Can you give me DRY code for getting VSI in python? Here is the matlab code:

function sim = VSI(image1, image2)
% ========================================================================
% Visual Saliency based Index
% Copyright(c) 2013 Lin ZHANG
% All Rights Reserved.
% ----------------------------------------------------------------------
% Permission to use, copy, or modify this software and its documentation
% for educational and research purposes only and without fee is hereQ
% granted, provided that this copyright notice and the original authors'
% names appear on all copies and supporting documentation. This program
% shall not be used, rewritten, or adapted as the basis of a commercial
% software or hardware product without first obtaining permission of the
% authors. The authors make no representations about the suitability of
% this software for any purpose. It is provided "as is" without express
% or implied warranty.
%----------------------------------------------------------------------
% For more information, please refer to
% Lin Zhang et al., "VSI: A Visual Saliency Induced Index for Perceptual
% Image Quality Assessment", submitted to TIP
%----------------------------------------------------------------------
%
%Input : (1) image1: the first image being compared, which is a RGB image
%        (2) image2: the second image being compared, which is a RGB image
%
%Output: sim: the similarity score between two images, a real number
%        
%-----------------------------------------------------------------------

constForVS = 1.27;%fixed
constForGM = 386;%fixed
constForChrom = 130;%fixed
alpha = 0.40;%fixed
lambda = 0.020;%fixed
sigmaF = 1.34;%fixed donot change
omega0 = 0.0210;%fixed
sigmaD = 145;%fixed
sigmaC = 0.001;%fixed

%compute the visual saliency map using SDSP
saliencyMap1 = SDSP(image1,sigmaF,omega0,sigmaD,sigmaC);
saliencyMap2 = SDSP(image2,sigmaF,omega0,sigmaD,sigmaC);

[rows, cols, junk] = size(image1);
%transform into an opponent color space
L1 = 0.06 * double(image1(:,:,1)) + 0.63 * double(image1(:,:,2)) + 0.27 * double(image1(:,:,3));
L2 = 0.06 * double(image2(:,:,1)) + 0.63 * double(image2(:,:,2)) + 0.27 * double(image2(:,:,3));
M1 = 0.30 * double(image1(:,:,1)) + 0.04 * double(image1(:,:,2)) - 0.35 * double(image1(:,:,3));
M2 = 0.30 * double(image2(:,:,1)) + 0.04 * double(image2(:,:,2)) - 0.35 * double(image2(:,:,3));
N1 = 0.34 * double(image1(:,:,1)) - 0.60 * double(image1(:,:,2)) + 0.17 * double(image1(:,:,3));
N2 = 0.34 * double(image2(:,:,1)) - 0.60 * double(image2(:,:,2)) + 0.17 * double(image2(:,:,3));
%%%%%%%%%%%%%%%%%%%%%%%%%
% Downsample the image
%%%%%%%%%%%%%%%%%%%%%%%%%
minDimension = min(rows,cols);
F = max(1,round(minDimension / 256));
aveKernel = fspecial('average',F);

aveM1 = conv2(M1, aveKernel,'same');
aveM2 = conv2(M2, aveKernel,'same');
M1 = aveM1(1:F:rows,1:F:cols);
M2 = aveM2(1:F:rows,1:F:cols);

aveN1 = conv2(N1, aveKernel,'same');
aveN2 = conv2(N2, aveKernel,'same');
N1 = aveN1(1:F:rows,1:F:cols);
N2 = aveN2(1:F:rows,1:F:cols);

aveL1 = conv2(L1, aveKernel,'same');
aveL2 = conv2(L2, aveKernel,'same');
L1 = aveL1(1:F:rows,1:F:cols);
L2 = aveL2(1:F:rows,1:F:cols);

aveSM1 = conv2(saliencyMap1, aveKernel,'same');
aveSM2 = conv2(saliencyMap2, aveKernel,'same');
saliencyMap1 = aveSM1(1:F:rows,1:F:cols);
saliencyMap2 = aveSM2(1:F:rows,1:F:cols);
%%%%%%%%%%%%%%%%%%%%%%%%%
% Calculate the gradient map
%%%%%%%%%%%%%%%%%%%%%%%%%
dx = [3 0 -3; 10 0 -10;  3  0 -3]/16;
dy = [3 10 3; 0  0   0; -3 -10 -3]/16;

IxL1 = conv2(L1, dx, 'same');     
IyL1 = conv2(L1, dy, 'same');    
gradientMap1 = sqrt(IxL1.^2 + IyL1.^2);

