410 lines
14 KiB
C++
410 lines
14 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) 2008-2012, 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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// * 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 <precomp.hpp>
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#if !defined (HAVE_CUDA)
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cv::gpu::SoftCascade::SoftCascade() : filds(0) { throw_nogpu(); }
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cv::gpu::SoftCascade::SoftCascade( const string&, const float, const float) : filds(0) { throw_nogpu(); }
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cv::gpu::SoftCascade::~SoftCascade() { throw_nogpu(); }
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bool cv::gpu::SoftCascade::load( const string&, const float, const float) { throw_nogpu(); }
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void cv::gpu::SoftCascade::detectMultiScale(const GpuMat&, const GpuMat&, GpuMat&, const int, Stream) { throw_nogpu(); }
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#else
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#include <icf.hpp>
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struct cv::gpu::SoftCascade::Filds
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{
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// scales range
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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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GpuMat octaves;
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GpuMat stages;
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GpuMat nodes;
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GpuMat leaves;
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GpuMat features;
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GpuMat levels;
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// preallocated buffer 640x480x10
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GpuMat dmem;
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// 160x120x10
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GpuMat shrunk;
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// 161x121x10
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GpuMat hogluv;
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std::vector<float> scales;
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icf::Cascade cascade;
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icf::ChannelStorage storage;
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bool fill(const FileNode &root, const float mins, const float maxs);
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void detect() const {}
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enum { BOOST = 0 };
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enum
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{
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FRAME_WIDTH = 640,
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FRAME_HEIGHT = 480,
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TOTAL_SCALES = 55,
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CLASSIFIERS = 5,
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ORIG_OBJECT_WIDTH = 64,
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ORIG_OBJECT_HEIGHT = 128,
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HOG_BINS = 6,
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HOG_LUV_BINS = 10
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};
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private:
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void calcLevels(const std::vector<icf::Octave>& octs,
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int frameW, int frameH, int nscales);
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typedef std::vector<icf::Octave>::const_iterator octIt_t;
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int fitOctave(const std::vector<icf::Octave>& octs, const float& logFactor) const
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{
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float minAbsLog = FLT_MAX;
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int res = 0;
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for (int oct = 0; oct < (int)octs.size(); ++oct)
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{
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const icf::Octave& octave =octs[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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};
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inline bool cv::gpu::SoftCascade::Filds::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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static const char *const SC_OCT_SCALE = "scale";
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static const char *const SC_OCT_STAGES = "stageNum";
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static const char *const SC_OCT_SHRINKAGE = "shrinkingFactor";
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static const char *const SC_STAGE_THRESHOLD = "stageThreshold";
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static const char * const SC_F_CHANNEL = "channel";
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static const char * const SC_F_RECT = "rect";
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// only Ada 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 == ORIG_OBJECT_WIDTH);
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origObjHeight = (int)root[SC_ORIG_H];
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CV_Assert(origObjHeight == ORIG_OBJECT_HEIGHT);
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FileNode fn = root[SC_OCTAVES];
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if (fn.empty()) return false;
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std::vector<icf::Octave> voctaves;
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std::vector<float> vstages;
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std::vector<icf::Node> vnodes;
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std::vector<float> vleaves;
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std::vector<icf::Feature> vfeatures;
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scales.clear();
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// std::vector<Level> levels;
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FileNodeIterator it = fn.begin(), it_end = fn.end();
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int feature_offset = 0;
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ushort octIndex = 0;
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ushort shrinkage = 1;
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for (; it != it_end; ++it)
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{
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FileNode fns = *it;
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float scale = (float)fns[SC_OCT_SCALE];
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scales.push_back(scale);
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ushort nstages = saturate_cast<ushort>((int)fn[SC_OCT_STAGES]);
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ushort2 size;
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size.x = cvRound(ORIG_OBJECT_WIDTH * scale);
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size.y = cvRound(ORIG_OBJECT_HEIGHT * scale);
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shrinkage = saturate_cast<ushort>((int)fn[SC_OCT_SHRINKAGE]);
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icf::Octave octave(octIndex, nstages, shrinkage, size, scale);
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CV_Assert(octave.stages > 0);
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voctaves.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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vstages.push_back((float)fn[SC_STAGE_THRESHOLD]);
