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 = 1:rows  
    for j = 1:cols  
        sum = 0;  
        for k = 1:colors  
            sum = sum + MyYuanLaiPic(i , j , k) / 3;%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(1);  
imshow(MyYuanLaiPic);  
  
%显示经过系统函数运算过的灰度图像  
figure(2);  
imshow(MyFirstGrayPic);  
  
%显示转化之后的灰度图像  
figure(3);  
imshow(MidGrayPic);

3. 对图像进行旋转和翻转

function I = data_augmentation(I, K)


if K == 1
    return;
elseif K == 2 % flipped
    I = flipud(I);
    return;
elseif K == 3 % rotation 90
    I = rot90(I,1);
    return;
elseif K == 4 % rotation 90 & flipped
    I = rot90(I,1);
    I = flipud(I);
    return;
elseif K == 5 % rotation 180
    I = rot90(I,2);
    return;
elseif K == 6 % rotation 180 & flipped
    I = rot90(I,2);
    I = flipud(I);
    return;
elseif K == 7 % rotation 270
    I = rot90(I,3);
    return;
elseif K == 8 % rotation 270 & flipped
    I = rot90(I,3);
    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 = 1+step1 : stride : (hei-patchsize+1)
            for y = 1+step2 : stride : (wid-patchsize+1)
                count = count + 1;
                subim_label  = HR_current(x : x+patchsize-1, y : y+patchsize-1,1:nch);
                imdb.HRlabels(:, :, :, count) = subim_label;
                if count<=diffPatches   %不够一个patch进行填充
                    imdb.HRlabels(:, :, :, end-count+1)   = HR_current(x : x+patchsize-1, y : y+patchsize-1,1: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     = 60;  % control the number of image patches
patchsize  = 70;

batchSize  = 256; % important for BNorm
count      = 0;
nch        = 1;   % 1 for grayscale image, 3 for color image

step1      = 0;
step2      = 0;

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

for j = 1:length(folders)
    for i = 1 : length(ext)
        filepaths = cat(1,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       = 1;
nscales  = min(1,0.45 + 0.05*randi(21,[ns,nimages])); %产生随机矩阵 1*5 all values are less than 1 
naugment = randi(8,[1,nimages]); %产生一个随机矩阵1*5    all values are form 1 - 8

for i = 1 : nimages
    % HR = imread(fullfile(filepaths(cshuffle(i)).folder,filepaths(cshuffle(i)).name));
    HR = imread(fullfile(folders{1},filepaths(cshuffle(i)).name)); %从文件夹里随机读取一张图片
    HR = HR(:,:,1); %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, 7); 转到data_augmentation.m里面就将图片旋转90度
    disp([i,nimages,round(count/batchSize)])
    
    for j = 1: size(nscales,1) %  size(nscales,1) 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 = 1+step1 : stride : (hei-patchsize+1)   % 产生的patch  (hei-patchsize+1)这个value是一个临界值，最后放不了一个patch
            for y = 1+step2 : stride : (wid-patchsize+1)
                count=count+1;
            end
        end
    end
end


numPatches  = ceil(count/batchSize)*batchSize; %ceil(1.2)=>2 ceil(0.2)=>1 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 = 0;
imdb.HRlabels  = zeros(patchsize, patchsize, nch, numPatches,'single');  %imdb是一个结构体 70*70*1*256

for i = 1 : nimages
    % HR = imread(fullfile(filepaths(cshuffle(i)).folder,filepaths(cshuffle(i)).name));
    HR = imread(fullfile(folders{1},filepaths(cshuffle(i)).name)); %从文件夹里随机读取一张图片
    
    if nch ==1 && size(HR,3) == 3  %rgb2gray
        HR = rgb2gray(HR);
    end
    
    HR = data_augmentation(HR, naugment(i));  % 图像旋转操作 
    disp([i,nimages,round(count/256)])
    
    for j = 1: size(nscales,1)
        
        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 = 1+step1 : stride : (hei-patchsize+1)
            for y = 1+step2 : stride : (wid-patchsize+1)
                count = count + 1;
                subim_label  = HR_current(x : x+patchsize-1, y : y+patchsize-1,1:nch);
                imdb.HRlabels(:, :, :, count) = subim_label;
                if count<=diffPatches   %不够一个patch进行填充
                    imdb.HRlabels(:, :, :, end-count+1)   = HR_current(x : x+patchsize-1, y : y+patchsize-1,1:nch);
                end
            end
        end
    end
end

imdb.set    = uint8(ones(1,size(imdb.HRlabels,4)));

 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