How would find the average within each region?

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James hart
James hart 2024-12-10
评论： James hart 2024-12-11
This code generates regions within a user defined image. I want to find the average value within each region and save it as a new image.
clc,clear, close all
% Load the grayscale image
img = imread('peppers.png'); % Replace with your file path
img = rgb2gray(img);
img = mat2gray(img);
threshold = 0.05; % Adjust this value for different levels of similarity
% Ensure the dimensions are a power of 2 by padding
[m, n] = size(img);
M = 2^nextpow2(m); % Nearest power of 2 greater than or equal to m
N = 2^nextpow2(n); % Nearest power of 2 greater than or equal to n
paddedImg = padarray(img, [M-m, N-n], 'post');
% Initialize the quadtree decomposition
quadtreeDecomp(paddedImg, threshold);
function quadtreeDecomp(img, threshold)
% Display the image
imshow(img, 'InitialMagnification', 'fit');
hold on;
% Call recursive function for quadtree decomposition
recursiveSplit(img, [1, 1], size(img, 1), threshold);
hold off;
end
function recursiveSplit(img, origin, sizeBlock, threshold)
% Extract the current block
x = origin(1);
y = origin(2);
block = img(x:x+sizeBlock-1, y:y+sizeBlock-1);
% Check homogeneity (using variance)
if std2(block) > threshold && sizeBlock > 4
% Split into four sub-blocks
halfSize = sizeBlock / 2;
% Recursively process each sub-block
recursiveSplit(img, [x, y], halfSize, threshold); % Top-left
recursiveSplit(img, [x, y+halfSize], halfSize, threshold); % Top-right
recursiveSplit(img, [x+halfSize, y], halfSize, threshold); % Bottom-left
recursiveSplit(img, [x+halfSize, y+halfSize], halfSize, threshold); % Bottom-right
else
% Draw rectangle for this block
rectangle('Position', [y, x, sizeBlock, sizeBlock], 'EdgeColor', 'r', 'LineWidth', 0.5);
end
end

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采纳的回答

Image Analyst
Image Analyst 2024-12-10
Try (untested)
function blockMean = recursiveSplit(img, origin, sizeBlock, threshold)
% Extract the current block
x = origin(1);
y = origin(2);
block = img(x:x+sizeBlock-1, y:y+sizeBlock-1);
% Check homogeneity (using variance)
if std2(block) > threshold && sizeBlock > 4
% Split into four sub-blocks
halfSize = sizeBlock / 2;
% Recursively process each sub-block
recursiveSplit(img, [x, y], halfSize, threshold); % Top-left
recursiveSplit(img, [x, y+halfSize], halfSize, threshold); % Top-right
recursiveSplit(img, [x+halfSize, y], halfSize, threshold); % Bottom-left
recursiveSplit(img, [x+halfSize, y+halfSize], halfSize, threshold); % Bottom-right
blockMean = []; % Don't return a mean if we're still splitting up the block.
else
% Draw rectangle for this block
rectangle('Position', [y, x, sizeBlock, sizeBlock], 'EdgeColor', 'r', 'LineWidth', 0.5);
blockMean = mean(block, 'all'); % No splitting needed, so return the mean.
end
end
  2 个评论
Image Analyst
Image Analyst 2024-12-10
From the documentation for qtdecomp:
% Read image into the workspace.
I = imread('liftingbody.png');
% Perform the quadtree decomposition and display the block representation in a figure.
S = qtdecomp(I,.27);
blocks = repmat(uint8(0),size(S));
for dim = [512 256 128 64 32 16 8 4 2 1];
numblocks = length(find(S==dim));
if (numblocks > 0)
values = repmat(uint8(1),[dim dim numblocks]);
values(2:dim,2:dim,:) = 0;
blocks = qtsetblk(blocks,S,dim,values);
end
end
blocks(end,1:end) = 1;
blocks(1:end,end) = 1;
imshow(I)
James hart
James hart 2024-12-11
Thank you so much. Your answer worked perfectly.

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