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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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{
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int feature = (int)(*(inIt +=2)++) + feature_offset;
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vnodes.push_back(icf::Node(feature, (float)(*(inIt++))));
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}
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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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vleaves.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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{
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cv::FileNode rn = (*st)[SC_F_RECT];
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cv::FileNodeIterator r_it = rn.begin();
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uchar4 rect;
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rect.x = saturate_cast<uchar>((int)*(r_it++));
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rect.y = saturate_cast<uchar>((int)*(r_it++));
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rect.z = saturate_cast<uchar>((int)*(r_it++));
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rect.w = saturate_cast<uchar>((int)*(r_it++));
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vfeatures.push_back(icf::Feature((int)(*st)[SC_F_CHANNEL], rect));
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}
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feature_offset += octave.stages * 3;
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++octIndex;
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}
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// upload in gpu memory
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octaves.upload(cv::Mat(1, voctaves.size() * sizeof(icf::Octave), CV_8UC1, (uchar*)&(voctaves[0]) ));
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CV_Assert(!octaves.empty());
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stages.upload(cv::Mat(vstages).reshape(1,1));
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CV_Assert(!stages.empty());
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nodes.upload(cv::Mat(1, vnodes.size() * sizeof(icf::Node), CV_8UC1, (uchar*)&(vnodes[0]) ));
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CV_Assert(!nodes.empty());
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leaves.upload(cv::Mat(vleaves).reshape(1,1));
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CV_Assert(!leaves.empty());
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features.upload(cv::Mat(1, vfeatures.size() * sizeof(icf::Feature), CV_8UC1, (uchar*)&(vfeatures[0]) ));
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CV_Assert(!features.empty());
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// compute levels
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calcLevels(voctaves, FRAME_WIDTH, FRAME_HEIGHT, TOTAL_SCALES);
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CV_Assert(!levels.empty());
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// init Cascade
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cascade = icf::Cascade(octaves, stages, nodes, leaves, features, levels);
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// allocate buffers
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dmem.create(FRAME_HEIGHT * HOG_LUV_BINS, FRAME_WIDTH, CV_8UC1);
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shrunk.create(FRAME_HEIGHT / shrinkage * HOG_LUV_BINS, FRAME_WIDTH / shrinkage, CV_8UC1);
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hogluv.create( (FRAME_HEIGHT / shrinkage * HOG_LUV_BINS) + 1, (FRAME_WIDTH / shrinkage) + 1, CV_16UC1);
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storage = icf::ChannelStorage(dmem, shrunk, hogluv, shrinkage);
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return true;
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}
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namespace {
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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 (fabs(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's and Dallal's papers
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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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{ 0.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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printf("!!! scaling: %f %f %f -> %f\n", scaling, a, b, a * pow(scaling, b));
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return a * pow(scaling, b);
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}
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};
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}
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inline void cv::gpu::SoftCascade::Filds::calcLevels(const std::vector<icf::Octave>& octs,
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int frameW, int frameH, int nscales)
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{
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CV_Assert(nscales > 1);
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std::vector<icf::Level> vlevels;
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float logFactor = (::log(maxScale) - ::log(minScale)) / (nscales -1);
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float scale = minScale;
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for (int sc = 0; sc < nscales; ++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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int fit = fitOctave(octs, logScale);
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icf::Level level(fit, octs[fit], scale, width, height);
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level.scaling[0] = CascadeIntrinsics::getFor(0, level.relScale);
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level.scaling[1] = CascadeIntrinsics::getFor(9, level.relScale);
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if (!width || !height)
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break;
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else
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vlevels.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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levels.upload(cv::Mat(1, vlevels.size() * sizeof(icf::Level), CV_8UC1, (uchar*)&(vlevels[0]) ));
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// std::cout << "level " << sc << " 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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}
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cv::gpu::SoftCascade::SoftCascade() : filds(0) {}
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cv::gpu::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::gpu::SoftCascade::~SoftCascade()
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{
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delete filds;
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}
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bool cv::gpu::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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return true;
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}
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void cv::gpu::SoftCascade::detectMultiScale(const GpuMat& image, const GpuMat& /*rois*/,
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GpuMat& /*objects*/, const int /*rejectfactor*/, Stream /*stream*/)
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{
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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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Filds& flds = *filds;
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flds.storage.frame(image);
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flds.detect();
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}
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#endif
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