#include "opencv2/imgproc.hpp"

#include "opencv2/highgui.hpp"

#include "opencv2/ml.hpp"

#include "opencv2/objdetect.hpp"

#include "opencv2/videoio.hpp"

#include <iostream>

#include <time.h>

using namespace cv;

using namespace cv::ml;

using namespace std;

vector< float > get_svm_detector( const Ptr< SVM >& svm );

void convert_to_ml( const std::vector< Mat > & train_samples, Mat& trainData );

void load_images( const String & dirname, vector< Mat > & img_lst, bool showImages );

void sample_neg( const vector< Mat > & full_neg_lst, vector< Mat > & neg_lst, const Size & size );

void computeHOGs( const Size wsize, const vector< Mat > & img_lst, vector< Mat > & gradient_lst, bool use_flip );

void test_trained_detector( String obj_det_filename, String test_dir, String videofilename );

vector< float > get_svm_detector( const Ptr< SVM >& svm )

{

// get the support vectors

Mat sv = svm->getSupportVectors();

const int sv_total = sv.rows;

// get the decision function

Mat alpha, svidx;

double rho = svm->getDecisionFunction( 0, alpha, svidx );

CV_Assert( alpha.total() == 1 && svidx.total() == 1 && sv_total == 1 );

CV_Assert( (alpha.type() == CV_64F && alpha.at<double>(0) == 1.) ||

(alpha.type() == CV_32F && alpha.at<float>(0) == 1.f) );

CV_Assert( sv.type() == CV_32F );

vector< float > hog_detector( sv.cols + 1 );

memcpy( &hog_detector[0], sv.ptr(), sv.cols*sizeof( hog_detector[0] ) );

hog_detector[sv.cols] = (float)-rho;

return hog_detector;

}

/*

* Convert training/testing set to be used by OpenCV Machine Learning algorithms.

* TrainData is a matrix of size (#samples x max(#cols,#rows) per samples), in 32FC1.

* Transposition of samples are made if needed.

*/

void convert_to_ml( const vector< Mat > & train_samples, Mat& trainData )

{

//--Convert data

const int rows = (int)train_samples.size();

const int cols = (int)std::max( train_samples[0].cols, train_samples[0].rows );

Mat tmp( 1, cols, CV_32FC1 ); //< used for transposition if needed

trainData = Mat( rows, cols, CV_32FC1 );

for( size_t i = 0 ; i < train_samples.size(); ++i )

{

CV_Assert( train_samples[i].cols == 1 || train_samples[i].rows == 1 );

if( train_samples[i].cols == 1 )

{

transpose( train_samples[i], tmp );

tmp.copyTo( trainData.row( (int)i ) );

}

else if( train_samples[i].rows == 1 )

{

train_samples[i].copyTo( trainData.row( (int)i ) );

}

}

}

void load_images( const String & dirname, vector< Mat > & img_lst, bool showImages = false )

{

vector< String > files;

glob( dirname, files );

for ( size_t i = 0; i < files.size(); ++i )

{

Mat img = imread( files[i] ); // load the image

if ( img.empty() ) // invalid image, skip it.

{

cout << files[i] << " is invalid!" << endl;

continue;

}

if ( showImages )

{

imshow( "image", img );

waitKey( 1 );

}

img_lst.push_back( img );

}

}

void sample_neg( const vector< Mat > & full_neg_lst, vector< Mat > & neg_lst, const Size & size )

{

Rect box;

box.width = size.width;

box.height = size.height;

const int size_x = box.width;

const int size_y = box.height;

srand( (unsigned int)time( NULL ) );

for ( size_t i = 0; i < full_neg_lst.size(); i++ )

if ( full_neg_lst[i].cols > box.width && full_neg_lst[i].rows > box.height )

{

box.x = rand() % ( full_neg_lst[i].cols - size_x );

box.y = rand() % ( full_neg_lst[i].rows - size_y );

Mat roi = full_neg_lst[i]( box );

neg_lst.push_back( roi.clone() );

}

}

void computeHOGs( const Size wsize, const vector< Mat > & img_lst, vector< Mat > & gradient_lst, bool use_flip )

{

HOGDescriptor hog;

hog.winSize = wsize;

Mat gray;

vector< float > descriptors;

for( size_t i = 0 ; i < img_lst.size(); i++ )

{

if ( img_lst[i].cols >= wsize.width && img_lst[i].rows >= wsize.height )

