winding1989
2012-05-05, 17:29
小弟的毕业设计是《基于图像处理的目标识别设计》，导师给了个matlab的例程，但是看不懂 不知道有些步骤的意思 ，还望请高人帮忙翻译一下！不胜感激！！！！！！
%% 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']);
displayEndOfDemoMessage(mfilename)
%% 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']);
displayEndOfDemoMessage(mfilename)