clc clear all %% img_in = imread('0066.jpg'); m = size(img_in,1); n = size(img_in,2); img_lab = rgb2lab(img_in); img_L_mean = mean(mean(img_lab(:,:,1))); img_a_mean = mean(mean(img_lab(:,:,2))); img_b_mean = mean(mean(img_lab(:,:,3))); %% %高斯滤波 img_R = img_in(:,:,1); img_G = img_in(:,:,2); img_B = img_in(:,:,3); w = fspecial('gaussian',[7 7]); img_R_blur = imfilter(img_R,w); img_G_blur = imfilter(img_G,w); img_B_blur = imfilter(img_B,w); img_blur = cat(3,img_R_blur,img_G_blur,img_B_blur); figure('name','滤波') imshow(img_blur) img_lab_blur = rgb2lab(img_blur); %% %计算显著图 Sd = zeros(m,n); for i = 1:m for j = 1:n Sd(i,j) = sqrt((img_L_mean - img_lab_blur(i,j,1))^2 + (img_a_mean - img_lab_blur(i,j,2))^2 + (img_b_mean - img_lab_blur(i,j,3))^2); end end %归一化 Sd_normalized = figure_normalize(Sd); imwrite(Sd_normalized,'FT_saliency.jpg') figure imshow(Sd_normalized) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% end运行代码时报错错误: 非法使用保留关键字 "end"。改进代码

clc clear all %% img_in = imread('ILSVRC2017_test_00000237.jpg'); m = size(img_in,1); n = size(img_in,2); img_lab = rgb2lab(img_in); img_L_mean = mean(mean(img_lab(:,:,1))); img_a_mean = mean(mean(img_lab(:,:,2))); img_b_mean = mean(mean(img_lab(:,:,3))); %% %高斯滤波 img_R = img_in(:,:,1); img_G = img_in(:,:,2); img_B = img_in(:,:,3); w = fspecial('gaussian',[7 7]); img_R_blur = imfilter(img_R,w); img_G_blur = imfilter(img_G,w); img_B_blur = imfilter(img_B,w); img_blur = cat(3,img_R_blur,img_G_blur,img_B_blur); figure('name','滤波') imshow(img_blur) img_lab_blur = rgb2lab(img_blur); %% %计算显著图 Sd = zeros(m,n); for i = 1:m for j = 1:n Sd(i,j) = sqrt((img_L_mean - img_lab_blur(i,j,1))^2 + (img_a_mean - img_lab_blur(i,j,2))^2 + (img_b_mean - img_lab_blur(i,j,3))^2); end end %归一化 Sd_normalized = figure_normalize(Sd); imwrite(Sd_normalized,'FT_saliency.jpg') figure imshow(Sd_normalized) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function Out_image = figure_normalize(In_image) % 归一化至0-1 o_max_image = max(max(In_image)); o_min_image = min(min(In_image)); Out_image = double(In_image - o_min_image)/double(o_max_image - o_min_image); end改进该代码使其能在matlab上运行

m = size(img_in,1); n = size(img_in,2); img_lab = rgb2lab(img_in); img_L_mean = mean(mean(img_lab(:,:,1))); img_a_mean = mean(mean(img_lab(:,:,2))); img_b_mean = mean(mean(img_lab(:,:,3))); %高斯...

