def ctf(self, **kwargs):
"""
Convert color space coordinates to XYZ tristimulus values.
Args:
:dtype:
| 'xyz'
| Convert to this color space.
:**kwargs:
| additional input arguments required for
| color space transformation.
| See specific luxpy function for more info
| (e.g. ?luxpy.xyz_to_lab)
Returns:
:returns:
| luxpy.XYZ with .value field that is a ndarray
with tristimulus values
"""
db = put_args_in_db(self.cspace_par, locals().copy())
return XYZ(value=colortf(self.value,
tf='{:s}>xyz'.format(self.dtype),
bwtf=db),
relative=self.relative,
cieobs=self.cieobs,
dtype='xyz')
def plotDL(ccts = None, cieobs =_CIEOBS, cspace = _CSPACE, axh = None, \
show = True, force_daylight_below4000K = False, cspace_pars = {}, \
formatstr = 'k-', **kwargs):
"""
Plot daylight locus.
Args:
:ccts:
| None or list[float], optional
| None defaults to [4000 K to 1e19 K] in 100 steps on a log10 scale.
:force_daylight_below4000K:
| False or True, optional
| CIE daylight phases are not defined below 4000 K.
| If True plot anyway.
:axh:
| None or axes handle, optional
| Determines axes to plot data in.
| None: make new figure.
:show:
| True or False, optional
| Invoke matplotlib.pyplot.show() right after plotting
:cieobs:
| luxpy._CIEOBS or str, optional
| Determines CMF set to calculate spectrum locus or other.
:cspace:
| luxpy._CSPACE or str, optional
| Determines color space / chromaticity diagram to plot data in.
| Note that data is expected to be in specified :cspace:
:formatstr:
| 'k-' or str, optional
| Format str for plotting (see ?matplotlib.pyplot.plot)
:cspace_pars:
| {} or dict, optional
| Dict with parameters required by color space specified in :cspace:
(for use with luxpy.colortf())
:kwargs:
| additional keyword arguments for use with matplotlib.pyplot.
Returns:
:returns:
| None (:show: == True)
| or
| handle to current axes (:show: == False)
"""
if ccts is None:
ccts = 10**np.linspace(np.log10(4000.0),np.log10(10.0**19.0),100.0)
xD,yD = daylightlocus(ccts, force_daylight_below4000K = force_daylight_below4000K)
Y = 100*np.ones(xD.shape)
DL = Yxy_to_xyz(np.vstack((Y, xD,yD)).T)
DL = colortf(DL, tf = cspace, tfa0 = cspace_pars)
Y,x,y = asplit(DL)
axh = plot_color_data(x,y,axh = axh, cieobs = cieobs, cspace = cspace, show=show, formatstr=formatstr, **kwargs)
if show == False:
return axh
def optfcn(x, rgbcal, xyzcal, tr, xyz_black, cspace, p_grays, p_whites,out,verbosity):
M = x.reshape((3,3))
xyzest = rgb_to_xyz(rgbcal, M, tr, xyz_black, tr_type)
xyzw = xyzcal[p_whites,:].mean(axis=0) # get xyzw for input into xyz_to_lab() or colortf()
labcal, labest = colortf(xyzcal,tf=cspace,xyzw=xyzw), colortf(xyzest,tf=cspace,xyzw=xyzw) # calculate lab coord. of cal. and est.
DEs = ((labcal-labest)**2).sum(axis=1)**0.5
DEg = DEs[p_grays]
DEw = DEs[p_whites]
F = (avg(DEs)**2 + avg(DEg)**2 + avg(DEw**2))**0.5
if verbosity > 1:
print('\nPerformance of TR + rgb-to-xyz conversion matrix M:')
print('all: DE(jab): avg = {:1.4f}, std = {:1.4f}'.format(avg(DEs),np.std(DEs)))
print('grays: DE(jab): avg = {:1.4f}, std = {:1.4f}'.format(avg(DEg),np.std(DEg)))
print('whites(s) DE(jab): avg = {:1.4f}, std = {:1.4f}'.format(avg(DEw),np.std(DEw)))
if out == 'F':
return F
else:
return eval(out)
def plotceruleanline(cieobs=_CIEOBS,
cspace=_CSPACE,
axh=None,
formatstr='ko-',
cspace_pars={}):
"""
Plot cerulean (yellow (577 nm) - blue (472 nm)) line
| Kuehni, CRA, 2014:
| Table II: spectral lights.
Args:
:axh:
| None or axes handle, optional
| Determines axes to plot data in.
| None: make new figure.
:cieobs:
| luxpy._CIEOBS or str, optional
| Determines CMF set to calculate spectrum locus or other.
:cspace:
| luxpy._CSPACE or str, optional
| Determines color space / chromaticity diagram to plot data in.
| Note that data is expected to be in specified :cspace:
:formatstr:
| 'k-' or str, optional
| Format str for plotting (see ?matplotlib.pyplot.plot)
:cspace_pars:
| {} or dict, optional
| Dict with parameters required by color space specified in :cspace:
| (for use with luxpy.colortf())
:kwargs:
| additional keyword arguments for use with matplotlib.pyplot.
Returns:
:returns:
| handle to cerulean line
References:
1. `Kuehni, R. G. (2014).
Unique hues and their stimuli—state of the art.
Color Research & Application, 39(3), 279–287.
<https://doi.org/10.1002/col.21793>`_
(see Table II, IV)
"""
if isinstance(cieobs, str):
cmf = _CMF[cieobs]['bar']
else:
cmf = cieobs
p_y = cmf[0] == 577.0 #Kuehni, CRA 2013 (mean, table IV)
p_b = cmf[0] == 472.0 #Kuehni, CRA 2013 (mean, table IV)
xyz_y = cmf[1:, p_y].T
xyz_b = cmf[1:, p_b].T
lab = colortf(np.vstack((xyz_b, xyz_y)), tf=cspace, tfa0=cspace_pars)
if axh is None:
axh = plt.gca()
hcerline = axh.plot(lab[:, 1], lab[:, 2], formatstr, label='Cerulean line')
return hcerline
def to_xyz(self, **kwargs):
"""
Convert color space coordinates to XYZ tristimulus values.
"""
db = put_args_in_db(self.cspace_par, locals().copy())
return XYZ(value=colortf(self.value,
tf='{:s}>xyz'.format(self.dtype),
bwtf=db),
relative=self.relative,
cieobs=self.cieobs,
dtype='xyz')
def xyz_to_rfl(xyz, CSF = None, rfl = None, out = 'rfl_est', \
refspd = None, D = None, cieobs = _CIEOBS, \
cspace = 'xyz', cspace_tf = {},\
interp_type = 'nd', k_neighbours = 4, verbosity = 0):
"""
Approximate spectral reflectance of xyz values based on nd-dimensional linear interpolation
or k nearest neighbour interpolation of samples from a standard reflectance set.
Args:
:xyz:
| ndarray with xyz values of target points.
:CSF:
| None, optional
| RGB camera response functions.
| If None: input :xyz: contains raw rgb (float) values. Override :cspace:
| argument and perform estimation directly in raw rgb space!!!
:rfl:
| ndarray, optional
| Reflectance set for color coordinate to rfl mapping.
:out:
| 'rfl_est' or str, optional
:refspd:
| None, optional
| Refer ence spectrum for color coordinate to rfl mapping.
| None defaults to D65.
:cieobs:
| _CIEOBS, optional
| CMF set used for calculation of xyz from spectral data.
:cspace:
| 'xyz', optional
| Color space for color coordinate to rfl mapping.
| Tip: Use linear space (e.g. 'xyz', 'Yuv',...) for (interp_type == 'nd'),
| and perceptually uniform space (e.g. 'ipt') for (interp_type == 'nearest')
:cspace_tf:
| {}, optional
| Dict with parameters for xyz_to_cspace and cspace_to_xyz transform.
:interp_type:
| 'nd', optional
| Options:
| - 'nd': perform n-dimensional linear interpolation using Delaunay triangulation.
| - 'nearest': perform nearest neighbour interpolation.
:k_neighbours:
| 4 or int, optional
| Number of nearest neighbours for reflectance spectrum interpolation.
| Neighbours are found using scipy.spatial.cKDTree
:verbosity:
| 0, optional
| If > 0: make a plot of the color coordinates of original and
| rendered image pixels.
Returns:
:returns:
| :rfl_est:
| ndarrays with estimated reflectance spectra.
"""
# get rfl set:
if rfl is None: # use IESTM30['4880'] set
rfl = _CRI_RFL['ies-tm30']['4880']['5nm']
wlr = rfl[0]
# get Ref spd:
if refspd is None:
refspd = _CIE_ILLUMINANTS['D65'].copy()
refspd = cie_interp(
refspd, wlr,
kind='linear') # force spd to same wavelength range as rfl
# Calculate rgb values of standard rfl set under refspd:
if CSF is None:
# Calculate lab coordinates:
xyz_rr, xyz_wr = spd_to_xyz(refspd,
relative=True,
rfl=rfl,
cieobs=cieobs,
out=2)
cspace_tf_copy = cspace_tf.copy()
cspace_tf_copy[
'xyzw'] = xyz_wr # put correct white point in param. dict
lab_rr = colortf(xyz_rr,
tf=cspace,
fwtf=cspace_tf_copy,
bwtf=cspace_tf_copy)[:, 0, :]
else:
# Calculate rgb coordinates from camera sensitivity functions
rgb_rr = rfl_to_rgb(rfl, spd=refspd, CSF=CSF, wl=None)
lab_rr = rgb_rr
xyz = xyz
lab_rr = np.round(lab_rr, _ROUNDING) # speed up search
# Convert xyz to lab-type values under refspd:
if CSF is None:
lab = colortf(xyz, tf=cspace, fwtf=cspace_tf_copy, bwtf=cspace_tf_copy)
else:
lab = xyz # xyz contained rgb values !!!
