Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
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// For Open Source Computer Vision Library
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#include "test_precomp.hpp"
class AllignedFrameSource : public cv::superres::FrameSource
{
public:
AllignedFrameSource(const cv::Ptr<cv::superres::FrameSource>& base, int scale);
void nextFrame(cv::OutputArray frame);
void reset();
private:
cv::Ptr<cv::superres::FrameSource> base_;
cv::Mat origFrame_;
int scale_;
};
AllignedFrameSource::AllignedFrameSource(const cv::Ptr<cv::superres::FrameSource>& base, int scale) :
base_(base), scale_(scale)
{
CV_Assert( !base_.empty() );
}
void AllignedFrameSource::nextFrame(cv::OutputArray frame)
{
base_->nextFrame(origFrame_);
if (origFrame_.rows % scale_ == 0 && origFrame_.cols % scale_ == 0)
{
cv::superres::arrCopy(origFrame_, frame);
}
else
{
cv::Rect ROI(0, 0, (origFrame_.cols / scale_) * scale_, (origFrame_.rows / scale_) * scale_);
cv::superres::arrCopy(origFrame_(ROI), frame);
}
}
void AllignedFrameSource::reset()
{
base_->reset();
}
class DegradeFrameSource : public cv::superres::FrameSource
{
public:
DegradeFrameSource(const cv::Ptr<cv::superres::FrameSource>& base, int scale);
void nextFrame(cv::OutputArray frame);
void reset();
private:
cv::Ptr<cv::superres::FrameSource> base_;
cv::Mat origFrame_;
cv::Mat blurred_;
cv::Mat deg_;
double iscale_;
};
DegradeFrameSource::DegradeFrameSource(const cv::Ptr<cv::superres::FrameSource>& base, int scale) :
base_(base), iscale_(1.0 / scale)
{
CV_Assert( !base_.empty() );
}
void addGaussNoise(cv::Mat& image, double sigma)
{
cv::Mat noise(image.size(), CV_32FC(image.channels()));
cvtest::TS::ptr()->get_rng().fill(noise, cv::RNG::NORMAL, 0.0, sigma);
cv::addWeighted(image, 1.0, noise, 1.0, 0.0, image, image.depth());
}
void addSpikeNoise(cv::Mat& image, int frequency)
{
cv::Mat_<uchar> mask(image.size(), 0);
for (int y = 0; y < mask.rows; ++y)
{
for (int x = 0; x < mask.cols; ++x)
{
if (cvtest::TS::ptr()->get_rng().uniform(0, frequency) < 1)
mask(y, x) = 255;
}
}
image.setTo(cv::Scalar::all(255), mask);
}
void DegradeFrameSource::nextFrame(cv::OutputArray frame)
{
base_->nextFrame(origFrame_);
cv::GaussianBlur(origFrame_, blurred_, cv::Size(5, 5), 0);
cv::resize(blurred_, deg_, cv::Size(), iscale_, iscale_, cv::INTER_NEAREST);
addGaussNoise(deg_, 10.0);
addSpikeNoise(deg_, 500);
cv::superres::arrCopy(deg_, frame);
}
void DegradeFrameSource::reset()
{
base_->reset();
}
double MSSIM(const cv::Mat& i1, const cv::Mat& i2)
{
const double C1 = 6.5025;
const double C2 = 58.5225;
const int depth = CV_32F;
cv::Mat I1, I2;
i1.convertTo(I1, depth);
i2.convertTo(I2, depth);
cv::Mat I2_2 = I2.mul(I2); // I2^2
cv::Mat I1_2 = I1.mul(I1); // I1^2
cv::Mat I1_I2 = I1.mul(I2); // I1 * I2
cv::Mat mu1, mu2;
cv::GaussianBlur(I1, mu1, cv::Size(11, 11), 1.5);
cv::GaussianBlur(I2, mu2, cv::Size(11, 11), 1.5);
cv::Mat mu1_2 = mu1.mul(mu1);
cv::Mat mu2_2 = mu2.mul(mu2);
cv::Mat mu1_mu2 = mu1.mul(mu2);
