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Commit 318257f3 authored by marina.kolpakova's avatar marina.kolpakova
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refactor cpp files naming

parent 02118430
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modules/softcascade/src/detector.cpp
View file @ 318257f3
......@@ -48,4 +48,548 @@ cv::softcascade::Detection::Detection(const cv::Rect& b, const float c, int k)
cv::Rect cv::softcascade::Detection::bb() const
{
return cv::Rect(x, y, w, h);
}
namespace {
struct SOctave
{
SOctave(const int i, const cv::Size& origObjSize, const cv::FileNode& fn)
: index(i), weaks((int)fn[SC_OCT_WEAKS]), scale((float)std::pow(2,(float)fn[SC_OCT_SCALE])),
size(cvRound(origObjSize.width * scale), cvRound(origObjSize.height * scale)) {}
int index;
int weaks;
float scale;
cv::Size size;
static const char *const SC_OCT_SCALE;
static const char *const SC_OCT_WEAKS;
static const char *const SC_OCT_SHRINKAGE;
};
struct Weak
{
Weak(){}
Weak(const cv::FileNode& fn) : threshold((float)fn[SC_WEAK_THRESHOLD]) {}
float threshold;
static const char *const SC_WEAK_THRESHOLD;
};
struct Node
{
Node(){}
Node(const int offset, cv::FileNodeIterator& fIt)
: feature((int)(*(fIt +=2)++) + offset), threshold((float)(*(fIt++))) {}
int feature;
float threshold;
};
struct Feature
{
Feature() {}
Feature(const cv::FileNode& fn, bool useBoxes = false) : channel((int)fn[SC_F_CHANNEL])
{
cv::FileNode rn = fn[SC_F_RECT];
cv::FileNodeIterator r_it = rn.begin();
int x = *r_it++;
int y = *r_it++;
int w = *r_it++;
int h = *r_it++;
// ToDo: fix me
if (useBoxes)
rect = cv::Rect(x, y, w, h);
else
rect = cv::Rect(x, y, w + x, h + y);
// 1 / area
rarea = 1.f / ((rect.width - rect.x) * (rect.height - rect.y));
}
int channel;
cv::Rect rect;
float rarea;
static const char *const SC_F_CHANNEL;
static const char *const SC_F_RECT;
};
const char *const SOctave::SC_OCT_SCALE = "scale";
const char *const SOctave::SC_OCT_WEAKS = "weaks";
const char *const SOctave::SC_OCT_SHRINKAGE = "shrinkingFactor";
const char *const Weak::SC_WEAK_THRESHOLD = "treeThreshold";
const char *const Feature::SC_F_CHANNEL = "channel";
const char *const Feature::SC_F_RECT = "rect";
struct Level
{
const SOctave* octave;
float origScale;
float relScale;
int scaleshift;
cv::Size workRect;
cv::Size objSize;
float scaling[2]; // 0-th for channels <= 6, 1-st otherwise
Level(const SOctave& oct, const float scale, const int shrinkage, const int w, const int h)
: octave(&oct), origScale(scale), relScale(scale / oct.scale),
workRect(cv::Size(cvRound(w / (float)shrinkage),cvRound(h / (float)shrinkage))),
objSize(cv::Size(cvRound(oct.size.width * relScale), cvRound(oct.size.height * relScale)))
{
scaling[0] = ((relScale >= 1.f)? 1.f : (0.89f * std::pow(relScale, 1.099f / std::log(2.f)))) / (relScale * relScale);
scaling[1] = 1.f;
scaleshift = static_cast<int>(relScale * (1 << 16));
}
void addDetection(const int x, const int y, float confidence, std::vector<cv::softcascade::Detection>& detections) const
{
// fix me
int shrinkage = 4;//(*octave).shrinkage;
cv::Rect rect(cvRound(x * shrinkage), cvRound(y * shrinkage), objSize.width, objSize.height);
detections.push_back(cv::softcascade::Detection(rect, confidence));
}
float rescale(cv::Rect& scaledRect, const float threshold, int idx) const
{
#define SSHIFT(a) ((a) + (1 << 15)) >> 16
// rescale
scaledRect.x = SSHIFT(scaleshift * scaledRect.x);
scaledRect.y = SSHIFT(scaleshift * scaledRect.y);
scaledRect.width = SSHIFT(scaleshift * scaledRect.width);