rgb = xyz
lab = np.round(lab, _ROUNDING) # speed up search
if interp_type == 'nearest':
# Find rfl (cfr. lab_rr) from rfl set that results in 'near' metameric
# color coordinates for each value in lab_ur (i.e. smallest DE):
# Construct cKDTree:
tree = sp.spatial.cKDTree(lab_rr, copy_data=True)
# Interpolate rfls using k nearest neightbours and inverse distance weigthing:
d, inds = tree.query(lab, k=k_neighbours)
if k_neighbours > 1:
d += _EPS
w = (1.0 / d**2)[:, :, None] # inverse distance weigthing
rfl_est = np.sum(w * rfl[inds + 1, :], axis=1) / np.sum(w, axis=1)
else:
rfl_est = rfl[inds + 1, :].copy()
elif interp_type == 'nd':
rfl_est = math.ndinterp1_scipy(lab_rr, rfl[1:], lab)
_isnan = np.isnan(rfl_est[:, 0])
if (
_isnan.any()
): #do nearest neigbour method for those that fail using Delaunay (i.e. ndinterp1_scipy)
# Find rfl (cfr. lab_rr) from rfl set that results in 'near' metameric
# color coordinates for each value in lab_ur (i.e. smallest DE):
# Construct cKDTree:
tree = sp.spatial.cKDTree(lab_rr, copy_data=True)
# Interpolate rfls using k nearest neightbours and inverse distance weigthing:
d, inds = tree.query(lab[_isnan, ...], k=k_neighbours)
if k_neighbours > 1:
d += _EPS
w = (1.0 / d**2)[:, :, None] # inverse distance weigthing
rfl_est_isnan = np.sum(w * rfl[inds + 1, :], axis=1) / np.sum(
w, axis=1)
else:
rfl_est_isnan = rfl[inds + 1, :].copy()
rfl_est[_isnan, :] = rfl_est_isnan
else:
raise Exception('xyz_to_rfl(): unsupported interp_type!')
rfl_est[
rfl_est <
0] = 0 #can occur for points outside convexhull of standard rfl set.
rfl_est = np.vstack((rfl[0], rfl_est))
if ((verbosity > 0) | ('xyz_est' in out.split(',')) |
('lab_est' in out.split(',')) | ('DEi_ab' in out.split(',')) |
('DEa_ab' in out.split(','))) & (CSF is None):
xyz_est, _ = spd_to_xyz(refspd,
rfl=rfl_est,
relative=True,
cieobs=cieobs,
out=2)
cspace_tf_copy = cspace_tf.copy()
cspace_tf_copy[
'xyzw'] = xyz_wr # put correct white point in param. dict
lab_est = colortf(xyz_est, tf=cspace, fwtf=cspace_tf_copy)[:, 0, :]
DEi_ab = np.sqrt(((lab_est[:, 1:3] - lab[:, 1:3])**2).sum(axis=1))
DEa_ab = DEi_ab.mean()
elif ((verbosity > 0) | ('xyz_est' in out.split(',')) |
('rgb_est' in out.split(',')) | ('DEi_rgb' in out.split(',')) |
('DEa_rgb' in out.split(','))) & (CSF is not None):
rgb_est = rfl_to_rgb(rfl_est[1:], spd=refspd, CSF=CSF, wl=wlr)
xyz_est = rgb_est
DEi_rgb = np.sqrt(((rgb_est - rgb)**2).sum(axis=1))
DEa_rgb = DEi_rgb.mean()
if verbosity > 0:
if CSF is None:
ax = plot_color_data(lab[...,1], lab[...,2], z = lab[...,0], \
show = False, cieobs = cieobs, cspace = cspace, \
formatstr = 'ro', label = 'Original')
plot_color_data(lab_est[...,1], lab_est[...,2], z = lab_est[...,0], \
show = True, axh = ax, cieobs = cieobs, cspace = cspace, \
formatstr = 'bd', label = 'Rendered')
else:
n = 100 #min(rfl.shape[0]-1,rfl_est.shape[0]-1)
s = np.random.permutation(rfl.shape[0] -
1)[:min(n, rfl.shape[0] - 1)]
st = np.random.permutation(rfl_est.shape[0] -
1)[:min(n, rfl_est.shape[0] - 1)]
fig = plt.figure()
ax = np.zeros((3, ), dtype=np.object)
ax[0] = fig.add_subplot(131)
ax[1] = fig.add_subplot(132)
ax[2] = fig.add_subplot(133, projection='3d')
ax[0].plot(rfl[0], rfl[1:][s].T, linestyle='-')
ax[0].set_title('Original RFL set (random selection of all)')
ax[0].set_ylim([0, 1])
ax[1].plot(rfl_est[0], rfl_est[1:][st].T, linestyle='--')
ax[0].set_title('Estimated RFL set (random selection of targets)')
ax[1].set_ylim([0, 1])
ax[2].plot(rgb[st, 0],
rgb[st, 1],
rgb[st, 2],
'ro',
label='Original')
ax[2].plot(rgb_est[st, 0],
rgb_est[st, 1],
rgb_est[st, 2],
'bd',
label='Rendered')
ax[2].legend()
if out == 'rfl_est':
return rfl_est
elif out == 'rfl_est,xyz_est':
return rfl_est, xyz_est
else:
return eval(out)
def render_image(img = None, spd = None, rfl = None, out = 'img_hyp', \
refspd = None, D = None, cieobs = _CIEOBS, \
cspace = 'xyz', cspace_tf = {}, CSF = None,\
interp_type = 'nd', k_neighbours = 4, show = True,
verbosity = 0, show_ref_img = True,\
stack_test_ref = 12,\
write_to_file = None):
"""
Render image under specified light source spd.
Args:
:img:
| None or str or ndarray with float (max = 1) rgb image.
| None load a default image.
:spd:
| ndarray, optional
| Light source spectrum for rendering
| If None: use CIE illuminant F4
:rfl:
| ndarray, optional
| Reflectance set for color coordinate to rfl mapping.
:out:
| 'img_hyp' or str, optional
| (other option: 'img_ren': rendered image under :spd:)
:refspd:
| None, optional
| Reference spectrum for color coordinate to rfl mapping.
| None defaults to D65 (srgb has a D65 white point)
:D:
| None, optional
| Degree of (von Kries) adaptation from spd to refspd.
:cieobs:
| _CIEOBS, optional
| CMF set for calculation of xyz from spectral data.
:cspace:
| 'xyz', optional
| Color space for color coordinate to rfl mapping.
| Tip: Use linear space (e.g. 'xyz', 'Yuv',...) for (interp_type == 'nd'),
| and perceptually uniform space (e.g. 'ipt') for (interp_type == 'nearest')
:cspace_tf:
| {}, optional
| Dict with parameters for xyz_to_cspace and cspace_to_xyz transform.
:CSF:
| None, optional
| RGB camera response functions.
| If None: input :xyz: contains raw rgb values. Override :cspace:
| argument and perform estimation directly in raw rgb space!!!
:interp_type:
| 'nd', optional
| Options:
| - 'nd': perform n-dimensional linear interpolation using Delaunay triangulation.
| - 'nearest': perform nearest neighbour interpolation.
:k_neighbours:
| 4 or int, optional
| Number of nearest neighbours for reflectance spectrum interpolation.
| Neighbours are found using scipy.spatial.cKDTree
:show:
| True, optional
| Show images.
:verbosity:
| 0, optional
| If > 0: make a plot of the color coordinates of original and
rendered image pixels.
:show_ref_img:
| True, optional
| True: shows rendered image under reference spd. False: shows
| original image.
:write_to_file:
| None, optional
| None: do nothing, else: write to filename(+path) in :write_to_file:
:stack_test_ref:
| 12, optional
| - 12: left (test), right (ref) format for show and imwrite
| - 21: top (test), bottom (ref)
| - 1: only show/write test
| - 2: only show/write ref
| - 0: show both, write test
Returns:
:returns:
| img_hyp, img_ren,
| ndarrays with float hyperspectral image and rendered images
"""
# Get image:
#imread = lambda x: plt.imread(x) #matplotlib.pyplot
if img is not None:
if isinstance(img, str):
img = plt.imread(img) # use matplotlib.pyplot's imread
else:
img = plt.imread(_HYPSPCIM_DEFAULT_IMAGE)
if isinstance(img, np.uint8):
img = img / 255
elif isinstance(img, np.uint16):
img = img / (2**16 - 1)
# Convert to 2D format:
rgb = img.reshape(img.shape[0] * img.shape[1], 3) # *1.0: make float
rgb[rgb == 0] = _EPS # avoid division by zero for pure blacks.
# Get unique rgb values and positions:
rgb_u, rgb_indices = np.unique(rgb, return_inverse=True, axis=0)
# get rfl set:
if rfl is None: # use IESTM30['4880'] set
rfl = _CRI_RFL['ies-tm30']['4880']['5nm']
wlr = rfl[
0] # spectral reflectance set determines wavelength range for estimation (xyz_to_rfl())
# get Ref spd:
if refspd is None:
refspd = _CIE_ILLUMINANTS['D65'].copy()
refspd = cie_interp(
refspd, wlr,
kind='linear') # force spd to same wavelength range as rfl
# Convert rgb_u to xyz and lab-type values under assumed refspd:
if CSF is None:
xyz_wr = spd_to_xyz(refspd, cieobs=cieobs, relative=True)
xyz_ur = colortf(rgb_u * 255, tf='srgb>xyz')
else:
xyz_ur = rgb_u # for input in xyz_to_rfl (when CSF is not None: this functions assumes input is indeed rgb !!!)
