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renamed gpu namespace -> cuda

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This commit is contained in:
Vladislav Vinogradov
2013-08-28 15:45:13 +04:00
parent e12496d150
commit e895b7455e
343 changed files with 3882 additions and 3882 deletions
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8
samples/cpp/stitching_detailed.cpp
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@@ -357,7 +357,7 @@ int main(int argc, char* argv[])
if (features_type == "surf")
{
#ifdef HAVE_OPENCV_NONFREE
if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)
if (try_gpu && cuda::getCudaEnabledDeviceCount() > 0)
finder = new SurfFeaturesFinderGpu();
else
#endif
@@ -553,7 +553,7 @@ int main(int argc, char* argv[])
Ptr<WarperCreator> warper_creator;
#ifdef HAVE_OPENCV_GPUWARPING
if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)
if (try_gpu && cuda::getCudaEnabledDeviceCount() > 0)
{
if (warp_type == "plane") warper_creator = new cv::PlaneWarperGpu();
else if (warp_type == "cylindrical") warper_creator = new cv::CylindricalWarperGpu();
@@ -618,7 +618,7 @@ int main(int argc, char* argv[])
else if (seam_find_type == "gc_color")
{
#ifdef HAVE_OPENCV_GPU
if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)
if (try_gpu && cuda::getCudaEnabledDeviceCount() > 0)
seam_finder = new detail::GraphCutSeamFinderGpu(GraphCutSeamFinderBase::COST_COLOR);
else
#endif
@@ -627,7 +627,7 @@ int main(int argc, char* argv[])
else if (seam_find_type == "gc_colorgrad")
{
#ifdef HAVE_OPENCV_GPU
if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)
if (try_gpu && cuda::getCudaEnabledDeviceCount() > 0)
seam_finder = new detail::GraphCutSeamFinderGpu(GraphCutSeamFinderBase::COST_COLOR_GRAD);
else
#endif

138
samples/cpp/tutorial_code/gpu/gpu-basics-similarity/gpu-basics-similarity.cpp
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@@ -21,28 +21,28 @@ Scalar getMSSIM_GPU( const Mat& I1, const Mat& I2);
struct BufferPSNR // Optimized GPU versions
{ // Data allocations are very expensive on GPU. Use a buffer to solve: allocate once reuse later.
gpu::GpuMat gI1, gI2, gs, t1,t2;
cuda::GpuMat gI1, gI2, gs, t1,t2;
gpu::GpuMat buf;
cuda::GpuMat buf;
};
double getPSNR_GPU_optimized(const Mat& I1, const Mat& I2, BufferPSNR& b);
struct BufferMSSIM // Optimized GPU versions
{ // Data allocations are very expensive on GPU. Use a buffer to solve: allocate once reuse later.
gpu::GpuMat gI1, gI2, gs, t1,t2;
cuda::GpuMat gI1, gI2, gs, t1,t2;
gpu::GpuMat I1_2, I2_2, I1_I2;
vector<gpu::GpuMat> vI1, vI2;
cuda::GpuMat I1_2, I2_2, I1_I2;
vector<cuda::GpuMat> vI1, vI2;
gpu::GpuMat mu1, mu2;
gpu::GpuMat mu1_2, mu2_2, mu1_mu2;
cuda::GpuMat mu1, mu2;
cuda::GpuMat mu1_2, mu2_2, mu1_mu2;
gpu::GpuMat sigma1_2, sigma2_2, sigma12;
gpu::GpuMat t3;
cuda::GpuMat sigma1_2, sigma2_2, sigma12;
cuda::GpuMat t3;
gpu::GpuMat ssim_map;
cuda::GpuMat ssim_map;
gpu::GpuMat buf;
cuda::GpuMat buf;
};
