@ -1,46 +1,20 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
/*
By downloading , copying , installing or using the software you agree to this
license . If you do not agree to this license , do not download , install ,
copy or use the software .
License Agreement
For Open Source Computer Vision Library
( 3 - clause BSD License )
Copyright ( C ) 2013 , OpenCV Foundation , all rights reserved .
Third party copyrights are property of their respective owners .
Redistribution and use in source and binary forms , with or without modification ,
are permitted provided that the following conditions are met :
* Redistributions of source code must retain the above copyright notice ,
this list of conditions and the following disclaimer .
* Redistributions in binary form must reproduce the above copyright notice ,
this list of conditions and the following disclaimer in the documentation
and / or other materials provided with the distribution .
* Neither the names of the copyright holders nor the names of the contributors
may be used to endorse or promote products derived from this software
without specific prior written permission .
This software is provided by the copyright holders and contributors " as is " and
any express or implied warranties , including , but not limited to , the implied
warranties of merchantability and fitness for a particular purpose are
disclaimed . In no event shall copyright holders or contributors be liable for
any direct , indirect , incidental , special , exemplary , or consequential damages
( including , but not limited to , procurement of substitute goods or services ;
loss of use , data , or profits ; or business interruption ) however caused
and on any theory of liability , whether in contract , strict liability ,
or tort ( including negligence or otherwise ) arising in any way out of
the use of this software , even if advised of the possibility of such damage .
This file was part of GSoC Project : Facemark API for OpenCV
This file contains results of GSoC Project : Facemark API for OpenCV
Final report : https : //gist.github.com/kurnianggoro/74de9121e122ad0bd825176751d47ecc
Student : Laksono Kurnianggoro
Mentor : Delia Passalacqua
*/
# include "opencv2/face.hpp"
# include "precomp.hpp"
# include "opencv2/face.hpp"
# include <fstream>
# include <cmath>
# include <ctime>
# include <cstdio>
# include <cassert>
# include <cstdarg>
namespace cv {
@ -108,8 +82,8 @@ public:
void loadModel ( String fs ) ;
bool setFaceDetector ( bool ( * f ) ( InputArray , OutputArray , void * extra_params ) ) ;
bool getFaces ( InputArray image , OutputArray faces , void * extra_param s ) ;
bool setFaceDetector ( bool ( * f ) ( InputArray , OutputArray , void * extra_params ) , void * userData ) ;
bool getFaces ( InputArray image , OutputArray faces ) ;
bool getData ( void * items ) ;
Params params ;
@ -128,12 +102,11 @@ protected:
void data_augmentation ( std : : vector < Mat > & imgs , std : : vector < Mat > & gt_shapes , std : : vector < BBox > & bboxes ) ;
Mat getMeanShape ( std : : vector < Mat > & gt_shapes , std : : vector < BBox > & bboxes ) ;
bool configFaceDetector ( ) ;
bool defaultFaceDetector ( const Mat image , std : : vector < Rect > & faces ) ;
bool defaultFaceDetector ( const Mat & image , std : : vector < Rect > & faces ) ;
CascadeClassifier face_cascade ;
bool ( * faceDetector ) ( InputArray , OutputArray , void * ) ;
bool isSetDetector ;
FN_FaceDetector faceDetector ;
void * faceDetectorData ;
/*training data*/
std : : vector < std : : vector < Point2f > > data_facemarks ; //original position
@ -251,98 +224,85 @@ Ptr<FacemarkLBF> FacemarkLBF::create(const FacemarkLBF::Params ¶meters){
return Ptr < FacemarkLBFImpl > ( new FacemarkLBFImpl ( parameters ) ) ;
}
FacemarkLBFImpl : : FacemarkLBFImpl ( const FacemarkLBF : : Params & parameters )
FacemarkLBFImpl : : FacemarkLBFImpl ( const FacemarkLBF : : Params & parameters ) :
faceDetector ( NULL ) , faceDetectorData ( NULL )
{
isSetDetector = false ;
isModelTrained = false ;
params = parameters ;
}
bool FacemarkLBFImpl : : setFaceDetector ( bool ( * f ) ( InputArray , OutputArray , void * extra_params ) ) {