# Estimate rfl's for xyz_ur:
rfl_est, xyzri = xyz_to_rfl(xyz_ur, rfl = rfl, out = 'rfl_est,xyz_est', \
refspd = refspd, D = D, cieobs = cieobs, \
cspace = cspace, cspace_tf = cspace_tf, CSF = CSF,\
interp_type = interp_type, k_neighbours = k_neighbours,
verbosity = verbosity)
# Get default test spd if none supplied:
if spd is None:
spd = _CIE_ILLUMINANTS['F4']
if CSF is None:
# calculate xyz values under test spd:
xyzti, xyztw = spd_to_xyz(spd, rfl=rfl_est, cieobs=cieobs, out=2)
# Chromatic adaptation from test spd to refspd:
if D is not None:
xyzti = cat.apply(xyzti, xyzw1=xyztw, xyzw2=xyz_wr, D=D)
# Convert xyzti under test spd to srgb:
rgbti = colortf(xyzti, tf='srgb') / 255
else:
# Calculate rgb coordinates from camera sensitivity functions under spd:
rgbti = rfl_to_rgb(rfl_est, spd=spd, CSF=CSF, wl=None)
# Chromatic adaptation from test spd to refspd:
if D is not None:
white = np.ones_like(spd)
white[0] = spd[0]
rgbwr = rfl_to_rgb(white, spd=refspd, CSF=CSF, wl=None)
rgbwt = rfl_to_rgb(white, spd=spd, CSF=CSF, wl=None)
rgbti = cat.apply_vonkries2(rgbti,
rgbwt,
rgbwr,
xyzw0=np.array([[1.0, 1.0, 1.0]]),
in_='rgb',
out_='rgb',
D=1)
# Reconstruct original locations for rendered image rgbs:
img_ren = rgbti[rgb_indices]
img_ren.shape = img.shape # reshape back to 3D size of original
img_ren = img_ren
# For output:
if show_ref_img == True:
rgb_ref = colortf(xyzri, tf='srgb') / 255 if (
CSF is None
) else xyzri # if CSF not None: xyzri contains rgbri !!!
img_ref = rgb_ref[rgb_indices]
img_ref.shape = img.shape # reshape back to 3D size of original
img_str = 'Rendered (under ref. spd)'
img = img_ref
else:
img_str = 'Original'
img = img
if (stack_test_ref > 0) | show == True:
if stack_test_ref == 21:
img_original_rendered = np.vstack(
(img_ren, np.ones((4, img.shape[1], 3)), img))
img_original_rendered_str = 'Rendered (under test spd)\n ' + img_str
elif stack_test_ref == 12:
img_original_rendered = np.hstack(
(img_ren, np.ones((img.shape[0], 4, 3)), img))
img_original_rendered_str = 'Rendered (under test spd) | ' + img_str
elif stack_test_ref == 1:
img_original_rendered = img_ren
img_original_rendered_str = 'Rendered (under test spd)'
elif stack_test_ref == 2:
img_original_rendered = img
img_original_rendered_str = img_str
elif stack_test_ref == 0:
img_original_rendered = img_ren
img_original_rendered_str = 'Rendered (under test spd)'
if write_to_file is not None:
# Convert from RGB to BGR formatand write:
#print('Writing rendering results to image file: {}'.format(write_to_file))
with warnings.catch_warnings():
warnings.simplefilter("ignore")
imsave(write_to_file, img_original_rendered)
if show == True:
# show images using pyplot.show():
plt.figure()
plt.imshow(img_original_rendered)
plt.title(img_original_rendered_str)
plt.gca().get_xaxis().set_ticklabels([])
plt.gca().get_yaxis().set_ticklabels([])
if stack_test_ref == 0:
plt.figure()
plt.imshow(img)
plt.title(img_str)
plt.axis('off')
if 'img_hyp' in out.split(','):
# Create hyper_spectral image:
rfl_image_2D = rfl_est[
rgb_indices +
1, :] # create array with all rfls required for each pixel
img_hyp = rfl_image_2D.reshape(img.shape[0], img.shape[1],
rfl_image_2D.shape[1])
# Setup output:
if out == 'img_hyp':
return img_hyp
elif out == 'img_ren':
return img_ren
else:
return eval(out)
def calibration_performance(rgb, xyztarget, M, N, tr, xyz_black, xyz_white, tr_type = 'lut',
cspace='lab', avg = lambda x: ((x**2).mean()**0.5),
rgb_is_xyz = False, is_verification_data = False,
nbit = 8, verbosity = 1, sep = ',', header = None):
"""
Check calibration performance. Calculate DE for each stimulus.
Args:
:rgb:
| ndarray [Nx3] or string with filename of RGB values
| (or xyz values if argument rgb_to_xyz == True!)
:xyztarget:
| ndarray [Nx3] or string with filename of target XYZ values corresponding
| to the RGB settings (or the measured XYZ values, if argument rgb_to_xyz == True).
:M:
| linear rgb to xyz conversion matrix
:N:
| xyz to linear rgb conversion matrix
:tr:
| Tone Response function parameters or lut
:xyz_black:
| ndarray with XYZ tristimulus values of black
:xyz_white:
| ndarray with tristimlus values of white
:tr_type:
| 'lut', optional
| options:
| - 'lut': Derive/specify Tone-Response as a look-up-table
| - 'gog': Derive/specify Tone-Response as a gain-offset-gamma function
:cspace:
| color space or chromaticity diagram to calculate color differences in.
:avg:
| lambda x: ((x**2).mean()**0.5), optional
| Function used to average the color differences of the individual RGB settings
| in the optimization of the xyz_to_rgb and rgb_to_xyz conversion matrices.
:rgb_is_xyz:
| False, optional
| If True: the data in argument rgb are actually measured XYZ tristimulus values
| and are directly compared to the target xyz.
:is_verification_data:
| False, optional
| If False: the data is assumed to be corresponding to RGB value settings used
| in the calibration (i.e. containing whites, blacks, grays, pure and binary mixtures)
| If True: no assumptions on content of rgb, so use this settings when
| checking the performance for a set of measured and target xyz data
| different than the ones used in the actual calibration measurements.
:nbit:
| 8, optional
| RGB values in nbit format (e.g. 8, 16, ...)
:verbosity:
| 1, optional
| > 0: print and plot optimization results
:sep:
| ',', optional
| separator in files with rgbcal and xyzcal data
:header:
| None, optional
| header specifier for files with rgbcal and xyzcal data
| (see pandas.read_csv)
Returns:
:M:
| linear rgb to xyz conversion matrix
:N:
| xyz to linear rgb conversion matrix
:tr:
| Tone Response function parameters or lut
:xyz_black:
| ndarray with XYZ tristimulus values of black
:xyz_white:
| ndarray with tristimlus values of white
"""
# process rgb, xyzcal inputs:
rgb, xyz = _parse_rgbxyz_input(rgb, xyz = xyztarget, sep = sep, header=header)
if rgb_is_xyz == False: # estimate xyz, otherwise assume rgb already contains xyzs
xyzest = rgb_to_xyz(rgb,M,tr, xyz_black, tr_type = tr_type) # convert rgb of all samples to xyz
else:
xyzest = rgb
lab, labest = colortf(xyz,tf=cspace,xyzw=xyz_white), colortf(xyzest,tf=cspace,xyzw=xyz_white) # calculate lab coord. of cal. and est.
DElabi,DEli, DEabi = ((lab-labest)**2).sum(axis=1)**0.5, ((lab[:,:1]-labest[:,:1])**2).sum(axis=1)**0.5, ((lab[:,1:]-labest[:,1:])**2).sum(axis=1)**0.5 # calculate DE of all samples
if verbosity > 0:
print("\nCalibration performance (all colors): \n DE(l*a*b*): avg = {:1.2f}, std = {:1.2f}".format(avg(DElabi),DElabi.std())) # print mean DEl*a*b*
print(" DE(l*) : avg = {:1.2f}, std = {:1.2f}".format(avg(DEli),DEli.std())) # print mean DEl*
print(" DE(a*b*) : avg = {:1.2f}, std = {:1.2f}".format(avg(DEabi),DEabi.std())) # print mean DEa*b*
if is_verification_data == False:
_plot_DEs_vs_digital_values(DElabi, DEli, DEabi, rgb, nbit = nbit, avg = avg, verbosity = verbosity)
_plot_target_vs_predicted_lab(lab, labest, cspace = cspace, verbosity = verbosity)
return DElabi,DEli, DEabi
def xyz_to_rfl(xyz, rfl = None, out = 'rfl_est', \
refspd = None, D = None, cieobs = _CIEOBS, \
cspace = 'ipt', cspace_tf = {},\
k_neighbours = 4, verbosity = 0):
"""
Approximate spectral reflectance of xyz based on k nearest neighbour
interpolation of samples from a standard reflectance set.
Args:
:xyz:
| ndarray with tristimulus values of target points.
:rfl:
| ndarray, optional
| Reflectance set for color coordinate to rfl mapping.
:out:
| 'rfl_est' or str, optional
:refspd:
| None, optional
| Refer ence spectrum for color coordinate to rfl mapping.
| None defaults to D65.
:cieobs:
| _CIEOBS, optional
| CMF set used for calculation of xyz from spectral data.
:cspace:
| 'ipt', optional
| Color space for color coordinate to rfl mapping.
:cspace_tf:
| {}, optional
| Dict with parameters for xyz_to_... and ..._to_xyz transform.
:k_neighbours:
| 4 or int, optional
| Number of nearest neighbours for reflectance spectrum interpolation.
| Neighbours are found using scipy.cKDTree
:verbosity:
| 0, optional
| If > 0: make a plot of the color coordinates of original and
rendered image pixels.
Returns:
:returns:
| :rfl_est:
| ndarrays with estimated reflectance spectra.
"""
# get rfl set:
if rfl is None: # use IESTM30['4880'] set
rfl = _CRI_RFL['ies-tm30']['4880']['5nm']
# get Ref spd:
if refspd is None:
refspd = _CIE_ILLUMINANTS['D65'].copy()
# Calculate lab-type coordinates of standard rfl set under refspd:
xyz_rr, xyz_wr = spd_to_xyz(refspd,
relative=True,
rfl=rfl,
cieobs=cieobs,
out=2)
cspace_tf_copy = cspace_tf.copy()
cspace_tf_copy['xyzw'] = xyz_wr # put correct white point in param. dict
lab_rr = colortf(xyz_rr,
tf=cspace,
fwtf=cspace_tf_copy,
bwtf=cspace_tf_copy)[:, 0, :]
# Convert xyz to lab-type values under refspd:
lab = colortf(xyz, tf=cspace, fwtf=cspace_tf_copy, bwtf=cspace_tf_copy)
# Find rfl (cfr. lab_rr) from rfl set that results in 'near' metameric
# color coordinates for each value in lab_ur (i.e. smallest DE):
# Construct cKDTree:
tree = cKDTree(lab_rr, copy_data=True)
# Interpolate rfls using k nearest neightbours and inverse distance weigthing:
d, inds = tree.query(lab, k=k_neighbours)
if k_neighbours > 1:
w = (1.0 / d**2)[:, :, None] # inverse distance weigthing
rfl_est = np.sum(w * rfl[inds + 1, :], axis=1) / np.sum(w, axis=1)
else:
rfl_est = rfl[inds + 1, :].copy()
rfl_est = np.vstack((rfl[0], rfl_est))
if (verbosity > 0) | ('xyz_est' in out.split(',')) | (
'lab_est' in out.split(',')) | ('DEi_ab' in out.split(',')) | (
'DEa_ab' in out.split(',')):
xyz_est, _ = spd_to_xyz(refspd,
rfl=rfl_est,
relative=True,
cieobs=cieobs,
out=2)
cspace_tf_copy = cspace_tf.copy()
cspace_tf_copy[
'xyzw'] = xyz_wr # put correct white point in param. dict
lab_est = colortf(xyz_est, tf=cspace, fwtf=cspace_tf_copy)[:, 0, :]
DEi_ab = np.sqrt(((lab_est[:, 1:3] - lab[:, 1:3])**2).sum(axis=1))
DEa_ab = DEi_ab.mean()
if verbosity > 0:
ax = plot_color_data(lab[...,1], lab[...,2], z = lab[...,0], \
show = False, cieobs = cieobs, cspace = cspace, \
formatstr = 'ro', label = 'Original')
plot_color_data(lab_est[...,1], lab_est[...,2], z = lab_est[...,0], \
show = True, axh = ax, cieobs = cieobs, cspace = cspace, \
formatstr = 'bd', label = 'Rendered')
if out == 'rfl_est':
return rfl_est
elif out == 'rfl_est,xyz_est':
return rfl_est, xyz_est
else:
return eval(out)
def xyz_to_rfl(xyz, rfl = None, out = 'rfl_est', \
refspd = None, D = None, cieobs = _CIEOBS, \
cspace = 'xyz', cspace_tf = {},\
interp_type = 'nd', k_neighbours = 4, verbosity = 0):
"""
Approximate spectral reflectance of xyz based on nd-dimensional linear interpolation
or k nearest neighbour interpolation of samples from a standard reflectance set.
