mirror of
https://github.com/opencv/opencv.git
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2a6fb2867e
Made all STL usages explicit to be able automatically find all usages of particular class or function.
348 lines
11 KiB
C++
348 lines
11 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) 2008, 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 Intel Corporation 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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size_t KeyPoint::hash() const
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{
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size_t _Val = 2166136261U, scale = 16777619U;
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Cv32suf u;
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u.f = pt.x; _Val = (scale * _Val) ^ u.u;
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u.f = pt.y; _Val = (scale * _Val) ^ u.u;
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u.f = size; _Val = (scale * _Val) ^ u.u;
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u.f = angle; _Val = (scale * _Val) ^ u.u;
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u.f = response; _Val = (scale * _Val) ^ u.u;
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_Val = (scale * _Val) ^ ((size_t) octave);
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_Val = (scale * _Val) ^ ((size_t) class_id);
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return _Val;
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}
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void write(FileStorage& fs, const std::string& objname, const std::vector<KeyPoint>& keypoints)
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{
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WriteStructContext ws(fs, objname, CV_NODE_SEQ + CV_NODE_FLOW);
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int i, npoints = (int)keypoints.size();
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for( i = 0; i < npoints; i++ )
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{
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const KeyPoint& kpt = keypoints[i];
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write(fs, kpt.pt.x);
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write(fs, kpt.pt.y);
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write(fs, kpt.size);
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write(fs, kpt.angle);
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write(fs, kpt.response);
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write(fs, kpt.octave);
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write(fs, kpt.class_id);
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}
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}
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void read(const FileNode& node, std::vector<KeyPoint>& keypoints)
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{
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keypoints.resize(0);
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FileNodeIterator it = node.begin(), it_end = node.end();
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for( ; it != it_end; )
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{
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KeyPoint kpt;
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it >> kpt.pt.x >> kpt.pt.y >> kpt.size >> kpt.angle >> kpt.response >> kpt.octave >> kpt.class_id;
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keypoints.push_back(kpt);
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}
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}
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void KeyPoint::convert(const std::vector<KeyPoint>& keypoints, std::vector<Point2f>& points2f,
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const std::vector<int>& keypointIndexes)
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{
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if( keypointIndexes.empty() )
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{
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points2f.resize( keypoints.size() );
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for( size_t i = 0; i < keypoints.size(); i++ )
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points2f[i] = keypoints[i].pt;
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}
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else
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{
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points2f.resize( keypointIndexes.size() );
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for( size_t i = 0; i < keypointIndexes.size(); i++ )
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{
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int idx = keypointIndexes[i];
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if( idx >= 0 )
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points2f[i] = keypoints[idx].pt;
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else
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{
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CV_Error( CV_StsBadArg, "keypointIndexes has element < 0. TODO: process this case" );
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//points2f[i] = Point2f(-1, -1);
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}
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}
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}
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}
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void KeyPoint::convert( const std::vector<Point2f>& points2f, std::vector<KeyPoint>& keypoints,
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float size, float response, int octave, int class_id )
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{
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keypoints.resize(points2f.size());
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for( size_t i = 0; i < points2f.size(); i++ )
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keypoints[i] = KeyPoint(points2f[i], size, -1, response, octave, class_id);
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}
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float KeyPoint::overlap( const KeyPoint& kp1, const KeyPoint& kp2 )
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{
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float a = kp1.size * 0.5f;
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float b = kp2.size * 0.5f;
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float a_2 = a * a;
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float b_2 = b * b;
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Point2f p1 = kp1.pt;
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Point2f p2 = kp2.pt;
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float c = (float)norm( p1 - p2 );
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float ovrl = 0.f;
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// one circle is completely encovered by the other => no intersection points!
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if( std::min( a, b ) + c <= std::max( a, b ) )
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return std::min( a_2, b_2 ) / std::max( a_2, b_2 );
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if( c < a + b ) // circles intersect
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{
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float c_2 = c * c;
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float cosAlpha = ( b_2 + c_2 - a_2 ) / ( kp2.size * c );
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float cosBeta = ( a_2 + c_2 - b_2 ) / ( kp1.size * c );
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float alpha = acos( cosAlpha );
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float beta = acos( cosBeta );
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float sinAlpha = sin(alpha);
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float sinBeta = sin(beta);
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float segmentAreaA = a_2 * beta;
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float segmentAreaB = b_2 * alpha;
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float triangleAreaA = a_2 * sinBeta * cosBeta;
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float triangleAreaB = b_2 * sinAlpha * cosAlpha;
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float intersectionArea = segmentAreaA + segmentAreaB - triangleAreaA - triangleAreaB;
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float unionArea = (a_2 + b_2) * (float)CV_PI - intersectionArea;
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ovrl = intersectionArea / unionArea;
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}
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return ovrl;
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}
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struct KeypointResponseGreaterThanThreshold
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{
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KeypointResponseGreaterThanThreshold(float _value) :
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value(_value)
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{
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}
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inline bool operator()(const KeyPoint& kpt) const
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{
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return kpt.response >= value;
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}
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float value;
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};
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struct KeypointResponseGreater
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{
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inline bool operator()(const KeyPoint& kp1, const KeyPoint& kp2) const
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{
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return kp1.response > kp2.response;
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}
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};
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// takes keypoints and culls them by the response
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void KeyPointsFilter::retainBest(std::vector<KeyPoint>& keypoints, int n_points)
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{
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//this is only necessary if the keypoints size is greater than the number of desired points.
