def test_masks_CFA_Bayer(self):
"""
Tests :func:`colour_demosaicing.bayer.masks.masks_CFA_Bayer`
definition.
"""
for pattern in ('RGGB', 'BGGR', 'GRBG', 'GBRG'):
mask = os.path.join(BAYER_DIRECTORY, '{0}_Masks.exr')
np.testing.assert_almost_equal(
tstack(masks_CFA_Bayer((8, 8), pattern)),
read_image(str(mask.format(pattern))),
decimal=7)
def test_masks_CFA_Bayer(self):
"""
Tests :func:`colour_demosaicing.bayer.masks.masks_CFA_Bayer`
definition.
"""
for pattern in ('RGGB', 'BGGR', 'GRBG', 'GBRG'):
np.testing.assert_almost_equal(
colour.tstack(masks_CFA_Bayer((8, 8), pattern)),
colour.read_image(
str(os.path.join(BAYER_DIRECTORY,
'{0}_Masks.exr'.format(pattern)))),
decimal=7)
def mosaicing_CFA_Bayer(RGB, pattern='RGGB'):
"""
Returns the *Bayer* CFA mosaic for a given *RGB* colourspace array.
Parameters
----------
RGB : array_like
*RGB* colourspace array.
pattern : unicode, optional
**{'RGGB', 'BGGR', 'GRBG', 'GBRG'}**,
Arrangement of the colour filters on the pixel array.
Returns
-------
ndarray
*Bayer* CFA mosaic.
Examples
--------
>>> import numpy as np
>>> RGB = np.array([[[0, 1, 2],
... [0, 1, 2]],
... [[0, 1, 2],
... [0, 1, 2]]])
>>> mosaicing_CFA_Bayer(RGB)
array([[ 0., 1.],
[ 1., 2.]])
>>> mosaicing_CFA_Bayer(RGB, pattern='BGGR')
array([[ 2., 1.],
[ 1., 0.]])
"""
RGB = as_float_array(RGB)
R, G, B = tsplit(RGB)
R_m, G_m, B_m = masks_CFA_Bayer(RGB.shape[0:2], pattern)
CFA = R * R_m + G * G_m + B * B_m
return CFA
def mosaicing_CFA_Bayer(RGB, pattern='RGGB'):
"""
Returns the *Bayer* CFA mosaic for a given *RGB* colourspace array.
Parameters
----------
RGB : array_like
*RGB* colourspace array.
pattern : unicode, optional
**{'RGGB', 'BGGR', 'GRBG', 'GBRG'}**,
Arrangement of the colour filters on the pixel array.
Returns
-------
ndarray
*Bayer* CFA mosaic.
Examples
--------
>>> RGB = np.array([[[0, 1, 2],
... [0, 1, 2]],
... [[0, 1, 2],
... [0, 1, 2]]])
>>> mosaicing_CFA_Bayer(RGB)
array([[0, 1],
[1, 2]])
>>> mosaicing_CFA_Bayer(RGB, pattern='BGGR')
array([[2, 1],
[1, 0]])
"""
RGB = np.asarray(RGB)
R, G, B = tsplit(RGB)
R_m, G_m, B_m = masks_CFA_Bayer(RGB.shape[0:2], pattern)
CFA = R * R_m + G * G_m + B * B_m
return CFA
def demosaicing_CFA_Bayer_Malvar2004(CFA, pattern='RGGB'):
"""
Returns the demosaiced *RGB* colourspace array from given *Bayer* CFA using
Malvar (2004) demosaicing algorithm.
Parameters
----------
CFA : array_like
*Bayer* CFA.
pattern : unicode, optional
**{'RGGB', 'BGGR', 'GRBG', 'GBRG'}**,
Arrangement of the colour filters on the pixel array.
Returns
-------
ndarray
*RGB* colourspace array.