{

Rect r = Rect(( img_lst[i].cols - wsize.width ) / 2,

( img_lst[i].rows - wsize.height ) / 2,

wsize.width,

wsize.height);

cvtColor( img_lst[i](r), gray, COLOR_BGR2GRAY );

hog.compute( gray, descriptors, Size( 8, 8 ), Size( 0, 0 ) );

gradient_lst.push_back( Mat( descriptors ).clone() );

if ( use_flip )

{

flip( gray, gray, 1 );

hog.compute( gray, descriptors, Size( 8, 8 ), Size( 0, 0 ) );

gradient_lst.push_back( Mat( descriptors ).clone() );

}

}

}

}

void test_trained_detector( String obj_det_filename, String test_dir, String videofilename )

{

cout << "Testing trained detector..." << endl;

HOGDescriptor hog;

hog.load( obj_det_filename );

vector< String > files;

glob( test_dir, files );

int delay = 0;

VideoCapture cap;

if ( videofilename != "" )

{

if ( videofilename.size() == 1 && isdigit( videofilename[0] ) )

cap.open( videofilename[0] - '0' );

else

cap.open( videofilename );

}

obj_det_filename = "testing " + obj_det_filename;

namedWindow( obj_det_filename, WINDOW_NORMAL );

for( size_t i=0;; i++ )

{

Mat img;

if ( cap.isOpened() )

{

cap >> img;

delay = 1;

}

else if( i < files.size() )

{

img = imread( files[i] );

}

if ( img.empty() )

{

return;

}

vector< Rect > detections;

vector< double > foundWeights;

hog.detectMultiScale( img, detections, foundWeights );

for ( size_t j = 0; j < detections.size(); j++ )

{

Scalar color = Scalar( 0, foundWeights[j] * foundWeights[j] * 200, 0 );

rectangle( img, detections[j], color, img.cols / 400 + 1 );

}

imshow( obj_det_filename, img );

if( waitKey( delay ) == 27 )

{

return;

}

}

}

int main( int argc, char** argv )

{

const char* keys =

{

"{help h| | show help message}"

"{pd | | path of directory contains positive images}"

"{nd | | path of directory contains negative images}"

"{td | | path of directory contains test images}"

"{tv | | test video file name}"

"{dw | | width of the detector}"

"{dh | | height of the detector}"

"{f |false| indicates if the program will generate and use mirrored samples or not}"

"{d |false| train twice}"

"{t |false| test a trained detector}"

"{v |false| visualize training steps}"

"{fn |my_detector.yml| file name of trained SVM}"

};

CommandLineParser parser( argc, argv, keys );

if ( parser.has( "help" ) )

{

parser.printMessage();

exit( 0 );

}

String pos_dir = parser.get< String >( "pd" );

String neg_dir = parser.get< String >( "nd" );

String test_dir = parser.get< String >( "td" );

String obj_det_filename = parser.get< String >( "fn" );

String videofilename = parser.get< String >( "tv" );

int detector_width = parser.get< int >( "dw" );

int detector_height = parser.get< int >( "dh" );

bool test_detector = parser.get< bool >( "t" );

bool train_twice = parser.get< bool >( "d" );

bool visualization = parser.get< bool >( "v" );

bool flip_samples = parser.get< bool >( "f" );

if ( test_detector )

{

test_trained_detector( obj_det_filename, test_dir, videofilename );

exit( 0 );

}

if( pos_dir.empty() || neg_dir.empty() )

{

parser.printMessage();

cout << "Wrong number of parameters.\n\n"

<< "Example command line:\n" << argv[0] << " -dw=64 -dh=128 -pd=/INRIAPerson/96X160H96/Train/pos -nd=/INRIAPerson/neg -td=/INRIAPerson/Test/pos -fn=HOGpedestrian64x128.xml -d\n"

<< "\nExample command line for testing trained detector:\n" << argv[0] << " -t -fn=HOGpedestrian64x128.xml -td=/INRIAPerson/Test/pos";

exit( 1 );

}

vector< Mat > pos_lst, full_neg_lst, neg_lst, gradient_lst;

vector< int > labels;

clog << "Positive images are being loaded..." ;

load_images( pos_dir, pos_lst, visualization );

if ( pos_lst.size() > 0 )

{

clog << "...[done]" << endl;

}

else

{

clog << "no image in " << pos_dir <<endl;

return 1;

}

Size pos_image_size = pos_lst[0].size();

if ( detector_width && detector_height )

