您好，下面是我从博客园中找的一个边缘检测的程序和数据，并且一步一步的理了下思路，我是用的是41*41的数据矩阵，运行到最后会出现一个问题，不知怎么改正，能帮我看下吗？
% thresholdRatio: Low thresh is this fraction of the high.

% -------------------------------------------------------------------------

function imgCanny = edge_canny(I,gaussDim,sigma,percentOfPixelsNotEdges,thresholdRatio)

%% Gaussian smoothing filter.

m = gaussDim(1);

n = gaussDim(2);

if mod(m,2) == 0 || mod(n,2) == 0

error('The dimensionality of Gaussian must be odd!');

end

% Generate gaussian convolution kernel.

gaussKernel = fspecial('gaussian', [m,n], sigma);

% Image edge copy.

[m,n] = size(gaussKernel);

[row,col,dim] = size(I);

if dim > 1

imgGray = rgb2gray(I);

else

imgGray = I;

end

imgCopy = imgReplicate(imgGray,(m-1)/2,(n-1)/2);

% Gaussian smoothing filter.

imgData = zeros(row,col);

for ii = 1:row

for jj = 1:col

    window = imgCopy(ii:ii+m-1,jj:jj+n-1);

    GSF = window.*gaussKernel;

    imgData(ii,jj) = sum(GSF(:));

end

end

%% Calculate the gradient values for each pixel.

% Sobel operator.

dgau2Dx = [-1 0 1;-2 0 2;-1 0 1];

dgau2Dy = [1 2 1;0 0 0;-1 -2 -1];

[m,n] = size(dgau2Dx);

% Image edge copy.

imgCopy = imgReplicate(imgData,(m-1)/2,(n-1)/2);

% To store the gradient and direction information.

gradx = zeros(row,col);

grady = zeros(row,col);

gradm = zeros(row,col);

dir = zeros(row,col); % Direction of gradient.

% Calculate the gradient values for each pixel.

for ii = 1:row

for jj = 1:col

    window = imgCopy(ii:ii+m-1,jj:jj+n-1);

    dx = window.*dgau2Dx;

    dy = window.*dgau2Dy;

    dx = dx'; % Make the sum more accurate.

    dx = sum(dx(:));

    dy = sum(dy(:));

    gradx(ii,jj) = dx;

    grady(ii,jj) = dy;

    gradm(ii,jj) = sqrt(dx^2 + dy^2);

    % Calculate the angle of the gradient.

    theta = atand(dy/dx) + 90; % 0~180.

    % Determine the direction of the gradient.

    if (theta >= 0 && theta < 45)

        dir(ii,jj) = 2;

    elseif (theta >= 45 && theta < 90)

        dir(ii,jj) = 3;

    elseif (theta >= 90 && theta < 135)

        dir(ii,jj) = 0;

    else

        dir(ii,jj) = 1;

    end

end

end

% Normalize for threshold selection.

magMax = max(gradm(😃);

if magMax ~= 0

gradm = gradm / magMax;

end

%% Plot 3D gradient graph.

% [xx, yy] = meshgrid(1:col, 1:row);

% figure;

% surf(xx,yy,gradm);

%% Threshold selection.

counts = imhist(gradm, 64);

highThresh = find(cumsum(counts) > percentOfPixelsNotEdgesrowcol,1,'first') / 64;

lowThresh = thresholdRatio*highThresh;

%% Non-Maxima Suppression(NMS) Using Linear Interpolation.

gradmCopy = zeros(row,col);

imgBW = zeros(row,col);

for ii = 2:row-1

for jj = 2:col-1

    E =  gradm(ii,jj+1);

    S =  gradm(ii+1,jj);

    W =  gradm(ii,jj-1);

    N =  gradm(ii-1,jj);

    NE = gradm(ii-1,jj+1);

    NW = gradm(ii-1,jj-1);

    SW = gradm(ii+1,jj-1);

    SE = gradm(ii+1,jj+1);

    % Linear interpolation.

    % dy/dx = tan(theta).

    % dx/dy = tan(90-theta).

    gradValue = gradm(ii,jj);

    if dir(ii,jj) == 0

        d = abs(grady(ii,jj)/gradx(ii,jj));

        gradm1 = E*(1-d) + NE*d;

        gradm2 = W*(1-d) + SW*d;

    elseif dir(ii,jj) == 1

        d = abs(gradx(ii,jj)/grady(ii,jj));

        gradm1 = N*(1-d) + NE*d;

        gradm2 = S*(1-d) + SW*d;

    elseif dir(ii,jj) == 2

        d = abs(gradx(ii,jj)/grady(ii,jj));

        gradm1 = N*(1-d) + NW*d;

        gradm2 = S*(1-d) + SE*d;

    elseif dir(ii,jj) == 3

        d = abs(grady(ii,jj)/gradx(ii,jj));

        gradm1 = W*(1-d) + NW*d;

        gradm2 = E*(1-d) + SE*d;

    else

        gradm1 = highThresh;

        gradm2 = highThresh;

    end

    % Non-Maxima Suppression.

    if gradValue >= gradm1 && gradValue >= gradm2

        if gradValue >= highThresh

            imgBW(ii,jj) = 1;

            gradmCopy(ii,jj) = highThresh;

        elseif gradValue >= lowThresh

            gradmCopy(ii,jj) = lowThresh;

        else

            gradmCopy(ii,jj) = 0;

        end

    else

        gradmCopy(ii,jj) = 0;

    end

end

end

%% High-Low threshold detection.Double-Threshold.

