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opencv/modules/gpu/src/softcascade.cpp
marina.kolpakova 672cf1f445 implement different behaviour for up- and down-scaling
2012-11-10 05:07:40 +04:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
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#include <precomp.hpp>
#include <opencv2/highgui/highgui.hpp>
#if !defined (HAVE_CUDA)
cv::gpu::SoftCascade::SoftCascade() : filds(0) { throw_nogpu(); }
cv::gpu::SoftCascade::SoftCascade( const string&, const float, const float) : filds(0) { throw_nogpu(); }
cv::gpu::SoftCascade::~SoftCascade() { throw_nogpu(); }
bool cv::gpu::SoftCascade::load( const string&, const float, const float) { throw_nogpu(); return false; }
void cv::gpu::SoftCascade::detectMultiScale(const GpuMat&, const GpuMat&, GpuMat&, const int, int) { throw_nogpu();}
void cv::gpu::SoftCascade::detectMultiScale(const GpuMat&, const GpuMat&, GpuMat&, int, GpuMat&, Stream)
{
throw_nogpu();
}
#else
#include <icf.hpp>
namespace cv { namespace gpu { namespace device {
namespace icf {
void fillBins(cv::gpu::PtrStepSzb hogluv, const cv::gpu::PtrStepSzf& nangle,
const int fw, const int fh, const int bins);
void detect(const PtrStepSzb& levels, const PtrStepSzb& octaves, const PtrStepSzf& stages,
const PtrStepSzb& nodes, const PtrStepSzf& leaves, const PtrStepSzi& hogluv, PtrStepSz<uchar4> objects,
PtrStepSzi counter, const int downscales);
void detectAtScale(const int scale, const PtrStepSzb& levels, const PtrStepSzb& octaves, const PtrStepSzf& stages,
const PtrStepSzb& nodes, const PtrStepSzf& leaves, const PtrStepSzi& hogluv, PtrStepSz<uchar4> objects,
PtrStepSzi counter, const int downscales);
}
}}}
struct cv::gpu::SoftCascade::Filds
{
Filds()
{
plane.create(FRAME_HEIGHT * (HOG_LUV_BINS + 1), FRAME_WIDTH, CV_8UC1);
fplane.create(FRAME_HEIGHT * 6, FRAME_WIDTH, CV_32FC1);
luv.create(FRAME_HEIGHT, FRAME_WIDTH, CV_8UC3);
shrunk.create(FRAME_HEIGHT / 4 * HOG_LUV_BINS, FRAME_WIDTH / 4, CV_8UC1);
integralBuffer.create(shrunk.rows + 1 * HOG_LUV_BINS, shrunk.cols + 1, CV_32SC1);
hogluv.create((FRAME_HEIGHT / 4 + 1) * HOG_LUV_BINS, FRAME_WIDTH / 4 + 1, CV_32SC1);
detCounter.create(1,1, CV_32SC1);
}
// scales range
float minScale;
float maxScale;
int origObjWidth;
int origObjHeight;
int downscales;
GpuMat octaves;
GpuMat stages;
GpuMat nodes;
GpuMat leaves;
GpuMat levels;
GpuMat detCounter;
// preallocated buffer 640x480x10 for hogluv + 640x480 got gray
GpuMat plane;
// preallocated buffer for floating point operations
GpuMat fplane;
// temporial mat for cvtColor
GpuMat luv;
// 160x120x10
GpuMat shrunk;
// temporial mat for integrall
GpuMat integralBuffer;
// 161x121x10
GpuMat hogluv;
std::vector<float> scales;
enum { BOOST = 0 };
enum
{
FRAME_WIDTH = 640,
FRAME_HEIGHT = 480,
TOTAL_SCALES = 55,
ORIG_OBJECT_WIDTH = 64,
ORIG_OBJECT_HEIGHT = 128,
HOG_BINS = 6,
LUV_BINS = 3,
HOG_LUV_BINS = 10
};
bool fill(const FileNode &root, const float mins, const float maxs);
void detect(cv::gpu::GpuMat objects, cudaStream_t stream) const
{
