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Normalize line endings and whitespace

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This commit is contained in:
OpenCV Buildbot
2012-10-17 03:18:30 +04:00
committed by Andrey Kamaev
parent 69020da607
commit 04384a71e4
1516 changed files with 258846 additions and 258162 deletions
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80
modules/video/src/bgfg_gaussmix.cpp
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@@ -57,7 +57,7 @@
namespace cv
{
BackgroundSubtractor::~BackgroundSubtractor() {}
void BackgroundSubtractor::operator()(InputArray, OutputArray, double)
{
@@ -73,12 +73,12 @@ static const double defaultBackgroundRatio = 0.7;
static const double defaultVarThreshold = 2.5*2.5;
static const double defaultNoiseSigma = 30*0.5;
static const double defaultInitialWeight = 0.05;
BackgroundSubtractorMOG::BackgroundSubtractorMOG()
{
frameSize = Size(0,0);
frameType = 0;
nframes = 0;
nmixtures = defaultNMixtures;
history = defaultHistory;
@@ -86,14 +86,14 @@ BackgroundSubtractorMOG::BackgroundSubtractorMOG()
backgroundRatio = defaultBackgroundRatio;
noiseSigma = defaultNoiseSigma;
}
BackgroundSubtractorMOG::BackgroundSubtractorMOG(int _history, int _nmixtures,
double _backgroundRatio,
double _noiseSigma)
{
frameSize = Size(0,0);
frameType = 0;
nframes = 0;
nmixtures = min(_nmixtures > 0 ? _nmixtures : defaultNMixtures, 8);
history = _history > 0 ? _history : defaultHistory;
@@ -101,7 +101,7 @@ BackgroundSubtractorMOG::BackgroundSubtractorMOG(int _history, int _nmixtures,
backgroundRatio = min(_backgroundRatio > 0 ? _backgroundRatio : 0.95, 1.);
noiseSigma = _noiseSigma <= 0 ? defaultNoiseSigma : _noiseSigma;
}
BackgroundSubtractorMOG::~BackgroundSubtractorMOG()
{
}
@@ -112,10 +112,10 @@ void BackgroundSubtractorMOG::initialize(Size _frameSize, int _frameType)
frameSize = _frameSize;
frameType = _frameType;
nframes = 0;
int nchannels = CV_MAT_CN(frameType);
CV_Assert( CV_MAT_DEPTH(frameType) == CV_8U );
// for each gaussian mixture of each pixel bg model we store ...
// the mixture sort key (w/sum_of_variances), the mixture weight (w),
// the mean (nchannels values) and
@@ -124,7 +124,7 @@ void BackgroundSubtractorMOG::initialize(Size _frameSize, int _frameType)
bgmodel = Scalar::all(0);
}
template<typename VT> struct MixData
{
float sortKey;
@@ -133,7 +133,7 @@ template<typename VT> struct MixData
VT var;
};
static void process8uC1( const Mat& image, Mat& fgmask, double learningRate,
Mat& bgmodel, int nmixtures, double backgroundRatio,
double varThreshold, double noiseSigma )
@@ -142,17 +142,17 @@ static void process8uC1( const Mat& image, Mat& fgmask, double learningRate,
float alpha = (float)learningRate, T = (float)backgroundRatio, vT = (float)varThreshold;
int K = nmixtures;
MixData<float>* mptr = (MixData<float>*)bgmodel.data;
const float w0 = (float)defaultInitialWeight;
const float sk0 = (float)(w0/(defaultNoiseSigma*2));
const float var0 = (float)(defaultNoiseSigma*defaultNoiseSigma*4);
const float minVar = (float)(noiseSigma*noiseSigma);
for( y = 0; y < rows; y++ )
{
const uchar* src = image.ptr<uchar>(y);
uchar* dst = fgmask.ptr<uchar>(y);
if( alpha > 0 )
{
for( x = 0; x < cols; x++, mptr += K )
@@ -160,7 +160,7 @@ static void process8uC1( const Mat& image, Mat& fgmask, double learningRate,
float wsum = 0;
float pix = src[x];
int kHit = -1, kForeground = -1;
for( k = 0; k < K; k++ )
{
float w = mptr[k].weight;
@@ -180,19 +180,19 @@ static void process8uC1( const Mat& image, Mat& fgmask, double learningRate,
var = max(var + alpha*(d2 - var), minVar);
mptr[k].var = var;
mptr[k].sortKey = w/sqrt(var);
for( k1 = k-1; k1 >= 0; k1-- )
{
if( mptr[k1].sortKey >= mptr[k1+1].sortKey )
break;
std::swap( mptr[k1], mptr[k1+1] );
}
kHit = k1+1;
break;
}
}
if( kHit < 0 ) // no appropriate gaussian mixture found at all, remove the weakest mixture and create a new one
{
kHit = k = min(k, K-1);
@@ -205,7 +205,7 @@ static void process8uC1( const Mat& image, Mat& fgmask, double learningRate,
else
for( ; k < K; k++ )
wsum += mptr[k].weight;
float wscale = 1.f/wsum;
wsum = 0;
for( k = 0; k < K; k++ )
@@ -215,7 +215,7 @@ static void process8uC1( const Mat& image, Mat& fgmask, double learningRate,
