add feature rescaling according to Dollal's paper FPDW
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@ -1,31 +1,31 @@
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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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// 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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// 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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// 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) 2008-2011, Willow Garage Inc., all rights reserved.
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// Copyright (C) 2008-2012, 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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// * Redistributions 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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// * 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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// * Redistributions 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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// * 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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// * 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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@ -37,6 +37,7 @@
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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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@ -137,6 +138,43 @@ struct Level
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workRect(cv::Size(cvRound(w / (float)shrinkage),cvRound(h / (float)shrinkage))),
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objSize(cv::Size(cvRound(oct.size.width * relScale), cvRound(oct.size.height * relScale)))
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{}
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void markDetection(const int x, const int dx, std::vector<cv::Rect>& detections) const
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{
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}
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};
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struct CascadeIntrinsics
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{
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static const float lambda = 1.099f, a = 0.89f;
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static float getFor(int channel, float scaling)
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{
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CV_Assert(channel < 10);
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if ((scaling - 1.f) < FLT_EPSILON)
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return 1.f;
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// according to R. Benenson, M. Mathias, R. Timofte and L. Van Gool paper
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static const float A[2][2] =
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{ //channel <= 6, otherwise
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{ 0.89f, 1.f}, // down
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{ 1.00f, 1.f} // up
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};
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static const float B[2][2] =
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{ //channel <= 6, otherwise
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{ 1.099f / log(2), 2.f}, // down
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{ 2.f, 2.f} // up
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};
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float a = A[(int)(scaling >= 1)][(int)(channel >= 6)];
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float b = B[(int)(scaling >= 1)][(int)(channel >= 6)];
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return a * pow(scaling, b);
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}
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};
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// Feature rescale(float relScale)
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@ -148,42 +186,6 @@ struct Level
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// return res;
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// }
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// // according to R. Benenson, M. Mathias, R. Timofte and L. Van Gool paper
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// struct CascadeIntrinsics
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// {
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// static const float lambda = 1.099f, a = 0.89f;
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// static const float intrinsics[10][4];
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// static float getFor(int channel, float scaling)
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// {
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// CV_Assert(channel < 10);
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// if ((scaling - 1.f) < FLT_EPSILON)
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// return 1.f;
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// int ud = (int)(scaling < 1.f);
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// return intrinsics[channel][(ud << 1)] * pow(scaling, intrinsics[channel][(ud << 1) + 1]);
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// }
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// };
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// const float CascadeIntrinsics::intrinsics[10][4] =
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// { //da, db, ua, ub
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// // hog-like orientation bins
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// {a, lambda / log(2), 1, 2},
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// {a, lambda / log(2), 1, 2},
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// {a, lambda / log(2), 1, 2},
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// {a, lambda / log(2), 1, 2},
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// {a, lambda / log(2), 1, 2},
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// {a, lambda / log(2), 1, 2},
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// // gradient magnitude
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// {a, lambda / log(2), 1, 2},
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// // luv color channels
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// {1, 2, 1, 2},
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// {1, 2, 1, 2},
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// {1, 2, 1, 2}
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// };
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void calcHistBins(const cv::Mat& grey, cv::Mat& magIntegral, std::vector<cv::Mat>& histInts,
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const int bins, int shrinkage)
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@ -236,6 +238,7 @@ void calcHistBins(const cv::Mat& grey, cv::Mat& magIntegral, std::vector<cv::Mat
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cv::resize(mag, shrMag, cv::Size(), scale, scale, cv::INTER_AREA);
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cv::integral(shrMag, magIntegral, mag.depth());
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histInts.push_back(magIntegral);
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}
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struct ChannelStorage
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@ -246,25 +249,40 @@ struct ChannelStorage
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int shrinkage;
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enum {HOG_BINS = 6};
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enum {HOG_BINS = 6, HOG_LUV_BINS = 10};
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ChannelStorage() {}
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ChannelStorage(const cv::Mat& colored, int shr) : shrinkage(shr)
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{
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cv::Mat _luv;
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cv::Mat _luv, shrLuv;
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cv::cvtColor(colored, _luv, CV_BGR2Luv);
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cv::resize(_luv, shrLuv, cv::Size(), 1.f / shr, 1.f / shr, cv::INTER_AREA);
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cv::integral(luv, luv);
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cv::integral(shrLuv, luv);
