lm opencv 算法_OpenCV进行图像相似度对比的几种办法

// PSNR.cpp : 定义控制台应用程序的入口点。

//

#include "stdafx.h"

#include // Console I/O

#include // String to number conversion

#include // Basic OpenCV structures

#include // Image processing methods for the CPU

#include // Read images

#include // GPU structures and methods

using namespace std;

using namespace cv;

double getPSNR(const Mat& I1, const Mat& I2); // CPU versions

Scalar getMSSIM( const Mat& I1, const Mat& I2);

double getPSNR_GPU(const Mat& I1, const Mat& I2); // Basic GPU versions

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;

gpu::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;

gpu::GpuMat I1_2, I2_2, I1_I2;

vector<:gpumat> vI1, vI2;

gpu::GpuMat mu1, mu2;

gpu::GpuMat mu1_2, mu2_2, mu1_mu2;

gpu::GpuMat sigma1_2, sigma2_2, sigma12;

gpu::GpuMat t3;

gpu::GpuMat ssim_map;

gpu::GpuMat buf;

};

Scalar getMSSIM_GPU_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b);

void help()

{

cout

<< "\n--------------------------------------------------------------------------" << endl

<< "This program shows how to port your CPU code to GPU or write that from scratch." << endl

<< "You can see the performance improvement for the similarity check methods (PSNR and SSIM)." << endl

<< "Usage:" << endl

<< "./gpu-basics-similarity referenceImage comparedImage numberOfTimesToRunTest(like 10)." << endl

<< "--------------------------------------------------------------------------" << endl

<< endl;

}

int main(int argc, char *argv[])

{

help();

Mat I1 = imread("swan1.jpg",1); // Read the two images

Mat I2 = imread("swan2.jpg",1);

if (!I1.data || !I2.data) // Check for success

{

cout << "Couldn't read the image";

return 0;

}

BufferPSNR bufferPSNR;

BufferMSSIM bufferMSSIM;

int TIMES;

stringstream sstr("500");

sstr >> TIMES;

double time, result;

//------------------------------- PSNR CPU ----------------------------------------------------

time = (double)getTickCount();

for (int i = 0; i < TIMES; ++i)

result = getPSNR(I1,I2);

time = 1000*((double)getTickCount() - time)/getTickFrequency();

time /= TIMES;

cout << "Time of PSNR CPU (averaged for " << TIMES << " runs): " << time << " milliseconds."

<< " With result of: " << result << endl;

------------------------------- PSNR GPU ----------------------------------------------------

//time = (double)getTickCount();

//for (int i = 0; i < TIMES; ++i)

// result = getPSNR_GPU(I1,I2);

//time = 1000*((double)getTickCount() - time)/getTickFrequency();

//time /= TIMES;

//cout << "Time of PSNR GPU (averaged for " << TIMES << " runs): " << time << " milliseconds."

// << " With result of: " << result << endl;

/*

//------------------------------- PSNR GPU Optimized--------------------------------------------

time = (double)getTickCount(); // Initial call

result = getPSNR_GPU_optimized(I1, I2, bufferPSNR);

time = 1000*((double)getTickCount() - time)/getTickFrequency();

cout << "Initial call GPU optimized: " << time <

<< " With result of: " << result << endl;

time = (double)getTickCount();

for (int i = 0; i < TIMES; ++i)

result = getPSNR_GPU_optimized(I1, I2, bufferPSNR);

time = 1000*((double)getTickCount() - time)/getTickFrequency();

time /= TIMES;

cout << "Time of PSNR GPU OPTIMIZED ( / " << TIMES << " runs): " << time

<< " milliseconds." << " With result of: " << result << endl << endl;

//------------------------------- SSIM CPU -----------------------------------------------------

Scalar x;

time = (double)getTickCount();

for (int i = 0; i < TIMES; ++i)

x = getMSSIM(I1,I2);

time = 1000*((double)getTickCount() - time)/getTickFrequency();

time /= TIMES;

cout << "Time of MSSIM CPU (averaged for " << TIMES << " runs): " << time << " milliseconds."

<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl;

//------------------------------- SSIM GPU -----------------------------------------------------

time = (double)getTickCount();

for (int i = 0; i < TIMES; ++i)

x = getMSSIM_GPU(I1,I2);

time = 1000*((double)getTickCount() - time)/getTickFrequency();

time /= TIMES;

cout << "Time of MSSIM GPU (averaged for " << TIMES << " runs): " << time << " milliseconds."

<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl;

//------------------------------- SSIM GPU Optimized--------------------------------------------

time = (double)getTickCount();

x = getMSSIM_GPU_optimized(I1,I2, bufferMSSIM);

time = 1000*((double)getTickCount() - time)/getTickFrequency();

cout << "Time of MSSIM GPU Initial Call " << time << " milliseconds."

<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl;

time = (double)getTickCount();

for (int i = 0; i < TIMES; ++i)

x = getMSSIM_GPU_optimized(I1,I2, bufferMSSIM);

time = 1000*((double)getTickCount() - time)/getTickFrequency();

time /= TIMES;

cout << "Time of MSSIM GPU OPTIMIZED ( / " << TIMES << " runs): " << time << " milliseconds."

