VOC分割数据集数据增强

痛定思痛。 2022-10-01 13:47 277阅读 0赞

分割数据集的正常的数据增强方式：对原始数据和ground-truth做相同的增强变换，代码实现如下。

对于原始数据进行增强的代码：my\_Augment\_data.m

%图像的水平翻转和垂直翻转
    clear;clc
    
    % file_path =  './data1/JPEGImages/';% 图像文件夹路径
    % new_file_path = './aug1/JPEGImages/';
    
    file_path =  '~/dataset/mydatababy/full-VOC/JPEGImages/';% 图像文件夹路径
    new_file_path = '~/dataset/mydatababy/full-VOC-new/JPEGImages/';
    
    img_path_list = dir(strcat(file_path,'*.jpg'));%获取该文件夹中所有jpg格式的图像
    
    img_num = length(img_path_list);%获取图像总数量
    
    img_num
    if img_num > 0 %有满足条件的图像
        for j = 1:img_num %逐一读取图像
            image_name = img_path_list(j).name(1:end-4);% 图像名
            
            image_name
            image =  imread(strcat(file_path,image_name,'.jpg'));
            
            imageBB = image;
            imageB = image;
            
            for k=1:3
                imageBB(:,:,k)=flipud(image(:,:,k));%上下翻转
                imageB(:,:,k)=fliplr(image(:,:,k));%左右翻转
            end
                                   
    %         subplot(1,3,1);
    %         imshow(image);
    %         title('yuan')
    % 
    %         subplot(1,3,2);
    %         imshow(imageBB);
    %         title('up-down')
    % 
    %         subplot(1,3,3);
    %         imshow(imageB);
    %         title('left-right')
    %         
            
            file_name1 = strcat(new_file_path,image_name,'.jpg');
            imwrite(image,file_name1);
            
            new_file_name2 = strcat(new_file_path,image_name,'_2.jpg');
            imwrite(imageBB,new_file_name2);
            
            new_file_name3 = strcat(new_file_path,image_name,'_3.jpg');
            imwrite(imageB,new_file_name3);
            
          
            imageI1=rot90(image,1);%逆时针转90度
            imageI2=rot90(image,2);%逆时针转180度
            imageI3=rot90(image,3);%逆时针转270度
            
            new_file_name4 = strcat(new_file_path,image_name,'_4.jpg');
            imwrite(imageI1,new_file_name4);
            
            new_file_name5 = strcat(new_file_path,image_name,'_5.jpg');
            imwrite(imageI2,new_file_name5);
            
            new_file_name6 = strcat(new_file_path,image_name,'_6.jpg');
            imwrite(imageI3,new_file_name6);
            
            
        end
    end
    
    
    % 
    % AA=imread('test/label24/2018_010001.png');%文件名是你自己的哦
    % 
    % 
    % figure;
    % 
    % subplot(1,3,1);
    % imshow(AA);
    % title('yuan')
    % 
    % subplot(1,3,2);
    % imshow(BB);
    % title('up-down')
    % 
    % subplot(1,3,3);
    % imshow(B);
    % title('left-right')
    % 
    % 
    % I=imread('test/label24/2018_010001.png');
    % I1=rot90(I,1);%逆时针转90度
    % I2=rot90(I,2);%逆时针转180度
    % I3=rot90(I,3);%逆时针转270度
    % figure
    % subplot(2,2,1);
    % imshow(I);
    % title('原图')
    % subplot(2,2,2);
    % imshow(I1)
    % title('逆时针转90度')
    % subplot(2,2,3);
    % imshow(I2);
    % title('逆时针转180度')
    % subplot(2,2,4);
    % imshow(I3);
    % title('逆时针转270度')

对于ground-truth进行数据增强的代码：my\_Augment\_labels.m

%图像的水平翻转和垂直翻转
    clear;clc
    
    %file_path =  './data/JPEGImages/';% 图像文件夹路径
    %new_file_path = './aug/JPEGImages/';
    
    % file_path =  './data1/SegmentationClass/';% 图像文件夹路径 %##labels
    % new_file_path = './aug1/SegmentationClass/';
    
    file_path =  '~/dataset/mydatababy/full-VOC/SegmentationClass/';% 图像文件夹路径 %##labels
    new_file_path = '~/dataset/mydatababy/full-VOC-new/SegmentationClass/';
    
