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)
Exit:
>>> 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
therefore, all arrays have a different shape and type.
For additional processing, I would like these arrays to be represented as in the last lena.mpl variable, or simply to convert the array values to their normalized type [0..1] float32.
What is the best way to do this?
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