clc; clear all; img_in = imread('ILSVRC2017_test_00000237.jpg'); m = size(img_in,1); n = size(img_in,2); img_lab = rgb2lab(img_in); img_L_mean = mean(mean(img_lab(:,:,1))); img_a_mean = mean(mean(img_lab(:,:,2))); img_b_mean = mean(mean(img_lab(:,:,3))); %高斯滤波 img_R = img_in(:,:,1); img_G = img_in(:,:,2); img_B = img_in(:,:,3); w = fspecial('gaussian',[7 7]); img_R_blur = imfilter(img_R,w); img_G_blur = imfilter(img_G,w); img_B_blur = imfilter(img_B,w); img_blur = cat(3,img_R_blur,img_G_blur,img_B_blur); figure('Name','滤波'); imshow(img_blur); img_lab_blur = rgb2lab(img_blur); %计算显著图 Sd = zeros(m,n); for i = 1:m for j = 1:n Sd(i,j) = sqrt((img_L_mean - img_lab_blur(i,j,1))^2 + (img_a_mean - img_lab_blur(i,j,2))^2 + (img_b_mean - img_lab_blur(i,j,3))^2); end end %归一化 Sd_normalized = figure_normalize(Sd); imwrite(Sd_normalized,'FT_saliency.jpg'); figure; imshow(Sd_normalized); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function Out_image = figure_normalize(In_image) o_max_image = max(max(In_image)); o_min_image = min(min(In_image)); Out_image = double(In_image - o_min_image)/double(o_max_image - o_min_image); end运行该代码报错未定义函数或变量 'figure_normalize'。

function Out_image = figure_normalize(In_image) o_max_image = max(max(In_image)); o_min_image = min(min(In_image)); Out_image = double(In_image - o_min_image)/double(o_max_image - o_min_image); end ...

这段代码优化巡行时间：clc;clear; tic %遍历循环读取所有tiff文件 file_path = 'D:\JIANGXiaoYu\5_20230709_24dpf\';% 图像文件夹路径 img_path_list = dir(strcat(file_path,'.tiff'));%获取该文件夹中所有TIFF格式的图像 img_num = length(img_path_list);%获取图像总数量 Output_path='D:\JIANGXiaoYu\5_20230709_24dpf_tif\';%文件夹的路径 for jj = 1:img_num image_name = img_path_list(jj).name; % 图像名 image=imread(strcat(file_path,img_path_list(jj).name)); Info=imfinfo(image_name); Slice=size(Info,1); %%获取图片z向帧数 Width=Info.Width; Height=Info.Height; Image=zeros(Height,Width,Slice); for i=1:Slice Image(:,:,i)=imread(strcat(file_path,img_path_list(jj).name),i); %%一层一层的读入图像 J=uint8(Image(:,:,i)); %%一层一层写出图像 imwrite(J,[Output_path,num2str(209(jj-1)+i,'%04d'),'.tif']);%2G209/4G419 end fprintf(' %d %s\n',jj,strcat(file_path,image_name));% 显示正在处理的图像名 end toc disp(['运行时间: ',num2str(toc)]); %遍历循环读取所有tiff文件

slice = size(Info, 1); width = Info.Width; height = Info.Height; temp_images = zeros(height, width, slice); for i = 1:slice temp_images(:, :, i) = imread(fullfile(file_path, image_name), i); ...

clear clc pathname = uigetdir; name_list=dir(pathname); for i=3:22 name_list(i).引用了不存在的字段 'num'。出错 LoadData (line 33)num=zeros(20,1); name_list(i).num(i-2)=1; end %pathname = uigetdir; img_name1=importdata('E:\\train.txt'); img_train_num=size(img_name1,1); num=0; for i=1:img_train_num img_name_char=cell2mat(img_name1(i)); label_1(i).name=extractBefore(cell2mat(img_name1(i)),'_'); imgPath = [pathname,'\',label_1(i).name,'\',cell2mat(img_name1(i))]; temp = imread(imgPath); train_x(:,:,i)=temp; % temp = double(temp(:)); % num=num+1; % imagedata1(:,num)=temp; end for i=1:img_train_num for j=1:20 if strcmp(name_list(j+2).name,label_1(i).name) label_1(i).num=name_list(j+2).num; end end train_y(:,i)=uint8(label_1(i).num); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %pathname = uigetdir; img_name1=importdata('E\\test.txt'); img_test_num=size(img_name1,1); num=0; for i=1:img_test_num img_name_char=cell2mat(img_name1(i)); label_2(i).name=extractBefore(cell2mat(img_name1(i)),'_'); imgPath = [pathname,'\',label_2(i).name,'\',cell2mat(img_name1(i))]; temp = imread(imgPath); test_x(:,:,i)=temp; % temp = double(temp(:)); % num=num+1; % imagedata2(:,num)=temp; end for i=1:img_test_num for j=1:20 if strcmp(name_list(j+2).name,label_2(i).name) label_2(i).num=name_list(j+2).num; end end test_y(:,i)=uint8(label_2(i).num); end save('E\\imgdata_uint8.mat', 'train_x','train_y','test_x','test_y');

