基于火灾监控数字图像程序

clear;
clc;
file_name =input('D:\我的文档\桌面\数字图像资料\火灾.jpg','s');
I_rgb =imread('D:\我的文档\桌面\数字图像资料\火灾.jpg');
figure();
imshow(I_rgb)
title('原始图像');
C=makecform('srgb2lab');
I_lab =applycform(I_rgb,C);
ab=double(I_lab (:,:,2:3));
nrows = size(ab,1);
ncols = size(ab,2);
ab = reshape(ab,nrows*ncols,2);
nColors = 3;
[cluster_idx cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean','Replicates',3); pixel_labels = reshape(cluster_idx,nrows,ncols);
segmented_images = cell(1,3);
rgb_label = repmat(pixel_labels,[1 1 3]);
for k = 1:nColors
color = I_rgb ;
color(rgb_label ~= k) = 0;
segmented_images{k} = color;
end
figure(),imshow(segmented_images{3}), title('分割结果——火焰');
segmented_images{3}= rgb2gray(segmented_images{3});%以下灰度处理
[rows , cols , colors] = size(segmented_images{3});
B = zeros(rows,cols);
B = uint8(B);
C=zeros(rows,cols);
C=uint8(C);
for i = 1:rows
for j = 1:cols
sum1 = 0;
sum2 = 0;
for k = 1:colors
sum1=sum1+ segmented_images{3} ( i,j,k )/3;
sum2=sum2+max(segmented_images{3} ( i,j,k ));
end
B(i,j) = sum1;
C(i,j)=sum2;
end
end
figure();
imshow(segmented_images{3}),
title('灰度');
[N,M]=size(segmented_images{3});%以下二值化length=N*M;
L=256;
count=0;
for k=0:L-1
for i=1:N
for j=1:M
if segmented_images{3}(i,j)==k
count=count+1;
end
end
end
P(k+1)=count/length;
count=0;
end
for i=1:L
if P(i)~=0
first=i;
break
end
end
for i=L:-1:1
if P(i)~=0
last=i;
break
end
end
entropy1=0;
for i=first:last
if (P(i)~=0)
entropy1=entropy1+P(i)*log2(1/P(i));
end
end
ep=0;
averF=0;
averB=0;
for t=0:L
y=t+1;
if (y>first)&&(y<last)
for k=1:y
ep=ep+P(k);
end
w0(y)=ep;
w1(y)=1-w0(y);
for i=1:t
ep=averF+(i)*P(i)/w0(y);
end
u0(y)=averF;
for i=t:L
averB=averB+(i)*P(i)/w1(y);
end
u1(y)=averB;
u=w0(y)*u0(y)+w1(y)*u1(y);
arg(y)=w0(y)*(u0(y)-u)*(u0(y)-u)+w1(y)*(u1(y)-u)*(u1(y)-u); end
ep=0;
averF=0;
averB=0;
end
high=arg(1);
for i=2:last-first-3
if high<arg(i)
high=arg(i);
x=i;
end
end
t=x-1;
I0=0;
I1=0;
for i=1:N
for j=1:M
if (segmented_images{3}(i,j)>=t)
y1(i,j)=255;
I1=I1+1;
else
y1(i,j)=0;
I0=I0+1;
end
end
end
figure,imshow(y1);
title('二值化');%（以上采用Otsu算法）
figure;%以下为平滑
J=fspecial('average');
J1=filter2(J, y1)/255;
subplot(1,3,2);
imshow(J1);
title('平滑');
j=edge(J1,'canny' ,[0.04,0.25],1.5);%以下轮廓提取figure();
imshow(j);
title('轮廓');
bw = J1;%以下质心分析
L = bwlabel(bw);
s = regionprops(L, 'centroid');
centroids = cat(1, s.Centroid);
figure();
imshow(bw);
title('质心分析');
hold on;
plot(centroids(:,1), centroids(:,2), 'b*');
hold off。