#!/usr/bin/env python
# coding: utf-8

# ## MAT 201A Winter 2016 

# ### HW2
# Junxiang Yao

# In[104]:


get_ipython().run_line_magic('pylab', 'inline')
from __future__ import print_function
from __future__ import division


# In[105]:


img = imread('M.jpg')
imshow(img)


# In[106]:


img.shape


# #### I get the width and length of the image, and use those number to creat an two dimentional array below.

# In[107]:


newimg = zeros([1024,1024],uint8)


# #### I put the mean of the r,g,b value from the pixel on the image into the newimg array to create an acromatic image which will enable ifft with ease later. 

# In[108]:


height = 1024
width = 1024
for j in range(0,width):
  for i in range(0,height):
    newimg [i,j] = img[i,j,0]/3 + img[i,j,1]/3 + img[i,j,2]/3
imshow(newimg,cmap=cm.gray)
colorbar()
print


# #### I regarded the image as both magnitude spectrum and phase spectrum.

# In[175]:


mag_spec = zeros([width*height],uint8)
phs_spec = zeros([width*height],uint8)
for j in range(0,width):
  for i in range(0,height):
    mag_spec[j+i*width] = newimg [i,j]
    phs_spec[j+i*width] = newimg [i,j]/255.0*pi-pi/2


# #### I make two one dimentional arrays to save the value from the newimg array. The mag_spec array is using the value directly as the magnitude value. The phs_spec array changes range of the value from the new img array into -pi/2~pi/2, and uses this value as the phase value. 

# In[176]:


X = [np.complex(cos(phs)* mag, -sin(phs)* mag) for mag, phs in zip(mag_spec, phs_spec)]
index = range(0, len(X), 1024)
sig = []
for i in range(len(index)-1):
    tempt = (X[index[i]:index[i+1]])
    x = fft.ifft(tempt) * 8
    sig.append(x)
sig=array(sig).flatten()
print(array(sig).shape)


# #### I used the elements from the phs_spec array and the the mag_spec array to create a complex array. And I used 1024, which is the original width of the picture as the window width value, and  I did ifft to each lines from the original image since i supposed to regard this picture as a spectrum. 

# In[177]:


sigout = list((x.astype(int16)) * 150)
plot(sigout)
title('output signal')
xlabel('time')
ylabel('magnitude')


# In[178]:


from IPython.display import Audio
Audio(sigout,rate = 3000)


# #### I think the result is kind of weird and short, and I think that may caused by the phase spectrum I used. So I tried another way.

# In[179]:


a = []


# #### This time I assumed that all the phase are 0, so I used the whole picture as magnitude spectrum and use irfft on each rows directly.

# In[180]:


for i in range(0,height):
    a.append(fft.irfft(newimg [i,:]))


# In[181]:


sig = array(a).flatten()


# In[182]:


sigout = list((sig.astype(int16)) * 200)
plot(sigout)
title('output signal')
xlabel('time')
ylabel('magnitude')


# In[183]:


sigout = list((sig.astype(int16)-200) * 50)
plot(sigout)
title('output signal')
xlabel('time')
ylabel('magnitude')


# In[184]:


from IPython.display import Audio
Audio(sigout,rate = 176400)