def __getattr__(self, attribute):
"""
Reimplements the :meth:`MutableSequence.__getattr__` method.
Parameters
----------
attribute : unicode
Attribute to retrieve the value.
Returns
-------
object
Attribute value.
"""
try:
return self.__dict__[attribute]
except KeyError:
if hasattr(Image, attribute):
value = [getattr(image, attribute) for image in self]
if attribute == 'data':
return tstack(value)
else:
return tuple(value)
elif hasattr(Metadata, attribute):
value = [getattr(image.metadata, attribute) for image in self]
return np.asarray(value)
else:
raise AttributeError(
"'{0}' object has no attribute '{1}'".format(
self.__class__.__name__, attribute))
def camera_response_functions_Debevec1997(image_stack,
s=samples_Grossberg2003,
samples=1000,
l=30,
w=weighting_function_Debevec1997,
n=256,
normalise=True):
"""
Returns the camera response functions for given image stack using Debevec
(1997) method.
Image channels are sampled with :math:`s` sampling function and the output
samples are passed to :func:`g_solve`.
Parameters
----------
image_stack : ImageStack
Stack of single channel or multi-channel floating point images.
s : callable, optional
Sampling function :math:`s`.
samples : int, optional
Samples count per images.
l : numeric, optional
:math:`\lambda` smoothing term.
w : callable, optional
Weighting function :math:`w`.
n : int, optional
:math:`n` constant.
normalise : bool, optional
Enables the camera response functions normalisation. Uncertain camera
response functions values resulting from :math:`w` function are
set to zero.
Returns
-------
ndarray
Camera response functions :math:`g(z)`.
"""
samples = s(image_stack.data, samples, n)
L_l = np.log(average_luminance(image_stack.f_number,
image_stack.exposure_time,
image_stack.iso))
crfs = np.exp(tstack(np.array([g_solve(samples[..., x], L_l, l, w, n)[0]
for x in range(samples.shape[-1])])))
if normalise:
# TODO: Investigate if the normalisation value should account for the
# percentage of uncertain camera response functions values or be
# correlated to it and scaled accordingly. As an alternative of setting
# the uncertain camera response functions values to zero, it would be
# interesting to explore extrapolation as the camera response functions
# are essentially smooth. It is important to note that camera sensors
# are usually acting non linearly when reaching saturation level.
crfs[w(np.linspace(0, 1, crfs.shape[0])) == 0] = 0
crfs /= np.max(crfs, axis=0)
return crfs
def chromaticity_diagram_visual(
samples=256,
cmfs='CIE 1931 2 Degree Standard Observer',
transformation='CIE 1931',
parent=None):
"""
Creates a chromaticity diagram visual based on
:class:`colour_analysis.visuals.Primitive` class.
Parameters
----------
samples : int, optional
Inner samples count used to construct the chromaticity diagram
triangulation.
cmfs : unicode, optional
Standard observer colour matching functions used for the chromaticity
diagram boundaries.
transformation : unicode, optional
{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}
Chromaticity diagram transformation.
parent : Node, optional
Parent of the chromaticity diagram in the `SceneGraph`.
Returns
-------
Primitive
Chromaticity diagram visual.
