)((base_shape[39].x+base_shape[36].x)/2.0),(float)((base_shape[39].y+base_shape[36].y)/2.0));
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ey2 = Point2f((float)((base_shape[45].x+base_shape[42].x)/2.0),(float)((base_shape[45].y+base_shape[42].y)/2.0));
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T.push_back(Point2f(pivot.x-ey1.x,pivot.y-ey1.y));
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Trans = Point2f(pivot.x-ey1.x,pivot.y-ey1.y);
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return true;
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}else{
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Trans = Point2f( (float)(face.x + face.width*0.5),(float)(face.y + face.height*0.5));
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}
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return found;
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}
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bool parseArguments(int argc, char** argv,
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String & cascade,
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String & model,
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String & images,
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String & annotations,
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String & test_images
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){
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const String keys =
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"{ @f face-cascade | | (required) path to the cascade model file for the face detector }"
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"{ @e eyes-cascade | | (required) path to the cascade model file for the eyes detector }"
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"{ @i images | | (required) path of a text file contains the list of paths to all training images}"
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"{ @a annotations | | (required) Path of a text file contains the list of paths to all annotations files}"
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"{ @t test-images | | Path of a text file contains the list of paths to the test images}"
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"{ help h usage ? | | facemark_demo_aam -face-cascade -eyes-cascade -images -annotations [-t]\n"
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" example: facemark_demo_aam ../face_cascade.xml ../eyes_cascade.xml ../images_train.txt ../points_train.txt ../test.txt}"
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;
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CommandLineParser parser(argc, argv,keys);
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parser.about("hello");
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if (parser.has("help")){
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parser.printMessage();
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return false;
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}
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cascade = String(parser.get<String>("face-cascade"));
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model = String(parser.get<string>("eyes-cascade"));
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images = String(parser.get<string>("images"));
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annotations = String(parser.get<string>("annotations"));
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test_images = String(parser.get<string>("test-images"));
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if(cascade.empty() || model.empty() || images.empty() || annotations.empty()){
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std::cerr << "one or more required arguments are not found" << '\n';
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cout<<"face-cascade : "<<cascade.c_str()<<endl;
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cout<<"eyes-cascade : "<<model.c_str()<<endl;
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cout<<"images : "<<images.c_str()<<endl;
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cout<<"annotations : "<<annotations.c_str()<<endl;
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parser.printMessage();
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return false;
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}
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return true;
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}
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