2. img_name1=importdata('E:\\train.txt'); 和 img_name1=importdata('E\\test.txt'); 这两行代码中的路径分隔符不一致，应该都使用反斜杠\。 3. save('E\\imgdata_uint8.mat', 'train_x','train_y','test_...

clear all close all clc %addpath('./two') %addpath('./groundtruth') %rmpath('./two') %rmpath('./groundtruth') I_Out = imread('3.bmp'); I_Mask = imread('img3.jpg'); I_Mask = I_Mask(:,:,1) I_Mask = I_Mask>0; cross_region = I_Out & I_Mask; numerator = sum(sum(cross_region)); denominator = sum(sum(I_Out)) + sum(sum(I_Mask)); DC_rate = 2*numerator/denominator

DC是用来衡量分割结果与真实标注之间重合度的指标，其取值范围在[0,1]之间，值越大代表分割结果与真实标注越相似。代码中，首先读取了原图和二值化标注图，然后将标注图的像素值二值化为0和1，接着利用位运算AND操作...

clear clc pathname = uigetdir; name_list=dir(pathname); for i=3:22 name_list(i).num=zeros(20,1); name_list(i).num(i-2)=1; end %pathname = uigetdir; img_name1=importdata('train_30_32.txt'); img_train_num=size(img_name1,1); num=0; for i=1:img_train_num img_name_char=cell2mat(img_name1(i)); label_1(i).name=extractBefore(cell2mat(img_name1(i)),'_'); imgPath = [pathname,'\',label_1(i).name,'\',cell2mat(img_name1(i))]; temp = imread(imgPath); train_x(:,:,i)=temp; % temp = double(temp(:)); % num=num+1; % imagedata1(:,num)=temp; end for i=1:img_train_num for j=1:20 if strcmp(name_list(j+2).name,label_1(i).name) label_1(i).num=name_list(j+2).num; end end train_y(:,i)=uint8(label_1(i).num); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %pathname = uigetdir; img_name1=importdata('test_30_32.txt'); img_test_num=size(img_name1,1); num=0; for i=1:img_test_num img_name_char=cell2mat(img_name1(i)); label_2(i).name=extractBefore(cell2mat(img_name1(i)),'_'); imgPath = [pathname,'\',label_2(i).name,'\',cell2mat(img_name1(i))]; temp = imread(imgPath); test_x(:,:,i)=temp; % temp = double(temp(:)); % num=num+1; % imagedata2(:,num)=temp; end for i=1:img_test_num for j=1:20 if strcmp(name_list(j+2).name,label_2(i).name) label_2(i).num=name_list(j+2).num; end end test_y(:,i)=uint8(label_2(i).num); end save('E:\\imgdata_uint8.mat', 'train_x','train_y','test_x','test_y');逐行解释

接着，使用循环遍历前 20 个文件夹（name_list 中的前两个元素为 . 和 ..），并为每个文件夹创建一个标签向量 num，其中第 i-2 个元素为 1，其余为 0，表示该文件夹对应的标签。接下来，读取训练集图像...

