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515 lines
16 KiB
C++
515 lines
16 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) 2008-2011, 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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//
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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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//
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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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//M*/
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#include <precomp.hpp>
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#include <opencv2/objdetect/objdetect.hpp>
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#include <opencv2/core/core.hpp>
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#include <vector>
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#include <string>
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#include <iostream>
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namespace {
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struct Octave
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{
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float scale;
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int stages;
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cv::Size size;
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int shrinkage;
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static const char *const SC_OCT_SCALE;
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static const char *const SC_OCT_STAGES;
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static const char *const SC_OCT_SHRINKAGE;
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Octave() : scale(0), stages(0), size(cv::Size()), shrinkage(0) {}
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Octave(cv::Size origObjSize, const cv::FileNode& fn)
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: scale((float)fn[SC_OCT_SCALE]), stages((int)fn[SC_OCT_STAGES]),
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size(cvRound(origObjSize.width * scale), cvRound(origObjSize.height * scale)),
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shrinkage((int)fn[SC_OCT_SHRINKAGE])
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{}
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};
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const char *const Octave::SC_OCT_SCALE = "scale";
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const char *const Octave::SC_OCT_STAGES = "stageNum";
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const char *const Octave::SC_OCT_SHRINKAGE = "shrinkingFactor";
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struct Stage
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{
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float threshold;
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static const char *const SC_STAGE_THRESHOLD;
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Stage(){}
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Stage(const cv::FileNode& fn) : threshold((float)fn[SC_STAGE_THRESHOLD]){}
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};
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const char *const Stage::SC_STAGE_THRESHOLD = "stageThreshold";
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struct Node
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{
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int feature;
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float threshold;
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Node(){}
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Node(cv::FileNodeIterator& fIt) : feature((int)(*(fIt +=2)++)), threshold((float)(*(fIt++))){}
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};
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struct Feature
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{
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int channel;
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cv::Rect rect;
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static const char * const SC_F_CHANNEL;
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static const char * const SC_F_RECT;
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Feature() {}
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Feature(const cv::FileNode& fn) : channel((int)fn[SC_F_CHANNEL])
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{
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cv::FileNode rn = fn[SC_F_RECT];
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cv::FileNodeIterator r_it = rn.end();
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rect = cv::Rect(*(--r_it), *(--r_it), *(--r_it), *(--r_it));
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// std::cout << "feature: " << rect.x << " " << rect.y << " " << rect.width
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//<< " " << rect.height << " " << channel << std::endl;
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}
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};
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const char * const Feature::SC_F_CHANNEL = "channel";
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const char * const Feature::SC_F_RECT = "rect";
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struct Level
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{
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const Octave* octave;
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float origScale;
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float relScale;
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float shrScale;
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cv::Size workRect;
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cv::Size objSize;
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// TiDo not reounding
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Level(const Octave& oct, const float scale, const int shrinkage, const int w, const int h)
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: octave(&oct), origScale(scale), relScale(scale / oct.scale), shrScale (relScale / shrinkage),
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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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};
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// Feature rescale(float relScale)
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// {
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// Feature res(*this);
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// res.rect = cv::Rect (cvRound(rect.x * relScale), cvRound(rect.y * relScale),
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// cvRound(rect.width * relScale), cvRound(rect.height * relScale));
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// res.threshold = threshold * CascadeIntrinsics::getFor(channel, relScale);
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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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}
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struct cv::SoftCascade::Filds
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{
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float minScale;
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float maxScale;
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int origObjWidth;
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int origObjHeight;
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int shrinkage;
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std::vector<Octave> octaves;
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std::vector<Stage> stages;
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std::vector<Node> nodes;
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std::vector<float> leaves;
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std::vector<Feature> features;
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std::vector<Level> levels;
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// typedef std::vector<Stage>::iterator stIter_t;
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// // carrently roi must be save for out of ranges.
