Hi guys,
I'm, trying to replicate what posted at:
http://docs.opencv.org/trunk/modules/contrib/doc/facerec/tutorial/facerec_video_recognition.html
(it's Facial Recognition from Video, using FisherFace algorithm)
I've changed the quoted code only to retrieve vector<mat> images and vector<int> labels from Database. They are input parameters for my function, and I've verified that the right .bmp images are opened
What is happening is that I always get result 1 for "prediction" vector..Even if i use different photos with different expressions (of my brother too).
I know it may seem vague, and for that reason I attach the code, but have you any suggestion on what the problem can be?
thanks
A
include "F_imageprocessing.h"
include <iostream>
include <fstream>
include <sstream>
include "opencv/cv.h"
include "opencv/cvaux.h"
include "main_derp.h"
include "opencv2/contrib/contrib.hpp"
#include "F_imageprocessing.h"
#include <iostream>
#include <fstream>
#include <sstream>
#include "opencv/cv.h"
#include "opencv/cvaux.h"
#include "main_derp.h"
#include "opencv2/contrib/contrib.hpp"
//http://docs.opencv.org/trunk/modules/contrib/doc/facerec/tutorial/facerec_video_recognition.html
imageprocessing::imageprocessing(QObject *parent) :
QObject(parent)
{
} }
int
imageprocessing::imagecheck(QVector<qstring> imageprocessing::imagecheck(QVector<QString> img_paths, QVector<int> img_ID)
{
int prediction = 0;
//string fn_haar = (QDir::currentPath()+"/haarcascade_frontalface_default.xml").toStdString();
string fn_haar = "/opt/local/share/OpenCV/haarcascades/haarcascade_frontalface_default.xml";
vector<mat> vector<Mat> images;
vector<int> labels;
for (int j=0; j<img_paths.count();
j++)="" {="" if(!img_paths[j].tostdstring().empty())="" {="" qimage="" q_image;="" q_image.load(qstring(img_paths[j]));="" images.push_back(imread(img_paths[j].tostdstring().c_str(),="" 0));="" labels.push_back(img_id[j]);="" }else="" return="" -1;="" }="" int="" im_width="images[0].cols;" int="" im_height="images[0].rows;</p">
j++)
{
if(!img_paths[j].toStdString().empty())
{
QImage q_image;
q_image.load(QString(img_paths[j]));
images.push_back(imread(img_paths[j].toStdString().c_str(), 0));
labels.push_back(img_ID[j]);
}else return -1;
}
int im_width = images[0].cols;
int im_height = images[0].rows;
// Get a handle to the Video device:
VideoCapture cap(0);
cap.isOpened();
Mat frame;
//start of Algorithm (fisherface)
Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
//Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
model->train(images, labels);
CascadeClassifier haar_cascade;
haar_cascade.load(fn_haar);
for(int zz=0;zz<3;zz++) {
//for(;;) {
cap >> frame;
// Clone the current frame:
Mat original = frame.clone();
// Convert the current frame to grayscale:
Mat gray;
cvtColor(original, gray, CV_BGR2GRAY);
// Find the faces in the frame:
vector< Rect_<int> > faces;
haar_cascade.detectMultiScale(gray, faces);
for(int i = 0; i < faces.size(); i++) {
Rect face_i = faces[i];
Mat face = gray(face_i);
Mat face_resized;
cv::resize(face, face_resized, Size(im_width, im_height), 1.0, 1.0, INTER_CUBIC);
prediction = model->predict(face_resized);
qDebug()<<prediction;
/*
//create rectangle around detected image
rectangle(original, face_i, CV_RGB(0, 255,0), 1);
string box_text = format("Prediction = %d", prediction);
// Calculate the position for annotated text and put the rectangle into image
int pos_x = std::max(face_i.tl().x - 10, 0);
int pos_y = std::max(face_i.tl().y - 10, 0);
putText(original, box_text, Point(pos_x, pos_y), FONT_HERSHEY_PLAIN, 1.0, CV_RGB(0,255,0), 2.0);*/
}
/* Show the result:
imshow("face_recognizer", original);
char key = (char) waitKey(20);*/
// Exit this loop on escape:
}
qDebug()<<prediction;
return prediction;
}
}
