基于外围特征和SURF特征的印刷体汉字识别方法matlab程序

function Iout=affine_warp(Iin,M,mode) % Affine transformation function (Rotation, Translation, Resize) % This function transforms a volume with a 3x3 transformation matrix % % Iout=affine_warp(Iin,Minv,mode) % % inputs, % Iin: The input image % Minv: The (inverse) 3x3 transformation matrix % mode: If 0: linear interpolation and outside pixels set to nearest pixel % 1: linear interpolation and outside pixels set to zero % (cubic interpolation only support by compiled mex file) % 2: cubic interpolation and outsite pixels set to nearest pixel % 3: cubic interpolation and outside pixels set to zero % % output, % Iout: The transformed image % % example, % % Read image % I=im2double(imread('lenag2.png')) % % Make a transformation matrix % M=make_transformation_matrix([2 3],2,[1.0 1.1]); % % Transform the image % Iout=affine_warp(I,M,0) % % Show the image % figure, imshow(Iout); % % Function is written by D.Kroon University of Twente (February 2009) % Make all x,y indices [x,y]=ndgrid(0:size(Iin,1)-1,0:size(Iin,2)-1); % Calculate center of the image % mean= size(Iin)/2; % Make center of the image coordinates 0,0 %xd=x-mean(1); %yd=y-mean(2); xd=x; yd=y; % Calculate the Transformed coordinates Tlocalx = mean(1) + M(1,1) * xd + M(1,2) *yd + M(1,3) * 1; Tlocaly = mean(2) + M(2,1) * xd + M(2,2) *yd + M(2,3) * 1; switch(mode) case 0 Interpolation='bilinear'; Boundary='replicate'; case 1 Interpolation='bilinear'; Boundary='zero'; case 2 Interpolation='bicubic'; Boundary='replicate'; otherwise Interpolation='bicubic'; Boundary='zero'; end Iout=image_interpolation(Iin,Tlocalx,Tlocaly,Interpolation,Boundary); function Iout = image_interpolation(Iin,Tlocalx,Tlocaly,Interpolation,Boundary,ImageSize) % This function is used to transform an 2D image, in a backwards way with an % transformation image. % % Iout = image_interpolation(Iin,Tlocalx,Tlocaly,Interpolation,Boundary,ImageSize) % % inputs, % Iin : 2D greyscale or color input image % Tlocalx,Tlocaly : (Backwards) Transformation images for all image pixels % Interpolation: % 'nearest' - nearest-neighbor interpolation % 'bilinear' - bilinear interpolation % 'bicubic' - cubic interpolation; the default method % Boundary: % 'zero' - outside input image are implicilty assumed to be zero % 'replicate' - Input array values outside the bounds of the array % are assumed to equal the nearest array border value % (optional) % ImageSize: - Size of output image % outputs, % Iout : The transformed image % % Function is written by D.Kroon University of Twente (September 2010) if(~isa(Iin,'double')), Iin=double(Iin); end if(nargin<6), ImageSize=[size(Iin,1) size(Iin,2)]; end if(ndims(Iin)==2), lo=1; else lo=3; end switch(lower(Interpolation)) case 'nearest' xBas0=round(Tlocalx); yBas0=round(Tlocaly); case 'bilinear' xBas0=floor(Tlocalx); yBas0=floor(Tlocaly); xBas1=xBas0+1; yBas1=yBas0+1; % Linear interpolation constants (percentages) tx=Tlocalx-xBas0; ty=Tlocaly-yBas0; perc0=(1-tx).*(1-ty); perc1=(1-tx).*ty; perc2=tx.*(1-ty); perc3=tx.*ty; case 'bicubic' xBas0=floor(Tlocalx); yBas0=floor(Tlocaly); tx=Tlocalx-xBas0; ty=Tlocaly-yBas0; % Determine the t vectors vec_tx0= 0.5; vec_tx1= 0.5*tx; vec_tx2= 0.5*tx.^2; vec_tx3= 0.5*tx.^3; vec_ty0= 0.5; vec_ty1= 0.5*ty; vec_ty2= 0.5*ty.