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【图像分割】图像检测分割、特征提取、各种特征面积等的测量和过滤
本文提供了一个适合初学者的教程旨在演示图像检测分割、特征提取以及各种特征如面积的测量和过滤只提取某些对象。
首先该教程介绍了如何找到图像中的所有对象硬币然后根据指定的直径过滤结果筛选出特定直径的对象。通过一个简单的示例展示了阈值处理、标记和区域属性的基本概念。
对于那些刚开始接触 MATLAB 图像处理功能的同学来说这个教程是一个很好的起点。在他们深入学习更复杂的算法之前可以通过这个教程加深对基本概念和技术的理解。
为了完成这个教程需要安装图像处理工具箱因为它演示了该工具箱提供的某些功能。同时教程使用了工具箱自带的一个名为“硬币”的示例图像作为演示对象。
该教程的优点在于它提供了一种直观和实用的方法帮助初学者理解如何使用 MATLAB 对图像进行处理和分析。通过学习如何进行图像分割、特征提取和过滤读者将受益于这些基本概念并能够应用它们解决更为复杂的图像处理问题。这个教程对于那些有兴趣进一步探索图像处理领域的学生、研究人员和工程师来说都是一个很好的起点。
2 运行结果 部分代码
% Read in a standard MATLAB demo image of coins (US nickles and dimes, which are 5 cent and 10 cent coins). This image ships with MATLAB.
baseFileName coins.png;
folder fileparts(which(baseFileName)); % Determine where demo folder is (works with all versions).
fullFileName fullfile(folder, baseFileName);
fprintf(Full File Name %s.\n, fullFileName);
if ~exist(fullFileName, file)% It doesnt exist in the current folder.% Look on the search path.if ~exist(baseFileName, file)% It doesnt exist on the search path either.% Alert user that we cant find the image.warningMessage sprintf(Error: the input image file\n%s\nwas not found.\nClick OK to exit the demo., fullFileName);uiwait(warndlg(warningMessage));fprintf(1, Finished running BlobsDemo.m.\n);return;end% Found it on the search path. Construct the file name.fullFileName baseFileName; % Note: dont prepend the folder.
end
% If we get here, we should have found the image file.
originalImage imread(fullFileName);
% Check to make sure that it is grayscale, just in case the user substituted their own image.
[rows, columns, numberOfColorChannels] size(originalImage);
if numberOfColorChannels 1promptMessage sprintf(Your image file has %d color channels.\nThis demo was designed for grayscale images.\nDo you want me to convert it to grayscale for you so you can continue?, numberOfColorChannels);button questdlg(promptMessage, Continue, Convert and Continue, Cancel, Convert and Continue);if strcmp(button, Cancel)fprintf(1, Finished running BlobsDemo.m.\n);return;end% Do the conversion using standard book formulaoriginalImage rgb2gray(originalImage);
end% Display the grayscale image.
subplot(3, 3, 1);
imshow(originalImage);
% Maximize the figure window.
hFig1 gcf;
hFig1.Units normalized;
hFig1.WindowState maximized; % Go to full screen.
hFig1.NumberTitle off; % Get rid of Figure 1
hFig1.Name Demo by Image Analyst; % Put this into title bar.
% Force it to display RIGHT NOW (otherwise it might not display until its all done, unless youve stopped at a breakpoint.)
drawnow;
caption sprintf(Original coins image showing\n6 nickels (the larger coins) and 4 dimes (the smaller coins).);
title(caption, FontSize, captionFontSize);
axis(on, image); % Make sure image is not artificially stretched because of screens aspect ratio.% Just for fun, lets get its histogram and display it.
[pixelCount, grayLevels] imhist(originalImage);
subplot(3, 3, 2);
bar(pixelCount);
title(Histogram of original image, FontSize, captionFontSize);
xlim([0 grayLevels(end)]); % Scale x axis manually.
grid on;%------------------------------------------------------------------------------------------------------------------------------------------------------
% Threshold the image to get a binary image (only 0s and 1s) of class logical.
