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137 lines
4.0 KiB
137 lines
4.0 KiB
/* |
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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.empty()) |
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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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