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September Morn Update

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Ingy döt Net 2019-09-12 10:33:56 -07:00
parent 4e2d22a71d
commit aac6731f2c
6856 changed files with 141342 additions and 21127 deletions
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10
Task/Image-convolution/MATLAB/image-convolution.m
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@ -9,7 +9,7 @@ function testConvImage
1 2 1 ]; % Gaussian smoothing (without normalizing)
fprintf('Original image:\n')
disp(Im)
fprintf('Original kernal:\n')
fprintf('Original kernel:\n')
disp(Ker)
fprintf('Padding with zeroes:\n')
disp(convImage(Im, Ker, 'zeros'))
@ -17,7 +17,7 @@ function testConvImage
disp(convImage(Im, Ker, 'value', 5))
fprintf('Duplicating border pixels to pad image:\n')
disp(convImage(Im, Ker, 'extend'))
fprintf('Renormalizing kernal and using only values within image:\n')
fprintf('Renormalizing kernel and using only values within image:\n')
disp(convImage(Im, Ker, 'partial'))
fprintf('Only processing inner (non-border) pixels:\n')
disp(convImage(Im, Ker, 'none'))
@ -39,7 +39,7 @@ function testConvImage
% title('Duplicating border pixels to pad image')
% figure
% imshow(imresize(convImage(Im, Ker, 'partial'), 10))
% title('Renormalizing kernal and using only values within image')
% title('Renormalizing kernel and using only values within image')
% figure
% imshow(imresize(convImage(Im, Ker, 'none'), 10))
% title('Only processing inner (non-border) pixels')
@ -47,7 +47,7 @@ end
function ImOut = convImage(Im, Ker, varargin)
% ImOut = convImage(Im, Ker)
% Filters an image using sliding-window kernal convolution.
% Filters an image using sliding-window kernel convolution.
% Convolution is done layer-by-layer. Use rgb2gray if single-layer needed.
% Zero-padding convolution will be used if no border handling is specified.
% Im - Array containing image data (output from imread)
@ -60,7 +60,7 @@ function ImOut = convImage(Im, Ker, varargin)
%
% ImOut = convImage(Im, Ker, 'value', padVal)
% Image will be padded with padVal when calculating convolution
% (possibly useful for emphasizing certain data with unusual kernal)
% (possibly useful for emphasizing certain data with unusual kernel)
%
% ImOut = convImage(Im, Ker, 'extend')
% Image will be padded with the value of the closest image pixel