Examples
--------
>>> CFA = np.array([[0.30980393, 0.36078432, 0.30588236, 0.3764706],
... [0.35686275, 0.39607844, 0.36078432, 0.40000001]])
>>> demosaicing_CFA_Bayer_Malvar2004(CFA)
array([[[ 0.30980393, 0.31666668, 0.32941177],
[ 0.33039216, 0.36078432, 0.38112746],
[ 0.30588236, 0.32794118, 0.34877452],
[ 0.36274511, 0.3764706 , 0.38480393]],
<BLANKLINE>
[[ 0.34828432, 0.35686275, 0.36568628],
[ 0.35318628, 0.38186275, 0.39607844],
[ 0.3379902 , 0.36078432, 0.3754902 ],
[ 0.37769609, 0.39558825, 0.40000001]]])
>>> CFA = np.array([[0.3764706, 0.360784320, 0.40784314, 0.3764706],
... [0.35686275, 0.30980393, 0.36078432, 0.29803923]])
>>> demosaicing_CFA_Bayer_Malvar2004(CFA, 'BGGR')
array([[[ 0.35539217, 0.37058825, 0.3764706 ],
[ 0.34264707, 0.36078432, 0.37450981],
[ 0.36568628, 0.39607844, 0.40784314],
[ 0.36568629, 0.3764706 , 0.3882353 ]],
<BLANKLINE>
[[ 0.34411765, 0.35686275, 0.36200981],
[ 0.30980393, 0.32990197, 0.34975491],
[ 0.33039216, 0.36078432, 0.38063726],
[ 0.29803923, 0.30441178, 0.31740197]]])
"""
CFA = np.asarray(CFA)
R_m, G_m, B_m = masks_CFA_Bayer(CFA.shape, pattern)
GR_GB = np.asarray([[0, 0, -1, 0, 0], [0, 0, 2, 0, 0], [-1, 2, 4, 2, -1],
[0, 0, 2, 0, 0], [0, 0, -1, 0, 0]]) / 8
Rg_RB_Bg_BR = np.asarray([[0, 0, 0.5, 0, 0], [0, -1, 0, -1, 0],
[-1, 4, 5, 4, -1], [0, -1, 0, -1, 0],
[0, 0, 0.5, 0, 0]]) / 8
Rg_BR_Bg_RB = np.transpose(Rg_RB_Bg_BR)
Rb_BB_Br_RR = np.asarray([[0, 0, -1.5, 0, 0], [0, 2, 0, 2, 0],
[-1.5, 0, 6, 0, -1.5], [0, 2, 0, 2, 0],
[0, 0, -1.5, 0, 0]]) / 8
R = CFA * R_m
G = CFA * G_m
B = CFA * B_m
G = np.where(np.logical_or(R_m == 1, B_m == 1), convolve(CFA, GR_GB), G)
RBg_RBBR = convolve(CFA, Rg_RB_Bg_BR)
RBg_BRRB = convolve(CFA, Rg_BR_Bg_RB)
RBgr_BBRR = convolve(CFA, Rb_BB_Br_RR)
# Red rows.
R_r = np.transpose(np.any(R_m == 1, axis=1)[np.newaxis]) * np.ones(R.shape)
# Red columns.
R_c = np.any(R_m == 1, axis=0)[np.newaxis] * np.ones(R.shape)
# Blue rows.
B_r = np.transpose(np.any(B_m == 1, axis=1)[np.newaxis]) * np.ones(B.shape)
# Blue columns
B_c = np.any(B_m == 1, axis=0)[np.newaxis] * np.ones(B.shape)
R = np.where(np.logical_and(R_r == 1, B_c == 1), RBg_RBBR, R)
R = np.where(np.logical_and(B_r == 1, R_c == 1), RBg_BRRB, R)
B = np.where(np.logical_and(B_r == 1, R_c == 1), RBg_RBBR, B)
B = np.where(np.logical_and(R_r == 1, B_c == 1), RBg_BRRB, B)
R = np.where(np.logical_and(B_r == 1, B_c == 1), RBgr_BBRR, R)
B = np.where(np.logical_and(R_r == 1, R_c == 1), RBgr_BBRR, B)
return tstack((R, G, B))
def demosaicing_CFA_Bayer_Menon2007(CFA, pattern='RGGB', refining_step=True):
"""
Returns the demosaiced *RGB* colourspace array from given *Bayer* CFA using
DDFAPD - *Menon (2007)* demosaicing algorithm.
Parameters
----------
CFA : array_like
*Bayer* CFA.
pattern : unicode, optional
**{'RGGB', 'BGGR', 'GRBG', 'GBRG'}**,
Arrangement of the colour filters on the pixel array.
refining_step : bool
Perform refining step.
Returns
-------
ndarray
*RGB* colourspace array.
Notes
-----
- The definition output is not clipped in range [0, 1] : this allows for
direct HDRI / radiance image generation on *Bayer* CFA data and post
demosaicing of the high dynamic range data as showcased in this
`Jupyter Notebook <https://github.com/colour-science/colour-hdri/\
blob/develop/colour_hdri/examples/\
examples_merge_from_raw_files_with_post_demosaicing.ipynb>`__.
