mirror of https://github.com/opencv/opencv.git
marina.kolpakova
12 years ago
parent
0211843062
commit
318257f3a3
3 changed files with 544 additions and 594 deletions
@ -1,594 +0,0 @@ |
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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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//
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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) 2008-2013, 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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using cv::softcascade::Detection; |
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using cv::softcascade::Detector; |
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using cv::softcascade::ChannelFeatureBuilder; |
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using namespace cv; |
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namespace { |
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struct SOctave |
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{ |
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SOctave(const int i, const cv::Size& origObjSize, const cv::FileNode& fn) |
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: index(i), weaks((int)fn[SC_OCT_WEAKS]), scale((float)std::pow(2,(float)fn[SC_OCT_SCALE])), |
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size(cvRound(origObjSize.width * scale), cvRound(origObjSize.height * scale)) {} |
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int index; |
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int weaks; |
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float scale; |
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cv::Size size; |
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static const char *const SC_OCT_SCALE; |
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static const char *const SC_OCT_WEAKS; |
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static const char *const SC_OCT_SHRINKAGE; |
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}; |
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struct Weak |
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{ |
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Weak(){} |
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Weak(const cv::FileNode& fn) : threshold((float)fn[SC_WEAK_THRESHOLD]) {} |
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float threshold; |
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static const char *const SC_WEAK_THRESHOLD; |
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}; |
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struct Node |
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{ |
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Node(){} |
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Node(const int offset, cv::FileNodeIterator& fIt) |
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: feature((int)(*(fIt +=2)++) + offset), threshold((float)(*(fIt++))) {} |
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int feature; |
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float threshold; |
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}; |
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struct Feature |
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{ |
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Feature() {} |
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Feature(const cv::FileNode& fn, bool useBoxes = false) : 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.begin(); |
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int x = *r_it++; |
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int y = *r_it++; |
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int w = *r_it++; |
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int h = *r_it++; |
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// ToDo: fix me
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if (useBoxes) |
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rect = cv::Rect(x, y, w, h); |
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else |
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rect = cv::Rect(x, y, w + x, h + y); |
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// 1 / area
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rarea = 1.f / ((rect.width - rect.x) * (rect.height - rect.y)); |
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} |
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int channel; |
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cv::Rect rect; |
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float rarea; |
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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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}; |
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const char *const SOctave::SC_OCT_SCALE = "scale"; |
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const char *const SOctave::SC_OCT_WEAKS = "weaks"; |
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const char *const SOctave::SC_OCT_SHRINKAGE = "shrinkingFactor"; |
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const char *const Weak::SC_WEAK_THRESHOLD = "treeThreshold"; |
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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 SOctave* octave; |
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float origScale; |
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float relScale; |
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int scaleshift; |
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cv::Size workRect; |
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cv::Size objSize; |
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float scaling[2]; // 0-th for channels <= 6, 1-st otherwise
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Level(const SOctave& 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), |
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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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scaling[0] = ((relScale >= 1.f)? 1.f : (0.89f * std::pow(relScale, 1.099f / std::log(2.f)))) / (relScale * relScale); |
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scaling[1] = 1.f; |
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scaleshift = static_cast<int>(relScale * (1 << 16)); |
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} |
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void addDetection(const int x, const int y, float confidence, std::vector<Detection>& detections) const |
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{ |
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// fix me
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int shrinkage = 4;//(*octave).shrinkage;
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cv::Rect rect(cvRound(x * shrinkage), cvRound(y * shrinkage), objSize.width, objSize.height); |
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detections.push_back(Detection(rect, confidence)); |
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} |
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float rescale(cv::Rect& scaledRect, const float threshold, int idx) const |
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{ |
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#define SSHIFT(a) ((a) + (1 << 15)) >> 16 |
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// rescale
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scaledRect.x = SSHIFT(scaleshift * scaledRect.x); |
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scaledRect.y = SSHIFT(scaleshift * scaledRect.y); |
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scaledRect.width = SSHIFT(scaleshift * scaledRect.width); |
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scaledRect.height = SSHIFT(scaleshift * scaledRect.height); |
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#undef SSHIFT |
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float sarea = static_cast<float>((scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y)); |
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// compensation areas rounding
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return (sarea == 0.0f)? threshold : (threshold * scaling[idx] * sarea); |