% Method #1: using im2bw()
% normalizedThresholdValue 0.4; % In range 0 to 1.
% thresholdValue normalizedThresholdValue * max(max(originalImage)); % Gray Levels.
% binaryImage im2bw(originalImage, normalizedThresholdValue); % One way to threshold to binary
% Method #2: using a logical operation.
thresholdValue 100;
binaryImage originalImage thresholdValue; % Bright objects will be chosen if you use .
% IMPORTANT OPTION
% Use if you want to find dark objects instead of bright objects.
% binaryImage originalImage thresholdValue; % Dark objects will be chosen if you use .% Do a hole fill to get rid of any background pixels or holes inside the blobs.
binaryImage imfill(binaryImage, holes);% Show the threshold as a vertical red bar on the histogram.
hold on;
maxYValue ylim;
line([thresholdValue, thresholdValue], maxYValue, Color, r);
% Place a text label on the bar chart showing the threshold.
annotationText sprintf(Thresholded at %d gray levels, thresholdValue);
% For text(), the x and y need to be of the data class double so lets cast both to double.
text(double(thresholdValue 5), double(0.5 * maxYValue(2)), annotationText, FontSize, 10, Color, [0 .5 0]);
text(double(thresholdValue - 70), double(0.94 * maxYValue(2)), Background, FontSize, 10, Color, [0 0 .5]);
text(double(thresholdValue 50), double(0.94 * maxYValue(2)), Foreground, FontSize, 10, Color, [0 0 .5]);% Display the binary image.
subplot(3, 3, 3);
imshow(binaryImage);
title(Binary Image, obtained by thresholding, FontSize, captionFontSize);%------------------------------------------------------------------------------------------------------------------------------------------------------
% Identify individual blobs by seeing which pixels are connected to each other. This is called Connected Components Labeling.
% Each group of connected pixels will be given a label, a number, to identify it and distinguish it from the other blobs.
% Do connected components labeling with either bwlabel() or bwconncomp().
[labeledImage, numberOfBlobs] bwlabel(binaryImage, 8); % Label each blob so we can make measurements of it
% labeledImage is an integer-valued image where all pixels in the blobs have values of 1, or 2, or 3, or ... etc.
subplot(3, 3, 4);
imshow(labeledImage, []); % Show the gray scale image.
title(Labeled Image, from bwlabel(), FontSize, captionFontSize);
drawnow;% Lets assign each blob a different color to visually show the user the distinct blobs.
coloredLabels label2rgb (labeledImage, hsv, k, shuffle); % pseudo random color labels
% coloredLabels is an RGB image. We could have applied a colormap instead (but only with R2014b and later)
subplot(3, 3, 5);
imshow(coloredLabels);
axis image; % Make sure image is not artificially stretched because of screens aspect ratio.
caption sprintf(Pseudo colored labels, from label2rgb().\nBlobs are numbered from top to bottom, then from left to right.);
title(caption, FontSize, captionFontSize);%
% MAIN PART IS RIGHT HERE!!!
% Get all the blob properties.
props regionprops(labeledImage, originalImage, all);
% Or, if you want, you can ask for only a few specific measurements. This will be faster since we dont have to compute everything.
% props regionprops(labeledImage, originalImage, MeanIntensity, Area, Perimeter, Centroid, EquivDiameter);
numberOfBlobs numel(props); % Will be the same as we got earlier from bwlabel().
%%------------------------------------------------------------------------------------------------------------------------------------------------------
% PLOT BOUNDARIES.
% Plot the borders of all the coins on the original grayscale image using the coordinates returned by bwboundaries().
% bwboundaries() returns a cell array, where each cell contains the row/column coordinates for an object in the image.
subplot(3, 3, 6);
imshow(originalImage);
title(Outlines, from bwboundaries(), FontSize, captionFontSize);
axis(on, image); % Make sure image is not artificially stretched because of screens aspect ratio.