References
----------
:cite:`Menon2007c`
Examples
--------
>>> CFA = np.array(
... [[ 0.30980393, 0.36078432, 0.30588236, 0.3764706 ],
... [ 0.35686275, 0.39607844, 0.36078432, 0.40000001]])
>>> demosaicing_CFA_Bayer_Menon2007(CFA)
array([[[ 0.30980393, 0.35686275, 0.39215687],
[ 0.30980393, 0.36078432, 0.39607844],
[ 0.30588236, 0.36078432, 0.39019608],
[ 0.32156864, 0.3764706 , 0.40000001]],
<BLANKLINE>
[[ 0.30980393, 0.35686275, 0.39215687],
[ 0.30980393, 0.36078432, 0.39607844],
[ 0.30588236, 0.36078432, 0.39019609],
[ 0.32156864, 0.3764706 , 0.40000001]]])
>>> CFA = np.array(
... [[ 0.3764706 , 0.36078432, 0.40784314, 0.3764706 ],
... [ 0.35686275, 0.30980393, 0.36078432, 0.29803923]])
>>> demosaicing_CFA_Bayer_Menon2007(CFA, 'BGGR')
array([[[ 0.30588236, 0.35686275, 0.3764706 ],
[ 0.30980393, 0.36078432, 0.39411766],
[ 0.29607844, 0.36078432, 0.40784314],
[ 0.29803923, 0.3764706 , 0.42352942]],
<BLANKLINE>
[[ 0.30588236, 0.35686275, 0.3764706 ],
[ 0.30980393, 0.36078432, 0.39411766],
[ 0.29607844, 0.36078432, 0.40784314],
[ 0.29803923, 0.3764706 , 0.42352942]]])
"""
CFA = as_float_array(CFA)
R_m, G_m, B_m = masks_CFA_Bayer(CFA.shape, pattern)
h_0 = np.array([0, 0.5, 0, 0.5, 0])
h_1 = np.array([-0.25, 0, 0.5, 0, -0.25])
R = CFA * R_m
G = CFA * G_m
B = CFA * B_m
G_H = np.where(G_m == 0, _cnv_h(CFA, h_0) + _cnv_h(CFA, h_1), G)
G_V = np.where(G_m == 0, _cnv_v(CFA, h_0) + _cnv_v(CFA, h_1), G)
C_H = np.where(R_m == 1, R - G_H, 0)
C_H = np.where(B_m == 1, B - G_H, C_H)
C_V = np.where(R_m == 1, R - G_V, 0)
C_V = np.where(B_m == 1, B - G_V, C_V)
D_H = np.abs(C_H - np.pad(C_H, ((0, 0),
(0, 2)), mode=str('reflect'))[:, 2:])
D_V = np.abs(C_V - np.pad(C_V, ((0, 2),
(0, 0)), mode=str('reflect'))[2:, :])
del h_0, h_1, CFA, C_V, C_H
k = np.array(
[[0, 0, 1, 0, 1],
[0, 0, 0, 1, 0],
[0, 0, 3, 0, 3],
[0, 0, 0, 1, 0],
[0, 0, 1, 0, 1]]) # yapf: disable
d_H = convolve(D_H, k, mode='constant')
d_V = convolve(D_V, np.transpose(k), mode='constant')
del D_H, D_V
mask = d_V >= d_H
G = np.where(mask, G_H, G_V)
M = np.where(mask, 1, 0)
del d_H, d_V, G_H, G_V
# Red rows.
R_r = np.transpose(np.any(R_m == 1, axis=1)[np.newaxis]) * np.ones(R.shape)
# Blue rows.
B_r = np.transpose(np.any(B_m == 1, axis=1)[np.newaxis]) * np.ones(B.shape)
k_b = np.array([0.5, 0, 0.5])
R = np.where(
np.logical_and(G_m == 1, R_r == 1),
G + _cnv_h(R, k_b) - _cnv_h(G, k_b),
R,
)
R = np.where(
np.logical_and(G_m == 1, B_r == 1) == 1,
G + _cnv_v(R, k_b) - _cnv_v(G, k_b),
R,
)
B = np.where(
np.logical_and(G_m == 1, B_r == 1),
G + _cnv_h(B, k_b) - _cnv_h(G, k_b),
B,
)
B = np.where(
np.logical_and(G_m == 1, R_r == 1) == 1,
G + _cnv_v(B, k_b) - _cnv_v(G, k_b),
B,
)
R = np.where(
np.logical_and(B_r == 1, B_m == 1),
np.where(
M == 1,
B + _cnv_h(R, k_b) - _cnv_h(B, k_b),
B + _cnv_v(R, k_b) - _cnv_v(B, k_b),
),
R,
)
B = np.where(
np.logical_and(R_r == 1, R_m == 1),
np.where(
M == 1,
R + _cnv_h(B, k_b) - _cnv_h(R, k_b),
R + _cnv_v(B, k_b) - _cnv_v(R, k_b),
),
B,
)
RGB = tstack([R, G, B])
del R, G, B, k_b, R_r, B_r
if refining_step:
RGB = refining_step_Menon2007(RGB, tstack([R_m, G_m, B_m]), M)
del M, R_m, G_m, B_m
return RGB
def demosaicing_CFA_Bayer_Malvar2004(CFA, pattern='RGGB'):
"""
Returns the demosaiced *RGB* colourspace array from given *Bayer* CFA using
*Malvar (2004)* demosaicing algorithm.
Parameters
----------
CFA : array_like
*Bayer* CFA.
pattern : unicode, optional
**{'RGGB', 'BGGR', 'GRBG', 'GBRG'}**,
Arrangement of the colour filters on the pixel array.
Returns
-------
ndarray
*RGB* colourspace array.
Notes
-----
- The definition output is not clipped in range [0, 1] : this allows for
direct HDRI / radiance image generation on *Bayer* CFA data and post
demosaicing of the high dynamic range data as showcased in this
`Jupyter Notebook <https://github.com/colour-science/colour-hdri/\
blob/develop/colour_hdri/examples/\
examples_merge_from_raw_files_with_post_demosaicing.ipynb>`_.