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} |
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}; |
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struct ChannelStorage |
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{ |
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cv::Mat hog; |
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int shrinkage; |
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int offset; |
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size_t step; |
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int model_height; |
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cv::Ptr<ChannelFeatureBuilder> builder; |
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enum {HOG_BINS = 6, HOG_LUV_BINS = 10}; |
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ChannelStorage(const cv::Mat& colored, int shr, std::string featureTypeStr) : shrinkage(shr) |
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{ |
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model_height = cvRound(colored.rows / (float)shrinkage); |
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if (featureTypeStr == "ICF") featureTypeStr = "HOG6MagLuv"; |
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builder = ChannelFeatureBuilder::create(featureTypeStr); |
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(*builder)(colored, hog, cv::Size(cvRound(colored.cols / (float)shrinkage), model_height)); |
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step = hog.step1(); |
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} |
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float get(const int channel, const cv::Rect& area) const |
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{ |
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const int *ptr = hog.ptr<const int>(0) + model_height * channel * step + offset; |
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int a = ptr[area.y * step + area.x]; |
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int b = ptr[area.y * step + area.width]; |
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int c = ptr[area.height * step + area.width]; |
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int d = ptr[area.height * step + area.x]; |
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return static_cast<float>(a - b + c - d); |
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} |
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}; |
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} |
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struct Detector::Fields |
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{ |
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float minScale; |
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float maxScale; |
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int scales; |
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int origObjWidth; |
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int origObjHeight; |
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int shrinkage; |
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std::vector<SOctave> octaves; |
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std::vector<Weak> weaks; |
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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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cv::Size frameSize; |
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typedef std::vector<SOctave>::iterator octIt_t; |
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typedef std::vector<Detection> dvector; |
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std::string featureTypeStr; |
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void detectAt(const int dx, const int dy, const Level& level, const ChannelStorage& storage, dvector& detections) const |
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{ |
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float detectionScore = 0.f; |
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const SOctave& octave = *(level.octave); |
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int stBegin = octave.index * octave.weaks, stEnd = stBegin + octave.weaks; |
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for(int st = stBegin; st < stEnd; ++st) |
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{ |
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const Weak& weak = weaks[st]; |
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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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cv::Rect scaledRect(feature.rect); |
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float threshold = level.rescale(scaledRect, node.threshold, (int)(feature.channel > 6)) * feature.rarea; |
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float sum = storage.get(feature.channel, scaledRect); |
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int next = (sum >= threshold)? 2 : 1; |
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// leaves
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const Node& leaf = nodes[nId + next]; |
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const Feature& fLeaf = features[leaf.feature]; |
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scaledRect = fLeaf.rect; |
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threshold = level.rescale(scaledRect, leaf.threshold, (int)(fLeaf.channel > 6)) * fLeaf.rarea; |
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sum = storage.get(fLeaf.channel, scaledRect); |
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int lShift = (next - 1) * 2 + ((sum >= threshold) ? 1 : 0); |
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float impact = leaves[(st * 4) + lShift]; |
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detectionScore += impact; |
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if (detectionScore <= weak.threshold) return; |
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} |
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if (detectionScore > 0) |
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level.addDetection(dx, dy, detectionScore, detections); |
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} |
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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 SOctave& octave =*oct; |
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float logOctave = std::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(const cv::Size& curr, float mins, float maxs, int total) |
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{ |
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if (frameSize == curr && maxs == maxScale && mins == minScale && total == scales) return; |
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frameSize = curr; |
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maxScale = maxs; minScale = mins; scales = total; |
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CV_Assert(scales > 1); |
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levels.clear(); |
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float logFactor = (std::log(maxScale) - std::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 = static_cast<int>(std::max(0.0f, frameSize.width - (origObjWidth * scale))); |
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int height = static_cast<int>(std::max(0.0f, frameSize.height - (origObjHeight * scale))); |
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float logScale = std::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(std::log(scale) + logFactor)); |
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} |
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} |
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bool fill(const FileNode &root) |
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{ |
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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_HOG6_MAG_LUV = "HOG6MagLuv"; |
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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_TREES = "trees"; |
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static const char *const SC_FEATURES = "features"; |
