Question

请考虑这个可重复的例子：

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)

输出是：

>>> 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

因此所有阵列的形状和类型都不同。

对于其他处理，我希望这个数组表示为最后一个变量lena.mpl，或者只是将数组值转换为它们的标准化[0..1] float32类型。

这样做的最佳方式是什么？

Answer 1

def normalize(arr):
    arr=arr.astype('float32')
    if arr.max() > 1.0:
        arr/=255.0
    return arr

从各种“图像”对象中规范化numpy数组

1 个答案: