def conv2dsep(x,h,v):
(Nrx,Ncx)=x.shape
y=np.zeros((Nrx+len(h)-1, Ncx+len(v)-1))
for m in range(Nrx):
y[m,:]=mm.conv(x[m,:],h)
for n in range(Ncx+len(v)-1):
y[:,n]=mm.conv(y[:Nrx,n],v)
return(y)
import math
import cmath
import numpy as np
import imageio
import MyModule as mm
import time
x=[1,2,3,4]
x1=mm.complex_idft(mm.complex_dft(x))
x2=mm.complex_idft(mm.fft(x))
x3=mm.ifft(mm.complex_dft(x))
x4=mm.ifft(mm.fft(x))
y=[5,6,7,8]
output1=mm.conv(x,y)
output2=mm.ifft(mm.fft(x)*mm.fft(y))
img=imageio.imread('lena.png')#[256-128:256+128,256-128:256+128]
t1=time.time()
imageio.imwrite('lenareconstructed.png',mm.ifft2d(mm.fft2d(img)))
t2=time.time()
print('img ifft takes %10.2f sec' %( t2-t1))
window[N//2-M//2+n]=0.54-0.46*math.cos(2*math.pi*n/M)
lp_windowed=lpr*window
LP_windowed=mm.fft(lp_windowed)
plt.plot(lp_windowed.real)
plt.plot(np.abs(LP_windowed))
'''hp-windowed'''
hp=-1.0*lp_windowed
hp[N//2]+=1.0
plt.figure()
plt.plot(hp.real)
plt.plot(np.abs(mm.fft(hp)))
plt.plot(mm.conv(x,lp_windowed))
'''filter design'''
PP=np.zeros((N),dtype=complex)
Nc1=int(0.15*N)
Nc2=int(0.2*N)
Nc3=int(0.3*N)
Nc4=int(0.4*N)
Nc5=int(0.45*N)
for n in range(Nc1):
PP[n]=1.0
PP[(N-n)%N]=1.0
for n in range(Nc1,Nc2):
PP[n]=0.5
PP[(N-n)%N]=0.5
for n in range(Nc2,Nc3):