分析程序作用close all; %关闭当前所有图形窗口 clear all; %清空工作空间变量 clc; %清屏 function nearest_neighbor = nearest_neighbor(lena.png,2); % 初始化，读入图像，图像数据为mncolor img = imread(lena.png); % 变化后图像 [row,col,color] = size(img); % 获得图像的行列数及色板数 row = round(rowR); % 新图像行 col = round(colR); % 新图像列 % 新图像初始化 % 使用class获得原图像的数据类型，使得新图像数据类型与原图像保持一致 img_new = zeros(row,col,color,class(img)); % 对新图像的行、列、色板赋值 for i = 1:row for j = 1:col for n = 1:color x = round(i/R); y = round(j/R); % 为了避免x和y等于0而报错，采取+1处理即可 if x == 0 x = x+1; end if y == 0 y = y+1; end img_new(i,j,n) = img(x,y,n); end end end

第二行代码 "clear all;" 清空 MATLAB 的工作空间，以便删除之前的变量和数据。第三行代码 "clc;" 清空 MATLAB 命令窗口，以便清除之前的输出信息。第四行代码是一个函数调用，将文件 "lena.png" 读入到 MATLAB ...

%% close all; clc; clear all; % img = imread('GZY.jpg'); imshow(img); title('original image'); % 计算傅里叶变换 img_F = fft2(img); [M, N] = size(img); s = sum(abs(img_F(1:M, 1:N))); % 行求和 s = sum(s); % 列求和 ave = s / (M * N); disp(['average value of image is: ',num2str(ave)]); S = log(1 + abs(img_F)); figure; imshow(S); title('二维图像显示幅度谱'); x = 0:1:255; y = 0:1:255; [x, y] = meshgrid(x, y); figure; mesh(S); title('三维图像显示幅度谱'); img_Q = angle(img_F); figure; plot(img_Q); title('二维图像显示相位谱'); x = 0:1:255; y = 0:1:255; [x, y] = meshgrid(x, y); figure; mesh(img_Q); title('三维图像显示相位谱');这是我的代码，帮我修改这个问题average value of image is: 7772.7855 错误使用 matlab.graphics.chart.primitive.Surface 值必须是数值类型的标量、向量或数组。出错 mesh (第 98 行) hh = matlab.graphics.chart.primitive.Surface('ZData',z,'FaceColor',fc,'EdgeColor','flat', ... 出错 untitled (第 29 行) mesh(S);

[M, N] = size(img); s = sum(abs(img_F(1:M, 1:N))); % 行求和 s = sum(s); % 列求和 ave = s / (M * N); disp(['average value of image is: ',num2str(ave)]); S = log(1 + abs(img_F)); figure; imshow(S); ...

clc; clear,close; I = imread('flower.tif'); %使用fspecial函数创建一个运动模糊退化函数。假设我们想要一个方向为45度，长度为11像素的模糊核 motion_kernel = fspecial('motion', 11, 45); %应用运动模糊和高斯噪声 blurred = imfilter(I, motion_kernel, 'conv', 'circular'); blurred = imnoise(blurred, 'gaussian', 0, 0.01); % 添加均值为0，方差为0.01的高斯噪声 estimated_nsr = 0; % 估计信噪比 restored = deconvwnr(blurred, motion_kernel, estimated_nsr); % 维纳滤波 estimated_nsr = 0.001; % 估计信噪比 restored_wnr = deconvwnr(blurred, motion_kernel, estimated_nsr); % 自相关函数滤波 corr_img = xcorr2(blurred); % 计算自相关函数 corr_kernel = xcorr2(motion_kernel); % 计算模糊核的自相关函数 restored_wiener = deconvwnr(blurred, motion_kernel, corr_kernel, corr_img);这个程序改一下错

1. 在使用imfilter函数进行卷积时，可以指定边缘处理方式，如'conv'表示用0填充边缘，'replicate'表示用边缘像素填充，'symmetric'表示用图像对称的方式填充，等等。 2. 在添加高斯噪声时，可以根据需要调整均值和...