cv::Mat sigma1_2, sigma2_2, sigma12;
cv::GaussianBlur(I1_2, sigma1_2, cv::Size(11, 11), 1.5);
sigma1_2 -= mu1_2;
cv::GaussianBlur(I2_2, sigma2_2, cv::Size(11, 11), 1.5);
sigma2_2 -= mu2_2;
cv::GaussianBlur(I1_I2, sigma12, cv::Size(11, 11), 1.5);
sigma12 -= mu1_mu2;
cv::Mat t1, t2;
cv::Mat numerator;
cv::Mat denominator;
// t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
t1 = 2 * mu1_mu2 + C1;
t2 = 2 * sigma12 + C2;
numerator = t1.mul(t2);
// t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))
t1 = mu1_2 + mu2_2 + C1;
t2 = sigma1_2 + sigma2_2 + C2;
denominator = t1.mul(t2);
// ssim_map = numerator./denominator;
cv::Mat ssim_map;
cv::divide(numerator, denominator, ssim_map);
// mssim = average of ssim map
cv::Scalar mssim = cv::mean(ssim_map);
if (i1.channels() == 1)
return mssim[0];
return (mssim[0] + mssim[1] + mssim[3]) / 3;
}
class SuperResolution : public testing::Test
{
public:
void RunTest(cv::Ptr<cv::superres::SuperResolution> superRes);
};
void SuperResolution::RunTest(cv::Ptr<cv::superres::SuperResolution> superRes)
{
const std::string inputVideoName = cvtest::TS::ptr()->get_data_path() + "car.avi";
const int scale = 2;
const int iterations = 100;
const int temporalAreaRadius = 2;
ASSERT_FALSE( superRes.empty() );
const int btvKernelSize = superRes->getInt("btvKernelSize");
superRes->set("scale", scale);
superRes->set("iterations", iterations);
superRes->set("temporalAreaRadius", temporalAreaRadius);
cv::Ptr<cv::superres::FrameSource> goldSource(new AllignedFrameSource(cv::superres::createFrameSource_Video(inputVideoName), scale));
cv::Ptr<cv::superres::FrameSource> lowResSource(new DegradeFrameSource(new AllignedFrameSource(cv::superres::createFrameSource_Video(inputVideoName), scale), scale));
// skip first frame
cv::Mat frame;
lowResSource->nextFrame(frame);
goldSource->nextFrame(frame);
cv::Rect inner(btvKernelSize, btvKernelSize, frame.cols - 2 * btvKernelSize, frame.rows - 2 * btvKernelSize);
superRes->setInput(lowResSource);
double srAvgMSSIM = 0.0;
const int count = 10;
cv::Mat goldFrame, superResFrame;
for (int i = 0; i < count; ++i)
{
goldSource->nextFrame(goldFrame);
ASSERT_FALSE( goldFrame.empty() );
superRes->nextFrame(superResFrame);
ASSERT_FALSE( superResFrame.empty() );
const double srMSSIM = MSSIM(goldFrame(inner), superResFrame);
srAvgMSSIM += srMSSIM;
}
srAvgMSSIM /= count;
EXPECT_GE( srAvgMSSIM, 0.5 );
}
TEST_F(SuperResolution, BTVL1)
{
RunTest(cv::superres::createSuperResolution_BTVL1());
}
#if defined(HAVE_OPENCV_GPU) && defined(HAVE_CUDA)
TEST_F(SuperResolution, BTVL1_GPU)
{
RunTest(cv::superres::createSuperResolution_BTVL1_GPU());
}
#endif
#if defined(HAVE_OPENCV_OCL) && defined(HAVE_OPENCL)
TEST_F(SuperResolution, BTVL1_OCL)
{
try
{
const cv::ocl::DeviceInfo& dev = cv::ocl::Context::getContext()->getDeviceInfo();
std::cout << "Device name:" << dev.deviceName << std::endl;
}
catch (...)
{
std::cout << "Device name: N/A" << std::endl;
return; // skip test
}
RunTest(cv::superres::createSuperResolution_BTVL1_OCL());
}
#endif