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soft cascade: gpu representation

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marina.kolpakova 2012-09-20 16:22:10 +04:00
parent dd9c53497b
commit 267d140bfe
5 changed files with 473 additions and 6 deletions
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9
modules/gpu/include/opencv2/gpu/gpu.hpp
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@ -1554,9 +1554,14 @@ public:
virtual ~SoftCascade();
//! return vector of bounding boxes. Each box contains one detected object
//! detect specific objects on in the input frame for all scales computed flom minScale and maxscale values
//! Param image is input frame for detector. Cascade will be applied to it.
//! Param rois is a mask
//! Param objects 4-channel matrix thet contain detected rectangles
//! Param rejectfactor used for final object box computing
//! Param stream
virtual void detectMultiScale(const GpuMat& image, const GpuMat& rois, GpuMat& objects,
int rejectfactor = 1, Stream stream = Stream::Null()); // ToDo store objects in GPU mem
int rejectfactor = 1, Stream stream = Stream::Null());
protected:
enum { BOOST = 0 };

43
modules/gpu/src/cuda/isf-sc.cu Normal file
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@ -0,0 +1,43 @@
/*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) 2008-2012, 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:
//
// * 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 <icf.hpp>

118
modules/gpu/src/icf.hpp Normal file
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@ -0,0 +1,118 @@
/*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) 2008-2012, 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:
//
// * 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*/
#ifndef __OPENCV_ICF_HPP__
#define __OPENCV_ICF_HPP__
#if defined __CUDACC__
# define __hd__ __host__ __device__ __forceinline__
#else
# define __hd__
#endif
namespace icf {
struct Cascade
{
};
struct ChannelStorage
{
};
struct __align__(16) Octave
{
ushort index;
ushort stages;
ushort shrinkage;
ushort2 size;
float scale;
Octave(const ushort i, const ushort s, const ushort sh, const ushort2 sz, const float sc)
: index(i), stages(s), shrinkage(sh), size(sz), scale(sc) {}
};
struct __align__(8) Node
{
int feature;
float threshold;
Node(const int f, const float t) : feature(f), threshold(t) {}
};
struct __align__(8) Feature
{
int channel;
uchar4 rect;
Feature(const int c, const uchar4 r) : channel(c), rect(r) {}
};
struct __align__(8) Level //is actually 24 bytes
{
int octave;
// float origScale; //not actually used
float relScale;
float shrScale; // used for marking detection
float scaling[2]; // calculated according to Dollal paper
// for 640x480 we can not get overflow
uchar2 workRect;
uchar2 objSize;
Level(int idx, const Octave& oct, const float scale, const int w, const int h)
: octave(idx), relScale(scale / oct.scale), shrScale (relScale / (float)oct.shrinkage)
{
workRect.x = round(w / (float)oct.shrinkage);
workRect.y = round(h / (float)oct.shrinkage);
objSize.x = round(oct.size.x * relScale);
objSize.y = round(oct.size.y * relScale);
}
};
}
#endif