References
----------
:cite:`Malvar2004a`
Examples
--------
>>> CFA = np.array(
... [[0.30980393, 0.36078432, 0.30588236, 0.3764706],
... [0.35686275, 0.39607844, 0.36078432, 0.40000001]])
>>> demosaicing_CFA_Bayer_Malvar2004(CFA)
array([[[ 0.30980393, 0.31666668, 0.32941177],
[ 0.33039216, 0.36078432, 0.38112746],
[ 0.30588236, 0.32794118, 0.34877452],
[ 0.36274511, 0.3764706 , 0.38480393]],
<BLANKLINE>
[[ 0.34828432, 0.35686275, 0.36568628],
[ 0.35318628, 0.38186275, 0.39607844],
[ 0.3379902 , 0.36078432, 0.3754902 ],
[ 0.37769609, 0.39558825, 0.40000001]]])
>>> CFA = np.array(
... [[0.3764706, 0.360784320, 0.40784314, 0.3764706],
... [0.35686275, 0.30980393, 0.36078432, 0.29803923]])
>>> demosaicing_CFA_Bayer_Malvar2004(CFA, 'BGGR')
array([[[ 0.35539217, 0.37058825, 0.3764706 ],
[ 0.34264707, 0.36078432, 0.37450981],
[ 0.36568628, 0.39607844, 0.40784314],
[ 0.36568629, 0.3764706 , 0.3882353 ]],
<BLANKLINE>
[[ 0.34411765, 0.35686275, 0.36200981],
[ 0.30980393, 0.32990197, 0.34975491],
[ 0.33039216, 0.36078432, 0.38063726],
[ 0.29803923, 0.30441178, 0.31740197]]])
"""
CFA = as_float_array(CFA)
R_m, G_m, B_m = masks_CFA_Bayer(CFA.shape, pattern)
GR_GB = as_float_array(
[[0, 0, -1, 0, 0],
[0, 0, 2, 0, 0],
[-1, 2, 4, 2, -1],
[0, 0, 2, 0, 0],
[0, 0, -1, 0, 0]]) / 8 # yapf: disable
Rg_RB_Bg_BR = as_float_array(
[[0, 0, 0.5, 0, 0],
[0, -1, 0, -1, 0],
[-1, 4, 5, 4, - 1],
[0, -1, 0, -1, 0],
[0, 0, 0.5, 0, 0]]) / 8 # yapf: disable
Rg_BR_Bg_RB = np.transpose(Rg_RB_Bg_BR)
Rb_BB_Br_RR = as_float_array(
[[0, 0, -1.5, 0, 0],
[0, 2, 0, 2, 0],
[-1.5, 0, 6, 0, -1.5],
[0, 2, 0, 2, 0],
[0, 0, -1.5, 0, 0]]) / 8 # yapf: disable
R = CFA * R_m
G = CFA * G_m
B = CFA * B_m
del G_m
G = np.where(np.logical_or(R_m == 1, B_m == 1), convolve(CFA, GR_GB), G)
RBg_RBBR = convolve(CFA, Rg_RB_Bg_BR)
RBg_BRRB = convolve(CFA, Rg_BR_Bg_RB)
RBgr_BBRR = convolve(CFA, Rb_BB_Br_RR)
del GR_GB, Rg_RB_Bg_BR, Rg_BR_Bg_RB, Rb_BB_Br_RR
# Red rows.
R_r = np.transpose(np.any(R_m == 1, axis=1)[np.newaxis]) * np.ones(R.shape)
# Red columns.
R_c = np.any(R_m == 1, axis=0)[np.newaxis] * np.ones(R.shape)
# Blue rows.
B_r = np.transpose(np.any(B_m == 1, axis=1)[np.newaxis]) * np.ones(B.shape)
# Blue columns
B_c = np.any(B_m == 1, axis=0)[np.newaxis] * np.ones(B.shape)
del R_m, B_m
R = np.where(np.logical_and(R_r == 1, B_c == 1), RBg_RBBR, R)
R = np.where(np.logical_and(B_r == 1, R_c == 1), RBg_BRRB, R)
B = np.where(np.logical_and(B_r == 1, R_c == 1), RBg_RBBR, B)
B = np.where(np.logical_and(R_r == 1, B_c == 1), RBg_BRRB, B)
R = np.where(np.logical_and(B_r == 1, B_c == 1), RBgr_BBRR, R)
B = np.where(np.logical_and(R_r == 1, R_c == 1), RBgr_BBRR, B)
del RBg_RBBR, RBg_BRRB, RBgr_BBRR, R_r, R_c, B_r, B_c
return tstack([R, G, B])
def demosaicing_CFA_Bayer_bilinear(CFA, pattern='RGGB'):
"""
Returns the demosaiced *RGB* colourspace array from given *Bayer* CFA using
bilinear interpolation.
Parameters
----------
CFA : array_like
*Bayer* CFA.
pattern : unicode, optional
**{'RGGB', 'BGGR', 'GRBG', 'GBRG'}**,
Arrangement of the colour filters on the pixel array.
Returns
-------
ndarray
*RGB* colourspace array.
Notes
-----
- The definition output is not clipped in range [0, 1] : this allows for
direct HDRI / radiance image generation on *Bayer* CFA data and post
demosaicing of the high dynamic range data as showcased in this
`Jupyter Notebook <https://github.com/colour-science/colour-hdri/\
blob/develop/colour_hdri/examples/\
examples_merge_from_raw_files_with_post_demosaicing.ipynb>`_.
