귀찮으니(!) gpt를 통해서 딸깍~ 시전
| % 이미지 읽기 img = imread("test.jpg"); % RGB -> grayscale 직접 변환 if ndims(img) == 3 r = double(img(:,:,1)); g = double(img(:,:,2)); b = double(img(:,:,3)); gray = 0.299*r + 0.587*g + 0.114*b; else gray = double(img); end % 블럭 크기 blockSize = 32; [h, w] = size(gray); % 결과 저장 scoreMap = zeros(floor(h/blockSize), floor(w/blockSize)); % 블럭별 FFT 처리 for by = 1:blockSize:(h - blockSize + 1) for bx = 1:blockSize:(w - blockSize + 1) % 블럭 추출 block = gray(by:by+blockSize-1, bx:bx+blockSize-1); % 평균 제거 block = block - mean(block(:)); % FFT F = fft2(block); % magnitude mag = abs(F); % 중앙 이동 mag = fftshift(mag); center = floor(blockSize/2) + 1; radius = 4; [X, Y] = meshgrid(1:blockSize, 1:blockSize); dist = sqrt((X-center).^2 + (Y-center).^2); % 고주파만 사용 mask = dist > radius; % 고주파 에너지 energy = sum(mag(mask).^2); % diversity score score = log(1 + energy); iy = (by-1)/blockSize + 1; ix = (bx-1)/blockSize + 1; scoreMap(iy, ix) = score; end end % 정규화 scoreMap = scoreMap - min(scoreMap(:)); scoreMap = scoreMap / max(scoreMap(:)); % 결과 표시 figure; imshow(uint8(gray)); title("Original"); figure; imagesc(scoreMap); axis image; colorbar; title("FFT Diversity Map"); |
원본
그레이스케일
원인은 모르겠으나 blocksize 5 이하로는 검은색으로 나온다.
| blocksize = 6 | blocksize = 8 | blocksize = 10 |
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| blocksize = 12 | blocksize = 16 | blocksize = 32 |
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원본
그레이스케일
| blocksize = 6 | blocksize = 10 | blocksize = 12 |
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| blocksize = 16 | blocksize = 18 | blocksize = 20 |
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| blocksize = 24 | blocksize = 28 | blocksize = 32 |
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이미지 폭이 큰 차이가 없어서 그런진 모르겠지만..
blocksize 10~12 정도가 가장 무난한 것 같다.
rgb에 대해서 각각 처리 후 합산인데 눈에 보이는건 크게 달라 보이진 않았는데
| img = imread("test.jpg"); img = double(img); blockSize = 32; [h, w, c] = size(img); scoreMap = zeros(floor(h/blockSize), floor(w/blockSize)); for by = 1:blockSize:(h - blockSize + 1) for bx = 1:blockSize:(w - blockSize + 1) totalEnergy = 0; % RGB 각각 처리 for ch = 1:3 block = img(by:by+blockSize-1, ... bx:bx+blockSize-1, ch); block = block - mean(block(:)); F = fft2(block); mag = abs(fftshift(F)); center = floor(blockSize/2) + 1; radius = 4; [X, Y] = meshgrid(1:blockSize, 1:blockSize); dist = sqrt((X-center).^2 + (Y-center).^2); mask = dist > radius; energy = sum(mag(mask).^2); totalEnergy = totalEnergy + energy; end score = log(1 + totalEnergy); iy = (by-1)/blockSize + 1; ix = (bx-1)/blockSize + 1; scoreMap(iy, ix) = score; end end scoreMap = scoreMap - min(scoreMap(:)); scoreMap = scoreMap / max(scoreMap(:)); figure; imshow(uint8(img)); figure; imagesc(scoreMap); axis image; colorbar; title("RGB FFT Diversity"); |
blocksize = 10
두개를 비교해보면 하단에 파란색 마트 정도는 좀 더 잘 보이는 차이가 있는데
처리 시간대비로는 그리 큰 효과를 얻긴 힘들지도 모르겠다.
| grayscale | rgb 각각 처리후 합산 |
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LAB 으로 전환해서 색상에 대해서 처리, blocksize = 10
| % ========================================== % LAB 기반 Fabric Diversity / Anomaly Map % ========================================== img = imread("test.jpg"); img = double(img) / 255.0; %------------------------------------------ % RGB -> LAB 변환 %------------------------------------------ % sRGB gamma correction 제거 mask = img <= 0.04045; img_linear = zeros(size(img)); img_linear(mask) = img(mask) / 12.92; img_linear(~mask) = ((img(~mask)+0.055)/1.055).^2.4; % RGB -> XYZ M = [ ... 0.4124564 0.3575761 0.1804375; 0.2126729 0.7151522 0.0721750; 0.0193339 0.1191920 0.9503041]; [h, w, ~] = size(img); rgb_reshaped = reshape(img_linear, [], 3); xyz = rgb_reshaped * M'; X = xyz(:,1) / 0.95047; Y = xyz(:,2); Z = xyz(:,3) / 1.08883; % XYZ -> LAB helper f = @(t) ((t > 0.008856).*t.^(1/3) + ... (t <= 0.008856).*(7.787*t + 16/116)); fx = f(X); fy = f(Y); fz = f(Z); L = 116*fy - 16; A = 500*(fx - fy); B = 200*(fy - fz); lab = zeros(h, w, 3); lab(:,:,1) = reshape(L, h, w); lab(:,:,2) = reshape(A, h, w); lab(:,:,3) = reshape(B, h, w); %------------------------------------------ % FFT Block Analysis %------------------------------------------ blockSize = 32; step = 16; % overlap sliding outH = floor((h - blockSize)/step) + 1; outW = floor((w - blockSize)/step) + 1; scoreMap = zeros(outH, outW); % FFT mask 생성 center = floor(blockSize/2) + 1; [Xm, Ym] = meshgrid(1:blockSize, 1:blockSize); dist = sqrt((Xm-center).^2 + (Ym-center).^2); radius = 4; highMask = dist > radius; %------------------------------------------ % Sliding Window %------------------------------------------ oy = 1; for by = 1:step:(h - blockSize + 1) ox = 1; for bx = 1:step:(w - blockSize + 1) totalEnergy = 0; % LAB 각 채널 FFT for ch = 1:3 block = lab( ... by:by+blockSize-1, ... bx:bx+blockSize-1, ... ch); % 평균 제거 block = block - mean(block(:)); % FFT F = fft2(block); mag = abs(fftshift(F)); % 고주파 에너지 energy = sum(mag(highMask).^2); totalEnergy = totalEnergy + energy; end % 로그 스케일 score = log(1 + totalEnergy); scoreMap(oy, ox) = score; ox = ox + 1; end oy = oy + 1; end %------------------------------------------ % Normalize %------------------------------------------ scoreMap = scoreMap - min(scoreMap(:)); scoreMap = scoreMap / max(scoreMap(:)); %------------------------------------------ % Display %------------------------------------------ figure; imagesc(img); axis image; title("Original"); figure; imagesc(scoreMap); axis image; colorbar; title("LAB FFT Diversity / Anomaly Map"); |
block 10, step 16
block 10, step 10
| rgb 각각 처리후 합산 | RGB->LAB, 색상만 처리 |
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