如何用类似Python的edge（）Matlab函数制作canny边缘检测器

Question

我想在Python中使用edge()Matlab函数：

mask = double(edge(mask,'canny',threshold));

我试图找到一个函数，它相当于Python中的edge()Matlab函数。我尝试过Python中的cv2.Canny()函数，但是它与Matlab函数不同（您可以在这里阅读关于差异的解释：https://dsp.stackexchange.com/questions/4716/differences-between-opencv-canny-and-matlab-canny）。我试过在那个论坛上建议先用高斯模糊对图像进行模糊处理，但结果还是和matlab的不一样。在

所以，我找到了一些Matlab canny边缘检测器代码，我正试图将这些代码转换成内置的edge()函数，就像Matlab一样。我发现的密码是：

第一个代码：

^{pr2}$

发件人： https://www.mathworks.com/matlabcentral/mlc-downloads/downloads/submissions/63294/versions/9/previews/MaxPol%20Package/demo%20examples/forward%20signal%20and%20imaging%20problems/Canny%20edge%20detection/canny_edge.m/index.html?access_key=

第二个代码：

function [eout, dx, dy] = canny_edge(image_scan, smoothing_kernel, derivative_kernel)

thresh = [0.2 0.5];
sigma = sqrt(2);
thinning = true;
H = [];
kx = 1;
ky = 1;

[m,n] = size(image_scan);
e = false(m,n);
% Magic numbers
PercentOfPixelsNotEdges = .7; % Used for selecting thresholds
ThresholdRatio = .4;          % Low thresh is this fraction of the high.


dx = imfilter(image_scan, smoothing_kernel', 'conv', 'replicate');
dx = imfilter(dx, derivative_kernel, 'conv', 'replicate');

% Compute smoothed numerical gradient of image I along y (vertical)
% direction. GY corresponds to dG/dy, where G is the Gaussian Smoothed
% version of image I.
dy = imfilter(image_scan, smoothing_kernel, 'conv', 'replicate');
dy  = imfilter(dy, derivative_kernel', 'conv', 'replicate');

% Calculate Magnitude of Gradient
magGrad = hypot(dx, dy);

% Normalize for threshold selection
magmax = max(magGrad(:));
if magmax > 0
    magGrad = magGrad / magmax;
end

% Determine Hysteresis Thresholds
[lowThresh, highThresh] = selectThresholds(thresh, magGrad, PercentOfPixelsNotEdges, ThresholdRatio, mfilename);

% Perform Non-Maximum Suppression Thining and Hysteresis Thresholding of Edge
% Strength
eout = thinAndThreshold(e, dx, dy, magGrad, lowThresh, highThresh);
thresh = [lowThresh highThresh];


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
%   Local Function : selectThresholds
%
function [lowThresh, highThresh] = selectThresholds(thresh, magGrad, PercentOfPixelsNotEdges, ThresholdRatio, ~)

[m,n] = size(magGrad);

% Select the thresholds
if isempty(thresh)
    counts=imhist(magGrad, 64);
    highThresh = find(cumsum(counts) > PercentOfPixelsNotEdges*m*n,...
        1,'first') / 64;
    lowThresh = ThresholdRatio*highThresh;
elseif length(thresh)==1
    highThresh = thresh;
    if thresh>=1
        error(message('images:edge:thresholdMustBeLessThanOne'))
    end
    lowThresh = ThresholdRatio*thresh;
elseif length(thresh)==2
    lowThresh = thresh(1);
    highThresh = thresh(2);
    if (lowThresh >= highThresh) || (highThresh >= 1)
        error(message('images:edge:thresholdOutOfRange'))
    end
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
%   Local Function : thinAndThreshold
%
function H = thinAndThreshold(E, dx, dy, magGrad, lowThresh, highThresh)

% Perform Non-Maximum Suppression Thining and Hysteresis Thresholding of Edge
% Strength

% We will accrue indices which specify ON pixels in strong edgemap
% The array e will become the weak edge map.
idxStrong = [];
for dir = 1:4
    idxLocalMax = cannyFindLocalMaxima(dir,dx,dy,magGrad);
    idxWeak = idxLocalMax(magGrad(idxLocalMax) > lowThresh);
    E(idxWeak)=1;
    idxStrong = [idxStrong; idxWeak(magGrad(idxWeak) > highThresh)]; %#ok<AGROW>
end