References
----------
:cite:`Losson2010c`
Examples
--------
>>> import numpy as np
>>> CFA = np.array(
... [[0.30980393, 0.36078432, 0.30588236, 0.3764706],
... [0.35686275, 0.39607844, 0.36078432, 0.40000001]])
>>> demosaicing_CFA_Bayer_bilinear(CFA)
array([[[ 0.69705884, 0.17941177, 0.09901961],
[ 0.46176472, 0.4509804 , 0.19803922],
[ 0.45882354, 0.27450981, 0.19901961],
[ 0.22941177, 0.5647059 , 0.30000001]],
<BLANKLINE>
[[ 0.23235295, 0.53529412, 0.29705883],
[ 0.15392157, 0.26960785, 0.59411766],
[ 0.15294118, 0.4509804 , 0.59705884],
[ 0.07647059, 0.18431373, 0.90000002]]])
>>> CFA = np.array(
... [[0.3764706, 0.360784320, 0.40784314, 0.3764706],
... [0.35686275, 0.30980393, 0.36078432, 0.29803923]])
>>> demosaicing_CFA_Bayer_bilinear(CFA, 'BGGR')
array([[[ 0.07745098, 0.17941177, 0.84705885],
[ 0.15490197, 0.4509804 , 0.5882353 ],
[ 0.15196079, 0.27450981, 0.61176471],
[ 0.22352942, 0.5647059 , 0.30588235]],
<BLANKLINE>
[[ 0.23235295, 0.53529412, 0.28235295],
[ 0.4647059 , 0.26960785, 0.19607843],
[ 0.45588237, 0.4509804 , 0.20392157],
[ 0.67058827, 0.18431373, 0.10196078]]])
"""
CFA = as_float_array(CFA)
R_m, G_m, B_m = masks_CFA_Bayer(CFA.shape, pattern)
H_G = as_float_array(
[[0, 1, 0],
[1, 4, 1],
[0, 1, 0]]) / 4 # yapf: disable
H_RB = as_float_array(
[[1, 2, 1],
[2, 4, 2],
[1, 2, 1]]) / 4 # yapf: disable
R = convolve(CFA * R_m, H_RB)
G = convolve(CFA * G_m, H_G)
B = convolve(CFA * B_m, H_RB)
del R_m, G_m, B_m, H_RB, H_G
return tstack([R, G, B])
def demosaicing_CFA_Bayer_Malvar2004(CFA, pattern='RGGB'):
"""
Returns the demosaiced *RGB* colourspace array from given *Bayer* CFA using
Malvar (2004) demosaicing algorithm.
Parameters
----------
CFA : array_like
*Bayer* CFA.
pattern : unicode, optional
**{'RGGB', 'BGGR', 'GRBG', 'GBRG'}**,
Arrangement of the colour filters on the pixel array.
Returns
-------
ndarray
*RGB* colourspace array.
Examples
--------
>>> CFA = np.array([[0.30980393, 0.36078432, 0.30588236, 0.3764706],
... [0.35686275, 0.39607844, 0.36078432, 0.40000001]])
>>> demosaicing_CFA_Bayer_Malvar2004(CFA)
array([[[ 0.30980393, 0.31666668, 0.32941177],
[ 0.33039216, 0.36078432, 0.38112746],
[ 0.30588236, 0.32794118, 0.34877452],
[ 0.36274511, 0.3764706 , 0.38480393]],
<BLANKLINE>
[[ 0.34828432, 0.35686275, 0.36568628],
[ 0.35318628, 0.38186275, 0.39607844],
[ 0.3379902 , 0.36078432, 0.3754902 ],
[ 0.37769609, 0.39558825, 0.40000001]]])
>>> CFA = np.array([[0.3764706, 0.360784320, 0.40784314, 0.3764706],
... [0.35686275, 0.30980393, 0.36078432, 0.29803923]])
>>> demosaicing_CFA_Bayer_Malvar2004(CFA, 'BGGR')
array([[[ 0.35539217, 0.37058825, 0.3764706 ],
[ 0.34264707, 0.36078432, 0.37450981],
[ 0.36568628, 0.39607844, 0.40784314],
[ 0.36568629, 0.3764706 , 0.3882353 ]],
<BLANKLINE>
[[ 0.34411765, 0.35686275, 0.36200981],
[ 0.30980393, 0.32990197, 0.34975491],
[ 0.33039216, 0.36078432, 0.38063726],
[ 0.29803923, 0.30441178, 0.31740197]]])
"""
CFA = np.asarray(CFA)
R_m, G_m, B_m = masks_CFA_Bayer(CFA.shape, pattern)
GR_GB = np.asarray(
[[0, 0, -1, 0, 0],
[0, 0, 2, 0, 0],
[-1, 2, 4, 2, -1],
[0, 0, 2, 0, 0],
[0, 0, -1, 0, 0]]) / 8
Rg_RB_Bg_BR = np.asarray(
[[0, 0, 0.5, 0, 0],
[0, -1, 0, -1, 0],
[-1, 4, 5, 4, - 1],
[0, -1, 0, -1, 0],
[0, 0, 0.5, 0, 0]]) / 8
Rg_BR_Bg_RB = np.transpose(Rg_RB_Bg_BR)
Rb_BB_Br_RR = np.asarray(
[[0, 0, -1.5, 0, 0],
[0, 2, 0, 2, 0],
[-1.5, 0, 6, 0, -1.5],
[0, 2, 0, 2, 0],
[0, 0, -1.5, 0, 0]]) / 8
R = CFA * R_m
G = CFA * G_m
B = CFA * B_m
G = np.where(np.logical_or(R_m == 1, B_m == 1), convolve(CFA, GR_GB), G)
RBg_RBBR = convolve(CFA, Rg_RB_Bg_BR)
RBg_BRRB = convolve(CFA, Rg_BR_Bg_RB)
RBgr_BBRR = convolve(CFA, Rb_BB_Br_RR)
