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469 lines
15 KiB
C++
469 lines
15 KiB
C++
/*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) 2013, 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 "seamless_cloning.hpp"
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#include "opencv2/highgui.hpp"
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#include <iostream>
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#include <complex>
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using namespace cv;
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using namespace std;
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void Cloning::computeGradientX( const Mat &img, Mat &gx)
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{
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Mat kernel = Mat::zeros(1, 3, CV_8S);
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kernel.at<char>(0,2) = 1;
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kernel.at<char>(0,1) = -1;
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filter2D(img, gx, CV_32F, kernel);
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}
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void Cloning::computeGradientY( const Mat &img, Mat &gy)
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{
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Mat kernel = Mat::zeros(3, 1, CV_8S);
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kernel.at<char>(2,0) = 1;
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kernel.at<char>(1,0) = -1;
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filter2D(img, gy, CV_32F, kernel);
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}
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void Cloning::computeLaplacianX( const Mat &img, Mat &laplacianX)
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{
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Mat kernel = Mat::zeros(1, 3, CV_8S);
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kernel.at<char>(0,0) = -1;
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kernel.at<char>(0,1) = 1;
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filter2D(img, laplacianX, CV_32F, kernel);
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}
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void Cloning::computeLaplacianY( const Mat &img, Mat &laplacianY)
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{
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Mat kernel = Mat::zeros(3, 1, CV_8S);
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kernel.at<char>(0,0) = -1;
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kernel.at<char>(1,0) = 1;
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filter2D(img, laplacianY, CV_32F, kernel);
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}
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void Cloning::dst(const Mat& src, Mat& dest, bool invert)
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{
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Mat temp = Mat::zeros(src.rows, 2 * src.cols + 2, CV_32F);
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int flag = invert ? DFT_ROWS + DFT_SCALE + DFT_INVERSE: DFT_ROWS;
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src.copyTo(temp(Rect(1,0, src.cols, src.rows)));
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for(int j = 0 ; j < src.rows ; ++j)
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{
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for(int i = 0 ; i < src.cols ; ++i)
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{
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temp.ptr<float>(j)[src.cols + 2 + i] = - src.ptr<float>(j)[src.cols - 1 - i];
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}
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}
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Mat planes[] = {temp, Mat::zeros(temp.size(), CV_32F)};
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Mat complex;
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merge(planes, 2, complex);
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dft(complex, complex, flag);
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split(complex, planes);
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temp = Mat::zeros(src.cols, 2 * src.rows + 2, CV_32F);
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for(int j = 0 ; j < src.cols ; ++j)
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{
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for(int i = 0 ; i < src.rows ; ++i)
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{
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float val = planes[1].ptr<float>(i)[j + 1];
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temp.ptr<float>(j)[i + 1] = val;
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temp.ptr<float>(j)[temp.cols - 1 - i] = - val;
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}
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}
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Mat planes2[] = {temp, Mat::zeros(temp.size(), CV_32F)};
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merge(planes2, 2, complex);
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dft(complex, complex, flag);
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split(complex, planes2);
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temp = planes2[1].t();
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dest = Mat::zeros(src.size(), CV_32F);
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temp(Rect( 0, 1, src.cols, src.rows)).copyTo(dest);
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}
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void Cloning::idst(const Mat& src, Mat& dest)
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{
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dst(src, dest, true);
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}
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void Cloning::solve(const Mat &img, std::vector<float>& mod_diff, Mat &result)
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{
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const int w = img.size().width;
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const int h = img.size().height;
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Mat ModDiff(h-2, w-2, CV_32F, &mod_diff[0]);
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Mat res;
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dst(ModDiff, res);
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for(int j = 0 ; j < h-2; j++)
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{
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for(int i = 0 ; i < w-2; i++)
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{
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res.ptr<float>(j)[i] /= (filter_X[i] + filter_Y[j] - 4);
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}
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}
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idst(res, ModDiff);
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//first col
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for(int i = 0 ; i < w ; ++i)
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result.ptr<unsigned char>(0)[i] = img.ptr<unsigned char>(0)[i];
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for(int j = 1 ; j < h-1 ; ++j)
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{
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//first row
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result.ptr<unsigned char>(j)[0] = img.ptr<unsigned char>(j)[0];
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for(int i = 1 ; i < w-1 ; ++i)
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{
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//saturate cast is not used here, because it behaves differently from the previous implementation
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//most notable, saturate_cast rounds before truncating, here it's the opposite.
