please consider this reproducible example:
from PIL import Image
import numpy as np
import scipy.misc as sm
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import matplotlib.cbook as cbook
import urllib
datafile = cbook.get_sample_data('lena.jpg')
lena_pil = Image.open(datafile)
lena_pil_np = np.asarray(lena_pil)
lena_scipy = sm.lena()
lena_tmp = open('lena_tmp.png', 'wb')
lena_tmp.write(urllib.urlopen('http://optipng.sourceforge.net/pngtech/img/lena.png').read())
lena_tmp.close()
lena_mpl = mpimg.imread('lena_tmp.png')
sm.info(lena_pil_np)
sm.info(lena_scipy)
sm.info(lena_mpl)
Output is:
>>> sm.info(lena_pil_np)
class: ndarray
shape: (512, 512, 3)
strides: (1536, 3, 1)
itemsize: 1
aligned: True
contiguous: True
fortran: False
data pointer: 0xb707e01cL
byteorder: little
byteswap: False
type: uint8
>>> sm.info(lena_scipy)
class: ndarray
shape: (512, 512)
strides: (2048, 4)
itemsize: 4
aligned: True
contiguous: True
fortran: False
data pointer: 0xb6f7d008L
byteorder: little
byteswap: False
type: int32
>>> sm.info(lena_mpl)
class: ndarray
shape: (512, 512, 3)
strides: (6144, 12, 4)
itemsize: 4
aligned: True
contiguous: True
fortran: False
data pointer: 0xb6c7b008L
byteorder: little
byteswap: False
type: float32
so all arrays are of different shape and type.
For additional processing I would like this arrays to be represented as in last variable lena.mpl, or just to transform array values to their normalized [0..1] float32 type.
What is the best way to do this?