# Red rows.
R_r = np.transpose(np.any(R_m == 1, axis=1)[np.newaxis]) * np.ones(R.shape)
# Red columns.
R_c = np.any(R_m == 1, axis=0)[np.newaxis] * np.ones(R.shape)
# Blue rows.
B_r = np.transpose(np.any(B_m == 1, axis=1)[np.newaxis]) * np.ones(B.shape)
# Blue columns
B_c = np.any(B_m == 1, axis=0)[np.newaxis] * np.ones(B.shape)
R = np.where(np.logical_and(R_r == 1, B_c == 1), RBg_RBBR, R)
R = np.where(np.logical_and(B_r == 1, R_c == 1), RBg_BRRB, R)
B = np.where(np.logical_and(B_r == 1, R_c == 1), RBg_RBBR, B)
B = np.where(np.logical_and(R_r == 1, B_c == 1), RBg_BRRB, B)
R = np.where(np.logical_and(B_r == 1, B_c == 1), RBgr_BBRR, R)
B = np.where(np.logical_and(R_r == 1, R_c == 1), RBgr_BBRR, B)
return tstack((R, G, B))
def demosaicing_CFA_Bayer_Menon2007(CFA, pattern='RGGB', refining_step=True):
"""
Returns the demosaiced *RGB* colourspace array from given *Bayer* CFA using
DDFAPD - Menon (2007) demosaicing algorithm.
Parameters
----------
CFA : array_like
*Bayer* CFA.
pattern : unicode, optional
**{'RGGB', 'BGGR', 'GRBG', 'GBRG'}**,
Arrangement of the colour filters on the pixel array.
refining_step : bool
Perform refining step.
Returns
-------
ndarray
*RGB* colourspace array.
Examples
--------
>>> CFA = np.array([[ 0.30980393, 0.36078432, 0.30588236, 0.3764706 ],
... [ 0.35686275, 0.39607844, 0.36078432, 0.40000001]])
>>> demosaicing_CFA_Bayer_Menon2007(CFA)
array([[[ 0.30980393, 0.35686275, 0.39215687],
[ 0.30980393, 0.36078432, 0.39607844],
[ 0.30588236, 0.36078432, 0.39019608],
[ 0.32156864, 0.3764706 , 0.40000001]],
<BLANKLINE>
[[ 0.30980393, 0.35686275, 0.39215687],
[ 0.30980393, 0.36078432, 0.39607844],
[ 0.30588236, 0.36078432, 0.39019609],
[ 0.32156864, 0.3764706 , 0.40000001]]])
>>> CFA = np.array([[ 0.3764706 , 0.36078432, 0.40784314, 0.3764706 ],
... [ 0.35686275, 0.30980393, 0.36078432, 0.29803923]])
>>> demosaicing_CFA_Bayer_Menon2007(CFA, 'BGGR')
array([[[ 0.30588236, 0.35686275, 0.3764706 ],
[ 0.30980393, 0.36078432, 0.39411766],
[ 0.29607844, 0.36078432, 0.40784314],
[ 0.29803923, 0.3764706 , 0.42352942]],
<BLANKLINE>
[[ 0.30588236, 0.35686275, 0.3764706 ],
[ 0.30980393, 0.36078432, 0.39411766],
[ 0.29607844, 0.36078432, 0.40784314],
[ 0.29803923, 0.3764706 , 0.42352942]]])
"""
CFA = np.asarray(CFA)
R_m, G_m, B_m = masks_CFA_Bayer(CFA.shape, pattern)
h_0 = np.array([0, 0.5, 0, 0.5, 0])
h_1 = np.array([-0.25, 0, 0.5, 0, -0.25])
R = CFA * R_m
G = CFA * G_m
B = CFA * B_m
G_H = np.where(G_m == 0, _cnv_h(CFA, h_0) + _cnv_h(CFA, h_1), G)
G_V = np.where(G_m == 0, _cnv_v(CFA, h_0) + _cnv_v(CFA, h_1), G)
C_H = np.where(R_m == 1, R - G_H, 0)
C_H = np.where(B_m == 1, B - G_H, C_H)
C_V = np.where(R_m == 1, R - G_V, 0)
C_V = np.where(B_m == 1, B - G_V, C_V)
D_H = np.abs(C_H -
np.pad(C_H, ((0, 0), (0, 2)), mode=str('reflect'))[:, 2:])
D_V = np.abs(C_V -
np.pad(C_V, ((0, 2), (0, 0)), mode=str('reflect'))[2:, :])
k = np.array(
[[0, 0, 1, 0, 1],
[0, 0, 0, 1, 0],
[0, 0, 3, 0, 3],
[0, 0, 0, 1, 0],
[0, 0, 1, 0, 1]])
d_H = convolve(D_H, k, mode='constant')
d_V = convolve(D_V, np.transpose(k), mode='constant')
mask = d_V >= d_H
G = np.where(mask, G_H, G_V)
M = np.where(mask, 1, 0)