    %img_path_list = dir(strcat(file_path,'*.jpg'));%获取该文件夹中所有jpg格式的图像
    
    img_path_list = dir(strcat(file_path,'*.png'));%获取该文件夹中所有jpg格式的图像  %##labels
    
    img_num = length(img_path_list);%获取图像总数量
    
    img_num
    if img_num > 0 %有满足条件的图像
        for j = 1:img_num %逐一读取图像
            image_name = img_path_list(j).name(1:end-4);% 图像名
            
            image_name
            %image =  imread(strcat(file_path,image_name,'.jpg'));
            image =  imread(strcat(file_path,image_name,'.png')); %##labels
    
            imageBB = image;
            imageB = image;
            
    %         for k=1:3
    %             imageBB(:,:,k)=flipud(image(:,:,k));%上下翻转
    %             imageB(:,:,k)=fliplr(image(:,:,k));%左右翻转
    %         end
            
            
            imageBB(:,:,1)=flipud(image(:,:,1));%上下翻转  %##labels
            imageB(:,:,1)=fliplr(image(:,:,1));%左右翻转
            
    %         subplot(1,3,1);
    %         imshow(image);
    %         title('yuan')
    % 
    %         subplot(1,3,2);
    %         imshow(imageBB);
    %         title('up-down')
    % 
    %         subplot(1,3,3);
    %         imshow(imageB);
    %         title('left-right')
    %         
            
    %         file_name1 = strcat(new_file_path,image_name,'.jpg');
    %         imwrite(image,file_name1);
    %         
    %         new_file_name2 = strcat(new_file_path,image_name,'_2.jpg');
    %         imwrite(imageBB,new_file_name2);
    %         
    %         new_file_name3 = strcat(new_file_path,image_name,'_3.jpg');
    %         imwrite(imageB,new_file_name3);
            
    
    
            file_name1 = strcat(new_file_path,image_name,'.png'); %##labels
            imwrite(image,file_name1);
            
            new_file_name2 = strcat(new_file_path,image_name,'_2.png');
            imwrite(imageBB,new_file_name2);
            
            new_file_name3 = strcat(new_file_path,image_name,'_3.png');
            imwrite(imageB,new_file_name3);
    
    
    
            imageI1=rot90(image,1);%逆时针转90度
            imageI2=rot90(image,2);%逆时针转180度
            imageI3=rot90(image,3);%逆时针转270度
            
    %         new_file_name4 = strcat(new_file_path,image_name,'_4.jpg');
    %         imwrite(imageI1,new_file_name4);
    %         
    %         new_file_name5 = strcat(new_file_path,image_name,'_5.jpg');
    %         imwrite(imageI2,new_file_name5);
    %         
    %         new_file_name6 = strcat(new_file_path,image_name,'_6.jpg');
    %         imwrite(imageI3,new_file_name6);
            
            
            new_file_name4 = strcat(new_file_path,image_name,'_4.png');  %##labels
            imwrite(imageI1,new_file_name4);
            
            new_file_name5 = strcat(new_file_path,image_name,'_5.png');
            imwrite(imageI2,new_file_name5);
            
            new_file_name6 = strcat(new_file_path,image_name,'_6.png');
            imwrite(imageI3,new_file_name6);
            
        end
    end

**由于要加入噪声从而对数据进行增强，所以按照以下流程制作数据集，数据增强方式如下：**

1.将1198张原始图像和label随机分为训练集文件夹（959张）和测试集文件夹（239张）;同时，把对应的ground-truth分为训练文件夹和测试文件夹。  
2.对原始图像采用8种方式进行数据增强，包括加入4种噪声，4种亮度调节;同时，把对应的ground-truth进行复制进行增强。  
3.根据分成的训练和测试文件夹，生成train.txt和val.txt文件。  
4.手动把训练集和测试集合并为一个文件夹作为原始数据集;同时，把ground-truth合并为一个文件夹。  
5.根据合并后的文件夹，生成trainval.txt文件。  
6.用增强后的959×9=8631张数据集作为训练集，用原始分离出的239张测试集作为最终的测试集，进行训练和测试。

my\_split.m

clear all;
    clc;
    %step1: split to test and trian datasets
    
    file_path =  '~/dataset/mydata0611/full-VOC/JPEGImages/';%image path
    dst_test_path = '~/dataset/mydata0611/full-VOC/JPEGTest/'; %dst_test path
    dst_train_path = '~/dataset/mydata0611/full-VOC/JPEGTrain/'; %dst_train path
    
