标识圆形目标

此示例说明如何使用边界跟踪例程 bwboundaries 根据对象的圆度对其进行分类。

步骤 1：读取图像

在 pills_etc.png 中进行读取。

RGB = imread('pillsetc.png');
imshow(RGB)

Figure contains an axes. The axes contains an object of type image.

步骤 2：阈值化图像

将图像转换为黑白，以便使用 bwboundaries 为边界跟踪做准备。

I = rgb2gray(RGB);
bw = imbinarize(I);
imshow(bw)

Figure contains an axes. The axes contains an object of type image.

步骤 3：去除噪声

使用形态学函数，删除不属于关注对象的像素。

删除包含少于 30 个像素的所有对象。

bw = bwareaopen(bw,30);
imshow(bw)

Figure contains an axes. The axes contains an object of type image.

填充笔帽中的间隙。

se = strel('disk',2);
bw = imclose(bw,se);
imshow(bw)

Figure contains an axes. The axes contains an object of type image.

填充任何孔洞，以便可以使用 regionprops 来估计每个边界所包围的面积

bw = imfill(bw,'holes');
imshow(bw)

Figure contains an axes. The axes contains an object of type image.

步骤 4：找到边界

只关注外边界。选项 'noholes' 将通过阻止 bwboundaries 搜索内部轮廓来加快处理速度。

[B,L] = bwboundaries(bw,'noholes');

显示标签矩阵并绘制每个边界。

imshow(label2rgb(L,@jet,[.5 .5 .5]))
hold on
for k = 1:length(B)
  boundary = B{k};
  plot(boundary(:,2),boundary(:,1),'w','LineWidth',2)
end

Figure contains an axes. The axes contains 7 objects of type image, line.

步骤 5：确定哪些对象为圆形

估计每个对象的面积和周长。使用这些结果形成简单的度量来表示对象的圆度：

$metric = \frac{4 π * area}{{perimeter}^{2}}$

只有对于圆，该度量值等于 1；而对于任何其他形状，该度量值都小于 1。可以通过设置适当的阈值来控制判别过程。此示例中使用 0.94 的阈值，以便只将药丸分类为圆形。

使用 regionprops 获得所有对象的面积估计值。请注意，bwboundaries 返回的标签矩阵可以被 regionprops 重用。

stats = regionprops(L,'Area','Centroid');

threshold = 0.94;

% loop over the boundaries
for k = 1:length(B)

  % obtain (X,Y) boundary coordinates corresponding to label 'k'
  boundary = B{k};

  % compute a simple estimate of the object's perimeter
  delta_sq = diff(boundary).^2;    
  perimeter = sum(sqrt(sum(delta_sq,2)));
  
  % obtain the area calculation corresponding to label 'k'
  area = stats(k).Area;
  
  % compute the roundness metric
  metric = 4*pi*area/perimeter^2;
  
  % display the results
  metric_string = sprintf('%2.2f',metric);

  % mark objects above the threshold with a black circle
  if metric > threshold
    centroid = stats(k).Centroid;
    plot(centroid(1),centroid(2),'ko');
  end
  
  text(boundary(1,2)-35,boundary(1,1)+13,metric_string,'Color','y',...
       'FontSize',14,'FontWeight','bold')
  
end

title(['Metrics closer to 1 indicate that ',...
       'the object is approximately round'])

Figure contains an axes. The axes with title Metrics closer to 1 indicate that the object is approximately round contains 15 objects of type image, line, text.