欢迎来到海神之光博客之家✅博主简介热爱科研的Matlab仿真开发者修心和技术同步精进个人主页海神之光代码获取方式海神之光Matlab王者学习之路—代码获取方式⛳️座右铭行百里者半于九十。更多Matlab图像处理仿真内容点击①Matlab图像处理进阶版②付费专栏Matlab图像处理初级版⛳️关注CSDN海神之光更多资源等你来⛄一、硬币图像识别简介本设计为硬币图像识别统计装置通过数码相机获取平铺无重叠堆积的硬币的图像并通过Matlab工具处理后统计硬币的数目。1 图像格式转换取的图像格式为RGB彩色图像需要先将其转换为8位256级的灰度图像。本程序采用Matlab的图像处理工具箱的函数rgb2gray来实现。rgb2gray功能转换RGB图像或颜色映像表为灰度图像。语法I rgb2gray(RGB)newmap rgb2gray(map)2 去噪及特征提取上图1-1为硬币统计的局部图片图中可见硬币主体部分和背景以及图像有着明显的区别可以通过选取合适的阈值进行二值化从而提取出硬币的特征。图1-2为此图像的直方图从图中可见到比较明显的阈值分界点但是并不是非常的明显这是因为图中有很多的硬币因为反光的缘故导致主体部分有些发白如图1-3所示。3 灰度调整对于这些发白部分我们采用灰度调整及中值滤波进行处理在matlab中提供了两个函数进行相应的操作其中imadjust进行灰度调整其用法如下Imadjst(f,[low_in high_in],[low_out high_out],gamma)Gamma所表示的意义1 -------- 凹曲线1 -------- 凸直线1 -------- 直线medfilt2用于进行中值滤波处理其用法如下Fmedfilt2(f,[m n]);f为输入图像[m n]为中值滤波模板F是中值滤波后输出的图像。图4-1经过灰度调整及中值滤波后的图像如图1-4所示可见经过中值滤波后硬币的主体部分有了较大的改善。4 二值化处理经过滤波后即可对图像进行二值化处理首先我们采用人工选择阈值的方法进行二值化由图可见对于本幅图片其合适的阈值在50~100之间通过试验我们选取的值为80。对图像二值化处理的程序如下[M,N]size(F);for x1:Mfor y1:Nif F(x,y)80F(x,y)0; %低于阈值的值黑elseF(x,y)255; %高于阈值的值白endendend5 阈值分割当然仍有许多模糊的硬币管脚残影但已经将硬币的主体很好的识别了出来采用人工选择阈值的方法虽然可以成功分离出硬币的主体但是这个阈值这是针对这张图片有效对于获取的其它图片这个阈值并不能正确地对图像进行二值化处理因此我们决定采用自动阈值分割的方法来对图像进行二值化。我们所选用的自动阈值分割方法为Otsu法它是一种使类间方差最大的自动确定阈值的方法该方法具有简单、处理速度快的特点是一种常用的阈值选取方法。在matlab中提供了一个函数graythresh来实现Otsu法阈值分割其用法如下Tgraythresh(f);其中f为待进行阈值分割的灰度图像T为返回的分割灰度比例将其乘于256即为Otsu法划定的分割阈值。优化后的程序如下Tgraythresh(F);由图中可见噪声被有效的滤除了但是去除了噪声的同时也使部分接触紧密的硬币在闭运算后可能连成一个整体如图1-8中的红圈所示因此在此后的识别统计中需要对其进行特殊的处理。⛄二、部分源代码% function BlobsDemo()% echo on;% Startup code.tic; % Start timer.clc; % Clear command window.clearvars; % Get rid of variables from prior run of this m-file.fprintf(‘Running BlobsDemo.m…\n’); % Message sent to command window.workspace; % Make sure the workspace panel with all the variables is showing.imtool close all; % Close all imtool figures.format long g;format compact;captionFontSize 14;% Check that user has the Image Processing Toolbox installed.hasIPT license(‘test’, ‘image_toolbox’);if ~hasIPT% User does not have the toolbox installed.message sprintf(‘Sorry, but you do not seem to have the Image Processing Toolbox.\nDo you want to try to continue anyway?’);reply questdlg(message, ‘Toolbox missing’, ‘Yes’, ‘No’, ‘Yes’);if strcmpi(reply, ‘No’)% User said No, so exit.return;endend% Read in a standard MATLAB demo image of coins (US nickles and dimes, which are 5 cent and 10 cent coins)baseFileName ‘coins.png’;folder fileparts(which(baseFileName)); % Determine where demo folder is (works with all versions).fullFileName fullfile(folder, baseFileName);if ~exist(fullFileName, ‘file’)% It doesn’t exist in the current folder.% Look on the search path.if ~exist(baseFileName, ‘file’)% It doesn’t exist on the search path either.% Alert user that we can’t 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: don’t 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.set(gcf, ‘units’,‘normalized’,‘outerposition’,[0 0 1 1]);% Force it to display RIGHT NOW (otherwise it might not display until it’s all done, unless you’ve 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 image; % Make sure image is not artificially stretched because of screen’s aspect ratio.% Just for fun, let’s 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 0’s and 1’s) 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 let’s 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.% 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 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);% Let’s 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 screen’s 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);% Get all the blob properties. Can only pass in originalImage in version R2008a and later.blobMeasurements regionprops(labeledImage, originalImage, ‘all’);numberOfBlobs size(blobMeasurements, 1);% bwboundaries() returns a cell array, where each cell contains the row/column coordinates for an object in the image.