cudaMemset(detCounter.data, 0, detCounter.step * detCounter.rows * sizeof(int));
device::icf::detect(levels, octaves, stages, nodes, leaves, hogluv, objects , detCounter, downscales);
}
void detectAtScale(int scale, cv::gpu::GpuMat objects, cudaStream_t stream) const
{
cudaMemset(detCounter.data, 0, detCounter.step * detCounter.rows * sizeof(int));
device::icf::detectAtScale(scale, levels, octaves, stages, nodes, leaves, hogluv, objects,
detCounter, downscales);
}
private:
void calcLevels(const std::vector<device::icf::Octave>& octs,
int frameW, int frameH, int nscales);
typedef std::vector<device::icf::Octave>::const_iterator octIt_t;
int fitOctave(const std::vector<device::icf::Octave>& octs, const float& logFactor) const
{
float minAbsLog = FLT_MAX;
int res = 0;
for (int oct = 0; oct < (int)octs.size(); ++oct)
{
const device::icf::Octave& octave =octs[oct];
float logOctave = ::log(octave.scale);
float logAbsScale = ::fabs(logFactor - logOctave);
if(logAbsScale < minAbsLog)
{
res = oct;
minAbsLog = logAbsScale;
}
}
return res;
}
};
bool cv::gpu::SoftCascade::Filds::fill(const FileNode &root, const float mins, const float maxs)
{
using namespace device::icf;
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 == ORIG_OBJECT_WIDTH);
origObjHeight = (int)root[SC_ORIG_H];
CV_Assert(origObjHeight == ORIG_OBJECT_HEIGHT);
FileNode fn = root[SC_OCTAVES];
if (fn.empty()) return false;
std::vector<Octave> voctaves;
std::vector<float> vstages;
std::vector<Node> vnodes;
std::vector<float> vleaves;
scales.clear();
FileNodeIterator it = fn.begin(), it_end = fn.end();
int feature_offset = 0;
ushort octIndex = 0;
ushort shrinkage = 1;
for (; it != it_end; ++it)
{
FileNode fns = *it;
float scale = (float)fns[SC_OCT_SCALE];
bool isUPOctave = scale >= 1;
scales.push_back(scale);
ushort nstages = saturate_cast<ushort>((int)fns[SC_OCT_STAGES]);
ushort2 size;
size.x = cvRound(ORIG_OBJECT_WIDTH * scale);
size.y = cvRound(ORIG_OBJECT_HEIGHT * scale);
shrinkage = saturate_cast<ushort>((int)fns[SC_OCT_SHRINKAGE]);
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;
FileNodeIterator ftrs = ffs.begin();
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)fns[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;
inIt +=3;
// extract feature, Todo:check it
uint th = saturate_cast<uint>((float)(*(inIt++)));
cv::FileNode ftn = (*ftrs)[SC_F_RECT];
cv::FileNodeIterator r_it = ftn.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++));
if (isUPOctave)
{
rect.z -= rect.x;
rect.w -= rect.y;
}
uint channel = saturate_cast<uint>((int)(*ftrs)[SC_F_CHANNEL]);
vnodes.push_back(Node(rect, channel, th));
++ftrs;
}
fns = (*ftr)[SC_LEAF];
inIt = fns.begin(), inIt_end = fns.end();
for (; inIt != inIt_end; ++inIt)
vleaves.push_back((float)(*inIt));
}
}
feature_offset += octave.stages * 3;
++octIndex;
}
// upload in gpu memory
octaves.upload(cv::Mat(1, voctaves.size() * sizeof(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(Node), CV_8UC1, (uchar*)&(vnodes[0]) ));
CV_Assert(!nodes.empty());
leaves.upload(cv::Mat(vleaves).reshape(1,1));
CV_Assert(!leaves.empty());
// compute levels
