代码如下：

## LL 2020/4/11 14:15
# a demo to generate pattern maps of different frequence.
import numpy as  np
import matplotlib.pyplot as plt
import cv2 as cv

def signal_xHz(A,B ,fi, time,N,i):
	return A * np.sin( 2 * np.pi * fi * np.linspace(0, time , N)  + 2* np.pi/3  *  i)  +  B
 
def generate_sin_2D(x,y,A,B,fi,time,i):
    #生成一个x,y 大小的正选波模板，正弦波幅值为A，中心值为B，采样时长为time，频率为fi,模板序号为i
	
	# 注意： x/time 为采样的频率，采样的频率最好大于最高频的5倍即  x/time = 5~8 fi
	a = np.zeros([x, y], np.uint8)
	print(a.size)
	sin = signal_xHz(A,B,fi,time,x,i);
	a[:,:] = sin
	img = np.zeros([x, y, 3], np.uint8)
	img[:,:,1] = a;
	return img
	
for f in range(1,10):
	a1 = generate_sin_2D(480,480,60,120,0.1*f+1,10,0)
	name = str(f)+'hz.bmp'
	cv.imwrite(name,a1)
	
# 示例	
a = generate_sin_2D(480,480,60,120,1,10,0)
cv.imshow("iamge", a)
cv.waitKey(-1)
print(a)

最后生成的不同的频率模板如下：

补充：后面可能考试需要用到倾斜的相位图的版本，MATLAB代码（矩阵处理这一方面MATLAB还是比Python的Numpy要好用）如下：

% 2020/06/01    by LiuLi
%%  x:图像分辨率，N:正弦波周期，savepath:正弦模板图片组存放路径
function  save_pattermap_bank(x,N,savepath)
% x = 480;
% N = 8 ;
for i = [0,1,2] 
 color = uint8(zeros(x,x,3));
 deta_phi = 2*pi*N/x;
 map = zeros(x,x);
 for col = 1:x
    phi = linspace(0+(col-1)*deta_phi,2*pi*N+(col-1)*deta_phi,x);
    sin_ = 60*sin(phi+2*pi/3*i)+120;
    map(col,:) = uint8(sin_);
    
end
color(:,:,2) = map;
savename = ['pattern', mat2str(i+1) '.bmp'];
savepath_ = [savepath,'/',savename];
imwrite(color,savepath_);
end
end

结果是这样的：