基于彩色图像的Canny边缘检测算法

基于图像边缘检测的Canny算法，梯度幅值和角度，进行非极大值抑制

clear;
close all;
clc;

%%%%%%%%%%%%%%%%%%%%%%%%%
%%%Input parameters%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%
MaskSize = 3;
Sigma = 5;   %（for Gaussian）
Threshold = 0.30; %（building brick 0.13 monkey 0.22 lena0.18 building outline ）
% ThresholdRatio = 0;
ThresholdRatio = 1e-40;

%==========================================================================
%===================Opening the files of images============================
%==========================================================================
[filename pathname]=uigetfile（{‘*.jpg‘;...
    ‘*.bmp‘;...
    ‘*.gif‘;...
    ‘*.png‘;...
    ‘*.tif‘}...
    ‘Open the files of images‘）;
if filename~=0
    openfilename = strcat（pathnamefilename）; %processing diversified forms of images
    eval（[‘info=imfinfo（‘‘‘openfilename ‘‘‘）;‘]）;
    switch info.ColorType
        case ‘truecolor‘
            eval（[‘RGB = imread（‘‘‘openfilename ‘‘‘）;‘]）;  %[X map] = rgb2ind（RGB 256）;
            InImg = rgb2gray（RGB）;
            clear RGB;
        case ‘indexed‘
            eval（[‘[X map] = imread（‘‘‘openfilename ‘‘‘）;‘]）;
            InImg = ind2gray（X map）;
            clear X;
        case ‘grayscale‘
            eval（[‘InImg = imread（‘‘‘openfilename ‘‘‘）;‘]）;
    end
else
    return;
end
st = cputime;
% subplot（211）;imagesc（InImg）;title（‘lotus picture‘）;
InImg = double（InImg）;
[rows cols] = size（InImg）;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Calculate Filtered Gradient
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

GausDer_x = ones（MaskSizeMaskSize）;
GausDer_y = ones（MaskSizeMaskSize）;
Amp=-1/sqrt（2*pi*Sigma）;
for g_x=1:MaskSize;
    for g_y=1:MaskSize;
        x = g_x-（MaskSize+1）/2;
        y = g_y-（MaskSize+1）/2;
        GausDer_x（g_xg_y）=Amp*（x/Sigma^2）.*exp（-（x^2+y^2）/（2*Sigma^2））;
        GausDer_y（g_xg_y）=Amp*（y/Sigma^2）.*exp（-（x^2+y^2）/（2*Sigma^2））;   %derivative of a Gaussian
    end
end

GausDer_x（g_xg_y） = sum（sum（GausDer_x（g_xg_y）））;
GausDer_y（g_xg_y） = sum（sum（GausDer_x（g_xg_y）））;    %normalization

Img_gx = conv2（InImgGausDer_x‘same‘）;
Img_gy = conv2（InImgGausDer_y‘same‘）;

F = sqrt（Img_gx.*Img_gx+Img_gy.*Img_gy）;  %the magnitude of the gradient
D = atan（Img_gy./Img_gx）;                 %edge orientation of the gradient
figure（1）;
subplot（121）;
imagesc（F）;
title（‘Gradient Amplitude‘）;
subplot（122）;
imagesc（D）;
title（‘Gradient Angle‘）;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Nonmaximum suppression
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

EdgGrad = zeros（rows cols）;
EdgDirc = zeros（rows cols）;
for i = 2: （rows-1）
    for j = 2: （cols-1）
        switch fix（8*D（ij）/pi）
            case 0                                     % from 0 - pi/8  and 7pi/8 -pi
  

 属性            大小     日期    时间   名称
----------- ---------  ---------- -----  ----
     目录           0  2018-06-09 14:05  53623196Cmp_vis_canny\
     文件        7315  2018-06-09 10:56  53623196Cmp_vis_canny\Cmp_vis_canny.m
     文件       30046  2018-06-09 11:00  53623196Cmp_vis_canny\edge detection.jpg
     文件      460040  2018-06-09 10:59  53623196Cmp_vis_canny\edge gradient.fig
     文件      136304  2018-06-09 11:00  53623196Cmp_vis_canny\edge gradient.jpg
     文件       11914  2006-08-09 11:57  53623196Cmp_vis_canny\flower.jpg
     文件     2120064  2018-06-09 10:57  53623196Cmp_vis_canny\gradient.fig
     文件      147180  2018-06-09 11:00  53623196Cmp_vis_canny\gradient.jpg
     文件       26152  2018-06-09 10:21  53623196Cmp_vis_canny\lotus.jpg
     文件       25586  2018-06-09 11:01  53623196Cmp_vis_canny\untitled.jpg