In [1]:

    

import cv2


    

In [2]:

    

import urllib.request


    

In [3]:

    

import numpy as np


    

In [4]:

    

import glymur


    

In [5]:

    

#import matplotlib.pyplot as plt
from matplotlib import pyplot as plt


    

In [6]:

    

%matplotlib inline


    

In [7]:

    

HTTP_response = urllib.request.urlopen('http://www.hpca.ual.es/~vruiz/images/lena.png') # get the image


    

In [8]:

    

arr = np.asarray(bytearray(HTTP_response.read()), dtype=np.uint8) # convert the image to a numpy.ndarray


    

In [9]:

    

img = cv2.imdecode(arr,-1) # Decompress the image


    

In [10]:

    

img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # Convert to BGR -> RGB


    

In [11]:

    

plt.imshow(img)


    

    
Out[11]:





<matplotlib.image.AxesImage at 0x7fa645cd2518>






    

In [15]:

    

jp2 = glymur.Jp2k('lena.jp2', data=img, numres=5, cratios=[16,8,4,2,1])


    

In [22]:

    

#data = jp2.read(rlevel=2)


    

In [27]:

    

data = jp2.read(layer=10)


    

In [28]:

    

plt.imshow(data)


    

    
Out[28]:





<matplotlib.image.AxesImage at 0x7fa61f721b38>






    

In [15]:

    

for i in range(5):
    print(i)


    

    




0
1
2
3
4

In [44]:

    

for i in range(5):
    #jp2 = glymur.jp2box.ImageHeaderBox(laplacian[i+1].shape[0], laplacian[i+1].shape[1], num_components=3, signed=True, bits_per_component=8, compression=7, colorspace_unknown=False, ip_provided=False, length=0, offset=-1)
    jp2 = glymur.Jp2k('laplacian_{}.jp2'.format(i+1), data=laplacian[i+1], numres=1)
    #jp2 = glymur.Jp2k('laplacian_{}.jp2'.format(i+1), mode='wb')
    #jp2.write(laplacian[i+1])
    #print (jp2)


    

In [ ]:

    




    

In [ ]:

    




    

In [ ]: