samontab
10 years ago
committed by
Vladislav Sovrasov
parent
25575af653
commit
a39e3623ae
5 changed files with 361 additions and 0 deletions
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp" |
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namespace cv |
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{ |
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namespace saliency |
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{ |
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/**
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* Fine Grained Saliency |
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*/ |
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StaticSaliencyFineGrained::StaticSaliencyFineGrained() |
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{ |
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className = "FINE_GRAINED"; |
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} |
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StaticSaliencyFineGrained::~StaticSaliencyFineGrained() |
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{ |
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} |
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bool StaticSaliencyFineGrained::computeSaliencyImpl( const InputArray image, OutputArray saliencyMap ) |
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{ |
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Mat dst(Size(image.getMat().cols, image.getMat().rows), CV_8UC1); |
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calcIntensityChannel(image.getMat(), dst); |
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dst.copyTo(saliencyMap); |
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#ifdef SALIENCY_DEBUG |
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// visualize saliency map
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imshow( "Saliency Map Interna", saliencyMap ); |
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#endif |
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return true; |
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} |
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void StaticSaliencyFineGrained::copyImage(Mat srcArg, Mat dstArg) |
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{ |
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srcArg.copyTo(dstArg); |
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} |
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void StaticSaliencyFineGrained::calcIntensityChannel(Mat srcArg, Mat dstArg) |
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{ |
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if(dstArg.channels() > 1) |
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{ |
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//("Error: Destiny image must have only one channel.\n");
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return; |
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} |
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const int numScales = 6; |
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Mat intensityScaledOn[numScales]; |
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Mat intensityScaledOff[numScales]; |
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Mat gray = Mat::zeros(Size(srcArg.cols, srcArg.rows), CV_8UC1); |
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Mat integralImage(Size(srcArg.cols + 1, srcArg.rows + 1), CV_32FC1); |
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Mat intensity(Size(srcArg.cols, srcArg.rows), CV_8UC1); |
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Mat intensityOn(Size(srcArg.cols, srcArg.rows), CV_8UC1); |
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Mat intensityOff(Size(srcArg.cols, srcArg.rows), CV_8UC1); |
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int i; |
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int neighborhood; |
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int neighborhoods[] = {3*4, 3*4*2, 3*4*2*2, 7*4, 7*4*2, 7*4*2*2}; |
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for(i=0; i<numScales; i++) |
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{ |
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intensityScaledOn[i] = Mat(Size(srcArg.cols, srcArg.rows), CV_8UC1); |
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intensityScaledOff[i] = Mat(Size(srcArg.cols, srcArg.rows), CV_8UC1); |
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} |
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// Prepare the input image: put it into a grayscale image.
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if(srcArg.channels()==3) |
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{ |
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cvtColor(srcArg, gray, COLOR_BGR2GRAY); |
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} |
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else |
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{ |
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srcArg.copyTo(gray); |
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} |
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// smooth pixels at least twice, as done by Frintrop and Itti
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GaussianBlur( gray, gray, Size( 3, 3 ), 0, 0 ); |
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GaussianBlur( gray, gray, Size( 3, 3 ), 0, 0 ); |
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// Calculate integral image, only once.
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integral(gray, integralImage, CV_32F); |
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for(i=0; i< numScales; i++) |
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{ |
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neighborhood = neighborhoods[i] ; |
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getIntensityScaled(integralImage, gray, intensityScaledOn[i], intensityScaledOff[i], neighborhood); |
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} |
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mixScales(intensityScaledOn, intensityOn, intensityScaledOff, intensityOff, numScales); |
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mixOnOff(intensityOn, intensityOff, intensity); |
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intensity.copyTo(dstArg); |
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} |
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void StaticSaliencyFineGrained::getIntensityScaled(Mat integralImage, Mat gray, Mat intensityScaledOn, Mat intensityScaledOff, int neighborhood) |
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{ |
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float value, meanOn, meanOff; |
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Point2i point; |
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int x,y; |
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intensityScaledOn.setTo(Scalar::all(0)); |
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intensityScaledOff.setTo(Scalar::all(0)); |
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for(y = 0; y < gray.rows; y++) |
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{ |
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for(x = 0; x < gray.cols; x++) |
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{ |
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point.x = x; |
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point.y = y; |
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value = getMean(integralImage, point, neighborhood, gray.at<uchar>(y, x)); |
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meanOn = gray.at<uchar>(y, x) - value; |
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meanOff = value - gray.at<uchar>(y, x); |
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if(meanOn > 0) |
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intensityScaledOn.at<uchar>(y, x) = (uchar)meanOn; |
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else |
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intensityScaledOn.at<uchar>(y, x) = 0; |
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if(meanOff > 0) |
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intensityScaledOff.at<uchar>(y, x) = (uchar)meanOff; |
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else |
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intensityScaledOff.at<uchar>(y, x) = 0; |
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} |
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} |
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} |
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float StaticSaliencyFineGrained::getMean(Mat srcArg, Point2i PixArg, int neighbourhood, int centerVal) |
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{ |
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Point2i P1, P2; |