    img_path_list = dir(strcat(file_path,'*.jpg'));%get jpg-image list
    img_num = length(img_path_list);%get jpg-image number
    
    
    for i = 1:img_num
        name(i,1) = {img_path_list(i).name(1:end-4)};
    end
    image_orign = name; %get the origin name of image
    
    
    len2 = img_num;%get the origin_image number
    index = randperm(len2);  %get the random index
    test_num = floor(len2*0.2);% 0.2 as the test datasets
    train_num = (len2 - test_num);% 0.8 as the train datasets
    trainval_num = len2; %total number
    
    test_index = image_orign(index(1:test_num),:); %set the index as the test datasets
    train_index = image_orign(index(test_num+1:test_num+train_num),:); %set the index as the train datasets
    trainval_index = image_orign(index(1:end),:);%set the index as the all datasets
    
    for i = 1:length(test_index)
        %trainval_index{1} %get the data from cell
        
        imagename = [file_path,test_index{i},'.jpg'];
        disp(imagename);
        movefile(imagename,dst_test_path);
    end
    
    for i = 1:length(train_index)
        %trainval_index{1} %get the data from cell
        
        imagename = [file_path,train_index{i},'.jpg'];
        disp(imagename);
        movefile(imagename,dst_train_path); %move file to different folder
    end
    
    %copyfile(imagename,dst_train_path); %copy file to different folder
    
    
    
    label_path =  '~/dataset/mydata0611/full-VOC/SegmentationClass/';%image path
    dst_label_test_path = '~/dataset/mydata0611/full-VOC/SegmentationTest/'; %dst_test path
    dst_label_train_path = '~/dataset/mydata0611/full-VOC/SegmentationTrain/'; %dst_train path
    
    
    
    for i = 1:length(test_index)
        %trainval_index{1} %get the data from cell
        
        imagename = [label_path,test_index{i},'.png'];
        disp(imagename);
        movefile(imagename,dst_label_test_path);
    end
    
    for i = 1:length(train_index)
        %trainval_index{1} %get the data from cell
        
        imagename = [label_path,train_index{i},'.png'];
        disp(imagename);
        movefile(imagename,dst_label_train_path); %move file to different folder
    end
    
    
    
    
    
    % fid1=fopen('D:\temppic\data\full-VOC\ImageSets\Segmentation\trainval.txt','w');
    % for i = 1:length(trainval_index)
    %     %trainval_index{1} %get the data from cell
    %     fprintf(fid1,'%s\n',trainval_index{i});%
    % end
    % fclose(fid1);
    % 
    % fid2=fopen('D:\temppic\data\full-VOC\ImageSets\Segmentation\train.txt','w');
    % for i = 1:length(train_index)
    %     fprintf(fid1,'%s\n',train_index{i});%
    % end
    % fclose(fid2);
    % 
    % fid3=fopen('D:\temppic\data\full-VOC\ImageSets\Segmentation\val.txt','w');
    % for i = 1:length(test_index)
    %     fprintf(fid1,'%s\n',test_index{i});%
    % end
    % fclose(fid3);

my\_Augment\_noise\_data.m

close all;
    clear all;
    clc;
    
    %step2_1:
    %augment image use noise
    %8 methods: 4 noise, 4 light
    
    file_path =  '~/dataset/mydata0611/full-VOC/JPEGTrain/';% 图像文件夹路径
    new_file_path = '~/dataset/mydata0611/full-VOC/JPEGTrain/';
    
    img_path_list = dir(strcat(file_path,'*.jpg'));%获取该文件夹中所有jpg格式的图像
    
    img_num = length(img_path_list);%获取图像总数量
    
    img_num
    if img_num > 0 %有满足条件的图像
        for j = 1:img_num %逐一读取图像
            image_name = img_path_list(j).name(1:end-4);% 图像名
            
            image_name
            image =  imread(strcat(file_path,image_name,'.jpg'));
            
            file_name_yuan = strcat(new_file_path,image_name,'.jpg');
            imwrite(image,file_name_yuan);
            
            image_gaussian = imnoise(image, 'gaussian');
            file_name_gaussian = strcat(new_file_path,image_name,'_gaussian.jpg');
            imwrite(image_gaussian,file_name_gaussian);
            