IxL2 = conv2(L2, dx, 'same');     
IyL2 = conv2(L2, dy, 'same');    
gradientMap2 = sqrt(IxL2.^2 + IyL2.^2);

%%%%%%%%%%%%%%%%%%%%%%%%%
% Calculate the VSI
%%%%%%%%%%%%%%%%%%%%%%%%%
VSSimMatrix = (2 * saliencyMap1 .* saliencyMap2 + constForVS) ./ (saliencyMap1.^2 + saliencyMap2.^2 + constForVS);
gradientSimMatrix = (2*gradientMap1.*gradientMap2 + constForGM) ./(gradientMap1.^2 + gradientMap2.^2 + constForGM);

weight = max(saliencyMap1, saliencyMap2);

ISimMatrix = (2 * M1 .* M2 + constForChrom) ./ (M1.^2 + M2.^2 + constForChrom);
QSimMatrix = (2 * N1 .* N2 + constForChrom) ./ (N1.^2 + N2.^2 + constForChrom);

SimMatrixC = (gradientSimMatrix .^ alpha) .* VSSimMatrix .* real((ISimMatrix .* QSimMatrix) .^ lambda) .* weight;
sim = sum(sum(SimMatrixC)) / sum(weight(:));

return;
%===================================
function VSMap = SDSP(image,sigmaF,omega0,sigmaD,sigmaC)
% ========================================================================
% SDSP algorithm for salient region detection from a given image.
% Copyright(c) 2013 Lin ZHANG, School of Software Engineering, Tongji
% University
% All Rights Reserved.
% ----------------------------------------------------------------------
% Permission to use, copy, or modify this software and its documentation
% for educational and research purposes only and without fee is here
% granted, provided that this copyright notice and the original authors'
% names appear on all copies and supporting documentation. This program
% shall not be used, rewritten, or adapted as the basis of a commercial
% software or hardware product without first obtaining permission of the
% authors. The authors make no representations about the suitability of
% this software for any purpose. It is provided "as is" without express
% or implied warranty.
%----------------------------------------------------------------------
%
% This is an implementation of the algorithm for calculating the
% SDSP (Saliency Detection by combining Simple Priors).
%
% Please refer to the following paper
%
% Lin Zhang, Zhongyi Gu, and Hongyu Li,"SDSP: a novel saliency detection 
% method by combining simple priors", ICIP, 2013.
% 
%----------------------------------------------------------------------
%
%Input : image: an uint8 RGB image with dynamic range [0, 255] for each
%channel
%        
%Output: VSMap: the visual saliency map extracted by the SDSP algorithm.
%Data range for VSMap is [0, 255]. So, it can be regarded as a common
%gray-scale image.
%        
%-----------------------------------------------------------------------
%convert the image into LAB color space
[oriRows, oriCols, junk] = size(image);
image = double(image);
dsImage(:,:,1) = imresize(image(:,:,1), [256, 256],'bilinear');
dsImage(:,:,2) = imresize(image(:,:,2), [256, 256],'bilinear');
dsImage(:,:,3) = imresize(image(:,:,3), [256, 256],'bilinear');
lab = RGB2Lab(dsImage); 

LChannel = lab(:,:,1);
AChannel = lab(:,:,2);
BChannel = lab(:,:,3);

LFFT = fft2(double(LChannel));
AFFT = fft2(double(AChannel));
BFFT = fft2(double(BChannel));

[rows, cols, junk] = size(dsImage);
LG = logGabor(rows,cols,omega0,sigmaF);
FinalLResult = real(ifft2(LFFT.*LG));
FinalAResult = real(ifft2(AFFT.*LG));
FinalBResult = real(ifft2(BFFT.*LG));

SFMap = sqrt(FinalLResult.^2 + FinalAResult.^2 + FinalBResult.^2);

%the central areas will have a bias towards attention
coordinateMtx = zeros(rows, cols, 2);
coordinateMtx(:,:,1) = repmat((1:1:rows)', 1, cols);
coordinateMtx(:,:,2) = repmat(1:1:cols, rows, 1);

centerY = rows / 2;
centerX = cols / 2;
centerMtx(:,:,1) = ones(rows, cols) * centerY;
centerMtx(:,:,2) = ones(rows, cols) * centerX;
SDMap = exp(-sum((coordinateMtx - centerMtx).^2,3) / sigmaD^2);