"""
cmfs = get_cmfs(cmfs)
illuminant = DEFAULT_PLOTTING_ILLUMINANT
XYZ_to_ij = (
CHROMATICITY_DIAGRAM_TRANSFORMATIONS[transformation]['XYZ_to_ij'])
ij_to_XYZ = (
CHROMATICITY_DIAGRAM_TRANSFORMATIONS[transformation]['ij_to_XYZ'])
ij_c = XYZ_to_ij(cmfs.values, illuminant)
triangulation = Delaunay(ij_c, qhull_options='QJ')
samples = np.linspace(0, 1, samples)
ii, jj = np.meshgrid(samples, samples)
ij = tstack((ii, jj))
ij = np.vstack((ij_c, ij[triangulation.find_simplex(ij) > 0]))
ij_p = np.hstack((ij, np.full((ij.shape[0], 1), 0)))
triangulation = Delaunay(ij, qhull_options='QJ')
RGB = normalise(XYZ_to_sRGB(ij_to_XYZ(ij, illuminant), illuminant),
axis=-1)
diagram = Primitive(vertices=ij_p,
faces=triangulation.simplices,
vertex_colours=RGB,
parent=parent)
return diagram
def test_multi_signal_copy_operations():
domain = np.arange(0, 1000, 100)
range_1 = np.linspace(1, 10, domain.size)
range_2 = np.linspace(1, 10, domain.size) + 1
range_3 = np.linspace(1, 10, domain.size) + 2
cms1 = MultiSignal(tstack((domain, range_1, range_2, range_3)))
cms2 = cms1.copy()
assert id(cms1) != id(cms2)
def chromaticity_diagram_visual(samples=256,
cmfs='CIE 1931 2 Degree Standard Observer',
transformation='CIE 1931',
parent=None):
"""
Creates a chromaticity diagram visual based on
:class:`colour_analysis.visuals.Primitive` class.
Parameters
----------
samples : int, optional
Inner samples count used to construct the chromaticity diagram
triangulation.
cmfs : unicode, optional
Standard observer colour matching functions used for the chromaticity
diagram boundaries.
transformation : unicode, optional
**{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}**,
Chromaticity diagram transformation.
parent : Node, optional
Parent of the chromaticity diagram in the `SceneGraph`.
Returns
-------
Primitive
Chromaticity diagram visual.
"""
cmfs = get_cmfs(cmfs)
illuminant = DEFAULT_PLOTTING_ILLUMINANT
XYZ_to_ij = (
CHROMATICITY_DIAGRAM_TRANSFORMATIONS[transformation]['XYZ_to_ij'])
ij_to_XYZ = (
CHROMATICITY_DIAGRAM_TRANSFORMATIONS[transformation]['ij_to_XYZ'])
ij_c = XYZ_to_ij(cmfs.values, illuminant)
triangulation = Delaunay(ij_c, qhull_options='QJ')
samples = np.linspace(0, 1, samples)
ii, jj = np.meshgrid(samples, samples)
ij = tstack((ii, jj))
ij = np.vstack((ij_c, ij[triangulation.find_simplex(ij) > 0]))
ij_p = np.hstack((ij, np.full((ij.shape[0], 1), 0, DEFAULT_FLOAT_DTYPE)))
triangulation = Delaunay(ij, qhull_options='QJ')
RGB = normalise_maximum(
XYZ_to_sRGB(ij_to_XYZ(ij, illuminant), illuminant), axis=-1)
diagram = Primitive(
vertices=ij_p,
faces=triangulation.simplices,
vertex_colours=RGB,
parent=parent)
return diagram
def test_multi_signal_copy_operations():
domain = np.arange(0, 1000, 100)
range_1 = np.linspace(1, 10, domain.size)
range_2 = np.linspace(1, 10, domain.size) + 1
range_3 = np.linspace(1, 10, domain.size) + 2
cms1 = MultiSignal(tstack((domain, range_1, range_2, range_3)))
cms2 = cms1.copy()
assert id(cms1) != id(cms2)
def __repr__(self):
try:
representation = repr(tstack((self.domain, self.range)))
representation = representation.replace('array',
self.__class__.__name__)
representation = representation.replace(' [', '{0}['.format(
' ' * (len(self.__class__.__name__) + 2)))
return representation
except TypeError:
return super(Signal, self).__repr__()
def __repr__(self):
try:
representation = repr(tstack((self.domain, self.range)))
representation = representation.replace('array',
self.__class__.__name__)
representation = representation.replace(
' [',
'{0}['.format(' ' * (len(self.__class__.__name__) + 2)))
return representation
except TypeError:
return super(Signal, self).__repr__()
def __create_image(self):
"""
Creates the image used by the *Image View* according to
:attr:`GamutView.__display_input_colourspace_out_of_gamut`,
:attr:`GamutView.__display_correlate_colourspace_out_of_gamut`,
:attr:`GamutView.__display_out_of_pointer_gamut` and
:attr:`GamutView.__display_hdr_colours` attributes values.
