This example shows how to classify objects based on their roundness using `bwboundaries`

, a boundary tracing routine.

**Step 1: Read Image**

Read in `pills_etc.png`

.

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

**Step 2: Threshold the Image**

Convert the image to black and white in order to prepare for boundary tracing using `bwboundaries`

.

I = rgb2gray(RGB); threshold = graythresh(I); bw = im2bw(I,threshold); imshow(bw)

**Step 3: Remove the Noise**

Using morphology functions, remove pixels which do not belong to the objects of interest.

% remove all object containing fewer than 30 pixels bw = bwareaopen(bw,30); % fill a gap in the pen's cap se = strel('disk',2); bw = imclose(bw,se); % fill any holes, so that regionprops can be used to estimate % the area enclosed by each of the boundaries bw = imfill(bw,'holes'); imshow(bw)

**Step 4: Find the Boundaries**

Concentrate only on the exterior boundaries. Option 'noholes' will accelerate the processing by preventing `bwboundaries`

from searching for inner contours.

[B,L] = bwboundaries(bw,'noholes'); % Display the label matrix and draw each boundary 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

**Step 5: Determine which Objects are Round**

Estimate each object's area and perimeter. Use these results to form a simple metric indicating the roundness of an object:

metric = 4*pi*area/perimeter^2.

This metric is equal to one only for a circle and it is less than one for any other shape. The discrimination process can be controlled by setting an appropriate threshold. In this example use a threshold of 0.94 so that only the pills will be classified as round.

Use `regionprops`

to obtain estimates of the area for all of the objects. Notice that the label matrix returned by `bwboundaries`

can be reused by `regionprops`

.

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']);

Was this topic helpful?