Scalar getMSSIM_GPU_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b);
@@ -197,10 +197,10 @@ double getPSNR_GPU_optimized(const Mat& I1, const Mat& I2, BufferPSNR& b)
b.gI1.convertTo(b.t1, CV_32F);
b.gI2.convertTo(b.t2, CV_32F);
gpu::absdiff(b.t1.reshape(1), b.t2.reshape(1), b.gs);
gpu::multiply(b.gs, b.gs, b.gs);
cuda::absdiff(b.t1.reshape(1), b.t2.reshape(1), b.gs);
cuda::multiply(b.gs, b.gs, b.gs);
double sse = gpu::sum(b.gs, b.buf)[0];
double sse = cuda::sum(b.gs, b.buf)[0];
if( sse <= 1e-10) // for small values return zero
return 0;
@@ -214,7 +214,7 @@ double getPSNR_GPU_optimized(const Mat& I1, const Mat& I2, BufferPSNR& b)
double getPSNR_GPU(const Mat& I1, const Mat& I2)
{
gpu::GpuMat gI1, gI2, gs, t1,t2;
cuda::GpuMat gI1, gI2, gs, t1,t2;
gI1.upload(I1);
gI2.upload(I2);
@@ -222,10 +222,10 @@ double getPSNR_GPU(const Mat& I1, const Mat& I2)
gI1.convertTo(t1, CV_32F);
gI2.convertTo(t2, CV_32F);
gpu::absdiff(t1.reshape(1), t2.reshape(1), gs);
gpu::multiply(gs, gs, gs);
cuda::absdiff(t1.reshape(1), t2.reshape(1), gs);
cuda::multiply(gs, gs, gs);
Scalar s = gpu::sum(gs);
Scalar s = cuda::sum(gs);
double sse = s.val[0] + s.val[1] + s.val[2];
if( sse <= 1e-10) // for small values return zero
@@ -295,7 +295,7 @@ Scalar getMSSIM_GPU( const Mat& i1, const Mat& i2)
{
const float C1 = 6.5025f, C2 = 58.5225f;
/***************************** INITS **********************************/
gpu::GpuMat gI1, gI2, gs1, tmp1,tmp2;
cuda::GpuMat gI1, gI2, gs1, tmp1,tmp2;
gI1.upload(i1);
gI2.upload(i2);
@@ -303,57 +303,57 @@ Scalar getMSSIM_GPU( const Mat& i1, const Mat& i2)
gI1.convertTo(tmp1, CV_MAKE_TYPE(CV_32F, gI1.channels()));
gI2.convertTo(tmp2, CV_MAKE_TYPE(CV_32F, gI2.channels()));
vector<gpu::GpuMat> vI1, vI2;
gpu::split(tmp1, vI1);
gpu::split(tmp2, vI2);
vector<cuda::GpuMat> vI1, vI2;
cuda::split(tmp1, vI1);
cuda::split(tmp2, vI2);
Scalar mssim;
Ptr<gpu::Filter> gauss = gpu::createGaussianFilter(vI2[0].type(), -1, Size(11, 11), 1.5);
Ptr<cuda::Filter> gauss = cuda::createGaussianFilter(vI2[0].type(), -1, Size(11, 11), 1.5);
for( int i = 0; i < gI1.channels(); ++i )
{
gpu::GpuMat I2_2, I1_2, I1_I2;
cuda::GpuMat I2_2, I1_2, I1_I2;
gpu::multiply(vI2[i], vI2[i], I2_2); // I2^2
gpu::multiply(vI1[i], vI1[i], I1_2); // I1^2
gpu::multiply(vI1[i], vI2[i], I1_I2); // I1 * I2
cuda::multiply(vI2[i], vI2[i], I2_2); // I2^2
cuda::multiply(vI1[i], vI1[i], I1_2); // I1^2
cuda::multiply(vI1[i], vI2[i], I1_I2); // I1 * I2
/*************************** END INITS **********************************/
gpu::GpuMat mu1, mu2; // PRELIMINARY COMPUTING
cuda::GpuMat mu1, mu2; // PRELIMINARY COMPUTING
gauss->apply(vI1[i], mu1);
gauss->apply(vI2[i], mu2);
gpu::GpuMat mu1_2, mu2_2, mu1_mu2;
gpu::multiply(mu1, mu1, mu1_2);
gpu::multiply(mu2, mu2, mu2_2);
gpu::multiply(mu1, mu2, mu1_mu2);
cuda::GpuMat mu1_2, mu2_2, mu1_mu2;
cuda::multiply(mu1, mu1, mu1_2);
cuda::multiply(mu2, mu2, mu2_2);
cuda::multiply(mu1, mu2, mu1_mu2);
gpu::GpuMat sigma1_2, sigma2_2, sigma12;
cuda::GpuMat sigma1_2, sigma2_2, sigma12;
