想将density peaks密度峰值算法用于图像分割出现error

matlab

想将density peaks密度峰值算法用于图像分割出现error

2019-07-17 12:50发布

站内问答 / matlab

2858 3

下面是将density peaks密度峰值算法用于图像处理的改进代码，出现错误，希望大神帮忙看一下怎么改正。

clear all
close all
% disp('The only input needed is a distance matrix file')
% disp('The format of this file should be: ')
% disp('Column 1: id of element i')
% disp('Column 2: id of element j')
% disp('Column 3: dist(i,j)')
% mdist=input('name of the distance matrix file (with single quotes)? ');
% disp('Reading input distance matrix')
% xx=load(mdist);
% xx = load('D:example_distances.dat')
%x = load('C:UseTraceOfAllUsers.txt');
% 从文件中读取数据
x = imread('naochuxue.jpg');
x=double(x);
minX = min(x);
maxX = max(x);%取较大值
ran = maxX - minX;
nx(:,1) = (x(:,1) - minX(1,1)) / ran(1,1);
nx(:,2) = (x(:,2) - minX(1,2)) / ran(1,2);
dist = pdist2(nx, nx);
N = size(dist,1);%%第一个维度的长度，相当于文件的行数（即距离的总个数）
xx = zeros((N-1)*N/2, 3);%初始化为零
idx = 1;
% 这里不考虑对角线元素
for i=1:N
for j=i+1:N
xx(idx, 1) = i;
xx(idx, 2) = j;
xx(idx, 3) = dist(i, j);
idx = idx + 1;
end
end
N = size(xx, 1);
ND=max(xx(:,2));
NL=max(xx(:,1));
if (NL>ND)
ND=NL;
end
% N=size(xx,1);
% for i=1:ND
% for j=1:ND
% dist(i,j)=0;
% end
% end
% for i=1:N
% ii=xx(i,1);
% jj=xx(i,2);
% dist(ii,jj)=xx(i,3);
% dist(jj,ii)=xx(i,3);
% end
% percent=2;
% fprintf('average percentage of neighbours (hard coded): %5.6f ', percent);
%
% position=round(N*percent/100);
% sda=sort(xx(:,3));
% dc=sda(position);
dc = 0.15;
%计算局部密度 rho (利用 Gaussian 核)
fprintf('Computing Rho with gaussian kernel of radius: %12.6f ', dc);
% 将每个数据点的 rho 值初始化为零
for i=1:ND
rho(i)=0.;
end
%
% Gaussian kernel
%
for i=1:ND-1
for j=i+1:ND
rho(i)=rho(i)+exp(-(dist(i,j)/dc)*(dist(i,j)/dc));
rho(j)=rho(j)+exp(-(dist(i,j)/dc)*(dist(i,j)/dc));
end
end
%
% "Cut off" kernel
%
% for i=1:ND-1
% for j=i+1:ND
% if (dist(i,j)<dc)S
% rho(i)=rho(i)+1.;
% rho(j)=rho(j)+1.;
% end
% end
% end
% 先求矩阵列最大值，再求最大值，最后得到所有距离值中的最大值
maxd=max(max(dist));
% 将 rho 按降序排列，ordrho 保持序
[rho_sorted,ordrho]=sort(rho,'descend');
% 处理 rho 值最大的数据点
delta(ordrho(1))=-1.;
nneigh(ordrho(1))=0;
% 生成 delta 和 nneigh 数组
for ii=2:ND
delta(ordrho(ii))=maxd;
for jj=1:ii-1
if(dist(ordrho(ii),ordrho(jj))<delta(ordrho(ii)))
delta(ordrho(ii))=dist(ordrho(ii),ordrho(jj));
nneigh(ordrho(ii))=ordrho(jj);
% 记录 rho 值更大的数据点中与 ordrho(ii) 距离最近的点的编号 ordrho(jj)
end
end
end
% 生成 rho 值最大数据点的 delta 值
delta(ordrho(1))=max(delta(:));
% 决策图
disp('Generated file:DECISION GRAPH')
disp('column 1:Density')
disp('column 2:Delta')
fid = fopen('DECISION_GRAPH', 'w');
for i=1:ND
fprintf(fid, '%6.2f %6.2f ', rho(i),delta(i));
end
% 选择一个围住类中心的矩形
disp('Select a rectangle enclosing cluster centers')
scrsz = get(0,'ScreenSize');
figure('Position',[6 72 scrsz(3)/4. scrsz(4)/1.3]);