% Here is where we actually get the boundaries for each blob.
boundaries bwboundaries(binaryImage); % Note: this is a cell array with several boundaries -- one boundary per cell.
% boundaries is a cell array - one cell for each blob.
% In each cell is an N-by-2 list of coordinates in a (row, column) format. Note: NOT (x,y).
% Column 1 is rows, or y. Column 2 is columns, or x.
numberOfBoundaries size(boundaries, 1); % Count the boundaries so we can use it in our for loop
% Here is where we actually plot the boundaries of each blob in the overlay.
hold on; % Dont let boundaries blow away the displayed image.
for k 1 : numberOfBoundariesthisBoundary boundaries{k}; % Get boundary for this specific blob.x thisBoundary(:,2); % Column 2 is the columns, which is x.y thisBoundary(:,1); % Column 1 is the rows, which is x.plot(x, y, r-, LineWidth, 2); % Plot boundary in red.
end
hold off;%------------------------------------------------------------------------------------------------------------------------------------------------------
% Print out the measurements to the command window, and display blob numbers on the image.
textFontSize 14; % Used to control size of blob number labels put atop the image.
% Print header line in the command window.
fprintf(1,Blob # Mean Intensity Area Perimeter Centroid Diameter\n);
% Extract all the mean diameters into an array.
% The diameter is the Equivalent Circular Diameter, which is the diameter of a circle with the same number of pixels as the blob.
% Enclosing in brackets is a nice trick to concatenate all the values from all the structure fields (every structure in the props structure array).
blobECD [props.EquivDiameter];
% Loop over all blobs printing their measurements to the command window.
for k 1 : numberOfBlobs % Loop through all blobs.% Find the individual measurements of each blob. They are field of each structure in the props strucutre array.% You could use the bracket trick (like with blobECD above) OR you can get the value from the field of this particular structure.% Im showing you both ways and you can use the way you like best.meanGL props(k).MeanIntensity; % Get average intensity.blobArea props(k).Area; % Get area.blobPerimeter props(k).Perimeter; % Get perimeter.blobCentroid props(k).Centroid; % Get centroid one at a time% Now do the printing of this blobs measurements to the command window.fprintf(1,#%2d %17.1f %11.1f %8.1f %8.1f %8.1f % 8.1f\n, k, meanGL, blobArea, blobPerimeter, blobCentroid, blobECD(k));% Put the blob number labels on the grayscale image that is showing the red boundaries on it.text(blobCentroid(1), blobCentroid(2), num2str(k), FontSize, textFontSize, FontWeight, Bold, HorizontalAlignment, center, VerticalAlignment, middle);
end%------------------------------------------------------------------------------------------------------------------------------------------------------
% Now, Ill show you a way to get centroids into an N -by-2 array directly from props,
% rather than accessing them as a field of the props strcuture array.
% We can get the centroids of ALL the blobs into 2 arrays,
% one for the centroid x values and one for the centroid y values.
allBlobCentroids vertcat(props.Centroid); % A 10 row by 2 column array of (x,y) centroid coordinates.
centroidsX allBlobCentroids(:, 1); % Extract out the centroid x values into their own vector.
centroidsY allBlobCentroids(:, 2); % Extract out the centroid y values into their own vector.
% Put the labels on the rgb labeled image also.
subplot(3, 3, 5);
for k 1 : numberOfBlobs % Loop through all blobs.% Place the blob label number at the centroid of the blob.text(centroidsX(k), centroidsY(k), num2str(k), FontSize, textFontSize, FontWeight, Bold, HorizontalAlignment, center, VerticalAlignment, middle);
end%------------------------------------------------------------------------------------------------------------------------------------------------------
% Now Ill demonstrate how to select certain blobs based using the ismember() function and extract them into new subimages.
% Lets say that we wanted to find only those blobs
% with an intensity between 150 and 220 and an area less than 2000 pixels.
