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290 lines
8.3 KiB
C++
290 lines
8.3 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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class AllignedFrameSource : public cv::superres::FrameSource
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{
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public:
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AllignedFrameSource(const cv::Ptr<cv::superres::FrameSource>& base, int scale);
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void nextFrame(cv::OutputArray frame);
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void reset();
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private:
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cv::Ptr<cv::superres::FrameSource> base_;
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cv::Mat origFrame_;
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int scale_;
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};
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AllignedFrameSource::AllignedFrameSource(const cv::Ptr<cv::superres::FrameSource>& base, int scale) :
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base_(base), scale_(scale)
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{
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CV_Assert( !base_.empty() );
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}
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void AllignedFrameSource::nextFrame(cv::OutputArray frame)
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{
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base_->nextFrame(origFrame_);
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if (origFrame_.rows % scale_ == 0 && origFrame_.cols % scale_ == 0)
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{
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cv::superres::arrCopy(origFrame_, frame);
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}
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else
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{
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cv::Rect ROI(0, 0, (origFrame_.cols / scale_) * scale_, (origFrame_.rows / scale_) * scale_);
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cv::superres::arrCopy(origFrame_(ROI), frame);
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}
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}
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void AllignedFrameSource::reset()
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{
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base_->reset();
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}
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class DegradeFrameSource : public cv::superres::FrameSource
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{
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public:
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DegradeFrameSource(const cv::Ptr<cv::superres::FrameSource>& base, int scale);
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void nextFrame(cv::OutputArray frame);
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void reset();
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private:
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cv::Ptr<cv::superres::FrameSource> base_;
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cv::Mat origFrame_;
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cv::Mat blurred_;
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cv::Mat deg_;
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double iscale_;
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};
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DegradeFrameSource::DegradeFrameSource(const cv::Ptr<cv::superres::FrameSource>& base, int scale) :
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base_(base), iscale_(1.0 / scale)
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{
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CV_Assert( !base_.empty() );
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}
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void addGaussNoise(cv::Mat& image, double sigma)
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{
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cv::Mat noise(image.size(), CV_32FC(image.channels()));
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cvtest::TS::ptr()->get_rng().fill(noise, cv::RNG::NORMAL, 0.0, sigma);
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cv::addWeighted(image, 1.0, noise, 1.0, 0.0, image, image.depth());
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}
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void addSpikeNoise(cv::Mat& image, int frequency)
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{
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cv::Mat_<uchar> mask(image.size(), 0);
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for (int y = 0; y < mask.rows; ++y)
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{
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for (int x = 0; x < mask.cols; ++x)
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{
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if (cvtest::TS::ptr()->get_rng().uniform(0, frequency) < 1)
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mask(y, x) = 255;
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}
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}
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image.setTo(cv::Scalar::all(255), mask);
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}
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void DegradeFrameSource::nextFrame(cv::OutputArray frame)
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{
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base_->nextFrame(origFrame_);
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cv::GaussianBlur(origFrame_, blurred_, cv::Size(5, 5), 0);
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cv::resize(blurred_, deg_, cv::Size(), iscale_, iscale_, cv::INTER_NEAREST);
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addGaussNoise(deg_, 10.0);
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addSpikeNoise(deg_, 500);
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cv::superres::arrCopy(deg_, frame);
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}
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void DegradeFrameSource::reset()
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{
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base_->reset();
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}
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double MSSIM(const cv::Mat& i1, const cv::Mat& i2)
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{
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const double C1 = 6.5025;
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const double C2 = 58.5225;
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const int depth = CV_32F;
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cv::Mat I1, I2;
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i1.convertTo(I1, depth);
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i2.convertTo(I2, depth);
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cv::Mat I2_2 = I2.mul(I2); // I2^2
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cv::Mat I1_2 = I1.mul(I1); // I1^2
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cv::Mat I1_I2 = I1.mul(I2); // I1 * I2
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cv::Mat mu1, mu2;
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cv::GaussianBlur(I1, mu1, cv::Size(11, 11), 1.5);
