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私は主にデジタル顕微鏡 (DM) スクリプトを使用して電子顕微鏡画像処理に取り組んでおり、最近では、Python の幅広い汎用性、豊富なオープン ライブラリ、およびクロスプラットフォーム機能のために Python を学び始めています。

DMのi変数に似た2D（画像）または3D（スペクトル画像）配列にインデックスを付けるためのPython（numpy）に同様のツールがあるかどうかは誰にもわかりませんか？

i 変数は、DM スクリプトに関するこのチュートリアルの 11 ページで簡単に紹介されています 。 pdf

これらは、画像のような 2D または 3D オブジェクトにインデックスを付ける簡単な方法であり、マスク関数の生成などの画像処理に非常に便利です。

たとえば、次の DM スクリプト

image t1 := RealImage ("test1", 4, 5, 5)
image t2 := RealImage ("test2", 4, 5, 5)
image t3 := RealImage ("test3", 4, 5, 5)
t1 = irow   
// the value in each pixel equals to the row index
t2 = iradius  
// the value in each pixel equals to the radius 
// (i.e., distance to the center pixel)
t3 = itheta  
// the value in each pixel quals to the angle (radian)
// to the center pixel (i.e., angle in polar representation) 
t1.showimage(); t2.showimage(); t3.showimage()

次の画像が得られます（ここではスプレッドシートで表されているか、マトリックス形式と言います）：

t1 = 
0   0   0   0   0
1   1   1   1   1
2   2   2   2   2
3   3   3   3   3
4   4   4   4   4

t2=
3.5355339   2.9154758   2.5495098   2.5495098   2.9154758
2.9154758   2.1213202   1.5811388   1.5811388   2.1213202
2.5495098   1.5811388   0.70710677  0.70710677  1.5811388
2.5495098   1.5811388   0.70710677  0.70710677  1.5811388
2.9154758   2.1213202   1.5811388   1.5811388   2.1213202

t3=
-2.3561945  -2.1112158  -1.7681919  -1.3734008  -1.0303768
-2.6011732  -2.3561945  -1.8925469  -1.2490457  -0.78539819
-2.9441972  -2.8198421  -2.3561945  -0.78539819 -0.32175055
2.9441972   2.8198421   2.3561945   0.78539819  0.32175055
2.6011732   2.3561945   1.8925469   1.2490457   0.78539819
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2 に答える 2

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The equivalent way to do this in NumPy is to use the numpy.indices function.

So to do the same in NumPy as you did in DM (remember coordinates are always (y,x) in numpy, and that irow, say, is index of y-coordinates):

from __future__ import division
import numpy as np

test1 = np.random.random((5,5))
irow, icol = np.indices(test1.shape)

# to use iradius and itheta we need to get icol, irow centered
irow_centered = irow - test1.shape[0] / 2.0
icol_centered = icol - test1.shape[1] / 2.0

iradius = (icol_centered**2 + irow_centered**2)**0.5
itheta = np.arctan2(irow_centered, icol_centered)

Then

>>> irow
array([[0, 0, 0, 0, 0],
       [1, 1, 1, 1, 1],
       [2, 2, 2, 2, 2],
       [3, 3, 3, 3, 3],
       [4, 4, 4, 4, 4]])
>>> iradius
array([[ 3.53553391,  2.91547595,  2.54950976,  2.54950976,  2.91547595],
       [ 2.91547595,  2.12132034,  1.58113883,  1.58113883,  2.12132034],
       [ 2.54950976,  1.58113883,  0.70710678,  0.70710678,  1.58113883],
       [ 2.54950976,  1.58113883,  0.70710678,  0.70710678,  1.58113883],
       [ 2.91547595,  2.12132034,  1.58113883,  1.58113883,  2.12132034]])
>>> itheta
array([[-2.35619449, -2.11121583, -1.76819189, -1.37340077, -1.03037683],
       [-2.60117315, -2.35619449, -1.89254688, -1.24904577, -0.78539816],
       [-2.94419709, -2.8198421 , -2.35619449, -0.78539816, -0.32175055],
       [ 2.94419709,  2.8198421 ,  2.35619449,  0.78539816,  0.32175055],
       [ 2.60117315,  2.35619449,  1.89254688,  1.24904577,  0.78539816]])

It's also possible to do this using the mgrid and ogrid functions. mgrid will return the coordinates in a fully populated array (the same shape as the source image, identical to numpy.indices) while ogrid returns row and column vectors of the right shape that often get automatically broadcast correctly.

If you're using this to create masks for Fourier-transformed images there's also the np.fft.fftfreq function which will give you the frequencies of the pixels in the Fourier transform. I use the following to get the frequency squared at each pixel for a given image shape:

def get_freq_squared(shape, packed_complex=True):
    """Return the frequency squared for an n-d array.
    The returned image will match shape, if packed_complex is true the last
    dimension will be treated as 0,f,...,nf rather than 0,f,..., nf,...,-f
    """
    vecs = [np.fft.fftfreq(s, 1.0 / s) ** 2 for s in shape]

    if packed_complex:
        s = shape[-1]
        olds = (s-1)*2
        vecs[-1] = rfftfreq(olds, 1.0/olds)**2
    return reduce(np.add.outer, vecs)
于 2015-08-18T21:04:23.233 に答える
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Pillowは numpy 配列 IIRC を使用します。Scipy にはいくつかの画像機能がありますが、画像の読み込みと保存は苦手です。

于 2015-08-16T21:54:12.520 に答える