显著性区域检测matlab-code-20170412

Main_program%====test1clearcloseallclc%加8_皿,输入的待处理的图像%n，图像分块的patch的大小%img_rgb:缩放后的rgb图%img_lab：rgb2lab后的图%h,w,图像高宽%mex_store,存储矩阵img=imread('test1.jpg');%img=imread('E:^^^matlab\显著性分割\假设已知均值\testpicture\0010.tif);max_side=120;yu_value=0.8;img_lab=pre_rgb2lab(img,max_side);[img_scale_1,img_scale_2,img_scale_3,img_scale_4]=get4Scale(img_lab);[DistanceValue_scale_1_t1,DistanceValue_scale_1_exp,DistanceValue_scale_1_t1_rang,DistanceValue_scale_1_exp_rang]=distanceValueMap_search_onescale_2(img_scale_1,max_side);[DistanceValue_scale_2_t1,DistanceValue_scale_2_exp,DistanceValue_scale_2_t1_rang,DistanceValue_scale_2_exp_rang]=distanceValueMap_search_onescale_2(img_scale_2,max_side);[DistanceValue_scale_3_t1,DistanceValue_scale_3_exp,DistanceValue_scale_3_t1_rang,DistanceValue_scale_3_exp_rang]=distanceValueMap_search_onescale_2(img_scale_3,max_side);[DistanceValue_scale_4_t1,DistanceValue_scale_4_exp,DistanceValue_scale_4_t1_rang,DistanceValue_scale_4_exp_rang]=distanceValueMap_search_onescale_2(img_scale_4,max_side);value_C_1=Center_weight(DistanceValue_scale_1_exp_rang,yu_value);value_C_2=Center_weight(DistanceValue_scale_2_exp_rang,yu_value);value_C_3=Center_weight(DistanceValue_scale_3_exp_rang,yu_value);value_C_4=Center_weight(DistanceValue_scale_4_exp_rang,yu_value);[h,w]=size(value_C_1);value_C_1_resize=value_C_1;value_C_2_resize=imresize(value_C_2,[h,w]);value_C_3_resize=imresize(value_C_3,[h,w]);value_C_4_resize=imresize(value_C_4,[h,w]);value_C_sum=(value_C_1_resize+value_C_2_resize+value_C_3_resize+value_C_4_resize)/4;figure,imshow(value_C_sum);figure,imshow(value_C_1_resize);figure,imshow(value_C_2_resize);figure,imshow(value_C_3_resize);figure,imshow(value_C_4_resize);8888888888888888888888888888888888888888888888888888888888888888888888888888Center_weightfunction[img_out]=Center_weight(img_in,yu_value)%UNTITLEDSummaryofthisfunctiongoeshere%Detailedexplanationgoeshereimg_in=mat2gray(img_in);[h,w]=size(img_in);[row,col]=find(img_in>yu_value);%找到所有大于阈值的点，记录坐标l=length(row);a(1,:)=row;a(2,:)=col;fori0=1:hforj0=1:wfori=1:lc_store(i)=norm(a(:,i)-[i0;j0]);endmin_c(i0,j0)=min(c_store);endendmin_c_rang=mat2gray(min_c);img_out=img_in.*(1-min_c_rang);end888888888888888888888888888888888888888888888888888888888888888888888888888888888888distanceValueMap_search_onescalefunction[DistanceValue_t1,DistanceValue_exp,DistanceValue_t1_rang,DistanceValue_exp_rang]=distanceValueMap_search_onescale(img_in,max_side)%UNTITLED7Summaryofthisfunctiongoeshere%Detailedexplanationgoeshere[h,w]=size(img_in(:,:,1));img_scale1=img_in;%================================================================================%在计算块之前，考虑边缘点，所以先对矩阵进行填充q=3;%填充边缘像素个数img_scale1_pad(:,:,1)=padarray(img_scale1(:,:,1),[q,q],'replicate','both');img_scale1_pad(:,:,2)=padarray(img_scale1(:,:,2),[q,q],'replicate','both');img_scale1_pad(:,:,3)=padarray(img_scale1(:,:,3),[q,q],'replicate','both');% %计算该尺度下存储patch相量的矩阵，各三个像素取一个patchfori=1:hforj=1:wimg_scale1_patchVstore_all(i,j,:)=reshape(img_scale1_pad(i:i+6,j:j+6,:),1,147);endendimg_scale1_patchVstore_x=img_scale1_patchVstore_all(1:q:h,1:q:w,:);%将代表patch的相量存为一个矩阵disp('img_scale1_patchVstore_xcomplete');% %开始计算每一点的距离的值[h_x,w_x]=size(img_scale1_patchVstore_x(:,:,1));%记录存储矩阵的大小fori0=1:hdisp(num2str(i0));forj0=1:wtemp1=img_scale1_patchVstore_all(i0,j0,:);store_tp1_all=reshape(img_scale1_patchVstore_all(i0,j0,:),1,147);fori=1:h_xforj=1:w_xstore_tp1_x=reshape(img_scale1_patchVstore_x(i,j,:),1,147);store_o_disvalue_scale1_x(i,j)=norm(store_tp1_all-store_tp1_x);%将与mex_store_tp1像素和其他全图像素计算的欧式距离存储store_position_dis_x(i,j)=norm([i0,j0]-[(1+(i-1)*3),(1+(j-1)*3)]);endend%---将距离值进行归一化 max_1=max(max(store_o_disvalue_scale1_x));storemap_scale1_x_rang=mat2gray(store_o_disvalue_scale1_x,[0,max_1]);store_position_dis_x_rang=mat2gray(store_position_dis_x,[0,max_side]);%在0至U最大边长之间归一化%--结合相量值欧式距离和位置欧式距离，计算每个patch对于i0,j0的最终距离值 c=3;%设定该公式中的一个参数% 以下为该尺度中的距离值Dis_scale1=storemap_scale1_x_rang./(1+3*store_position_dis_x_rang);%--在上述该尺度的距离值中找距目标patch欧式距离最小的65个patch,并记录位置和欧式距离值-----Dis_all=Dis_scale1;num_K=65;%在矩阵中找65个最小值cout=0;%计数变量初始值min_v=[];%计数矩阵初始值whilecout<num_Kmm=min(min(Dis_all));f=find(Dis_all==mm);min_v=[min_v,Dis_all(f)'];cout=length(min_v);Dis_all(f)=10;endDistanceValue_t1(i0,j0)=sum(min_v)/num_K;DistanceValue_exp(i0,j0)=1-exp(-DistanceValue_t1(i0,j0));endend%对两个值进行归一化DistanceValue_t1_rang=mat2gray(DistanceValue_t1);DistanceValue_exp_rang=mat2gray(DistanceValue_exp);end888888888888.888888888888888888888888888888888888888888888888888888888888888888gaussianSmoothfunction[result]=gaussianSmooth(img)%UNTITLEDSummaryofthisfunctiongoeshere%Detailedexplanationgoeshere%将图像进行高斯平滑，用于生成高斯金字塔等或是去噪%采用横向，纵向两个向量平滑% filter=[1,5,10,10,5,1,1];filter=filter/sum(filter);[h,w]=size(img);ifmin(h,w)>3%bigimage-usestraight-forwardmethodresult=conv2(filter,filter,img,'same');%不进行降采样%horResult=convResult(:,[2:2:end]-1);%result=horResult([2:2:end]-1,:);else

%Imageissmallalongatleastonedimension.error('输入进行高斯平滑的图太小();endend5.RGB2Lab8888888888888888888888888888888888888888888888888888888888888888888888888888888888888function[L,a,b]=5.RGB2Lab%function[L,a,b]=RGB2Lab(R,G,B)%RGB2LabtakesmatricescorrespondingtoRed,Green,andBlue,and%transformsthemintoCIELab.ThistransformisbasedonITU-R%RecommendationBT.709usingtheD65whitepointreference.%TheerrorintransformingRGB->Lab->RGBisapproximately%10人-5.RGBvaluescanbeeitherbetween0and1orbetween0and255.%ByMarkRuzonfromCcodebyYossiRubner,23September1997.%UpdatedforMATLAB528January1998.%%Ifyourimageisloadedintouint8formatasanMxNx3tensor,you%canpassitinasoneargument.Ifyoubreakitinto3pieces,convert%themintodoublebeforecallingthisfunction.if(nargin==1)B=double(R(:,:,3));G=double(R(:,:,2));R=double(R(:,:,1));endif((max(max(R))>1.0)I(max(max(G))>1.0)I(max(max(B))>1.0))R=R/255;G=G/255;B=B/255;end[M,N]=size(R);=M*N;%SetathresholdT=0.008856;RGB=[reshape(R,1,s);reshape(G,1,s);reshape(B,1,s)];%RGBtoXYZMAT=[0.4124530.3575800.180423;0.2126710.7151600.072169;0.0193340.1191930.950227];XYZ=MAT*RGB;X=XYZ(1,:)/0.950456;Y=XYZ(2,:);Z=XYZ(3,:)/1.088754;XT=X>T;YT=Y>T;ZT=Z>T;fX=XT.