Args:
:xyz:
| ndarray with tristimulus values of target points.
:rfl:
| ndarray, optional
| Reflectance set for color coordinate to rfl mapping.
:out:
| 'rfl_est' or str, optional
:refspd:
| None, optional
| Refer ence spectrum for color coordinate to rfl mapping.
| None defaults to D65.
:cieobs:
| _CIEOBS, optional
| CMF set used for calculation of xyz from spectral data.
:cspace:
| 'xyz', optional
| Color space for color coordinate to rfl mapping.
| Tip: Use linear space (e.g. 'xyz', 'Yuv',...) for (interp_type == 'nd'),
| and perceptually uniform space (e.g. 'ipt') for (interp_type == 'nearest')
:cspace_tf:
| {}, optional
| Dict with parameters for xyz_to_cspace and cspace_to_xyz transform.
:interp_type:
| 'nd', optional
| Options:
| - 'nd': perform n-dimensional linear interpolation using Delaunay triangulation.
| - 'nearest': perform nearest neighbour interpolation.
:k_neighbours:
| 4 or int, optional
| Number of nearest neighbours for reflectance spectrum interpolation.
| Neighbours are found using scipy.spatial.cKDTree
:verbosity:
| 0, optional
| If > 0: make a plot of the color coordinates of original and
| rendered image pixels.
Returns:
:returns:
| :rfl_est:
| ndarrays with estimated reflectance spectra.
"""
# get rfl set:
if rfl is None: # use IESTM30['4880'] set
rfl = _CRI_RFL['ies-tm30']['4880']['5nm']
# get Ref spd:
if refspd is None:
refspd = _CIE_ILLUMINANTS['D65'].copy()
# Calculate lab-type coordinates of standard rfl set under refspd:
xyz_rr, xyz_wr = spd_to_xyz(refspd,
relative=True,
rfl=rfl,
cieobs=cieobs,
out=2)
cspace_tf_copy = cspace_tf.copy()
cspace_tf_copy['xyzw'] = xyz_wr # put correct white point in param. dict
lab_rr = colortf(xyz_rr,
tf=cspace,
fwtf=cspace_tf_copy,
bwtf=cspace_tf_copy)[:, 0, :]
# Convert xyz to lab-type values under refspd:
lab = colortf(xyz, tf=cspace, fwtf=cspace_tf_copy, bwtf=cspace_tf_copy)
if interp_type == 'nearest':
# Find rfl (cfr. lab_rr) from rfl set that results in 'near' metameric
# color coordinates for each value in lab_ur (i.e. smallest DE):
# Construct cKDTree:
tree = sp.spatial.cKDTree(lab_rr, copy_data=True)
# Interpolate rfls using k nearest neightbours and inverse distance weigthing:
d, inds = tree.query(lab, k=k_neighbours)
if k_neighbours > 1:
d += _EPS
w = (1.0 / d**2)[:, :, None] # inverse distance weigthing
rfl_est = np.sum(w * rfl[inds + 1, :], axis=1) / np.sum(w, axis=1)
else:
rfl_est = rfl[inds + 1, :].copy()
elif interp_type == 'nd':
rfl_est = math.ndinterp1_scipy(lab_rr, rfl[1:], lab)
_isnan = np.isnan(rfl_est[:, 0])
if (
_isnan.any()
): #do nearest neigbour method for those that fail using Delaunay (i.e. ndinterp1_scipy)
# Find rfl (cfr. lab_rr) from rfl set that results in 'near' metameric
# color coordinates for each value in lab_ur (i.e. smallest DE):
# Construct cKDTree:
tree = sp.spatial.cKDTree(lab_rr, copy_data=True)
# Interpolate rfls using k nearest neightbours and inverse distance weigthing:
d, inds = tree.query(lab[_isnan, ...], k=k_neighbours)
if k_neighbours > 1:
d += _EPS
w = (1.0 / d**2)[:, :, None] # inverse distance weigthing
rfl_est_isnan = np.sum(w * rfl[inds + 1, :], axis=1) / np.sum(
w, axis=1)
else:
rfl_est_isnan = rfl[inds + 1, :].copy()
rfl_est[_isnan, :] = rfl_est_isnan
else:
raise Exception('xyz_to_rfl(): unsupported interp_type!')
rfl_est[
rfl_est <
0] = 0 #can occur for points outside convexhull of standard rfl set.
rfl_est = np.vstack((rfl[0], rfl_est))
if (verbosity > 0) | ('xyz_est' in out.split(',')) | (
'lab_est' in out.split(',')) | ('DEi_ab' in out.split(',')) | (
'DEa_ab' in out.split(',')):
xyz_est, _ = spd_to_xyz(refspd,
rfl=rfl_est,
relative=True,
cieobs=cieobs,
out=2)
cspace_tf_copy = cspace_tf.copy()
cspace_tf_copy[
'xyzw'] = xyz_wr # put correct white point in param. dict
lab_est = colortf(xyz_est, tf=cspace, fwtf=cspace_tf_copy)[:, 0, :]
DEi_ab = np.sqrt(((lab_est[:, 1:3] - lab[:, 1:3])**2).sum(axis=1))
DEa_ab = DEi_ab.mean()
if verbosity > 0:
ax = plot_color_data(lab[...,1], lab[...,2], z = lab[...,0], \
show = False, cieobs = cieobs, cspace = cspace, \
formatstr = 'ro', label = 'Original')
plot_color_data(lab_est[...,1], lab_est[...,2], z = lab_est[...,0], \
show = True, axh = ax, cieobs = cieobs, cspace = cspace, \
formatstr = 'bd', label = 'Rendered')
if out == 'rfl_est':
return rfl_est
elif out == 'rfl_est,xyz_est':
return rfl_est, xyz_est
else:
return eval(out)
def plotellipse(v, cspace_in = 'Yxy', cspace_out = None, nsamples = 100, \
show = True, axh = None, \
line_color = 'darkgray', line_style = ':', line_width = 1, line_marker = '', line_markersize = 4,\
plot_center = False, center_marker = 'o', center_color = 'darkgray', center_markersize = 4,\
show_grid = False, llabel = '', label_fontname = 'Times New Roman', label_fontsize = 12,\
out = None):
"""
Plot ellipse(s) given in v-format [Rmax,Rmin,xc,yc,theta].
Args:
:v:
| (Nx5) ndarray
| ellipse parameters [Rmax,Rmin,xc,yc,theta]
:cspace_in:
| 'Yxy', optional
| Color space of v.
| If None: no color space assumed. Axis labels assumed ('x','y').
:cspace_out:
| None, optional
| Color space to plot ellipse(s) in.
| If None: plot in cspace_in.
:nsamples:
| 100 or int, optional
| Number of points (samples) in ellipse boundary
:show:
| True or boolean, optional
| Plot ellipse(s) (True) or not (False)
:axh:
| None, optional
| Ax-handle to plot ellipse(s) in.
| If None: create new figure with axes.
:line_color:
| 'darkgray', optional
| Color to plot ellipse(s) in.
:line_style:
| ':', optional
| Linestyle of ellipse(s).
:line_width':
| 1, optional
| Width of ellipse boundary line.
:line_marker:
| 'none', optional
| Marker for ellipse boundary.
:line_markersize:
| 4, optional
| Size of markers in ellipse boundary.
:plot_center:
| False, optional
| Plot center of ellipse: yes (True) or no (False)
:center_color:
| 'darkgray', optional
| Color to plot ellipse center in.
:center_marker:
| 'o', optional
| Marker for ellipse center.
:center_markersize:
| 4, optional
| Size of marker of ellipse center.
:show_grid:
| False, optional
| Show grid (True) or not (False)
:llabel:
| None,optional
| Legend label for ellipse boundary.
:label_fontname:
| 'Times New Roman', optional
| Sets font type of axis labels.
:label_fontsize:
| 12, optional
| Sets font size of axis labels.
:out:
| None, optional
| Output of function
| If None: returns None. Can be used to output axh of newly created
| figure axes or to return Yxys an ndarray with coordinates of
| ellipse boundaries in cspace_out (shape = (nsamples,3,N))
Returns:
:returns: None, or whatever set by :out:.