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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_SHRINKAGE = "shrinkage"; |
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static const char *const FEATURE_FORMAT = "featureFormat"; |
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// only Ada Boost supported
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std::string stageTypeStr = (std::string)root[SC_STAGE_TYPE]; |
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CV_Assert(stageTypeStr == SC_BOOST); |
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std::string fformat = (std::string)root[FEATURE_FORMAT]; |
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bool useBoxes = (fformat == "BOX"); |
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// only HOG-like integral channel features supported
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featureTypeStr = (std::string)root[SC_FEATURE_TYPE]; |
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CV_Assert(featureTypeStr == SC_ICF || featureTypeStr == SC_HOG6_MAG_LUV); |
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origObjWidth = (int)root[SC_ORIG_W]; |
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origObjHeight = (int)root[SC_ORIG_H]; |
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shrinkage = (int)root[SC_SHRINKAGE]; |
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FileNode fn = root[SC_OCTAVES]; |
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if (fn.empty()) return false; |
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// for each octave
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FileNodeIterator it = fn.begin(), it_end = fn.end(); |
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for (int octIndex = 0; it != it_end; ++it, ++octIndex) |
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{ |
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FileNode fns = *it; |
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SOctave octave(octIndex, cv::Size(origObjWidth, origObjHeight), fns); |
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CV_Assert(octave.weaks > 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_TREES]; |
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if (fn.empty()) return false; |
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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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weaks.push_back(Weak(*st)); |
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fns = (*st)[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((int)features.size(), inIt)); |
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fns = (*st)[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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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, useBoxes)); |
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} |
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return true; |
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} |
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}; |
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Detector::Detector(const double mins, const double maxs, const int nsc, const int rej) |
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: fields(0), minScale(mins), maxScale(maxs), scales(nsc), rejCriteria(rej) {} |
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Detector::~Detector() { delete fields;} |
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void Detector::read(const cv::FileNode& fn) |
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{ |
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Algorithm::read(fn); |
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} |
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bool Detector::load(const cv::FileNode& fn) |
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{ |
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if (fields) delete fields; |
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fields = new Fields; |
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return fields->fill(fn); |
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} |
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namespace { |
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using cv::softcascade::Detection; |
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typedef std::vector<Detection> dvector; |
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struct ConfidenceGt |
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{ |
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bool operator()(const Detection& a, const Detection& b) const |
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{ |
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return a.confidence > b.confidence; |
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} |
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}; |
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static float overlap(const cv::Rect &a, const cv::Rect &b) |
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{ |
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int w = std::min(a.x + a.width, b.x + b.width) - std::max(a.x, b.x); |
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int h = std::min(a.y + a.height, b.y + b.height) - std::max(a.y, b.y); |
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return (w < 0 || h < 0)? 0.f : (float)(w * h); |
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} |
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void DollarNMS(dvector& objects) |
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{ |
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static const float DollarThreshold = 0.65f; |
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std::sort(objects.begin(), objects.end(), ConfidenceGt()); |
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for (dvector::iterator dIt = objects.begin(); dIt != objects.end(); ++dIt) |
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{ |
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const Detection &a = *dIt; |
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for (dvector::iterator next = dIt + 1; next != objects.end(); ) |
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{ |
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const Detection &b = *next; |
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const float ovl = overlap(a.bb(), b.bb()) / std::min(a.bb().area(), b.bb().area()); |
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if (ovl > DollarThreshold) |
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next = objects.erase(next); |
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else |
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++next; |
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} |
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} |
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} |
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static void suppress(int type, std::vector<Detection>& objects) |
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{ |
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CV_Assert(type == Detector::DOLLAR); |
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DollarNMS(objects); |
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} |
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} |
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void Detector::detectNoRoi(const cv::Mat& image, std::vector<Detection>& objects) const |
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{ |
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Fields& fld = *fields; |
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// create integrals
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ChannelStorage storage(image, fld.shrinkage, fld.featureTypeStr); |
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typedef std::vector<Level>::const_iterator lIt; |
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for (lIt it = fld.levels.begin(); it != fld.levels.end(); ++it) |
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{ |
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const Level& level = *it; |
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// we train only 3 scales.
