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547 lines
17 KiB
C++
547 lines
17 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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#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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#include <cstdio>
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#include <stdarg.h>
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// used for noisy printfs
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// #define WITH_DEBUG_OUT
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#if defined WITH_DEBUG_OUT
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# define dprintf(format, ...) \
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do { printf(format, ##__VA_ARGS__); } while (0)
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#else
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# define dprintf(format, ...)
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#endif
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namespace {
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struct Octave
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{
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int index;
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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(const int i, cv::Size origObjSize, const cv::FileNode& fn)
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: index(i), 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 Weak
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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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Weak(){}
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Weak(const cv::FileNode& fn) : threshold((float)fn[SC_STAGE_THRESHOLD]){}
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};
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const char *const Weak::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(const int offset, cv::FileNodeIterator& fIt)
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: feature((int)(*(fIt +=2)++) + offset), 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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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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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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// 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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};
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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 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(bool isUp, float scaling)
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{
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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)(isUp)];
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float b = B[(int)(scaling >= 1)][(int)(isUp)];
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dprintf("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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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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int scaleshift;
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cv::Size workRect;
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cv::Size objSize;
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enum { R_SHIFT = 1 << 15 };
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float scaling[2];
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typedef cv::SoftCascade::Detection detection_t;
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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),
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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] = CascadeIntrinsics::getFor(false, relScale) / (relScale * relScale);
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scaling[1] = CascadeIntrinsics::getFor(true, relScale) / (relScale * relScale);
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scaleshift = relScale * (1 << 16);
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}
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void addDetection(const int x, const int y, float confidence, std::vector<detection_t>& detections) const
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{
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int shrinkage = (*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_t(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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// rescale
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scaledRect.x = (scaleshift * scaledRect.x + R_SHIFT) >> 16;
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scaledRect.y = (scaleshift * scaledRect.y + R_SHIFT) >> 16;
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scaledRect.width = (scaleshift * scaledRect.width + R_SHIFT) >> 16;
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scaledRect.height = (scaleshift * scaledRect.height + R_SHIFT) >> 16;
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float sarea = (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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std::vector<cv::Mat> hog;
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int shrinkage;
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int offset;
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int step;
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enum {HOG_BINS = 6, HOG_LUV_BINS = 10};
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ChannelStorage() {}
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ChannelStorage(const cv::Mat& colored, int shr) : shrinkage(shr)
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{
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hog.clear();
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cv::IntegralChannels ints(shr);
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// convert to grey
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cv::Mat grey;
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cv::cvtColor(colored, grey, CV_BGR2GRAY);
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ints.createHogBins(grey, hog, 6);
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ints.createLuvBins(colored, hog);
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step = hog[0].cols;
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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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// CV_Assert(channel < HOG_LUV_BINS);
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const cv::Mat& m = hog[channel];
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int *ptr = ((int*)(m.data)) + 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 (a - b + c - d);
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}
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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<Weak> 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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cv::Size frameSize;
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enum { BOOST = 0 };
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typedef std::vector<Octave>::iterator octIt_t;
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void detectAt(const int dx, const int dy, const Level& level, const ChannelStorage& storage,
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std::vector<Detection>& detections) const
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{
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dprintf("detect at: %d %d\n", dx, dy);
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float detectionScore = 0.f;
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const Octave& octave = *(level.octave);
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int stBegin = octave.index * octave.stages, stEnd = stBegin + 1024;//octave.stages;
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dprintf(" octave stages: %d to %d index %d %f level %f\n",
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stBegin, stEnd, octave.index, octave.scale, level.origScale);
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int st = stBegin;
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for(; st < stEnd; ++st)
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{
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dprintf("index: %d\n", st);
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const Weak& stage = stages[st];
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{
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int nId = st * 3;
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// work with root node
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const Node& node = nodes[nId];
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const Feature& feature = features[node.feature];
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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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dprintf("root feature %d %f\n",feature.channel, sum);
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int next = (sum >= threshold)? 2 : 1;
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dprintf("go: %d (%f >= %f)\n\n" ,next, sum, threshold);
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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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dprintf("decided: %d (%f >= %f) %d %f\n\n" ,next, sum, threshold, lShift, impact);
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detectionScore += impact;
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}
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dprintf("extracted stage:\n");
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dprintf("ct %f\n", stage.threshold);
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dprintf("computed score %f\n\n", detectionScore);
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#if defined WITH_DEBUG_OUT
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if (st - stBegin > 50 ) break;
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#endif
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if (detectionScore <= stage.threshold) return;
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}
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dprintf("x %d y %d: %d\n", dx, dy, st - stBegin);
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dprintf(" got %d\n", st);
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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 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(const cv::Size& curr, int scales)
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{
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if (frameSize == curr) return;
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frameSize = curr;
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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, frameSize.width - (origObjWidth * scale));
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int height = std::max(0.0f, frameSize.height - (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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}
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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 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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origObjHeight = (int)root[SC_ORIG_H];
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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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int feature_offset = 0;
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int octIndex = 0;
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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(octIndex, cv::Size(origObjWidth, origObjHeight), 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(Weak(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(feature_offset, 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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feature_offset += octave.stages * 3;
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++octIndex;
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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(const float mins, const float maxs, const int nsc)
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: filds(0), minScale(mins), maxScale(maxs), scales(nsc) {}
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cv::SoftCascade::SoftCascade(const cv::FileStorage& fs) : filds(0)
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{
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read(fs);
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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::read( const cv::FileStorage& fs)
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{
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if (!fs.isOpened()) return false;
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if (filds)
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delete filds;
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filds = 0;
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filds = new Filds;
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Filds& flds = *filds;
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return flds.fill(fs.getFirstTopLevelNode(), minScale, maxScale);
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}
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void cv::SoftCascade::detectMultiScale(const Mat& image, const std::vector<cv::Rect>& /*rois*/,
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std::vector<Detection>& objects, const int /*rejectfactor*/) const
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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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|
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Filds& fld = *filds;
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fld.calcLevels(image.size(), scales);
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objects.clear();
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// create integrals
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ChannelStorage storage(image, fld.shrinkage);
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|
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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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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 = 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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|
} |