Returns
-------
ndarray
Image
"""
image = np.copy(self.__image)
has_overlay = False
if (self.__display_input_colourspace_out_of_gamut or
self.__display_correlate_colourspace_out_of_gamut):
image[image >= 0] = 0
image[image < 0] = 1
has_overlay = True
if self.__display_correlate_colourspace_out_of_gamut:
image = RGB_to_RGB(image,
RGB_COLOURSPACES[self.__input_colourspace],
RGB_COLOURSPACES[self.__correlate_colourspace])
has_overlay = True
if self.__display_out_of_pointer_gamut:
colourspace = RGB_COLOURSPACES[self.__input_colourspace]
image = is_within_pointer_gamut(
RGB_to_XYZ(image,
colourspace.whitepoint,
colourspace.whitepoint,
colourspace.RGB_to_XYZ_matrix)).astype(int)
# TODO: Investigate why stacking implies that image needs to be
# inverted.
image = 1 - tstack((image, image, image))
has_overlay = True
if self.__display_hdr_colours:
image[image <= 1] = 0
# has_overlay = True
if self.__image_overlay and has_overlay:
image = self.__image + image
oecf = RGB_COLOURSPACES[DEFAULT_OECF].transfer_function
return oecf(image)
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 _create_image(self):
"""
Creates the image used by the *Image View* according to
:attr:`GamutView._display_input_colourspace_out_of_gamut`,
:attr:`GamutView._display_correlate_colourspace_out_of_gamut`,
:attr:`GamutView._display_out_of_pointer_gamut` and
:attr:`GamutView._display_hdr_colours` attributes values.
Returns
-------
ndarray
Image
"""
image = np.copy(self._image)
has_overlay = False
if (self._display_input_colourspace_out_of_gamut
or self._display_correlate_colourspace_out_of_gamut):
image[image >= 0] = 0
image[image < 0] = 1
has_overlay = True
if self._display_correlate_colourspace_out_of_gamut:
image = RGB_to_RGB(image,
RGB_COLOURSPACES[self._input_colourspace],
RGB_COLOURSPACES[self._correlate_colourspace])
has_overlay = True
if self._display_out_of_pointer_gamut:
colourspace = RGB_COLOURSPACES[self._input_colourspace]
image = is_within_pointer_gamut(
RGB_to_XYZ(image, colourspace.whitepoint,
colourspace.whitepoint,
colourspace.RGB_to_XYZ_matrix)).astype(int)
# TODO: Investigate why stacking implies that image needs to be
# inverted.
image = 1 - tstack((image, image, image))
has_overlay = True
if self._display_hdr_colours:
image[image <= 1] = 0
# has_overlay = True
if self._image_overlay and has_overlay:
image = self._image + image
oecf = RGB_COLOURSPACES[DEFAULT_ENCODING_CCTF].encoding_cctf
return oecf(image)
def test_multi_signal_oject_initialisation():
domain = np.arange(0, 1000, 100)
range_1 = np.linspace(1, 10, domain.size)
range_2 = np.linspace(1, 10, domain.size) + 1
range_3 = np.linspace(1, 10, domain.size) + 2
cms1 = MultiSignal(tstack((domain, range_1, range_2, range_3)))
np.testing.assert_array_equal(cms1.range,
tstack((range_1, range_2, range_3)))
np.testing.assert_array_equal(cms1.domain, domain)