236
modules/gpu/src/softcascade.cpp
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@ -56,12 +56,242 @@ void cv::gpu::SoftCascade::detectMultiScale(const GpuMat&, const GpuMat&, GpuMat
#else
#include <icf.hpp>
struct cv::gpu::SoftCascade::Filds
{
bool fill(const FileNode &root, const float mins, const float maxs){return true;}
void calcLevels(int frameW, int frameH, int scales) {}
// scales range
float minScale;
float maxScale;
int origObjWidth;
int origObjHeight;
GpuMat octaves;
GpuMat stages;
GpuMat nodes;
GpuMat leaves;
GpuMat features;
std::vector<float> scales;
icf::Cascade cascade;
bool fill(const FileNode &root, const float mins, const float maxs);
private:
void calcLevels(const std::vector<icf::Octave>& octs,
int frameW, int frameH, int nscales);
typedef std::vector<icf::Octave>::const_iterator octIt_t;
int fitOctave(const std::vector<icf::Octave>& octs, const float& logFactor)
{
float minAbsLog = FLT_MAX;
int res = 0;
for (int oct = 0; oct < (int)octs.size(); ++oct)
{
const icf::Octave& octave =octs[oct];
float logOctave = ::log(octave.scale);
float logAbsScale = ::fabs(logFactor - logOctave);
if(logAbsScale < minAbsLog)
{
res = oct;
minAbsLog = logAbsScale;
}
}
return res;
}
};
inline bool cv::gpu::SoftCascade::Filds::fill(const FileNode &root, const float mins, const float maxs)
{
minScale = mins;
maxScale = maxs;
// 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_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_STAGES = "stages";
static const char *const SC_FEATURES = "features";
static const char *const SC_WEEK = "weakClassifiers";
static const char *const SC_INTERNAL = "internalNodes";
static const char *const SC_LEAF = "leafValues";
static const char *const SC_OCT_SCALE = "scale";
static const char *const SC_OCT_STAGES = "stageNum";
static const char *const SC_OCT_SHRINKAGE = "shrinkingFactor";
static const char *const SC_STAGE_THRESHOLD = "stageThreshold";
static const char * const SC_F_CHANNEL = "channel";
static const char * const SC_F_RECT = "rect";
// only Ada Boost supported
std::string stageTypeStr = (string)root[SC_STAGE_TYPE];
CV_Assert(stageTypeStr == SC_BOOST);
// only HOG-like integral channel features cupported
string featureTypeStr = (string)root[SC_FEATURE_TYPE];
CV_Assert(featureTypeStr == SC_ICF);
origObjWidth = (int)root[SC_ORIG_W];
CV_Assert(origObjWidth == SoftCascade::ORIG_OBJECT_WIDTH);
origObjHeight = (int)root[SC_ORIG_H];
CV_Assert(origObjHeight == SoftCascade::ORIG_OBJECT_HEIGHT);
FileNode fn = root[SC_OCTAVES];
if (fn.empty()) return false;
std::vector<icf::Octave> voctaves;
std::vector<float> vstages;
std::vector<icf::Node> vnodes;
std::vector<float> vleaves;
std::vector<icf::Feature> vfeatures;
scales.clear();
// std::vector<Level> levels;
FileNodeIterator it = fn.begin(), it_end = fn.end();
int feature_offset = 0;
ushort octIndex = 0;
for (; it != it_end; ++it)
{
FileNode fns = *it;
float scale = (float)fns[SC_OCT_SCALE];
scales.push_back(scale);
ushort nstages = saturate_cast<ushort>((int)fn[SC_OCT_STAGES]);
ushort2 size;
size.x = cvRound(SoftCascade::ORIG_OBJECT_WIDTH * scale);
size.y = cvRound(SoftCascade::ORIG_OBJECT_HEIGHT * scale);
ushort shrinkage = saturate_cast<ushort>((int)fn[SC_OCT_SHRINKAGE]);
icf::Octave octave(octIndex, nstages, shrinkage, size, scale);
CV_Assert(octave.stages > 0);
voctaves.push_back(octave);
FileNode ffs = fns[SC_FEATURES];
if (ffs.empty()) return false;
fns = fns[SC_STAGES];
if (fn.empty()) return false;
// for each stage (~ decision tree with H = 2)
FileNodeIterator st = fns.begin(), st_end = fns.end();
for (; st != st_end; ++st )
{
fns = *st;
vstages.push_back((float)fn[SC_STAGE_THRESHOLD]);
fns = fns[SC_WEEK];
FileNodeIterator ftr = fns.begin(), ft_end = fns.end();
for (; ftr != ft_end; ++ftr)
{
fns = (*ftr)[SC_INTERNAL];
FileNodeIterator inIt = fns.begin(), inIt_end = fns.end();
for (; inIt != inIt_end;)
{
int feature = (int)(*(inIt +=2)++) + feature_offset;