gauss->apply(I1_2, sigma1_2);
gpu::subtract(sigma1_2, mu1_2, sigma1_2); // sigma1_2 -= mu1_2;
cuda::subtract(sigma1_2, mu1_2, sigma1_2); // sigma1_2 -= mu1_2;
gauss->apply(I2_2, sigma2_2);
gpu::subtract(sigma2_2, mu2_2, sigma2_2); // sigma2_2 -= mu2_2;
cuda::subtract(sigma2_2, mu2_2, sigma2_2); // sigma2_2 -= mu2_2;
gauss->apply(I1_I2, sigma12);
gpu::subtract(sigma12, mu1_mu2, sigma12); // sigma12 -= mu1_mu2;
cuda::subtract(sigma12, mu1_mu2, sigma12); // sigma12 -= mu1_mu2;
///////////////////////////////// FORMULA ////////////////////////////////
gpu::GpuMat t1, t2, t3;
cuda::GpuMat t1, t2, t3;
mu1_mu2.convertTo(t1, -1, 2, C1); // t1 = 2 * mu1_mu2 + C1;
sigma12.convertTo(t2, -1, 2, C2); // t2 = 2 * sigma12 + C2;
gpu::multiply(t1, t2, t3); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
cuda::multiply(t1, t2, t3); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
gpu::addWeighted(mu1_2, 1.0, mu2_2, 1.0, C1, t1); // t1 = mu1_2 + mu2_2 + C1;
gpu::addWeighted(sigma1_2, 1.0, sigma2_2, 1.0, C2, t2); // t2 = sigma1_2 + sigma2_2 + C2;
gpu::multiply(t1, t2, t1); // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))
cuda::addWeighted(mu1_2, 1.0, mu2_2, 1.0, C1, t1); // t1 = mu1_2 + mu2_2 + C1;
cuda::addWeighted(sigma1_2, 1.0, sigma2_2, 1.0, C2, t2); // t2 = sigma1_2 + sigma2_2 + C2;
cuda::multiply(t1, t2, t1); // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))
gpu::GpuMat ssim_map;
gpu::divide(t3, t1, ssim_map); // ssim_map = t3./t1;
cuda::GpuMat ssim_map;
cuda::divide(t3, t1, ssim_map); // ssim_map = t3./t1;
Scalar s = gpu::sum(ssim_map);
Scalar s = cuda::sum(ssim_map);
mssim.val[i] = s.val[0] / (ssim_map.rows * ssim_map.cols);
}
@@ -368,63 +368,63 @@ Scalar getMSSIM_GPU_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b)
b.gI1.upload(i1);
b.gI2.upload(i2);
gpu::Stream stream;
cuda::Stream stream;
b.gI1.convertTo(b.t1, CV_32F, stream);
b.gI2.convertTo(b.t2, CV_32F, stream);
gpu::split(b.t1, b.vI1, stream);
gpu::split(b.t2, b.vI2, stream);
cuda::split(b.t1, b.vI1, stream);
cuda::split(b.t2, b.vI2, stream);
Scalar mssim;
Ptr<gpu::Filter> gauss = gpu::createGaussianFilter(b.vI1[0].type(), -1, Size(11, 11), 1.5);
Ptr<cuda::Filter> gauss = cuda::createGaussianFilter(b.vI1[0].type(), -1, Size(11, 11), 1.5);
for( int i = 0; i < b.gI1.channels(); ++i )
{
gpu::multiply(b.vI2[i], b.vI2[i], b.I2_2, 1, -1, stream); // I2^2
gpu::multiply(b.vI1[i], b.vI1[i], b.I1_2, 1, -1, stream); // I1^2
gpu::multiply(b.vI1[i], b.vI2[i], b.I1_I2, 1, -1, stream); // I1 * I2
cuda::multiply(b.vI2[i], b.vI2[i], b.I2_2, 1, -1, stream); // I2^2
cuda::multiply(b.vI1[i], b.vI1[i], b.I1_2, 1, -1, stream); // I1^2
cuda::multiply(b.vI1[i], b.vI2[i], b.I1_I2, 1, -1, stream); // I1 * I2
gauss->apply(b.vI1[i], b.mu1, stream);
gauss->apply(b.vI2[i], b.mu2, stream);
gpu::multiply(b.mu1, b.mu1, b.mu1_2, 1, -1, stream);
gpu::multiply(b.mu2, b.mu2, b.mu2_2, 1, -1, stream);
gpu::multiply(b.mu1, b.mu2, b.mu1_mu2, 1, -1, stream);
cuda::multiply(b.mu1, b.mu1, b.mu1_2, 1, -1, stream);