*X.人(1/3)+(~XT).*(7.787.*X+16/116);%ComputeLY3=Y.A(1/3);fY=YT.*Y3+(~YT).*(7.787.*Y+16/116);L=YT.*(116*Y3-16.0)+(~YT).*(903.3*Y);fZ=ZT.*Z.人(1/3)+(~ZT).*(7.787.*Z+16/116);%Computeaandba=500*(fX-fY);b=200*(fY-fZ);L=reshape(L,M,N);a=reshape(a,M,N);b=reshape(b,M,N);if((nargout==1)I(nargout==0))L=cat(3,L,a,b);End8888888888888888888888888888888888888888888888888888888888888888888888888888888888pre_rgb2labfunction[img_lab]=pre_rgb2lab(img_in,max_side)%UNTITLED3Summaryofthisfunctiongoeshere%Detailedexplanationgoeshere%img_rgb:缩放后的rgb图%img_lab：rgb2lab后的图%h,w,图像高宽img=double(img_in);[h_t,w_t]=size(img(:,:,1));%得到原图的长宽ifw_t>h_t %看长和宽哪个比较大,将原图放缩至大值为250img_rgb(:,:,1)=imresize(img(:,:,1),[(max_side/w_t)*h_t,max_side],'bilinear');%用双线性差值法img_rgb(:,:,2)=imresize(img(:,:,2),[(max_side/w_t)*h_t,max_side],'bilinear');img_rgb(:,:,3)=imresize(img(:,:,3),[(max_side/w_t)*h_t,max_side],'bilinear');elseimg_rgb(:,:,1)=imresize(img(:,:,1),[max_side,(max_side/h_t)*w_t],'bilinear');img_rgb(:,:,2)=imresize(img(:,:,2),[max_side,(max_side/h_t)*w_t],'bilinear');img_rgb(:,:,3)=imresize(img(:,:,3),[max_side,(max_side/h_t)*w_t],'bilinear');end%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%=====================RGB2Lab=============================img_lab_o=RGB2Lab(img_rgb);img_lab(:,:,1)=img_lab_o(:,:,1)*255/100; %L通道从0到100转至l」0到255img_lab(:,:,2)=(img_lab_o(:,:,2)+120)*255/200;%a，通道从-120到120转到0到255img_lab(:,:,3)=(img_lab_o(:,:,3)+120)*255/200;%========================================================%归一化，从0到255归至U0到1img_lab=img_lab/255;end8888888888888888888888888888888888888888888888888888888888888888888888888888888888888Lab2RGBfunction[R,G,B]=Lab2RGB(L,a,b)%function[R,G,B]=Lab2RGB(L,a,b)%Lab2RGBtakesmatricescorrespondingtoL,a,andbinCIELabspace%andtransformsthemintoRGB.ThistransformisbasedonITU-R%RecommendationBT.709usingtheD65whitepointreference.%andtheerrorintransformingRGB->Lab->RGBisapproximately%10人-5.ByMarkRuzonfromCcodebyYossiRubner,23September1997.%UpdatedforMATLAB528January1998.%Fixedabuginconversionbacktouint89September1999.if(nargin==1)b=L(:,:,3);a=L(:,:,2);L=L(:,:,1);end%ThresholdsT1=0.008856;T2=0.206893;[M,N]=size(L);s=M*N;L=reshape(L,1,s);a=reshape(a,1,s);b=reshape(b,1,s);%ComputeYfY=((L+16)/116).人3;YT=fY>T1;fY=(-YT).*(L/903.3)+YT.*fY;丫=fY;%AlterfYslightlyforfurthercalculationsfY=YT.*(fY.人(1/3))+(〜YT).*(7.787.*fY+16/116);%ComputeXfX=a/500+fY;XT=fX>T2;X=(XT.*(fX.人3)+(-XT).*((fX-16/116)/7.787));%ComputeZfZ=fY-b/200;ZT=fZ>T2;Z=(ZT.*(fZ.人3)+(〜ZT).