Matlab的用法总结

1. 对序列进行洗牌 randperm()

randperm()产生随机的序列
%if filepaths 是一个5*1的结构体，then

cshuffle = randperm(length(filepaths)) %对filepaths进行随机的洗牌，得到了 cshuffle  => [2,5,4,1,3] 洗牌后的序列

2. 图像灰度化 rgb2gray()

MyYuanLaiPic = imread('e:/image/matlab/Cluo.jpg');%读取RGB格式的图像
MyFirstGrayPic = rgb2gray(MyYuanLaiPic);%用已有的函数进行RGB到灰度图像的转换  

[rows , cols , colors] = size(MyYuanLaiPic);%得到原来图像的矩阵的参数
MidGrayPic = zeros(rows , cols);%用得到的参数创建一个全零的矩阵，这个矩阵用来存储用下面的方法产生的灰度图像
MidGrayPic = uint8(MidGrayPic);%将创建的全零矩阵转化为uint8格式，因为用上面的语句创建之后图像是double型的  

for i = :rows
    for j = :cols
        sum = ;
        for k = :colors
            sum = sum + MyYuanLaiPic(i , j , k) / ;%进行转化的关键公式，sum每次都因为后面的数字而不能超过255 
        end
        MidGrayPic(i , j) = sum;
    end
end
imwrite(MidGrayPic , 'E:/image/matlab/Cluo.png' , 'png');  

%显示原来的RGB图像
figure();
imshow(MyYuanLaiPic);  

%显示经过系统函数运算过的灰度图像
figure();
imshow(MyFirstGrayPic);  

%显示转化之后的灰度图像
figure();
imshow(MidGrayPic);

3. 对图像进行旋转和翻转

function I = data_augmentation(I, K)

if K ==
    return;
elseif K ==  % flipped
    I = flipud(I);
    return;
elseif K ==  % rotation
    I = rot90(I,);
    return;
elseif K ==  % rotation  & flipped
    I = rot90(I,);
    I = flipud(I);
    return;
elseif K ==  % rotation
    I = rot90(I,);
    return;
elseif K ==  % rotation  & flipped
    I = rot90(I,);
    I = flipud(I);
    return;
elseif K ==  % rotation
    I = rot90(I,);
    return;
elseif K ==  % rotation  & flipped
    I = rot90(I,);
    I = flipud(I);
    return;
end

4. 对array进行连接 cat(dim, A, B)

、

5.对图像进缩放，imresize()

HR_current  = imresize(HR,nscales(j,i),'bicubic');
%第二个参数为缩放因子，e.g. 0.5、0.8
%第三个参数为缩放method

6.读取图像默认的数据类型uint8，最大值为255；但是在进行图形矩阵的操作和变化过程中非常容易溢出，所以需要转化为double（64为，0~1）或者single

im2double()将值0~255映射到0~1之间

7.两个循环搞定patch的获取

 for x = +step1 : stride : (hei-patchsize+)
            for y = +step2 : stride : (wid-patchsize+)
                count = count + ;
                subim_label  = HR_current(x : x+patchsize-, y : y+patchsize-,:nch);
                imdb.HRlabels(:, :, :, count) = subim_label;
                if count<=diffPatches   %不够一个patch进行填充
                    imdb.HRlabels(:, :, :, end-count+)   = HR_current(x : x+patchsize-, y : y+patchsize-,:nch);
                end
            end
        end

8.产生图像的patch

function [imdb] = generatepatches

%% Note, set your training image set first, large dataset is prefered!
folders    = {'path_of_your_training_dataset'}; % set this first!

stride     = ;  % control the number of image patches
patchsize  = ;

batchSize  = ; % important for BNorm
count      = ;
nch        = ;   %  for grayscale image,  for color image

step1      = ;
step2      = ;

ext               =  {'*.jpg','*.png','*.bmp'};
filepaths         =  [];

for j = :length(folders)
    for i =  : length(ext)
        filepaths = cat(,filepaths, dir(fullfile(folders{j}, ext{i}))); %获取folder获取所有figure
    end
end

cshuffle = randperm(length(filepaths)); % randperm获取filepaths的随机排列
nimages  = round(length(filepaths)); % control the number of image patches 去整数

ns       = ;
nscales  = min(,0.45 + 0.05*randi(,[ns,nimages])); %产生随机矩阵 * all values are less than
naugment = randi(,[,nimages]); %产生一个随机矩阵1*    all values are form  - 

for i =  : nimages
    % HR = imread(fullfile(filepaths(cshuffle(i)).folder,filepaths(cshuffle(i)).name));
    HR = imread(fullfile(folders{},filepaths(cshuffle(i)).name)); %从文件夹里随机读取一张图片
    HR = HR(:,:,); %如果是rgb图就获取一个通道的，如果是gray图则自然为该图像

    HR = data_augmentation(HR, naugment(i)); % 数据增大data_augmentation    data_augmentation(HR, ); 转到data_augmentation.m里面就将图片旋转90度
    disp([i,nimages,round(count/batchSize)])

    for j = : size(nscales,) %  size(nscales,) 为1

        HR_current  = imresize(HR,nscales(j,i),'bicubic'); %对图像进行尺度的缩放
        [hei,wid,~] = size(HR_current); % 得到此时的高和宽
        for x = +step1 : stride : (hei-patchsize+)   % 产生的patch  (hei-patchsize+)这个value是一个临界值，最后放不了一个patch
            for y = +step2 : stride : (wid-patchsize+)
                count=count+;
            end
        end
    end
end

numPatches  = ceil(count/batchSize)*batchSize; %ceil(1.2)=> ceil(0.2)=> 总共有多少个patch
diffPatches = numPatches - count;   % 真实的差了多少个patch
disp([numPatches,numPatches/batchSize,diffPatches]);

disp('-----------------------------');

%------------------------------------------------------------------
%------------------------------------------------------------------

count = ;
imdb.HRlabels  = zeros(patchsize, patchsize, nch, numPatches,'single');  %imdb是一个结构体 ***

for i =  : nimages
    % HR = imread(fullfile(filepaths(cshuffle(i)).folder,filepaths(cshuffle(i)).name));
    HR = imread(fullfile(folders{},filepaths(cshuffle(i)).name)); %从文件夹里随机读取一张图片

    if nch == && size(HR,) ==   %rgb2gray
        HR = rgb2gray(HR);
    end

    HR = data_augmentation(HR, naugment(i));  % 图像旋转操作
    disp([i,nimages,round(count/)])

    for j = : size(nscales,)

        HR_current  = imresize(HR,nscales(j,i),'bicubic'); %图像进行随机的缩放
        [hei,wid,~] = size(HR_current);
        HR_current  = im2single(HR_current);

        for x = +step1 : stride : (hei-patchsize+)
            for y = +step2 : stride : (wid-patchsize+)
                count = count + ;
                subim_label  = HR_current(x : x+patchsize-, y : y+patchsize-,:nch);
                imdb.HRlabels(:, :, :, count) = subim_label;
                if count<=diffPatches   %不够一个patch进行填充
                    imdb.HRlabels(:, :, :, end-count+)   = HR_current(x : x+patchsize-, y : y+patchsize-,:nch);
                end
            end
        end
    end
end

imdb.set    = uint8(ones(,size(imdb.HRlabels,)));

9.求某一个矩阵的指定维度的大小

size(A,dims) = value
# 如xx是一个16*401的矩阵，则求到第一维度的大小16

size(xx,1) =>16

#  如xx是一个16*401的矩阵，则求到第二维度的大小401

size(xx,2) =>401