Open Source Computer Vision Library https://opencv.org/
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/*
* BackgroundSubtractorGBH_test.cpp
*
* Created on: Jun 14, 2012
* Author: andrewgodbehere
*/
#include "test_precomp.hpp"
using namespace cv;
class CV_BackgroundSubtractorTest : public cvtest::BaseTest
{
public:
CV_BackgroundSubtractorTest();
protected:
void run(int);
};
CV_BackgroundSubtractorTest::CV_BackgroundSubtractorTest()
{
}
/**
* This test checks the following:
* (i) BackgroundSubtractorGMG can operate with matrices of various types and sizes
* (ii) Training mode returns empty fgmask
* (iii) End of training mode, and anomalous frame yields every pixel detected as FG
*/
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;
Ptr<BackgroundSubtractorGMG> fgbg = createBackgroundSubtractorGMG();
Mat fgmask;
if (fgbg.empty())
CV_Error(Error::StsError,"Failed to create Algorithm\n");
/**
* Set a few parameters
*/
fgbg->setSmoothingRadius(7);
fgbg->setDecisionThreshold(0.7);
fgbg->setNumFrames(120);
/**
* Generate bounds for the values in the matrix for each type
*/
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)
{
uchar half = UCHAR_MAX/2;
maxd = (unsigned char)rng.uniform(half+32, UCHAR_MAX);
mind = (unsigned char)rng.uniform(0, half-32);
}
else if (type == CV_8S)
{
maxd = (char)rng.uniform(32, CHAR_MAX);
mind = (char)rng.uniform(CHAR_MIN, -32);
}
else if (type == CV_16U)
{
ushort half = USHRT_MAX/2;
maxd = (unsigned int)rng.uniform(half+32, USHRT_MAX);
mind = (unsigned int)rng.uniform(0, half-32);
}
else if (type == CV_16S)
{
maxd = rng.uniform(32, SHRT_MAX);
mind = rng.uniform(SHRT_MIN, -32);
}
else if (type == CV_32S)
{
maxd = rng.uniform(32, INT_MAX);
mind = rng.uniform(INT_MIN, -32);
}
else if (type == CV_32F)
{
maxd = rng.uniform(32.0f, FLT_MAX);
mind = 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);
}
fgbg->setMinVal(mind);
fgbg->setMaxVal(maxd);
Mat simImage = Mat::zeros(height, width, channelsAndType);
int numLearningFrames = 120;
for (int i = 0; i < numLearningFrames; ++i)
{
/**
* Genrate simulated "image" for any type. Values always confined to upper half of range.
*/
rng.fill(simImage, RNG::UNIFORM, (mind + maxd)*0.5, maxd);
/**
* Feed simulated images into background subtractor
*/
fgbg->apply(simImage,fgmask);
Mat fullbg = Mat::zeros(simImage.rows, simImage.cols, CV_8U);
//! 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
rng.fill(simImage, RNG::UNIFORM, mind, maxd);
fgbg->apply(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 );
}
}
TEST(VIDEO_BGSUBGMG, accuracy) { CV_BackgroundSubtractorTest test; test.safe_run(); }