Convert the following matlab code to python 2.7?

github repo of the code:

https://github.com/KirillLykov/cvision-algorithms/blob/master/src/seamCarving.m

function image = seamCarving(newSize, image) % apply seam carving to the image % following paper by Avidan and Shamir '07 sizeReductionX = size(image, 1) - newSize(1); sizeReductionY = size(image, 2) - newSize(2);

mmax = @(left, right) max([left right]);

image = seamCarvingReduce([mmax(0, sizeReductionX), mmax(0, sizeReductionY)], image);

image = seamCarvingEnlarge([mmax(0, -sizeReductionX), mmax(0, -sizeReductionY)], image); end

function image = seamCarvingReduce(sizeReduction, image) if (sizeReduction == 0) return; end; [T, transBitMask] = findTransportMatrix(sizeReduction, image); image = addOrDeleteSeams(transBitMask, sizeReduction, image, @reduceImageByMask); end

% TODO Bug: enlarge gives artifacts althout I chouse different seams as described % in 4.3 in the paper function image = seamCarvingEnlarge(sizeEnlarge, image) if (sizeEnlarge == 0) return; end; [T, transBitMask] = findTransportMatrix(sizeEnlarge, image); image = addOrDeleteSeams(transBitMask, sizeEnlarge, image, @enlargeImageByMask); end

function [T, transBitMask] = findTransportMatrix(sizeReduction, image) % find optimal order of removing raws and columns

T = zeros(sizeReduction(1) + 1, sizeReduction(2) + 1, 'double'); transBitMask = ones(size(T)) * -1;

% fill in borders imageNoRow = image; for i = 2 : size(T, 1) energy = energyRGB(imageNoRow); [optSeamMask, seamEnergyRow] = findOptSeam(energy'); imageNoRow = reduceImageByMask(imageNoRow, optSeamMask, 0); transBitMask(i, 1) = 0;

T(i, 1) = T(i - 1, 1) + seamEnergyRow; end;

imageNoColumn = image; for j = 2 : size(T, 2) energy = energyRGB(imageNoColumn); [optSeamMask, seamEnergyColumn] = findOptSeam(energy); imageNoColumn = reduceImageByMask(imageNoColumn, optSeamMask, 1); transBitMask(1, j) = 1;

T(1, j) = T(1, j - 1) + seamEnergyColumn; end;

% on the borders, just remove one column and one row before proceeding energy = energyRGB(image); [optSeamMask, seamEnergyRow] = findOptSeam(energy'); image = reduceImageByMask(image, optSeamMask, 0);

energy = energyRGB(image); [optSeamMask, seamEnergyColumn] = findOptSeam(energy); image = reduceImageByMask(image, optSeamMask, 1);

% fill in internal part for i = 2 : size(T, 1)

imageWithoutRow = image; % copy for deleting columns

for j = 2 : size(T, 2) energy = energyRGB(imageWithoutRow);

[optSeamMaskRow, seamEnergyRow] = findOptSeam(energy'); imageNoRow = reduceImageByMask(imageWithoutRow, optSeamMaskRow, 0);

[optSeamMaskColumn, seamEnergyColumn] = findOptSeam(energy); imageNoColumn = reduceImageByMask(imageWithoutRow, optSeamMaskColumn, 1);

neighbors = [(T(i - 1, j) + seamEnergyRow) (T(i, j - 1) + seamEnergyColumn)]; [val, ind] = min(neighbors);

T(i, j) = val; transBitMask(i, j) = ind - 1;

% move from left to right imageWithoutRow = imageNoColumn; end;

energy = energyRGB(image); [optSeamMaskRow, seamEnergyRow] = findOptSeam(energy'); % move from top to bottom image = reduceImageByMask(image, optSeamMaskRow, 0); end;

end

function image = addOrDeleteSeams(transBitMask, sizeReduction, image, operation) % delete seams following optimal way i = size(transBitMask, 1); j = size(transBitMask, 2);

for it = 1 : (sizeReduction(1) + sizeReduction(2))

energy = energyRGB(image); if (transBitMask(i, j) == 0) [optSeamMask, seamEnergyRaw] = findOptSeam(energy'); image = operation(image, optSeamMask, 0); i = i - 1; else [optSeamMask, seamEnergyColumn] = findOptSeam(energy); image = operation(image, optSeamMask, 1); j = j - 1; end;

end; end

function [optSeamMask, seamEnergy] = findOptSeam(energy) % following paper by Avidan and Shamir `07 % finds optimal seam % returns mask with 0 mean a pixel is in the seam

% find M for vertical seams % for vertical - use I` M = padarray(energy, [0 1], realmax('double')); % to avoid handling border elements

sz = size(M); for i = 2 : sz(1) for j = 2 : (sz(2) - 1) neighbors = [M(i - 1, j - 1) M(i - 1, j) M(i - 1, j + 1)]; M(i, j) = M(i, j) + min(neighbors); end end