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float value; |
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P1.x = PixArg.x - neighbourhood + 1; |
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P1.y = PixArg.y - neighbourhood + 1; |
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P2.x = PixArg.x + neighbourhood + 1; |
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P2.y = PixArg.y + neighbourhood + 1; |
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if(P1.x < 0) |
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P1.x = 0; |
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else if(P1.x > srcArg.cols - 1) |
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P1.x = srcArg.cols - 1; |
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if(P2.x < 0) |
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P2.x = 0; |
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else if(P2.x > srcArg.cols - 1) |
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P2.x = srcArg.cols - 1; |
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if(P1.y < 0) |
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P1.y = 0; |
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else if(P1.y > srcArg.rows - 1) |
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P1.y = srcArg.rows - 1; |
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if(P2.y < 0) |
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P2.y = 0; |
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else if(P2.y > srcArg.rows - 1) |
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P2.y = srcArg.rows - 1; |
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// we use the integral image to compute fast features
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value = (float) ( |
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(srcArg.at<float>(P2.y, P2.x)) + |
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(srcArg.at<float>(P1.y, P1.x)) - |
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(srcArg.at<float>(P2.y, P1.x)) - |
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(srcArg.at<float>(P1.y, P2.x)) |
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); |
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value = (value - centerVal)/ (( (P2.x - P1.x) * (P2.y - P1.y))-1) ; |
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return value; |
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} |
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void StaticSaliencyFineGrained::mixScales(Mat *intensityScaledOn, Mat intensityOn, Mat *intensityScaledOff, Mat intensityOff, const int numScales) |
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{ |
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int i=0, x, y; |
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int width = intensityScaledOn[0].cols; |
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int height = intensityScaledOn[0].rows; |
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short int maxValOn = 0, currValOn=0; |
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short int maxValOff = 0, currValOff=0; |
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int maxValSumOff = 0, maxValSumOn=0; |
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Mat mixedValuesOn(Size(width, height), CV_16UC1); |
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Mat mixedValuesOff(Size(width, height), CV_16UC1); |
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mixedValuesOn.setTo(Scalar::all(0)); |
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mixedValuesOff.setTo(Scalar::all(0)); |
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for(i=0;i<numScales;i++) |
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{ |
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for(y=0;y<height;y++) |
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for(x=0;x<width;x++) |
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{ |
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currValOn = intensityScaledOn[i].at<uchar>(y, x); |
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if(currValOn > maxValOn) |
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maxValOn = currValOn; |
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currValOff = intensityScaledOff[i].at<uchar>(y, x); |
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if(currValOff > maxValOff) |
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maxValOff = currValOff; |
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mixedValuesOn.at<unsigned short>(y, x) += currValOn; |
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mixedValuesOff.at<unsigned short>(y, x) += currValOff; |
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} |
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} |
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for(y=0;y<height;y++) |
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for(x=0;x<width;x++) |
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{ |
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currValOn = mixedValuesOn.at<unsigned short>(y, x); |
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currValOff = mixedValuesOff.at<unsigned short>(y, x); |
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if(currValOff > maxValSumOff) |
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maxValSumOff = currValOff; |
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if(currValOn > maxValSumOn) |
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maxValSumOn = currValOn; |
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} |
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for(y=0;y<height;y++) |
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for(x=0;x<width;x++) |
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{ |
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intensityOn.at<uchar>(y, x) = (uchar)(255.*((float)(mixedValuesOn.at<unsigned short>(y, x) / (float)maxValSumOn))); |
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intensityOff.at<uchar>(y, x) = (uchar)(255.*((float)(mixedValuesOff.at<unsigned short>(y, x) / (float)maxValSumOff))); |
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} |
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} |
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void StaticSaliencyFineGrained::mixOnOff(Mat intensityOn, Mat intensityOff, Mat intensityArg) |
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{ |
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int x,y; |
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int width = intensityOn.cols; |
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int height= intensityOn.rows; |
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int maxVal=0; |
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int currValOn, currValOff, maxValSumOff, maxValSumOn; |
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Mat intensity(Size(width, height), CV_8UC1); |
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maxValSumOff = 0; |
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maxValSumOn = 0; |
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for(y=0;y<height;y++) |
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for(x=0;x<width;x++) |
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{ |
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currValOn = intensityOn.at<uchar>(y, x); |
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currValOff = intensityOff.at<uchar>(y, x); |
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if(currValOff > maxValSumOff) |
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maxValSumOff = currValOff; |
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if(currValOn > maxValSumOn) |
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maxValSumOn = currValOn; |
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} |
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if(maxValSumOn > maxValSumOff) |
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maxVal = maxValSumOn; |
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else |
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maxVal = maxValSumOff; |
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for(y=0;y<height;y++) |
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for(x=0;x<width;x++) |
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{ |
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intensity.at<uchar>(y, x) = (uchar) (255. * (float) (intensityOn.at<uchar>(y, x) + intensityOff.at<uchar>(y, x)) / (float)maxVal); |
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} |
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intensity.copyTo(intensityArg); |
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} |
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} /* namespace saliency */ |
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}/* namespace cv */ |
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