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Background subtractor GMG: removed flexitype, fixed build errors.

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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
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313
modules/video/test/test_backgroundsubtractor_gbh.cpp
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@@ -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 );
}
}