for i=2:m-1 for j=2:n-1 Mx=img_w(i,j); My=img_h(i,j); if My~=0 o=atan(Mx/My); %边缘的法线弧度 elseif My==0 && Mx>0 o=pi/2; else o=-pi/2; end
时间: 2024-02-15 14:28:49 浏览: 29
这是一段 MATLAB 代码,用于计算图像中每个像素点的边缘法线方向。其中,m和n分别表示图像的行数和列数,img_w和img_h分别表示经过水平方向和垂直方向滤波后的图像,Mx和My分别表示当前像素点在水平方向和垂直方向的滤波结果。代码使用了两重循环,遍历了图像中除去边缘的所有像素点。对于每个像素点,根据其在水平和垂直方向上的滤波结果计算出边缘法线方向,并将其存储在变量o中。具体而言:
- 如果My不等于0,则计算边缘法线方向的弧度值o = atan(Mx/My);
- 如果My等于0且Mx大于0,则边缘法线方向为pi/2;
- 如果My等于0且Mx小于0,则边缘法线方向为-pi/2。
最终,该代码会得到一个大小为m×n的矩阵,存储了图像中每个像素点的边缘法线方向。
相关问题
mx=8; my=8;%x轴和y轴阵元个数; sn=4;%信号个数 dw=0.5;%半径波长比 snr=10; %[50,50,50,50]; N=1000;%采样点数; fangwei=[10,25,135,170];%信号方位角 yangjiao=[60 80 20 10]; for i=1:sn for m=1:mx daoxiang1(m,i)=exp(-j2pidw(m-1)cos(fangwei(i)pi/180)cos(yangjiao(i)pi/180)); end for mm=1:my daoxiang2(mm,i)=exp(-j2pidwmmsin(fangwei(i)pi/180)cos(yangjiao(i)pi/180)); end ss=randn(sn,N); end daoxiang=[daoxiang1;daoxiang2]; Signal=daoxiangss; x = awgn(Signal,snr,'measured'); %加入高斯白噪声 R=xx'/N; [tzxiangliang,tzzhi]=eig(R); Nspace=tzxiangliang(:,1:mx+my-sn);%噪声子空间对应小的特征值(从小到大排列) for azi=1:1:180 for ele=1:1:90 for m=1:mx daoxiang3(m,1)=exp(-j2pidw(m-1)cos(azipi/180)cos(elepi/180)); end for mm=1:my daoxiang4(mm,1)=exp(-j2pidwmmsin(azipi/180)cos(elepi/180)); end AQ1=[daoxiang3;daoxiang4]; Power=AQ1'NspaceNspace'AQ1; %在1-180度范围内进行计算 P(ele,azi)=-10log10(abs(Power)); end end mesh(P); title('九元L阵;信噪比:[50,50,50,50];距离波长比:0.5'); xlabel('方位角');ylabel('仰角'); zlabel('空间谱/db');修改成能算出方位角和俯仰角的代码
以下是修改后的代码,可以计算出方位角和俯仰角:
```matlab
mx = 8; my = 8; % x轴和y轴阵元个数
sn = 4; % 信号个数
dw = 0.5; % 半径波长比
snr = 10; % 信噪比
N = 1000; % 采样点数
fangwei = [10, 25, 135, 170]; % 信号方位角
yangjiao = [60 80 20 10]; % 信号俯仰角
for i = 1:sn
for m = 1:mx
daoxiang1(m, i) = exp(-j * 2 * pi * dw * (m - 1) * cos(fangwei(i) * pi / 180) * cos(yangjiao(i) * pi / 180));
end
for mm = 1:my
daoxiang2(mm, i) = exp(-j * 2 * pi * dw * mm * sin(fangwei(i) * pi / 180) * cos(yangjiao(i) * pi / 180));
end
ss(i, :) = randn(1, N); % 生成高斯白噪声
end
daoxiang = [daoxiang1; daoxiang2];
Signal = daoxiang * ss;
x = awgn(Signal, snr, 'measured'); % 加入高斯白噪声
R = x * x' / N;
[tzxiangliang, tzzhi] = eig(R);
Nspace = tzxiangliang(:, 1:mx + my - sn); % 噪声子空间对应小的特征值(从小到大排列)
for azi = 1:180
for ele = 1:90
for m = 1:mx
daoxiang3(m, 1) = exp(-j * 2 * pi * dw * (m - 1) * cos(azi * pi / 180) * cos(ele * pi / 180));
end
for mm = 1:my
daoxiang4(mm, 1) = exp(-j * 2 * pi * dw * mm * sin(azi * pi / 180) * cos(ele * pi / 180));
end
AQ1 = [daoxiang3; daoxiang4];
Power = AQ1' * Nspace * Nspace' * AQ1; % 在1-180度范围内进行计算
P(ele, azi) = -10 * log10(abs(Power));
end
end
[ele_grid, azi_grid] = meshgrid(1:90, 1:180);
[x, y, z] = sph2cart(azi_grid / 180 * pi, (90 - ele_grid) / 180 * pi, P);
mesh(x, y, z);
title('九元L阵;信噪比:[50,50,50,50];距离波长比:0.5');
xlabel('x');
ylabel('y');
zlabel('z');
```
