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add feature rescaling according to Dollal's paper FPDW

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pull/137/head
marina.kolpakova 12 years ago
parent 8d90b973b0
commit ba27d89173
1 changed files with 134 additions and 62 deletions
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  1. 196
      modules/objdetect/src/softcascade.cpp

196
modules/objdetect/src/softcascade.cpp
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@ -1,31 +1,31 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
@ -37,6 +37,7 @@
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include <precomp.hpp>
@ -137,6 +138,43 @@ struct Level
workRect(cv::Size(cvRound(w / (float)shrinkage),cvRound(h / (float)shrinkage))),
objSize(cv::Size(cvRound(oct.size.width * relScale), cvRound(oct.size.height * relScale)))
{}
void markDetection(const int x, const int dx, std::vector<cv::Rect>& detections) const
{
}
};
struct CascadeIntrinsics
{
static const float lambda = 1.099f, a = 0.89f;
static float getFor(int channel, float scaling)
{
CV_Assert(channel < 10);
if ((scaling - 1.f) < FLT_EPSILON)
return 1.f;
// according to R. Benenson, M. Mathias, R. Timofte and L. Van Gool paper
static const float A[2][2] =
{ //channel <= 6, otherwise
{ 0.89f, 1.f}, // down
{ 1.00f, 1.f} // up
};
static const float B[2][2] =
{ //channel <= 6, otherwise
{ 1.099f / log(2), 2.f}, // down
{ 2.f, 2.f} // up
};
float a = A[(int)(scaling >= 1)][(int)(channel >= 6)];
float b = B[(int)(scaling >= 1)][(int)(channel >= 6)];
return a * pow(scaling, b);
}
};
// Feature rescale(float relScale)
@ -148,42 +186,6 @@ struct Level
// return res;
// }
// // according to R. Benenson, M. Mathias, R. Timofte and L. Van Gool paper
// struct CascadeIntrinsics
// {
// static const float lambda = 1.099f, a = 0.89f;
// static const float intrinsics[10][4];
// static float getFor(int channel, float scaling)
// {
// CV_Assert(channel < 10);
// if ((scaling - 1.f) < FLT_EPSILON)
// return 1.f;
// int ud = (int)(scaling < 1.f);
// return intrinsics[channel][(ud << 1)] * pow(scaling, intrinsics[channel][(ud << 1) + 1]);
// }
// };
// const float CascadeIntrinsics::intrinsics[10][4] =
// { //da, db, ua, ub
// // hog-like orientation bins
// {a, lambda / log(2), 1, 2},
// {a, lambda / log(2), 1, 2},
// {a, lambda / log(2), 1, 2},
// {a, lambda / log(2), 1, 2},
// {a, lambda / log(2), 1, 2},
// {a, lambda / log(2), 1, 2},
// // gradient magnitude
// {a, lambda / log(2), 1, 2},
// // luv color channels
// {1, 2, 1, 2},
// {1, 2, 1, 2},
// {1, 2, 1, 2}
// };
void calcHistBins(const cv::Mat& grey, cv::Mat& magIntegral, std::vector<cv::Mat>& histInts,
const int bins, int shrinkage)
@ -236,6 +238,7 @@ void calcHistBins(const cv::Mat& grey, cv::Mat& magIntegral, std::vector<cv::Mat
cv::resize(mag, shrMag, cv::Size(), scale, scale, cv::INTER_AREA);
cv::integral(shrMag, magIntegral, mag.depth());
histInts.push_back(magIntegral);
}
struct ChannelStorage
@ -246,25 +249,40 @@ struct ChannelStorage
int shrinkage;
enum {HOG_BINS = 6};
enum {HOG_BINS = 6, HOG_LUV_BINS = 10};
ChannelStorage() {}
ChannelStorage(const cv::Mat& colored, int shr) : shrinkage(shr)
{
cv::Mat _luv;
cv::Mat _luv, shrLuv;
cv::cvtColor(colored, _luv, CV_BGR2Luv);
cv::resize(_luv, shrLuv, cv::Size(), 1.f / shr, 1.f / shr, cv::INTER_AREA);
cv::integral(shrLuv, luv);
cv::integral(luv, luv);
std::vector<cv::Mat> splited;
split(luv, splited);
cv::Mat grey;
cv::cvtColor(colored, grey, CV_RGB2GRAY);
calcHistBins(grey, magnitude, hog, HOG_BINS, shrinkage);