# Red rows.
R_r = np.transpose(np.any(R_m == 1, axis=1)[np.newaxis]) * np.ones(R.shape)
# Blue rows.
B_r = np.transpose(np.any(B_m == 1, axis=1)[np.newaxis]) * np.ones(B.shape)
k_b = np.array([0.5, 0, 0.5])
R = np.where(np.logical_and(G_m == 1, R_r == 1),
G + _cnv_h(R, k_b) - _cnv_h(G, k_b),
R)
R = np.where(np.logical_and(G_m == 1, B_r == 1) == 1,
G + _cnv_v(R, k_b) - _cnv_v(G, k_b),
R)
B = np.where(np.logical_and(G_m == 1, B_r == 1),
G + _cnv_h(B, k_b) - _cnv_h(G, k_b),
B)
B = np.where(np.logical_and(G_m == 1, R_r == 1) == 1,
G + _cnv_v(B, k_b) - _cnv_v(G, k_b),
B)
R = np.where(np.logical_and(B_r == 1, B_m == 1),
np.where(M == 1,
B + _cnv_h(R, k_b) - _cnv_h(B, k_b),
B + _cnv_v(R, k_b) - _cnv_v(B, k_b)),
R)
B = np.where(np.logical_and(R_r == 1, R_m == 1),
np.where(M == 1,
R + _cnv_h(B, k_b) - _cnv_h(R, k_b),
R + _cnv_v(B, k_b) - _cnv_v(R, k_b)),
B)
RGB = tstack((R, G, B))
if refining_step:
RGB = refining_step_Menon2007(RGB, tstack((R_m, G_m, B_m)), M)
return RGB
def demosaicing_CFA_Bayer_Menon2007(CFA, pattern='RGGB', refining_step=True):
"""
Returns the demosaiced *RGB* colourspace array from given *Bayer* CFA using
DDFAPD - Menon (2007) demosaicing algorithm.
Parameters
----------
CFA : array_like
*Bayer* CFA.
pattern : unicode, optional
**{'RGGB', 'BGGR', 'GRBG', 'GBRG'}**,
Arrangement of the colour filters on the pixel array.
refining_step : bool
Perform refining step.
Returns
-------
ndarray
*RGB* colourspace array.
Examples
--------
>>> CFA = np.array([[ 0.30980393, 0.36078432, 0.30588236, 0.3764706 ],
... [ 0.35686275, 0.39607844, 0.36078432, 0.40000001]])
>>> demosaicing_CFA_Bayer_Menon2007(CFA)
array([[[ 0.30980393, 0.35686275, 0.39215687],
[ 0.30980393, 0.36078432, 0.39607844],
[ 0.30588236, 0.36078432, 0.39019608],
[ 0.32156864, 0.3764706 , 0.40000001]],
<BLANKLINE>
[[ 0.30980393, 0.35686275, 0.39215687],
[ 0.30980393, 0.36078432, 0.39607844],
[ 0.30588236, 0.36078432, 0.39019609],
[ 0.32156864, 0.3764706 , 0.40000001]]])
>>> CFA = np.array([[ 0.3764706 , 0.36078432, 0.40784314, 0.3764706 ],
... [ 0.35686275, 0.30980393, 0.36078432, 0.29803923]])
>>> demosaicing_CFA_Bayer_Menon2007(CFA, 'BGGR')
array([[[ 0.30588236, 0.35686275, 0.3764706 ],
[ 0.30980393, 0.36078432, 0.39411766],
[ 0.29607844, 0.36078432, 0.40784314],
[ 0.29803923, 0.3764706 , 0.42352942]],
<BLANKLINE>
[[ 0.30588236, 0.35686275, 0.3764706 ],
[ 0.30980393, 0.36078432, 0.39411766],
[ 0.29607844, 0.36078432, 0.40784314],
[ 0.29803923, 0.3764706 , 0.42352942]]])
"""
CFA = np.asarray(CFA)
R_m, G_m, B_m = masks_CFA_Bayer(CFA.shape, pattern)
h_0 = np.array([0, 0.5, 0, 0.5, 0])
h_1 = np.array([-0.25, 0, 0.5, 0, -0.25])
R = CFA * R_m
G = CFA * G_m
B = CFA * B_m
G_H = np.where(G_m == 0, _cnv_h(CFA, h_0) + _cnv_h(CFA, h_1), G)
G_V = np.where(G_m == 0, _cnv_v(CFA, h_0) + _cnv_v(CFA, h_1), G)
C_H = np.where(R_m == 1, R - G_H, 0)
C_H = np.where(B_m == 1, B - G_H, C_H)
C_V = np.where(R_m == 1, R - G_V, 0)
C_V = np.where(B_m == 1, B - G_V, C_V)
D_H = np.abs(C_H - np.pad(C_H, ((0, 0),
(0, 2)), mode=str('reflect'))[:, 2:])
D_V = np.abs(C_V - np.pad(C_V, ((0, 2),
(0, 0)), mode=str('reflect'))[2:, :])
k = np.array([[0, 0, 1, 0, 1], [0, 0, 0, 1, 0], [0, 0, 3, 0, 3],
[0, 0, 0, 1, 0], [0, 0, 1, 0, 1]])
d_H = convolve(D_H, k, mode='constant')
d_V = convolve(D_V, np.transpose(k), mode='constant')
mask = d_V >= d_H
G = np.where(mask, G_H, G_V)
M = np.where(mask, 1, 0)