解释代码clc;clear all;close all; img = imread('tire.tif'); figure,imshow(img); img3 = imrotate(img,90); figure,imshow(img3); imwrite(img3,'a3.jpg');

这段代码的作用是： ...7. 将 img3 图像保存为名为 'a3.jpg' 的 JPEG 格式文件其中，imread() 函数用于读取图像文件，imrotate() 函数用于旋转图像，imshow() 函数用于显示图像，imwrite() 函数用于保存图像。

clc;clear all; img = imread('‪C:\Users\210\Desktop\123\mouse.jpg'); figure; imshow(img),title("原图像"); [ROW,COL] = size(img); img = double(img); new_img = zeros(ROW,COL); %新建画布 %定义robert算子 roberts_x = [1,0;0,-1]; roberts_y = [0,-1;1,0]; for i = 1:ROW - 1 for j = 1:COL - 1 funBox = img(i:i+1,j:j+1); G_x = roberts_x .* funBox; G_x = abs(sum(G_x(:))); G_y = roberts_y .* funBox; G_y = abs(sum(G_y(:))); roberts_xy = G_x * 0.5 + G_y * 0.5; new_img(i,j) = roberts_xy; end end figure(2); imshow(new_img/255),title("robert算子的图像"); % 定义laplace算子 laplace = [0,1,0;1,-4,1;0,1,0]; for i = 1:ROW - 2 for j = 1:COL - 2 funBox = img(i:i+2,j:j+2); G = laplace .* funBox; G = abs(sum(G(:))); new_img(i+1,j+1) = G; end end figure(3) imshow(new_img/255),title("laplace算子的图像"); %定义sobel算子 sobel_x = [-1,0,1;-2,0,2;-1,0,1]; sobel_y = [-1,-2,-1;0,0,0;1,2,1]; for i = 1:ROW - 2 for j = 1:COL - 2 funBox = img(i:i+2,j:j+2); G_x = sobel_x .* funBox; G_x = abs(sum(G_x(:))); G_y = sobel_y .* funBox; G_y = abs(sum(G_y(:))); sobelxy = G_x * 0.5 + G_y * 0.5; new_img(i+1,j+1) = sobelxy; end end figure(4); imshow(new_img/255),title("sobel的图像"); %定义Prewitt算子 sobel_x = [-1,0,1;-1,0,1;-1,0,1]; sobel_y = [-1,-1,-1;0,0,0;1,1,1]; for i = 1:ROW - 2 for j = 1:COL - 2 funBox = img(i:i+2,j:j+2); G_x = sobel_x .* funBox; G_x = abs(sum(G_x(:))); G_y = sobel_y .* funBox; G_y = abs(sum(G_y(:))); sobelxy = G_x * 0.5 + G_y * 0.5; new_img(i+1,j+1) = sobelxy; end end figure(5); imshow(new_img/255),title("Prewitt的图像");

这段代码中有一行 img = imread('‪C:\Users\210\Desktop\123\mouse.jpg');，你需要注意到这行代码中的路径字符串包含一个隐藏字符，可能导致 imread 函数无法正确读取图像文件。你可以尝试重新输入路径字符串，...

