generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Background subtractor GMG: removed flexitype, fixed build errors.

Browse Source
This commit is contained in:
Andrey Kamaev 2012-06-30 20:47:09 +00:00
parent afe11f69fb
commit dec38e5949
3 changed files with 187 additions and 288 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

86
modules/video/include/opencv2/video/background_segm.hpp
View File

@ -199,75 +199,7 @@ protected:
*/
class CV_EXPORTS BackgroundSubtractorGMG: public cv::BackgroundSubtractor
{
private:
/**
* A general flexible datatype.
*
* Used internally to enable background subtraction algorithm to be robust to any input Mat type.
* Datatype can be char, unsigned char, int, unsigned int, long int, float, or double.
*/
union flexitype{
char c;
uchar uc;
int i;
unsigned int ui;
long int li;
float f;
double d;
flexitype(){d = 0.0;} //!< Default constructor, set all bits of the union to 0.
flexitype(char cval){c = cval;} //!< Char type constructor
bool operator ==(flexitype& rhs)
{
return d == rhs.d;
}
//! Char type assignment operator
flexitype& operator =(char cval){
if (this->c == cval){return *this;}
c = cval; return *this;
}
flexitype(unsigned char ucval){uc = ucval;} //!< unsigned char type constructor
//! unsigned char type assignment operator
flexitype& operator =(unsigned char ucval){
if (this->uc == ucval){return *this;}
uc = ucval; return *this;
}
flexitype(int ival){i = ival;} //!< int type constructor
//! int type assignment operator
flexitype& operator =(int ival){
if (this->i == ival){return *this;}
i = ival; return *this;
}
flexitype(unsigned int uival){ui = uival;} //!< unsigned int type constructor
//! unsigned int type assignment operator
flexitype& operator =(unsigned int uival){
if (this->ui == uival){return *this;}
ui = uival; return *this;
}
flexitype(float fval){f = fval;} //!< float type constructor
//! float type assignment operator
flexitype& operator =(float fval){
if (this->f == fval){return *this;}
f = fval; return *this;
}
flexitype(long int lival){li = lival;} //!< long int type constructor
//! long int type assignment operator
flexitype& operator =(long int lival){
if (this->li == lival){return *this;}
li = lival; return *this;
}
flexitype(double dval){d=dval;} //!< double type constructor
//! double type assignment operator
flexitype& operator =(double dval){
if (this->d == dval){return *this;}
d = dval; return *this;
}
};
protected:
/**
* Used internally to represent a single feature in a histogram.
* Feature is a color and an associated likelihood (weight in the histogram).
@ -387,7 +319,7 @@ public:
* @param min minimum value taken on by pixels in image sequence. Usually 0
* @param max maximum value taken on by pixels in image sequence. e.g. 1.0 or 255
*/
void initializeType(InputArray image, flexitype min, flexitype max);
void initializeType(InputArray image, double min, double max);
/**
* Selectively update the background model. Only update background model for pixels identified
* as background.
@ -417,24 +349,20 @@ protected:
double decisionThreshold; //!< value above which pixel is determined to be FG.
int smoothingRadius; //!< smoothing radius, in pixels, for cleaning up FG image.
flexitype maxVal, minVal;
double maxVal, minVal;
/*
* General Parameters
*/
size_t imWidth; //!< width of image.
size_t imHeight; //!< height of image.
size_t numPixels;
int imWidth; //!< width of image.
int imHeight; //!< height of image.
size_t numPixels;
int imageDepth; //!< Depth of image, e.g. CV_8U
unsigned int numChannels; //!< Number of channels in image.
unsigned int numChannels; //!< Number of channels in image.
bool isDataInitialized;
//!< After general parameters are set, data structures must be initialized.
