Realization of beauty system with MATLAB

1 brief introduction

With the rise of various beauty cameras in the market , People are also pursuing more perfect beauty photos . This paper presents a static image beauty method . Firstly, facial feature points are determined by face recognition , Then the image smoothing technology is introduced , The double-sided filter is used to filter the image noise and realize the whitening and brightening function ; Image fusion technology can be used , Wavelet transform image fusion algorithm is used to stack image pixels to realize decoration , A series of special effects such as filter . All algorithm results can be obtained by MATLAB Realization .​

2 Part of the code

 function  f = qudou( g,n )

% g = imread('E:\ digital image processing \work\meiyan\1.jpg');

% n For the number of operations , The default is 1,g For the original

g2 = g;

 if( nargin == 1 )

    n = 1;

 end

% Get the size of the original image

[ M,N,~ ] = size( g );

while( n ~=0 )

    n = n - 1; 

    % Interactively select the processing area

    mask = roipoly( g2 );

    figure,imshow(mask);

    x1 = immultiply( mask,g2( :,:,1 ) );

    x2 = immultiply( mask,g2( :,:,2 ) );

    x3 = immultiply( mask,g2( :,:,3 ) );

    x = cat( 3,x1,x2,x3 );

%     figure,imshow(x);

    % Pre allocated memory ,f1,f2,f3 Store the operation results of three channels

    f1 = zeros( M,N );

    f2 = zeros( M,N );

    f3 = zeros( M,N );

    % Jump out of the double loop setting flag

    flag = 0;

    % Find the first pixel value that is not 0 The point of , Get the pixel value of this point , As pixels filled after sampling

    for i = 1:M

        for j = 1:N

            if( x1( i,j ) ~= 0 )

                r = x( i,j,: );

                flag = 1;

                break

            end

            if( flag == 1 )

                break

            end

        end

    end

    

    % Random generation of fill images

    y = zeros(3,3,3);

    y( :,:,1 ) = randi([r(1)-5,r(1)+5],[3,3]);

    y( :,:,2 ) = randi([r(2)-5,r(2)+5],[3,3]);

    y( :,:,3 ) = randi([r(3)-5,r(3)+5],[3 3]);

    

    % Type conversion

    y = double(y);

    % Process the three channels separately , Replace the original pixels with the sampled pixels

    for i = 2:3:M-1

        for j = 2:3:N-1

            f1( i-1:i+1,j-1:j+1 ) = mask( i-1:i+1,j-1:j+1 ).* y( :,:,1 );

            f2( i-1:i+1,j-1:j+1 ) = mask( i-1:i+1,j-1:j+1 ).* y( :,:,2 );

            f3( i-1:i+1,j-1:j+1 ) = mask( i-1:i+1,j-1:j+1 ).* y( :,:,3 );

        end

    end

    % Connect the three channels together

    f = cat( 3,f1,f2,f3 );

    % Type conversion

    f = uint8( f );

    

    % Get the processed image

    a = g2 - x;

    f = f + a;

    g2 = f;

end

% Image filtering processing

f = double(f);

b = double( imguidedfilter( uint8( f ) ) ) - f + 128 ;

t = imfilter( b, fspecial( 'gaussian',[5 5]) );

f = ( f*50 + ( f+2*t-256 )*50 )/100;

f = uint8(f);

% % Display images

% subplot( 1,2,1 ),imshow( g ),title(' Original picture ');

% subplot( 1,2,2 ),imshow( f ),title(' Fill the processed image ');

% end

3 Simulation results

4 reference

[1] Li Yin , Li Xiaohong , Jin Jian . be based on MATLAB Development and implementation of image processing system [C]// No 21 Computer technology and application (CACIS) Academic Conference . 2010.

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