应用遗传算法进行匹配.zip

clear; %图象的总帧数 FrameCount=21; %每帧图象的特征点的个数 FeaturePointCount=9; %权重系数 w1=0.2;w2=0.3; %初始群体数 PopSize=100; %交叉概率 Pc=0.9; %变异概率 Pm=0.04; %定义种群的数据结构 Population=struct('chrom',{},'fitness',{}); BestIndividual=struct('chrom',{},'fitness',{}); %最大迭代次数 MaxGeneration=100; %状态模型误差的协方差矩阵Q q=[0.25 0 0 0 0.25 0 0 0 0.25]; %状态转移矩阵F T=0.12 f=[1 0.12 0.0072 0 1 0.12 0 0 1]; dt=0.012; %观测矩阵H h=[1.0 0 0]; %观测噪声协方差阵R r=[0.005]; %初始估计协方差阵为P for i=1:FeaturePointCount*2 P(:,:,i)=zeros(3); end %初始化状态向量 for i=1:FeaturePointCount X_Ordinate(:,i)=[0 0 0]'; Y_Ordinate(:,i)=[0 0 0]'; end %对实际和预测坐标开辟三维矩阵 for i=1:FrameCount Prospect_Ordinate(:,:,i)=zeros(FeaturePointCount,2); end for i=1:FrameCount Actual_Ordinate(:,:,i)=zeros(FeaturePointCount,2); end fid=fopen('1.txt'); CurrentFrame= fscanf(fid,'%d %d',[2 inf]); CurrentFrame=CurrentFrame'; for i=1:FeaturePointCount XX_Ordinate(i,1)=CurrentFrame(i,1); YY_Ordinate(i,1)=CurrentFrame(i,2); end Prospect_Ordinate(:,:,1)=CurrentFrame; Actual_Ordinate(:,:,1)=CurrentFrame; for FC=1:FrameCount-1 %读入当前帧特征点坐标 FrameNumber=FC; CurrentFrame=Actual_Ordinate(:,:,FC); % o=num2str(FrameNumber); % OpenFileName=strcat('',o,'.txt'); % fid=fopen(OpenFileName); % CurrentFrame= fscanf(fid,'%d %d',[2 inf]); % CurrentFrame=CurrentFrame'; % fclose(fid); %卡尔曼滤波预测本帧图象中的特征点在下一帧中的位置 for i=1:FeaturePointCount %预测x方向的向量 Zx=Actual_Ordinate(i,1,FC); [X_Estimate_Coordinate p]=kalman1(f,q,P(:,:,(i*2-1)),X_Ordinate(:,i),r,h,Zx); X_Ordinate(:,i)=X_Estimate_Coordinate; P(:,:,(i*2-1))=p; X_Coordinate_Prospect=f*X_Estimate_Coordinate; NextFrame_Prospect(i,1)=X_Coordinate_Prospect(1); %预测y方向的向量 Zy=Actual_Ordinate(i,2,FC); [Y_Estimate_Coordinate p]=kalman1(f,q,P(:,:,(i*2)),Y_Ordinate(:,i),r,h,Zy); Y_Ordinate(:,i)=Y_Estimate_Coordinate; P(:,:,i*2)=p; Y_Coordinate_Prospect=f*Y_Estimate_Coordinate; NextFrame_Prospect(i,2)=Y_Coordinate_Prospect(1); end Prospect_Ordinate(:,:,FC+1)=NextFrame_Prospect; %读入下一帧特征点坐标 o=num2str(FrameNumber+1); OpenFileName=strcat('',o,'.txt'); fid=fopen(OpenFileName); NextFrame=fscanf(fid,'%d %d',[2 inf]); NextFrame=NextFrame'; [Nm,Nn]=size(NextFrame); fclose(fid); if Nm<FeaturePointCount Count=zeros(1,FeaturePointCount); for i=1:Nm if (NextFrame(i,2)>70&NextFrame(i,2)<80) Count(1)=Count(1)+1; elseif (NextFrame(i,2)>80&NextFrame(i,2)<90) Count(2)=Count(2)+1; elseif (NextFrame(i,2)>105&NextFrame(i,2)<120) Count(3)=Count(3)+1; elseif (NextFrame(i,2)>125&NextFrame(i,2)<135) Count(4)=Count(4)+1; elseif (NextFrame(i,2)>135&NextFrame(i,2)<150) Count(5)=Count(5)+1; elseif (NextFrame(i,2)>165&NextFrame(i,2)<180) Count(6)=Count(6)+1; else %(NextFrame(i,2)>190) Count(7)=Count(7)+1; end end if (Count(4)==0&Count(6)==1) for i=1:3 Actual_Ordinate(i,:,FC+1)=NextFrame(i,:); end for i=5:-1:4 