MATLAB图像处理代写

顶一下。

<p>%颗粒圆度
clear;close all;
%%
%读取源图像
I = imread('999.png');
figure;imshow(I);
%%
%灰度化、取反
h = rgb2gray(I);
figure;imshow(h);%灰度图像
h = imcomplement(h);%取反
figure;imshow(h);
%%
%中值滤波、二值化
h = medfilt2(h,[4,4]);
bw = im2bw(h,graythresh(h));
%%
%消除噪点
se = strel('disk',2);
bw = imclose(bw,se);
figure;imshow(bw);
%%
%填补闭合图形，填充色为白色
bw = imfill(bw,'holes');
%%
%边界寻找
[B,L] = bwboundaries(bw,'noholes');
% 为每个闭合图形设置颜色显示
figure;imshow(label2rgb(L, @jet, [.5 .5 .5]))
hold on
for k = 1:length(B)
boundary = B{k};
plot(boundary(:,2), boundary(:,1), 'w', 'LineWidth', 2)
end
%%
%计算面积
stats = regionprops(L,'Area','Centroid');</p><p>threshold = 0.94;</p><p>% 循环处理每个边界，length(B)是闭合图形的个数,即检测到的陶粒对象个数
for k = 1:length(B)</p><p> % 获取边界坐标'
boundary = B{k};</p><p> % 计算周长
delta_sq = diff(boundary).^2;
perimeter = sum(sqrt(sum(delta_sq,2)));</p><p> % 对标记为K的对象获取面积
area = stats(k).Area;</p><p> % 圆度计算公式4*PI*A/P^2
metric = 4*pi*area/perimeter^2;</p><p> % 结果显示
metric_string = sprintf('%2.2f',metric);</p><p> % 用一个黑色小圆圈标记圆度大于threshold = 0.94 的对象
if metric > threshold
centroid = stats(k).Centroid;
plot(centroid(1),centroid(2),'ko');
end
%设置显示字体
text(boundary(1,2)-35,boundary(1,1)+13,metric_string,'Color','y',...
'FontSize',14,'FontWeight','bold');</p><p>end</p><p>title(['圆度识别结果，越圆越接近1，']);</p><p> </p>

clear all
fprintf('Reading data...')
data=imread('cameraman.tif');
data=uint8(data);%读入数据，并将数据限制为uint8
fprintf('Done!\n')
%编码压缩
fprintf('compressing data...');
[zipped,info]=norm2huff(data);
fprintf('Done!\n')
%解压缩
fprintf('compressing data...');
unzipped=huff2norm(zipped,info);
fprintf('Done!\n')
%测试是否无失真
isOK=isequal(data(:),unzipped(:))
%显示压缩效果
whos data zipped unzipped
function [zipped,info]=norm2huff(vector)
if~isa(vector,'uint8'),
error('input argument must be a uint8 vector')
end
vector=vector(:)';
%将输入向量转换为行向量
f=frequency(vector);
%计算个元素出现的概率
simbols=find(f~=0);
f=f(simbols);%将元素按出现的概率排列
[f,sortindex]=sot(f);
simbols=simbols(sortindex);
%产生码字 generate the codeword as the 52 bits of a double
len=length(simbols);
simbols_index=num2cell(1:len);
codeword_tmp=cell(len,1);
while length(f)>1,
index1=simbols_index{1};
index2=simbols_index{2};
codeword_tmp(index1)=addnode(codeword_tmp(index1),uint8(0));
codeword_tmp(index2)=addnode(codeword_tmp(index2),uint8(1));
f=[sum(f(1:2)) f(3:end)];
simbols_index=[{[index1 index2]} simbols_index(3:end)];
%将数据重新排列，是两个节点的频率尽量与前一个节点的频率想当
resort data in order to have two nodes with lower frequency as
first to
[f,sortindex]=sort(f);
simbols_index=simbols_index(sortindex);
end
%对应相应的元素与码字
codeword=cell(256:1);
codeword(simbols)=codeword_tmp;
%计算总的字符串长度
len=0;
for index=1:length(vector),
len=len+length(codeword{double(vector(index))+1});
end
%产生01序列
string=repmat(uint8(0),1,len);
pointer=1;
for index=1:length(vector),
code=codeword{double(vector(index))+1};
len=length(code);
string(pointer+(0:len-1))=code;
pointer=pointer+len;
end
%如果需要，加零
len=length(string);
pad=8-mod(len,8);
if pad>0,
string=[string uint8(zeros(1,pad))];
end
%保存实际有用的码字
codeword=codeword(simbols);
codelen=zeros(size(codeword));
weights=2.^(0:23);
maxcodelen=0;
for index 1:length(codeword),
len=length(codeword{index});
if len>maxcodelen,
maxcodelen=len;
end
if len>0,
code=sum(weights(codeword{index}==1));
code=bitset(code,len+1);
codeword{index}=code;
codelen(index)=len;
end
end
codeword=[codeword{:}]
%计算压缩后的向量
cols=length(string)/8;
string=reshape(string,8,cols);
weights=2.^(0:7);
zipped=uint8(weights*double(string));
%存储一个稀疏矩阵
huffcodes=sparse(1,1);% init sparse matrix
for index=1:numel(codeword),
huffcodes(codeword(index),1)=simbols(index);
end
%产生信息结构体
info.pad=pad;
info.ratio=cols./length(vector);
info.length=length(vector);
info.maxcodelen=maxcodelen;
function codeword_new=addnode(codeword_old,item)
codeword_new=cell(size(codeword_old));
for index=1:length(codeword_old),
codeword_new{index}=[item codeword_old{index}];
end
function vector=huff2norm(zipped,info)
%HUFF2NORM Huffman 解码器
%HUFF2NORM(X,INFO)根据信息体结构 info 返回向量 zipped 的解码结果%
%矩阵参数以X(:)形式输入
if~isa(zipped,'uint8'),
error('input argument must be a uint8 vector')
end
%产生01序列
len=length(zipped);
string=repmat(uint8(0),1,len.*8);
bitindex=1:8;
for index+1:len,
string(bitindex+8.*(index-1))=uint8(bitget(zipped(index),bitindex));
end
%调整字符串
string=logical(string(:)');% remove 0 padding
len=length(string);
%解码
weights=2.^(0:51);
vector=repmat(uint8(0),1,info,length);
vectorindex=1;
codeindex=1;
code=0;
for index=1:len,
code=bitset(code,codeindex,string(index));]
codeindex=codeindex+1;
byte=decode(bitset(code,codeindex),info);
if byte>0,%
vector(vectorindex)=byte-1;
codeindex=1;
code=0;
vectorindex=vectorindex+1;
end
end
function byte=decode(code,info)
byte=info.huffcodes(code);
function f=frequency(vector)
%FREQUENCY 计算元素出现概率
if~isa(vector,'uint8'),
error('input argument must be a uint8 vector')
end
f=repmat(0,1,256);
%扫描向量
len=length(vector);
for index=0:256,%
f(index+1)=sum(vector==uint8(index));
end
%归一化
f=f./len;