size_t elemSize; //!< store image mat element sizes
size_t elemSize1;
/*
* Data Structures
*/

76
modules/video/src/bgfg_gmg.cpp
View File

@ -67,7 +67,7 @@ BackgroundSubtractorGMG::BackgroundSubtractorGMG()
smoothingRadius = 7;
}
void BackgroundSubtractorGMG::initializeType(InputArray _image,flexitype min, flexitype max)
void BackgroundSubtractorGMG::initializeType(InputArray _image, double min, double max)
{
minVal = min;
maxVal = max;
@ -114,7 +114,6 @@ void BackgroundSubtractorGMG::initializeType(InputArray _image,flexitype min, fl
* Detect and accommodate the image depth
*/
Mat image = _image.getMat();
imageDepth = image.depth(); // 32f, 8u, etc.
numChannels = image.channels();
/*
@ -127,16 +126,15 @@ void BackgroundSubtractorGMG::initializeType(InputArray _image,flexitype min, fl
/*
* Data Structure Initialization
*/
Size imsize = image.size();
imWidth = imsize.width;
imHeight = imsize.height;
numPixels = imWidth*imHeight;
imWidth = image.cols;
imHeight = image.rows;
numPixels = image.total();
pixels.resize(numPixels);
frameNum = 0;
// used to iterate through matrix of type unknown at compile time
elemSize = image.elemSize();
elemSize1 = image.elemSize1();
//elemSize = image.elemSize();
//elemSize1 = image.elemSize1();
vector<PixelModelGMG>::iterator pixel;
vector<PixelModelGMG>::iterator pixel_end = pixels.end();
@ -145,8 +143,8 @@ void BackgroundSubtractorGMG::initializeType(InputArray _image,flexitype min, fl
pixel->setMaxFeatures(maxFeatures);
}
fgMaskImage = Mat::zeros(imHeight,imWidth,CV_8UC1); // 8-bit unsigned mask. 255 for FG, 0 for BG
posteriorImage = Mat::zeros(imHeight,imWidth,CV_32FC1); // float for storing probabilities. Can be viewed directly with imshow.
fgMaskImage = Mat::zeros(imHeight, imWidth, CV_8UC1); // 8-bit unsigned mask. 255 for FG, 0 for BG
posteriorImage = Mat::zeros(imHeight, imWidth, CV_32FC1); // float for storing probabilities. Can be viewed directly with imshow.
isDataInitialized = true;
}
@ -171,7 +169,7 @@ void BackgroundSubtractorGMG::operator()(InputArray _image, OutputArray _fgmask,
Mat image = _image.getMat();
_fgmask.create(Size(imHeight,imWidth),CV_8U);
_fgmask.create(imHeight,imWidth,CV_8U);
fgMaskImage = _fgmask.getMat(); // 8-bit unsigned mask. 255 for FG, 0 for BG
/*
@ -183,54 +181,32 @@ void BackgroundSubtractorGMG::operator()(InputArray _image, OutputArray _fgmask,
vector<PixelModelGMG>::iterator pixel;
vector<PixelModelGMG>::iterator pixel_end = pixels.end();
size_t i;
//#pragma omp parallel
//#pragma omp parallel
for (i = 0, pixel=pixels.begin(); pixel != pixel_end; ++i,++pixel)
{
HistogramFeatureGMG newFeature;
newFeature.color.clear();
int irow = int(i / imWidth);
int icol = i % imWidth;
for (size_t c = 0; c < numChannels; ++c)
{
/*
* Perform quantization. in each channel. (color-min)*(levels)/(max-min).
* Shifts min to 0 and scales, finally casting to an int.