calcLevels(voctaves, FRAME_WIDTH, FRAME_HEIGHT, TOTAL_SCALES);
CV_Assert(!levels.empty());
return true;
}
namespace {
struct CascadeIntrinsics
{
static const float lambda = 1.099f, a = 0.89f;
static float getFor(int channel, float scaling)
{
CV_Assert(channel < 10);
if (fabs(scaling - 1.f) < FLT_EPSILON)
return 1.f;
// according to R. Benenson, M. Mathias, R. Timofte and L. Van Gool's and Dallal's papers
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
{ 0.f, 2.f} // up
};
float a = A[(int)(scaling >= 1)][(int)(channel > 6)];
float b = B[(int)(scaling >= 1)][(int)(channel > 6)];
// printf("!!! scaling: %f %f %f -> %f\n", scaling, a, b, a * pow(scaling, b));
return a * pow(scaling, b);
}
};
}
inline void cv::gpu::SoftCascade::Filds::calcLevels(const std::vector<device::icf::Octave>& octs,
int frameW, int frameH, int nscales)
{
CV_Assert(nscales > 1);
using device::icf::Level;
std::vector<Level> vlevels;
float logFactor = (::log(maxScale) - ::log(minScale)) / (nscales -1);
float scale = minScale;
downscales = 0;
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);
Level level(fit, octs[fit], scale, width, height);
level.scaling[0] = CascadeIntrinsics::getFor(0, level.relScale);
level.scaling[1] = CascadeIntrinsics::getFor(9, level.relScale);
if (!width || !height)
break;
else
{
vlevels.push_back(level);
if (octs[fit].scale < 1) ++downscales;
}
if (::fabs(scale - maxScale) < FLT_EPSILON) break;
scale = ::std::min(maxScale, ::expf(::log(scale) + logFactor));
// printf("level: %d (%f %f) [%f %f] (%d %d) (%d %d)\n", level.octave, level.relScale, level.shrScale,
// level.scaling[0], level.scaling[1], level.workRect.x, level.workRect.y, level.objSize.x,
//level.objSize.y);
std::cout << "level " << sc
<< " octeve "
<< vlevels[sc].octave
<< " relScale "
<< vlevels[sc].relScale
<< " " << vlevels[sc].shrScale
<< " [" << (int)vlevels[sc].objSize.x
<< " " << (int)vlevels[sc].objSize.y << "] ["
<< (int)vlevels[sc].workRect.x << " " << (int)vlevels[sc].workRect.y << "]" << std::endl;
}
levels.upload(cv::Mat(1, vlevels.size() * sizeof(Level), CV_8UC1, (uchar*)&(vlevels[0]) ));
}
cv::gpu::SoftCascade::SoftCascade() : filds(0) {}
cv::gpu::SoftCascade::SoftCascade( const string& filename, const float minScale, const float maxScale) : filds(0)
{
load(filename, minScale, maxScale);
}
cv::gpu::SoftCascade::~SoftCascade()
{
delete filds;
}
bool cv::gpu::SoftCascade::load( const string& filename, const float minScale, const float maxScale)
{
if (filds)
delete filds;
filds = 0;
cv::FileStorage fs(filename, FileStorage::READ);
if (!fs.isOpened()) return false;
filds = new Filds;
Filds& flds = *filds;
if (!flds.fill(fs.getFirstTopLevelNode(), minScale, maxScale)) return false;
return true;
}
#define USE_REFERENCE_VALUES
namespace {
char *itoa(long i, char* s, int /*dummy_radix*/)
{
sprintf(s, "%ld", i);
return s;
}
}
//================================== synchronous version ============================================================//
void cv::gpu::SoftCascade::detectMultiScale(const GpuMat& colored, const GpuMat& /*rois*/,
