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如何区分黑白棋盘格

image-segmentation image-processing matlab

Automatik · 技术社区 · 7 年前

我想将棋盘图像的方块分类为黑色或白色方块的过程自动化。进一步的步骤是区分它是一个空正方形还是正方形包含一块。到目前为止,我几乎可以使用正方形中心的平均强度将每个正方形分类为白色或黑色,但很难设置阈值。对于进一步的步骤(空方块或带块),我尝试了 std2 但也有困难。

这是我的原始图像,也是我目前所接近的

这是我的剧本:

image = imerode(original,strel('disk',3));
image = imadjust(image,[],[],2);
figure,imshow(image),hold on;
for i = 1:length(cells)
  TL = cells(i).TL; %Cell's corner top left
  TR = cells(i).TR; %Cell's corner top right
  BL = cells(i).BL; %Cell's corner bottom left
  BR = cells(i).BR; %Cell's corner bottom right
  x = [TL(1) TR(1) BR(1) BL(1)];
  y = [TL(2) TR(2) BR(2) BL(2)];
  bw = poly2mask(x,y,size(image,1),size(image,2));
  measurements = regionprops(bw,'BoundingBox');
  cropped = imcrop(image, measurements.BoundingBox);
  m = regionprops(bw,'Centroid');
  x = m.Centroid(1);
  y = m.Centroid(2);
  w = 25; %width
  h = 25; %height
  tl = [round(x-w/2) round(y-h/2)];
  center = image(tl(1):tl(1)+w,tl(2):tl(2)+h,:);
  %stds(i) = std2(center);
  avgs(i) = mean2(center);
  if(avgs(i) > 55)
      str = "W";
  else
      str = "B";
  end
  text(x,y,str,'Color','red','FontSize',16);
end

编辑: 下图是之后的新结果

image = imerode(image,strel('disk',4));
image = image>160;

2 回复 | 直到 7 年前

Nicky Mattsson 7 年前

可以使用Matlabs内置的棋盘方法 detectCheckerboardPoints 和 checkerboard 找到棋盘的大小并构造一个新的大小合适的棋盘。由于只能存在两个可能的棋盘,请同时构建这两个棋盘,并检查哪一个棋盘最匹配。

img = imread('PNWSv.jpg'); %Load image

%Prepare image
I = rgb2gray(img);
I2 = imerode(I,strel('square',10));
bw = imbinarize(I2,'adaptive');

%Find checkerboard points
[imagePoints,boardSize] = detectCheckerboardPoints(bw);

%Find the size of the checkerboard fields
x = boardSize(2)-1;
y = boardSize(1)-1;
fields = cell(y,x);
for k = 1:length(imagePoints)
    [i,j] = ind2sub([y,x],k);
    fields{i,j} = imagePoints(k,:);
end
avgDx = mean(mean(diff(cellfun(@(x) x(1),fields),1,2)));
avgDy = mean(mean(diff(cellfun(@(x) x(2),fields),1,1)));

%Construct the two possibilities
ref1 = imresize(imbinarize(checkerboard),[avgDx,avgDy]*8);
ref2 = imcomplement(ref1);

%Check which ones fits the better
c1 = normxcorr2(ref1,I);
c2 = normxcorr2(ref2,I);

if max(c2(:))<max(c1(:))
    ref=ref1;
    c = c1;
else 
    ref=ref2;
    c = c2;
end

%Plot the checkerboard bounding box on top of the original image
[ypeak, xpeak] = find(c==max(c(:)));
yoffSet = ypeak-size(ref,1);
xoffSet = xpeak-size(ref,2);

imshow(img);
imrect(gca, [xoffSet+1, yoffSet+1, size(ref1,2), size(ref1,1)]);

Yves Daoust 7 年前

二值化之后的侵蚀将帮助您找到空白的白色方块。

通过这些,您可以更轻松地重建整个棋盘格,并估计占用的方块。