% This would give us the three brightest dimes (the smaller coin type).
allBlobIntensities [props.MeanIntensity];
allBlobAreas [props.Area];
subplot(3, 3, 7);
histogram(allBlobAreas);
% Get a list of the blobs that meet our criteria and we need to keep.
% These will be logical indices - lists of true or false depending on whether the feature meets the criteria or not.
% for example [1, 0, 0, 1, 1, 0, 1, .....]. Elements 1, 4, 5, 7, ... are true, others are false.
allowableIntensityIndexes (allBlobIntensities 150) (allBlobIntensities 220);
allowableAreaIndexes allBlobAreas 2000; % Take the small objects.
% Now lets get actual indexes, rather than logical indexes, of the features that meet the criteria.
% for example [1, 4, 5, 7, .....] to continue using the example from above.
keeperIndexes find(allowableIntensityIndexes allowableAreaIndexes);
% Extract only those blobs that meet our criteria, and
% eliminate those blobs that dont meet our criteria.
% Note how we use ismember() to do this. Result will be an image - the same as labeledImage but with only the blobs listed in keeperIndexes in it.
keeperBlobsImage ismember(labeledImage, keeperIndexes);
% Re-label with only the keeper blobs kept.
labeledDimeImage bwlabel(keeperBlobsImage, 8); % Label each blob so we can make measurements of it
% Now were done. We have a labeled image of blobs that meet our specified criteria.
subplot(3, 3, 7);
imshow(labeledDimeImage, []);
axis image;
title(Keeper blobs (3 brightest dimes in a re-labeled image), FontSize, captionFontSize);
elapsedTime toc;
fprintf(Blob detection and measurement took %.3f seconds.\n, elapsedTime)%------------------------------------------------------------------------------------------------------------------------------------------------------
% Plot the centroids in the overlay above the original image in the upper left axes.
% Dimes will have a red cross, nickels will have a blue X.
message sprintf(Now I will plot the centroids over the original image in the upper left.\nPlease look at the upper left image.);
reply questdlg(message, Plot Centroids?, OK, Cancel, Cancel);
% Note: reply will for Upper right X, OK for OK, and Cancel for Cancel.
if strcmpi(reply, Cancel)return;
end
subplot(3, 3, 1);
hold on; % Dont blow away image.
for k 1 : numberOfBlobs % Loop through all keeper blobs.% Identify if blob #k is a dime or nickel.itsADime allBlobAreas(k) 2200; % Dimes are small.if itsADime% Plot dimes with a red .plot(centroidsX(k), centroidsY(k), r, MarkerSize, 15, LineWidth, 2);else% Plot nickels with a blue x.plot(centroidsX(k), centroidsY(k), bx, MarkerSize, 15, LineWidth, 2);end
end%------------------------------------------------------------------------------------------------------------------------------------------------------
% Now use the keeper blobs as a mask on the original image so we will get a masked gray level image.
% This will keep the regions in the mask as original but erase (blacken) everything else (outside of the mask regions).
% This will let us display the original image in the regions of the keeper blobs.
maskedImageDime originalImage; % Simply a copy at first.
maskedImageDime(~keeperBlobsImage) 0; % Set all non-keeper pixels to zero.
subplot(3, 3, 8);
imshow(maskedImageDime);
axis image;
title(Only the 3 brightest dimes from the original image, FontSize, captionFontSize);%------------------------------------------------------------------------------------------------------------------------------------------------------
% Now lets get the nickels (the larger coin type).
keeperIndexes find(allBlobAreas 2000); % Take the larger objects.
% Note how we use ismember to select the blobs that meet our criteria. Get a binary image with only nickel regions present.
nickelBinaryImage ismember(labeledImage, keeperIndexes);
% Lets get the nickels from the original grayscale image, with the other non-nickel pixels blackened.