scaledRect.height = SSHIFT(scaleshift * scaledRect.height);
#undef SSHIFT
float sarea = static_cast<float>((scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y));
// compensation areas rounding
return (sarea == 0.0f)? threshold : (threshold * scaling[idx] * sarea);
}
};
struct ChannelStorage
{
cv::Mat hog;
int shrinkage;
int offset;
size_t step;
int model_height;
cv::Ptr<cv::softcascade::ChannelFeatureBuilder> builder;
enum {HOG_BINS = 6, HOG_LUV_BINS = 10};
ChannelStorage(const cv::Mat& colored, int shr, std::string featureTypeStr) : shrinkage(shr)
{
model_height = cvRound(colored.rows / (float)shrinkage);
if (featureTypeStr == "ICF") featureTypeStr = "HOG6MagLuv";
builder = cv::softcascade::ChannelFeatureBuilder::create(featureTypeStr);
(*builder)(colored, hog, cv::Size(cvRound(colored.cols / (float)shrinkage), model_height));
step = hog.step1();
}
float get(const int channel, const cv::Rect& area) const
{
const int *ptr = hog.ptr<const int>(0) + model_height * channel * step + offset;
int a = ptr[area.y * step + area.x];
int b = ptr[area.y * step + area.width];
int c = ptr[area.height * step + area.width];
int d = ptr[area.height * step + area.x];
return static_cast<float>(a - b + c - d);
}
};
}
struct cv::softcascade::Detector::Fields
{
float minScale;
float maxScale;
int scales;
int origObjWidth;
int origObjHeight;
int shrinkage;
std::vector<SOctave> octaves;
std::vector<Weak> weaks;
std::vector<Node> nodes;
std::vector<float> leaves;
std::vector<Feature> features;
std::vector<Level> levels;
cv::Size frameSize;
typedef std::vector<SOctave>::iterator octIt_t;
typedef std::vector<Detection> dvector;
std::string featureTypeStr;
void detectAt(const int dx, const int dy, const Level& level, const ChannelStorage& storage, dvector& detections) const
{
float detectionScore = 0.f;
const SOctave& octave = *(level.octave);
int stBegin = octave.index * octave.weaks, stEnd = stBegin + octave.weaks;
for(int st = stBegin; st < stEnd; ++st)
{
const Weak& weak = weaks[st];
int nId = st * 3;
// work with root node
const Node& node = nodes[nId];
const Feature& feature = features[node.feature];
cv::Rect scaledRect(feature.rect);
float threshold = level.rescale(scaledRect, node.threshold, (int)(feature.channel > 6)) * feature.rarea;
float sum = storage.get(feature.channel, scaledRect);
int next = (sum >= threshold)? 2 : 1;
// leaves
const Node& leaf = nodes[nId + next];
const Feature& fLeaf = features[leaf.feature];
scaledRect = fLeaf.rect;
threshold = level.rescale(scaledRect, leaf.threshold, (int)(fLeaf.channel > 6)) * fLeaf.rarea;
sum = storage.get(fLeaf.channel, scaledRect);
int lShift = (next - 1) * 2 + ((sum >= threshold) ? 1 : 0);
float impact = leaves[(st * 4) + lShift];
detectionScore += impact;
if (detectionScore <= weak.threshold) return;
}
if (detectionScore > 0)
level.addDetection(dx, dy, detectionScore, detections);
}
octIt_t fitOctave(const float& logFactor)
{
float minAbsLog = FLT_MAX;
octIt_t res = octaves.begin();
for (octIt_t oct = octaves.begin(); oct < octaves.end(); ++oct)
{
const SOctave& octave =*oct;
float logOctave = std::log(octave.scale);
float logAbsScale = fabs(logFactor - logOctave);
if(logAbsScale < minAbsLog)
{
res = oct;
minAbsLog = logAbsScale;
}
}
return res;
}
// compute levels of full pyramid
void calcLevels(const cv::Size& curr, float mins, float maxs, int total)
{
if (frameSize == curr && maxs == maxScale && mins == minScale && total == scales) return;
frameSize = curr;
maxScale = maxs; minScale = mins; scales = total;