for i=1:ND
ind(i)=i;
gamma(i)=rho(i)*delta(i);
end
subplot(2,1,1)
tt=plot(rho(:),delta(:),'o','MarkerSize',5,'MarkerFaceColor','k','MarkerEdgeColor','k');
title ('Decision Graph','FontSize',15.0)
xlabel (' ho')
ylabel ('delta')
% 利用 rho 和 delta 画出一个决策图
subplot(2,1,1)
rect = getrect(1);
rhomin=rect(1);
deltamin=rect(4);
% 初始化 cluster 个数
NCLUST=0;
% cl 为归属标志数组，cl(i)=j 表示第 i 号数据点归属于第 j 个 cluster
% 将 cl 初始化为 -1
for i=1:ND
cl(i)=-1;
end
% 在矩形区域内统计数据点（即聚类中心）的个数
for i=1:ND
if ( (rho(i)>rhomin) && (delta(i)>deltamin))
NCLUST=NCLUST+1;
cl(i)=NCLUST;
icl(NCLUST)=i;
end
end
fprintf('NUMBER OF CLUSTERS: %i ', NCLUST);
disp('Performing assignation')
%assignation
% 将其他数据点归类 (assignation)
for i=1:ND
if (cl(ordrho(i))==1)
cl(ordrho(i))=cl(nneigh(ordrho(i)));
end
end
%halo
for i=1:ND
halo(i)=cl(i);
end
if (NCLUST>1)
% 初始化数组 bord_rho 为 0,每个 cluster 定义一个 bord_rho 值
for i=1:NCLUST
bord_rho(i)=0.;
end
for i=1:ND-1
for j=i+1:ND
% 距离足够小但不属于同一个 cluster 的 i 和 j
if ((cl(i)~=cl(j))&& (dist(i,j)<=dc))
rho_aver=(rho(i)+rho(j))/2.;% 取 i,j 两点的平均局部密度
if (rho_aver>bord_rho(cl(i)))
bord_rho(cl(i))=rho_aver;
end
if (rho_aver>bord_rho(cl(j)))
bord_rho(cl(j))=rho_aver;
end
end
end
end
for i=1:ND
if (rho(i)<bord_rho(cl(i)))
halo(i)=0;
end
end
end
% 逐一处理每个 cluster
for i=1:NCLUST
nc=0;
nh=0;
for j=1:ND
if (cl(j)==i)
nc=nc+1;
end
if (halo(j)==i)
nh=nh+1;
end
end
fprintf('CLUSTER: %i CENTER: %i ELEMENTS: %i CORE: %i HALO: %i ', i,icl(i),nc,nh,nc-nh);
end
cmap=colormap;
for i=1:NCLUST
ic=int8((i*64.)/(NCLUST*1.));
subplot(2,1,1)
hold on
plot(rho(icl(i)),delta(icl(i)),'o','MarkerSize',8,'MarkerFaceColor',cmap(ic,:),'MarkerEdgeColor',cmap(ic,:));
end
subplot(2,1,2)
disp('Performing 2D nonclassical multidimensional scaling')
Y1 = mdscale(dist, 2, 'criterion','metricstress');
plot(Y1(:,1),Y1(:,2),'o','MarkerSize',2,'MarkerFaceColor','k','MarkerEdgeColor','k');
title ('2D Nonclassical multidimensional scaling','FontSize',15.0)
xlabel ('X')
ylabel ('Y')
for i=1:ND
A(i,1)=0.;
A(i,2)=0.;
end
for i=1:NCLUST
nn=0;
ic=int8((i*64.)/(NCLUST*1.));
for j=1:ND
if (halo(j)==i)
nn=nn+1;
A(nn,1)=Y1(j,1);
A(nn,2)=Y1(j,2);
end
end
hold on
plot(A(1:nn,1),A(1:nn,2),'o','MarkerSize',2,'MarkerFaceColor',cmap(ic,:),'MarkerEdgeColor',cmap(ic,:));
end
%for i=1:ND
% if (halo(i)>0)
% ic=int8((halo(i)*64.)/(NCLUST*1.));
% hold on
% plot(Y1(i,1),Y1(i,2),'o','MarkerSize',2,'MarkerFaceColor',cmap(ic,:),'MarkerEdgeColor',cmap(ic,:));
% end
%end
faa = fopen('CLUSTER_ASSIGNATION', 'w');
disp('Generated file:CLUSTER_ASSIGNATION')
disp('column 1:element id')
disp('column 2:cluster assignation without halo control')
disp('column 3:cluster assignation with halo control')
for i=1:ND
fprintf(faa, '%i %i %i ',i,cl(i),halo(i));
end