识别圆形物体

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 usingbwboundaries.

I = rgb2gray(RGB);bw = imbinarize(I);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 pixelsbw = bwareaopen(bw,30);% fill a gap in the pen's capse = 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 boundariesbw = imfill(bw,'holes');imshow(bw)

Step 4: Find the Boundaries

Concentrate only on the exterior boundaries. Option 'noholes' will accelerate the processing by preventingbwboundaries from searching for inner contours.

[B,L] = bwboundaries(bw,'noholes');% Display the label matrix and draw each boundaryimshow(label2rgb(L, @jet, [.5 .5 .5]))hold onfor 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 bybwboundaries can be reused by regionprops.

stats = regionprops(L,'Area','Centroid');threshold = 0.94;% loop over the boundariesfor 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');  endtitle(['Metrics closer to 1 indicate that ',...       'the object is approximately round']);