CV_Assert(scales > 1);
levels.clear();
float logFactor = (std::log(maxScale) - std::log(minScale)) / (scales -1);
float scale = minScale;
for (int sc = 0; sc < scales; ++sc)
{
int width = static_cast<int>(std::max(0.0f, frameSize.width - (origObjWidth * scale)));
int height = static_cast<int>(std::max(0.0f, frameSize.height - (origObjHeight * scale)));
float logScale = std::log(scale);
octIt_t fit = fitOctave(logScale);
Level level(*fit, scale, shrinkage, width, height);
if (!width || !height)
break;
else
levels.push_back(level);
if (fabs(scale - maxScale) < FLT_EPSILON) break;
scale = std::min(maxScale, expf(std::log(scale) + logFactor));
}
}
bool fill(const FileNode &root)
{
// cascade properties
static const char *const SC_STAGE_TYPE = "stageType";
static const char *const SC_BOOST = "BOOST";
static const char *const SC_FEATURE_TYPE = "featureType";
static const char *const SC_HOG6_MAG_LUV = "HOG6MagLuv";
static const char *const SC_ICF = "ICF";
static const char *const SC_ORIG_W = "width";
static const char *const SC_ORIG_H = "height";
static const char *const SC_OCTAVES = "octaves";
static const char *const SC_TREES = "trees";
static const char *const SC_FEATURES = "features";
static const char *const SC_INTERNAL = "internalNodes";
static const char *const SC_LEAF = "leafValues";
static const char *const SC_SHRINKAGE = "shrinkage";
static const char *const FEATURE_FORMAT = "featureFormat";
// only Ada Boost supported
std::string stageTypeStr = (std::string)root[SC_STAGE_TYPE];
CV_Assert(stageTypeStr == SC_BOOST);
std::string fformat = (std::string)root[FEATURE_FORMAT];
bool useBoxes = (fformat == "BOX");
// only HOG-like integral channel features supported
featureTypeStr = (std::string)root[SC_FEATURE_TYPE];
CV_Assert(featureTypeStr == SC_ICF || featureTypeStr == SC_HOG6_MAG_LUV);
origObjWidth = (int)root[SC_ORIG_W];
origObjHeight = (int)root[SC_ORIG_H];
shrinkage = (int)root[SC_SHRINKAGE];
FileNode fn = root[SC_OCTAVES];
if (fn.empty()) return false;
// for each octave
FileNodeIterator it = fn.begin(), it_end = fn.end();
for (int octIndex = 0; it != it_end; ++it, ++octIndex)
{
FileNode fns = *it;
SOctave octave(octIndex, cv::Size(origObjWidth, origObjHeight), fns);
CV_Assert(octave.weaks > 0);
octaves.push_back(octave);
FileNode ffs = fns[SC_FEATURES];
if (ffs.empty()) return false;
fns = fns[SC_TREES];
if (fn.empty()) return false;
FileNodeIterator st = fns.begin(), st_end = fns.end();
for (; st != st_end; ++st )
{
weaks.push_back(Weak(*st));
fns = (*st)[SC_INTERNAL];
FileNodeIterator inIt = fns.begin(), inIt_end = fns.end();
for (; inIt != inIt_end;)
nodes.push_back(Node((int)features.size(), inIt));
fns = (*st)[SC_LEAF];
inIt = fns.begin(), inIt_end = fns.end();
for (; inIt != inIt_end; ++inIt)
leaves.push_back((float)(*inIt));
}
st = ffs.begin(), st_end = ffs.end();
for (; st != st_end; ++st )
features.push_back(Feature(*st, useBoxes));
}
return true;
}
};
cv::softcascade::Detector::Detector(const double mins, const double maxs, const int nsc, const int rej)
: fields(0), minScale(mins), maxScale(maxs), scales(nsc), rejCriteria(rej) {}
cv::softcascade::Detector::~Detector() { delete fields;}
void cv::softcascade::Detector::read(const cv::FileNode& fn)
{
Algorithm::read(fn);
}
bool cv::softcascade::Detector::load(const cv::FileNode& fn)
{
if (fields) delete fields;
fields = new Fields;
return fields->fill(fn);
}
namespace {
using cv::softcascade::Detection;
typedef std::vector<Detection> dvector;
struct ConfidenceGt
{