if( wsum > T && kForeground < 0 )
kForeground = k+1;
}
dst[x] = (uchar)(-(kHit >= kForeground));
}
}
@@ -225,7 +225,7 @@ static void process8uC1( const Mat& image, Mat& fgmask, double learningRate,
{
float pix = src[x];
int kHit = -1, kForeground = -1;
for( k = 0; k < K; k++ )
{
if( mptr[k].weight < FLT_EPSILON )
@@ -240,7 +240,7 @@ static void process8uC1( const Mat& image, Mat& fgmask, double learningRate,
break;
}
}
if( kHit >= 0 )
{
float wsum = 0;
@@ -254,14 +254,14 @@ static void process8uC1( const Mat& image, Mat& fgmask, double learningRate,
}
}
}
dst[x] = (uchar)(kHit < 0 || kHit >= kForeground ? 255 : 0);
}
}
}
}
static void process8uC3( const Mat& image, Mat& fgmask, double learningRate,
Mat& bgmodel, int nmixtures, double backgroundRatio,
double varThreshold, double noiseSigma )
@@ -269,18 +269,18 @@ static void process8uC3( const Mat& image, Mat& fgmask, double learningRate,
int x, y, k, k1, rows = image.rows, cols = image.cols;
float alpha = (float)learningRate, T = (float)backgroundRatio, vT = (float)varThreshold;
int K = nmixtures;
const float w0 = (float)defaultInitialWeight;
const float sk0 = (float)(w0/(defaultNoiseSigma*2*sqrt(3.)));
const float var0 = (float)(defaultNoiseSigma*defaultNoiseSigma*4);
const float minVar = (float)(noiseSigma*noiseSigma);
MixData<Vec3f>* mptr = (MixData<Vec3f>*)bgmodel.data;
for( y = 0; y < rows; y++ )
{
const uchar* src = image.ptr<uchar>(y);
uchar* dst = fgmask.ptr<uchar>(y);
if( alpha > 0 )
{
for( x = 0; x < cols; x++, mptr += K )
@@ -288,7 +288,7 @@ static void process8uC3( const Mat& image, Mat& fgmask, double learningRate,
float wsum = 0;
Vec3f pix(src[x*3], src[x*3+1], src[x*3+2]);
int kHit = -1, kForeground = -1;
for( k = 0; k < K; k++ )
{
float w = mptr[k].weight;
@@ -310,19 +310,19 @@ static void process8uC3( const Mat& image, Mat& fgmask, double learningRate,
max(var[2] + alpha*(diff[2]*diff[2] - var[2]), minVar));
mptr[k].var = var;
mptr[k].sortKey = w/sqrt(var[0] + var[1] + var[2]);
for( k1 = k-1; k1 >= 0; k1-- )
{
if( mptr[k1].sortKey >= mptr[k1+1].sortKey )
break;
std::swap( mptr[k1], mptr[k1+1] );
}
kHit = k1+1;
break;
}
}
if( kHit < 0 ) // no appropriate gaussian mixture found at all, remove the weakest mixture and create a new one
{
kHit = k = min(k, K-1);
@@ -335,7 +335,7 @@ static void process8uC3( const Mat& image, Mat& fgmask, double learningRate,
else
for( ; k < K; k++ )
wsum += mptr[k].weight;
float wscale = 1.f/wsum;
wsum = 0;
for( k = 0; k < K; k++ )
@@ -345,7 +345,7 @@ static void process8uC3( const Mat& image, Mat& fgmask, double learningRate,
if( wsum > T && kForeground < 0 )
kForeground = k+1;
}
dst[x] = (uchar)(-(kHit >= kForeground));
}
}
@@ -355,7 +355,7 @@ static void process8uC3( const Mat& image, Mat& fgmask, double learningRate,
{
Vec3f pix(src[x*3], src[x*3+1], src[x*3+2]);
int kHit = -1, kForeground = -1;
for( k = 0; k < K; k++ )
{
if( mptr[k].weight < FLT_EPSILON )
@@ -370,7 +370,7 @@ static void process8uC3( const Mat& image, Mat& fgmask, double learningRate,
break;
}
}
if( kHit >= 0 )
{
float wsum = 0;
@@ -384,7 +384,7 @@ static void process8uC3( const Mat& image, Mat& fgmask, double learningRate,
}
}
}
dst[x] = (uchar)(kHit < 0 || kHit >= kForeground ? 255 : 0);
}
}
@@ -395,18 +395,18 @@ void BackgroundSubtractorMOG::operator()(InputArray _image, OutputArray _fgmask,
{
Mat image = _image.getMat();
bool needToInitialize = nframes == 0 || learningRate >= 1 || image.size() != frameSize || image.type() != frameType;
if( needToInitialize )
initialize(image.size(), image.type());
CV_Assert( image.depth() == CV_8U );
_fgmask.create( image.size(), CV_8U );
Mat fgmask = _fgmask.getMat();
++nframes;
learningRate = learningRate >= 0 && nframes > 1 ? learningRate : 1./min( nframes, history );
CV_Assert(learningRate >= 0);
if( image.type() == CV_8UC1 )
process8uC1( image, fgmask, learningRate, bgmodel, nmixtures, backgroundRatio, varThreshold, noiseSigma );
else if( image.type() == CV_8UC3 )
@@ -414,7 +414,7 @@ void BackgroundSubtractorMOG::operator()(InputArray _image, OutputArray _fgmask,
else
CV_Error( CV_StsUnsupportedFormat, "Only 1- and 3-channel 8-bit images are supported in BackgroundSubtractorMOG" );
}
}
/* End of file. */