bool FacemarkLBFImpl : : setFaceDetector ( bool ( * f ) ( InputArray , OutputArray , void * extra_params ) , void * userData ) {
faceDetector = f ;
isSetDetector = true ;
return true ;
}
bool FacemarkLBFImpl : : getFaces ( InputArray image , OutputArray roi , void * extra_params ) {
if ( ! isSetDetector ) {
return false ;
}
if ( extra_params ! = 0 ) {
//do nothing
}
std : : vector < Rect > & faces = * ( std : : vector < Rect > * ) roi . getObj ( ) ;
faces . clear ( ) ;
faceDetector ( image . getMat ( ) , faces , extra_params ) ;
faceDetectorData = userData ;
return true ;
}
bool FacemarkLBFImpl : : configFaceDetector ( ) {
if ( ! isSetDetector ) {
/*check the cascade classifier file*/
std : : ifstream infile ;
infile . open ( params . cascade_face . c_str ( ) , std : : ios : : in ) ;
if ( ! infile ) {
std : : string error_message = " The cascade classifier model is not found. " ;
CV_Error ( CV_StsBadArg , error_message ) ;
return false ;
}
face_cascade . load ( params . cascade_face . c_str ( ) ) ;
bool FacemarkLBFImpl : : getFaces ( InputArray image , OutputArray faces_ )
{
if ( ! faceDetector )
{
std : : vector < Rect > faces ;
defaultFaceDetector ( image . getMat ( ) , faces ) ;
Mat ( faces ) . copyTo ( faces_ ) ;
return true ;
}
return true ;
return faceDetector ( image , faces_ , faceDetectorData ) ;
}
bool FacemarkLBFImpl : : defaultFaceDetector ( const Mat image , std : : vector < Rect > & faces ) {
bool FacemarkLBFImpl : : defaultFaceDetector ( const Mat & image , std : : vector < Rect > & faces ) {
Mat gray ;
faces . clear ( ) ;
if ( image . channels ( ) > 1 ) {
cvtColor ( image , gray , CV_BGR2GRAY ) ;
} else {
if ( image . channels ( ) > 1 )
{
cvtColor ( image , gray , COLOR_BGR2GRAY ) ;
}
else
{
gray = image ;
}
equalizeHist ( gray , gray ) ;
face_cascade . detectMultiScale ( gray , faces , 1.05 , 2 , 0 | CV_HAAR_SCALE_IMAGE , Size ( 30 , 30 ) ) ;
equalizeHist ( gray , gray ) ;
if ( face_cascade . empty ( ) )
{
{ /* check the cascade classifier file */
std : : ifstream infile ;
infile . open ( params . cascade_face . c_str ( ) , std : : ios : : in ) ;
if ( ! infile )
CV_Error_ ( Error : : StsBadArg , ( " The cascade classifier model is not found: %s " , params . cascade_face . c_str ( ) ) ) ;
}
face_cascade . load ( params . cascade_face . c_str ( ) ) ;
CV_Assert ( ! face_cascade . empty ( ) ) ;
}
face_cascade . detectMultiScale ( gray , faces , 1.05 , 2 , CASCADE_SCALE_IMAGE , Size ( 30 , 30 ) ) ;
return true ;
}
bool FacemarkLBFImpl : : getData ( void * items ) {
if ( items ! = 0 ) {
// do nothing
}
return true ;
CV_UNUSED ( items ) ;
return false ;
}
bool FacemarkLBFImpl : : addTrainingSample ( InputArray image , InputArray landmarks ) {
// FIXIT
std : : vector < Point2f > & _landmarks = * ( std : : vector < Point2f > * ) landmarks . getObj ( ) ;
configFaceDetector ( ) ;
prepareTrainingData ( image . getMat ( ) , _landmarks , data_faces , data_shapes , data_boxes ) ;
return true ;
}
void FacemarkLBFImpl : : training ( void * parameters ) {
if ( parameters ! = 0 ) { /*do nothing*/ }
if ( data_faces . size ( ) < 1 ) {
std : : string error_message =
" Training data is not provided. Consider to add using addTrainingSample() function! " ;
CV_Error ( CV_StsBadArg , error_message ) ;
CV_UNUSED ( parameters ) ;
if ( data_faces . empty ( ) )
{
CV_Error ( Error : : StsBadArg , " Training data is not provided. Consider to add using addTrainingSample() function! " ) ;
}
if ( strcmp ( params . cascade_face . c_str ( ) , " " ) = = 0
| | ( strcmp ( params . model_filename . c_str ( ) , " " ) = = 0 & & params . save_model )
) {
std : : string error_message = " The parameter cascade_face and model_filename should be set! " ;
CV_Error ( CV_StsBadArg , error_message ) ;
if ( params . cascade_face . empty ( ) | | ( params . model_filename . empty ( ) & & params . save_model ) )
{
CV_Error ( Error : : StsBadArg , " The parameter cascade_face and model_filename should be set! " ) ;
}
// flip the image and swap the landmark position
@ -358,10 +318,8 @@ void FacemarkLBFImpl::training(void* parameters){
for ( int i = 0 ; i < N ; i + + ) {