"""
Yxys = np.zeros((nsamples, 3, v.shape[0]))
ellipse_vs = np.zeros((v.shape[0], 5))
for i, vi in enumerate(v):
# Set sample density of ellipse boundary:
t = np.linspace(0, 2 * np.pi, int(nsamples))
a = vi[0] # major axis
b = vi[1] # minor axis
xyc = vi[2:4, None] # center
theta = vi[-1] # rotation angle
# define rotation matrix:
R = np.hstack((np.vstack((np.cos(theta), np.sin(theta))),
np.vstack((-np.sin(theta), np.cos(theta)))))
# Calculate ellipses:
Yxyc = np.vstack((1, xyc)).T
Yxy = np.vstack(
(np.ones((1, nsamples)),
xyc + np.dot(R, np.vstack((a * np.cos(t), b * np.sin(t)))))).T
Yxys[:, :, i] = Yxy
# Convert to requested color space:
if (cspace_out is not None) & (cspace_in is not None):
Yxy = colortf(Yxy, cspace_in + '>' + cspace_out)
Yxyc = colortf(Yxyc, cspace_in + '>' + cspace_out)
Yxys[:, :, i] = Yxy
# get ellipse parameters in requested color space:
ellipse_vs[i, :] = math.fit_ellipse(Yxy[:, 1:])
#de = np.sqrt((Yxy[:,1]-Yxyc[:,1])**2 + (Yxy[:,2]-Yxyc[:,2])**2)
#ellipse_vs[i,:] = np.hstack((de.max(),de.min(),Yxyc[:,1],Yxyc[:,2],np.nan)) # nan because orientation is xy, but request is some other color space. Change later to actual angle when fitellipse() has been implemented
# plot ellipses:
if show == True:
if (axh is None) & (i == 0):
fig = plt.figure()
axh = fig.add_subplot(111)
if (cspace_in is None):
xlabel = 'x'
ylabel = 'y'
else:
xlabel = _CSPACE_AXES[cspace_in][1]
ylabel = _CSPACE_AXES[cspace_in][2]
if (cspace_out is not None):
xlabel = _CSPACE_AXES[cspace_out][1]
ylabel = _CSPACE_AXES[cspace_out][2]
if plot_center == True:
axh.plot(Yxyc[:, 1],
Yxyc[:, 2],
color=center_color,
linestyle='none',
marker=center_marker,
markersize=center_markersize)
if llabel is None:
axh.plot(Yxy[:, 1],
Yxy[:, 2],
color=line_color,
linestyle=line_style,
linewidth=line_width,
marker=line_marker,
markersize=line_markersize)
else:
axh.plot(Yxy[:, 1],
Yxy[:, 2],
color=line_color,
linestyle=line_style,
linewidth=line_width,
marker=line_marker,
markersize=line_markersize,
label=llabel)
axh.set_xlabel(xlabel,
fontname=label_fontname,
fontsize=label_fontsize)
axh.set_ylabel(ylabel,
fontname=label_fontname,
fontsize=label_fontsize)
if show_grid == True:
plt.grid(True)
#plt.show()
Yxys = np.transpose(Yxys, axes=(0, 2, 1))
if out is not None:
return eval(out)
else:
return None
def plotUH(xyz0=None,
uhues=[0, 1, 2, 3],
cieobs=_CIEOBS,
cspace=_CSPACE,
axh=None,
formatstr=['yo-.', 'bo-.', 'ro-.', 'go-.'],
excludefromlegend='',
cspace_pars={}):
"""
Plot unique hue lines from color space center point xyz0.
| Kuehni, CRA, 2014:
| uY,uB,uG: Table II: spectral lights;
| uR: Table IV: Xiao data.
Args:
:xyz0:
| None, optional
| Center of color space (unique hue lines are expected to cross here)
| None defaults to equi-energy-white.
:uhues:
| [0,1,2,3], optional
| Unique hue lines to plot [0:'yellow',1:'blue',2:'red',3:'green']
:axh:
| None or axes handle, optional
| Determines axes to plot data in.
| None: make new figure.
:cieobs:
| luxpy._CIEOBS or str, optional
| Determines CMF set to calculate spectrum locus or other.
:cspace:
| luxpy._CSPACE or str, optional
| Determines color space / chromaticity diagram to plot data in.
| Note that data is expected to be in specified :cspace:
:formatstr:
| ['yo-.','bo-.','ro-.','go-.'] or list[str], optional
| Format str for plotting the different unique lines
| (see also ?matplotlib.pyplot.plot)
:excludefromlegend:
| '' or str, optional
| To exclude certain hues from axes legend.
:cspace_pars:
| {} or dict, optional
| Dict with parameters required by color space specified in :cspace:
| (for use with luxpy.colortf())
Returns:
:returns:
| list[handles] to unique hue lines
References:
1. `Kuehni, R. G. (2014).
Unique hues and their stimuli—state of the art.
Color Research & Application, 39(3), 279–287.
<https://doi.org/10.1002/col.21793>`_
(see Table II, IV)
"""
hues = ['yellow', 'blue', 'red', 'green']
if isinstance(cieobs, str):
cmf = _CMF[cieobs]['bar']
else:
cmf = cieobs
p_y = cmf[
0] == 577.0 #unique yellow,#Kuehni, CRA 2013 (mean, table IV: spectral data)
p_b = cmf[
0] == 472.0 #unique blue,Kuehni, CRA 2013 (mean, table IV: spectral data)
p_g = cmf[
0] == 514.0 #unique green, Kuehni, CRA 2013 (mean, table II: spectral data)
p_r = cmf[
0] == 650.0 #unique red, Kuehni, CRA 2013 (Xiao data, table IV: display data)
xyz_y = 100.0 * cmf[1:, p_y].T
xyz_b = 100.0 * cmf[1:, p_b].T
xyz_g = 100.0 * cmf[1:, p_g].T
xyz_r = 100.0 * cmf[1:, p_r].T
xyz_uh = np.vstack((xyz_y, xyz_b, xyz_r, xyz_g))
huniquehues = []
if xyz0 is None:
xyz0 = np.array([100.0, 100.0, 100.0])
if axh is None:
axh = plt.gca()
for huenr in uhues:
lab = colortf(np.vstack((xyz0, xyz_uh[huenr])),
tf=cspace,
tfa0=cspace_pars)
huh = axh.plot(lab[:, 1],
lab[:, 2],
formatstr[huenr],
label=excludefromlegend + 'Unique ' + hues[huenr])
huniquehues = [huniquehues, huh]
return huniquehues
def plotSL(cieobs =_CIEOBS, cspace = _CSPACE, DL = False, BBL = True, D65 = False,\
EEW = False, cctlabels = False, axh = None, show = True,\
cspace_pars = {}, formatstr = 'k-',\
diagram_colors = False, diagram_samples = 100, diagram_opacity = 1.0,\
diagram_lightness = 0.25,\
**kwargs):
"""
Plot spectrum locus for cieobs in cspace.
Args:
:DL:
| True or False, optional
| True plots Daylight Locus as well.
:BBL:
| True or False, optional
| True plots BlackBody Locus as well.
:D65:
| False or True, optional
| True plots D65 chromaticity as well.
:EEW:
| False or True, optional
| True plots Equi-Energy-White chromaticity as well.
:cctlabels:
| False or True, optional
| Add cct text labels at various points along the blackbody locus.
:axh:
| None or axes handle, optional
| Determines axes to plot data in.
| None: make new figure.
:show:
| True or False, optional
| Invoke matplotlib.pyplot.show() right after plotting
:cieobs:
| luxpy._CIEOBS or str, optional
| Determines CMF set to calculate spectrum locus or other.
:cspace:
| luxpy._CSPACE or str, optional
| Determines color space / chromaticity diagram to plot data in.
| Note that data is expected to be in specified :cspace:
:formatstr:
| 'k-' or str, optional
| Format str for plotting (see ?matplotlib.pyplot.plot)
:cspace_pars:
| {} or dict, optional
| Dict with parameters required by color space specified in :cspace:
| (for use with luxpy.colortf())
:diagram_colors:
| False, optional
| True: plot colored chromaticity diagram.
:diagram_samples:
| 256, optional
| Sampling resolution of color space.
:diagram_opacity:
| 1.0, optional
| Sets opacity of chromaticity diagram
:diagram_lightness:
| 0.25, optional
| Sets lightness of chromaticity diagram
:kwargs:
| additional keyword arguments for use with matplotlib.pyplot.
Returns:
:returns:
| None (:show: == True)
| or
| handle to current axes (:show: == False)
"""
if isinstance(cieobs, str):
SL = _CMF[cieobs]['bar'][1:4].T
else:
SL = cieobs[1:4].T
SL = 100.0 * SL / (SL[:, 1, None] + _EPS)
SL = SL[SL.sum(axis=1) >
0, :] # avoid div by zero in xyz-to-Yxy conversion
SL = colortf(SL, tf=cspace, tfa0=cspace_pars)
plambdamax = SL[:, 1].argmax()
SL = np.vstack(
(SL[:(plambdamax + 1), :], SL[0])
) # add lowest wavelength data and go to max of gamut in x (there is a reversal for some cmf set wavelengths >~700 nm!)
Y, x, y = asplit(SL)
showcopy = show
if np.any([DL, BBL, D65, EEW]):
show = False
if diagram_colors == True:
axh_ = plot_chromaticity_diagram_colors(axh = axh, show = diagram_colors, cspace = cspace, cieobs = cieobs,\
cspace_pars = cspace_pars,\
diagram_samples = diagram_samples,\
diagram_opacity = diagram_opacity,\
diagram_lightness = diagram_lightness,\
label_fontname = None, label_fontsize = None)
else:
axh_ = axh
axh_ = plot_color_data(x,
y,
axh=axh_,
cieobs=cieobs,
cspace=cspace,
show=show,
formatstr=formatstr,
**kwargs)
if DL == True:
if 'label' in kwargs.keys(): # avoid label also being used for DL
kwargs.pop('label')
plotDL(ccts=None,
cieobs=cieobs,
cspace=cspace,
axh=axh_,
show=show,
cspace_pars=cspace_pars,
formatstr='k:',
**kwargs)
if BBL == True:
if 'label' in kwargs.keys(): # avoid label also being used for BB
kwargs.pop('label')
plotBB(ccts=None,
cieobs=cieobs,
cspace=cspace,
axh=axh_,
show=show,
cspace_pars=cspace_pars,
cctlabels=cctlabels,
formatstr='k-.',
**kwargs)
if D65 == True:
YxyD65 = colortf(spd_to_xyz(_CIE_ILLUMINANTS['D65'], cieobs=cieobs),
tf=cspace,
tfa0=cspace_pars)
axh.plot(YxyD65[..., 1], YxyD65[..., 2], 'bo')
if EEW == True:
YxyEEW = colortf(spd_to_xyz(_CIE_ILLUMINANTS['E'], cieobs=cieobs),
tf=cspace,
tfa0=cspace_pars)
axh.plot(YxyEEW[..., 1], YxyEEW[..., 2], 'ko')
if showcopy == False:
return axh_
else:
plt.show()
def plotBB(ccts=None,
cieobs=_CIEOBS,
cspace=_CSPACE,
axh=None,
cctlabels=True,
show=True,
cspace_pars={},
formatstr='k-',
**kwargs):
"""
Plot blackbody locus.