% find the min element in the last raw [val, indJ] = min(M(sz(1), :)); seamEnergy = val;

%optSeam = zeros(sz(1), 1, 'int32'); optSeamMask = zeros(size(energy), 'uint8');

%indJ = indJ - 1; % because of padding on 1 element from left

%go backward and save (i, j) for i = sz(1) : -1 : 2 %optSeam(i) = indJ - 1; optSeamMask(i, indJ - 1) = 1; % -1 because of padding on 1 element from left neighbors = [M(i - 1, indJ - 1) M(i - 1, indJ) M(i - 1, indJ + 1)]; [val, indIncr] = min(neighbors);

seamEnergy = seamEnergy + val;

indJ = indJ + (indIncr - 2); % (x - 2): [1,2]->[-1,1]] end %optSeam(1) = indJ; % to avoid if in loop becuase matlab is slow as hell

optSeamMask(1, indJ - 1) = 1; % -1 because of padding on 1 element from left optSeamMask = ~optSeamMask;

end

function imageReduced = reduceImageByMask( image, seamMask, isVerical ) % removes pixels by input mask % removes vertical line if isVerical == 1, otherwise horizontal if (isVerical) imageReduced = reduceImageByMaskVertical(image, seamMask); else imageReduced = reduceImageByMaskHorizontal(image, seamMask'); end; end

% could not find a more elegant way to do it function imageReduced = reduceImageByMaskVertical(image, seamMask) imageReduced = zeros(size(image, 1), size(image, 2) - 1, size(image, 3)); for i = 1 : size(seamMask, 1) imageReduced(i, :, 1) = image(i, seamMask(i, :), 1); imageReduced(i, :, 2) = image(i, seamMask(i, :), 2); imageReduced(i, :, 3) = image(i, seamMask(i, :), 3); end end

function imageReduced = reduceImageByMaskHorizontal(image, seamMask) imageReduced = zeros(size(image, 1) - 1, size(image, 2), size(image, 3)); for j = 1 : size(seamMask, 2) imageReduced(:, j, 1) = image(seamMask(:, j), j, 1); imageReduced(:, j, 2) = image(seamMask(:, j), j, 2); imageReduced(:, j, 3) = image(seamMask(:, j), j, 3); end end

function imageEnlarged = enlargeImageByMask(image, seamMask, isVerical) % removes pixels by input mask % removes vertical line if isVerical == 1, otherwise horizontal if (isVerical) imageEnlarged = enlargeImageByMaskVertical(image, seamMask); else imageEnlarged = enlargeImageByMaskHorizontal(image, seamMask'); end; end

function imageEnlarged = enlargeImageByMaskVertical(image, seamMask)

avg = @(image, i, j, k) (image(i, j-1, k) + image(i, j+1, k))/2;

imageEnlarged = zeros(size(image, 1), size(image, 2) + 1, size(image, 3)); for i = 1 : size(seamMask, 1) j = find(seamMask(i, :) ~= 1); imageEnlarged(i, :, 1) = [image(i, 1:j, 1), avg(image, i, j, 1), image(i, j+1:end, 1)]; imageEnlarged(i, :, 2) = [image(i, 1:j, 2), avg(image, i, j, 2), image(i, j+1:end, 2)]; imageEnlarged(i, :, 3) = [image(i, 1:j, 3), avg(image, i, j, 3), image(i, j+1:end, 3)]; end; end

function imageEnlarged = enlargeImageByMaskHorizontal(image, seamMask)

avg = @(image, i, j, k) (image(i-1, j, k) + image(i+1, j, k))/2;

imageEnlarged = zeros(size(image, 1) + 1, size(image, 2), size(image, 3)); for j = 1 : size(seamMask, 2) i = find(seamMask(:, j) ~= 1); imageEnlarged(:, j, 1) = [image(1:i, j, 1); avg(image, i, j, 1); image(i+1:end, j, 1)]; imageEnlarged(:, j, 2) = [image(1:i, j, 2); avg(image, i, j, 2); image(i+1:end, j, 2)]; imageEnlarged(:, j, 3) = [image(1:i, j, 3); avg(image, i, j, 3); image(i+1:end, j, 3)]; end; end

function res = energyRGB(I) % returns energy of all pixelels % e = |dI/dx| + |dI/dy| res = energyGrey(I(:, :, 1)) + energyGrey(I(:, :, 2)) + energyGrey(I(:, :, 3)); end

function res = energyGrey(I) % returns energy of all pixelels % e = |dI/dx| + |dI/dy| res = abs(imfilter(I, [-1,0,1], 'replicate')) + abs(imfilter(I, [-1;0;1], 'replicate')); end