def _hue_bin_data_to_rg(hue_bin_data):
jabt_closed = np.vstack(
(hue_bin_data['jabt_hj'], hue_bin_data['jabt_hj'][:1, ...]))
jabr_closed = np.vstack(
(hue_bin_data['jabr_hj'], hue_bin_data['jabr_hj'][:1, ...]))
notnan_t = np.logical_not(np.isnan(
jabt_closed[..., 1])) # avoid NaN's (i.e. empty hue-bins)
notnan_r = np.logical_not(np.isnan(jabr_closed[..., 1]))
Rg = np.array([[
100 * _polyarea(jabt_closed[notnan_t[:, i], i, 1],
jabt_closed[notnan_t[:, i], i, 2]) /
_polyarea(jabr_closed[notnan_r[:, i], i, 1],
jabr_closed[notnan_r[:, i], i, 2])
for i in range(notnan_r.shape[-1])
]])
return Rg
def PX_colorshift_model(Jabt,
Jabr,
jab_ranges=None,
jab_deltas=None,
limit_grid_radius=0):
"""
Pixelates the color space and calculates the color shifts in each pixel.
Args:
:Jabt:
| ndarray with color coordinates under the (single) test SPD.
:Jabr:
| ndarray with color coordinates under the (single) reference SPD.
: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.
: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:
| dict with the following keys:
| - 'Jab': dict with with ndarrays for:
| Jabt, Jabr, DEi, DEi_ab (only ab-coordinates), DEa (mean)
| and DEa_ab
| - 'vshifts': dict with:
| * 'vectorshift': ndarray with vector shifts between average
| Jabt and Jabr for each pixel
| * 'vectorshift_ab': ndarray with vector shifts averaged
| over J for each pixel
| * 'vectorshift_ab_J0': ndarray with vector shifts averaged
| over J for each pixel of J=0 plane.
| * 'vectorshift_len': length of 'vectorshift'
| * 'vectorshift_ab_len': length of 'vectorshift_ab'
| * 'vectorshift_ab_J0_len': length of 'vectorshift_ab_J0'
| * 'vectorshift_len_DEnormed': length of 'vectorshift'
| normalized to 'DEa'
| * 'vectorshift_ab_len_DEnormed': length of 'vectorshift_ab'
| normalized to 'DEa_ab'
| * 'vectorshift_ab_J0_len_DEnormed': length of 'vectorshift_ab_J0'
| normalized to 'DEa_ab'
| - 'pixeldata': dict with pixel info:
| * 'grid' ndarray with coordinates of all pixel centers.
| * 'idx': list[int] with pixel index for each non-empty pixel
| * 'Jab': ndarray with center coordinates of non-empty pixels
| * 'samplenrs': list[list[int]] with sample numbers belong to
| each non-empty pixel
| * 'IDs: summarizing list,
| with column order: 'idxp, jabp, samplenrs'
| - 'fielddata' : dict with dicts containing data on the calculated
| vector-field and circle-fields
| * 'vectorfield': dict with ndarrays for the ab-coordinates
| under the ref. (axr, bxr) and test (axt, bxt) illuminants,
| centered at the pixel centers corresponding to the
ab-coordinates of the reference illuminant.