            
            image_saltpepper = imnoise(image, 'salt & pepper');
            file_name_saltpepper = strcat(new_file_path,image_name,'_saltpepper.jpg');
            imwrite(image_saltpepper,file_name_saltpepper);
            
            
            image_speckle = imnoise(image, 'speckle');
            file_name_speckle = strcat(new_file_path,image_name,'_speckle.jpg');
            imwrite(image_speckle,file_name_speckle);
            
            
            image_poisson = imnoise(image, 'poisson');
            file_name_poisson = strcat(new_file_path,image_name,'_poisson.jpg');
            imwrite(image_poisson,file_name_poisson);
            
            
            image_stretch = intrans(image, 'stretch', mean2(im2double(image)),0.9);
            image_stretch_1 = im2uint8(image_stretch);
            file_name_stretch = strcat(new_file_path,image_name,'_stretch.jpg');
            imwrite(image_stretch_1,file_name_stretch);
            
            
            image_gscale = gscale(image, 'full8');
            file_name_gscale = strcat(new_file_path,image_name,'_gscale.jpg');
            imwrite(image_gscale,file_name_gscale);
            
            
            image_imadjust1 = imadjust(image, [0 1], [0 0.75]); 
            file_name_imadjust1 = strcat(new_file_path,image_name,'_imadjust1.jpg');
            imwrite(image_imadjust1,file_name_imadjust1);
            
            
            image_imadjust2 = imadjust(image, [0 1], [0.5 1]); 
            file_name_imadjust2 = strcat(new_file_path,image_name,'_imadjust2.jpg');
            imwrite(image_imadjust2,file_name_imadjust2);
            
        end
    end
    
    
    
    % AA=imread('~/dataset/mydata0611/full-VOC/JPEGTrain/2018_000258.jpg');%文件名是你自己的哦
    % 
    % figure;
    % 
    % subplot(1,3,1);
    % imshow(AA);
    % title('yuan')
    % 
    % 
    % BB = imnoise(AA, 'gaussian');
    % subplot(1,3,2);
    % imshow(BB);
    % title('gaussian')
    % 
    % % B = imnoise(AA, 'localvar');
    % % subplot(1,3,3);
    % % imshow(B);
    % % title('localvar')
    % 
    % 
    % figure;
    % 
    % BB1 = imnoise(AA, 'salt & pepper');
    % subplot(1,3,1);
    % imshow(BB1);
    % title('salt & pepper')
    % 
    % B1 = imnoise(AA, 'speckle');
    % subplot(1,3,2);
    % imshow(B1);
    % title('speckle')
    % 
    % 
    % B2 = imnoise(AA, 'poisson');
    % subplot(1,3,3);
    % imshow(B2);
    % title('poisson')
    % 
    % 
    % figure;
    % 
    % LL1 = imadjust(AA, [0 1], [1 0]); %fanzhuan method1  %buxuan
    % subplot(1,3,1);
    % imshow(LL1);
    % title('imadjust')
    % 
    % % LL3 = imcomplement(AA);%fanzhuan method2
    % % subplot(1,3,3);
    % % imshow(LL3);
    % % title('imcomplement')
    % 
    % 
    % 
    % % LL4 = im2bw(LL1,0.55);%erzhihua
    % % subplot(1,3,2);
    % % imshow(LL4);
    % 
    % 
    % 
    % 
    % LL2 = intrans(AA, 'stretch', mean2(im2double(AA)),0.9);
    % subplot(1,3,2);
    % imshow(LL2);
    % title('intrans')
    % LL2_1 = im2uint8(LL2);
    % 
    % 
    % LL3 = gscale(AA, 'full8');
    % subplot(1,3,3);
    % imshow(LL3);
    % title('gscale')
    % 
    % 
    % figure;
    % 
    % imadjust1 = imadjust(AA, [0.3 0.75], [0 1]);   %buxuan
    % subplot(1,3,1);
    % imshow(imadjust1);
    % title('imadjust1')
    % 
    % imadjust2 = imadjust(AA, [0 1], [0 0.75]); 
    % subplot(1,3,2);
    % imshow(imadjust2);
    % title('imadjust2')
    % 
    % 
    % imadjust3 = imadjust(AA, [0 1], [0.5 1]); 
    % subplot(1,3,3);
    % imshow(imadjust3);
    % title('imadjust3')
    % 
    % figure;
    % imshow(LL2_1);

my\_Augment\_noise\_labels.m

close all;
    clear all;
    clc;
    
