/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // 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 // // Copyright (C) 2014, 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: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's 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. // // * The name of the copyright holders may not 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 the Intel Corporation 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. // //M*/ #include "precomp.hpp" namespace cv { namespace saliency { /** * StaticSaliency */ bool StaticSaliency::computeBinaryMap( InputArray _saliencyMap, OutputArray _binaryMap ) { Mat saliencyMap = _saliencyMap.getMat(); CV_CheckTypeEQ(saliencyMap.type(), CV_32FC1, ""); Mat labels = Mat::zeros( saliencyMap.rows * saliencyMap.cols, 1, 1 ); Mat samples = Mat_<float>( saliencyMap.rows * saliencyMap.cols, 1 ); Mat centers; TermCriteria terminationCriteria; terminationCriteria.epsilon = 0.2; terminationCriteria.maxCount = 1000; terminationCriteria.type = TermCriteria::COUNT + TermCriteria::EPS; int elemCounter = 0; for ( int i = 0; i < saliencyMap.rows; i++ ) { for ( int j = 0; j < saliencyMap.cols; j++ ) { samples.at<float>( elemCounter, 0 ) = saliencyMap.at<float>( i, j ); elemCounter++; } } kmeans( samples, 5, labels, terminationCriteria, 5, KMEANS_RANDOM_CENTERS, centers ); Mat outputMat = Mat_<float>( saliencyMap.size() ); int intCounter = 0; for ( int x = 0; x < saliencyMap.rows; x++ ) { for ( int y = 0; y < saliencyMap.cols; y++ ) { outputMat.at<float>( x, y ) = centers.at<float>( labels.at<int>( intCounter, 0 ), 0 ); intCounter++; } } //Convert outputMat = outputMat * 255; outputMat.convertTo( outputMat, CV_8U ); // adaptative thresholding using Otsu's method, to make saliency map binary _binaryMap.createSameSize(outputMat, outputMat.type()); Mat BinaryMap = _binaryMap.getMat(); threshold( outputMat, BinaryMap, 0, 255, THRESH_BINARY | THRESH_OTSU ); return true; } }/* namespace saliency */ }/* namespace cv */