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if( n_points > 0 && keypoints.size() > (size_t)n_points )
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{
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if (n_points==0)
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{
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keypoints.clear();
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return;
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}
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//first use nth element to partition the keypoints into the best and worst.
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std::nth_element(keypoints.begin(), keypoints.begin() + n_points, keypoints.end(), KeypointResponseGreater());
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//this is the boundary response, and in the case of FAST may be ambigous
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float ambiguous_response = keypoints[n_points - 1].response;
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//use std::partition to grab all of the keypoints with the boundary response.
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std::vector<KeyPoint>::const_iterator new_end =
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std::partition(keypoints.begin() + n_points, keypoints.end(),
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KeypointResponseGreaterThanThreshold(ambiguous_response));
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//resize the keypoints, given this new end point. nth_element and partition reordered the points inplace
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keypoints.resize(new_end - keypoints.begin());
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}
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}
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struct RoiPredicate
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{
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RoiPredicate( const Rect& _r ) : r(_r)
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{}
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bool operator()( const KeyPoint& keyPt ) const
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{
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return !r.contains( keyPt.pt );
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}
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Rect r;
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};
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void KeyPointsFilter::runByImageBorder( std::vector<KeyPoint>& keypoints, Size imageSize, int borderSize )
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{
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if( borderSize > 0)
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{
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if (imageSize.height <= borderSize * 2 || imageSize.width <= borderSize * 2)
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keypoints.clear();
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else
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keypoints.erase( std::remove_if(keypoints.begin(), keypoints.end(),
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RoiPredicate(Rect(Point(borderSize, borderSize),
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Point(imageSize.width - borderSize, imageSize.height - borderSize)))),
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keypoints.end() );
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}
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}
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struct SizePredicate
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{
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SizePredicate( float _minSize, float _maxSize ) : minSize(_minSize), maxSize(_maxSize)
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{}
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bool operator()( const KeyPoint& keyPt ) const
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{
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float size = keyPt.size;
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return (size < minSize) || (size > maxSize);
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}
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float minSize, maxSize;
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};
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void KeyPointsFilter::runByKeypointSize( std::vector<KeyPoint>& keypoints, float minSize, float maxSize )
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{
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CV_Assert( minSize >= 0 );
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CV_Assert( maxSize >= 0);
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CV_Assert( minSize <= maxSize );
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keypoints.erase( std::remove_if(keypoints.begin(), keypoints.end(), SizePredicate(minSize, maxSize)),
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keypoints.end() );
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}
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class MaskPredicate
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{
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public:
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MaskPredicate( const Mat& _mask ) : mask(_mask) {}
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bool operator() (const KeyPoint& key_pt) const
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{
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return mask.at<uchar>( (int)(key_pt.pt.y + 0.5f), (int)(key_pt.pt.x + 0.5f) ) == 0;
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}
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private:
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const Mat mask;
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MaskPredicate& operator=(const MaskPredicate&);
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};
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void KeyPointsFilter::runByPixelsMask( std::vector<KeyPoint>& keypoints, const Mat& mask )
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{
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if( mask.empty() )
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return;
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keypoints.erase(std::remove_if(keypoints.begin(), keypoints.end(), MaskPredicate(mask)), keypoints.end());
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}
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struct KeyPoint_LessThan
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{
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KeyPoint_LessThan(const std::vector<KeyPoint>& _kp) : kp(&_kp) {}
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bool operator()(int i, int j) const
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{
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const KeyPoint& kp1 = (*kp)[i];
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const KeyPoint& kp2 = (*kp)[j];
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if( kp1.pt.x != kp2.pt.x )
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return kp1.pt.x < kp2.pt.x;
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if( kp1.pt.y != kp2.pt.y )
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return kp1.pt.y < kp2.pt.y;
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if( kp1.size != kp2.size )
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return kp1.size > kp2.size;
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if( kp1.angle != kp2.angle )
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return kp1.angle < kp2.angle;
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if( kp1.response != kp2.response )
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return kp1.response > kp2.response;
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if( kp1.octave != kp2.octave )
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return kp1.octave > kp2.octave;
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if( kp1.class_id != kp2.class_id )
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return kp1.class_id > kp2.class_id;
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return i < j;
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}
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const std::vector<KeyPoint>* kp;
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};
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void KeyPointsFilter::removeDuplicated( std::vector<KeyPoint>& keypoints )
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{
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int i, j, n = (int)keypoints.size();
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std::vector<int> kpidx(n);
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std::vector<uchar> mask(n, (uchar)1);
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for( i = 0; i < n; i++ )
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kpidx[i] = i;
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std::sort(kpidx.begin(), kpidx.end(), KeyPoint_LessThan(keypoints));
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for( i = 1, j = 0; i < n; i++ )
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{
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KeyPoint& kp1 = keypoints[kpidx[i]];
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KeyPoint& kp2 = keypoints[kpidx[j]];
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if( kp1.pt.x != kp2.pt.x || kp1.pt.y != kp2.pt.y ||
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kp1.size != kp2.size || kp1.angle != kp2.angle )
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j = i;
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else
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mask[kpidx[i]] = 0;
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}
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for( i = j = 0; i < n; i++ )
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{
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if( mask[i] )
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{
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if( i != j )
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keypoints[j] = keypoints[i];
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j++;
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}
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}
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keypoints.resize(j);
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}
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}
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