bool operator()(const Detection& a, const Detection& b) const
{
return a.confidence > b.confidence;
}
};
static float overlap(const cv::Rect &a, const cv::Rect &b)
{
int w = std::min(a.x + a.width, b.x + b.width) - std::max(a.x, b.x);
int h = std::min(a.y + a.height, b.y + b.height) - std::max(a.y, b.y);
return (w < 0 || h < 0)? 0.f : (float)(w * h);
}
void DollarNMS(dvector& objects)
{
static const float DollarThreshold = 0.65f;
std::sort(objects.begin(), objects.end(), ConfidenceGt());
for (dvector::iterator dIt = objects.begin(); dIt != objects.end(); ++dIt)
{
const Detection &a = *dIt;
for (dvector::iterator next = dIt + 1; next != objects.end(); )
{
const Detection &b = *next;
const float ovl = overlap(a.bb(), b.bb()) / std::min(a.bb().area(), b.bb().area());
if (ovl > DollarThreshold)
next = objects.erase(next);
else
++next;
}
}
}
static void suppress(int type, std::vector<Detection>& objects)
{
CV_Assert(type == cv::softcascade::Detector::DOLLAR);
DollarNMS(objects);
}
}
void cv::softcascade::Detector::detectNoRoi(const cv::Mat& image, std::vector<Detection>& objects) const
{
Fields& fld = *fields;
// create integrals
ChannelStorage storage(image, fld.shrinkage, 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)
{
for (int dx = 0; dx < level.workRect.width; ++dx)
{
storage.offset = (int)(dy * storage.step + dx);
fld.detectAt(dx, dy, level, storage, objects);
}
}
}
if (rejCriteria != NO_REJECT) suppress(rejCriteria, objects);
}
void cv::softcascade::Detector::detect(cv::InputArray _image, cv::InputArray _rois, std::vector<Detection>& objects) const
{
// only color images are suppered
cv::Mat image = _image.getMat();
CV_Assert(image.type() == CV_8UC3);
Fields& fld = *fields;
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 cv::softcascade::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;
}
}
\ No newline at end of file
modules/softcascade/src/softcascade.cpp deleted 100644 → 0
View file @ 02118430
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// 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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2013, 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:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * 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.
//
// 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
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// 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"
using cv::softcascade::Detection;
using cv::softcascade::Detector;
using cv::softcascade::ChannelFeatureBuilder;
using namespace cv;
namespace {
struct SOctave
{
SOctave(const int i, const cv::Size& origObjSize, const cv::FileNode& fn)
: index(i), weaks((int)fn[SC_OCT_WEAKS]), scale((float)std::pow(2,(float)fn[SC_OCT_SCALE])),
size(cvRound(origObjSize.width * scale), cvRound(origObjSize.height * scale)) {}
int index;
int weaks;
float scale;
cv::Size size;
static const char *const SC_OCT_SCALE;
static const char *const SC_OCT_WEAKS;
static const char *const SC_OCT_SHRINKAGE;
};
struct Weak
{
Weak(){}
Weak(const cv::FileNode& fn) : threshold((float)fn[SC_WEAK_THRESHOLD]) {}
float threshold;
static const char *const SC_WEAK_THRESHOLD;
};
struct Node
{
Node(){}
Node(const int offset, cv::FileNodeIterator& fIt)
: feature((int)(*(fIt +=2)++) + offset), threshold((float)(*(fIt++))) {}
int feature;
float threshold;
};
struct Feature
{
Feature() {}
Feature(const cv::FileNode& fn, bool useBoxes = false) : channel((int)fn[SC_F_CHANNEL])
{
cv::FileNode rn = fn[SC_F_RECT];
