傅里叶变换之展示图片轮廓

先贴上原始的完整的图片

>>> import numpy as np

>>> from numpy.fft import fft,ifft

>>> #fft是傅里叶转换,ifft傅里叶反转

>>> from PIL import  Image

>>> cat =Image.open('C:/a/a.jpg')#获取一张图片

>>> cat

<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1800x1273 at 0xCDF8EB8>

>>> cat.show()

>>> #转换成int类型数据，int8==128

>>> np.fromstring(cat.tobytes() ,dtype=np.int8)

array([ 11, 117, -55, ...,  92, -55, -12], dtype=int8)

>>> #之所以有负数，是因为int8<128，颜色值0-255

>>> #傅里叶转换，傅里叶转换的结果包含实数和虚数

>>> a=np.fromstring(cat.tobytes() ,dtype=np.int8)

>>> a=fft(a)

>>> a

array([-1.71761120e+08       +0.j        ,

       -2.20943522e+07+17384940.60301246j,

        3.10160552e+07 +9074920.52415055j, ...,

       -1.32824910e+07 -2714223.17490176j,

        3.10160552e+07 -9074920.52415086j,

       -2.20943522e+07-17384940.60301225j])

>>> #将真实的数据转换成频率

>>> #将傅里叶的数据去除低频的波，设置为零

>>> np.where(np.abs(a)<1e5,0,a)

array([-1.71761120e+08       +0.j        ,

       -2.20943522e+07+17384940.60301246j,

        3.10160552e+07 +9074920.52415055j, ...,

       -1.32824910e+07 -2714223.17490176j,

        3.10160552e+07 -9074920.52415086j,

       -2.20943522e+07-17384940.60301225j])

>>> #下面是傅里叶的反转

>>> a=ifft(a)

>>> a

array([ 11.-3.32958784e-13j, 117.+1.65327672e-13j, -55.+1.93279434e-14j,

       ...,  92.-3.39755573e-13j, -55.+3.72622577e-13j,

       -12.+1.22327445e-13j])

>>> #只获取实数部分则

>>> a=np.real(a)

>>> a

array([ 11., 117., -55., ...,  92., -55., -12.])

>>> aa=np.int8(a)#转换为int8

>>> aa

array([ 10, 116, -54, ...,  91, -55, -11], dtype=int8)

>>> pa=Image.frombytes (size=cat.size,mode=cat.mode,data=aa )

>>> pa

<PIL.Image.Image image mode=RGB size=1800x1273 at 0xCE1A208>

>>> pa.show()#获得图像的轮廓展示出来

>>> 

 轮廓如图：