*/
size_t quantizedColor;
// pixel at data+elemSize*i. Individual channel c at data+elemSize*i+elemSize1*c
if (imageDepth == CV_8U)
double color;
switch(image.depth())
{
uchar *color = (uchar*)(image.data+elemSize*i+elemSize1*c);
quantizedColor = (size_t)((double)(*color-minVal.uc)*quantizationLevels/(maxVal.uc-minVal.uc));
}
else if (imageDepth == CV_8S)
{
char *color = (char*)(image.data+elemSize*i+elemSize1*c);
quantizedColor = (size_t)((double)(*color-minVal.c)*quantizationLevels/(maxVal.c-minVal.c));
}
else if (imageDepth == CV_16U)
{
unsigned int *color = (unsigned int*)(image.data+elemSize*i+elemSize1*c);
quantizedColor = (size_t)((double)(*color-minVal.ui)*quantizationLevels/(maxVal.ui-minVal.ui));
}
else if (imageDepth == CV_16S)
{
int *color = (int*)(image.data+elemSize*i+elemSize1*c);
quantizedColor = (size_t)((double)(*color-minVal.i)*quantizationLevels/(maxVal.i-minVal.i));
}
else if (imageDepth == CV_32F)
{
float *color = (float*)image.data+elemSize*i+elemSize1*c;
quantizedColor = (size_t)((double)(*color-minVal.ui)*quantizationLevels/(maxVal.ui-minVal.ui));
}
else if (imageDepth == CV_32S)
{
long int *color = (long int*)(image.data+elemSize*i+elemSize1*c);
quantizedColor = (size_t)((double)(*color-minVal.li)*quantizationLevels/(maxVal.li-minVal.li));
}
else if (imageDepth == CV_64F)
{
double *color = (double*)image.data+elemSize*i+elemSize1*c;
quantizedColor = (size_t)((double)(*color-minVal.d)*quantizationLevels/(maxVal.d-minVal.d));
case CV_8U: color = image.ptr<uchar>(irow)[icol * numChannels + c]; break;
case CV_8S: color = image.ptr<schar>(irow)[icol * numChannels + c]; break;
case CV_16U: color = image.ptr<ushort>(irow)[icol * numChannels + c]; break;
case CV_16S: color = image.ptr<short>(irow)[icol * numChannels + c]; break;
case CV_32S: color = image.ptr<int>(irow)[icol * numChannels + c]; break;
case CV_32F: color = image.ptr<float>(irow)[icol * numChannels + c]; break;
case CV_64F: color = image.ptr<double>(irow)[icol * numChannels + c]; break;
default: color = 0; break;
}
size_t quantizedColor = (size_t)((color-minVal)*quantizationLevels/(maxVal-minVal));
newFeature.color.push_back(quantizedColor);
}
// now that the feature is ready for use, put it in the histogram
@ -251,7 +227,7 @@ void BackgroundSubtractorGMG::operator()(InputArray _image, OutputArray _fgmask,
*/
int row,col;
col = i%imWidth;
row = (i-col)/imWidth;
row = int(i-col)/imWidth;
posteriorImage.at<float>(row,col) = (1.0f-posterior);
}
pixel->setLastObservedFeature(newFeature);
@ -284,10 +260,10 @@ void BackgroundSubtractorGMG::updateBackgroundModel(InputArray _mask)
Mat maskImg = _mask.getMat();
//#pragma omp parallel
for (size_t i = 0; i < imHeight; ++i)
for (int i = 0; i < imHeight; ++i)
{
//#pragma omp parallel
for (size_t j = 0; j < imWidth; ++j)
for (int j = 0; j < imWidth; ++j)
{
if (frameNum <= numInitializationFrames + 1)
{

313
modules/video/test/test_backgroundsubtractor_gbh.cpp
View File

@ -12,9 +12,9 @@ using namespace cv;
class CV_BackgroundSubtractorTest : public cvtest::BaseTest
{
public:
CV_BackgroundSubtractorTest();
CV_BackgroundSubtractorTest();
protected:
void run(int);
void run(int);
};
CV_BackgroundSubtractorTest::CV_BackgroundSubtractorTest()
@ -29,172 +29,167 @@ CV_BackgroundSubtractorTest::CV_BackgroundSubtractorTest()
*/
void CV_BackgroundSubtractorTest::run(int)
{
int code = cvtest::TS::OK;
RNG& rng = ts->get_rng();
int type = ((unsigned int)rng)%7; //!< pick a random type, 0 - 6, defined in types_c.h
int channels = 1 + ((unsigned int)rng)%4; //!< random number of channels from 1 to 4.