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if (level.origScale > 2.5) break; |
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for (int dy = 0; dy < level.workRect.height; ++dy) |
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{ |
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for (int dx = 0; dx < level.workRect.width; ++dx) |
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{ |
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storage.offset = (int)(dy * storage.step + dx); |
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fld.detectAt(dx, dy, level, storage, objects); |
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} |
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} |
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} |
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if (rejCriteria != NO_REJECT) suppress(rejCriteria, objects); |
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} |
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void Detector::detect(cv::InputArray _image, cv::InputArray _rois, std::vector<Detection>& objects) const |
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{ |
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// only color images are suppered
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cv::Mat image = _image.getMat(); |
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CV_Assert(image.type() == CV_8UC3); |
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Fields& fld = *fields; |
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fld.calcLevels(image.size(),(float) minScale, (float)maxScale, scales); |
||||
|
||||
objects.clear(); |
||||
|
||||
if (_rois.empty()) |
||||
return detectNoRoi(image, objects); |
||||
|
||||
int shr = fld.shrinkage; |
||||
|
||||
cv::Mat roi = _rois.getMat(); |
||||
cv::Mat mask(image.rows / shr, image.cols / shr, CV_8UC1); |
||||
|
||||
mask.setTo(cv::Scalar::all(0)); |
||||
cv::Rect* r = roi.ptr<cv::Rect>(0); |
||||
for (int i = 0; i < (int)roi.cols; ++i) |
||||
cv::Mat(mask, cv::Rect(r[i].x / shr, r[i].y / shr, r[i].width / shr , r[i].height / shr)).setTo(cv::Scalar::all(1)); |
||||
|
||||
// create integrals
|
||||
ChannelStorage storage(image, shr, fld.featureTypeStr); |
||||
|
||||
typedef std::vector<Level>::const_iterator lIt; |
||||
for (lIt it = fld.levels.begin(); it != fld.levels.end(); ++it) |
||||
{ |
||||
const Level& level = *it; |
||||
|
||||
// we train only 3 scales.
|
||||
if (level.origScale > 2.5) break; |
||||
|
||||
for (int dy = 0; dy < level.workRect.height; ++dy) |
||||
{ |
||||
uchar* m = mask.ptr<uchar>(dy); |
||||
for (int dx = 0; dx < level.workRect.width; ++dx) |
||||
{ |
||||
if (m[dx]) |
||||
{ |
||||
storage.offset = (int)(dy * storage.step + dx); |
||||
fld.detectAt(dx, dy, level, storage, objects); |
||||
} |
||||
} |
||||
} |
||||
} |
||||
|
||||
if (rejCriteria != NO_REJECT) suppress(rejCriteria, objects); |
||||
} |
||||
|
||||
void Detector::detect(InputArray _image, InputArray _rois, OutputArray _rects, OutputArray _confs) const |
||||
{ |
||||
std::vector<Detection> objects; |
||||
detect( _image, _rois, objects); |
||||
|
||||
_rects.create(1, (int)objects.size(), CV_32SC4); |
||||
cv::Mat_<cv::Rect> rects = (cv::Mat_<cv::Rect>)_rects.getMat(); |
||||
cv::Rect* rectPtr = rects.ptr<cv::Rect>(0); |
||||
|
||||
_confs.create(1, (int)objects.size(), CV_32F); |
||||
cv::Mat confs = _confs.getMat(); |
||||
float* confPtr = confs.ptr<float>(0); |
||||
|
||||
typedef std::vector<Detection>::const_iterator IDet; |
||||
|
||||
int i = 0; |
||||
for (IDet it = objects.begin(); it != objects.end(); ++it, ++i) |
||||
{ |
||||
rectPtr[i] = (*it).bb(); |
||||
confPtr[i] = (*it).confidence; |
||||
} |
||||
} |
||||
Loading…
Reference in new issue