Args:
:ccts:
| None or list[float], optional
| None defaults to [1000 to 1e19 K].
| Range:
| [1000,1500,2000,2500,3000,3500,4000,5000,6000,8000,10000]
| + [15000 K to 1e19 K] in 100 steps on a log10 scale
:cctlabels:
| True or False, optional
| Add cct text labels at various points along the blackbody locus.
:axh:
| None or axes handle, optional
| Determines axes to plot data in.
| None: make new figure.
:show:
| True or False, optional
| Invoke matplotlib.pyplot.show() right after plotting
:cieobs:
| luxpy._CIEOBS or str, optional
| Determines CMF set to calculate spectrum locus or other.
:cspace:
| luxpy._CSPACE or str, optional
| Determines color space / chromaticity diagram to plot data in.
| Note that data is expected to be in specified :cspace:
:formatstr:
| 'k-' or str, optional
| Format str for plotting (see ?matplotlib.pyplot.plot)
:cspace_pars:
| {} or dict, optional
| Dict with parameters required by color space specified in :cspace:
| (for use with luxpy.colortf())
:kwargs:
| additional keyword arguments for use with matplotlib.pyplot.
Returns:
:returns:
| None (:show: == True)
| or
| handle to current axes (:show: == False)
"""
if ccts is None:
ccts1 = np.array([
1000.0, 1500.0, 2000.0, 2500.0, 3000.0, 3500.0, 4000.0, 5000.0,
6000.0, 8000.0, 10000.0
])
ccts2 = 10**np.linspace(np.log10(15000.0), np.log10(10.0**19.0), 100)
ccts = np.hstack((ccts1, ccts2))
else:
ccts1 = None
BB = cri_ref(ccts, ref_type='BB')
xyz = spd_to_xyz(BB, cieobs=cieobs)
Yxy = colortf(xyz, tf=cspace, tfa0=cspace_pars)
Y, x, y = asplit(Yxy)
axh = plot_color_data(x,
y,
axh=axh,
cieobs=cieobs,
cspace=cspace,
show=show,
formatstr=formatstr,
**kwargs)
if (cctlabels == True) & (ccts1 is not None):
for i in range(ccts1.shape[0]):
if ccts1[i] >= 3000.0:
if i % 2 == 0.0:
axh.plot(x[i], y[i], 'k+', color='0.5')
axh.text(x[i] * 1.05,
y[i] * 0.95,
'{:1.0f}K'.format(ccts1[i]),
color='0.5')
axh.plot(x[-1], y[-1], 'k+', color='0.5')
axh.text(x[-1] * 1.05,
y[-1] * 0.95,
'{:1.0e}K'.format(ccts[-1]),
color='0.5')
if show == False:
return axh
def plot_chromaticity_diagram_colors(diagram_samples = 256, diagram_opacity = 1.0, diagram_lightness = 0.25,\
cieobs = _CIEOBS, cspace = 'Yxy', cspace_pars = {},\
show = True, axh = None,\
show_grid = True, label_fontname = 'Times New Roman', label_fontsize = 12,\
**kwargs):
"""
Plot the chromaticity diagram colors.
Args:
:diagram_samples:
| 256, optional
| Sampling resolution of color space.
:diagram_opacity:
| 1.0, optional
| Sets opacity of chromaticity diagram
:diagram_lightness:
| 0.25, optional
| Sets lightness of chromaticity diagram
:axh:
| None or axes handle, optional
| Determines axes to plot data in.
| None: make new figure.
:show:
| True or False, optional
| Invoke matplotlib.pyplot.show() right after plotting
:cieobs:
| luxpy._CIEOBS or str, optional
| Determines CMF set to calculate spectrum locus or other.
:cspace:
| luxpy._CSPACE or str, optional
| Determines color space / chromaticity diagram to plot data in.
| Note that data is expected to be in specified :cspace:
:cspace_pars:
| {} or dict, optional
| Dict with parameters required by color space specified in :cspace:
| (for use with luxpy.colortf())
:show_grid:
| True, optional
| Show grid (True) or not (False)
:label_fontname:
| 'Times New Roman', optional
| Sets font type of axis labels.
:label_fontsize:
| 12, optional
| Sets font size of axis labels.
:kwargs:
| additional keyword arguments for use with matplotlib.pyplot.
Returns:
"""
offset = _EPS
ii, jj = np.meshgrid(np.linspace(offset, 1 + offset, diagram_samples), np.linspace(1+offset, offset, diagram_samples))
ij = np.dstack((ii, jj))
SL = _CMF[cieobs]['bar'][1:4].T
SL = np.vstack((SL,SL[0]))
SL = 100.0*SL/SL[:,1,None]
SL = colortf(SL, tf = cspace, tfa0 = cspace_pars)
Y,x,y = asplit(SL)
SL = np.vstack((x,y)).T
ij2D = ij.reshape((diagram_samples**2,2))
ij2D = np.hstack((diagram_lightness*100*np.ones((ij2D.shape[0],1)), ij2D))
xyz = colortf(ij2D, tf = cspace + '>xyz', tfa0 = cspace_pars)
xyz[xyz < 0] = 0
xyz[np.isinf(xyz.sum(axis=1)),:] = np.nan
xyz[np.isnan(xyz.sum(axis=1)),:] = offset
srgb = xyz_to_srgb(xyz)
srgb = srgb/srgb.max()
srgb = srgb.reshape((diagram_samples,diagram_samples,3))
if show == True:
if axh is None:
fig = plt.figure()
axh = fig.add_subplot(111)
polygon = Polygon(SL, facecolor='none', edgecolor='none')
axh.add_patch(polygon)
image = axh.imshow(
srgb,
interpolation='bilinear',
extent = (0.0, 1, -0.05, 1),
clip_path=None,
alpha=diagram_opacity)
image.set_clip_path(polygon)
plt.plot(x,y, color = 'darkgray')
if cspace == 'Yxy':
plt.xlim([0,1])
plt.ylim([0,1])
elif cspace == 'Yuv':
plt.xlim([0,0.6])
plt.ylim([0,0.6])
if (cspace is not None):
xlabel = _CSPACE_AXES[cspace][1]
ylabel = _CSPACE_AXES[cspace][2]
if (label_fontname is not None) & (label_fontsize is not None):
plt.xlabel(xlabel, fontname = label_fontname, fontsize = label_fontsize)
plt.ylabel(ylabel, fontname = label_fontname, fontsize = label_fontsize)
if show_grid == True:
plt.grid()
#plt.show()
return axh
else:
return None
def subsample_RFL_set(rfl, rflpath = '', samplefcn = 'rand', S = _CIE_ILLUMINANTS['E'], \
jab_ranges = None, jab_deltas = None, cieobs = _VF_CIEOBS, cspace = _VF_CSPACE, \
ax = np.arange(-_VF_MAXR,_VF_MAXR+_VF_DELTAR,_VF_DELTAR), \
bx = np.arange(-_VF_MAXR,_VF_MAXR+_VF_DELTAR,_VF_DELTAR), \
jx = None, limit_grid_radius = 0):
"""
Sub-samples a spectral reflectance set by pixelization of color space.
Args:
:rfl:
| ndarray or str
| Array with of str referring to a set of spectral reflectance
functions to be subsampled.
| If str to file: file must contain data as columns, with first
column the wavelengths.
:rflpath:
| '' or str, optional
| Path to folder with rfl-set specified in a str :rfl: filename.
:samplefcn:
| 'rand' or 'mean', optional
| -'rand': selects a random sample from the samples within each pixel
| -'mean': returns the mean spectral reflectance in each pixel.
:S:
| _CIE_ILLUMINANTS['E'], optional
| Illuminant used to calculate the color coordinates of the spectral
reflectance samples.
:jab_ranges:
| None or ndarray, optional
| Specifies the pixelization of color space.
(ndarray.shape = (3,3), with first axis: J,a,b, and second
axis: min, max, delta)
:jab_deltas:
| float or ndarray, optional
| Specifies the sampling range.
| A float uses jab_deltas as the maximum Euclidean distance to select
samples around each pixel center. A ndarray of 3 deltas, uses
a city block sampling around each pixel center.
:cspace:
| _VF_CSPACE or dict, optional
| Specifies color space. See _VF_CSPACE_EXAMPLE for example structure.
:cieobs:
| _VF_CIEOBS or str, optional
| Specifies CMF set used to calculate color coordinates.
:ax:
| default ndarray or user defined ndarray, optional
| default = np.arange(-_VF_MAXR,_VF_MAXR+_VF_DELTAR,_VF_DELTAR)
:bx:
| default ndarray or user defined ndarray, optional
| default = np.arange(-_VF_MAXR,_VF_MAXR+_VF_DELTAR,_VF_DELTAR)
:jx:
| None, optional
| Note that not-None :jab_ranges: override :ax:, :bx: and :jx input.
:limit_grid_radius:
| 0, optional
| A value of zeros keeps grid as specified by axr,bxr.
| A value > 0 only keeps (a,b) coordinates within :limit_grid_radius:
Returns:
:returns:
| rflsampled, jabp
| ndarrays with resp. the subsampled set of spectral reflectance
functions and the pixel coordinate centers.
"""
# Testing effects of sample set, pixel size and gamut size:
if type(rfl) == str:
rfl = pd.read_csv(os.path.join(rflpath,rfl),header = None).get_values().T
# Calculate Jab coordinates of samples:
xyz,xyzw = spd_to_xyz(S, cieobs = cieobs, rfl = rfl.copy(), out = 2)
cspace_pars = cspace.copy()
cspace_pars.pop('type')
cspace_pars['xyzw'] = xyzw
jab = colortf(xyz,tf = cspace['type'],fwtf = cspace_pars)
# Generate grid and get samples in each grid:
gridp,idxp, jabp, pixelsamplenrs, pixelIDs = get_pixel_coordinates(jab, jab_ranges = jab_ranges, jab_deltas = jab_deltas, limit_grid_radius = limit_grid_radius)
# Get rfls from set using sampling function (mean or rand):
W = rfl[:1]
R = rfl[1:]
rflsampled = np.nan*np.ones((len(idxp),R.shape[1]))
for i in range(len(idxp)):
if samplefcn == 'mean':
rfl_i = np.nanmean(rfl[pixelsamplenrs[i],:],axis = 0)
else:
samplenr_i = np.random.randint(len(pixelsamplenrs[i]))
rfl_i = rfl[pixelsamplenrs[i][samplenr_i],:]
rflsampled[i,:] = rfl_i
rflsampled = np.vstack((W,rflsampled))
return rflsampled, jabp
def DE_cspace(xyzt, xyzr, dtype = 'xyz', tf = _CSPACE, DEtype = 'jab', avg = None, avg_axis = 0, out = 'DEi',
xyzwt = None, xyzwr = None, fwtft = {}, fwtfr = {}, KLCH = None,\
camtype = cam._CAM_DEFAULT_TYPE, ucstype = 'ucs'):
"""
Calculate color difference DE in specific color space.
