def test_translationpositive1_2(self):
image = np.array(io.imread('images/sample1024x1024.jpg'),
dtype=np.float)
tf_image = idct2(translation(dct2(image), 100, axis=1))
image[:, 100:] = image[:, :-100]
image[:, :100] = 0
self.assertTrue(np.max(np.abs(image - tf_image)) < 1e-10)
def test_translationnegative1_3(self):
image = np.array(io.imread('images/sample2048x2048.jpg'),
dtype=np.float)
tf_image = idct2(translation(dct2(image), -100, axis=1))
image[:, :-100] = image[:, 100:]
image[:, -100:] = 0
self.assertTrue(np.max(np.abs(image - tf_image)) < 1e-10)
def test_translationnegative0_2(self):
image = np.array(io.imread('images/sample1024x1024.jpg'),
dtype=np.float)
tf_image = idct2(translation(dct2(image), -100, axis=0))
image[:-100, :] = image[100:, :]
image[-100:, :] = 0
self.assertTrue(np.max(np.abs(image - tf_image)) < 1e-10)
def test_mask_small_3(self):
image = np.array(io.imread('images/sample2048x2048.jpg'),
dtype=np.float)
ulx, lrx, uly, lry = 104, 112, 104, 112
tf_image = idct2(mask(dct2(image), ulx=ulx, lrx=lrx, uly=uly, lry=lry))
image[ulx:lrx, uly:lry] = 0
self.assertTrue(np.max(np.abs(image - tf_image)) < 1e-10)
def test_mask_horizontal_2(self):
image = np.array(io.imread('images/sample1024x1024.jpg'),
dtype=np.float)
ulx, lrx, uly, lry = 105, 108, 200, 304
tf_image = idct2(mask(dct2(image), ulx=ulx, lrx=lrx, uly=uly, lry=lry))
image[ulx:lrx, uly:lry] = 0
self.assertTrue(np.max(np.abs(image - tf_image)) < 1e-10)
def test_dct2_3(self):
image = np.array(io.imread('images/sample2048x2048.jpg'),
dtype=np.float)
dct_image = dct2(image)
blocks = skimage.util.view_as_blocks(image, (8, 8))
blocks[:] = dctn(blocks, axes=(2, 3), norm='ortho')
self.assertTrue(np.max(np.abs(image - dct_image)) < 1e-10)
def test_mask_small_1(self):
image = np.array(io.imread('images/sample512x512.jpg'), dtype=np.float)
for _ in range(100):
original_image = image.copy()
ulx = random.randint(0, image.shape[0] - 1)
uly = random.randint(0, image.shape[1] - 1)
lrx = random.randint(ulx + 1, image.shape[0])
lry = random.randint(uly + 1, image.shape[1])
tf_image = idct2(
mask(dct2(image), ulx=ulx, lrx=lrx, uly=uly, lry=lry))
original_image[ulx:lrx, uly:lry] = 0
self.assertTrue(np.max(np.abs(original_image - tf_image)) < 1e-10)
def _translation(image, sigma):
im = image.copy()
if im.shape[1] <= np.abs(sigma):
im[:] = 0
return im
im[:, sigma // 8 * 8:] = im[:, :im.shape[1] - sigma // 8 * 8]
im[:, :sigma // 8 * 8] = 0
sigma = sigma % 8
# Vの定義
V1 = np.zeros((8, 8), dtype=np.float) # V[sigma]
V2 = np.zeros((8, 8), dtype=np.float) # V[w-sigma]
for y in range(8 - sigma):
V1[y, sigma + y] = 1
V1 = dct2(V1)
for y in range(sigma):
V2[8 - sigma + y, y] = 1
V2 = dct2(V2)
im[:, :8] = np.dot(im[:, :8], V1)
for j in range(im.shape[1] // 8 - 1, 0, -1):
im[:, j * 8:j * 8 + 8] = np.dot(im[:, j * 8:j * 8 + 8], V1) + np.dot(
im[:, j * 8 - 8:j * 8], V2)
return im
def test_rotate90_1(self):
image = np.array(io.imread('images/sample512x512.jpg'), dtype=np.float)
tf_image = idct2(rotate90(dct2(image)))
image = np.rot90(image, k=1)
self.assertTrue(np.max(np.abs(image - tf_image)) < 1e-10)
def test_rotate270_3(self):
image = np.array(io.imread('images/sample2048x2048.jpg'),
dtype=np.float)
tf_image = idct2(rotate270(dct2(image)))
image = np.rot90(image, k=3)
self.assertTrue(np.max(np.abs(image - tf_image)) < 1e-10)
def test_rotate180_2(self):
image = np.array(io.imread('images/sample1024x1024.jpg'),
dtype=np.float)
tf_image = idct2(rotate180(dct2(image)))
image = np.rot90(image, k=2)
self.assertTrue(np.max(np.abs(image - tf_image)) < 1e-10)
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)
def test_mask_vertical_4(self):
image = np.array(io.imread('images/sample512x512.jpg'), dtype=np.float)
ulx, lrx, uly, lry = 200, 304, 104, 112
tf_image = idct2(mask(dct2(image), ulx=ulx, lrx=lrx, uly=uly, lry=lry))
image[ulx:lrx, uly:lry] = 0
self.assertTrue(np.max(np.abs(image - tf_image)) < 1e-10)
def test_translationpositive0_1(self):
image = np.array(io.imread('images/sample512x512.jpg'), dtype=np.float)
tf_image = idct2(translation(dct2(image), 100, axis=0))
image[100:, :] = image[:-100, :]
image[:100, :] = 0
self.assertTrue(np.max(np.abs(image - tf_image)) < 1e-10)
def test_translationzero_2(self):
image = np.array(io.imread('images/sample512x512.jpg'), dtype=np.float)
tf_image = idct2(translation(dct2(image), image.shape[1] * 2, axis=0))
image[:] = 0
self.assertTrue(np.max(np.abs(image - tf_image)) < 1e-10)
def test_dctidct_3(self):
image = np.array(io.imread('images/sample2048x2048.jpg'),
dtype=np.float)
dct_image = idct2(dct2(image))
self.assertTrue(np.max(np.abs(image - dct_image)) < 1e-10)
def reflection(image, 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)を鏡像変換する.
Parameters
----------
image:幅と高さが8の倍数である画像を表す2次元配列. 8の倍数でない場合の動作は未定義.
axis:変換する軸. defalutは`axis=0`
Returns
-------
`image`を鏡像変換した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.reflection.reflection(a,axis=0)
array([[ 5.77395663e-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]])
'''
R = np.zeros((8, 8), dtype=np.float)
for i in range(8):
R[i, 7 - i] = 1
R = dct2(R)
if axis == 0:
return np.vstack(
list(
map(lambda m: np.dot(R, m),
np.flip(np.vsplit(image, range(8, image.shape[1], 8)),
0))))
elif axis == 1:
return np.hstack(
list(
map(lambda m: np.dot(m, R),
np.flip(np.hsplit(image, range(8, image.shape[1], 8)),
0))))