% In other words, we will create a masked image.
maskedImageNickel originalImage; % Simply a copy at first.
maskedImageNickel(~nickelBinaryImage) 0; % Set all non-nickel pixels to zero.
subplot(3, 3, 9);
imshow(maskedImageNickel, []);
axis image;
title(Only the nickels from the original image, FontSize, captionFontSize);%------------------------------------------------------------------------------------------------------------------------------------------------------
% WERE BASICALLY DONE WITH THE DEMO NOW.
elapsedTime toc;
% Alert user that the demo is done and give them the option to save an image.
message sprintf(Done making measurements of the features.\n\nElapsed time %.2f seconds., elapsedTime);
message sprintf(%s\n\nCheck out the figure window for the images.\nCheck out the command window for the numerical results., message);
message sprintf(%s\n\nDo you want to save the pseudo-colored image?, message);
reply questdlg(message, Save image?, Yes, No, No);
% Note: reply will for Upper right X, Yes for Yes, and No for No.
if strcmpi(reply, Yes)% Ask user for a filename.FilterSpec {*.PNG, PNG Images (*.png); *.tif, TIFF images (*.tif); *.*, All Files (*.*)};DialogTitle Save image file name;% Get the default filename. Make sure its in the folder where this m-file lives.% (If they run this file but the cd is another folder then pwd will show that folder, not this one.thisFile mfilename(fullpath);[thisFolder, baseFileName, ext] fileparts(thisFile);DefaultName sprintf(%s/%s.tif, thisFolder, baseFileName);[fileName, specifiedFolder] uiputfile(FilterSpec, DialogTitle, DefaultName);if fileName ~ 0% Parse what they actually specified.[folder, baseFileName, ext] fileparts(fileName);% Create the full filename, making sure it has a tif filename.fullImageFileName fullfile(specifiedFolder, [baseFileName .tif]);% Save the labeled image as a tif image.imwrite(uint8(coloredLabels), fullImageFileName);% Just for fun, read image back into the imtool utility to demonstrate that tool.tifimage imread(fullImageFileName);imtool(tifimage, []);end
end%------------------------------------------------------------------------------------------------------------------------------------------------------
% OPTIONAL : CROP EACH COIN OUT TO A SEPARATE SUB-IMAGE ON A NEW FIGURE.
message sprintf(Would you like to crop out each coin to individual images?);
reply questdlg(message, Extract Individual Images?, Yes, No, Yes);
% Note: reply will for Upper right X, Yes for Yes, and No for No.
if strcmpi(reply, Yes)% Maximize the figure window.hFig2 figure; % Create a new figure window.hFig2.Units normalized;hFig2.WindowState maximized; % Go to full screen.hFig2.NumberTitle off; % Get rid of Figure 1hFig2.Name Demo by Image Analyst; % Put this into title bar.for k 1 : numberOfBlobs % Loop through all blobs.% Find the bounding box of each blob.thisBlobsBoundingBox props(k).BoundingBox; % Get list of pixels in current blob.% Extract out this coin into its own image.subImage imcrop(originalImage, thisBlobsBoundingBox);% Determine if its a dime (small) or a nickel (large coin).if props(k).Area 2200coinType nickel;elsecoinType dime;end% Display the image with informative caption.subplot(3, 4, k);imshow(subImage);caption sprintf(Coin #%d is a %s.