%warm colors have a bias towards attention
maxA = max(AChannel(:));
minA = min(AChannel(:));
normalizedA = (AChannel - minA) / (maxA - minA);

maxB = max(BChannel(:));
minB = min(BChannel(:));
normalizedB = (BChannel - minB) / (maxB - minB);

labDistSquare = normalizedA.^2 + normalizedB.^2;
SCMap = 1 - exp(-labDistSquare / (sigmaC^2));
% VSMap = SFMap .* SDMap;
VSMap = SFMap .* SDMap .* SCMap;

VSMap =  imresize(VSMap, [oriRows, oriCols],'bilinear');
VSMap = mat2gray(VSMap);
return;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function labImage = RGB2Lab(image)

image = double(image);
normalizedR = image(:,:,1) / 255;
normalizedG = image(:,:,2) / 255;
normalizedB = image(:,:,3) / 255;

RSmallerOrEqualto4045 = normalizedR <= 0.04045;
RGreaterThan4045 = 1 - RSmallerOrEqualto4045;
tmpR = (normalizedR / 12.92) .* RSmallerOrEqualto4045;
tmpR = tmpR + power((normalizedR + 0.055)/1.055,2.4) .* RGreaterThan4045;

GSmallerOrEqualto4045 = normalizedG <= 0.04045;
GGreaterThan4045 = 1 - GSmallerOrEqualto4045;
tmpG = (normalizedG / 12.92) .* GSmallerOrEqualto4045;
tmpG = tmpG + power((normalizedG + 0.055)/1.055,2.4) .* GGreaterThan4045;

BSmallerOrEqualto4045 = normalizedB <= 0.04045;
BGreaterThan4045 = 1 - BSmallerOrEqualto4045;
tmpB = (normalizedB / 12.92) .* BSmallerOrEqualto4045;
tmpB = tmpB + power((normalizedB + 0.055)/1.055,2.4) .* BGreaterThan4045;

X = tmpR*0.4124564 + tmpG*0.3575761 + tmpB*0.1804375;
Y = tmpR*0.2126729 + tmpG*0.7151522 + tmpB*0.0721750;
Z = tmpR*0.0193339 + tmpG*0.1191920 + tmpB*0.9503041;

epsilon = 0.008856; %actual CIE standard
kappa   = 903.3;  %actual CIE standard
Xr = 0.9642; %reference white D50
Yr = 1.0;  %reference white
Zr = 0.8251; %reference white

xr = X/Xr;
yr = Y/Yr;
zr = Z/Zr;

xrGreaterThanEpsilon = xr > epsilon;
xrSmallerOrEqualtoEpsilon = 1 - xrGreaterThanEpsilon;
fx = power(xr, 1.0/3.0) .* xrGreaterThanEpsilon;
fx = fx + (kappa*xr + 16.0)/116.0 .* xrSmallerOrEqualtoEpsilon;

yrGreaterThanEpsilon = yr > epsilon;
yrSmallerOrEqualtoEpsilon = 1 - yrGreaterThanEpsilon;
fy = power(yr, 1.0/3.0) .* yrGreaterThanEpsilon;
fy = fy + (kappa*yr + 16.0)/116.0 .* yrSmallerOrEqualtoEpsilon;

zrGreaterThanEpsilon = zr > epsilon;
zrSmallerOrEqualtoEpsilon = 1 - zrGreaterThanEpsilon;
fz = power(zr, 1.0/3.0) .* zrGreaterThanEpsilon;
fz = fz + (kappa*zr + 16.0)/116.0 .* zrSmallerOrEqualtoEpsilon;

[rows,cols,junk] = size(image);
labImage = zeros(rows,cols,3);
labImage(:,:,1) = 116.0 * fy - 16.0;
labImage(:,:,2) = 500.0 * (fx - fy);
labImage(:,:,3) = 200.0 * (fy - fz);
return;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function LG = logGabor(rows,cols,omega0,sigmaF)
     [u1, u2] = meshgrid(([1:cols]-(fix(cols/2)+1))/(cols-mod(cols,2)), ...
               ([1:rows]-(fix(rows/2)+1))/(rows-mod(rows,2)));
     mask = ones(rows, cols);
     for rowIndex = 1:rows
         for colIndex = 1:cols
             if u1(rowIndex, colIndex)^2 + u2(rowIndex, colIndex)^2 > 0.25
                 mask(rowIndex, colIndex) = 0;
             end
         end
     end
     u1 = u1 .* mask;
     u2 = u2 .* mask;
     
     u1 = ifftshift(u1);  
     u2 = ifftshift(u2);
     
     radius = sqrt(u1.^2 + u2.^2);    
     radius(1,1) = 1;
            
     LG = exp((-(log(radius/omega0)).^2) / (2 * (sigmaF^2)));  
     LG(1,1) = 0; 
return;