cms2 = MultiSignal(range_1)
np.testing.assert_array_equal(cms2.range, range_1[:, np.newaxis])
np.testing.assert_array_equal(cms2.domain, np.linspace(0, 1, domain.size))
cms3 = MultiSignal(range_1, labels=['My Label'])
assert list(cms3.signals.keys()) == ['My Label']
cms4 = MultiSignal(
tstack((range_1, range_2, range_3)), domain / 1000, ('a', 'b', 'c'))
np.testing.assert_array_equal(cms4.range,
tstack((range_1, range_2, range_3)))
np.testing.assert_array_equal(cms4.domain, domain / 1000)
assert cms4.labels == ['a', 'b', 'c']
cms5 = MultiSignal(Series(range_1, domain))
np.testing.assert_array_equal(cms5.range, range_1[:, np.newaxis])
np.testing.assert_array_equal(cms5.domain, domain)
dataframe = DataFrame(
OrderedDict([('aa', range_1), ('bb', range_2), ('cc', range_3)]))
cms6 = MultiSignal(dataframe)
np.testing.assert_array_equal(cms6.range,
tstack((range_1, range_2, range_3)))
assert cms6.labels == ['aa', 'bb', 'cc']
np.testing.assert_array_equal(cms6.domain, np.arange(0, np.size(domain)))
def test_multi_signal_oject_initialisation():
domain = np.arange(0, 1000, 100)
range_1 = np.linspace(1, 10, domain.size)
range_2 = np.linspace(1, 10, domain.size) + 1
range_3 = np.linspace(1, 10, domain.size) + 2
cms1 = MultiSignal(tstack((domain, range_1, range_2, range_3)))
np.testing.assert_array_equal(cms1.range,
tstack((range_1, range_2, range_3)))
np.testing.assert_array_equal(cms1.domain, domain)
cms2 = MultiSignal(range_1)
np.testing.assert_array_equal(cms2.range, range_1[:, np.newaxis])
np.testing.assert_array_equal(cms2.domain, np.linspace(0, 1, domain.size))
cms3 = MultiSignal(range_1, labels=['My Label'])
assert list(cms3.signals.keys()) == ['My Label']
cms4 = MultiSignal(tstack((range_1, range_2, range_3)), domain / 1000,
('a', 'b', 'c'))
np.testing.assert_array_equal(cms4.range,
tstack((range_1, range_2, range_3)))
np.testing.assert_array_equal(cms4.domain, domain / 1000)
assert cms4.labels == ['a', 'b', 'c']
cms5 = MultiSignal(Series(range_1, domain))
np.testing.assert_array_equal(cms5.range, range_1[:, np.newaxis])
np.testing.assert_array_equal(cms5.domain, domain)
dataframe = DataFrame(
OrderedDict([('aa', range_1), ('bb', range_2), ('cc', range_3)]))
cms6 = MultiSignal(dataframe)
np.testing.assert_array_equal(cms6.range,
tstack((range_1, range_2, range_3)))
assert cms6.labels == ['aa', 'bb', 'cc']
np.testing.assert_array_equal(cms6.domain, np.arange(0, np.size(domain)))
def samples_Grossberg2003(image_stack, samples=1000, n=256):
"""
Returns the samples for given image stack intensity histograms using
Grossberg (2003) method.
Parameters
----------
image_stack : array_like
Stack of single channel or multi-channel floating point images.
samples : int, optional
Samples count.
n : int, optional
Histograms bins count.
Returns
-------
ndarray
Intensity histograms samples.
