matlab - 用像素值“0”填充剩余区域

Question

在下图中，我用两种不同的阴影填充了两个区域：

如何用像素值（黑色）填充图像的剩余部分（未填充的部分） ？0MATLAB

谢谢。

Answer 1

假设这是此问题的后续，当您获取 roi 时，除了使用它来创建上面的图像之外，您还可以使用它来制作具有黑色背景的图像。这避免了在图像其他地方具有相同值的任何问题（归功于Bee对上一个问题的回答）：

img = im2double(imread('cameraman.tif'));
imshow(img);
roi = imfreehand(gca);

img2 = img;
img3 = zeros(size(img));

img2(roi.createMask) = val_1;
img3(roi.createMask) = val_1;
% and repeat to add additional roi

或者，如果您想稍后再次使用它们，您可以将这些区域存储为单独的 BW 掩码：

imshow(img);

% create masks
roi = imfreehand(gca);
BW = roi.createMask;
roi2 = imfreehand(gca);
BW2 = roi.createMask;

% original image + roi
img2 = img;
img2(BW) = val_1;
img2(BW2) = val_2;

% B&W image 
img3 = BW*val_1+BW2*val_2;

Answer 2

Assuming that the values of the pixel values of the two regions are known as val_1 and val_2, you could do something like so:

Algorithm

1. Get two images, one with everything except val_1 set to 0, the other doing the same with val_2. This will most likely contain a lot of noise points as well.
2. Erode the two images with masks of appropriate threshold size. This eliminates the points of that value not in the region.
3. Grow the high regions of the resulting image to the connected component in the original image.
4. Change the shade of the high regions in the respective images as necessary and add them to get the resultant image.

If the shades are not known beforehand, you could use ginput as illustrated @TryHard's answer.

Answer 3

试试这个在两个灰色形状中挑选点，然后把其他的都涂黑。

img = imread(myimagefilename);
imshow(img);

% you can skip the following part and set clr1 and clr2 manually 
% if you already know the grayscale values in the patches

pts=ginput(2);  % <-- pick points within the regions you colored in

clr1=img(pts(1,2),pts(1,1));
clr2=img(pts(2,2),pts(2,1));

img2=img;
img2(find(img~=clr1 & img~=clr2)) = 0;

img2=im2bw(img2,0.2);  % <-- 0.2 is the threshold

[xxx idx1]= bwfill(~img2,pts(1,1),pts(1,2),8);
[xxx idx2]= bwfill(~img2,pts(2,1),pts(2,2),8);
idx=setxor(union(idx1,idx2),[1:numel(img)]);

img2 = img;
img2(idx)=0;
imshow(img2)

感觉过于复杂，但它确实有效。它使用两个步骤，首先是粗略的“过滤器”，然后使用通过将最初过滤的图像转换为黑白（需要阈值）生成的掩码进行更彻底的去除，然后进行填充操作以识别补丁中的像素。

Answer 4

您可以使用matlab 文件交换（下方或下载Simple single-seeded region growing）中的函数（或其等效函数之一）。此函数将创建一个逻辑掩码，您可以使用它来使图像变黑（针对多个区域连续调用此函数）

I = im2double(imread('5Yo8l.png'));  
J = segCroissRegion(0.01,I,0,0)  

imshow(I+J);  



function Phi = segCroissRegion(tolerance,Igray,x,y)  

    if(x == 0 || y == 0)    
        imshow(Igray);    
        [y,x] = ginput(1);    %%% beware of the (x,y) mix-up here
    end    
    Phi = false(size(Igray,1),size(Igray,2));    
    ref = true(size(Igray,1),size(Igray,2));    
    PhiOld = Phi;    
    Phi(uint8(x),uint8(y)) = 1;    
    while(sum(Phi(:)) ~= sum(PhiOld(:)))    
        PhiOld = Phi;    
        segm_val = Igray(Phi);    
        meanSeg = mean(segm_val);    
        posVoisinsPhi = imdilate(Phi,strel('disk',1,0)) - Phi;    
        voisins = find(posVoisinsPhi);    
        valeursVoisins = Igray(voisins);    
        Phi(voisins(valeursVoisins > meanSeg - tolerance & valeursVoisins < meanSeg + tolerance)) = 1;    
    end