1、主要思路：
1）最大最小歸一化，對(duì)模值進(jìn)行
2）利用幅角轉(zhuǎn)換為復(fù)數(shù)數(shù)據(jù)
實(shí)現(xiàn)代碼文章來(lái)源：http://www.zghlxwxcb.cn/news/detail-627000.html
normalized_image_list = []
for image in croped_image_list:
assert image.dtype == np.complex128
maximum = np.abs(image).max()
minimum = np.abs(image).min()
magni=np.abs(image)
magni=(magni-minimum)/(maximum-minimum)
ang=np.angle(image)
real=magni*np.cos(ang)
img=magni*np.sin(ang)*1j
normalized_image = real+img
normalized_image_list.append(normalized_image)
測(cè)試原理代碼（基于numpy）文章來(lái)源地址http://www.zghlxwxcb.cn/news/detail-627000.html
import numpy as np
a=1+1j
print('np.abs(a)',np.abs(a))
magnitude=np.abs(a)
ang=np.angle(a)
print(magnitude)
print(np.cos (ang))
real=magnitude* np.cos( ang)
img=magnitude* np.cos( ang)
print(real)
print(img)
b=real+img*1j
print(b)
print('np.abs(b)',np.abs(b))
'''
對(duì)應(yīng)的運(yùn)行結(jié)果：
np.abs(a) 1.4142135623730951
1.4142135623730951
0.7071067811865476
1.0000000000000002
1.0000000000000002
(1.0000000000000002+1.0000000000000002j)
np.abs(b) 1.4142135623730954
'''
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