# Red rows.
R_r = np.transpose(np.any(R_m == 1, axis=1)[np.newaxis]) * np.ones(R.shape)
# Blue rows.
B_r = np.transpose(np.any(B_m == 1, axis=1)[np.newaxis]) * np.ones(B.shape)
k_b = np.array([0.5, 0, 0.5])
R = np.where(np.logical_and(G_m == 1, R_r == 1),
G + _cnv_h(R, k_b) - _cnv_h(G, k_b), R)
R = np.where(
np.logical_and(G_m == 1, B_r == 1) == 1,
G + _cnv_v(R, k_b) - _cnv_v(G, k_b), R)
B = np.where(np.logical_and(G_m == 1, B_r == 1),
G + _cnv_h(B, k_b) - _cnv_h(G, k_b), B)
B = np.where(
np.logical_and(G_m == 1, R_r == 1) == 1,
G + _cnv_v(B, k_b) - _cnv_v(G, k_b), B)
R = np.where(
np.logical_and(B_r == 1, B_m == 1),
np.where(M == 1, B + _cnv_h(R, k_b) - _cnv_h(B, k_b),
B + _cnv_v(R, k_b) - _cnv_v(B, k_b)), R)
B = np.where(
np.logical_and(R_r == 1, R_m == 1),
np.where(M == 1, R + _cnv_h(B, k_b) - _cnv_h(R, k_b),
R + _cnv_v(B, k_b) - _cnv_v(R, k_b)), B)
RGB = tstack((R, G, B))
if refining_step:
RGB = refining_step_Menon2007(RGB, tstack((R_m, G_m, B_m)), M)
return RGB
def demosaicing_CFA_Bayer_bilinear(CFA, pattern='RGGB'):
"""
Returns the demosaiced *RGB* colourspace array from given *Bayer* CFA using
bilinear interpolation.
Parameters
----------
CFA : array_like
*Bayer* CFA.
pattern : unicode, optional
**{'RGGB', 'BGGR', 'GRBG', 'GBRG'}**,
Arrangement of the colour filters on the pixel array.
Returns
-------
ndarray
*RGB* colourspace array.
Examples
--------
>>> CFA = np.array([[0.30980393, 0.36078432, 0.30588236, 0.3764706],
... [0.35686275, 0.39607844, 0.36078432, 0.40000001]])
>>> demosaicing_CFA_Bayer_bilinear(CFA)
array([[[ 0.69705884, 0.17941177, 0.09901961],
[ 0.46176472, 0.4509804 , 0.19803922],
[ 0.45882354, 0.27450981, 0.19901961],
[ 0.22941177, 0.5647059 , 0.30000001]],
<BLANKLINE>
[[ 0.23235295, 0.53529412, 0.29705883],
[ 0.15392157, 0.26960785, 0.59411766],
[ 0.15294118, 0.4509804 , 0.59705884],
[ 0.07647059, 0.18431373, 0.90000002]]])
>>> CFA = np.array([[0.3764706, 0.360784320, 0.40784314, 0.3764706],
... [0.35686275, 0.30980393, 0.36078432, 0.29803923]])
>>> demosaicing_CFA_Bayer_bilinear(CFA, 'BGGR')
array([[[ 0.07745098, 0.17941177, 0.84705885],
[ 0.15490197, 0.4509804 , 0.5882353 ],
[ 0.15196079, 0.27450981, 0.61176471],
[ 0.22352942, 0.5647059 , 0.30588235]],
<BLANKLINE>
[[ 0.23235295, 0.53529412, 0.28235295],
[ 0.4647059 , 0.26960785, 0.19607843],
[ 0.45588237, 0.4509804 , 0.20392157],
[ 0.67058827, 0.18431373, 0.10196078]]])
"""
CFA = np.asarray(CFA)
R_m, G_m, B_m = masks_CFA_Bayer(CFA.shape, pattern)
H_G = np.asarray(
[[0, 1, 0],
[1, 4, 1],
[0, 1, 0]]) / 4
H_RB = np.asarray(
[[1, 2, 1],
[2, 4, 2],
[1, 2, 1]]) / 4
R = convolve(CFA * R_m, H_RB)
G = convolve(CFA * G_m, H_G)
B = convolve(CFA * B_m, H_RB)
return tstack((R, G, B))