[m,n] = size(E);

if ~isempty(idxStrong) % result is all zeros if idxStrong is empty
    rstrong = rem(idxStrong-1, m)+1;
    cstrong = floor((idxStrong-1)/m)+1;
    H = bwselect(E, cstrong, rstrong, 8);
else
    H = zeros(m, n);
end


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
%   Local Function : cannyFindLocalMaxima
%
function idxLocalMax = cannyFindLocalMaxima(direction,ix,iy,mag)
%
% This sub-function helps with the non-maximum suppression in the Canny
% edge detector.  The input parameters are:
%
%   direction - the index of which direction the gradient is pointing,
%               read from the diagram below. direction is 1, 2, 3, or 4.
%   ix        - input image filtered by derivative of gaussian along x
%   iy        - input image filtered by derivative of gaussian along y
%   mag       - the gradient magnitude image
%
%    there are 4 cases:
%
%                         The X marks the pixel in question, and each
%         3     2         of the quadrants for the gradient vector
%       O----0----0       fall into two cases, divided by the 45
%     4 |         | 1     degree line.  In one case the gradient
%       |         |       vector is more horizontal, and in the other
%       O    X    O       it is more vertical.  There are eight
%       |         |       divisions, but for the non-maximum suppression
%    (1)|         |(4)    we are only worried about 4 of them since we
%       O----O----O       use symmetric points about the center pixel.
%        (2)   (3)


[m,n] = size(mag);

% Find the indices of all points whose gradient (specified by the
% vector (ix,iy)) is going in the direction we're looking at.

switch direction
    case 1
        idx = find((iy<=0 & ix>-iy)  | (iy>=0 & ix<-iy));
    case 2
        idx = find((ix>0 & -iy>=ix)  | (ix<0 & -iy<=ix));
    case 3
        idx = find((ix<=0 & ix>iy) | (ix>=0 & ix<iy));
    case 4
        idx = find((iy<0 & ix<=iy) | (iy>0 & ix>=iy));
end

% Exclude the exterior pixels
if ~isempty(idx)
    v = mod(idx,m);
    extIdx = (v==1 | v==0 | idx<=m | (idx>(n-1)*m));
    idx(extIdx) = [];
end

ixv = ix(idx);
iyv = iy(idx);
gradmag = mag(idx);

% Do the linear interpolations for the interior pixels
switch direction
    case 1
        d = abs(iyv./ixv);
        gradmag1 = mag(idx+m).*(1-d) + mag(idx+m-1).*d;
        gradmag2 = mag(idx-m).*(1-d) + mag(idx-m+1).*d;
    case 2
        d = abs(ixv./iyv);
        gradmag1 = mag(idx-1).*(1-d) + mag(idx+m-1).*d;
        gradmag2 = mag(idx+1).*(1-d) + mag(idx-m+1).*d;
    case 3
        d = abs(ixv./iyv);
        gradmag1 = mag(idx-1).*(1-d) + mag(idx-m-1).*d;
        gradmag2 = mag(idx+1).*(1-d) + mag(idx+m+1).*d;
    case 4
        d = abs(iyv./ixv);
        gradmag1 = mag(idx-m).*(1-d) + mag(idx-m-1).*d;
        gradmag2 = mag(idx+m).*(1-d) + mag(idx+m+1).*d;
end
idxLocalMax = idx(gradmag>=gradmag1 & gradmag>=gradmag2);

发件人： https://www.mathworks.com/matlabcentral/fileexchange/46859-canny-edge-detection

我试图将其转换为内置的edge()matlab函数：

function [eout,thresh,gv_45,gh_135] = edge(varargin)
args = matlab.images.internal.stringToChar(varargin);
[a,method,thresh,sigma,thinning,H,kx,ky] = parse_inputs(args{:});

% Check that the user specified a valid number of output arguments
if ~any(strcmp(method,{'sobel','roberts','prewitt'})) && (nargout>2)
    error(message('images:edge:tooManyOutputs'))
end

% Transform to a double precision intensity image if necessary
isPrewittOrSobel = strcmp(method,'sobel') || strcmp(method,'prewitt');
if ~isPrewittOrSobel && ~isfloat(a) && ~strcmp(method,'approxcanny')
    a = im2single(a);
end


[m,n] = size(a);

if strcmp(method,'canny')
    % Magic numbers
    PercentOfPixelsNotEdges = .7; % Used for selecting thresholds
    ThresholdRatio = .4;          % Low thresh is this fraction of the high.