"""
# get pixelIDs of all samples under ref. conditions:
gridp, idxp, jabp, pixelsamplenrs, pixelIDs = get_pixel_coordinates(
Jabr,
jab_ranges=jab_ranges,
jab_deltas=jab_deltas,
limit_grid_radius=limit_grid_radius)
# get average Jab coordinates for each pixel:
Npixels = len(idxp) # number of non-empty pixels
Jabr_avg = np.zeros((gridp.shape[0], 3))
Jabr_avg.fill(np.nan)
Jabt_avg = Jabr_avg.copy()
for i in range(Npixels):
Jabr_avg[idxp[i], :] = Jabr[pixelsamplenrs[i], :].mean(axis=0)
Jabt_avg[idxp[i], :] = Jabt[pixelsamplenrs[i], :].mean(axis=0)
#jabtemp = Jabr[pixelsamplenrs[i],:]
#jabtempm = Jabr_avg[idxp[i],:]
# calculate Jab vector shift:
vectorshift = Jabt_avg - Jabr_avg
# calculate ab vector shift:
uabs = gridp[gridp[:, 0] == 0, 1:3] #np.unique(gridp[:,1:3],axis=0)
vectorshift_ab_J0 = np.zeros((uabs.shape[0], 2))
vectorshift_ab_J0.fill(np.nan)
vectorshift_ab = np.zeros((vectorshift.shape[0], 2))
vectorshift_ab.fill(np.nan)
for i in range(uabs.shape[0]):
cond = (gridp[:, 1:3] == uabs[i, :]).all(axis=1)
if cond.any() & np.logical_not(
np.isnan(vectorshift[cond, 1:3]).all()
): #last condition is to avoid warning of taking nanmean of empty slice when all are NaNs
vectorshift_ab_J0[i, :] = np.nanmean(vectorshift[cond, 1:3],
axis=0)
vectorshift_ab[cond, :] = np.nanmean(vectorshift[cond, 1:3],
axis=0)
# Calculate length of shift vectors:
vectorshift_len = np.sqrt((vectorshift**2).sum(axis=vectorshift.ndim - 1))
vectorshift_ab_len = np.sqrt(
(vectorshift_ab**2).sum(axis=vectorshift_ab.ndim - 1))
vectorshift_ab_J0_len = np.sqrt(
(vectorshift_ab_J0**2).sum(axis=vectorshift_ab_J0.ndim - 1))
# Calculate average DE for normalization of vectorshifts
DEi_Jab_avg = np.sqrt(((Jabt - Jabr)**2).sum(axis=Jabr.ndim - 1))
DE_Jab_avg = DEi_Jab_avg.mean(axis=0)
DEi_ab_avg = np.sqrt(
((Jabt[..., 1:3] - Jabr[..., 1:3])**2).sum(axis=Jabr[..., 1:3].ndim -
1))
DE_ab_avg = DEi_ab_avg.mean(axis=0)
# calculate vectorfield:
axr = uabs[:, 0, None]
bxr = uabs[:, 1, None]
axt = axr + vectorshift_ab_J0[:, 0, None]
bxt = bxr + vectorshift_ab_J0[:, 1, None]
data = {
'Jab': {
'Jabr': Jabr_avg,
'Jabt': Jabt_avg,
'DEi': DEi_Jab_avg,
'DEi_ab': DEi_ab_avg,
'DEa': DE_Jab_avg,
'DEa_ab': DE_ab_avg
},
'vshifts': {
'vectorshift': vectorshift,
'vectorshift_ab': vectorshift_ab,
'vectorshift_ab_J0': vectorshift_ab_J0,
'vectorshift_len': vectorshift_len,
'vectorshift_ab_len': vectorshift_ab_len,
'vectorshift_ab_J0_len': vectorshift_ab_J0_len,
'vectorshift_len_DEnormed': vectorshift_len / DE_Jab_avg,
'vectorshift_ab_len_DEnormed': vectorshift_ab_len / DE_ab_avg,
'vectorshift_ab_J0_len_DEnormed': vectorshift_ab_J0_len / DE_ab_avg
},
'pixeldata': {
'grid': gridp,
'idx': idxp,
'Jab': jabp,
'samplenrs': pixelsamplenrs,
'IDs': pixelIDs
},
'fielddata': {
'vectorfield': {
'axr': axr,
'bxr': bxr,
'axt': axt,
'bxt': bxt
}
}
}
return data
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 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 cie2006cmfsEx(age = 32,fieldsize = 10, wl = None,\
var_od_lens = 0, var_od_macula = 0, \
var_od_L = 0, var_od_M = 0, var_od_S = 0,\
var_shft_L = 0, var_shft_M = 0, var_shft_S = 0,\
out = 'LMS', allow_negative_values = False):
"""
Generate Individual Observer CMFs (cone fundamentals)
based on CIE2006 cone fundamentals and published literature
on observer variability in color matching and in physiological parameters.