clear;clc;close all; img=imread('flower.tif'); gray=rgbimage2gray(img); %灰度化 %加入噪声 gray_noise=imnoise(gray,'salt & pepper',0.2); % 自适应中值滤波 f1 = adaptive_median_filter(gray_noise,11); if(size(img, 3) == 3) % Check if the image is a truecolor image f1 = gray2rgb(f1,img); end figure('color',[1,1,1]); subplot(221) imshow(img) title("原图") subplot(222) imshow(gray_noise) title("gray with noise") subplot(224) imshow(f1); title("自适应中值滤波") function f = adaptive_median_filter (g, Smax) % 判断邻域是否合理 if (Smax <= 1) || (Smax/2 == round(Smax/2)) || (Smax ~= round(Smax)) error ('SMAX must be an odd integer > 1.') end % f = g; f(:) = 0; % 标记是否已处理过 alreadyProcessed = false (size(g)); % 开始自适应滤波 for k = 3:2:Smax zmin = ordfilt2(g, 1, ones(k, k),'symmetric'); zmax = ordfilt2(g, k * k, ones(k, k), 'symmetric'); zmed = medfilt2(g, [k k], 'symmetric'); % 判断是否进入进程B processUsingLevelB = (zmed > zmin) & (zmax > zmed) & ~alreadyProcessed; % 若g不是脉冲，保留原值 zB = (g > zmin) & (zmax > g); outputZxy = processUsingLevelB & zB; %若是脉冲，用Zmed替换 outputZmed = processUsingLevelB & ~zB; f (outputZxy) = g(outputZxy); f (outputZmed) = zmed(outputZmed); % 已处理记录 alreadyProcessed = alreadyProcessed | processUsingLevelB; % 是否退出 if all (alreadyProcessed (:)) break; end end % 大于窗口尺寸后，Zxy替换成Zmed输出 f (~alreadyProcessed) = zmed (~alreadyProcessed); end function img_gray=rgbimage2gray(img) % 灰度变换,公式：f(x,y)=0.2989R+ 0.5870G + 0.1140B img_gray = img(:,:,1)0.2989+ img(:,:,2)0.5870+ img(:,:,3)*0.1140; end function img_rgb=gray2rgb(img_gray,img) % 将灰度图像转化为RGB图像 img_rgb = zeros(size(img)); img_rgb(:,:,1) = img_gray; img_rgb(:,:,2) = img_gray; img_rgb(:,:,3) = img_gray; end带有下标的赋值维度不匹配。出错 Untitled13>gray2rgb (line 75) img_rgb(:,:,1) = img_gray; 出错 Untitled13 (line 10) f1 = gray2rgb(f1,img);

这段代码中出现了错误，原因在于在函数gray2rgb中进行了赋值操作时，维度不匹配。...img_rgb = zeros(size(img)); img_rgb(:,:,1) = img_gray; img_rgb(:,:,2) = img_gray; img_rgb(:,:,3) = img_gray; end

补全代码：clear;clc % 读取图像文件 img = imread('Lena01.bmp'); img =imresize(img,0.2); % 显示原始图像 subplot(2, 2, 1); imshow(img); title('Original Image'); % 计算傅里叶变换 f = double(img); F = zeros(size(f)); [M, N] = size(f); for u = 0:M-1 for v = 0:N-1 for x = 0:M-1 for y = 0:N-1 end end end end % 显示傅里叶变换后的图像 subplot(2, 2, 2); imshow(log(1+abs(F)), []); title('Fourier Transform'); % 计算傅里叶逆变换 f2 = zeros(size(f)); for x = 0:M-1 for y = 0:N-1 for u = 0:M-1 for v = 0:N-1 end end end end

f2(x+1, y+1) = f2(x+1, y+1) + F(u+1, v+1) * exp(2j*pi*((u*x/M)+(v*y/N))); end end end end % 显示傅里叶逆变换后的图像 subplot(2, 2, 3); imshow(uint8(f2)); title('Inverse Fourier Transform'...

canny边缘检测及matlab实现.pdf

[m, n] = size(img); img = double(img); % 高斯滤波 w = fspecial('gaussian', [5 5]); img = imfilter(img, w, 'replicate'); figure; imshow(uint8(img)); % Sobel算子 w = fspecial('sobel'); img_w = ...

机器视觉大作业报告-matlab

clear all; close all; % 读取图像 I0 = imread('lena.bmp'); I1 = imread('lena_noisy01.jpg'); img2 = imread('lena_noisy02.bmp'); img1 = rgb2gray(I1); % 均值滤波 mean_filtered1 = imfilter(img1, fspecial...

matlab函数大全matlab函数大全.docx

例如，img = imread('image.jpg')会将名为'image.jpg'的图像加载到变量img中。 2. **imshow**：用于在句柄图形图窗中显示图像。例如，imshow(img)会将图像img显示出来。 3. **imwrite**：与imread相反，...

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