int channelsAndType = CV_MAKETYPE(type,channels);
int width = 2 + ((unsigned int)rng)%98; //!< Mat will be 2 to 100 in width and height
int height = 2 + ((unsigned int)rng)%98;
int code = cvtest::TS::OK;
RNG& rng = ts->get_rng();
int type = ((unsigned int)rng)%7; //!< pick a random type, 0 - 6, defined in types_c.h
int channels = 1 + ((unsigned int)rng)%4; //!< random number of channels from 1 to 4.
int channelsAndType = CV_MAKETYPE(type,channels);
int width = 2 + ((unsigned int)rng)%98; //!< Mat will be 2 to 100 in width and height
int height = 2 + ((unsigned int)rng)%98;
Ptr<BackgroundSubtractorGMG> fgbg =
Algorithm::create<BackgroundSubtractorGMG>("BackgroundSubtractor.GMG");
Mat fgmask;
Ptr<BackgroundSubtractorGMG> fgbg =
Algorithm::create<BackgroundSubtractorGMG>("BackgroundSubtractor.GMG");
Mat fgmask;
if (fgbg == NULL)
CV_Error(CV_StsError,"Failed to create Algorithm\n");
if (fgbg == NULL)
CV_Error(CV_StsError,"Failed to create Algorithm\n");
/**
* Set a few parameters
*/
fgbg->set("smoothingRadius",7);
fgbg->set("decisionThreshold",0.7);
fgbg->set("initializationFrames",120);
/**
* Set a few parameters
*/
fgbg->set("smoothingRadius",7);
fgbg->set("decisionThreshold",0.7);
fgbg->set("initializationFrames",120);
/**
* Generate bounds for the values in the matrix for each type
*/
uchar maxuc = 0, minuc = 0;
char maxc = 0, minc = 0;
uint maxui = 0, minui = 0;
int maxi=0, mini = 0;
long int maxli = 0, minli = 0;
float maxf = 0, minf = 0;
double maxd = 0, mind = 0;
/**
* Generate bounds for the values in the matrix for each type
*/
uchar maxuc = 0, minuc = 0;
char maxc = 0, minc = 0;
unsigned int maxui = 0, minui = 0;
int maxi=0, mini = 0;
long int maxli = 0, minli = 0;
float maxf = 0, minf = 0;
double maxd = 0, mind = 0;
/**
* Max value for simulated images picked randomly in upper half of type range
* Min value for simulated images picked randomly in lower half of type range
*/
if (type == CV_8U)
{
unsigned char half = UCHAR_MAX/2;
maxuc = (unsigned char)rng.uniform(half+32,UCHAR_MAX);
minuc = (unsigned char)rng.uniform(0,half-32);
}
else if (type == CV_8S)
{
char half = CHAR_MAX/2 + CHAR_MIN/2;
maxc = (char)rng.uniform(half+32,CHAR_MAX);
minc = (char)rng.uniform(CHAR_MIN,half-32);
}
else if (type == CV_16U)
{
uint half = UINT_MAX/2;
maxui = (unsigned int)rng.uniform((int)half+32,UINT_MAX);
minui = (unsigned int)rng.uniform(0,(int)half-32);
}
else if (type == CV_16S)
{
int half = INT_MAX/2 + INT_MIN/2;
maxi = rng.uniform(half+32,INT_MAX);
mini = rng.uniform(INT_MIN,half-32);
}
else if (type == CV_32S)
{
long int half = LONG_MAX/2 + LONG_MIN/2;
maxli = rng.uniform((int)half+32,(int)LONG_MAX);
minli = rng.uniform((int)LONG_MIN,(int)half-32);
}
else if (type == CV_32F)
{
float half = FLT_MAX/2.0 + FLT_MIN/2.0;