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std::vector<cv::Mat> splited;
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split(luv, splited);
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cv::Mat grey;
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cv::cvtColor(colored, grey, CV_RGB2GRAY);
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calcHistBins(grey, magnitude, hog, HOG_BINS, shrinkage);
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hog.insert(hog.end(), splited.begin(), splited.end());
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}
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float get(int chennel, cv::Rect area) const
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float get(const int x, const int y, const int channel, const cv::Rect& area) const
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{
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return 1.f;
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CV_Assert(channel < HOG_LUV_BINS);
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const cv::Mat m = hog[channel];
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float a = m.ptr(y + area.y)[x + area.x];
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float b = m.ptr(y + area.y)[x + area.width];
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float c = m.ptr(y + area.height)[x + area.width];
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float d = m.ptr(y + area.height)[x + area.x];
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return (a - b + c - d);
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}
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};
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}
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@ -291,33 +309,87 @@ struct cv::SoftCascade::Filds
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typedef std::vector<Octave>::iterator octIt_t;
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void detectAt(const Level& level, const int dx, const int dy, const ChannelStorage& storage,
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const std::vector<cv::Rect>& detections) const
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std::vector<cv::Rect>& detections) const
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{
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float detectionScore = 0.f;
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const Octave& octave = *(level.octave);
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int stBegin = octave.index() * octave.stages, stEnd = stBegin + octave.stages;
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for(int st = stBegin; st < stEnd; ++st)
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int st = stBegin;
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for(; st < stEnd; ++st)
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{
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const Stage& stage = stages[st];
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if (detectionScore > stage.threshold)
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{
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int nId = st * 3;
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// work with root node
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const Node& node = nodes[nId];
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const Feature& feature = features[node.feature];
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float sum = storage.get(feature.channel, feature.rect);
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int next = (sum >= node.threshold)? 2 : 1;
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// rescaling
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float scaling = CascadeIntrinsics::getFor(feature.channel, level.relScale);
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cv::Rect scaledRect = feature.rect;
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float farea = (scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y);
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// rescale
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scaledRect.x = cvRound(scaling * scaledRect.x);
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scaledRect.y = cvRound(scaling * scaledRect.y);
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scaledRect.width = cvRound(scaling * scaledRect.width);
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scaledRect.height = cvRound(scaling * scaledRect.height);
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float sarea = (scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y);
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float approx = 1.f;
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if ((farea - 0.f) > FLT_EPSILON && (farea - 0.f) > FLT_EPSILON)
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{
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const float expected_new_area = farea*level.relScale*level.relScale;
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approx = expected_new_area / sarea;
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}
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float rootThreshold = node.threshold / approx; // ToDo check
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rootThreshold *= scaling;
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// use rescaled
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float sum = storage.get(dx, dy, feature.channel, scaledRect);
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int next = (sum >= rootThreshold)? 2 : 1;
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// leaces
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const Node& leaf = nodes[nId + next];
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const Feature& fLeaf = features[node.feature];
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sum = storage.get(feature.channel, feature.rect);
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int lShift = (next - 1) * 2 + (sum >= leaf.threshold) ? 1 : 0;
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// rescaling
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scaling = CascadeIntrinsics::getFor(fLeaf.channel, level.relScale);
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scaledRect = fLeaf.rect;
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farea = (scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y);
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// rescale
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scaledRect.x = cvRound(scaling * scaledRect.x);
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scaledRect.y = cvRound(scaling * scaledRect.y);
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scaledRect.width = cvRound(scaling * scaledRect.width);
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scaledRect.height = cvRound(scaling * scaledRect.height);
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sarea = (scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y);
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approx = 1.f;
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if ((farea - 0.f) > FLT_EPSILON && (farea - 0.f) > FLT_EPSILON)
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{
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const float expected_new_area = farea*level.relScale*level.relScale;
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approx = expected_new_area / sarea;
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}
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rootThreshold = leaf.threshold / approx; // ToDo check
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rootThreshold *= scaling;
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sum = storage.get(dx, dy, feature.channel, scaledRect);
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int lShift = (next - 1) * 2 + (sum >= rootThreshold) ? 1 : 0;
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float impact = leaves[nId + lShift];
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detectionScore += impact;
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}
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if (detectionScore <= stage.threshold) break;
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}
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if (st == octave.stages - 1)
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level.markDetection(dx, dy, detections);
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}
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octIt_t fitOctave(const float& logFactor)
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