    %step2_2:
    %augment label
    %8 same
    
    file_path =  '~/dataset/mydata0611/full-VOC/SegmentationTrain/';% 图像文件夹路径
    new_file_path = '~/dataset/mydata0611/full-VOC/SegmentationTrain/';
    
    img_path_list = dir(strcat(file_path,'*.png'));%获取该文件夹中所有jpg格式的图像
    
    img_num = length(img_path_list);%获取图像总数量
    
    img_num
    if img_num > 0 %有满足条件的图像
        for j = 1:img_num %逐一读取图像
            image_name = img_path_list(j).name(1:end-4);% 图像名
            
            image_name
            image =  imread(strcat(file_path,image_name,'.png'));
            
            file_name_yuan = strcat(new_file_path,image_name,'.png');
            imwrite(image,file_name_yuan);
            
            %image_gaussian = imnoise(image, 'gaussian');
            file_name_gaussian = strcat(new_file_path,image_name,'_gaussian.png');
            imwrite(image,file_name_gaussian);
            
            
            %image_saltpepper = imnoise(image, 'salt & pepper');
            file_name_saltpepper = strcat(new_file_path,image_name,'_saltpepper.png');
            imwrite(image,file_name_saltpepper);
            
            
            %image_speckle = imnoise(image, 'speckle');
            file_name_speckle = strcat(new_file_path,image_name,'_speckle.png');
            imwrite(image,file_name_speckle);
            
            
            %image_poisson = imnoise(image, 'poisson');
            file_name_poisson = strcat(new_file_path,image_name,'_poisson.png');
            imwrite(image,file_name_poisson);
            
            
            %image_stretch = intrans(image, 'stretch', mean2(im2double(image)),0.9);
            %image_stretch_1 = im2uint8(image_stretch);
            file_name_stretch = strcat(new_file_path,image_name,'_stretch.png');
            imwrite(image,file_name_stretch);
            
            
            %image_gscale = gscale(image, 'full8');
            file_name_gscale = strcat(new_file_path,image_name,'_gscale.png');
            imwrite(image,file_name_gscale);
            
            
            %image_imadjust1 = imadjust(image, [0 1], [0 0.75]); 
            file_name_imadjust1 = strcat(new_file_path,image_name,'_imadjust1.png');
            imwrite(image,file_name_imadjust1);
            
            
            %image_imadjust2 = imadjust(image, [0 1], [0.5 1]); 
            file_name_imadjust2 = strcat(new_file_path,image_name,'_imadjust2.png');
            imwrite(image,file_name_imadjust2);
            
        end
    end
    
    
    
    % AA=imread('~/dataset/mydata0611/full-VOC/JPEGTrain/2018_000258.jpg');%文件名是你自己的哦
    % 
    % figure;
    % 
    % subplot(1,3,1);
    % imshow(AA);
    % title('yuan')
    % 
    % 
    % BB = imnoise(AA, 'gaussian');
    % subplot(1,3,2);
    % imshow(BB);
    % title('gaussian')
    % 
    % % B = imnoise(AA, 'localvar');
    % % subplot(1,3,3);
    % % imshow(B);
    % % title('localvar')
    % 
    % 
    % figure;
    % 
    % BB1 = imnoise(AA, 'salt & pepper');
    % subplot(1,3,1);
    % imshow(BB1);
    % title('salt & pepper')
    % 
    % B1 = imnoise(AA, 'speckle');
    % subplot(1,3,2);
    % imshow(B1);
    % title('speckle')
    % 
    % 
    % B2 = imnoise(AA, 'poisson');
    % subplot(1,3,3);
    % imshow(B2);
    % title('poisson')
    % 
    % 
    % figure;
    % 
    % LL1 = imadjust(AA, [0 1], [1 0]); %fanzhuan method1  %buxuan
    % subplot(1,3,1);
    % imshow(LL1);
    % title('imadjust')
    % 
    % % LL3 = imcomplement(AA);%fanzhuan method2
    % % subplot(1,3,3);
    % % imshow(LL3);
    % % title('imcomplement')
    % 
    % 
    % 
    % % LL4 = im2bw(LL1,0.55);%erzhihua
    % % subplot(1,3,2);
    % % imshow(LL4);
    % 
    % 
    % 
    % 
    % LL2 = intrans(AA, 'stretch', mean2(im2double(AA)),0.9);
    % subplot(1,3,2);
    % imshow(LL2);
    % title('intrans')
    % LL2_1 = im2uint8(LL2);
    % 
    % 
    % LL3 = gscale(AA, 'full8');
    % subplot(1,3,3);
    % imshow(LL3);
    % title('gscale')
    % 
    % 
    % figure;
    % 
    % imadjust1 = imadjust(AA, [0.3 0.75], [0 1]);   %buxuan
    % subplot(1,3,1);
    % imshow(imadjust1);
    % title('imadjust1')
    % 
    % imadjust2 = imadjust(AA, [0 1], [0 0.75]); 
    % subplot(1,3,2);
    % imshow(imadjust2);
    % title('imadjust2')
    % 
    % 
    % imadjust3 = imadjust(AA, [0 1], [0.5 1]); 
    % subplot(1,3,3);
    % imshow(imadjust3);
    % title('imadjust3')
    % 
    % figure;
    % imshow(LL2_1);