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float value = ModDiff.ptr<float>(j-1)[i-1];
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if(value < 0.)
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result.ptr<unsigned char>(j)[i] = 0;
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else if (value > 255.0)
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result.ptr<unsigned char>(j)[i] = 255;
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else
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result.ptr<unsigned char>(j)[i] = static_cast<unsigned char>(value);
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}
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//last row
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result.ptr<unsigned char>(j)[w-1] = img.ptr<unsigned char>(j)[w-1];
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}
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//last col
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for(int i = 0 ; i < w ; ++i)
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result.ptr<unsigned char>(h-1)[i] = img.ptr<unsigned char>(h-1)[i];
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}
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void Cloning::poissonSolver(const Mat &img, Mat &laplacianX , Mat &laplacianY, Mat &result)
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{
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const int w = img.size().width;
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const int h = img.size().height;
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unsigned long int idx;
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Mat lap = Mat(img.size(),CV_32FC1);
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lap = laplacianX + laplacianY;
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Mat bound = img.clone();
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rectangle(bound, Point(1, 1), Point(img.cols-2, img.rows-2), Scalar::all(0), -1);
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std::vector<float> boundary_point(h*w, 0.);
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for(int i =1;i<h-1;i++)
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for(int j=1;j<w-1;j++)
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{
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idx=i*w + j;
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boundary_point[idx] = -4*(int)bound.at<uchar>(i,j) + (int)bound.at<uchar>(i,(j+1)) + (int)bound.at<uchar>(i,(j-1))
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+ (int)bound.at<uchar>(i-1,j) + (int)bound.at<uchar>(i+1,j);
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}
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Mat diff = Mat(h,w,CV_32FC1);
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for(int i =0;i<h;i++)
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{
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for(int j=0;j<w;j++)
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{
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idx = i*w+j;
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diff.at<float>(i,j) = (float) (lap.at<float>(i,j) - boundary_point[idx]);
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}
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}
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std::vector<float> mod_diff((h-2)*(w-2), 0.);
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for(int i = 0 ; i < h-2;i++)
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{
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for(int j = 0 ; j < w-2; j++)
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{
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idx = i*(w-2) + j;
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mod_diff[idx] = diff.at<float>(i+1,j+1);
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}
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}
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///////////////////////////////////////////////////// Find DST /////////////////////////////////////////////////////
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solve(img,mod_diff,result);
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}
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void Cloning::initVariables(const Mat &destination, const Mat &binaryMask)
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{
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destinationGradientX = Mat(destination.size(),CV_32FC3);
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destinationGradientY = Mat(destination.size(),CV_32FC3);
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patchGradientX = Mat(destination.size(),CV_32FC3);
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patchGradientY = Mat(destination.size(),CV_32FC3);
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binaryMaskFloat = Mat(binaryMask.size(),CV_32FC1);
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binaryMaskFloatInverted = Mat(binaryMask.size(),CV_32FC1);
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//init of the filters used in the dst
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const int w = destination.size().width;
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filter_X.resize(w - 2);
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for(int i = 0 ; i < w-2 ; ++i)
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filter_X[i] = 2.0f * std::cos(CV_PI * (i + 1) / (w - 1));
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const int h = destination.size().height;
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filter_Y.resize(h - 2);
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for(int j = 0 ; j < h - 2 ; ++j)
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filter_Y[j] = 2.0f * std::cos(CV_PI * (j + 1) / (h - 1));
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}
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void Cloning::computeDerivatives(const Mat& destination, const Mat &patch, const Mat &binaryMask)
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{
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initVariables(destination,binaryMask);
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computeGradientX(destination,destinationGradientX);
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computeGradientY(destination,destinationGradientY);
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computeGradientX(patch,patchGradientX);