for ( int j = 0 ; j < params . initShape_n ; j + + ) {
int idx = i * params . initShape_n + j ;
int k = 0 ;
do {
k = rng . uniform ( 0 , N ) ;
} while ( k = = i ) ;
int k = rng . uniform ( 0 , N - 1 ) ;
k = ( k > = i ) ? k + 1 : k ; // require k != i
imgs [ idx ] = data_faces [ i ] ;
gt_shapes [ idx ] = data_shapes [ i ] ;
bboxes [ idx ] = data_boxes [ i ] ;
@ -382,12 +340,11 @@ void FacemarkLBFImpl::training(void* parameters){
bool FacemarkLBFImpl : : fit ( InputArray image , InputArray roi , InputOutputArray _landmarks , void * runtime_params )
{
if ( runtime_params ! = 0 ) {
// do nothing
}
CV_UNUSED ( runtime_params ) ;
// FIXIT
std : : vector < Rect > & faces = * ( std : : vector < Rect > * ) roi . getObj ( ) ;
if ( faces . siz e( ) < 1 ) return false ;
if ( faces . empty ( ) ) return false ;
std : : vector < std : : vector < Point2f > > & landmarks =
* ( std : : vector < std : : vector < Point2f > > * ) _landmarks . getObj ( ) ;
@ -407,13 +364,12 @@ bool FacemarkLBFImpl::fitImpl( const Mat image, std::vector<Point2f>& landmarks)
landmarks . clear ( ) ;
if ( ! isModelTrained ) {
std : : string error_message = " The LBF model is not trained yet. Please provide a trained model. " ;
CV_Error ( CV_StsBadArg , error_message ) ;
CV_Error ( Error : : StsBadArg , " The LBF model is not trained yet. Please provide a trained model. " ) ;
}
Mat img ;
if ( image . channels ( ) > 1 ) {
cvtColor ( image , img , CV _BGR2GRAY ) ;
cvtColor ( image , img , COLOR _BGR2GRAY ) ;
} else {
img = image ;
}
@ -424,13 +380,8 @@ bool FacemarkLBFImpl::fitImpl( const Mat image, std::vector<Point2f>& landmarks)
} else {
std : : vector < Rect > rects ;
if ( ! isSetDetector ) {
defaultFaceDetector ( img , rects ) ;
} else {
faceDetector ( img , rects , 0 ) ;
}
if ( rects . size ( ) = = 0 ) return 0 ; //failed to get face
if ( ! getFaces ( img , rects ) ) return 0 ;
if ( rects . empty ( ) ) return 0 ; //failed to get face
box = rects [ 0 ] ;
}
@ -470,8 +421,7 @@ void FacemarkLBFImpl::loadModel(String s){
std : : ifstream infile ;
infile . open ( s . c_str ( ) , std : : ios : : in ) ;
if ( ! infile ) {
std : : string error_message = " No valid input file was given, please check the given filename. " ;
CV_Error ( CV_StsBadArg , error_message ) ;
CV_Error ( Error : : StsBadArg , " No valid input file was given, please check the given filename. " ) ;
}
FileStorage fs ( s . c_str ( ) , FileStorage : : READ ) ;
@ -483,7 +433,7 @@ void FacemarkLBFImpl::loadModel(String s){
Rect FacemarkLBFImpl : : getBBox ( Mat & img , const Mat_ < double > shape ) {
std : : vector < Rect > rects ;
if ( ! isSet Detector) {
if ( ! face Detector) {
defaultFaceDetector ( img , rects ) ;
} else {
faceDetector ( img , rects , 0 ) ;
@ -523,7 +473,7 @@ void FacemarkLBFImpl::prepareTrainingData(Mat img, std::vector<Point2f> facePoin
std : : vector < Mat > & cropped , std : : vector < Mat > & shapes , std : : vector < BBox > & boxes )
{
if ( img . channels ( ) > 1 ) {
cvtColor ( img , img , CV _BGR2GRAY ) ;
cvtColor ( img , img , COLOR _BGR2GRAY ) ;
}
Mat shape ;
@ -697,8 +647,8 @@ void FacemarkLBFImpl::LBF::calcSimilarityTransform(const Mat &shape1, const Mat
Mat_ < double > covar1 , covar2 ;
Mat_ < double > mean1 , mean2 ;
calcCovarMatrix ( temp1 , covar1 , mean1 , CV_C OVAR_COLS ) ;
calcCovarMatrix ( temp2 , covar2 , mean2 , CV_C OVAR_COLS ) ;
calcCovarMatrix ( temp1 , covar1 , mean1 , COVAR_COLS ) ;
calcCovarMatrix ( temp2 , covar2 , mean2 , COVAR_COLS ) ;
double s1 = sqrt ( cv : : norm ( covar1 ) ) ;
double s2 = sqrt ( cv : : norm ( covar2 ) ) ;
@ -1075,7 +1025,7 @@ void FacemarkLBFImpl::Regressor::initRegressor(Params config) {
void FacemarkLBFImpl : : Regressor : : trainRegressor ( std : : vector < Mat > & imgs , std : : vector < Mat > & gt_shapes , std : : vector < Mat > & current_shapes ,
std : : vector < BBox > & bboxes , Mat & mean_shape_ , int start_from , Params config ) {
a ssert( start_from > = 0 & & start_from < stages_n ) ;
CV_A ssert( start_from > = 0 & & start_from < stages_n ) ;
mean_shape = mean_shape_ ;
int N = ( int ) imgs . size ( ) ;
Alexander Alekhin
7 years ago