^2;vec_ty3= 0.5*ty.^3; % t vector multiplied with 4x4 bicubic kernel gives the to q vectors vec_qx0= -1.0*vec_tx1 + 2.0*vec_tx2 - 1.0*vec_tx3; vec_qx1= 2.0*vec_tx0 - 5.0*vec_tx2 + 3.0*vec_tx3; vec_qx2= 1.0*vec_tx1 + 4.0*vec_tx2 - 3.0*vec_tx3; vec_qx3= -1.0*vec_tx2 + 1.0*vec_tx3; vec_qy0= -1.0*vec_ty1 + 2.0*vec_ty2 - 1.0*vec_ty3; vec_qy1= 2.0*vec_ty0 - 5.0*vec_ty2 + 3.0*vec_ty3; vec_qy2= 1.0*vec_ty1 + 4.0*vec_ty2 - 3.0*vec_ty3; vec_qy3= -1.0*vec_ty2 + 1.0*vec_ty3; % Determine 1D neighbour coordinates xn0=xBas0-1; xn1=xBas0; xn2=xBas0+1; xn3=xBas0+2; yn0=yBas0-1; yn1=yBas0; yn2=yBas0+1; yn3=yBas0+2; otherwise error('image_interpolation:inputs','unknown interpolation method'); end % limit indexes to boundaries switch(lower(Interpolation)) case 'nearest' check_xBas0=(xBas0<0)|(xBas0>(size(Iin,1)-1)); check_yBas0=(yBas0<0)|(yBas0>(size(Iin,2)-1)); xBas0=min(max(xBas0,0),size(Iin,1)-1); yBas0=min(max(yBas0,0),size(Iin,2)-1); case 'bilinear' check_xBas0=(xBas0<0)|(xBas0>(size(Iin,1)-1)); check_yBas0=(yBas0<0)|(yBas0>(size(Iin,2)-1)); check_xBas1=(xBas1<0)|(xBas1>(size(Iin,1)-1)); check_yBas1=(yBas1<0)|(yBas1>(size(Iin,2)-1)); xBas0=min(max(xBas0,0),size(Iin,1)-1); yBas0=min(max(yBas0,0),size(Iin,2)-1); xBas1=min(max(xBas1,0),size(Iin,1)-1); yBas1=min(max(yBas1,0),size(Iin,2)-1); case 'bicubic' check_xn0=(xn0<0)|(xn0>(size(Iin,1)-1)); check_xn1=(xn1<0)|(xn1>(size(Iin,1)-1)); check_xn2=(xn2<0)|(xn2>(size(Iin,1)-1)); check_xn3=(xn3<0)|(xn3>(size(Iin,1)-1)); check_yn0=(yn0<0)|(yn0>(size(Iin,2)-1)); check_yn1=(yn1<0)|(yn1>(size(Iin,2)-1)); check_yn2=(yn2<0)|(yn2>(size(Iin,2)-1)); check_yn3=(yn3<0)|(yn3>(size(Iin,2)-1)); xn0=min(max(xn0,0),size(Iin,1)-1); xn1=min(max(xn1,0),size(Iin,1)-1); xn2=min(max(xn2,0),size(Iin,1)-1); xn3=min(max(xn3,0),size(Iin,1)-1); yn0=min(max(yn0,0),size(Iin,2)-1); yn1=min(max(yn1,0),size(Iin,2)-1); yn2=min(max(yn2,0),size(Iin,2)-1); yn3=min(max(yn3,0),size(Iin,2)-1); end Iout=zeros([ImageSize(1:2) lo]); for i=1:lo; % Loop incase of RGB Iin_one=Iin(:,:,i); switch(lower(Interpolation)) case 'nearest' % Get the intensities intensity_xyz0=Iin_one(1+xBas0+yBas0*size(Iin,1)); % Set pixels outside the image switch(lower(Boundary)) case 'zero' intensity_xyz0(check_xBas0|check_yBas0)=0; otherwise end % Combine the weighted neighbour pixel intensities Iout_one=intensity_xyz0; case 'bilinear' % Get the intensities intensity_xyz0=Iin_one(1+xBas0+yBas0*size(Iin,1)); intensity_xyz1=Iin_one(1+xBas0+yBas1*size(Iin,1)); intensity_xyz2=Iin_one(1+xBas1+yBas0*size(Iin,1)); intensity_xyz3=Iin_one(1+xBas1+yBas1*size(Iin,1)); % Set pixels outside the image switch(lower(Boundary)) case 'zero' intensity_xyz0(check_xBas0|check_yBas0)=0; intensity_xyz1(check_xBas0|check_yBas1)=0; intensity_xyz2(check_xBas1|check_yBas0)=0; intensity_xyz3(check_xBas1|check_yBas1)=0; otherwise end % Combine the weighted neighbour pixel intensities Iout_one=intensity_xyz0.*perc0+intensity_xyz1.*perc1+intensity_xyz2.*perc2+intensity_xyz3.*perc3; case 'bicubic' % Get the intensities Iy0x0=Iin_one(1+xn0+yn0*size(Iin,1));Iy0x1=Iin_one(1+xn1+yn0*size(Iin,1)); Iy0x2=Iin_one(1+xn2+yn0*size(Iin,1));Iy0x3=Iin_one(1+xn3+yn0*size(Iin,1)); Iy1x0=Iin_one(1+xn0+yn1*size(Iin,1));Iy1x1=Iin_one(1+xn1+yn1*size(Iin,1)); Iy1x2=Iin_one(1+xn2+yn1*size(Iin,1));