vnodes.push_back(icf::Node(feature, (float)(*(inIt++))));
}
fns = (*ftr)[SC_LEAF];
inIt = fns.begin(), inIt_end = fns.end();
for (; inIt != inIt_end; ++inIt)
vleaves.push_back((float)(*inIt));
}
}
st = ffs.begin(), st_end = ffs.end();
for (; st != st_end; ++st )
{
cv::FileNode rn = (*st)[SC_F_RECT];
cv::FileNodeIterator r_it = rn.begin();
uchar4 rect;
rect.x = saturate_cast<uchar>((int)*(r_it++));
rect.y = saturate_cast<uchar>((int)*(r_it++));
rect.z = saturate_cast<uchar>((int)*(r_it++));
rect.w = saturate_cast<uchar>((int)*(r_it++));
vfeatures.push_back(icf::Feature((int)(*st)[SC_F_CHANNEL], rect));
}
feature_offset += octave.stages * 3;
++octIndex;
}
// upload in gpu memory
octaves.upload(cv::Mat(1, voctaves.size() * sizeof(icf::Octave), CV_8UC1, (uchar*)&(voctaves[0]) ));
CV_Assert(!octaves.empty());
stages.upload(cv::Mat(vstages).reshape(1,1));
CV_Assert(!stages.empty());
nodes.upload(cv::Mat(1, vnodes.size() * sizeof(icf::Node), CV_8UC1, (uchar*)&(vnodes[0]) ));
CV_Assert(!nodes.empty());
leaves.upload(cv::Mat(vleaves).reshape(1,1));
CV_Assert(!leaves.empty());
features.upload(cv::Mat(1, vfeatures.size() * sizeof(icf::Feature), CV_8UC1, (uchar*)&(vfeatures[0]) ));
CV_Assert(!features.empty());
// compute levels
calcLevels(voctaves, (int)SoftCascade::FRAME_WIDTH, (int)SoftCascade::FRAME_HEIGHT, (int)SoftCascade::TOTAL_SCALES);
return true;
}
inline void cv::gpu::SoftCascade::Filds::calcLevels(const std::vector<icf::Octave>& octs,
int frameW, int frameH, int nscales)
{
CV_Assert(nscales > 1);
std::vector<icf::Level> levels;
float logFactor = (::log(maxScale) - ::log(minScale)) / (nscales -1);
float scale = minScale;
for (int sc = 0; sc < nscales; ++sc)
{
int width = ::std::max(0.0f, frameW - (origObjWidth * scale));
int height = ::std::max(0.0f, frameH - (origObjHeight * scale));
float logScale = ::log(scale);
int fit = fitOctave(octs, logScale);
icf::Level level(fit, octs[fit], scale, width, height);
if (!width || !height)
break;
else
levels.push_back(level);
if (::fabs(scale - maxScale) < FLT_EPSILON) break;
scale = ::std::min(maxScale, ::expf(::log(scale) + logFactor));
// std::cout << "level " << sc << " scale "
// << levels[sc].origScale
// << " octeve "
// << levels[sc].octave->scale
// << " "
// << levels[sc].relScale
// << " " << levels[sc].shrScale
// << " [" << levels[sc].objSize.width
// << " " << levels[sc].objSize.height << "] ["
// << levels[sc].workRect.width << " " << levels[sc].workRect.height << "]" << std::endl;
}
}
cv::gpu::SoftCascade::SoftCascade() : filds(0) {}
cv::gpu::SoftCascade::SoftCascade( const string& filename, const float minScale, const float maxScale) : filds(0)
@ -86,8 +316,6 @@ bool cv::gpu::SoftCascade::load( const string& filename, const float minScale, c
filds = new Filds;
Filds& flds = *filds;
if (!flds.fill(fs.getFirstTopLevelNode(), minScale, maxScale)) return false;
flds.calcLevels(FRAME_WIDTH, FRAME_HEIGHT, TOTAL_SCALES);
return true;
}

73
modules/gpu/test/test_softcascade.cpp Normal file
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@ -0,0 +1,73 @@
/*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) 2008-2012, 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:
//
// * 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 <test_precomp.hpp>
#ifdef HAVE_CUDA
using cv::gpu::GpuMat;
TEST(SoftCascade, readCascade)
{
std::string xml = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/icf-template.xml";
cv::gpu::SoftCascade cascade;
ASSERT_TRUE(cascade.load(xml));
}
TEST(SoftCascade, detect)
{
std::string xml = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/sc_cvpr_2012_to_opencv.xml";
cv::gpu::SoftCascade cascade;
ASSERT_TRUE(cascade.load(xml));
cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path() + "cascadeandhog/bahnhof/image_00000000_0.png");
ASSERT_FALSE(coloredCpu.empty());
GpuMat colored(coloredCpu), objectBoxes, rois;
// ASSERT_NO_THROW(
// {
cascade.detectMultiScale(colored, rois, objectBoxes);
// });
}
#endif