"""
image_stack = np.asarray(image_stack)
if image_stack.ndim == 3:
channels_c = 1
else:
channels_c = image_stack.shape[-2]
cdf_i = []
for image in tsplit(image_stack):
histograms = tstack(
[np.histogram(image[..., c], n, range=(0, 1))[0]
for c in np.arange(channels_c)])
cdf = np.cumsum(histograms, axis=0)
cdf_i.append(cdf.astype(np.float_) / np.max(cdf, axis=0))
samples_cdf_i = np.zeros((samples, len(cdf_i), channels_c))
samples_u = np.linspace(0, 1, samples)
for i in np.arange(samples):
for j in np.arange(channels_c):
for k, cdf in enumerate(cdf_i):
samples_cdf_i[i, k, j] = np.argmin(np.abs(cdf[:, j] -
samples_u[i]))
return samples_cdf_i
def test_multi_signal_getter():
domain = np.arange(0, 1000, 100)
range_1 = np.linspace(1, 10, domain.size)
range_2 = np.linspace(1, 10, domain.size) + 1
range_3 = np.linspace(1, 10, domain.size) + 2
cms1 = MultiSignal(tstack((domain, range_1, range_2, range_3)))
np.testing.assert_array_almost_equal(cms1[150.25],
[2.5025, 3.5025, 4.5025])
np.testing.assert_array_almost_equal(
cms1[np.linspace(100, 400, 10)],
np.array([[2.00000000, 3.00000000,
4.00000000], [2.33333333, 3.33333333, 4.33333333], [
2.66666667, 3.66666667, 4.66666667
], [3.00000000, 4.00000000,
5.00000000], [3.33333333, 4.33333333, 5.33333333],
[3.66666667, 4.66666667,
5.66666667], [4.00000000, 5.00000000, 6.00000000], [
4.33333333, 5.33333333, 6.33333333
], [4.66666667, 5.66666667, 6.66666667],
[5.00000000, 6.00000000, 7.00000000]]))
def test_multi_signal_getter():
domain = np.arange(0, 1000, 100)
range_1 = np.linspace(1, 10, domain.size)
range_2 = np.linspace(1, 10, domain.size) + 1
range_3 = np.linspace(1, 10, domain.size) + 2
cms1 = MultiSignal(tstack((domain, range_1, range_2, range_3)))
np.testing.assert_array_almost_equal(cms1[150.25],
[2.5025, 3.5025, 4.5025])
np.testing.assert_array_almost_equal(
cms1[np.linspace(100, 400, 10)],
np.array([[2.00000000, 3.00000000, 4.00000000],
[2.33333333, 3.33333333, 4.33333333],
[2.66666667, 3.66666667, 4.66666667],
[3.00000000, 4.00000000, 5.00000000],
[3.33333333, 4.33333333, 5.33333333],
[3.66666667, 4.66666667, 5.66666667],
[4.00000000, 5.00000000, 6.00000000],
[4.33333333, 5.33333333, 6.33333333],
[4.66666667, 5.66666667, 6.66666667],
[5.00000000, 6.00000000, 7.00000000]]))
def __str__(self):
try:
return str(tstack((self.domain, self.range)))
except TypeError:
return super(Signal, self).__str__()
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 refining_step_Menon2007(RGB, RGB_m, M):
"""
Performs the refining step on given *RGB* colourspace array.
Parameters
----------
RGB : array_like
*RGB* colourspace array.
RGB_m : array_like
*Bayer* CFA red, green and blue masks.
M : array_like
Estimation for the best directional reconstruction.
Returns
-------
ndarray
Refined *RGB* colourspace array.
Examples
--------
>>> RGB = np.array([[[0.30588236, 0.35686275, 0.3764706],
... [0.30980393, 0.36078432, 0.39411766],
... [0.29607844, 0.36078432, 0.40784314],
... [0.29803923, 0.37647060, 0.42352942]],
... [[0.30588236, 0.35686275, 0.3764706],
... [0.30980393, 0.36078432, 0.39411766],
... [0.29607844, 0.36078432, 0.40784314],
... [0.29803923, 0.37647060, 0.42352942]]])
>>> RGB_m = np.array([[[0, 0, 1],
... [0, 1, 0],
... [0, 0, 1],
... [0, 1, 0]],
... [[0, 1, 0],
... [1, 0, 0],
... [0, 1, 0],
... [1, 0, 0]]])
>>> M = np.array([[0, 1, 0, 1],
... [1, 0, 1, 0]])
>>> refining_step_Menon2007(RGB, RGB_m, M)
array([[[ 0.30588236, 0.35686275, 0.3764706 ],
[ 0.30980393, 0.36078432, 0.39411765],
[ 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]]])
"""
R, G, B = tsplit(RGB)
R_m, G_m, B_m = tsplit(RGB_m)
M = np.asarray(M)
