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Added to the tutorials the "Support Vector Machines for Non-Linearly Separable Data" contributed by Fernando Iglesias García. Corrected a mistake in the gpu-basics-similarity.rst file.

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This commit is contained in:
Bernat Gabor
2011-08-14 19:58:20 +00:00
parent a51a8ad577
commit fec995391f
9 changed files with 257 additions and 31 deletions
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134
samples/cpp/tutorial_code/gpu/gpu-basics-similarity/gpu-basics-similarity.cpp
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@@ -56,7 +56,7 @@ void help()
int main(int argc, char *argv[])
{
help();
help();
Mat I1 = imread(argv[1]); // Read the two images
Mat I2 = imread(argv[2]);
@@ -68,7 +68,7 @@ int main(int argc, char *argv[])
BufferPSNR bufferPSNR;
BufferMSSIM bufferMSSIM;
int TIMES;
stringstream sstr(argv[3]);
sstr >> TIMES;
@@ -103,7 +103,7 @@ int main(int argc, char *argv[])
result = getPSNR_GPU_optimized(I1, I2, bufferPSNR);
time = 1000*((double)getTickCount() - time)/getTickFrequency();
cout << "Initial call GPU optimized: " << time <<" milliseconds."
<< " With result of: " << result << endl;
<< " With result of: " << result << endl;
time = (double)getTickCount();
for (int i = 0; i < TIMES; ++i)
@@ -113,7 +113,7 @@ int main(int argc, char *argv[])
time /= TIMES;
cout << "Time of PSNR GPU OPTIMIZED ( / " << TIMES << " runs): " << time
<< " milliseconds." << " With result of: " << result << endl << endl;
<< " milliseconds." << " With result of: " << result << endl << endl;
//------------------------------- SSIM CPU -----------------------------------------------------
@@ -183,31 +183,7 @@ double getPSNR(const Mat& I1, const Mat& I2)
}
}
double getPSNR_GPU(const Mat& I1, const Mat& I2)
{
gpu::GpuMat gI1, gI2, gs, t1,t2;
gI1.upload(I1);
gI2.upload(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);
Scalar s = gpu::sum(gs);
double sse = s.val[0] + s.val[1] + s.val[2];
if( sse <= 1e-10) // for small values return zero
return 0;
else
{
double mse =sse /(double)(gI1.channels() * I1.total());
double psnr = 10.0*log10((255*255)/mse);
return psnr;
}
}
double getPSNR_GPU_optimized(const Mat& I1, const Mat& I2, BufferPSNR& b)
{
@@ -232,9 +208,36 @@ 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;
gI1.upload(I1);
gI2.upload(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);
Scalar s = gpu::sum(gs);
double sse = s.val[0] + s.val[1] + s.val[2];
if( sse <= 1e-10) // for small values return zero
return 0;
else
{
double mse =sse /(double)(gI1.channels() * I1.total());
double psnr = 10.0*log10((255*255)/mse);
return psnr;
}
}
Scalar getMSSIM( const Mat& i1, const Mat& i2)
{
const double C1 = 6.5025, C2 = 58.5225;
/***************************** INITS **********************************/
int d = CV_32F;
Mat I1, I2;
@@ -244,8 +247,10 @@ Scalar getMSSIM( const Mat& i1, const Mat& i2)
Mat I2_2 = I2.mul(I2); // I2^2
Mat I1_2 = I1.mul(I1); // I1^2
Mat I1_I2 = I1.mul(I2); // I1 * I2
Mat mu1, mu2;
/*************************** END INITS **********************************/
Mat mu1, mu2; // PRELIMINARY COMPUTING
GaussianBlur(I1, mu1, Size(11, 11), 1.5);
GaussianBlur(I2, mu2, Size(11, 11), 1.5);
@@ -282,10 +287,79 @@ Scalar getMSSIM( const Mat& i1, const Mat& i2)
return mssim;
}
Scalar getMSSIM_GPU( const Mat& i1, const Mat& i2)
{
const float C1 = 6.5025f, C2 = 58.5225f;
/***************************** INITS **********************************/
gpu::GpuMat gI1, gI2, gs1, t1,t2;
gI1.upload(i1);
gI2.upload(i2);
gI1.convertTo(t1, CV_MAKE_TYPE(CV_32F, gI1.channels()));
gI2.convertTo(t2, CV_MAKE_TYPE(CV_32F, gI2.channels()));
vector<gpu::GpuMat> vI1, vI2;
gpu::split(t1, vI1);
gpu::split(t2, vI2);
Scalar mssim;
for( int i = 0; i < gI1.channels(); ++i )
{
gpu::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
/*************************** END INITS **********************************/
gpu::GpuMat mu1, mu2; // PRELIMINARY COMPUTING
gpu::GaussianBlur(vI1[i], mu1, Size(11, 11), 1.5);
gpu::GaussianBlur(vI2[i], mu2, Size(11, 11), 1.5);
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);
gpu::GpuMat sigma1_2, sigma2_2, sigma12;
gpu::GaussianBlur(I1_2, sigma1_2, Size(11, 11), 1.5);
sigma1_2 -= mu1_2;
gpu::GaussianBlur(I2_2, sigma2_2, Size(11, 11), 1.5);
sigma2_2 -= mu2_2;
gpu::GaussianBlur(I1_I2, sigma12, Size(11, 11), 1.5);
sigma12 -= mu1_mu2;
///////////////////////////////// FORMULA ////////////////////////////////
gpu::GpuMat t1, t2, t3;
t1 = 2 * mu1_mu2 + C1;
t2 = 2 * sigma12 + C2;
gpu::multiply(t1, t2, t3); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
t1 = mu1_2 + mu2_2 + C1;
t2 = sigma1_2 + sigma2_2 + C2;
gpu::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;
Scalar s = gpu::sum(ssim_map);
mssim.val[i] = s.val[0] / (ssim_map.rows * ssim_map.cols);
}
return mssim;
}
Scalar getMSSIM_GPU_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b)
{
int cn = i1.channels();
const float C1 = 6.5025f, C2 = 58.5225f;
/***************************** INITS **********************************/
b.gI1.upload(i1);
b.gI2.upload(i2);