make\_files\_val\_and\_train.m

clear all;
    clc;
    
    %step3: make train.txt and val.txt
    
    file_train_path =  '~/dataset/mydata0611/full-VOC/JPEGTrain/';% 图像文件夹路径 Train
    img_train_path_list = dir(strcat(file_train_path,'*.jpg'));%获取该文件夹中所有jpg格式的图像
    img_train_num = length(img_train_path_list);%获取图像总数量
    
    file_test_path =  '~/dataset/mydata0611/full-VOC/JPEGTest/';% 图像文件夹路径
    img_test_path_list = dir(strcat(file_test_path,'*.jpg'));%获取该文件夹中所有jpg格式的图像
    img_test_num = length(img_test_path_list);%获取图像总数量
    
    %fid1=fopen('~/dataset/mydata0611/full-VOC/ImageSets/Segmentation/trainval.txt','w');
    fid2=fopen('~/dataset/mydata0611/full-VOC/ImageSets/Segmentation/train.txt','w');
    fid3=fopen('~/dataset/mydata0611/full-VOC/ImageSets/Segmentation/val.txt','w');
    
    if img_train_num > 0 %有满足条件的图像
        for j = 1:img_train_num %逐一读取图像
            image_name = img_train_path_list(j).name(1:end-4);% 图像名
            %image =  imread(strcat(file_path,image_name,'.jpg'));
            fprintf(fid2,'%s\n',image_name);% 显示正在处理的图像名
            %图像处理过程 省略
            %imshow(image)
        end   
        fclose(fid2);
    end
    
    if img_test_num > 0 %有满足条件的图像
        for j = 1:img_test_num %逐一读取图像
            image_name = img_test_path_list(j).name(1:end-4);% 图像名
            %image =  imread(strcat(file_path,image_name,'.jpg'));
            fprintf(fid3,'%s\n',image_name);% 显示正在处理的图像名
            %图像处理过程 省略
            %imshow(image)
        end   
        fclose(fid3);
    end

make\_files\_trainval.m

clear all;
    clc;
    
    %step5: make trainval.txt
    
    file_trainval_path =  '~/dataset/mydata0611/full-VOC/JPEGImages/';% 图像文件夹路径 Train
    img_trainval_path_list = dir(strcat(file_trainval_path,'*.jpg'));%获取该文件夹中所有jpg格式的图像
    img_trainval_num = length(img_trainval_path_list);%获取图像总数量
    
    fid1=fopen('~/dataset/mydata0611/full-VOC/ImageSets/Segmentation/trainval.txt','w');
    
    
    if img_trainval_num > 0 %有满足条件的图像
        for j = 1:img_trainval_num %逐一读取图像
            image_name = img_trainval_path_list(j).name(1:end-4);% 图像名
            %image =  imread(strcat(file_path,image_name,'.jpg'));
            fprintf(fid1,'%s\n',image_name);% 显示正在处理的图像名
            %图像处理过程 省略
            %imshow(image)
        end
        fclose(fid1);
    end