*((fZ-16/116)/7.787));X=X*0.950456;Z=Z*1.088754;MAT=[3.240479-1.537150-0.498535;-0.9692561.8759920.041556;0.055648-0.2040431.057311];RGB=max(min(MAT*[X;Y;Z],1),0);R=reshape(RGB(1,:),M,N)*255;G=reshape(RGB(2,:),M,N)*255;B=reshape(RGB(3,:),M,N)*255;if((nargout==1)I(nargout==0))R=uint8(round(cat(3,R,G,B)));End8888888888888888888888888888888888888888888888888888888888888888888888888888888get4Scalefunction[img_scale_1,img_scale_2,img_scale_3,img_scale_4]=get4Scale(img_in)%UNTITLED4Summaryofthisfunctiongoeshere%Detailedexplanationgoeshere********************************************************************************%1.scale_11img_scale_1=img_in;[h,w]=size(img_scale_1(:,:,1));%记录原始图像尺寸% %2.scale_20.8%以下缩放为最邻近差值img_scale_2_g(:,:,1)=gaussianSmooth(img_scale_1(:,:,1));%先每一层进行高斯平滑，模糊时使用原图img_scale_2_g(:,:,2)=gaussianSmooth(img_scale_1(:,:,2));img_scale_2_g(:,:,3)=gaussianSmooth(img_scale_1(:,:,3));img_scale_2(:,:,1)=imresize(img_scale_2_g(:,:,1),[h*0.8,w*0.8]);%图像缩小为第二尺度，0.8img_scale_2(:,:,2)=imresize(img_scale_2_g(:,:,2),[h*0.8,w*0.8]);img_scale_2(:,:,3)=imresize(img_scale_2_g(:,:,3),[h*0.8,w*0.8]);% %3.scale_30.5img_scale_3_g(:,:,1)=gaussianSmooth(img_scale_2(:,:,1));%先每一层进行高斯平滑，模糊时使用放缩0.8的图作为原图img_scale_3_g(:,:,2)=gaussianSmooth(img_scale_2(:,:,2));img_scale_3_g(:,:,3)=gaussianSmooth(img_scale_2(:,:,3));img_scale_3(:,:,1)=imresize(img_scale_3_g(:,:,1),[h*0.5,w*0.5]);%图像缩小为第3尺度，0.5img_scale_3(:,:,2)=imresize(img_scale_3_g(:,:,2),[h*0.5,w*0.5]);img_scale_3(:,:,3)=imresize(img_scale_3_g(:,:,3),[h*0.5,w*0.5]);% %4.scale_40.3img_scale_4_g(:,:,1)=gaussianSmooth(img_scale_3(:,:,1));%先每一层进行高斯平滑，模糊时使用放缩后的尺度0.5作为原图img_scale_4_g(:,:,2)=gaussianSmooth(img_scale_3(:,:,2));img_scale_4_g(:,:,3)=gaussianSmooth(img_scale_3(:,:,3));img_scale_4(:,:,1)=imresize(img_scale_4_g(:,:,1),[h*0.3,w*0.3]);%图像缩小为第4尺度，0.3img_scale_4(:,:,2)=imresize(img_scale_4_g(:,:,2),[h*0.3,w*0.3]);img_scale_4(:,:,3)=imresize(img_scale_4_g(:,:,3),[h*0.3,w*0.3]);end8888888888888888888888888888888888888888888888888888888888888888888888888888888888888888888distanceValueMap_search_onescale_2function[DistanceValue_t1,DistanceValue_exp,DistanceValue_t1_rang,DistanceValue_exp_rang]=distanceValueMap_search_onescale_2(img_in,max_side)%UNTITLED7Summaryofthisfunctiongoeshere%Detailedexplanationgoeshere[h,w]=size(img_in(:,:,1));img_scale1=img_in;%================================================================================%在计算块之前，考虑边缘点，所以先对矩阵进行填充q=3;%填充边缘像素个数img_scale1_pad(:,:,1)=padarray(img_scale1(:,:,1),[q,q],'replicate','both');img_scale1_pad(:,:,2)=padarray(img_scale1(:,:,2),[q,q],'replicate','both');img_scale1_pad(:,:,3)=padarray(img_scale1(:,:,3),[q,q],'replicate','both');% %计算该尺度下存储patch相量的矩阵，各三个像素取一个patchfori=1:hforj=1:wimg_scale1_patchVstore_all(i,j,:)=reshape(img_scale1_pad(i:i+6,j:j+6,:),1,147);endendimg_scale1_patchVstore_x=img_scale1_patchVstore_all(1:q:h,1:q:w,:);%将代表patch的相量存为一个矩阵ones_Vstore_temp=ones(size(img_scale1_patchVstore_x));%生成全一矩阵disp('img_scale1_patchVstore_xcomplete');% %开始计算每一点的距离的值[h_x,w_x]=size(img_scale1_patchVstore_x(:,:,1));%记录存储矩阵的大小fori0=1:hdisp(num2str(i0));forj0=1:wtemp1=img_scale1_patchVstore_all(i0,j0,:);fori2=1:length(temp1)temp_x_1(:,:,i2)=temp1(:,:,i2)*ones_Vstore_temp(:,:,i2);endtemp_x_2=temp_x_1-img_scale1_patchVstore_x;temp_x_2=temp_x_2.*tem