如何使用MATLAB檢索任意圖像的背景顏色？

Question

這是從this one，

springed另一個問題如何以編程方式獲取圖像的背景顏色？

實施例：

對於上述圖像中，背景顏色是白色。

Answer 1

如對問題本身的意見討論，「背景色」的概念是相當主觀的，所以它不是真的有可能寫一個算法，以保證對所有輸入所需的結果。這就是說，但是，我想我明白你想要完成什麼，而且我已經寫了幾個MATLAB函數，它們很成功地爲許多輸入圖像識別可能的背景顏色。試過。

我使用了啓發式是基於這樣的觀察，一般說來，圖像的背景顏色很可能是低頻信息區，而前景很可能是高頻。 （請注意，這是不是這樣的，我getBackgroundColor功能就會失敗。）所以我要做的就是隔離在頻域的高頻信息，變換回空間域，「散開的」選擇以覆蓋寬廣的高頻區域，然後簡單地去除這些像素。

有很多可以收緊，以提高性能爲具體應用撥弄代碼的地方，但它似乎很好地進行各種各樣的測試案例就是工作。

getBackgroundColor.m：

function [img, meanColor, modeColor] = getBackgroundColor (img) 
% 
% function [img, meanColor, modeColor] = getBackgroundColor (img) 
% 
% img - Either a string representing the filename of an image to open 
%    or an image itself. If the latter, it must be either a 
%    3-dimensional matrix representing an RGB image or a 2-dimensional 
%    matrix representing a grayscale image. 

if ischar(img) 
    img = imread(imageFile); 
end 
img = double(img); 

% Handle RGB and Grayscale separately. 
if ndims(img)==3 
    % There are probably some spiffy ways to consolidate this sprawl 
    % so that the R, G, and B channels are not being processed 
    % independently, but for the time being, this does work. 
    red = getBG(img(:, :, 1)); 
    green = getBG(img(:, :, 2)); 
    blue = getBG(img(:, :, 3)); 

    % For each channel, remove the "foreground" regions identified in 
    % each of the other channels. 
    red(isnan(green)) = NaN; 
    red(isnan(blue)) = NaN; 

    green(isnan(red)) = NaN; 
    green(isnan(blue)) = NaN; 

    blue(isnan(red)) = NaN; 
    blue(isnan(green)) = NaN; 

    % Compute the mean and mode colors. 
    meanColor = [ ... 
     mean(mean(red(~isnan(red)))) ... 
     mean(mean(green(~isnan(green)))) ... 
     mean(mean(blue(~isnan(blue)))) ]; 
    modeColor = [ ... 
     mode(mode(red(~isnan(red)))) ... 
     mode(mode(green(~isnan(green)))) ... 
     mode(mode(blue(~isnan(blue)))) ]; 

    % Update each the foreground regions of each channel and set them 
    % to their mean colors. This is only necessary for visualization. 
    red(isnan(red)) = meanColor(1); 
    green(isnan(green)) = meanColor(2); 
    blue(isnan(blue)) = meanColor(3); 

    img(:, :, 1) = red; 
    img(:, :, 2) = green; 
    img(:, :, 3) = blue; 
else 
    img = getBG(img); 
    meanColor = mean(mean(img(~isnan(img)))); 
    modeColor = mode(mode(img(~isnan(img)))); 
    img(isnan(img)) = meanColor; 
end 

% Convert the image back to integers (optional) 
img = uint8(img); 

% Display the results before returning 
display(meanColor) 
display(modeColor) 



    function image = getBG (image) 
     mask = getAttenuationMask(size(image), min(size(image))/2, 0, 1); 

     % Assume that the background is mostly constant, so isolate the high-frequency 
     % parts of the image in the frequency domain and then transform it back into the spatial domain 
     fftImage = fftshift(fft2(image)); 
     fftImage = fftImage .* mask; 
     invFftImage = abs(ifft2(fftImage)); 

     % Expand the high-frequency areas of the image and fill in any holes. This should 
     % cover all but the (hopefully) low frequency background areas. 
     edgeRegion = imfill(imdilate(invFftImage, strel('disk', 4, 4)), 'holes'); 

     % Now remove the parts of the image that are covered by edgeRegion 
     edgeMean = mean(mean(edgeRegion)); 
     image(edgeRegion>edgeMean) = NaN; 
    end 
end 

getAttenuationMask.m：

function mask = getAttenuationMask (maskSize, radius, centerValue, edgeValue) 
% 
% function mask = getAttenuationMask (maskSize, radius, centerValue, edgeValue) 
% 

if nargin==2 
    centerValue = 1; 
    edgeValue = 0; 
end 

width = maskSize(1); 
height = maskSize(2); 

mx = width/2; 
my = height/2; 

mask=zeros(maskSize); 

for i=1:width 
    for j=1:height 
     d = sqrt((i-mx)^2 + (j-my)^2); 
     if (d >= radius) 
     d = edgeValue; 
     else 
     d = (centerValue * (1 - (d/radius))) + (edgeValue * (d/radius)); 
     end 

     mask(i, j) = d; 
    end 
end