filedetect.cpp

This example shows how to train and use a planar object detector

/*
Copyright 2005, 2006 Computer Vision Lab, 
Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland. 
All rights reserved.

This file is part of BazAR.

BazAR is free software; you can redistribute it and/or modify it under the
terms of the GNU General Public License as published by the Free Software
Foundation; either version 2 of the License, or (at your option) any later
version.

BazAR is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with
BazAR; if not, write to the Free Software Foundation, Inc., 51 Franklin
Street, Fifth Floor, Boston, MA 02110-1301, USA 
*/
#include <iostream>

// include everything garfeild needs
#include <garfeild.h>

// image loading and saving with OpenCV
#include <highgui.h>

using namespace std;

int main(int argc, char *argv[])
{
        // model.png is the default model image file name.
        char *modelFile= "model.jpg";

        if (argc<2) {
                cerr << "Usage: " << argv[0] 
                        << " [-m <model image>] <image> [<image> ...]\n";
                return -1;
        }

        // search for model name
        for (int i=1; i<argc-1; i++) {
                if (strcmp(argv[i], "-m") ==0) {
                        modelFile = argv[i+1];
                        break;
                }
        }

        // Allocate the detector object
        planar_object_recognizer detector;

        // A lower threshold will allow detection in harder conditions, but
        // might lead to false positives.
        detector.match_score_threshold=.03f;

        detector.ransac_dist_threshold = 5;
        detector.max_ransac_iterations = 500;
        detector.non_linear_refine_threshold = 1.5;
        detector.min_view_rate=.2;
        detector.views_number = 100;

        // Train or load classifier
        if(!detector.build_with_cache(
                                string(modelFile), // mode image file name
                                400,               // maximum number of keypoints on the model
                                32,                // patch size in pixels
                                3,                 // yape radius. Use 3,5 or 7.
                                16,                // number of trees for the classifier. Somewhere between 12-50
                                3                  // number of levels in the gaussian pyramid
                                ))
        {
                cerr << modelFile << ": Error while loading model image or classifier!\n";
                return -2;
        }

        // The detector is now ready. Load input images.
        for (int i=1; i<argc; ++i) {
                if (argv[i][0] == '-') { ++i; continue; }
                IplImage *im = cvLoadImage(argv[i],0);
                if (!im) {
                        cerr << argv[i] << ": unable to load image.\n";
                        continue;
                } 
                if (detector.detect(im)) {
                        cout << argv[i] << ": detection succeeded!\n";

                        // save an image showing the result
                        IplImage *result = detector.create_result_image(im, false,true,false,true);
                        char fn[512]; sprintf(fn,"result_%s", argv[i]);
                        cvSaveImage(fn, result);
                        cvReleaseImage(&result);
                } else {
                        cout << argv[i] << ": detection failed.\n";
                }
        }
        return 0;
}