Args:
:xyzt:
| ndarray with tristimulus values of test data.
:xyzr:
| ndarray with tristimulus values of reference data.
:dtype:
| 'xyz' or 'jab', optional
| Specifies data type in :xyzt: and :xyzr:.
:xyzwt:
| None or ndarray, optional
| White point tristimulus values of test data
| None defaults to the one set in :fwtft:
| or else to the default of cspace.
:xyzwr:
| None or ndarray, optional
| Whitepoint tristimulus values of reference data
| None defaults to the one set in non-empty :fwtfr:
| or else to default of cspace.
:tf:
| _CSPACE, optional
| Color space to use for color difference calculation.
:fwtft:
| {}, optional
| Dict with parameters for forward transform
from xyz to cspace for test data.
:fwtfr:
| {}, optional
| Dict with parameters for forward transform
| from xyz to cspace for reference data.
:KLCH:
| None, optional
| Weigths for L, C, H
| None: default to [1,1,1]
| KLCH is not used when tf == 'camucs'.
:DEtype:
| 'jab' or str, optional
| Options:
| - 'jab' : calculates full color difference over all 3 dimensions.
| - 'ab' : calculates chromaticity difference.
| - 'j' : calculates lightness or brightness difference
| (depending on :outin:).
| - 'j,ab': calculates both 'j' and 'ab' options
| and returns them as a tuple.
:avg:
| None, optional
| None: don't calculate average DE,
| otherwise use function handle in :avg:.
:avg_axis:
| axis to calculate average over, optional
:out:
| 'DEi' or str, optional
| Requested output.
:camtype:
| luxpy.cam._CAM_DEFAULT_TYPE, optional
| Str specifier for CAM type to use, options: 'ciecam02' or 'ciecam16'.
| Only when DEtype == 'camucs'.
:ucstype:
| 'ucs' or 'lcd' or 'scd', optional
| Str specifier for which type of color attribute compression
| parameters to use:
| -'ucs': uniform color space,
| -'lcd', large color differences,
| -'scd': small color differences
| Only when DEtype == 'camucs'.
Note:
For the other input arguments, see specific color space used.
Returns:
:returns:
| ndarray with DEi [, DEa] or other as specified by :out:
"""
# Get xyzw from dict if xyzw is None & dict is Not None
if xyzwr is not None:
fwtfr['xyzw'] = xyzwr
else:
if bool(fwtfr):
xyzwr = fwtfr['xyzw']
if xyzwt is not None:
fwtft['xyzw'] = xyzwt
else:
if bool(fwtft):
xyzwt = fwtft['xyzw']
if tf == 'camucs':
if dtype == 'xyz':
if fwtfr['xyzw'] is None:
fwtfr['xyzw'] = cam._CAM_DEFAULT_WHITE_POINT
if fwtft['xyzw'] is None:
fwtft['xyzw'] = cam._CAM_DEFAULT_WHITE_POINT
jabt = cam.camXucs(xyzt, camtype=camtype, ucstype=ucstype, **fwtft)
jabr = cam.camXucs(xyzr, camtype=camtype, ucstype=ucstype, **fwtfr)
else:
jabt = xyzt
jabr = xyzr
KL, c1, c2 = [
cam._CAM_UCS_PARAMETERS[camtype][ucstype][x]
for x in sorted(cam._CAM_UCS_PARAMETERS[camtype][ucstype].keys())
]
# Calculate color difference and take account of KL:
DEi = ((((jabt[...,0:1]-jabr[...,0:1])/KL)**2).sum(axis = jabt[...,0:1].ndim - 1, keepdims = True),\
((jabt[...,1:3]-jabr[...,1:3])**2).sum(axis = jabt[...,1:3].ndim - 1, keepdims = True))
elif (tf == 'DE2000') | (tf == 'DE00'):
return DE2000(xyzt, xyzr, dtype = 'xyz', DEtype = DEtype, avg = avg,\
avg_axis = avg_axis, out = out,
xyzwt = xyzwt, xyzwr = xyzwr, KLCH = KLCH)
else:
if dtype == 'xyz':
# Use colortf:
jabt = colortf(xyzt, tf=tf, fwtf=fwtft)
jabr = colortf(xyzr, tf=tf, fwtf=fwtfr)
else:
jabt = xyzt
jabr = xyzr
if (KLCH == None) | (KLCH == [1, 1, 1]):
# Calculate color difference and take account of KL:
DEi = (((jabt[...,0:1]-jabr[...,0:1])**2).sum(axis = jabt[...,0:1].ndim - 1, keepdims = True),\
((jabt[...,1:3]-jabr[...,1:3])**2).sum(axis = jabt[...,1:3].ndim - 1, keepdims = True))
else: #using LCH specification for use with KLCH weights:
Jt = jabt[..., 0:1]
at = jabt[..., 1:2]
bt = jabt[..., 2:3]
Ct = np.sqrt(at**2 + bt**2)
ht = cam.hue_angle(at, bt, htype='rad')
Jr = jabr[..., 0:1]
ar = jabr[..., 1:2]
br = jabr[..., 2:3]
Cr = np.sqrt(ar**2 + br**2)
hr = cam.hue_angle(at, bt, htype='rad')
dJ = Jt - Jr
dC = Ct - Cr
dH = ht - hr
DEab2 = ((at - ar)**2 + (bt - br)**2)
dH = np.sqrt(DEab2 - dC**2)
DEi = ((dJ / KLCH[0])**2, (dC / KLCH[1])**2 + (dH / KLCH[2])**2)
return _process_DEi(DEi,
DEtype=DEtype,
avg=avg,
avg_axis=avg_axis,
out=out)
def plot_chromaticity_diagram_colors(diagram_samples = 256, diagram_opacity = 1.0, diagram_lightness = 0.25,\
cieobs = _CIEOBS, cspace = 'Yxy', cspace_pars = {},\
show = True, axh = None,\
show_grid = False, label_fontname = 'Times New Roman', label_fontsize = 12,\
**kwargs):
"""
Plot the chromaticity diagram colors.
Args:
:diagram_samples:
| 256, optional
| Sampling resolution of color space.
:diagram_opacity:
| 1.0, optional
| Sets opacity of chromaticity diagram
:diagram_lightness:
| 0.25, optional
| Sets lightness of chromaticity diagram
:axh:
| None or axes handle, optional
| Determines axes to plot data in.
| None: make new figure.
:show:
| True or False, optional
| Invoke matplotlib.pyplot.show() right after plotting
:cieobs:
| luxpy._CIEOBS or str, optional
| Determines CMF set to calculate spectrum locus or other.
:cspace:
| luxpy._CSPACE or str, optional
| Determines color space / chromaticity diagram to plot data in.
| Note that data is expected to be in specified :cspace:
:cspace_pars:
| {} or dict, optional
| Dict with parameters required by color space specified in :cspace:
| (for use with luxpy.colortf())
:show_grid:
| False, optional
| Show grid (True) or not (False)
:label_fontname:
| 'Times New Roman', optional
| Sets font type of axis labels.
:label_fontsize:
| 12, optional
| Sets font size of axis labels.
:kwargs:
| additional keyword arguments for use with matplotlib.pyplot.
Returns:
"""
if isinstance(cieobs, str):
SL = _CMF[cieobs]['bar'][1:4].T
else:
SL = cieobs[1:4].T
SL = 100.0 * SL / (SL[:, 1, None] + _EPS)
SL = SL[SL.sum(axis=1) >
0, :] # avoid div by zero in xyz-to-Yxy conversion
SL = colortf(SL, tf=cspace, tfa0=cspace_pars)
plambdamax = SL[:, 1].argmax()
SL = np.vstack(
(SL[:(plambdamax + 1), :], SL[0])
) # add lowest wavelength data and go to max of gamut in x (there is a reversal for some cmf set wavelengths >~700 nm!)
Y, x, y = asplit(SL)
SL = np.vstack((x, y)).T
# create grid for conversion to srgb
offset = _EPS
min_x = min(offset, x.min())
max_x = max(1, x.max())
min_y = min(offset, y.min())
max_y = max(1, y.max())
ii, jj = np.meshgrid(
np.linspace(min_x - offset, max_x + offset, int(diagram_samples)),
np.linspace(max_y + offset, min_y - offset, int(diagram_samples)))
ij = np.dstack((ii, jj))
ij[ij == 0] = offset
ij2D = ij.reshape((diagram_samples**2, 2))
ij2D = np.hstack((diagram_lightness * 100 * np.ones(
(ij2D.shape[0], 1)), ij2D))
xyz = colortf(ij2D, tf=cspace + '>xyz', tfa0=cspace_pars)
xyz[xyz < 0] = 0
xyz[np.isinf(xyz.sum(axis=1)), :] = np.nan
xyz[np.isnan(xyz.sum(axis=1)), :] = offset
srgb = xyz_to_srgb(xyz)
srgb = srgb / srgb.max()
srgb = srgb.reshape((diagram_samples, diagram_samples, 3))
if show == True:
if axh is None:
fig = plt.figure()
axh = fig.add_subplot(111)
polygon = Polygon(SL, facecolor='none', edgecolor='none')
axh.add_patch(polygon)
image = axh.imshow(srgb,
interpolation='bilinear',
extent=(min_x, max_x, min_y - 0.05, max_y),
clip_path=None,
alpha=diagram_opacity)
image.set_clip_path(polygon)
axh.plot(x, y, color='darkgray')
if (cspace == 'Yxy') & (isinstance(cieobs, str)):
axh.set_xlim([0, 1])
axh.set_ylim([0, 1])
elif (cspace == 'Yuv') & (isinstance(cieobs, str)):
axh.set_xlim([0, 0.6])
axh.set_ylim([0, 0.6])
if (cspace is not None):
xlabel = _CSPACE_AXES[cspace][1]
ylabel = _CSPACE_AXES[cspace][2]
if (label_fontname is not None) & (label_fontsize is not None):
axh.set_xlabel(xlabel,
fontname=label_fontname,
fontsize=label_fontsize)
axh.set_ylabel(ylabel,
fontname=label_fontname,
fontsize=label_fontsize)
if show_grid == True:
axh.grid(True)
#plt.show()
return axh
else:
return None
def render_image(img = None, spd = None, rfl = None, out = 'img_hyp', \
refspd = None, D = None, cieobs = _CIEOBS, \
cspace = 'ipt', cspace_tf = {},\
k_neighbours = 4, show = True,
verbosity = 0, show_ref_img = True,\
stack_test_ref = 12,\
write_to_file = None):
"""
Render image under specified light source spd.