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// void detectInRoi(const cv::Rect& roi, const Integral& ints, std::vector<cv::Rect>& objects, const int step)
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// {
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// for (int dy = roi.y; dy < roi.height; dy+=step)
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// for (int dx = roi.x; dx < roi.width; dx += step)
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// {
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// applyCascade(ints, dx, dy);
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// }
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// }
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// void applyCascade(const Integral& ints, const int x, const int y)
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// {
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// for (stIter_t sIt = stages.begin(); sIt != stages.end(); ++sIt)
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// {
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// Stage& stage = *sIt;
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// }
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// }
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typedef std::vector<Octave>::iterator octIt_t;
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octIt_t fitOctave(const float& logFactor)
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{
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float minAbsLog = FLT_MAX;
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octIt_t res = octaves.begin();
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for (octIt_t oct = octaves.begin(); oct < octaves.end(); ++oct)
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{
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const Octave& octave =*oct;
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float logOctave = log(octave.scale);
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float logAbsScale = fabs(logFactor - logOctave);
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if(logAbsScale < minAbsLog)
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{
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res = oct;
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minAbsLog = logAbsScale;
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}
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}
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return res;
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}
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// compute levels of full pyramid
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void calcLevels(int frameW, int frameH, int scales)
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{
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CV_Assert(scales > 1);
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levels.clear();
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float logFactor = (log(maxScale) - log(minScale)) / (scales -1);
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float scale = minScale;
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for (int sc = 0; sc < scales; ++sc)
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{
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int width = std::max(0.0f, frameW - (origObjWidth * scale));
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int height = std::max(0.0f, frameH - (origObjHeight * scale));
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float logScale = log(scale);
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octIt_t fit = fitOctave(logScale);
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Level level(*fit, scale, shrinkage, width, height);
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if (!width || !height)
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break;
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else
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levels.push_back(level);
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if (fabs(scale - maxScale) < FLT_EPSILON) break;
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scale = std::min(maxScale, expf(log(scale) + logFactor));
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// std::cout << "level scale "
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// << levels[sc].origScale
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// << " octeve "
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// << levels[sc].octave->scale
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// << " "
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// << levels[sc].relScale
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// << " " << levels[sc].shrScale
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// << " [" << levels[sc].objSize.width
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// << " " << levels[sc].objSize.height << "] ["
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// << levels[sc].workRect.width << " " << levels[sc].workRect.height << std::endl;
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}
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return;
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std::cout << std::endl << std::endl << std::endl;
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}
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bool fill(const FileNode &root, const float mins, const float maxs)
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{
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minScale = mins;
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maxScale = maxs;
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// cascade properties
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static const char *const SC_STAGE_TYPE = "stageType";
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static const char *const SC_BOOST = "BOOST";
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static const char *const SC_FEATURE_TYPE = "featureType";
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static const char *const SC_ICF = "ICF";
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static const char *const SC_ORIG_W = "width";
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static const char *const SC_ORIG_H = "height";
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static const char *const SC_OCTAVES = "octaves";
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static const char *const SC_STAGES = "stages";
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static const char *const SC_FEATURES = "features";
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static const char *const SC_WEEK = "weakClassifiers";
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static const char *const SC_INTERNAL = "internalNodes";
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static const char *const SC_LEAF = "leafValues";
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// only boost supported
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std::string stageTypeStr = (string)root[SC_STAGE_TYPE];
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CV_Assert(stageTypeStr == SC_BOOST);
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// only HOG-like integral channel features cupported
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string featureTypeStr = (string)root[SC_FEATURE_TYPE];
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CV_Assert(featureTypeStr == SC_ICF);
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origObjWidth = (int)root[SC_ORIG_W];
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CV_Assert(origObjWidth == SoftCascade::ORIG_OBJECT_WIDTH);
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origObjHeight = (int)root[SC_ORIG_H];
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CV_Assert(origObjHeight == SoftCascade::ORIG_OBJECT_HEIGHT);
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// for each octave (~ one cascade in classic OpenCV xml)
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FileNode fn = root[SC_OCTAVES];
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if (fn.empty()) return false;
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// octaves.reserve(noctaves);
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FileNodeIterator it = fn.begin(), it_end = fn.end();
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for (; it != it_end; ++it)
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{
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FileNode fns = *it;
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Octave octave(cv::Size(SoftCascade::ORIG_OBJECT_WIDTH, SoftCascade::ORIG_OBJECT_HEIGHT), fns);
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CV_Assert(octave.stages > 0);
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octaves.push_back(octave);
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FileNode ffs = fns[SC_FEATURES];
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if (ffs.empty()) return false;
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fns = fns[SC_STAGES];
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if (fn.empty()) return false;
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// for each stage (~ decision tree with H = 2)
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FileNodeIterator st = fns.begin(), st_end = fns.end();
