Improving and Optimizing Blurring Function
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William Rowe
am 21 Jul. 2020
Bearbeitet: Matt J
am 22 Jul. 2020
I wrote a very simple sort of blurring function that takes an image and integer (n) as inputs and outputs an image of the same size that divides up the original image into n x n sections and takes the mean of each section and assigns that color to the output image. (Hopefully that makes sense?) I know my code is by no means great and there's plenty of ways to break it, but I was wondering what might be a better way to accomplish this task rather than actually looping through each section and calculating means? Just hoping to learn some tips on how to optimize a function like this or if there's a better way entirely to accomplish this. Thanks!
Here's my code:
inputArg1 is the image and inputArg2 is the integer n
function [outImg] = justChannel(inputArg1,inputArg2)
outImg = zeros(size(inputArg1));
[x,y] = size(inputArg1);
deltaX = x/inputArg2;
deltaY = y/inputArg2;
redChannel = inputArg1(:,:,1);
greenChannel = inputArg1(:,:,2);
blueChannel = inputArg1(:,:,3);
across=0;
down=0;
while(across < inputArg2)
while(down < inputArg2)
currentX = floor(across * deltaX + 1);
currentY = floor(down * deltaY + 1);
nextX = ceil(currentX + deltaX - 1);
nextY = ceil(currentY + deltaY - 1);
redMean = mean(mean(redChannel(currentX:nextX,currentY:nextY)));
outImg(currentX:nextX,currentY:nextY,1) = redMean;
greenMean = mean(mean(greenChannel(currentX:nextX,currentY:nextY)));
outImg(currentX:nextX,currentY:nextY,2) = greenMean;
blueMean = mean(mean(blueChannel(currentX:nextX,currentY:nextY)));
outImg(currentX:nextX,currentY:nextY,3) = blueMean;
down=down+1;
end
across = across + 1;
down=0;
end
outImg = uint8(outImg);
imshow(outImg);
end
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