cuda::multiply(b.mu2, b.mu2, b.mu2_2, 1, -1, stream);
cuda::multiply(b.mu1, b.mu2, b.mu1_mu2, 1, -1, stream);
gauss->apply(b.I1_2, b.sigma1_2, stream);
gpu::subtract(b.sigma1_2, b.mu1_2, b.sigma1_2, gpu::GpuMat(), -1, stream);
cuda::subtract(b.sigma1_2, b.mu1_2, b.sigma1_2, cuda::GpuMat(), -1, stream);
//b.sigma1_2 -= b.mu1_2; - This would result in an extra data transfer operation
gauss->apply(b.I2_2, b.sigma2_2, stream);
gpu::subtract(b.sigma2_2, b.mu2_2, b.sigma2_2, gpu::GpuMat(), -1, stream);
cuda::subtract(b.sigma2_2, b.mu2_2, b.sigma2_2, cuda::GpuMat(), -1, stream);
//b.sigma2_2 -= b.mu2_2;
gauss->apply(b.I1_I2, b.sigma12, stream);
gpu::subtract(b.sigma12, b.mu1_mu2, b.sigma12, gpu::GpuMat(), -1, stream);
cuda::subtract(b.sigma12, b.mu1_mu2, b.sigma12, cuda::GpuMat(), -1, stream);
//b.sigma12 -= b.mu1_mu2;
//here too it would be an extra data transfer due to call of operator*(Scalar, Mat)
gpu::multiply(b.mu1_mu2, 2, b.t1, 1, -1, stream); //b.t1 = 2 * b.mu1_mu2 + C1;
gpu::add(b.t1, C1, b.t1, gpu::GpuMat(), -1, stream);
gpu::multiply(b.sigma12, 2, b.t2, 1, -1, stream); //b.t2 = 2 * b.sigma12 + C2;
gpu::add(b.t2, C2, b.t2, gpu::GpuMat(), -12, stream);
cuda::multiply(b.mu1_mu2, 2, b.t1, 1, -1, stream); //b.t1 = 2 * b.mu1_mu2 + C1;
cuda::add(b.t1, C1, b.t1, cuda::GpuMat(), -1, stream);
cuda::multiply(b.sigma12, 2, b.t2, 1, -1, stream); //b.t2 = 2 * b.sigma12 + C2;
cuda::add(b.t2, C2, b.t2, cuda::GpuMat(), -12, stream);
gpu::multiply(b.t1, b.t2, b.t3, 1, -1, stream); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
cuda::multiply(b.t1, b.t2, b.t3, 1, -1, stream); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
gpu::add(b.mu1_2, b.mu2_2, b.t1, gpu::GpuMat(), -1, stream);
gpu::add(b.t1, C1, b.t1, gpu::GpuMat(), -1, stream);
cuda::add(b.mu1_2, b.mu2_2, b.t1, cuda::GpuMat(), -1, stream);
cuda::add(b.t1, C1, b.t1, cuda::GpuMat(), -1, stream);
gpu::add(b.sigma1_2, b.sigma2_2, b.t2, gpu::GpuMat(), -1, stream);
gpu::add(b.t2, C2, b.t2, gpu::GpuMat(), -1, stream);
cuda::add(b.sigma1_2, b.sigma2_2, b.t2, cuda::GpuMat(), -1, stream);
cuda::add(b.t2, C2, b.t2, cuda::GpuMat(), -1, stream);
gpu::multiply(b.t1, b.t2, b.t1, 1, -1, stream); // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))
gpu::divide(b.t3, b.t1, b.ssim_map, 1, -1, stream); // ssim_map = t3./t1;
cuda::multiply(b.t1, b.t2, b.t1, 1, -1, stream); // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))
cuda::divide(b.t3, b.t1, b.ssim_map, 1, -1, stream); // ssim_map = t3./t1;
stream.waitForCompletion();
Scalar s = gpu::sum(b.ssim_map, b.buf);
Scalar s = cuda::sum(b.ssim_map, b.buf);
mssim.val[i] = s.val[0] / (b.ssim_map.rows * b.ssim_map.cols);
}

2
samples/cpp/videostab.cpp
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@@ -347,7 +347,7 @@ int main(int argc, const char **argv)
{
cout << "initializing GPU..."; cout.flush();
Mat hostTmp = Mat::zeros(1, 1, CV_32F);
gpu::GpuMat deviceTmp;
cuda::GpuMat deviceTmp;
deviceTmp.upload(hostTmp);
cout << endl;
}