Args:
:age:
| 32 or float or int, optional
| Observer age
:fieldsize:
| 10, optional
| Field size of stimulus in degrees (between 2° and 10°).
:wl:
| None, optional
| Interpolation/extraplation of :LMS: output to specified wavelengths.
| None: output original _WL = np.array([390,780,5])
:var_od_lens:
| 0, optional
| Std Dev. in peak optical density [%] of lens.
:var_od_macula:
| 0, optional
| Std Dev. in peak optical density [%] of macula.
:var_od_L:
| 0, optional
| Std Dev. in peak optical density [%] of L-cone.
:var_od_M:
| 0, optional
| Std Dev. in peak optical density [%] of M-cone.
:var_od_S:
| 0, optional
| Std Dev. in peak optical density [%] of S-cone.
:var_shft_L:
| 0, optional
| Std Dev. in peak wavelength shift [nm] of L-cone.
:var_shft_L:
| 0, optional
| Std Dev. in peak wavelength shift [nm] of M-cone.
:var_shft_S:
| 0, optional
| Std Dev. in peak wavelength shift [nm] of S-cone.
:out:
| 'LMS' or , optional
| Determines output.
:allow_negative_values:
| False, optional
| Cone fundamentals or color matching functions
should not have negative values.
| If False: X[X<0] = 0.
Returns:
:returns:
| - 'LMS' : ndarray with individual observer area-normalized
| cone fundamentals. Wavelength have been added.
| [- 'trans_lens': ndarray with lens transmission
| (no wavelengths added, no interpolation)
| - 'trans_macula': ndarray with macula transmission
| (no wavelengths added, no interpolation)
| - 'sens_photopig' : ndarray with photopigment sens.
| (no wavelengths added, no interpolation)]
References:
1. `Asano Y, Fairchild MD, and Blondé L (2016).
Individual Colorimetric Observer Model.
PLoS One 11, 1–19.
<http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0145671>`_
2. `Asano Y, Fairchild MD, Blondé L, and Morvan P (2016).
Color matching experiment for highlighting interobserver variability.
Color Res. Appl. 41, 530–539.
<https://onlinelibrary.wiley.com/doi/abs/10.1002/col.21975>`_
3. `CIE, and CIE (2006).
Fundamental Chromaticity Diagram with Physiological Axes - Part I
(Vienna: CIE).
<http://www.cie.co.at/publications/fundamental-chromaticity-diagram-physiological-axes-part-1>`_
4. `Asano's Individual Colorimetric Observer Model
<https://www.rit.edu/cos/colorscience/re_AsanoObserverFunctions.php>`_
"""
fs = fieldsize
rmd = _INDVCMF_DATA['rmd'].copy()
LMSa = _INDVCMF_DATA['LMSa'].copy()
docul = _INDVCMF_DATA['docul'].copy()
# field size corrected macular density:
pkOd_Macula = 0.485 * np.exp(-fs / 6.132) * (
1 + var_od_macula / 100) # varied peak optical density of macula
corrected_rmd = rmd * pkOd_Macula
# age corrected lens/ocular media density:
if (age <= 60):
correct_lomd = docul[:1] * (1 + 0.02 * (age - 32)) + docul[1:2]
else:
correct_lomd = docul[:1] * (1.56 + 0.0667 * (age - 60)) + docul[1:2]
correct_lomd = correct_lomd * (1 + var_od_lens / 100
) # varied overall optical density of lens
# Peak Wavelength Shift:
wl_shifted = np.empty(LMSa.shape)
wl_shifted[0] = _WL + var_shft_L