<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl << endl;

return 0;

*/

getchar();

}

double getPSNR(const Mat& I1, const Mat& I2)

{

Mat s1;

absdiff(I1, I2, s1); // |I1 - I2|

s1.convertTo(s1, CV_32F); // cannot make a square on 8 bits

s1 = s1.mul(s1); // |I1 - I2|^2

Scalar s = sum(s1); // sum elements per channel

double sse = s.val[0] + s.val[1] + s.val[2]; // sum channels

if( sse <= 1e-10) // for small values return zero

return 0;

else

{

double mse =sse /(double)(I1.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)

{

b.gI1.upload(I1);

b.gI2.upload(I2);

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);

double sse = gpu::sum(b.gs, b.buf)[0];

if( sse <= 1e-10) // for small values return zero

return 0;

else

{

double mse = sse /(double)(I1.channels() * I1.total());

double psnr = 10.0*log10((255*255)/mse);

return psnr;

}

}

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;

i1.convertTo(I1, d); // cannot calculate on one byte large values

i2.convertTo(I2, d);

Mat I2_2 = I2.mul(I2); // I2^2

Mat I1_2 = I1.mul(I1); // I1^2

Mat I1_I2 = I1.mul(I2); // I1 * I2

/*************************** END INITS **********************************/

Mat mu1, mu2; // PRELIMINARY COMPUTING

GaussianBlur(I1, mu1, Size(11, 11), 1.5);

GaussianBlur(I2, mu2, Size(11, 11), 1.5);

Mat mu1_2 = mu1.mul(mu1);

Mat mu2_2 = mu2.mul(mu2);

Mat mu1_mu2 = mu1.mul(mu2);

Mat sigma1_2, sigma2_2, sigma12;

GaussianBlur(I1_2, sigma1_2, Size(11, 11), 1.5);

sigma1_2 -= mu1_2;

GaussianBlur(I2_2, sigma2_2, Size(11, 11), 1.5);

sigma2_2 -= mu2_2;

GaussianBlur(I1_I2, sigma12, Size(11, 11), 1.5);

sigma12 -= mu1_mu2;

/ FORMULA

Mat t1, t2, t3;

t1 = 2 * mu1_mu2 + C1;

t2 = 2 * sigma12 + C2;

t3 = t1.mul(t2); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))

t1 = mu1_2 + mu2_2 + C1;

t2 = sigma1_2 + sigma2_2 + C2;

t1 = t1.mul(t2); // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))

Mat ssim_map;

divide(t3, t1, ssim_map); // ssim_map = t3./t1;

Scalar mssim = mean( ssim_map ); // mssim = average of ssim map

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<: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 = sigma1_2 - mu1_2;

gpu::subtract(sigma1_2,mu1_2,sigma1_2);

gpu::GaussianBlur(I2_2, sigma2_2, Size(11, 11), 1.5);

//sigma2_2 = sigma2_2 - mu2_2;

gpu::GaussianBlur(I1_I2, sigma12, Size(11, 11), 1.5);

(Mat)sigma12 =(Mat)sigma12 - (Mat)mu1_mu2;

//sigma12 = 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);

gpu::Stream stream;

stream.enqueueConvert(b.gI1, b.t1, CV_32F);

stream.enqueueConvert(b.gI2, b.t2, CV_32F);

gpu::split(b.t1, b.vI1, stream);

gpu::split(b.t2, b.vI2, stream);

Scalar mssim;

for( int i = 0; i < b.gI1.channels(); ++i )

{

gpu::multiply(b.vI2[i], b.vI2[i], b.I2_2, stream); // I2^2

gpu::multiply(b.vI1[i], b.vI1[i], b.I1_2, stream); // I1^2

gpu::multiply(b.vI1[i], b.vI2[i], b.I1_I2, stream); // I1 * I2

//gpu::GaussianBlur(b.vI1[i], b.mu1, Size(11, 11), 1.5, 0, BORDER_DEFAULT, -1, stream);

//gpu::GaussianBlur(b.vI2[i], b.mu2, Size(11, 11), 1.5, 0, BORDER_DEFAULT, -1, stream);

gpu::multiply(b.mu1, b.mu1, b.mu1_2, stream);

gpu::multiply(b.mu2, b.mu2, b.mu2_2, stream);

gpu::multiply(b.mu1, b.mu2, b.mu1_mu2, stream);

//gpu::GaussianBlur(b.I1_2, b.sigma1_2, Size(11, 11), 1.5, 0, BORDER_DEFAULT, -1, stream);

//gpu::subtract(b.sigma1_2, b.mu1_2, b.sigma1_2, stream);

//b.sigma1_2 -= b.mu1_2; - This would result in an extra data transfer operation

//gpu::GaussianBlur(b.I2_2, b.sigma2_2, Size(11, 11), 1.5, 0, BORDER_DEFAULT, -1, stream);

//gpu::subtract(b.sigma2_2, b.mu2_2, b.sigma2_2, stream);

//b.sigma2_2 -= b.mu2_2;

//gpu::GaussianBlur(b.I1_I2, b.sigma12, Size(11, 11), 1.5, 0, BORDER_DEFAULT, -1, stream);

//gpu::subtract(b.sigma12, b.mu1_mu2, b.sigma12, 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, stream); //b.t1 = 2 * b.mu1_mu2 + C1;

//gpu::add(b.t1, C1, b.t1, stream);

gpu::multiply(b.sigma12, 2, b.t2, stream); //b.t2 = 2 * b.sigma12 + C2;

//gpu::add(b.t2, C2, b.t2, stream);

gpu::multiply(b.t1, b.t2, b.t3, stream); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))

//gpu::add(b.mu1_2, b.mu2_2, b.t1, stream);

//gpu::add(b.t1, C1, b.t1, stream);

//gpu::add(b.sigma1_2, b.sigma2_2, b.t2, stream);

//gpu::add(b.t2, C2, b.t2, stream);

gpu::multiply(b.t1, b.t2, b.t1, stream); // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))

gpu::divide(b.t3, b.t1, b.ssim_map, stream); // ssim_map = t3./t1;

stream.waitForCompletion();

Scalar s = gpu::sum(b.ssim_map, b.buf);

mssim.val[i] = s.val[0] / (b.ssim_map.rows * b.ssim_map.cols);

}

return mssim;

}

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