cv::FileNodeIterator r_it = rn.begin();
int x = *r_it++;
int y = *r_it++;
int w = *r_it++;
int h = *r_it++;
// ToDo: fix me
if (useBoxes)
rect = cv::Rect(x, y, w, h);
else
rect = cv::Rect(x, y, w + x, h + y);
// 1 / area
rarea = 1.f / ((rect.width - rect.x) * (rect.height - rect.y));
}
int channel;
cv::Rect rect;
float rarea;
static const char *const SC_F_CHANNEL;
static const char *const SC_F_RECT;
};
const char *const SOctave::SC_OCT_SCALE = "scale";
const char *const SOctave::SC_OCT_WEAKS = "weaks";
const char *const SOctave::SC_OCT_SHRINKAGE = "shrinkingFactor";
const char *const Weak::SC_WEAK_THRESHOLD = "treeThreshold";
const char *const Feature::SC_F_CHANNEL = "channel";
const char *const Feature::SC_F_RECT = "rect";
struct Level
{
const SOctave* octave;
float origScale;
float relScale;
int scaleshift;
cv::Size workRect;
cv::Size objSize;
float scaling[2]; // 0-th for channels <= 6, 1-st otherwise
Level(const SOctave& oct, const float scale, const int shrinkage, const int w, const int h)
: octave(&oct), origScale(scale), relScale(scale / oct.scale),
workRect(cv::Size(cvRound(w / (float)shrinkage),cvRound(h / (float)shrinkage))),
objSize(cv::Size(cvRound(oct.size.width * relScale), cvRound(oct.size.height * relScale)))
{
scaling[0] = ((relScale >= 1.f)? 1.f : (0.89f * std::pow(relScale, 1.099f / std::log(2.f)))) / (relScale * relScale);
scaling[1] = 1.f;
scaleshift = static_cast<int>(relScale * (1 << 16));
}
void addDetection(const int x, const int y, float confidence, std::vector<Detection>& detections) const
{
// fix me
int shrinkage = 4;//(*octave).shrinkage;
cv::Rect rect(cvRound(x * shrinkage), cvRound(y * shrinkage), objSize.width, objSize.height);
detections.push_back(Detection(rect, confidence));
}
float rescale(cv::Rect& scaledRect, const float threshold, int idx) const
{
#define SSHIFT(a) ((a) + (1 << 15)) >> 16
// rescale
scaledRect.x = SSHIFT(scaleshift * scaledRect.x);
scaledRect.y = SSHIFT(scaleshift * scaledRect.y);
scaledRect.width = SSHIFT(scaleshift * scaledRect.width);
scaledRect.height = SSHIFT(scaleshift * scaledRect.height);
#undef SSHIFT
float sarea = static_cast<float>((scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y));
// compensation areas rounding
return (sarea == 0.0f)? threshold : (threshold * scaling[idx] * sarea);
}
};
struct ChannelStorage
{
cv::Mat hog;
int shrinkage;
int offset;
size_t step;
int model_height;
cv::Ptr<ChannelFeatureBuilder> builder;
enum {HOG_BINS = 6, HOG_LUV_BINS = 10};
ChannelStorage(const cv::Mat& colored, int shr, std::string featureTypeStr) : shrinkage(shr)
{
model_height = cvRound(colored.rows / (float)shrinkage);
if (featureTypeStr == "ICF") featureTypeStr = "HOG6MagLuv";
builder = ChannelFeatureBuilder::create(featureTypeStr);
(*builder)(colored, hog, cv::Size(cvRound(colored.cols / (float)shrinkage), model_height));
step = hog.step1();
}
float get(const int channel, const cv::Rect& area) const
{
const int *ptr = hog.ptr<const int>(0) + model_height * channel * step + offset;
int a = ptr[area.y * step + area.x];
int b = ptr[area.y * step + area.width];
int c = ptr[area.height * step + area.width];
int d = ptr[area.height * step + area.x];
return static_cast<float>(a - b + c - d);
}
};
}
struct Detector::Fields
{
float minScale;
float maxScale;
int scales;
int origObjWidth;
int origObjHeight;
int shrinkage;
std::vector<SOctave> octaves;
std::vector<Weak> weaks;
std::vector<Node> nodes;
std::vector<float> leaves;
std::vector<Feature> features;
std::vector<Level> levels;
cv::Size frameSize;
