141 lines
5.1 KiB
C++
141 lines
5.1 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) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., 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 "opencv2/core/core.hpp"
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#include "precomp.hpp"
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#include <iostream>
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using namespace cv;
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void downsamplePoints( const Mat& src, Mat& dst, size_t count )
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{
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CV_Assert( count >= 2 );
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CV_Assert( src.cols == 1 || src.rows == 1 );
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CV_Assert( src.total() >= count );
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CV_Assert( src.type() == CV_8UC3);
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dst.create( 1, count, CV_8UC3 );
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//TODO: optimize by exploiting symmetry in the distance matrix
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Mat dists( src.total(), src.total(), CV_32FC1, Scalar(0) );
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if( dists.empty() )
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std::cerr << "Such big matrix cann't be created." << std::endl;
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for( int i = 0; i < dists.rows; i++ )
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{
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for( int j = i; j < dists.cols; j++ )
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{
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float dist = norm(src.at<Point3_<uchar> >(i) - src.at<Point3_<uchar> >(j));
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dists.at<float>(j, i) = dists.at<float>(i, j) = dist;
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}
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}
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double maxVal;
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Point maxLoc;
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minMaxLoc(dists, 0, &maxVal, 0, &maxLoc);
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dst.at<Point3_<uchar> >(0) = src.at<Point3_<uchar> >(maxLoc.x);
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dst.at<Point3_<uchar> >(1) = src.at<Point3_<uchar> >(maxLoc.y);
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Mat activedDists( 0, dists.cols, dists.type() );
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Mat candidatePointsMask( 1, dists.cols, CV_8UC1, Scalar(255) );
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activedDists.push_back( dists.row(maxLoc.y) );
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candidatePointsMask.at<uchar>(0, maxLoc.y) = 0;
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for( size_t i = 2; i < count; i++ )
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{
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activedDists.push_back(dists.row(maxLoc.x));
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candidatePointsMask.at<uchar>(0, maxLoc.x) = 0;
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Mat minDists;
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reduce( activedDists, minDists, 0, CV_REDUCE_MIN );
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minMaxLoc( minDists, 0, &maxVal, 0, &maxLoc, candidatePointsMask );
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dst.at<Point3_<uchar> >(i) = src.at<Point3_<uchar> >(maxLoc.x);
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}
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}
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void cv::generateColors( std::vector<Scalar>& colors, size_t count, size_t factor )
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{
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if( count < 1 )
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return;
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colors.resize(count);
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if( count == 1 )
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{
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colors[0] = Scalar(0,0,255); // red
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return;
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}
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if( count == 2 )
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{
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colors[0] = Scalar(0,0,255); // red
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colors[1] = Scalar(0,255,0); // green
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return;
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}
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// Generate a set of colors in RGB space. A size of the set is severel times (=factor) larger then
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// the needed count of colors.
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Mat bgr( 1, count*factor, CV_8UC3 );
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randu( bgr, 0, 256 );
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// Convert the colors set to Lab space.
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// Distances between colors in this space correspond a human perception.
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Mat lab;
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cvtColor( bgr, lab, CV_BGR2Lab);
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// Subsample colors from the generated set so that
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// to maximize the minimum distances between each other.
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// Douglas-Peucker algorithm is used for this.
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Mat lab_subset;
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downsamplePoints( lab, lab_subset, count );
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// Convert subsampled colors back to RGB
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Mat bgr_subset;
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cvtColor( lab_subset, bgr_subset, CV_Lab2BGR );
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CV_Assert( bgr_subset.total() == count );
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for( size_t i = 0; i < count; i++ )
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{
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Point3_<uchar> c = bgr_subset.at<Point3_<uchar> >(i);
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colors[i] = Scalar(c.x, c.y, c.z);
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}
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}
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