特别地，对于加入噪声和调节亮度的代码中，有两个函数gscale.m和intrans.m，并不是matlab的内置函数，特地附上代码，只需要将该函数文件和调用函数的.m文件置于同一个目录下即可正常调用。

gscale.m

function g=gscale(f,varargin)
    if length(varargin)==0
      method='full8';
    else method=varargin{1};
    end
    if strcmp(class(f),'double')&(max(f(:))>1 | min(f(:))<0)
       f=mat2gray(f);
    end
    
    
    switch method
    case 'full8'
            g=im2uint8(mat2gray(double(f)));
    case 'full16'
            g=im2uint16(mat2gray(double(f)));
    case 'minmax'
           low = varargin{2};high = varargin{3};
           if low>1 | low<0 |high>1 | high<0
                 error('Parameters low and high must be in the range [0,1]')
           end
           if strcmp(class(f),'double')
                low_in=min(f(:));
                high_in=max(f(:));
           elseif  strcmp(class(f),'uint8')
                low_in=double(min(f(:)))./255;
                high_in=double(max(f(:)))./255;
           elseif   strcmp(class(f),'uint16')
                low_in=double(min(f(:)))./65535;
                high_in=double(max(f(:)))./65535;
           end
    
           g=imadjust(f,[low_in high_in],[low high]);
    otherwise
           error('Unknown method')
    end

intrans.m

function g = intrans(f, varargin)
    %INTRANS Performs intensity (gray-level) transformations.
    %   G = INTRANS(F, 'neg') computes the negative of input image F.
    %
    %   G = INTRANS(F, 'log', C, CLASS) computes C*log(1 + F) and
    %   multiplies the result by (positive) constant C. If the last two
    %   parameters are omitted, C defaults to 1. Because the log is used
    %   frequently to display Fourier spectra, parameter CLASS offers the
    %   option to specify the class of the output as 'uint8' or
    %   'uint16'. If parameter CLASS is omitted, the output is of the
    %   same class as the input.
    %
    %   G = INTRANS(F, 'gamma', GAM) performs a gamma transformation on
    %   the input image using parameter GAM (a required input). 
    %
    %   G = INTRANS(F, 'stretch', M, E) computes a contrast-stretching
    %   transformation using the expression 1./(1 + (M./(F +
    %   eps)).^E).  Parameter M must be in the range [0, 1].  The default
    %   value for M is mean2(im2double(F)), and the default value for E
    %   is 4.
    %
    %   For the 'neg', 'gamma', and 'stretch' transformations, double
    %   input images whose maximum value is greater than 1 are scaled
    %   first using MAT2GRAY.  Other images are converted to double first
    %   using IM2DOUBLE.  For the 'log' transformation, double images are
    %   transformed without being scaled; other images are converted to
    %   double first using IM2DOUBLE.
    %
    %   The output is of the same class as the input, except if a
    %   different class is specified for the 'log' option.
    %   Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins
    %   Digital Image Processing Using MATLAB, Prentice-Hall, 2004
    %   $Revision: 1.7 $  $Date: 2003/10/13 00:45:53 $
    % Verify the correct number of inputs.
    error(nargchk(2, 4, nargin))
    % Store the class of the input for use later.
    classin = class(f);
    % If the input is of class double, and it is outside the range
    % [0, 1], and the specified transformation is not 'log', convert the
    % input to the range [0, 1].
    if strcmp(class(f), 'double') & max(f(:)) > 1 & ...
          ~strcmp(varargin{1}, 'log')
       f = mat2gray(f);
    else % Convert to double, regardless of class(f).
       f = im2double(f);
    end
    % Determine the type of transformation specified.
    method = varargin{1};
    % Perform the intensity transformation specified.   
    switch method
    case 'neg'
       g = imcomplement(f);
    case 'log'
       if length(varargin) == 1 
          c = 1;
       elseif length(varargin) == 2 
          c = varargin{2};
       elseif length(varargin) == 3
          c = varargin{2};
          classin = varargin{3};
       else
          error('Incorrect number of inputs for the log option.')
       end
       g = c*(log(1 + double(f)));
    case 'gamma'
       if length(varargin) < 2
          error('Not enough inputs for the gamma option.')
       end
       gam = varargin{2};
       g = imadjust(f, [ ], [ ], gam);
    
    case 'stretch'
       if length(varargin) == 1
          % Use defaults.
          m = mean2(f); 
          E = 4.0;          
       elseif length(varargin) == 3
          m = varargin{2}; 
          E = varargin{3};
       else error('Incorrect number of inputs for the stretch option.')
       end
       g = 1./(1 + (m./(f + eps)).^E);
    otherwise
       error('Unknown enhancement method.')
    end
    % Convert to the class of the input image.
    %g = changeclass(classin, g);