typedef std::vector<SOctave>::iterator octIt_t;
typedef std::vector<Detection> dvector;
std::string featureTypeStr;
void detectAt(const int dx, const int dy, const Level& level, const ChannelStorage& storage, dvector& detections) const
{
float detectionScore = 0.f;
const SOctave& octave = *(level.octave);
int stBegin = octave.index * octave.weaks, stEnd = stBegin + octave.weaks;
for(int st = stBegin; st < stEnd; ++st)
{
const Weak& weak = weaks[st];
int nId = st * 3;
// work with root node
const Node& node = nodes[nId];
const Feature& feature = features[node.feature];
cv::Rect scaledRect(feature.rect);
float threshold = level.rescale(scaledRect, node.threshold, (int)(feature.channel > 6)) * feature.rarea;
float sum = storage.get(feature.channel, scaledRect);
int next = (sum >= threshold)? 2 : 1;
// leaves
const Node& leaf = nodes[nId + next];
const Feature& fLeaf = features[leaf.feature];
scaledRect = fLeaf.rect;
threshold = level.rescale(scaledRect, leaf.threshold, (int)(fLeaf.channel > 6)) * fLeaf.rarea;
sum = storage.get(fLeaf.channel, scaledRect);
int lShift = (next - 1) * 2 + ((sum >= threshold) ? 1 : 0);
float impact = leaves[(st * 4) + lShift];
detectionScore += impact;
if (detectionScore <= weak.threshold) return;
}
if (detectionScore > 0)
level.addDetection(dx, dy, detectionScore, detections);
}
octIt_t fitOctave(const float& logFactor)
{
float minAbsLog = FLT_MAX;
octIt_t res = octaves.begin();
for (octIt_t oct = octaves.begin(); oct < octaves.end(); ++oct)
{
const SOctave& octave =*oct;
float logOctave = std::log(octave.scale);
float logAbsScale = fabs(logFactor - logOctave);
if(logAbsScale < minAbsLog)
{
res = oct;
minAbsLog = logAbsScale;
}
}
return res;
}
// compute levels of full pyramid
void calcLevels(const cv::Size& curr, float mins, float maxs, int total)
{
if (frameSize == curr && maxs == maxScale && mins == minScale && total == scales) return;
frameSize = curr;
maxScale = maxs; minScale = mins; scales = total;
CV_Assert(scales > 1);
levels.clear();
float logFactor = (std::log(maxScale) - std::log(minScale)) / (scales -1);
float scale = minScale;
for (int sc = 0; sc < scales; ++sc)
{
int width = static_cast<int>(std::max(0.0f, frameSize.width - (origObjWidth * scale)));
int height = static_cast<int>(std::max(0.0f, frameSize.height - (origObjHeight * scale)));
float logScale = std::log(scale);
octIt_t fit = fitOctave(logScale);
Level level(*fit, scale, shrinkage, width, height);
if (!width || !height)
break;
else
levels.push_back(level);
if (fabs(scale - maxScale) < FLT_EPSILON) break;
scale = std::min(maxScale, expf(std::log(scale) + logFactor));
}
}
bool fill(const FileNode &root)
{
// cascade properties
static const char *const SC_STAGE_TYPE = "stageType";
static const char *const SC_BOOST = "BOOST";
static const char *const SC_FEATURE_TYPE = "featureType";
static const char *const SC_HOG6_MAG_LUV = "HOG6MagLuv";
static const char *const SC_ICF = "ICF";
static const char *const SC_ORIG_W = "width";
static const char *const SC_ORIG_H = "height";
static const char *const SC_OCTAVES = "octaves";
static const char *const SC_TREES = "trees";
static const char *const SC_FEATURES = "features";
static const char *const SC_INTERNAL = "internalNodes";
static const char *const SC_LEAF = "leafValues";
static const char *const SC_SHRINKAGE = "shrinkage";
static const char *const FEATURE_FORMAT = "featureFormat";
// only Ada Boost supported
std::string stageTypeStr = (std::string)root[SC_STAGE_TYPE];
CV_Assert(stageTypeStr == SC_BOOST);