    % Calculate gradients using a derivative of Gaussian filter
    [dx, dy] = smoothGradient(a, sigma);

    % Calculate Magnitude of Gradient
    magGrad = hypot(dx, dy);

    % Normalize for threshold selection
    magmax = max(magGrad(:));
    if magmax > 0
        magGrad = magGrad / magmax;
    end

    % Determine Hysteresis Thresholds
    [lowThresh, highThresh] = selectThresholds(thresh, magGrad, PercentOfPixelsNotEdges, ThresholdRatio, mfilename);

    % Perform Non-Maximum Suppression Thining and Hysteresis Thresholding of Edge
    % Strength
    e = thinAndThreshold(dx, dy, magGrad, lowThresh, highThresh);
    thresh = [lowThresh highThresh];

elseif strcmp(method,'approxcanny')
    e = computeapproxcanny(a, thresh);

elseif strcmp(method,'canny_old')
    % Magic numbers
    GaussianDieOff = .0001;
    PercentOfPixelsNotEdges = .7; % Used for selecting thresholds
    ThresholdRatio = .4;          % Low thresh is this fraction of the high.

    % Design the filters - a gaussian and its derivative

    pw = 1:30; % possible widths
    ssq = sigma^2;
    width = find(exp(-(pw.*pw)/(2*ssq))>GaussianDieOff,1,'last');
    if isempty(width)
        width = 1;  % the user entered a really small sigma
    end

    t = (-width:width);
    gau = exp(-(t.*t)/(2*ssq))/(2*pi*ssq);     % the gaussian 1D filter

    % Find the directional derivative of 2D Gaussian (along X-axis)
    % Since the result is symmetric along X, we can get the derivative along
    % Y-axis simply by transposing the result for X direction.
    [x,y]=meshgrid(-width:width,-width:width);
    dgau2D=-x.*exp(-(x.*x+y.*y)/(2*ssq))/(pi*ssq);

    % Convolve the filters with the image in each direction
    % The canny edge detector first requires convolution with
    % 2D Gaussian, and then with the derivative of a Gaussian.
    % Since Gaussian filter is separable, for smoothing, we can use
    % two 1D convolutions in order to achieve the effect of convolving
    % with 2D Gaussian.  We convolve along rows and then columns.

    %smooth the image out
    aSmooth=imfilter(a,gau,'conv','replicate');   % run the filter across rows
    aSmooth=imfilter(aSmooth,gau','conv','replicate'); % and then across columns

    %apply directional derivatives
    ax = imfilter(aSmooth, dgau2D, 'conv','replicate');
    ay = imfilter(aSmooth, dgau2D', 'conv','replicate');

    mag = sqrt((ax.*ax) + (ay.*ay));
    magmax = max(mag(:));
    if magmax>0
        mag = mag / magmax;   % normalize
    end

    % Select the thresholds
    if isempty(thresh)
        counts=imhist(mag, 64);
        highThresh = find(cumsum(counts) > PercentOfPixelsNotEdges*m*n,...
            1,'first') / 64;
        lowThresh = ThresholdRatio*highThresh;
        thresh = [lowThresh highThresh];
    elseif length(thresh)==1
        highThresh = thresh;
        if thresh>=1
            error(message('images:edge:singleThresholdOutOfRange'))
        end
        lowThresh = ThresholdRatio*thresh;
        thresh = [lowThresh highThresh];
    elseif length(thresh)==2
        lowThresh = thresh(1);
        highThresh = thresh(2);
        if (lowThresh >= highThresh) || (highThresh >= 1)
            error(message('images:edge:thresholdOutOfRange'))
        end
    end