\nDiameter %.1f pixels\nArea %d pixels, ...k, coinType, blobECD(k), props(k).Area);title(caption, FontSize, textFontSize);end%------------------------------------------------------------------------------------------------------------------------------------------------------% Display the MATLAB peaks logo.logoSubplot subplot(3, 4, 11:12);caption sprintf(A MATLAB Tutorial by ImageAnalyst);text(0.5,1.15, caption, Color,r, FontSize, 18, FontWeight,b, HorizontalAlignment, center, VerticalAlignment, middle) ;positionOfLowerRightPlot get(logoSubplot, position);L 40*membrane(1,25);logoax axes(CameraPosition, [-193.4013, -265.1546, 220.4819],...Box, off, ...CameraTarget,[26, 26, 10], ...CameraUpVector,[0, 0, 1], ...CameraViewAngle,9.5, ...DataAspectRatio, [1, 1, .9],...Position, positionOfLowerRightPlot, ...Visible,off, ...XLim,[1, 51], ...YLim,[1, 51], ...ZLim,[-13, 40], ...parent, gcf);axis(logoSubplot, off);s surface(L, ...EdgeColor,none, ...FaceColor,[0.9, 0.2, 0.2], ...FaceLighting,phong, ...AmbientStrength,0.3, ...DiffuseStrength,0.6, ...Clipping,off,...BackFaceLighting,lit, ...SpecularStrength,1, ...SpecularColorReflectance,1, ...SpecularExponent,7, ...Tag,TheMathWorksLogo, ...parent,logoax);l1 light(Position,[40, 100, 20], ...% OPTIONAL : CROP EACH COIN OUT TO A SEPARATE SUB-IMAGE ON A NEW FIGURE. message sprintf(Would you like to crop out each coin to individual images?); reply questdlg(message, Extract Individual Images?, Yes, No, Yes); % Note: reply will for Upper right X, Yes for Yes, and No for No. if strcmpi(reply, Yes) % Maximize the figure window. hFig2 figure; % Create a new figure window. hFig2.Units normalized; hFig2.WindowState maximized; % Go to full screen. hFig2.NumberTitle off; % Get rid of Figure 1 hFig2.Name Demo by Image Analyst; % Put this into title bar. for k 1 : numberOfBlobs % Loop through all blobs. % Find the bounding box of each blob. thisBlobsBoundingBox props(k).BoundingBox; % Get list of pixels in current blob. % Extract out this coin into its own image. subImage imcrop(originalImage, thisBlobsBoundingBox); % Determine if its a dime (small) or a nickel (large coin). if props(k).Area 2200 coinType nickel; else coinType dime; end % Display the image with informative caption. subplot(3, 4, k); imshow(subImage); caption sprintf(Coin #%d is a %s.\nDiameter %.1f pixels\nArea %d pixels, ... k, coinType, blobECD(k), props(k).Area); title(caption, FontSize, textFontSize); end %------------------------------------------------------------------------------------------------------------------------------------------------------ % Display the MATLAB peaks logo. logoSubplot subplot(3, 4, 11:12); caption sprintf(A MATLAB Tutorial by ImageAnalyst); text(0.5,1.15, caption, Color,r, FontSize, 18, FontWeight,b, HorizontalAlignment, center, VerticalAlignment, middle) ; positionOfLowerRightPlot get(logoSubplot, position); L 40*membrane(1,25); logoax axes(CameraPosition, [-193.4013, -265.1546, 220.4819],... Box, off, ... CameraTarget,[26, 26, 10], ... CameraUpVector,[0, 0, 1], ... CameraViewAngle,9.5, ... DataAspectRatio, [1, 1, .9],... Position, positionOfLowerRightPlot, ... 3 参考文献 文章中一些内容引自网络会注明出处或引用为参考文献难免有未尽之处如有不妥请随时联系删除。 [1]马寅.基于CCD的图像特征提取与识别[D].东北大学,2012.DOI:10.7666/d.J0120301.
[2]王妞,康辉英.基于图像检测的船舶特征分割与提取优化算法[J].舰船科学技术, 2018(4X):3.DOI:CNKI:SUN:JCKX.0.2018-08-049.
[3]尹聪.彩色图像人脸检测与特征提取认证[J].信息技术与信息化, 2009.DOI:JournalArticle/5af35bd8c095d718d80b8d86.
[4]罗文辉,王三武.基于面积和结构特征的水表图像二步分割方法[J].武汉理工大学学报信息与管理工程版, 2006, 28(5):4.DOI:10.3963/j.issn.1007-144X.2006.05.014.
4 Matlab代码实现
