clear;clc;close all;

%%%%%%%%%%%%%%%%% 读取原图，并处理成二值图像 %%%%%%%%%%%%%%%%%%%%%%%

I=imread(‘tao.png’);

I2=rgb2gray(I);

BW=im2bw(I2,0.9);

figure(1),subplot(1,3,1),imshow(I),title(‘原始图像’);

subplot(1,3,2),imshow(I2),title(‘灰度图像’);

subplot(1,3,3),imshow(BW),title(‘二值图像’);

%%%%%%%%%%%%%%%% 进行边缘检测 得到不连续的图形边界 %%%%%%%%%%%%%%%%%%%%%%

%得到各个图形的连续边界

SE=strel(‘rectangle’,[40 30]); % 结构定义

J2=imopen(BW,SE); % 开运算

figure(2),imshow(J2),title(‘对二值图像进行开运算’);

SE=strel(‘square’,5); % 采用方形结构元素进行腐蚀

J=imerode(~J2,SE);

BW2=(~J2)-J; % 检测边缘

figure(3),imshow(BW2),title(‘边缘检测’);

%填充了已有的检测的连续形状边界

B = imfill(BW2,‘holes’);%图像填充

B = bwmorph(B,‘remove’);%移除内部像素

figure(4),imshow(B),title(‘提取出的边界图像’);

%将不同的图形进行分别标记，num表示连接的图形对象的个数

[Label,num] = bwlabel(B,8);

%%%%%%%%%%%%%%%%%%%计算各个图形单元边界像素点数%%%%%%%%%%%%%%%%%%%%%%%%%%%

for i = 1 : num

Premeter(i) = 0;

end

[row,col] = size(Label);

for i = 1 : row

for j = 1 : col

if(Label(i,j) > 0)

Premeter(Label(i,j)) = Premeter(Label(i,j)) + 1;%计算标记后的各块图形边界中像素的个数的总数

end

end

end

%%%%%%%%%%%%%%%%%%% 计算各个图形单元的面积%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

FilledLabel = imfill(Label,‘holes’); %填充标记过的边界线中间围成的图形区域

figure,imshow(FilledLabel),title(‘标记过并被填充的结果’);

for i = 1 : num

Area(i) = 0;

end

[row,col] = size(FilledLabel);

for i = 1 : row

for j = 1 : col

if(FilledLabel(i,j) > 0)

Area(FilledLabel(i,j)) = Area(FilledLabel(i,j)) + 1; %通过统计像素点个数的方式来求各形状的面积

end

end

end

%%%%%%%%%%%%%%%%%% 计算各个图形单元的圆度 %%%%%%%%%%%%%%%%%%%%%%%%%%

for i = 1 : num

Ecllipseratio(i) = 4piArea(i)/Premeter(i)^2;

end

%%%%%%%%%%%%%%%%%%%% 计算各个图像的色度 %%%%%%%%%%%%%%%%%%%%%

HSV = rgb2hsv(I); %转换为HSV，为后面的色度元素的提取做准备

[row,col] = size(FilledLabel); %统计填充后的图形中各块图形所含像素的个数的多少

MeanHue = zeros(1,num);

for i = 1 : num

Hue = zeros(Area(i),1);

nPoint = 0;

for j = 1 : row

for k = 1 : col

if(FilledLabel(j,k) == i)

nPoint = nPoint + 1;

Hue(nPoint,1) = HSV(j,k,1);

end

end

end

Hue(:,i) = sort(Hue(:,1));

for j = floor(nPoint*0.1) : floor(nPoint*0.9)

MeanHue(i) = MeanHue(i) + Hue(j,1);

end

MeanHue(i) = MeanHue(i) / (0.8*nPoint); %计算出平均的色度值

end

%%%%%%%%%%%%%%%%%%%%%%%% 各种水果识别 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%识别梨，在二维特征空间对各个图像进行类别区分

pear=0;result1=0;

for i=1:num

if(MeanHue(i)<0.125) %判断各个图形中平均色度值小于0.125的为梨

pear=i;

result1=1;

end

end

%对分出来的梨构建相应的图像掩膜，并用对原图的亮度图像进行掩膜操作

pearHSV=HSV;

for j = 1 : row

for k = 1 : col

if(FilledLabel(j,k) ~=pear)

pearHSV(j,k,3)=0;

end

end

end

%变换生成最终的结果图像

pearmatrix = hsv2rgb(pearHSV);%转换为RGB彩图，彩图中已经滤去了其余水果，只剩下梨

if(result1==1)

figure,imshow(pearmatrix);title(‘梨’);

end

%%%%%%%%%识别桃，在二维特征空间对各个图像进行类别区分

pitch=0;result2=0;

for i=1:num

if(MeanHue(i)>0.5) %判断各个图形中平均色度值大于0.5的为桃

pitch=i;

result2=1;

end

end

%对分别出来的桃构建相应的图像掩膜，并用对原图的亮度图像进行掩膜操作

pitchHSV=HSV;

[row,col] = size(FilledLabel); %统计填充后的图形中各块图形所含像素的个数的多少

for j = 1 : row

for k = 1 : col

if(FilledLabel(j,k) ~=pitch)

pitchHSV(j,k,3)=0;

end

end

end

%变换生成最终的结果图像

pitchmatrix = hsv2rgb(pitchHSV); %转换为RGB彩图，彩图中已经滤去了其余水果，只剩下桃

if(result2==1)

figure,imshow(pitchmatrix);title(‘桃子’);

end

%%%%%%%识别苹果，在二维特征空间对各个图像进行类别区分

apple=0;result3=0;

for i=1:num

if((Ecllipseratio(i)<1.25)&&(Ecllipseratio(i)>1.0)) %判断各个图形中圆度居于1.0与1.25之间的的为苹果

apple=i;

result3=1;

end

end

%对分出来的苹果构建相应的图像掩膜，并用对原图的亮度图像进行掩膜操作

appleHSV=HSV;

for j = 1 : row

for k = 1 : col

if(FilledLabel(j,k) ~=apple)

appleHSV(j,k,3)=0;

end

end

end

%变换生成最终的结果图像

applematrix = hsv2rgb(appleHSV); %转换为RGB彩图，彩图中已经滤去了其余水果，只剩下苹果

if(result3==1)

figure,imshow(applematrix);title(‘苹果’);

end

%%%%%%当没有梨，桃子，苹果，出现时

if(result10&&result20&&result3==0)

figure,imshow(‘无.png’);

end