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canny.m
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clc,clear,close all
% image_src = imread('01.jpeg');
image_src = imread('D:\File\Program\matlab\EdgeDetection\obm.png');
[m, n, dim] = size(image_src);
%%================图像预处理========================
image_gray=rgb2gray(image_src);
%% 直方图均值化
image_histeq = histeq(image_gray,256);
%%=================边缘检测=======================
%% 高斯平滑降噪
sigma = 0.5;
%使用标准差为1的二维高斯平滑核对灰度图像进行滤波
image_gray2 = imgaussfilt(image_gray,sigma);
%% 锐化
% sharp=[-1 -1 -1,-1 8.5 -1,-1 -1 -1];
% sharp=[1 1 1,1 -7 1,1 1 1];
% sharp=[0 -1 0,-1 4.5 -1,0 -1 0];
% sharp=[0 -1 -1 -1 0,-1 1 1 1 -1,-1 1 5 1 -1,-1 1 1 1 -1,0 -1 -1 -1 0];
% image_sharp=conv2(double(image_gray),sharp,'same');
figure;
subplot(1,4,1);
imshow(image_src);%原图
title('原始图像');
subplot(1,4,2);
imshow(image_gray);
title('灰度图像');
subplot(1,4,3);
imshow(image_histeq);
title('均值化');
% subplot(1,4,4);
% imshow(image_gray2);
% title('锐化图像');
%% Sobel算子
I2 = double(image_gray2);
sobel = [1 2 1,0 0 0,-1 -2 -1];
I1_y = conv2(I2,sobel,'same');
I1_x = conv2(I2,sobel','same');
theta = zeros(m,n);
sector = zeros(m,n);
for x = 1:(m-1)
for y = 1:(n-1)
I2(x,y) = (I1_y(x,y)^2 + I1_x(x,y)^2)^0.5;%几何平均求梯度幅值
theta(x,y) = atand(I1_y(x,y) / I1_x(x,y));%反正切求梯度方向
tem = theta(x,y);
if (tem >= 22.5) && (tem < 67.5)
sector(x,y) = 0;
elseif (tem >= -22.5) && (tem < 22.5)
sector(x,y) = 3;
elseif (tem >= -67.5) && (tem < -22.5)
sector(x,y) = 2;
else
sector(x,y) = 1;
end
end
end
%% 非极大值抑制
canny1=zeros(m,n);
for x = 2:(m-1)
for y = 2:(n-1)
%先置零，若邻域四个方向有更大值，更新
canny1(x,y) = 0;
if 0 == sector(x,y) %右上左下
if (I2(x,y) >= I2(x+1,y+1)) && (I2(x,y) > I2(x-1,y-1))
canny1(x,y) = I2(x,y);
end
elseif 1 == sector(x,y) %垂直方向
if (I2(x,y) >= I2(x,y+1)) && (I2(x,y) > I2(x,y-1))
canny1(x,y) = I2(x,y);
end
elseif 2 == sector(x,y) %左上右下
if (I2(x,y) >= I2(x-1,y+1)) && (I2(x,y) > I2(x+1,y-1))
canny1(x,y) = I2(x,y);
end
elseif 3 == sector(x,y) %水平方向
if (I2(x,y) >= I2(x-1,y)) && (I2(x,y) > I2(x+1,y))
canny1(x,y) = I2(x,y);
end
end
end
end
%% 双阈值检测
ratio = 2;%高低阈值比
canny2 = zeros(m,n);
bin = zeros(m,n);
lowTh = 50;
for y = 2:(m-1)
for x = 2:(n-1)
if canny1(y,x)<lowTh %低阈值
canny2(y,x) = 0;
bin(y,x) = 0;
elseif canny1(y,x)>ratio*lowTh %高阈值
canny2(y,x) = canny1(y,x);
bin(y,x) = 1;
else %介于之间的看其8领域有没有高于高阈值的，有则可以为边缘
tem = [canny1(y-1,x-1), canny1(y-1,x), canny1(y-1,x+1);
canny1(y,x-1), canny1(y,x), canny1(y,x+1);
canny1(y+1,x-1), canny1(y+1,x), canny1(y+1,x+1)];
temMax = max(tem);
if temMax(1) > ratio*lowTh
canny2(y,x) = temMax(1);
bin(y,x) = 1;
%高阈值减半查找
elseif temMax(1) > ratio*lowTh/2
canny2(y,x) = temMax(1);
bin(y,x) = 1;
else
canny2(y,x) = 0;
bin(y,x) = 0;
end
end
end
end
%% Matlab自带Canny算子
I2_3 = edge(double(image_gray),'canny');
%% 绘图
figure;
subplot(3,2,1);
imshow(uint8(I1_y));%原图
title('y轴方向卷积');
subplot(3,2,2);
imshow(uint8(I1_x));
title('x轴方向卷积');
subplot(3,2,3);
imshow(uint8(I2));
title('梯度幅值图');
subplot(3,2,4);
imshow(uint8(canny2));
title('非最大值抑制结果');
subplot(3,2,5);
imshow(bin);
title('My Canny');
subplot(3,2,6);
imshow(I2_3);
title('Matlab canny算子分割结果');