{

pos_image_size = Size( detector_width, detector_height );

}

else

{

for ( size_t i = 0; i < pos_lst.size(); ++i )

{

if( pos_lst[i].size() != pos_image_size )

{

cout << "All positive images should be same size!" << endl;

exit( 1 );

}

}

pos_image_size = pos_image_size / 8 * 8;

}

clog << "Negative images are being loaded...";

load_images( neg_dir, full_neg_lst, false );

sample_neg( full_neg_lst, neg_lst, pos_image_size );

clog << "...[done]" << endl;

clog << "Histogram of Gradients are being calculated for positive images...";

computeHOGs( pos_image_size, pos_lst, gradient_lst, flip_samples );

size_t positive_count = gradient_lst.size();

labels.assign( positive_count, +1 );

clog << "...[done] ( positive count : " << positive_count << " )" << endl;

clog << "Histogram of Gradients are being calculated for negative images...";

computeHOGs( pos_image_size, neg_lst, gradient_lst, flip_samples );

size_t negative_count = gradient_lst.size() - positive_count;

labels.insert( labels.end(), negative_count, -1 );

CV_Assert( positive_count < labels.size() );

clog << "...[done] ( negative count : " << negative_count << " )" << endl;

Mat train_data;

convert_to_ml( gradient_lst, train_data );

clog << "Training SVM...";

Ptr< SVM > svm = SVM::create();

/* Default values to train SVM */

svm->setCoef0( 0.0 );

svm->setDegree( 3 );

svm->setTermCriteria( TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, 1e-3 ) );

svm->setGamma( 0 );

svm->setKernel( SVM::LINEAR );

svm->setNu( 0.5 );

svm->setP( 0.1 ); // for EPSILON_SVR, epsilon in loss function?

svm->setC( 0.01 ); // From paper, soft classifier

svm->setType( SVM::EPS_SVR ); // C_SVC; // EPSILON_SVR; // may be also NU_SVR; // do regression task

svm->train( train_data, ROW_SAMPLE, labels );

clog << "...[done]" << endl;

if ( train_twice )

{

clog << "Testing trained detector on negative images. This may take a few minutes...";

HOGDescriptor my_hog;

my_hog.winSize = pos_image_size;

// Set the trained svm to my_hog

my_hog.setSVMDetector( get_svm_detector( svm ) );

vector< Rect > detections;

vector< double > foundWeights;

for ( size_t i = 0; i < full_neg_lst.size(); i++ )

{

if ( full_neg_lst[i].cols >= pos_image_size.width && full_neg_lst[i].rows >= pos_image_size.height )

my_hog.detectMultiScale( full_neg_lst[i], detections, foundWeights );

else

detections.clear();

for ( size_t j = 0; j < detections.size(); j++ )

{

Mat detection = full_neg_lst[i]( detections[j] ).clone();

resize( detection, detection, pos_image_size, 0, 0, INTER_LINEAR_EXACT);

neg_lst.push_back( detection );

}

if ( visualization )

{

for ( size_t j = 0; j < detections.size(); j++ )

{

rectangle( full_neg_lst[i], detections[j], Scalar( 0, 255, 0 ), 2 );

}

imshow( "testing trained detector on negative images", full_neg_lst[i] );

waitKey( 5 );

}

}

clog << "...[done]" << endl;

gradient_lst.clear();

clog << "Histogram of Gradients are being calculated for positive images...";

computeHOGs( pos_image_size, pos_lst, gradient_lst, flip_samples );

positive_count = gradient_lst.size();

clog << "...[done] ( positive count : " << positive_count << " )" << endl;

clog << "Histogram of Gradients are being calculated for negative images...";

computeHOGs( pos_image_size, neg_lst, gradient_lst, flip_samples );

negative_count = gradient_lst.size() - positive_count;

clog << "...[done] ( negative count : " << negative_count << " )" << endl;

labels.clear();

labels.assign(positive_count, +1);

labels.insert(labels.end(), negative_count, -1);

clog << "Training SVM again...";

convert_to_ml( gradient_lst, train_data );

svm->train( train_data, ROW_SAMPLE, labels );

clog << "...[done]" << endl;

}

HOGDescriptor hog;

hog.winSize = pos_image_size;

hog.setSVMDetector( get_svm_detector( svm ) );

hog.save( obj_det_filename );

test_trained_detector( obj_det_filename, test_dir, videofilename );

return 0;

}