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computeGradientY(patch,patchGradientY);
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Mat Kernel(Size(3, 3), CV_8UC1);
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Kernel.setTo(Scalar(1));
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erode(binaryMask, binaryMask, Kernel, Point(-1,-1), 3);
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binaryMask.convertTo(binaryMaskFloat,CV_32FC1,1.0/255.0);
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}
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void Cloning::scalarProduct(Mat mat, float r, float g, float b)
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{
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vector <Mat> channels;
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split(mat,channels);
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multiply(channels[2],r,channels[2]);
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multiply(channels[1],g,channels[1]);
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multiply(channels[0],b,channels[0]);
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merge(channels,mat);
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}
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void Cloning::arrayProduct(const cv::Mat& lhs, const cv::Mat& rhs, cv::Mat& result) const
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{
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vector <Mat> lhs_channels;
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vector <Mat> result_channels;
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split(lhs,lhs_channels);
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split(result,result_channels);
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for(int chan = 0 ; chan < 3 ; ++chan)
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multiply(lhs_channels[chan],rhs,result_channels[chan]);
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merge(result_channels,result);
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}
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void Cloning::poisson(const Mat &destination)
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{
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Mat laplacianX = Mat(destination.size(),CV_32FC3);
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Mat laplacianY = Mat(destination.size(),CV_32FC3);
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laplacianX = destinationGradientX + patchGradientX;
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laplacianY = destinationGradientY + patchGradientY;
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computeLaplacianX(laplacianX,laplacianX);
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computeLaplacianY(laplacianY,laplacianY);
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split(laplacianX,rgbx_channel);
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split(laplacianY,rgby_channel);
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split(destination,output);
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for(int chan = 0 ; chan < 3 ; ++chan)
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{
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poissonSolver(output[chan], rgbx_channel[chan], rgby_channel[chan], output[chan]);
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}
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}
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void Cloning::evaluate(const Mat &I, const Mat &wmask, const Mat &cloned)
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{
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bitwise_not(wmask,wmask);
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wmask.convertTo(binaryMaskFloatInverted,CV_32FC1,1.0/255.0);
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arrayProduct(destinationGradientX,binaryMaskFloatInverted, destinationGradientX);
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arrayProduct(destinationGradientY,binaryMaskFloatInverted, destinationGradientY);
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poisson(I);
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merge(output,cloned);
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}
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void Cloning::normalClone(const Mat &destination, const Mat &patch, const Mat &binaryMask, Mat &cloned, int flag)
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{
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int w = destination.size().width;
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int h = destination.size().height;
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int channel = destination.channels();
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computeDerivatives(destination,patch,binaryMask);
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switch(flag)
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{
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case NORMAL_CLONE:
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arrayProduct(patchGradientX,binaryMaskFloat, patchGradientX);
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arrayProduct(patchGradientY,binaryMaskFloat, patchGradientY);
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break;
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case MIXED_CLONE:
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for(int i=0;i < h; i++)
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{
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for(int j=0; j < w; j++)
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{
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for(int c=0;c<channel;++c)
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{
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if(abs(patchGradientX.at<float>(i,j*channel+c) - patchGradientY.at<float>(i,j*channel+c)) >
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abs(destinationGradientX.at<float>(i,j*channel+c) - destinationGradientY.at<float>(i,j*channel+c)))
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{
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patchGradientX.at<float>(i,j*channel+c) = patchGradientX.at<float>(i,j*channel+c)
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* binaryMaskFloat.at<float>(i,j);
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patchGradientY.at<float>(i,j*channel+c) = patchGradientY.at<float>(i,j*channel+c)
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* binaryMaskFloat.at<float>(i,j);
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}
