基于机器视觉的人眼检测算法matlab仿真【包括程序，注释，参考文献，操作步骤】

% 定义眼睛检测函数，返回左右眼的x和y坐标 function [XL, XR, YL, YR] = func_eye_pos(img) % 初始化左右眼坐标默认值 XL = 100; XR = 300; YL = 50; YR = 50; % 复制输入图像 X = img; X2 = X; [y1,x1,z1] = size(X);% 获取图像尺寸 res=false;% 初始化是否缩放标志 if(y1>1300 || x1>1300)% 若图像尺寸超过1300x1300像素，则进行缩放 display('resized'); res = true; X = imresize(X,.5);% 缩小图像至原来的一半 end % 保存原始图像用于裁剪 forcrop=X; X=imcomplement(X);% 图像反色处理 sup = X;% 保存反色后的图像 % 显示反色图像 imagesc(X) % 将图像转换为HSV色彩空间并仅保留饱和度通道 X=rgb2hsv(X); X=X(:,:,2); % 自适应阈值二值化处理 BW = imbinarize(X, 'adaptive', 'Sensitivity', 0.900000, 'ForegroundPolarity', 'bright'); % 反色二值图 BW = imcomplement(BW); radius = 4;% 定义结构元素用于形态学操作 decomposition = 0; se = strel('disk', radius, decomposition); BW = imopen(BW, se);% 开运算去除小物体 BW2= im2bw(BW);% 转换为二值图像 [B,L] = bwboundaries(BW); % 分析区域属性 stats = regionprops(L,'Centroid','MajorAxisLength','MinorAxisLength','Area','BoundingBox','Orientation') % 初始化存储变量 pos= []; circles = []; rad= []; met=[]; bbnum=[]; for k = 1:size(B)% 遍历区域属性 imarea = y1*x1; % 筛选符合条件的区域 if (stats(k).Orientation > -80) && (stats(k).Area > 100) && (stats(k).Area < (imarea*.40)) thisBB = stats(k).BoundingBox; I2 = imcrop(sup,stats(k).BoundingBox);% 根据边界框裁剪图像 I2 = rgb2gray(I2); rgbImage = cat(3, I2, I2, I2);% 重复彩色通道以适应后续处理 I = rgb2hsv(rgbImage); % 定义HSV通道的阈值范围 V1min = 0.000; V1max = 1.000; V2min = 0.000; V2max = 1.000; V3min = 0.256; V3max = 1.000; % 应用阈值生成掩模 BW = (I(:,:,1) >= V1min ) & (I(:,:,1) <= V1max) & ... (I(:,:,2) >= V2min ) & (I(:,:,2) <= V2max) & ... (I(:,:,3) >= V3min ) & (I(:,:,3) <= V3max); % 用掩模处理后的图像 maskedRGBImage = rgbImage; % 将不在掩模内的像素置为0 maskedRGBImage(repmat(~BW,[1 1 3])) = 0; % 绘制边界框 rectangle('Position', [thisBB(1),thisBB(2),thisBB(3),thisBB(4)],'EdgeColor','r','LineWidth',2 ); % 在掩模图像上寻找圆形 [centers,radii,metric]=imfindcircles(maskedRGBImage,[10 30],'Method','TwoStage','ObjectPolarity','bright','Sensitivity',.87,'EdgeThreshold',.03); [centers2,radii2,metric2]=imfindcircles(maskedRGBImage,[30 80],'Method','TwoStage','ObjectPolarity','bright','Sensitivity',.87,'EdgeThreshold',.03); % 合并两组圆形结果 if (~isempty(centers2)) centers= cat(1,centers,centers2); radii=cat(1,radii,radii2); metric=cat(1,metric,metric2); end % 处理找到的圆形 [m,n]=size(centers); if m~=0 && m<6 count = m; % 记录有效圆形的位置、半径等信息 circles = cat(1,circles,centers); rad = cat(1,rad,radii); met =cat(1,met,metric); pos = cat(1,pos,[thisBB(1),thisBB(2),thisBB(3),thisBB(4)]); end end end % 继续处理找到的顶部圆形 center=[]; radius=[]; [m n] =size(pos); % figure,imshow(sup);% 显示图像 for j=1:m if(res == false) main= sup; % 裁剪图像 crop = imcrop(main,[pos(j,1),pos(j,2),pos(j,3),pos(j,4)]); % 在裁剪图像上寻找圆形 [centers,radii,metric]=imfindcircles(crop,[5 20],'Method','TwoStage','ObjectPolarity','bright', 'Sensitivity',.96,'EdgeThreshold',.02 ); % 绘制已找到的圆形 viscircles([pos(j,1) + circles(j,1),(pos(j,2)+ circles(j,2))],rad(j)); [k na] = size(centers); % 处理找到的圆形 if(~isempty(centers)) center = cat(1,center,[pos(j,1) + centers(1,1),(pos(j,2)+ centers(1,2))]); radius= cat(1,radius,radii(1,1)); rectangle('Position', [pos(j,1),pos(j,2),pos(j,3),pos(j,4)],'EdgeColor','r','LineWidth',2 ); % 绘制边界框 end else main= sup; rectangle('Position', [pos(j,1),pos(j,2),pos(j,3),pos(j,4)],'EdgeColor','r','LineWidth',2);% 绘制边界框 crop = imcrop(main,[pos(j,1),pos(j,2),pos(j,3),pos(j,4)]); [centers,radii,metric]=imfindcircles(crop,[5 20],'Method','TwoStage','ObjectPolarity','bright', 'Sensitivity',.96,'EdgeThreshold',.02 ); viscircles([pos(j,1) + circles(j,1),(pos(j,2)+ circles(j,2))],rad(j)); [k na] = size(centers); if(~isempty(centers)) center = cat(1,center,[pos(j,1)/.5 + centers(1,1),(pos(j,2)/.5+ centers(1,2))]); radius= cat(1,radius,radii(1,1)/.5); rectangle('Position', [pos(j,1),pos(j,2),pos(j,3),pos(j,4)],'EdgeColor','r','LineWidth',2 ); end end end % 显示处理后的图像与圆形 % figure, imshow(X2) % viscircles(center,radius); % 计算眼睛间的相似度 [m n] = size(center); max_corr= 0; max_sim=0; scalar=8; if(res == true) sup=X2; sup= imcomplement(sup);% 反色处理 scalar=16; end for i= 1:m temp = imcrop(sup,[center(i,1)-(radius(i)*2),center(i,2)-(radius(i)*2),(radius(i)*scalar),radius(i)*scalar]); temp= rgb2gray(temp); temp=flip(temp,2); for o=i+1:m-1 cr= imcrop(sup,[center(o,1)-(radius(o)*2),center(o,2)-(radius(o)*2),(radius(i)*scalar),radius(i)*scalar]); cr= imresize(cr,size(temp)); cr= rgb2gray(cr); % 图像配准设置 [optimizer, metric] = imregconfig('multimodal') optimizer.InitialRadius = 0.009; optimizer.Epsilon = 1.5e-4; optimizer.GrowthFactor = 1.01; optimizer.MaximumIterations = 300; tform = imregtform(cr,temp,'similarity',optimizer,metric); cr = imwarp(cr,tform,'OutputView',imref2d(size(temp))); % 计算模板匹配的相关系数 C1 = normxcorr2((temp),(cr)); % 更新最大相关系数和眼睛坐标 if(max(C1(:))>max_corr) max_corr = max(C1(:)); XL=center(i,1); YL=center(i,2); XR=center(o,1); YR=center(o,2); end end end end