Neighborhood operations of the image
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Hi,
I have an image of size 256x256.
I need to apply the sliding window and block processing (neighborhood)operations to the image. As a result the pixel in the center of the window should have the mean value of the window.
Can you suggest anything about this?
Thanks in advance
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回答(3 个)
Andrei Bobrov
2012-5-5
try function conv2
eg:
I - your array
[m n] - size your sliding window
solution:
out = conv2(I,ones([m n])/(m*n),'same');
ADD
use function imfilter and fspecial from Image Processing Toolbox
out = imfilter(I,fspecial('average',[m n]));
here I - your image
or:
out = imfilter(I, ones([m n])/(m*n));
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Takuya Otani
2012-5-5
Try blockproc function in image processing toolbox It does exactly what you are looking for You may also try colfilt also found in image processing toolbox. It is similar.
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Image Analyst
2012-5-5
Here's a demo that shows two different ways of using blockproc. Save the code below as blockproc_demo.m and run it.
function blockproc_demo()
try
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
workspace; % Make sure the workspace panel is showing.
fontSize = 20;
% Change the current folder to the folder of this m-file.
if(~isdeployed)
cd(fileparts(which(mfilename)));
end
% Read in standard MATLAB demo image.
grayImage = imread('cameraman.tif');
[rows columns numberOfColorChannels] = size(grayImage);
% Display the original image.
subplot(2, 2, 1);
imshow(grayImage, []);
caption = sprintf('Original Image\n%d by %d pixels', ...
rows, columns);
title(caption, 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'Position', get(0,'Screensize'));
set(gcf, 'name','Demo by ImageAnalyst', 'numbertitle','off')
% Block process the image.
windowSize = 3;
% Each 3x3 block will get replaced by one value.
% Output image will be smaller by a factor of windowSize.
myFilterHandle = @myFilter;
blockyImage = blockproc(grayImage,[windowSize windowSize], myFilterHandle);
[rowsP columnsP numberOfColorChannelsP] = size(blockyImage);
% Display the processed image.
% It is smaller, but the display routine imshow() replicates
% the image so that it looks bigger than it really is.
subplot(2, 2, 2);
imshow(blockyImage, []);
caption = sprintf('Image Processed in %d by %d Blocks\n%d by %d pixels\nCustom Box Filter', ...
windowSize, windowSize, rowsP, columnsP);
title(caption, 'FontSize', fontSize);
% Now let's do it an alternate way where we use an anonymous function.
% We'll take the standard deviation in the blocks.
windowSize = 8;
myFilterHandle2 = @(block_struct) ...
std2(block_struct.data) * ones(size(block_struct.data));
blockyImageSD = blockproc(grayImage, [windowSize windowSize], myFilterHandle2);
[rowsSD columnsSD numberOfColorChannelsSD] = size(blockyImageSD);
subplot(2, 2, 4);
imshow(blockyImageSD, []);
caption = sprintf('Image Processed in %d by %d Blocks\n%d by %d pixels\nAnonymous Standard Deviation Filter', ...
windowSize, windowSize, rowsSD, columnsSD);
title(caption, 'FontSize', fontSize);
% Note: the image size of blockyImageSD is 256x256, NOT smaller.
% That's because we're returning an 8x8 array instead of a scalar.
uiwait(msgbox('Done with demo'));
catch ME
errorMessage = sprintf('Error in blockproc_demo():\n\nError Message:\n%s', ME.message);
uiwait(warndlg(errorMessage));
end
return;
% Takes one 3x3 block of image data and multiplies it
% element by element by the kernel and
% returns a single value.
function singleValue = myFilter(blockStruct)
try
% Assign default value.
% Will be used near sides of image (due to boundary effects),
% or in the case of errors, etc.
singleValue = 0;
% Create a 2D filter.
kernel = [0 0.2 0; 0.2 0.2 0.2; 0 0.2 0];
% kernel = ones(blockStruct.blockSize); % Box filter.
% Make sure filter size matches image block size.
if any(blockStruct.blockSize ~= size(kernel))
% If any of the dimensions don't match.
% You'll get here near the edges,
% if the image is not a multiple of the block size.
% warndlg('block size does not match kernel size');
return;
end
% Size matches if we get here, so we're okay.
% Extract our block out of the structure.
array3x3 = blockStruct.data;
% Do the filtering. Multiply by kernel and sum.
singleValue = sum(sum(double(array3x3) .* kernel));
catch ME
% Some kind of problem...
errorMessage = sprintf('Error in myFilter():\n\nError Message:\n%s', ME.message);
% uiwait(warndlg(errorMessage));
fprintf(1, '%s\n', errorMessage);
end
return;
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