    % The next step is to do the non-maximum suppression. We will accrue
    % indices which specify ON pixels in strong edgemap The array e will become
    % the weak edge map.
    e = cannyFindLocalMaxima(ax,ay,mag,lowThresh);

    if ~isempty(e)
        [rstrong,cstrong] = find(mag>highThresh & e);

        if ~isempty(rstrong) % result is all zeros if idxStrong is empty
            e = bwselect(e, cstrong, rstrong, 8);
            e = bwmorph(e, 'thin', 1); % Thin double (or triple) pixel wide contours
        end
    end

elseif any(strcmp(method, {'log','zerocross'}))
    % The output edge map:
    e = false(m,n);

    rr = 2:m-1; cc=2:n-1;

    % We don't use image blocks here
    if isempty(H)
        fsize = ceil(sigma*3) * 2 + 1;  % choose an odd fsize > 6*sigma;
        op = fspecial('log',fsize,sigma);
    else
        op = H;
    end

    op = op - sum(op(:))/numel(op); % make the op to sum to zero
    b = imfilter(a,op,'replicate');

    if isempty(thresh)
        thresh = 0.75 * sum(abs(b(:)),'double') / numel(b);
    end

    % Look for the zero crossings:  +-, -+ and their transposes
    % We arbitrarily choose the edge to be the negative point
    [rx,cx] = find( b(rr,cc) < 0 & b(rr,cc+1) > 0 ...
        & abs( b(rr,cc)-b(rr,cc+1) ) > thresh );   % [- +]
    e((rx+1) + cx*m) = 1;
    [rx,cx] = find( b(rr,cc-1) > 0 & b(rr,cc) < 0 ...
        & abs( b(rr,cc-1)-b(rr,cc) ) > thresh );   % [+ -]
    e((rx+1) + cx*m) = 1;
    [rx,cx] = find( b(rr,cc) < 0 & b(rr+1,cc) > 0 ...
        & abs( b(rr,cc)-b(rr+1,cc) ) > thresh);   % [- +]'
    e((rx+1) + cx*m) = 1;
    [rx,cx] = find( b(rr-1,cc) > 0 & b(rr,cc) < 0 ...
        & abs( b(rr-1,cc)-b(rr,cc) ) > thresh);   % [+ -]'
    e((rx+1) + cx*m) = 1;

    % Most likely this covers all of the cases.   Just check to see if there
    % are any points where the LoG was precisely zero:
    [rz,cz] = find( b(rr,cc)==0 );
    if ~isempty(rz)
        % Look for the zero crossings: +0-, -0+ and their transposes
        % The edge lies on the Zero point
        zero = (rz+1) + cz*m;   % Linear index for zero points
        zz = (b(zero-1) < 0 & b(zero+1) > 0 ...
            & abs( b(zero-1)-b(zero+1) ) > 2*thresh);     % [- 0 +]'
        e(zero(zz)) = 1;
        zz = (b(zero-1) > 0 & b(zero+1) < 0 ...
            & abs( b(zero-1)-b(zero+1) ) > 2*thresh);     % [+ 0 -]'
        e(zero(zz)) = 1;
        zz = (b(zero-m) < 0 & b(zero+m) > 0 ...
            & abs( b(zero-m)-b(zero+m) ) > 2*thresh);     % [- 0 +]
        e(zero(zz)) = 1;
        zz = (b(zero-m) > 0 & b(zero+m) < 0 ...
            & abs( b(zero-m)-b(zero+m) ) > 2*thresh);     % [+ 0 -]
        e(zero(zz)) = 1;
    end

else  % one of the easy methods (roberts,sobel,prewitt)

    if isPrewittOrSobel
        isSobel = strcmp(method, 'sobel');
        scale = 4; % for calculating the automatic threshold
        offset = [0 0 0 0]; % offsets used in the computation of the threshold

        [bx, by, b] = edgesobelprewittmex(a, isSobel, kx, ky); 

    elseif strcmp(method, 'roberts')
        x_mask = [1 0; 0 -1]/2; % Roberts approximation to diagonal derivative
        y_mask = [0 1;-1  0]/2;

        scale = 6;
        offset = [-1 1 1 -1];

        % compute the gradient in x and y direction
        bx = imfilter(a,x_mask,'replicate');
        by = imfilter(a,y_mask,'replicate');