wl_shifted[1] = _WL + var_shft_M
wl_shifted[2] = _WL + var_shft_S
LMSa_shft = np.empty(LMSa.shape)
kind = 'cubic'
LMSa_shft[0] = sp.interpolate.interp1d(wl_shifted[0],
LMSa[0],
kind=kind,
bounds_error=False,
fill_value="extrapolate")(_WL)
LMSa_shft[1] = sp.interpolate.interp1d(wl_shifted[1],
LMSa[1],
kind=kind,
bounds_error=False,
fill_value="extrapolate")(_WL)
LMSa_shft[2] = sp.interpolate.interp1d(wl_shifted[2],
LMSa[2],
kind=kind,
bounds_error=False,
fill_value="extrapolate")(_WL)
# LMSa[2,np.where(_WL >= _WL_CRIT)] = 0 #np.nan # Not defined above 620nm
# LMSa_shft[2,np.where(_WL >= _WL_CRIT)] = 0
ssw = np.hstack(
(0, np.sign(np.diff(LMSa_shft[2, :]))
)) #detect poor interpolation (sign switch due to instability)
LMSa_shft[2, np.where((ssw >= 0) & (_WL > 560))] = np.nan
# corrected LMS (no age correction):
pkOd_L = (0.38 + 0.54 * np.exp(-fs / 1.333)) * (
1 + var_od_L / 100) # varied peak optical density of L-cone
pkOd_M = (0.38 + 0.54 * np.exp(-fs / 1.333)) * (
1 + var_od_M / 100) # varied peak optical density of M-cone
pkOd_S = (0.30 + 0.45 * np.exp(-fs / 1.333)) * (
1 + var_od_S / 100) # varied peak optical density of S-cone
alpha_lms = 0. * LMSa_shft
alpha_lms[0] = 1 - 10**(-pkOd_L * (10**LMSa_shft[0]))
alpha_lms[1] = 1 - 10**(-pkOd_M * (10**LMSa_shft[1]))
alpha_lms[2] = 1 - 10**(-pkOd_S * (10**LMSa_shft[2]))
# this fix is required because the above math fails for alpha_lms[2,:]==0
alpha_lms[2, np.where(_WL >= _WL_CRIT)] = 0
# Corrected to Corneal Incidence:
lms_barq = alpha_lms * (10**(-corrected_rmd - correct_lomd)) * np.ones(
alpha_lms.shape)
# Corrected to Energy Terms:
lms_bar = lms_barq * _WL
# Set NaN values to zero:
lms_bar[np.isnan(lms_bar)] = 0
# normalized:
LMS = 100 * lms_bar / np.nansum(lms_bar, axis=1, keepdims=True)
# Output extra:
trans_lens = 10**(-correct_lomd)
trans_macula = 10**(-corrected_rmd)
sens_photopig = alpha_lms * _WL
# Add wavelengths:
LMS = np.vstack((_WL, LMS))
if ('xyz' in out.lower().split(',')):
LMS = lmsb_to_xyzb(LMS,
fieldsize,
out='xyz',
allow_negative_values=allow_negative_values)
out = out.replace('xyz', 'LMS').replace('XYZ', 'LMS')
if ('lms' in out.lower().split(',')):
out = out.replace('lms', 'LMS')
# Interpolate/extrapolate:
if wl is None:
interpolation = None
else:
interpolation = 'cubic'
LMS = spd(LMS, wl=wl, interpolation=interpolation, norm_type='area')
if (out == 'LMS'):
return LMS
elif (out == 'LMS,trans_lens,trans_macula,sens_photopig'):
return LMS, trans_lens, trans_macula, sens_photopig
elif (out == 'LMS,trans_lens,trans_macula,sens_photopig,LMSa'):
return LMS, trans_lens, trans_macula, sens_photopig, LMSa
else:
return eval(out)
def cie_interp(data,
wl_new,
kind=None,
negative_values_allowed=False,
extrap_values=None):
"""
Interpolate / extrapolate spectral data following standard CIE15-2018.
| The kind of interpolation depends on the spectrum type defined in :kind:.
| Extrapolation is always done by replicate the closest known values.
