Gitea Mirror Bot
119 lines
3.8 KiB
C++
119 lines
3.8 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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//
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// Author: andrewgodbehere
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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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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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typedef testing::TestWithParam<std::tuple<perf::MatDepth,int>> bgsubgmg_allTypes;
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TEST_P(bgsubgmg_allTypes, accuracy)
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{
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const int depth = get<0>(GetParam());
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const int ncn = get<1>(GetParam());
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const int mtype = CV_MAKETYPE(depth, ncn);
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const int width = 64;
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const int height = 64;
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RNG& rng = TS::ptr()->get_rng();
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Ptr<BackgroundSubtractorGMG> fgbg = createBackgroundSubtractorGMG();
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ASSERT_TRUE(fgbg != nullptr) << "Failed to call createBackgroundSubtractorGMG()";
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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 (depth == 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 (depth == 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 (depth == 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 (depth == 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 (depth == 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
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{
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ASSERT_TRUE( (depth == CV_32F)||(depth == CV_64F) ) << "Unsupported depth";
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const double harf = 0.5;
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const double bias = 0.125; // = 32/256 (Like CV_8U)
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maxd = rng.uniform(harf + bias, 1.0);
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mind = rng.uniform(0.0, harf - bias );
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}
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fgbg->setMinVal(mind);
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fgbg->setMaxVal(maxd);
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Mat simImage(height, width, mtype);
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Mat fgmask;
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const Mat fullbg(height, width, CV_8UC1, cv::Scalar(0)); // all background.
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const 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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EXPECT_EQ(cv::norm(fgmask, fullbg, NORM_INF), 0) << "foreground mask should be entirely background during training";
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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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const Mat fullfg(height, width, CV_8UC1, cv::Scalar(255)); // all foreground.
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EXPECT_EQ(cv::norm(fgmask, fullfg, NORM_INF), 0) << "foreground mask should be entirely foreground finally";
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}
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INSTANTIATE_TEST_CASE_P(/**/,
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bgsubgmg_allTypes,
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testing::Combine(
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testing::Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F),
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testing::Values(1,2,3,4)));
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}} // namespace
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