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for (; st != st_end; ++st )
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{
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fns = *st;
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stages.push_back(Stage(fns));
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fns = fns[SC_WEEK];
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FileNodeIterator ftr = fns.begin(), ft_end = fns.end();
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for (; ftr != ft_end; ++ftr)
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{
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fns = (*ftr)[SC_INTERNAL];
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FileNodeIterator inIt = fns.begin(), inIt_end = fns.end();
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for (; inIt != inIt_end;)
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nodes.push_back(Node(inIt));
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fns = (*ftr)[SC_LEAF];
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inIt = fns.begin(), inIt_end = fns.end();
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for (; inIt != inIt_end; ++inIt)
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leaves.push_back((float)(*inIt));
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}
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}
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st = ffs.begin(), st_end = ffs.end();
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for (; st != st_end; ++st )
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features.push_back(Feature(*st));
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}
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shrinkage = octaves[0].shrinkage;
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return true;
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}
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};
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cv::SoftCascade::SoftCascade() : filds(0) {}
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cv::SoftCascade::SoftCascade( const string& filename, const float minScale, const float maxScale) : filds(0)
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{
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load(filename, minScale, maxScale);
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}
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cv::SoftCascade::~SoftCascade()
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{
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delete filds;
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}
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bool cv::SoftCascade::load( const string& filename, const float minScale, const float maxScale)
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{
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if (filds)
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delete filds;
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filds = 0;
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cv::FileStorage fs(filename, FileStorage::READ);
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if (!fs.isOpened()) return false;
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filds = new Filds;
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Filds& flds = *filds;
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if (!flds.fill(fs.getFirstTopLevelNode(), minScale, maxScale)) return false;
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flds.calcLevels(FRAME_WIDTH, FRAME_HEIGHT, TOTAL_SCALES);
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return true;
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}
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namespace {
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void calcHistBins(const cv::Mat& grey, cv::Mat& magIntegral, std::vector<cv::Mat>& histInts, const int bins, int shrinkage)
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{
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CV_Assert( grey.type() == CV_8U);
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float scale = 1.f / shrinkage;
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const int rows = grey.rows + 1;
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const int cols = grey.cols + 1;
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cv::Size intSumSize(cols, rows);
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histInts.clear();
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std::vector<cv::Mat> hist;
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for (int bin = 0; bin < bins; ++bin)
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{
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hist.push_back(cv::Mat(rows, cols, CV_32FC1));
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}
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cv::Mat df_dx, df_dy, mag, angle;
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cv::Sobel(grey, df_dx, CV_32F, 1, 0);
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cv::Sobel(grey, df_dy, CV_32F, 0, 1);
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cv::cartToPolar(df_dx, df_dy, mag, angle, true);
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const float magnitudeScaling = 1.0 / sqrt(2);
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mag *= magnitudeScaling;
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angle /= 60;
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for (int h = 0; h < mag.rows; ++h)
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{
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float* magnitude = mag.ptr<float>(h);
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float* ang = angle.ptr<float>(h);
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for (int w = 0; w < mag.cols; ++w)
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{
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hist[(int)ang[w]].ptr<float>(h)[w] = magnitude[w];
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}
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}
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for (int bin = 0; bin < bins; ++bin)
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{
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cv::Mat shrunk, sum;
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cv::resize(hist[bin], shrunk, cv::Size(), scale, scale, cv::INTER_AREA);
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cv::integral(shrunk, sum);
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histInts.push_back(sum);
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}
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cv::Mat shrMag;
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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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}
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struct ChannelStorage
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{
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std::vector<cv::Mat> hog;
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cv::Mat luv;
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cv::Mat magnitude;
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int shrinkage;
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enum {HOG_BINS = 6};
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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::cvtColor(colored, _luv, CV_BGR2Luv);
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cv::integral(luv, luv);
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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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}
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};
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}
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void cv::SoftCascade::detectMultiScale(const Mat& image, const std::vector<cv::Rect>& rois, std::vector<cv::Rect>& objects,
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const int step, const int rejectfactor)// add step scaling
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{
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typedef std::vector<cv::Rect>::const_iterator RIter_t;
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// only color images are supperted
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CV_Assert(image.type() == CV_8UC3);
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// only this window size allowed
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CV_Assert(image.cols == 640 && image.rows == 480);
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objects.clear();
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const Filds& fld = *filds;
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// create integrals
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ChannelStorage storage(image, fld.shrinkage);
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// for (RIter_t it = rois.begin(); it != rois.end(); ++it)
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// {
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// const cv::Rect& roi = *it;
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// (*filds).detectInRoi(roi, integrals, objects, step);
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// }
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} |