GpuMat& objects, const int /*rejectfactor*/, int specificScale)
{
// only color images are supperted
CV_Assert(colored.type() == CV_8UC3);
// only this window size allowed
CV_Assert(colored.cols == Filds::FRAME_WIDTH && colored.rows == Filds::FRAME_HEIGHT);
Filds& flds = *filds;
#if defined USE_REFERENCE_VALUES
cudaMemset(flds.hogluv.data, 0, flds.hogluv.step * flds.hogluv.rows);
cv::FileStorage imgs("/home/kellan/testInts.xml", cv::FileStorage::READ);
char buff[33];
for(int i = 0; i < Filds::HOG_LUV_BINS; ++i)
{
cv::Mat channel;
imgs[std::string("channel") + itoa(i, buff, 10)] >> channel;
// std::cout << "channel " << i << std::endl << channel << std::endl;
GpuMat gchannel(flds.hogluv, cv::Rect(0, 121 * i, 161, 121));
gchannel.upload(channel);
}
#else
GpuMat& plane = flds.plane;
GpuMat& shrunk = flds.shrunk;
cudaMemset(plane.data, 0, plane.step * plane.rows);
int fw = Filds::FRAME_WIDTH;
int fh = Filds::FRAME_HEIGHT;
GpuMat gray(plane, cv::Rect(0, fh * Filds::HOG_LUV_BINS, fw, fh));
//cv::gpu::cvtColor(colored, gray, CV_RGB2GRAY);
cv::gpu::cvtColor(colored, gray, CV_BGR2GRAY);
//create hog
GpuMat dfdx(flds.fplane, cv::Rect(0, 0, fw, fh));
GpuMat dfdy(flds.fplane, cv::Rect(0, fh, fw, fh));
cv::gpu::Sobel(gray, dfdx, CV_32F, 1, 0, 3, 0.125f);
cv::gpu::Sobel(gray, dfdy, CV_32F, 0, 1, 3, 0.125f);
GpuMat mag(flds.fplane, cv::Rect(0, 2 * fh, fw, fh));
GpuMat ang(flds.fplane, cv::Rect(0, 3 * fh, fw, fh));
cv::gpu::cartToPolar(dfdx, dfdy, mag, ang, true);
// normolize magnitude to uchar interval and angles to 6 bins
GpuMat nmag(flds.fplane, cv::Rect(0, 4 * fh, fw, fh));
GpuMat nang(flds.fplane, cv::Rect(0, 5 * fh, fw, fh));
cv::gpu::multiply(mag, cv::Scalar::all(1.f / ::log(2)), nmag);
cv::gpu::multiply(ang, cv::Scalar::all(1.f / 60.f), nang);
//create uchar magnitude
GpuMat cmag(plane, cv::Rect(0, fh * Filds::HOG_BINS, fw, fh));
nmag.convertTo(cmag, CV_8UC1);
// create luv
cv::gpu::cvtColor(colored, flds.luv, CV_BGR2Luv);
std::vector<GpuMat> splited;
for(int i = 0; i < Filds::LUV_BINS; ++i)
{
splited.push_back(GpuMat(plane, cv::Rect(0, fh * (7 + i), fw, fh)));
}
cv::gpu::split(flds.luv, splited);
device::icf::fillBins(plane, nang, fw, fh, Filds::HOG_BINS);
GpuMat hogluv(plane, cv::Rect(0, 0, fw, fh * Filds::HOG_LUV_BINS));
cv::gpu::resize(hogluv, flds.shrunk, cv::Size(), 0.25, 0.25, CV_INTER_AREA);
fw /= 4;
fh /= 4;
for(int i = 0; i < Filds::HOG_LUV_BINS; ++i)
{
GpuMat channel(shrunk, cv::Rect(0, fh * i, fw, fh ));
GpuMat sum(flds.hogluv, cv::Rect(0, (fh + 1) * i, fw + 1, fh + 1));
cv::gpu::integralBuffered(channel, sum, flds.integralBuffer);
}
#endif
if (specificScale == -1)
flds.detect(objects, 0);
else
flds.detectAtScale(specificScale, objects, 0);
cv::Mat out(flds.detCounter);
int ndetections = *(out.data);
objects = GpuMat(objects, cv::Rect(0, 0, ndetections * sizeof(Detection), 1));
}
void cv::gpu::SoftCascade::detectMultiScale(const GpuMat&, const GpuMat&, GpuMat&, int, GpuMat&, Stream)
{
// cudaStream_t stream = StreamAccessor::getStream(s);
}
#endif
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