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138 lines
4.0 KiB
C++
138 lines
4.0 KiB
C++
/*
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* BackgroundSubtractorGBH_test.cpp
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*
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* Created on: Jun 14, 2012
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* Author: andrewgodbehere
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*/
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#include "test_precomp.hpp"
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using namespace cv;
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class CV_BackgroundSubtractorTest : public cvtest::BaseTest
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{
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public:
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CV_BackgroundSubtractorTest();
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protected:
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void run(int);
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};
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CV_BackgroundSubtractorTest::CV_BackgroundSubtractorTest()
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{
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}
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/**
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* This test checks the following:
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* (i) BackgroundSubtractorGMG can operate with matrices of various types and sizes
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* (ii) Training mode returns empty fgmask
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* (iii) End of training mode, and anomalous frame yields every pixel detected as FG
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*/
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void CV_BackgroundSubtractorTest::run(int)
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{
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int code = cvtest::TS::OK;
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RNG& rng = ts->get_rng();
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int type = ((unsigned int)rng)%7; //!< pick a random type, 0 - 6, defined in types_c.h
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int channels = 1 + ((unsigned int)rng)%4; //!< random number of channels from 1 to 4.
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int channelsAndType = CV_MAKETYPE(type,channels);
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int width = 2 + ((unsigned int)rng)%98; //!< Mat will be 2 to 100 in width and height
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int height = 2 + ((unsigned int)rng)%98;
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Ptr<BackgroundSubtractorGMG> fgbg = createBackgroundSubtractorGMG();
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Mat fgmask;
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if (!fgbg)
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CV_Error(Error::StsError,"Failed to create Algorithm\n");
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/**
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* Set a few parameters
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*/
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fgbg->setSmoothingRadius(7);
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fgbg->setDecisionThreshold(0.7);
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fgbg->setNumFrames(120);
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/**
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* Generate bounds for the values in the matrix for each type
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*/
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double maxd = 0, mind = 0;
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/**
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* Max value for simulated images picked randomly in upper half of type range
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* Min value for simulated images picked randomly in lower half of type range
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*/
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if (type == CV_8U)
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{
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uchar half = UCHAR_MAX/2;
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maxd = (unsigned char)rng.uniform(half+32, UCHAR_MAX);
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mind = (unsigned char)rng.uniform(0, half-32);
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}
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else if (type == CV_8S)
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{
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maxd = (char)rng.uniform(32, CHAR_MAX);
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mind = (char)rng.uniform(CHAR_MIN, -32);
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}
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else if (type == CV_16U)
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{
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ushort half = USHRT_MAX/2;
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maxd = (unsigned int)rng.uniform(half+32, USHRT_MAX);
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mind = (unsigned int)rng.uniform(0, half-32);
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}
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else if (type == CV_16S)
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{
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maxd = rng.uniform(32, SHRT_MAX);
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mind = rng.uniform(SHRT_MIN, -32);
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}
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else if (type == CV_32S)
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{
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maxd = rng.uniform(32, INT_MAX);
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mind = rng.uniform(INT_MIN, -32);
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}
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else if (type == CV_32F)
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{
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maxd = rng.uniform(32.0f, FLT_MAX);
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mind = rng.uniform(-FLT_MAX, -32.0f);
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}
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else if (type == CV_64F)
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{
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maxd = rng.uniform(32.0, DBL_MAX);
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mind = rng.uniform(-DBL_MAX, -32.0);
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}
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fgbg->setMinVal(mind);
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fgbg->setMaxVal(maxd);
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Mat simImage = Mat::zeros(height, width, channelsAndType);
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int numLearningFrames = 120;
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for (int i = 0; i < numLearningFrames; ++i)
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{
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/**
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* Genrate simulated "image" for any type. Values always confined to upper half of range.
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*/
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rng.fill(simImage, RNG::UNIFORM, (mind + maxd)*0.5, maxd);
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/**
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* Feed simulated images into background subtractor
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*/
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fgbg->apply(simImage,fgmask);
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Mat fullbg = Mat::zeros(simImage.rows, simImage.cols, CV_8U);
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//! fgmask should be entirely background during training
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code = cvtest::cmpEps2( ts, fgmask, fullbg, 0, false, "The training foreground mask" );
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if (code < 0)
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ts->set_failed_test_info( code );
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}
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//! generate last image, distinct from training images
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rng.fill(simImage, RNG::UNIFORM, mind, maxd);
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fgbg->apply(simImage,fgmask);
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//! now fgmask should be entirely foreground
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Mat fullfg = 255*Mat::ones(simImage.rows, simImage.cols, CV_8U);
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code = cvtest::cmpEps2( ts, fgmask, fullfg, 255, false, "The final foreground mask" );
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if (code < 0)
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{
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ts->set_failed_test_info( code );
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}
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}
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TEST(VIDEO_BGSUBGMG, accuracy) { CV_BackgroundSubtractorTest test; test.safe_run(); }
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