Args:
:img:
| None or str or ndarray with uint8 rgb image.
| None load a default image.
:spd:
| ndarray, optional
| Light source spectrum for rendering
:rfl:
| ndarray, optional
| Reflectance set for color coordinate to rfl mapping.
:out:
| 'img_hyp' or str, optional
| (other option: 'img_ren': rendered image under :spd:)
:refspd:
| None, optional
| Reference spectrum for color coordinate to rfl mapping.
| None defaults to D65 (srgb has a D65 white point)
:D:
| None, optional
| Degree of (von Kries) adaptation from spd to refspd.
:cieobs:
| _CIEOBS, optional
| CMF set for calculation of xyz from spectral data.
:cspace:
| 'ipt', optional
| Color space for color coordinate to rfl mapping.
:cspace_tf:
| {}, optional
| Dict with parameters for xyz_to_cspace and cspace_to_xyz transform.
:k_neighbours:
| 4 or int, optional
| Number of nearest neighbours for reflectance spectrum interpolation.
| Neighbours are found using scipy.cKDTree
:show:
| True, optional
| Show images.
:verbosity:
| 0, optional
| If > 0: make a plot of the color coordinates of original and
rendered image pixels.
:show_ref_img:
| True, optional
| True: shows rendered image under reference spd. False: shows
original image.
:write_to_file:
| None, optional
| None: do nothing, else: write to filename(+path) in :write_to_file:
:stack_test_ref:
| 12, optional
| - 12: left (test), right (ref) format for show and imwrite
| - 21: top (test), bottom (ref)
| - 1: only show/write test
| - 2: only show/write ref
| - 0: show both, write test
Returns:
:returns:
| img_hyp, img_ren,
| ndarrays with hyperspectral image and rendered images
"""
# Get image:
#imread = lambda x: plt.imread(x) #matplotlib.pyplot
if img is not None:
if isinstance(img, str):
img = plt.imread(img) # use matplotlib.pyplot's imread
else:
img = plt.imread(_HYPSPCIM_DEFAULT_IMAGE)
# Convert to 2D format:
rgb = img.reshape(img.shape[0] * img.shape[1], 3) * 1.0 # *1.0: make float
rgb[rgb == 0] = _EPS # avoid division by zero for pure blacks.
# Get unique rgb values and positions:
rgb_u, rgb_indices = np.unique(rgb, return_inverse=True, axis=0)
# get Ref spd:
if refspd is None:
refspd = _CIE_ILLUMINANTS['D65'].copy()
# Convert rgb_u to xyz and lab-type values under assumed refspd:
xyz_wr = spd_to_xyz(refspd, cieobs=cieobs, relative=True)
xyz_ur = colortf(rgb_u, tf='srgb>xyz')
# Estimate rfl's for xyz_ur:
rfl_est, xyzri = xyz_to_rfl(xyz_ur, rfl = rfl, out = 'rfl_est,xyz_est', \
refspd = refspd, D = D, cieobs = cieobs, \
cspace = cspace, cspace_tf = cspace_tf,\
k_neighbours = k_neighbours, verbosity = verbosity)
# Get default test spd if none supplied:
if spd is None:
spd = _CIE_ILLUMINANTS['F4']
# calculate xyz values under test spd:
xyzti, xyztw = spd_to_xyz(spd, rfl=rfl_est, cieobs=cieobs, out=2)
# Chromatic adaptation from test spd to refspd:
if D is not None:
xyzti = cat.apply(xyzti, xyzw1=xyztw, xyzw2=xyz_wr, D=D)
# Convert xyzti under test spd to srgb:
rgbti = colortf(xyzti, tf='srgb') / 255
# Reconstruct original locations for rendered image rgbs:
img_ren = rgbti[rgb_indices]
img_ren.shape = img.shape # reshape back to 3D size of original
# For output:
if show_ref_img == True:
rgb_ref = colortf(xyzri, tf='srgb') / 255
img_ref = rgb_ref[rgb_indices]
img_ref.shape = img.shape # reshape back to 3D size of original
img_str = 'Rendered (under ref. spd)'
img = img_ref
else:
img_str = 'Original'
img = img / 255
if (stack_test_ref > 0) | show == True:
if stack_test_ref == 21:
img_original_rendered = np.vstack(
(img_ren, np.ones((4, img.shape[1], 3)), img))
img_original_rendered_str = 'Rendered (under test spd)\n ' + img_str
elif stack_test_ref == 12:
img_original_rendered = np.hstack(
(img_ren, np.ones((img.shape[0], 4, 3)), img))
img_original_rendered_str = 'Rendered (under test spd) | ' + img_str
elif stack_test_ref == 1:
img_original_rendered = img_ren
img_original_rendered_str = 'Rendered (under test spd)'
elif stack_test_ref == 2:
img_original_rendered = img
img_original_rendered_str = img_str
elif stack_test_ref == 0:
img_original_rendered = img_ren
img_original_rendered_str = 'Rendered (under test spd)'
if write_to_file is not None:
# Convert from RGB to BGR formatand write:
#print('Writing rendering results to image file: {}'.format(write_to_file))
with warnings.catch_warnings():
warnings.simplefilter("ignore")
imsave(write_to_file, img_original_rendered)
if show == True:
# show images using pyplot.show():
plt.figure()
plt.imshow(img_original_rendered)
plt.title(img_original_rendered_str)
plt.gca().get_xaxis().set_ticklabels([])
plt.gca().get_yaxis().set_ticklabels([])
if stack_test_ref == 0:
plt.figure()
plt.imshow(img_str)
plt.title(img_str)
plt.axis('off')
if 'img_hyp' in out.split(','):
# Create hyper_spectral image:
rfl_image_2D = rfl_est[
rgb_indices +
1, :] # create array with all rfls required for each pixel
img_hyp = rfl_image_2D.reshape(img.shape[0], img.shape[1],
rfl_image_2D.shape[1])
# Setup output:
if out == 'img_hyp':
return img_hyp
elif out == 'img_ren':
return img_ren
else:
return eval(out)
def plotSL(cieobs=_CIEOBS,
cspace=_CSPACE,
DL=True,
BBL=True,
D65=False,
EEW=False,
cctlabels=False,
axh=None,
show=True,
cspace_pars={},
formatstr='k-',
**kwargs):
"""
Plot spectrum locus for cieobs in cspace.
Args:
:DL:
| True or False, optional
| True plots Daylight Locus as well.
:BBL:
| True or False, optional
| True plots BlackBody Locus as well.
:D65:
| False or True, optional
| True plots D65 chromaticity as well.
:EEW:
| False or True, optional
| True plots Equi-Energy-White chromaticity as well.
:cctlabels:
| False or True, optional
| Add cct text labels at various points along the blackbody locus.
:axh:
| None or axes handle, optional
| Determines axes to plot data in.
| None: make new figure.
:show:
| True or False, optional
| Invoke matplotlib.pyplot.show() right after plotting
:cieobs:
| luxpy._CIEOBS or str, optional
| Determines CMF set to calculate spectrum locus or other.
:cspace:
| luxpy._CSPACE or str, optional
| Determines color space / chromaticity diagram to plot data in.
| Note that data is expected to be in specified :cspace:
:formatstr:
| 'k-' or str, optional
| Format str for plotting (see ?matplotlib.pyplot.plot)
:cspace_pars:
| {} or dict, optional
| Dict with parameters required by color space specified in :cspace:
| (for use with luxpy.colortf())
:kwargs:
| additional keyword arguments for use with matplotlib.pyplot.
Returns:
:returns:
| None (:show: == True)
| or
| handle to current axes (:show: == False)
"""
SL = _CMF[cieobs]['bar'][1:4].T
SL = np.vstack((SL, SL[0]))
SL = 100.0 * SL / SL[:, 1, None]
SL = colortf(SL, tf=cspace, tfa0=cspace_pars)
Y, x, y = asplit(SL)
showcopy = show
if np.any([DL, BBL, D65, EEW]):
show = False
axh_ = plot_color_data(x,
y,
axh=axh,
cieobs=cieobs,
cspace=cspace,
show=show,
formatstr=formatstr,
**kwargs)
if DL == True:
if 'label' in kwargs.keys(): # avoid label also being used for DL
kwargs.pop('label')
plotDL(ccts=None,
cieobs=cieobs,
cspace=cspace,
axh=axh,
show=show,
cspace_pars=cspace_pars,
formatstr='k:',
**kwargs)
if BBL == True:
if 'label' in kwargs.keys(): # avoid label also being used for BB
kwargs.pop('label')
plotBB(ccts=None,
cieobs=cieobs,
cspace=cspace,
axh=axh,
show=show,
cspace_pars=cspace_pars,
cctlabels=cctlabels,
formatstr='k-.',
**kwargs)
if D65 == True:
YxyD65 = colortf(spd_to_xyz(_CIE_ILLUMINANTS['D65']),
tf=cspace,
tfa0=cspace_pars)
plt.plot(YxyD65[..., 1], YxyD65[..., 2], 'bo')
if EEW == True:
YxyEEW = colortf(spd_to_xyz(_CIE_ILLUMINANTS['E']),
tf=cspace,
tfa0=cspace_pars)
plt.plot(YxyEEW[..., 1], YxyEEW[..., 2], 'ko')
if showcopy == False:
return axh_
else:
plt.show()