std::string fformat = (std::string)root[FEATURE_FORMAT];
bool useBoxes = (fformat == "BOX");
// only HOG-like integral channel features supported
featureTypeStr = (std::string)root[SC_FEATURE_TYPE];
CV_Assert(featureTypeStr == SC_ICF || featureTypeStr == SC_HOG6_MAG_LUV);
origObjWidth = (int)root[SC_ORIG_W];
origObjHeight = (int)root[SC_ORIG_H];
shrinkage = (int)root[SC_SHRINKAGE];
FileNode fn = root[SC_OCTAVES];
if (fn.empty()) return false;
// for each octave
FileNodeIterator it = fn.begin(), it_end = fn.end();
for (int octIndex = 0; it != it_end; ++it, ++octIndex)
{
FileNode fns = *it;
SOctave octave(octIndex, cv::Size(origObjWidth, origObjHeight), fns);
CV_Assert(octave.weaks > 0);
octaves.push_back(octave);
FileNode ffs = fns[SC_FEATURES];
if (ffs.empty()) return false;
fns = fns[SC_TREES];
if (fn.empty()) return false;
FileNodeIterator st = fns.begin(), st_end = fns.end();
for (; st != st_end; ++st )
{
weaks.push_back(Weak(*st));
fns = (*st)[SC_INTERNAL];
FileNodeIterator inIt = fns.begin(), inIt_end = fns.end();
for (; inIt != inIt_end;)
nodes.push_back(Node((int)features.size(), inIt));
fns = (*st)[SC_LEAF];
inIt = fns.begin(), inIt_end = fns.end();
for (; inIt != inIt_end; ++inIt)
leaves.push_back((float)(*inIt));
}
st = ffs.begin(), st_end = ffs.end();
for (; st != st_end; ++st )
features.push_back(Feature(*st, useBoxes));
}
return true;
}
};
Detector::Detector(const double mins, const double maxs, const int nsc, const int rej)
: fields(0), minScale(mins), maxScale(maxs), scales(nsc), rejCriteria(rej) {}
Detector::~Detector() { delete fields;}
void Detector::read(const cv::FileNode& fn)
{
Algorithm::read(fn);
}
bool Detector::load(const cv::FileNode& fn)
{
if (fields) delete fields;
fields = new Fields;
return fields->fill(fn);
}
namespace {
using cv::softcascade::Detection;
typedef std::vector<Detection> dvector;
struct ConfidenceGt
{
bool operator()(const Detection& a, const Detection& b) const
{
return a.confidence > b.confidence;
}
};
static float overlap(const cv::Rect &a, const cv::Rect &b)
{
int w = std::min(a.x + a.width, b.x + b.width) - std::max(a.x, b.x);
int h = std::min(a.y + a.height, b.y + b.height) - std::max(a.y, b.y);
return (w < 0 || h < 0)? 0.f : (float)(w * h);
}
void DollarNMS(dvector& objects)
{
static const float DollarThreshold = 0.65f;
std::sort(objects.begin(), objects.end(), ConfidenceGt());
for (dvector::iterator dIt = objects.begin(); dIt != objects.end(); ++dIt)
{
const Detection &a = *dIt;
for (dvector::iterator next = dIt + 1; next != objects.end(); )
{
const Detection &b = *next;
const float ovl = overlap(a.bb(), b.bb()) / std::min(a.bb().area(), b.bb().area());
if (ovl > DollarThreshold)
next = objects.erase(next);
else
++next;
}
}
}
static void suppress(int type, std::vector<Detection>& objects)
{
CV_Assert(type == Detector::DOLLAR);
DollarNMS(objects);
}
}
void Detector::detectNoRoi(const cv::Mat& image, std::vector<Detection>& objects) const
{
Fields& fld = *fields;
// create integrals
ChannelStorage storage(image, fld.shrinkage, 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)
{
for (int dx = 0; dx < level.workRect.width; ++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(cv::InputArray _image, cv::InputArray _rois, std::vector<Detection>& objects) const
{
// only color images are suppered
cv::Mat image = _image.getMat();
CV_Assert(image.type() == CV_8UC3);
Fields& fld = *fields;
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;
}
}
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