mx = 8; my = 8; % x轴和y轴阵元个数 sn = 4; % 信号个数 dw = 0.5; % 半径波长比 snr = 10; % 信噪比 N = 1000; % 采样点数 fangwei = [10, 25, 135, 170]; % 信号方位角 yangjiao = [60 80 20 10]; % 信号俯仰角 for i = 1:sn for m = 1:mx daoxiang1(m, i) = exp(-j * 2 * pi * dw * (m - 1) * cos(fangwei(i) * pi / 180) * cos(yangjiao(i) * pi / 180)); end for mm = 1:my daoxiang2(mm, i) = exp(-j * 2 * pi * dw * mm * sin(fangwei(i) * pi / 180) * cos(yangjiao(i) * pi / 180)); end ss(i, :) = randn(1, N); % 生成高斯白噪声 end daoxiang = [daoxiang1; daoxiang2]; Signal = daoxiang * ss; x = awgn(Signal, snr, 'measured'); % 加入高斯白噪声 R = x * x' / N; [tzxiangliang, tzzhi] = eig(R); Nspace = tzxiangliang(:, 1:mx + my - sn); % 噪声子空间对应小的特征值(从小到大排列) for azi = 1:180 for ele = 1:90 for m = 1:mx daoxiang3(m, 1) = exp(-j * 2 * pi * dw * (m - 1) * cos(azi * pi / 180) * cos(ele * pi / 180)); end for mm = 1:my daoxiang4(mm, 1) = exp(-j * 2 * pi * dw * mm * sin(azi * pi / 180) * cos(ele * pi / 180)); end AQ1 = [daoxiang3; daoxiang4]; Power = AQ1' * Nspace * Nspace' * AQ1; % 在1-180度范围内进行计算 P(ele, azi) = -10 * log10(abs(Power)); end end [ele_grid, azi_grid] = meshgrid(1:90, 1:180); [x, y, z] = sph2cart(azi_grid / 180 * pi, (90 - ele_grid) / 180 * pi, P); mesh(x, y, z); title('九元L阵;信噪比:[50,50,50,50];距离波长比:0.5'); xlabel('x'); ylabel('y'); zlabel('z'); 把这段代码改成经过10次蒙特卡洛实验后,求出方位角和俯仰角的角度均值
以下是修改后的代码:
```matlab
mx = 8; my = 8; % x轴和y轴阵元个数
sn = 4; % 信号个数
dw = 0.5; % 半径波长比
snr = 10; % 信噪比
N = 1000; % 采样点数
fangwei = [10, 25, 135, 170]; % 信号方位角
yangjiao = [60 80 20 10]; % 信号俯仰角
num_monte_carlo = 10; % 蒙特卡洛实验次数
azimuth_mean = zeros(num_monte_carlo, sn); % 存储方位角均值
elevation_mean = zeros(num_monte_carlo, sn); % 存储俯仰角均值
for monte_carlo_idx = 1:num_monte_carlo
for i = 1:sn
for m = 1:mx
daoxiang1(m, i) = exp(-j * 2 * pi * dw * (m - 1) * cos(fangwei(i) * pi / 180) * cos(yangjiao(i) * pi / 180));
end
for mm = 1:my
daoxiang2(mm, i) = exp(-j * 2 * pi * dw * mm * sin(fangwei(i) * pi / 180) * cos(yangjiao(i) * pi / 180));
end
ss(i, :) = randn(1, N); % 生成高斯白噪声
end
daoxiang = [daoxiang1; daoxiang2];
Signal = daoxiang * ss;
x = awgn(Signal, snr, 'measured'); % 加入高斯白噪声
R = x * x' / N;
[tzxiangliang, tzzhi] = eig(R);
Nspace = tzxiangliang(:, 1:mx + my - sn); % 噪声子空间对应小的特征值(从小到大排列)
P = zeros(90, 180); % 存储每个方位角和俯仰角的功率值
for azi = 1:180
for ele = 1:90
for m = 1:mx
daoxiang3(m, 1) = exp(-j * 2 * pi * dw * (m - 1) * cos(azi * pi / 180) * cos(ele * pi / 180));
end
for mm = 1:my
daoxiang4(mm, 1) = exp(-j * 2 * pi * dw * mm * sin(azi * pi / 180) * cos(ele * pi / 180));
end
AQ1 = [daoxiang3; daoxiang4];
Power = AQ1' * Nspace * Nspace' * AQ1; % 在1-180度范围内进行计算
P(ele, azi) = -10 * log10(abs(Power));
end
end
% 求出方位角和俯仰角的均值
for i = 1:sn
[ele_idx, azi_idx] = find(P == max(P(:)));
azimuth_mean(monte_carlo_idx, i) = mean(azi_idx);
elevation_mean(monte_carlo_idx, i) = mean(ele_idx);
P(ele_idx, azi_idx) = -100; % 将已经找到的最大值置为极小值,以便寻找下一个最大值
end
end
% 显示结果
disp(['方位角均值:', num2str(mean(azimuth_mean)), ' 度']);
disp(['俯仰角均值:', num2str(mean(elevation_mean)), ' 度']);
```
以上代码进行了10次蒙特卡洛实验,每次实验都会生成不同的随机信号和高斯白噪声,以求得方位角和俯仰角的角度均值。最终输出结果显示方位角和俯仰角的角度均值。
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