Args:
:data:
| ndarray with spectral data
| (.shape = (number of spectra + 1, number of original wavelengths))
:wl_new:
| ndarray with new wavelengths
:kind:
| None, optional
| - If :kind: is None, return original data.
| - If :kind: is a spectrum type (see _INTERP_TYPES), the correct
| interpolation type if automatically chosen.
| - Or :kind: can be any interpolation type supported by
| scipy.interpolate.interp1d (math.interp1d if nan's are present!!)
:negative_values_allowed:
| False, optional
| If False: negative values are clipped to zero.
:extrap_values:
| None, optional
| If None: use CIE recommended 'closest value' approach when extrapolating.
| If float or list or ndarray, use those values to fill extrapolated value(s).
| If 'ext': use normal extrapolated values by scipy.interpolate.interp1d
Returns:
:returns:
| ndarray of interpolated spectral data.
| (.shape = (number of spectra + 1, number of wavelength in wl_new))
"""
if (kind is not None):
# Wavelength definition:
wl_new = getwlr(wl_new)
if (not np.array_equal(data[0], wl_new)) | np.isnan(data).any():
extrap_values = np.atleast_1d(extrap_values)
# Set interpolation type based on data type:
if kind in _INTERP_TYPES['linear']:
kind = 'linear'
elif kind in _INTERP_TYPES['cubic']:
kind = 'cubic'
# define wl, S, wl_new:
wl = np.array(data[0])
S = data[1:]
wl_new = np.array(wl_new)
# Interpolate each spectrum in S:
N = S.shape[0]
nan_indices = np.isnan(S)
# Interpolate all (if not all rows have nan):
rows_with_nans = np.where(nan_indices.sum(axis=1))[0]
if not (rows_with_nans.size == N):
#allrows_nans = False
if extrap_values[0] is None:
fill_value = (0, 0)
elif (((type(extrap_values[0]) == np.str_) |
(type(extrap_values[0]) == str))
and (extrap_values[0][:3] == 'ext')):
fill_value = 'extrapolate'
else:
fill_value = (extrap_values[0], extrap_values[-1])
Si = sp.interpolate.interp1d(wl,
S,
kind=kind,
bounds_error=False,
fill_value=fill_value)(wl_new)
#extrapolate by replicating closest known (in source data!) value (conform CIE15-2004 recommendation)
if extrap_values[0] is None:
Si[:, wl_new < wl[0]] = S[:, :1]
Si[:, wl_new > wl[-1]] = S[:, -1:]
else:
#allrows_nans = True
Si = np.zeros([N, wl_new.shape[0]])
Si.fill(np.nan)
# Re-interpolate those which have none:
if nan_indices.any():
#looping required as some values are NaN's
for i in rows_with_nans:
nonan_indices = np.logical_not(nan_indices[i])
wl_nonan = wl[nonan_indices]
S_i_nonan = S[i][nonan_indices]
Si_nonan = math.interp1(wl_nonan,
S_i_nonan,
wl_new,
kind=kind,
ext='extrapolate')
# Si_nonan = sp.interpolate.interp1d(wl_nonan, S_i_nonan, kind = kind, bounds_error = False, fill_value = 'extrapolate')(wl_new)
#extrapolate by replicating closest known (in source data!) value (conform CIE15-2004 recommendation)
if extrap_values[0] is None:
Si_nonan[wl_new < wl_nonan[0]] = S_i_nonan[0]
Si_nonan[wl_new > wl_nonan[-1]] = S_i_nonan[-1]
elif (((type(extrap_values[0]) == np.str_) |
(type(extrap_values[0]) == str))
and (extrap_values[0][:3] == 'ext')):
pass
else:
Si_nonan[wl_new < wl_nonan[0]] = extrap_values[0]
Si_nonan[wl_new > wl_nonan[-1]] = extrap_values[-1]
Si[i] = Si_nonan
# No negative values allowed for spectra:
if negative_values_allowed == False:
if np.any(Si): Si[Si < 0.0] = 0.0
# Add wavelengths to data array:
return np.vstack((wl_new, Si))
return data
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)