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else
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{
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patchGradientX.at<float>(i,j*channel+c) = destinationGradientX.at<float>(i,j*channel+c)
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* binaryMaskFloat.at<float>(i,j);
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patchGradientY.at<float>(i,j*channel+c) = destinationGradientY.at<float>(i,j*channel+c)
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* binaryMaskFloat.at<float>(i,j);
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}
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}
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}
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}
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break;
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case MONOCHROME_TRANSFER:
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Mat gray = Mat(patch.size(),CV_8UC1);
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Mat gray8 = Mat(patch.size(),CV_8UC3);
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cvtColor(patch, gray, COLOR_BGR2GRAY );
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vector <Mat> temp;
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split(destination,temp);
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gray.copyTo(temp[2]);
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gray.copyTo(temp[1]);
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gray.copyTo(temp[0]);
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merge(temp,gray8);
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computeGradientX(gray8,patchGradientX);
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computeGradientY(gray8,patchGradientY);
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arrayProduct(patchGradientX, binaryMaskFloat, patchGradientX);
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arrayProduct(patchGradientY, binaryMaskFloat, patchGradientY);
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break;
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}
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evaluate(destination,binaryMask,cloned);
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}
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void Cloning::localColorChange(Mat &I, Mat &mask, Mat &wmask, Mat &cloned, float red_mul=1.0,
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float green_mul=1.0, float blue_mul=1.0)
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{
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computeDerivatives(I,mask,wmask);
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arrayProduct(patchGradientX,binaryMaskFloat, patchGradientX);
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arrayProduct(patchGradientY,binaryMaskFloat, patchGradientY);
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scalarProduct(patchGradientX,red_mul,green_mul,blue_mul);
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scalarProduct(patchGradientY,red_mul,green_mul,blue_mul);
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evaluate(I,wmask,cloned);
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}
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void Cloning::illuminationChange(Mat &I, Mat &mask, Mat &wmask, Mat &cloned, float alpha, float beta)
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{
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computeDerivatives(I,mask,wmask);
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arrayProduct(patchGradientX,binaryMaskFloat, patchGradientX);
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arrayProduct(patchGradientY,binaryMaskFloat, patchGradientY);
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Mat mag = Mat(I.size(),CV_32FC3);
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magnitude(patchGradientX,patchGradientY,mag);
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Mat multX, multY, multx_temp, multy_temp;
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multiply(patchGradientX,pow(alpha,beta),multX);
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pow(mag,-1*beta, multx_temp);
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multiply(multX,multx_temp, patchGradientX);
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patchNaNs(patchGradientX);
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multiply(patchGradientY,pow(alpha,beta),multY);
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pow(mag,-1*beta, multy_temp);
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multiply(multY,multy_temp,patchGradientY);
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patchNaNs(patchGradientY);
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Mat zeroMask = (patchGradientX != 0);
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patchGradientX.copyTo(patchGradientX, zeroMask);
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patchGradientY.copyTo(patchGradientY, zeroMask);
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evaluate(I,wmask,cloned);
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}
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void Cloning::textureFlatten(Mat &I, Mat &mask, Mat &wmask, float low_threshold,
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float high_threshold, int kernel_size, Mat &cloned)
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{
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computeDerivatives(I,mask,wmask);
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Mat out = Mat(mask.size(),CV_8UC1);
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Canny(mask,out,low_threshold,high_threshold,kernel_size);
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Mat zeros(patchGradientX.size(), CV_32FC3);
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zeros.setTo(0);
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Mat zerosMask = (out != 255);
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zeros.copyTo(patchGradientX, zerosMask);
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zeros.copyTo(patchGradientY, zerosMask);
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arrayProduct(patchGradientX,binaryMaskFloat, patchGradientX);
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arrayProduct(patchGradientY,binaryMaskFloat, patchGradientY);
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evaluate(I,wmask,cloned);
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}
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