maxf = rng.uniform(half+(float)32.0*FLT_EPSILON,FLT_MAX);
minf = rng.uniform(FLT_MIN,half-(float)32.0*FLT_EPSILON);
}
else if (type == CV_64F)
{
double half = DBL_MAX/2.0 + DBL_MIN/2.0;
maxd = rng.uniform(half+(double)32.0*DBL_EPSILON,DBL_MAX);
mind = rng.uniform(DBL_MIN,half-(double)32.0*DBL_EPSILON);
}
/**
* Max value for simulated images picked randomly in upper half of type range
* Min value for simulated images picked randomly in lower half of type range
*/
if (type == CV_8U)
{
uchar half = UCHAR_MAX/2;
maxuc = (unsigned char)rng.uniform(half+32, UCHAR_MAX);
minuc = (unsigned char)rng.uniform(0, half-32);
}
else if (type == CV_8S)
{
maxc = (char)rng.uniform(32, CHAR_MAX);
minc = (char)rng.uniform(CHAR_MIN, -32);
}
else if (type == CV_16U)
{
ushort half = USHRT_MAX/2;
maxui = (unsigned int)rng.uniform(half+32, USHRT_MAX);
minui = (unsigned int)rng.uniform(0, half-32);
}
else if (type == CV_16S)
{
maxi = rng.uniform(32, SHRT_MAX);
mini = rng.uniform(SHRT_MIN, -32);
}
else if (type == CV_32S)
{
maxli = rng.uniform(32, INT_MAX);
minli = rng.uniform(INT_MIN, -32);
}
else if (type == CV_32F)
{
maxf = rng.uniform(32.0f, FLT_MAX);
minf = rng.uniform(-FLT_MAX, -32.0f);
}
else if (type == CV_64F)
{
maxd = rng.uniform(32.0, DBL_MAX);
mind = rng.uniform(-DBL_MAX, -32.0);
}
Mat simImage = Mat::zeros(height,width,channelsAndType);
const uint numLearningFrames = 120;
for (uint i = 0; i < numLearningFrames; ++i)
{
/**
* Genrate simulated "image" for any type. Values always confined to upper half of range.
*/
if (type == CV_8U)
{
rng.fill(simImage,RNG::UNIFORM,(unsigned char)(minuc/2+maxuc/2),maxuc);
if (i == 0)
fgbg->initializeType(simImage,minuc,maxuc);
}
else if (type == CV_8S)
{
rng.fill(simImage,RNG::UNIFORM,(char)(minc/2+maxc/2),maxc);
if (i==0)
fgbg->initializeType(simImage,minc,maxc);
}
else if (type == CV_16U)
{
rng.fill(simImage,RNG::UNIFORM,(unsigned int)(minui/2+maxui/2),maxui);
if (i==0)
fgbg->initializeType(simImage,minui,maxui);
}
else if (type == CV_16S)
{
rng.fill(simImage,RNG::UNIFORM,(int)(mini/2+maxi/2),maxi);
if (i==0)
fgbg->initializeType(simImage,mini,maxi);
}
else if (type == CV_32F)
{
rng.fill(simImage,RNG::UNIFORM,(float)(minf/2.0+maxf/2.0),maxf);
if (i==0)
fgbg->initializeType(simImage,minf,maxf);
}
else if (type == CV_32S)
{
rng.fill(simImage,RNG::UNIFORM,(long int)(minli/2+maxli/2),maxli);
if (i==0)
fgbg->initializeType(simImage,minli,maxli);
}
else if (type == CV_64F)
{
rng.fill(simImage,RNG::UNIFORM,(double)(mind/2.0+maxd/2.0),maxd);
if (i==0)
fgbg->initializeType(simImage,mind,maxd);
}
Mat simImage = Mat::zeros(height, width, channelsAndType);
const unsigned int numLearningFrames = 120;
for (unsigned int i = 0; i < numLearningFrames; ++i)
{
/**
* Genrate simulated "image" for any type. Values always confined to upper half of range.