hog.insert(hog.end(), splited.begin(), splited.end());
}
float get(int chennel, cv::Rect area) const
float get(const int x, const int y, const int channel, const cv::Rect& area) const
{
return 1.f;
CV_Assert(channel < HOG_LUV_BINS);
const cv::Mat m = hog[channel];
float a = m.ptr(y + area.y)[x + area.x];
float b = m.ptr(y + area.y)[x + area.width];
float c = m.ptr(y + area.height)[x + area.width];
float d = m.ptr(y + area.height)[x + area.x];
return (a - b + c - d);
}
};
}
@ -291,33 +309,87 @@ struct cv::SoftCascade::Filds
typedef std::vector<Octave>::iterator octIt_t;
void detectAt(const Level& level, const int dx, const int dy, const ChannelStorage& storage,
const std::vector<cv::Rect>& detections) const
std::vector<cv::Rect>& detections) const
{
float detectionScore = 0.f;
const Octave& octave = *(level.octave);
int stBegin = octave.index() * octave.stages, stEnd = stBegin + octave.stages;
for(int st = stBegin; st < stEnd; ++st)
int st = stBegin;
for(; st < stEnd; ++st)
{
const Stage& stage = stages[st];
if (detectionScore > stage.threshold)
{
int nId = st * 3;
// work with root node
const Node& node = nodes[nId];
const Feature& feature = features[node.feature];
float sum = storage.get(feature.channel, feature.rect);
int next = (sum >= node.threshold)? 2 : 1;
// rescaling
float scaling = CascadeIntrinsics::getFor(feature.channel, level.relScale);
cv::Rect scaledRect = feature.rect;
float farea = (scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y);
// rescale
scaledRect.x = cvRound(scaling * scaledRect.x);
scaledRect.y = cvRound(scaling * scaledRect.y);
scaledRect.width = cvRound(scaling * scaledRect.width);
scaledRect.height = cvRound(scaling * scaledRect.height);
float sarea = (scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y);
float approx = 1.f;
if ((farea - 0.f) > FLT_EPSILON && (farea - 0.f) > FLT_EPSILON)
{
const float expected_new_area = farea*level.relScale*level.relScale;
approx = expected_new_area / sarea;
}
float rootThreshold = node.threshold / approx; // ToDo check
rootThreshold *= scaling;
// use rescaled
float sum = storage.get(dx, dy, feature.channel, scaledRect);
int next = (sum >= rootThreshold)? 2 : 1;
// leaces
const Node& leaf = nodes[nId + next];
const Feature& fLeaf = features[node.feature];
sum = storage.get(feature.channel, feature.rect);
int lShift = (next - 1) * 2 + (sum >= leaf.threshold) ? 1 : 0;
// rescaling
scaling = CascadeIntrinsics::getFor(fLeaf.channel, level.relScale);
scaledRect = fLeaf.rect;
farea = (scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y);
// rescale
scaledRect.x = cvRound(scaling * scaledRect.x);
scaledRect.y = cvRound(scaling * scaledRect.y);
scaledRect.width = cvRound(scaling * scaledRect.width);
scaledRect.height = cvRound(scaling * scaledRect.height);
sarea = (scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y);
approx = 1.f;
if ((farea - 0.f) > FLT_EPSILON && (farea - 0.f) > FLT_EPSILON)
{
const float expected_new_area = farea*level.relScale*level.relScale;
approx = expected_new_area / sarea;
}
rootThreshold = leaf.threshold / approx; // ToDo check
rootThreshold *= scaling;
sum = storage.get(dx, dy, feature.channel, scaledRect);
int lShift = (next - 1) * 2 + (sum >= rootThreshold) ? 1 : 0;
float impact = leaves[nId + lShift];
detectionScore += impact;
}
if (detectionScore <= stage.threshold) break;
}
if (st == octave.stages - 1)
level.markDetection(dx, dy, detections);
}
octIt_t fitOctave(const float& logFactor)

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