% If the 8 pixels around the low threshold point have high threshold, then

% the low threshold pixel should be retained.

for ii = 2:row-1

for jj = 2:col-1

    if gradmCopy(ii,jj) == lowThresh

        neighbors = [

            gradmCopy(ii-1,jj-1),   gradmCopy(ii-1,jj), gradmCopy(ii-1,jj+1),...

            gradmCopy(ii,  jj-1),                       gradmCopy(ii,  jj+1),...

            gradmCopy(ii+1,jj-1),   gradmCopy(ii+1,jj), gradmCopy(ii+1,jj+1)...

            ];

        if ~isempty(find(neighbors) == highThresh)

            imgBW(ii,jj) = 1;

        end

    end

end

end

imgCanny = logical(imgBW);

end

数据阵41*41：
-1 -0.744210248293034 -0.474851342941551 -0.317764340384943 -0.121328289375456 -0.0315434866713129 0.0794508631425337 0.0991848394884942 0.120612418771561 0.0884677731860635 0.0197625979881901 -0.0536440147998797 -0.162529766354390 -0.290221453097769 -0.399737789065978 -0.509254125034186 -0.558708778245124 -0.621978562281264 -0.614928813667215 -0.566677327687363 -0.518425841706442 -0.383062688915457 -0.228963003794228 -0.108124656000880 0.0780375383337753 0.279813304248855 0.477755362262235 0.675697420274569 0.774455113332948 0.878743116078552 1 0.981757672460018 0.939547149820456 0.818128131180397 0.696709112539292 0.524506990411887 0.291143730438762 0.0218591107472457 -0.223601126687357 -0.538475146073296 -0.833257034016823
-0.951633719271810 -0.699069815353227 -0.503092933761353 -0.311211498969871 -0.155558784464108 -0.0677734982028244 0.0218116599189273 0.0595365147190814 0.112296076208869 0.0102093732544224 -0.0524750666405827 -0.224402568775190 -0.346303700179250 -0.576673035611148 -0.680767286162968 -0.858420024991380 -0.938351040215472 -1 -0.975210924743339 -0.988525027565161 -0.980619517292785 -0.771614287977750 -0.654762749086537 -0.452487937687298 -0.181802159878958 0.0203336690747351 0.296343381328867 0.515001809043435 0.696015684267088 0.890247203784040 1.00000000000000 0.998905396753651 0.953793326575649 0.860484494241928 0.731627489011390 0.496260526586470 0.286613699615293 -0.0267367674488005 -0.239949040154128 -0.580630812986733 -0.855518205605465
-0.554317208609300 -0.380359872832717 -0.225496646250146 -0.0744322173705792 0.0633246352456443 0.151851059732465 0.205689528020756 0.253131450187452 0.228618904884857 0.172118273538368 0.0472141519624176 -0.0821484956462104 -0.270664530933234 -0.458395143439077 -0.626721209224518 -0.748225643157057 -0.910755340062439 -0.952398088107639 -1 -0.985702390388286 -0.911146999701513 -0.803370191725298 -0.648073021230985 -0.480669701339200 -0.273576502196054 -0.0187174615605711 0.213509404675503 0.499525564313121 0.637071303797140 0.856038164418202 0.925347015034566 1 0.946901557668253 0.929377159454274 0.743977366257413 0.577729235555812 0.358365491240831 0.167304125756351 -0.103275286507871 -0.307371980596934 -0.542131957150764
-0.295578568983048 -0.176786417622321 -0.0300146897826498 0.0936774322226865 0.203738549919365 0.290752286018120 0.330641843179128 0.365155302062259 0.312997222554176 0.291085917740853 0.156484206079750 0.0264466276359929 -0.155405497158131 -0.367059911638252 -0.555141360479637 -0.696847564860563 -0.838173788921476 -0.911130324595387 -0.973557936099796 -1 -0.877116747473586 -0.844398010010055 -0.666608390351151 -0.475476508897038 -0.285519313797089 -0.0704600520233560 0.228853011840129 0.408272602364442 0.627536800534654 0.803130036596908 0.925644359735097 1 0.961095843863689 0.897964074053625 0.761692669429334 0.612468151225017 0.427036611035270 0.250171668498815 0.0650120200306732 -0.148404036398611 -0.299281946206398
-0.110517263300729 -0.0268740543103976 0.0772038289601584 0.191879047162677 0.280936228571276 0.372107198521370 0.421791183656238 0.452062236859551 0.442226563974869 0.378558273658150 0.249931739409642 0.0716733488407166 -0.112328616090111 -0.286295420903672 -0.509377836490825 -0.702582548156951 -0.827956651144086 -0.923962301765865 -1 -0.980727582278397 -0.962555346008811 -0.847332143235891 -0.706907470408319 -0.562815306251207 -0.260000378576102 -0.115389592504890 0.146910678594661 0.394779517787297 0.616338190806020 0.792835608662332 0.943869154032895 1 0.988787675145019 0.907672002813228 0.802857333479098 0.633222629006232 0.461742372740987 0.294555325392566 0.152330484908594 -0.0325999624048533 -0.122439523928999
-0.0373339117904070 0.0634932023538499 0.159470690119676 0.257085665023765 0.333759316101933 0.421726241947565 0.486163378599972 0.511913127741309 0.522367598909791 0.452397343952614 0.354708445526353 0.172983382959798 -0.0358843844744834 -0.264274384399258 -0.463645631190322 -0.642160561134328 -0.822605240011974 -0.878972205985351 -1 -0.973437265225122 -0.959044282904143 -0.872094453557532 -0.741917814017205 -0.580314167081979 -0.405994850660412 -0.156660796904560 0.116822581872144 0.368263702250405 0.587718603803619 0.793509229805406 0.925621240316656 1 0.979743044836785 0.917827061395671 0.766403100564238 0.640083401881332 0.467534651454201 0.312082323427364 0.172604608031012 0.0326055454959158 -0.0934458561306110