% Plot the borders of all the coins on the original grayscale image using the coordinates returned by bwboundaries.subplot(3, 3, 6);imshow(originalImage);title(‘Outlines, from bwboundaries()’, ‘FontSize’, captionFontSize);axis image; % Make sure image is not artificially stretched because of screen’s aspect ratio.hold on;boundaries bwboundaries(binaryImage);numberOfBoundaries size(boundaries, 1);for k 1 : numberOfBoundariesthisBoundary boundaries{k};plot(thisBoundary(:,2), thisBoundary(:,1), ‘g’, ‘LineWidth’, 2);endhold off;textFontSize 14; % Used to control size of “blob number” labels put atop the image.labelShiftX -7; % Used to align the labels in the centers of the coins.blobECD zeros(1, numberOfBlobs);% Print header line in the command window.fprintf(1,‘Blob # Mean Intensity Area Perimeter Centroid Diameter\n’);% Loop over all blobs printing their measurements to the command window.for k 1 : numberOfBlobs % Loop through all blobs.% Find the mean of each blob. (R2008a has a better way where you can pass the original image% directly into regionprops. The way below works for all versions including earlier versions.)thisBlobsPixels blobMeasurements(k).PixelIdxList; % Get list of pixels in current blob.meanGL mean(originalImage(thisBlobsPixels)); % Find mean intensity (in original image!)meanGL2008a blobMeasurements(k).MeanIntensity; % Mean again, but only for version R2008ablobArea blobMeasurements(k).Area; % Get area. blobPerimeter blobMeasurements(k).Perimeter; % Get perimeter. blobCentroid blobMeasurements(k).Centroid; % Get centroid one at a time blobECD(k) sqrt(4 * blobArea / pi); % Compute ECD - Equivalent Circular Diameter. 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 boundaries grayscale image. text(blobCentroid(1) labelShiftX, blobCentroid(2), num2str(k), FontSize, textFontSize, FontWeight, Bold);end% Now, I’ll show you another way to get centroids.% 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 [blobMeasurements.Centroid];centroidsX allBlobCentroids(1:2:end-1);centroidsY allBlobCentroids(2:2:end);% Put the labels on the rgb labeled image also.subplot(3, 3, 5);for k 1 : numberOfBlobs % Loop through all blobs.text(centroidsX(k) labelShiftX, centroidsY(k), num2str(k), ‘FontSize’, textFontSize, ‘FontWeight’, ‘Bold’);end% Now I’ll demonstrate how to select certain blobs based using the ismember() function.% Let’s 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 [blobMeasurements.MeanIntensity];allBlobAreas [blobMeasurements.Area];% 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 let’s 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 don’t 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 we’re 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);% Plot the centroids in the original image in the upper left.% 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;endsubplot(3, 3, 1);hold on; % Don’t 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’, 10, ‘LineWidth’, 2);else% Plot dimes with a blue x.plot(centroidsX(k), centroidsY(k), ‘bx’, ‘MarkerSize’, 10, ‘LineWidth’, 2);endend⛄三、运行结果⛄四、matlab版本及参考文献1 matlab版本2014a2 参考文献[1]朱俊达.硬币分拣机的分拣与计数[J].技术与市场. 2018,25(02)3 备注简介此部分摘自互联网仅供参考若侵权联系删除 仿真咨询1 各类智能优化算法改进及应用生产调度、经济调度、装配线调度、充电优化、车间调度、发车优化、水库调度、三维装箱、物流选址、货位优化、公交排班优化、充电桩布局优化、车间布局优化、集装箱船配载优化、水泵组合优化、解医疗资源分配优化、设施布局优化、可视域基站和无人机选址优化2 机器学习和深度学习方面卷积神经网络CNN、LSTM、支持向量机SVM、最小二乘支持向量机LSSVM、极限学习机ELM、核极限学习机KELM、BP、RBF、宽度学习、DBN、RF、RBF、DELM、XGBOOST、TCN实现风电预测、光伏预测、电池寿命预测、辐射源识别、交通流预测、负荷预测、股价预测、PM2.5浓度预测、电池健康状态预测、水体光学参数反演、NLOS信号识别、地铁停车精准预测、变压器故障诊断3 图像处理方面图像识别、图像分割、图像检测、图像隐藏、图像配准、图像拼接、图像融合、图像增强、图像压缩感知4 路径规划方面旅行商问题TSP、车辆路径问题VRP、MVRP、CVRP、VRPTW等、无人机三维路径规划、无人机协同、无人机编队、机器人路径规划、栅格地图路径规划、多式联运运输问题、车辆协同无人机路径规划、天线线性阵列分布优化、车间布局优化5 无人机应用方面无人机路径规划、无人机控制、无人机编队、无人机协同、无人机任务分配6 无线传感器定位及布局方面传感器部署优化、通信协议优化、路由优化、目标定位优化、Dv-Hop定位优化、Leach协议优化、WSN覆盖优化、组播优化、RSSI定位优化7 信号处理方面信号识别、信号加密、信号去噪、信号增强、雷达信号处理、信号水印嵌入提取、肌电信号、脑电信号、信号配时优化8 电力系统方面微电网优化、无功优化、配电网重构、储能配置9 元胞自动机方面交通流 人群疏散 病毒扩散 晶体生长10 雷达方面卡尔曼滤波跟踪、航迹关联、航迹融合