# Updating of the green component.
R_G = R - G
B_G = B - G
FIR = np.ones(3) / 3
B_G_m = np.where(B_m == 1,
np.where(M == 1, _cnv_h(B_G, FIR), _cnv_v(B_G, FIR)), 0)
R_G_m = np.where(R_m == 1,
np.where(M == 1, _cnv_h(R_G, FIR), _cnv_v(R_G, FIR)), 0)
G = np.where(R_m == 1, R - R_G_m, G)
G = np.where(B_m == 1, B - B_G_m, G)
# Updating of the red and blue components in the green locations.
# 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_G = R - G
B_G = B - G
k_b = np.array([0.5, 0, 0.5])
R_G_m = np.where(np.logical_and(G_m == 1, B_r == 1),
_cnv_v(R_G, k_b),
R_G_m)
R = np.where(np.logical_and(G_m == 1, B_r == 1), G + R_G_m, R)
R_G_m = np.where(np.logical_and(G_m == 1, B_c == 1),
_cnv_h(R_G, k_b),
R_G_m)
R = np.where(np.logical_and(G_m == 1, B_c == 1), G + R_G_m, R)
B_G_m = np.where(np.logical_and(G_m == 1, R_r == 1),
_cnv_v(B_G, k_b),
B_G_m)
B = np.where(np.logical_and(G_m == 1, R_r == 1), G + B_G_m, B)
B_G_m = np.where(np.logical_and(G_m == 1, R_c == 1),
_cnv_h(B_G, k_b),
B_G_m)
B = np.where(np.logical_and(G_m == 1, R_c == 1), G + B_G_m, B)
# Updating of the red (blue) component in the blue (red) locations.
R_B = R - B
R_B_m = np.where(B_m == 1,
np.where(M == 1, _cnv_h(R_B, FIR), _cnv_v(R_B, FIR)), 0)
R = np.where(B_m == 1, B + R_B_m, R)
R_B_m = np.where(R_m == 1,
np.where(M == 1, _cnv_h(R_B, FIR), _cnv_v(R_B, FIR)), 0)
B = np.where(R_m == 1, R - R_B_m, B)
return tstack((R, G, B))
def __getitem__(self, x):
return tstack([signal[x] for signal in self._signals.values()])
def range(self):
if len(self._signals) != 0:
return tstack([signal.range for signal in self._signals.values()])
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 range(self):
if len(self._signals) != 0:
return tstack([signal.range for signal in self._signals.values()])
def __getitem__(self, x):
return tstack([signal[x] for signal in self._signals.values()])
def image_stack_to_radiance_image(
image_stack,
weighting_function=weighting_function_Debevec1997,
weighting_average=False,
camera_response_functions=None):
"""
Generates a HDRI / radiance image from given image stack.
Parameters
----------
image_stack : ImageStack
Stack of single channel or multi-channel floating point images. The
stack is assumed to be representing linear values except if
``camera_response_functions`` argument is provided.
weighting_function : callable, optional
Weighting function :math:`w`.
weighting_average : bool, optional
Enables weighting function :math:`w` computation on channels average
instead of on a per channel basis.
camera_response_functions : array_like, optional
Camera response functions :math:`g(z)` of the imaging system / camera
if the stack is representing non linear values.
Returns
-------
ndarray
Radiance image.
"""
image_c = None
weight_c = None
for image in image_stack:
if image_c is None:
image_c = np.zeros(image.data.shape)
weight_c = np.zeros(image.data.shape)
L = average_luminance(
image.metadata.f_number,
image.metadata.exposure_time,
image.metadata.iso)
if weighting_average and image.data.ndim == 3:
weight = weighting_function(np.average(image.data, axis=-1))
weight = np.rollaxis(weight[np.newaxis], 0, 3)
else:
weight = weighting_function(image.data)
image_data = image.data
if camera_response_functions is not None:
samples = np.linspace(0, 1, camera_response_functions.shape[0])
R, G, B = tsplit(image.data)
R = np.interp(R, samples, camera_response_functions[..., 0])
G = np.interp(G, samples, camera_response_functions[..., 1])
B = np.interp(B, samples, camera_response_functions[..., 2])
image_data = tstack((R, G, B))
image_c += weight * image_data / L
weight_c += weight
if image_c is not None:
image_c /= weight_c
image_c[np.isnan(image_c)] = 0
return image_c
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 refining_step_Menon2007(RGB, RGB_m, M):
"""
Performs the refining step on given *RGB* colourspace array.