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cv::GaussianBlur(I2, mu2, cv::Size(11, 11), 1.5);
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cv::Mat mu1_2 = mu1.mul(mu1);
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cv::Mat mu2_2 = mu2.mul(mu2);
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cv::Mat mu1_mu2 = mu1.mul(mu2);
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cv::Mat sigma1_2, sigma2_2, sigma12;
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cv::GaussianBlur(I1_2, sigma1_2, cv::Size(11, 11), 1.5);
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sigma1_2 -= mu1_2;
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cv::GaussianBlur(I2_2, sigma2_2, cv::Size(11, 11), 1.5);
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sigma2_2 -= mu2_2;
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cv::GaussianBlur(I1_I2, sigma12, cv::Size(11, 11), 1.5);
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sigma12 -= mu1_mu2;
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cv::Mat t1, t2;
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cv::Mat numerator;
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cv::Mat denominator;
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// t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
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t1 = 2 * mu1_mu2 + C1;
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t2 = 2 * sigma12 + C2;
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numerator = t1.mul(t2);
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// t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))
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t1 = mu1_2 + mu2_2 + C1;
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t2 = sigma1_2 + sigma2_2 + C2;
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denominator = t1.mul(t2);
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// ssim_map = numerator./denominator;
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cv::Mat ssim_map;
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cv::divide(numerator, denominator, ssim_map);
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// mssim = average of ssim map
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cv::Scalar mssim = cv::mean(ssim_map);
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if (i1.channels() == 1)
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return mssim[0];
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return (mssim[0] + mssim[1] + mssim[3]) / 3;
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}
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class SuperResolution : public testing::Test
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{
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public:
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void RunTest(cv::Ptr<cv::superres::SuperResolution> superRes);
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};
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void SuperResolution::RunTest(cv::Ptr<cv::superres::SuperResolution> superRes)
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{
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const std::string inputVideoName = cvtest::TS::ptr()->get_data_path() + "car.avi";
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const int scale = 2;
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const int iterations = 100;
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const int temporalAreaRadius = 2;
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ASSERT_FALSE( superRes.empty() );
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const int btvKernelSize = superRes->getInt("btvKernelSize");
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superRes->set("scale", scale);
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superRes->set("iterations", iterations);
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superRes->set("temporalAreaRadius", temporalAreaRadius);
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cv::Ptr<cv::superres::FrameSource> goldSource(new AllignedFrameSource(cv::superres::createFrameSource_Video(inputVideoName), scale));
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cv::Ptr<cv::superres::FrameSource> lowResSource(new DegradeFrameSource(new AllignedFrameSource(cv::superres::createFrameSource_Video(inputVideoName), scale), scale));
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// skip first frame
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cv::Mat frame;
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lowResSource->nextFrame(frame);
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goldSource->nextFrame(frame);
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cv::Rect inner(btvKernelSize, btvKernelSize, frame.cols - 2 * btvKernelSize, frame.rows - 2 * btvKernelSize);
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superRes->setInput(lowResSource);
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double srAvgMSSIM = 0.0;
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const int count = 10;
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cv::Mat goldFrame, superResFrame;
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for (int i = 0; i < count; ++i)
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{
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goldSource->nextFrame(goldFrame);
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ASSERT_FALSE( goldFrame.empty() );
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superRes->nextFrame(superResFrame);
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ASSERT_FALSE( superResFrame.empty() );
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const double srMSSIM = MSSIM(goldFrame(inner), superResFrame);
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srAvgMSSIM += srMSSIM;
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}
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srAvgMSSIM /= count;
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EXPECT_GE( srAvgMSSIM, 0.5 );
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}
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TEST_F(SuperResolution, BTVL1)
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{
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RunTest(cv::superres::createSuperResolution_BTVL1());
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}
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#if defined(HAVE_CUDA) && defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAWARPING) && defined(HAVE_OPENCV_GPUFILTERS)
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TEST_F(SuperResolution, BTVL1_GPU)
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{
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RunTest(cv::superres::createSuperResolution_BTVL1_GPU());
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}
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#endif
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#if defined(HAVE_OPENCV_OCL) && defined(HAVE_OPENCL)
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TEST_F(SuperResolution, BTVL1_OCL)
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{
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std::vector<cv::ocl::Info> infos;
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cv::ocl::getDevice(infos);
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RunTest(cv::superres::createSuperResolution_BTVL1_OCL());
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}
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#endif
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