*/
if (type == CV_8U)
{
rng.fill(simImage,RNG::UNIFORM,(unsigned char)(minuc/2+maxuc/2),maxuc);
if (i == 0)
fgbg->initializeType(simImage,minuc,maxuc);
}
else if (type == CV_8S)
{
rng.fill(simImage,RNG::UNIFORM,(char)(minc/2+maxc/2),maxc);
if (i==0)
fgbg->initializeType(simImage,minc,maxc);
}
else if (type == CV_16U)
{
rng.fill(simImage,RNG::UNIFORM,(unsigned int)(minui/2+maxui/2),maxui);
if (i==0)
fgbg->initializeType(simImage,minui,maxui);
}
else if (type == CV_16S)
{
rng.fill(simImage,RNG::UNIFORM,(int)(mini/2+maxi/2),maxi);
if (i==0)
fgbg->initializeType(simImage,mini,maxi);
}
else if (type == CV_32F)
{
rng.fill(simImage,RNG::UNIFORM,(float)(minf/2.0+maxf/2.0),maxf);
if (i==0)
fgbg->initializeType(simImage,minf,maxf);
}
else if (type == CV_32S)
{
rng.fill(simImage,RNG::UNIFORM,(long int)(minli/2+maxli/2),maxli);
if (i==0)
fgbg->initializeType(simImage,minli,maxli);
}
else if (type == CV_64F)
{
rng.fill(simImage,RNG::UNIFORM,(double)(mind/2.0+maxd/2.0),maxd);
if (i==0)
fgbg->initializeType(simImage,mind,maxd);
}
/**
* Feed simulated images into background subtractor
*/
(*fgbg)(simImage,fgmask);
Mat fullbg = Mat::zeros(Size(simImage.cols,simImage.rows),CV_8U);
fgbg->updateBackgroundModel(fullbg);
/**
* Feed simulated images into background subtractor
*/
(*fgbg)(simImage,fgmask);
Mat fullbg = Mat::zeros(simImage.rows, simImage.cols, CV_8U);
fgbg->updateBackgroundModel(fullbg);
//! fgmask should be entirely background during training
code = cvtest::cmpEps2( ts, fgmask, fullbg, 0, false, "The training foreground mask" );
if (code < 0)
ts->set_failed_test_info( code );
}
//! generate last image, distinct from training images
if (type == CV_8U)
rng.fill(simImage,RNG::UNIFORM,minuc,minuc);
else if (type == CV_8S)
rng.fill(simImage,RNG::UNIFORM,minc,minc);
else if (type == CV_16U)
rng.fill(simImage,RNG::UNIFORM,minui,minui);
else if (type == CV_16S)
rng.fill(simImage,RNG::UNIFORM,mini,mini);
else if (type == CV_32F)
rng.fill(simImage,RNG::UNIFORM,minf,minf);
else if (type == CV_32S)
rng.fill(simImage,RNG::UNIFORM,minli,minli);
else if (type == CV_64F)
rng.fill(simImage,RNG::UNIFORM,mind,mind);
//! fgmask should be entirely background during training
code = cvtest::cmpEps2( ts, fgmask, fullbg, 0, false, "The training foreground mask" );
if (code < 0)
ts->set_failed_test_info( code );
}
//! generate last image, distinct from training images
if (type == CV_8U)
rng.fill(simImage,RNG::UNIFORM,minuc,minuc);
else if (type == CV_8S)
rng.fill(simImage,RNG::UNIFORM,minc,minc);
else if (type == CV_16U)
rng.fill(simImage,RNG::UNIFORM,minui,minui);
else if (type == CV_16S)
rng.fill(simImage,RNG::UNIFORM,mini,mini);
else if (type == CV_32F)
rng.fill(simImage,RNG::UNIFORM,minf,minf);
else if (type == CV_32S)
rng.fill(simImage,RNG::UNIFORM,minli,minli);
else if (type == CV_64F)
rng.fill(simImage,RNG::UNIFORM,mind,mind);
(*fgbg)(simImage,fgmask);
//! now fgmask should be entirely foreground
Mat fullfg = 255*Mat::ones(Size(simImage.cols,simImage.rows),CV_8U);
code = cvtest::cmpEps2( ts, fgmask, fullfg, 255, false, "The final foreground mask" );
if (code < 0)
{
ts->set_failed_test_info( code );
}
(*fgbg)(simImage,fgmask);
//! now fgmask should be entirely foreground
Mat fullfg = 255*Mat::ones(simImage.rows, simImage.cols, CV_8U);
code = cvtest::cmpEps2( ts, fgmask, fullfg, 255, false, "The final foreground mask" );
if (code < 0)
{
ts->set_failed_test_info( code );
}
}