        % compute the magnitude
        b = kx*bx.*bx + ky*by.*by;

    else
        error(message('images:edge:invalidEdgeDetectionMethod', method))
    end

    if (nargout > 2) % if gradients are requested
        gv_45  = bx;
        gh_135 = by;
    end

    % Determine the threshold; see page 514 of
    % "Digital Imaging Processing" by William K. Pratt
    if isempty(thresh) % Determine cutoff based on RMS estimate of noise
        % Mean of the magnitude squared image is a
        % value that's roughly proportional to SNR
        cutoff = scale * sum(b(:),'double') / numel(b);
        thresh = sqrt(cutoff);
    else
        % Use relative tolerance specified by the user
        cutoff = (thresh).^2;
    end

    if thinning
        e = computeedge(b,bx,by,kx,ky,int8(offset),100*eps,cutoff);
    else
        e = b > cutoff;
    end

end

if nargout==0
    imshow(e);
else
    eout = e;
end

if isempty(a)
    if nargout==2
        if nargin == 2
            if strcmp(method,'canny')
                thresh = nan(1,2);
            else
                thresh = nan(1);
            end
        end
    end    
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
%   Local Function : parse_inputs
%
function [I,Method,Thresh,Sigma,Thinning,H,kx,ky] = parse_inputs(varargin)
% OUTPUTS:
%   I      Image Data
%   Method Edge detection method
%   Thresh Threshold value
%   Sigma  standard deviation of Gaussian
%   H      Filter for Zero-crossing detection
%   kx,ky  From Directionality vector

narginchk(1,5)

I = varargin{1};

validateattributes(I,{'numeric','logical'},{'real','nonsparse','2d'},mfilename,'I',1);

% Defaults
Method    = 'sobel';
Direction = 'both';
Thinning  = true;

methods    = {'canny','approxcanny','canny_old','prewitt','sobel','marr-hildreth','log','roberts','zerocross'};
directions = {'both','horizontal','vertical'};
options    = {'thinning','nothinning'};

% Now parse the nargin-1 remaining input arguments

% First get the strings - we do this because the interpretation of the
% rest of the arguments will depend on the method.
nonstr = [];   % ordered indices of non-string arguments
for i = 2:nargin
    if ischar(varargin{i})
        str = lower(varargin{i});
        j = find(strcmp(str,methods));
        k = find(strcmp(str,directions));
        l = find(strcmp(str,options));
        if ~isempty(j)
            Method = methods{j(1)};
            if strcmp(Method,'marr-hildreth')
                error(message('images:removed:syntax','EDGE(I,''marr-hildreth'',...)','EDGE(I,''log'',...)')) 
            end
        elseif ~isempty(k)
            Direction = directions{k(1)};
        elseif ~isempty(l)
            if strcmp(options{l(1)},'thinning')
                Thinning = true;
            else
                Thinning = false;
            end
        else
            error(message('images:edge:invalidInputString', varargin{ i }))
        end
    else
        nonstr = [nonstr i]; %#ok<AGROW>
    end
end

% Now get the rest of the arguments
[Thresh,Sigma,H,kx,ky] = images.internal.parseNonStringInputsEdge(varargin,Method,Direction,nonstr);
validateattributes(Thresh,{'numeric'},{'real'},mfilename,'thresh',3);


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
%   Local Function : smoothGradient
%
function [GX, GY] = smoothGradient(I, sigma)

% Create an even-length 1-D separable Derivative of Gaussian filter

% Determine filter length
filterExtent = ceil(4*sigma);
x = -filterExtent:filterExtent;

% Create 1-D Gaussian Kernel
c = 1/(sqrt(2*pi)*sigma);
gaussKernel = c * exp(-(x.^2)/(2*sigma^2));

% Normalize to ensure kernel sums to one
gaussKernel = gaussKernel/sum(gaussKernel);

% Create 1-D Derivative of Gaussian Kernel
derivGaussKernel = gradient(gaussKernel);

% Normalize to ensure kernel sums to zero
negVals = derivGaussKernel < 0;
posVals = derivGaussKernel > 0;
derivGaussKernel(posVals) = derivGaussKernel(posVals)/sum(derivGaussKernel(posVals));
derivGaussKernel(negVals) = derivGaussKernel(negVals)/abs(sum(derivGaussKernel(negVals)));