0.0351010810973385 0.144747450502097 0.220792044061772 0.302662760067395 0.382476006888786 0.471157266383073 0.535379750388720 0.580285873522898 0.594726740050004 0.528238111542176 0.419066884722455 0.236718603283862 0.0344096866800137 -0.194002109433711 -0.413292477829177 -0.614190012152459 -0.772837739412021 -0.858799898158196 -0.945748706933659 -1 -0.944218827852143 -0.814687055800867 -0.749677783543506 -0.560592049741469 -0.391649817964580 -0.139007424887300 0.0560700093832061 0.379567515968591 0.605855729656558 0.779622944327709 0.939974808726761 1 0.994916939076744 0.908415293531940 0.786025025296743 0.628496944243565 0.502403221250299 0.342614607194426 0.206658174528050 0.0950331698215701 0.00697546409175098
0.107377398425006 0.164455807055443 0.238768361210544 0.309409691817853 0.393222182406522 0.476714235976865 0.553360290241833 0.613653189051756 0.651579256183527 0.572856762480372 0.462953280905798 0.293825167674283 0.0997449365974785 -0.157316246702046 -0.413769002207999 -0.610081721062107 -0.783441261418496 -0.895274842132699 -0.972845612062953 -1 -0.988256074210210 -0.901240310101496 -0.797922877703371 -0.631660730445558 -0.439234695546590 -0.206282740602805 0.0623011801485278 0.301566031725190 0.548506963714134 0.767090778685562 0.926053416644243 0.992690088512537 1 0.902586596862566 0.767952866667108 0.606961328073258 0.462658228039419 0.325804953839230 0.197687295690830 0.107740566530232 0.0224364670317643
0.118666560101831 0.174782114903665 0.230847379179458 0.309745772867179 0.379523761935046 0.477964514560604 0.560006144953465 0.631073904952542 0.667668792932364 0.614887697022593 0.490973879442718 0.289647852460244 0.0710346631328918 -0.170409880428912 -0.382956434624341 -0.594132212358147 -0.763978593400532 -0.903534560343553 -0.980799359245450 -0.978835203137447 -1 -0.913707920735521 -0.833383237916808 -0.658132903608141 -0.471462182307603 -0.242492599728782 -0.0395441251430996 0.309940242878910 0.499896863248484 0.786505427777748 0.920146290114879 1 0.973529016225725 0.883267695308941 0.741985914009687 0.579968881167806 0.434113898503578 0.310997582034422 0.185868162870253 0.107902391333852 0.0401174983103023
0.167425034459522 0.201769353653152 0.252211225928949 0.323973410047615 0.402226238738161 0.492085377856130 0.584740904959971 0.668455692405724 0.697113091667560 0.650160060348332 0.536971577325918 0.356374370083614 0.140231199540366 -0.139321551865435 -0.358903028903163 -0.573125514750881 -0.735272930676966 -0.874246821031921 -0.948838132811875 -1 -0.936376959536353 -0.911805685894992 -0.795272828128246 -0.663861111191417 -0.469568804095345 -0.252153051716583 0.00418339848442084 0.268642413040113 0.528186302285629 0.752052038241779 0.921904357822556 1.00000000000000 0.970161481254449 0.885990170990777 0.733929655223893 0.581191543718812 0.430892114144925 0.326457246634577 0.210538168238527 0.144900768300756 0.0596389778510766
0.156940046079987 0.199508029430186 0.249575585060753 0.315549489903876 0.393323385604759 0.476679915118251 0.581697107017467 0.663213823140062 0.699739207916093 0.691921006906983 0.550599853523900 0.364605957708405 0.116618301868771 -0.109017587280267 -0.366983265233706 -0.564930268925301 -0.751214221912488 -0.861021619600182 -0.950406850145245 -1 -0.967106766519209 -0.909797040507540 -0.818668109328347 -0.667397749250659 -0.483833424912697 -0.265117209222304 -0.0225405327844729 0.244199879918632 0.532070746616038 0.735053765136196 0.893907783900628 1 0.956840235635048 0.864002157284677 0.716000603712859 0.560617451838860 0.412554467127948 0.299010654502751 0.196596516225329 0.132493193670729 0.0680082882009825
0.141873675795034 0.186752683577269 0.234680858178104 0.298491170648037 0.376380448020561 0.464678749937933 0.564979350733469 0.656893127762195 0.712446537563335 0.659900408918112 0.549357472163905 0.367148257936328 0.124457220295138 -0.119067400706266 -0.368607309502677 -0.583466916267934 -0.741896736037216 -0.889154916036164 -0.961145755140391 -1 -0.980275645087442 -0.948964145260016 -0.831911672490831 -0.688911424938177 -0.527350897177053 -0.291297316143579 -0.0591144408103509 0.233997901032954 0.502849273678328 0.704283266609534 0.881908768739610 1 0.924050743927217 0.838473373935292 0.684058138847851 0.530313118325924 0.394350737767331 0.277904406931594 0.182143851478568 0.118105944119483 0.0710256659282420
0.167361877557396 0.206971338318640 0.259648416506659 0.320810135315422 0.396530281092904 0.491119241249405 0.599490234863850 0.681837621009361 0.745584126833485 0.703868283820703 0.600182112042252 0.407164804101372 0.150380317898890 -0.0967535435809273 -0.361502693329854 -0.559305555390947 -0.751686772523816 -0.880779791092209 -0.963346353918104 -1 -0.986797642710393 -0.962366770005113 -0.829668443773002 -0.717661771534146 -0.508948473334845 -0.283545845537708 -0.0406556149157985 0.253428425281792 0.522836240019415 0.737907809390968 0.917847946143517 1.00000000000000 0.956883687919270 0.869727607394066 0.716592404482990 0.553272168470676 0.411905347020443 0.295780017615446 0.203241391094634 0.138926988785009 0.0926144832846019
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还有一个函数：
%% Local functions. Image Replicate.

