def translation(image, sigma, axis=0):
'''
8x8のブロックごとに離散コサイン変換された画像(以下DCT画像)を平行移動する.
Parameters
----------
image:幅と高さが8の倍数であるDCT画像を表す2次元配列. 8の倍数でない場合の動作は未定義.
sigma:移動距離. 負数も可(ただし, 少し遅い)
axis:移動方向の軸. defaultは`axis=0`
Returns
-------
`image`を平行移動したDCT画像を表す2次元配列を返す. `image`の値は変わらない.
Examples
--------
>>> import numpy as np
>>> a = np.arange(64).reshape((8,8))
>>> a
array([[ 0, 1, 2, 3, 4, 5, 6, 7],
[ 8, 9, 10, 11, 12, 13, 14, 15],
[16, 17, 18, 19, 20, 21, 22, 23],
[24, 25, 26, 27, 28, 29, 30, 31],
[32, 33, 34, 35, 36, 37, 38, 39],
[40, 41, 42, 43, 44, 45, 46, 47],
[48, 49, 50, 51, 52, 53, 54, 55],
[56, 57, 58, 59, 60, 61, 62, 63]])
>>> dct_image_transform.translation.translation(a,4,axis=1)
array([[ 0, 1, -2, 0, 3, 0, -7, 8],
[ 6, 0, -11, 3, 10, -5, -16, 22],
[ 12, -1, -19, 7, 17, -10, -25, 36],
[ 19, -3, -28, 11, 25, -16, -34, 50],
[ 25, -4, -36, 16, 32, -22, -43, 64],
[ 32, -6, -45, 20, 39, -27, -52, 79],
[ 38, -7, -53, 24, 47, -33, -61, 93],
[ 45, -9, -62, 28, 54, -38, -71, 107]])
'''
if sigma < 0:
sigma = -sigma
if axis == 0:
return np.transpose(
reflection(_translation(
reflection(np.transpose(image), axis=1), sigma),
axis=1))
elif axis == 1:
return reflection(_translation(reflection(image, axis=1), sigma),
axis=1)
else:
if axis == 0:
return np.transpose(_translation(np.transpose(image), sigma))
elif axis == 1:
return _translation(image, sigma)
def rotate180(image):
'''
8x8のブロックごとに離散コサイン変換された画像(以下DCT画像)を180度回転する.
Parameters
----------
image:幅と高さが8の倍数であるDCT画像を表す2次元配列. 8の倍数でない場合の動作は未定義.
Returns
-------
`image`を180度回転したDCT画像を表す2次元配列を返す. `image`の値は変わらない.
Examples
--------
>>> import numpy as np
>>> a = np.arange(64).reshape((8,8))
>>> a
array([[ 0, 1, 2, 3, 4, 5, 6, 7],
[ 8, 9, 10, 11, 12, 13, 14, 15],
[16, 17, 18, 19, 20, 21, 22, 23],
[24, 25, 26, 27, 28, 29, 30, 31],
[32, 33, 34, 35, 36, 37, 38, 39],
[40, 41, 42, 43, 44, 45, 46, 47],
[48, 49, 50, 51, 52, 53, 54, 55],
[56, 57, 58, 59, 60, 61, 62, 63]])
>>> dct_image_transform.rotate.rotate180(a)
array([[ 5.76134986e-15, -1.00000000e+00, 2.00000000e+00,
-3.00000000e+00, 4.00000000e+00, -5.00000000e+00,
6.00000000e+00, -7.00000000e+00],
[-8.00000000e+00, 9.00000000e+00, -1.00000000e+01,
1.10000000e+01, -1.20000000e+01, 1.30000000e+01,
-1.40000000e+01, 1.50000000e+01],
[ 1.60000000e+01, -1.70000000e+01, 1.80000000e+01,
-1.90000000e+01, 2.00000000e+01, -2.10000000e+01,
2.20000000e+01, -2.30000000e+01],
[-2.40000000e+01, 2.50000000e+01, -2.60000000e+01,
2.70000000e+01, -2.80000000e+01, 2.90000000e+01,
-3.00000000e+01, 3.10000000e+01],
[ 3.20000000e+01, -3.30000000e+01, 3.40000000e+01,
-3.50000000e+01, 3.60000000e+01, -3.70000000e+01,
3.80000000e+01, -3.90000000e+01],
[-4.00000000e+01, 4.10000000e+01, -4.20000000e+01,
4.30000000e+01, -4.40000000e+01, 4.50000000e+01,
-4.60000000e+01, 4.70000000e+01],
[ 4.80000000e+01, -4.90000000e+01, 5.00000000e+01,
-5.10000000e+01, 5.20000000e+01, -5.30000000e+01,
5.40000000e+01, -5.50000000e+01],
[-5.60000000e+01, 5.70000000e+01, -5.80000000e+01,
5.90000000e+01, -6.00000000e+01, 6.10000000e+01,
-6.20000000e+01, 6.30000000e+01]])
'''
return reflection(reflection(image,axis=0),axis=1)
def test_reflecion0_1(self):
image = np.array(io.imread('images/sample512x512.jpg'), dtype=np.float)
tf_image = idct2(reflection(dct2(image), axis=0))
image = np.flipud(image)
self.assertTrue(np.max(np.abs(image - tf_image)) < 1e-10)
def test_reflecion1_3(self):
image = np.array(io.imread('images/sample2048x2048.jpg'),
dtype=np.float)
tf_image = idct2(reflection(dct2(image), axis=1))
image = np.fliplr(image)
self.assertTrue(np.max(np.abs(image - tf_image)) < 1e-10)