% Compute smoothed numerical gradient of image I along x (horizontal)
% direction. GX corresponds to dG/dx, where G is the Gaussian Smoothed
% version of image I.
GX = imfilter(I, gaussKernel', 'conv', 'replicate');
GX = imfilter(GX, derivGaussKernel, 'conv', 'replicate');

% Compute smoothed numerical gradient of image I along y (vertical)
% direction. GY corresponds to dG/dy, where G is the Gaussian Smoothed
% version of image I.
GY = imfilter(I, gaussKernel, 'conv', 'replicate');
GY  = imfilter(GY, derivGaussKernel', 'conv', 'replicate');


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
%   Local Function : selectThresholds
%
function [lowThresh, highThresh] = selectThresholds(thresh, magGrad, PercentOfPixelsNotEdges, ThresholdRatio, ~)

[m,n] = size(magGrad);

% Select the thresholds
if isempty(thresh)
    counts=imhist(magGrad, 64);
    highThresh = find(cumsum(counts) > PercentOfPixelsNotEdges*m*n,...
        1,'first') / 64;
    lowThresh = ThresholdRatio*highThresh;
elseif length(thresh)==1
    highThresh = thresh;
    if thresh>=1
        error(message('images:edge:singleThresholdOutOfRange'))
    end
    lowThresh = ThresholdRatio*thresh;
elseif length(thresh)==2
    lowThresh = thresh(1);
    highThresh = thresh(2);
    if (lowThresh >= highThresh) || (highThresh >= 1)
        error(message('images:edge:thresholdOutOfRange'))
    end
end


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
%   Local Function : thinAndThreshold
%
function H = thinAndThreshold(dx, dy, magGrad, lowThresh, highThresh)
% Perform Non-Maximum Suppression Thining and Hysteresis Thresholding of
% Edge Strength

% We will accrue indices which specify ON pixels in strong edgemap
% The array e will become the weak edge map.

E = cannyFindLocalMaxima(dx,dy,magGrad,lowThresh);

if ~isempty(E)
    [rstrong,cstrong] = find(magGrad>highThresh & E);

    if ~isempty(rstrong) % result is all zeros if idxStrong is empty
        H = bwselect(E, cstrong, rstrong, 8);
    else
        H = false(size(E));
    end
else
    H = false(size(E));
end


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
%   Local Function : computeapproxcanny
%
function e = computeapproxcanny(a, thresh)
    a = im2uint8(a);
    if isempty(a)
        e = logical([]);
    else
        if isempty(thresh)
            e = images.internal.ocvcanny(a);
        else
            if numel(thresh) == 1
                e = images.internal.ocvcanny(a, 0.4*thresh, thresh);
            else
                e = images.internal.ocvcanny(a, thresh(2), thresh(1));
            end
        end
        e = logical(e);
    end

（您可以通过在matlab中键入“edit edge”而不加引号来查看代码）

有没有人有什么建议，或者有与matlab edge()函数相同的canny边缘检测器Python代码？任何建议或答案都会很有帮助。谢谢您。在

Answer 1

请澄清您的意图：

1. 您想用python编写一个与MATLABs edge（）函数等价的函数，并实现Canny？ （如果是的话，你为什么把所有的选项都放在最后的代码引用中？您只需要strcmp（method，'canny'）节）

2. 如果你已经有了这么好的MATLAB代码，它揭示了canny impl。你为什么不逐行翻译呢？

总的来说，解决方法是：

1. 编写您自己的自定义MATLAB函数，它只执行您需要的功能，没有其他功能。如果你想要canny，编写你自己的MATLAB函数来完成这个任务。

2. 在有了最少的可工作的MATLAB代码之后，逐行将其翻译成python，同时在每个步骤之后输出MATLAB代码和python代码的调试图像，以验证您是否正确。

最后注意：如果你看到使用OpenCV等效函数对你不起作用，你必须在C++中编写OpenCV代码，因为只有这样你才能进入调试器的OpenCV实现，并找出它与Matlab实现的区别。在