function imgRep = imgReplicate(I,rExt,cExt)

[row,col] = size(I);

imgCopy = zeros(row+2rExt,col+2cExt);

% 4 edges and 4 corners pixels.

top = I(1,:);

bottom = I(row,:);

left = I(:,1);

right = I(:,col);

topLeftCorner = I(1,1);

topRightCorner = I(1,col);

bottomLeftCorner = I(row,1);

bottomRightCorner = I(row,col);

% The coordinates of the oroginal image after the expansion in the new graph.

topLeftR = rExt+1;

topLeftC = cExt+1;

bottomLeftR = topLeftR+row-1;

bottomLeftC = topLeftC;

topRightR = topLeftR;

topRightC = topLeftC+col-1;

bottomRightR = topLeftR+row-1;

bottomRightC = topLeftC+col-1;

% Copy original image and 4 edges.

imgCopy(topLeftR:bottomLeftR,topLeftC:topRightC) = I;

imgCopy(1:rExt,topLeftC:topRightC) = repmat(top,[rExt,1]);

imgCopy(bottomLeftR+1:end,bottomLeftC:bottomRightC) = repmat(bottom,[rExt,1]);

imgCopy(topLeftR:bottomLeftR,1:cExt) = repmat(left,[1,cExt]);

imgCopy(topRightR:bottomRightR,topRightC+1:end) = repmat(right,[1,cExt]);

% Copy 4 corners.

for ii = 1:rExt

for jj = 1:cExt

    imgCopy(ii,jj) = topLeftCorner;

    imgCopy(ii,jj+topRightC) = topRightCorner;

    imgCopy(ii+bottomLeftR,jj) = bottomLeftCorner;

    imgCopy(ii+bottomRightR,jj+bottomRightC) = bottomRightCorner;

end

end

imgRep = imgCopy;

end

%% End of file.