Actual_Ordinate(i+1,:,FC+1)=NextFrame(i,:); end for i=Nm:-1:6 Actual_Ordinate(i+2,:,FC+1)=NextFrame(i,:); end Actual_Ordinate(4,:,FC+1)=Prospect_Ordinate(4,:,FC+1); Actual_Ordinate(7,:,FC+1)=Prospect_Ordinate(7,:,FC+1); elseif (Count(4)==0) for i=1:3 Actual_Ordinate(i,:,FC+1)=NextFrame(i,:); end for i=Nm:-1:4 Actual_Ordinate(i+1,:,FC+1)=NextFrame(i,:); end Actual_Ordinate(4,:,FC+1)=Prospect_Ordinate(4,:,FC+1); elseif (Count(6)==1) for i=1:6 Actual_Ordinate(i,:,FC+1)=NextFrame(i,:); end for i=Nm:-1:7 Actual_Ordinate(i+1,:,FC+1)=NextFrame(i,:); end Actual_Ordinate(7,:,FC+1)=Prospect_Ordinate(7,:,FC+1); else %(Count(7)==1) for i=1:7 Actual_Ordinate(i,:,FC+1)=NextFrame(i,:); end for i=Nm:-1:8 Actual_Ordinate(i+1,:,FC+1)=NextFrame(i,:); end Actual_Ordinate(8,:,FC+1)=Prospect_Ordinate(8,:,FC+1); end else Actual_Ordinate(:,:,FC+1)=NextFrame; end %初始化代数 Generation=1; %产生初始群体 GenerationInitialPopulation for i=1:PopSize InitialPopulation(i).chrom=randperm(FeaturePointCount); end %对群体进行估计 EvaluatePopulation % 计算初始群体进行估计 [Population,BestIndividual(Generation)]=EvaluatePopulation(Prospect_Ordinate(:,:,FC+1),Actual_Ordinate(:,:,FC+1),InitialPopulation,w1,w2,FeaturePointCount,PopSize); % [Population,BestIndividual(Generation)]=EvaluatePopulation(Actual_Ordinate(:,:,FC),Actual_Ordinate(:,:,FC+1),InitialPopulation,w1,w2,FeaturePointCount,PopSize); MaxIndividual=BestIndividual(1); while (Generation<MaxGeneration) Generation=Generation+1; %选择算子 NewPopulation=SelectOperator(Population,PopSize); Population=NewPopulation; %交叉算子 Population=CrossoverOperator2(Population,Pc,PopSize,FeaturePointCount); %变异算子 Population=MutationOperator2(Population,Pm,PopSize,FeaturePointCount); %对群体进行估计 [Population,BestIndividual(Generation)]=EvaluatePopulation(Prospect_Ordinate(:,:,FC+1),Actual_Ordinate(:,:,FC+1),Population,w1,w2,FeaturePointCount,PopSize); % [Population,BestIndividual(Generation)]=EvaluatePopulation(Actual_Ordinate(:,:,FC),Actual_Ordinate(:,:,FC+1),Population,w1,w2,FeaturePointCount,PopSize); if (MaxIndividual.fitness<BestIndividual(Generation).fitness) MaxIndividual=BestIndividual(Generation); end end MF=FC+1 MaxIndividual for i=1:FeaturePointCount XX_Ordinate(i,FC+1)=Actual_Ordinate(MaxIndividual.chrom(i),1,FC+1); YY_Ordinate(i,FC+1)=Actual_Ordinate(MaxIndividual.chrom(i),2,FC+1); % XX_Ordinate(i,FC+1)=NextFrame(MaxIndividual.chrom(i),1); % YY_Ordinate(i,FC+1)=NextFrame(MaxIndividual.chrom(i),2); end end plot(XX_Ordinate(1,:),YY_Ordinate(1,:),XX_Ordinate(2,:),YY_Ordinate(2,:),XX_Ordinate(3,:),YY_Ordinate(3,:),XX_Ordinate(4,:),YY_Ordinate(4,:),XX_Ordinate(5,:),YY_Ordinate(5,:),XX_Ordinate(6,:),YY_Ordinate(6,:)); hold on; for i=1:FeaturePointCount x = XX_Ordinate(i,:); y = YY_Ordinate(i,:); plot(x,y,'-k.'); end