Parameters
----------
RGB : array_like
*RGB* colourspace array.
RGB_m : array_like
*Bayer* CFA red, green and blue masks.
M : array_like
Estimation for the best directional reconstruction.
Returns
-------
ndarray
Refined *RGB* colourspace array.
Examples
--------
>>> RGB = np.array([[[0.30588236, 0.35686275, 0.3764706],
... [0.30980393, 0.36078432, 0.39411766],
... [0.29607844, 0.36078432, 0.40784314],
... [0.29803923, 0.37647060, 0.42352942]],
... [[0.30588236, 0.35686275, 0.3764706],
... [0.30980393, 0.36078432, 0.39411766],
... [0.29607844, 0.36078432, 0.40784314],
... [0.29803923, 0.37647060, 0.42352942]]])
>>> RGB_m = np.array([[[0, 0, 1],
... [0, 1, 0],
... [0, 0, 1],
... [0, 1, 0]],
... [[0, 1, 0],
... [1, 0, 0],
... [0, 1, 0],
... [1, 0, 0]]])
>>> M = np.array([[0, 1, 0, 1],
... [1, 0, 1, 0]])
>>> refining_step_Menon2007(RGB, RGB_m, M)
array([[[ 0.30588236, 0.35686275, 0.3764706 ],
[ 0.30980393, 0.36078432, 0.39411765],
[ 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]]])
"""
R, G, B = tsplit(RGB)
R_m, G_m, B_m = tsplit(RGB_m)
M = np.asarray(M)
# Updating of the green component.
R_G = R - G
B_G = B - G
FIR = np.ones(3) / 3
B_G_m = np.where(B_m == 1,
np.where(M == 1, _cnv_h(B_G, FIR), _cnv_v(B_G, FIR)), 0)
R_G_m = np.where(R_m == 1,
np.where(M == 1, _cnv_h(R_G, FIR), _cnv_v(R_G, FIR)), 0)
G = np.where(R_m == 1, R - R_G_m, G)
G = np.where(B_m == 1, B - B_G_m, G)
# Updating of the red and blue components in the green locations.
# 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_G = R - G
B_G = B - G
k_b = np.array([0.5, 0, 0.5])
R_G_m = np.where(np.logical_and(G_m == 1, B_r == 1), _cnv_v(R_G, k_b),
R_G_m)
R = np.where(np.logical_and(G_m == 1, B_r == 1), G + R_G_m, R)
R_G_m = np.where(np.logical_and(G_m == 1, B_c == 1), _cnv_h(R_G, k_b),
R_G_m)
R = np.where(np.logical_and(G_m == 1, B_c == 1), G + R_G_m, R)
B_G_m = np.where(np.logical_and(G_m == 1, R_r == 1), _cnv_v(B_G, k_b),
B_G_m)
B = np.where(np.logical_and(G_m == 1, R_r == 1), G + B_G_m, B)
B_G_m = np.where(np.logical_and(G_m == 1, R_c == 1), _cnv_h(B_G, k_b),
B_G_m)
B = np.where(np.logical_and(G_m == 1, R_c == 1), G + B_G_m, B)
# Updating of the red (blue) component in the blue (red) locations.
R_B = R - B
R_B_m = np.where(B_m == 1,
np.where(M == 1, _cnv_h(R_B, FIR), _cnv_v(R_B, FIR)), 0)
R = np.where(B_m == 1, B + R_B_m, R)
R_B_m = np.where(R_m == 1,
np.where(M == 1, _cnv_h(R_B, FIR), _cnv_v(R_B, FIR)), 0)
B = np.where(R_m == 1, R - R_B_m, B)
return tstack((R, G, B))
def __str__(self):
try:
return str(tstack((self.domain, self.range)))
except TypeError:
return super(Signal, self).__str__()
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_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))
