def spd_to_tm30(St):
# calculate CIE 1931 2° white point xyz:
xyzw_cct, _ = spd_to_xyz(St, cieobs = '1931_2', relative = True, out = 2)
# calculate cct, duv:
cct, duv = xyz_to_cct(xyzw_cct, cieobs = '1931_2', out = 'cct,duv')
# calculate ref illuminant:
Sr = _cri_ref(cct, mix_range = [4000, 5000], cieobs = '1931_2', wl3 = St[0])
# calculate CIE 1964 10° sample and white point xyz under test and ref. illuminants:
xyz, xyzw = spd_to_xyz(np.vstack((St,Sr[1:])), cieobs = '1964_10',
rfl = _TM30_SAMPLE_SET, relative = True, out = 2)
N = St.shape[0]-1
xyzt, xyzr = xyz[:,:N,:], xyz[:,N:,:]
xyzwt, xyzwr = xyzw[:N,:], xyzw[N:,:]
# calculate CAM02-UCS coordinates
# (standard conditions = {'La':100.0,'Yb':20.0,'surround':'avg','D':1.0):
jabt = _xyz_to_jab_cam02ucs(xyzt, xyzw = xyzwt)
jabr = _xyz_to_jab_cam02ucs(xyzr, xyzw = xyzwr)
# calculate DEi, Rfi:
DEi = (((jabt-jabr)**2).sum(axis=-1,keepdims=True)**0.5)[...,0]
Rfi = log_scale(DEi, scale_factor = [6.73])
# calculate Rf
DEa = DEi.mean(axis = 0,keepdims = True)
Rf = log_scale(DEa, scale_factor = [6.73])
# calculate hue-bin data:
hue_bin_data = _get_hue_bin_data(jabt, jabr, start_hue = 0, nhbins = 16)
# calculate Rg:
Rg = _hue_bin_data_to_rg(hue_bin_data)
# calculate local color fidelity values, Rfhj,
# local hue shift, Rhshj and local chroma shifts, Rcshj:
Rcshj, Rhshj, Rfhj, DEhj = _hue_bin_data_to_Rxhj(hue_bin_data,
scale_factor = [6.73])
# Fit ellipse to gamut shape of samples under test source:
gamut_ellipse_fit = _hue_bin_data_to_ellipsefit(hue_bin_data)
hue_bin_data['gamut_ellipse_fit'] = gamut_ellipse_fit
# return output dict:
return {'St' : St, 'Sr' : Sr,
'xyzw_cct' : xyzw_cct, 'xyzwt' : xyzwt, 'xyzwr' : xyzwr,
'xyzt' : xyzt, 'xyzr' : xyzr,
'cct': cct.T, 'duv': duv.T,
'jabt' : jabt, 'jabr' : jabr,
'DEi' : DEi, 'DEa' : DEa, 'Rfi' : Rfi, 'Rf' : Rf,
'hue_bin_data' : hue_bin_data, 'Rg' : Rg,
'DEhj' : DEhj, 'Rfhj' : Rfhj,
'Rcshj': Rcshj,'Rhshj':Rhshj,
'hue_bin_data' : hue_bin_data}
def test_spd_to_xyz(self, getvars):
# check spd_to_xyz:
S, spds, rfls, xyz, xyzw = getvars
xyz = lx.spd_to_xyz(spds)
xyz = lx.spd_to_xyz(spds, relative=False)
xyz = lx.spd_to_xyz(spds, rfl=rfls)
xyz, xyzw = lx.spd_to_xyz(spds, rfl=rfls, cieobs='1931_2', out=2)
def _setup_default_adaptation_field(dataw=None,
Lw=100,
cie_illuminant='D65',
inputtype='xyz',
relative=True,
cieobs=_CAM_DEFAULT_CIEOBS):
"""
Setup a default illuminant adaptation field with Lw = 100 cd/m² for selected CIE observer.
Args:
:dataw:
| None or ndarray, optional
| Input tristimulus values or spectral data of white point.
| None defaults to the use of the illuminant specified in :cie_illuminant:.
:cie_illuminant:
| 'D65', optional
| String corresponding to one of the illuminants (keys)
| in luxpy._CIE_ILLUMINANT
| If ndarray, then use this one.
| This is ONLY USED WHEN dataw is NONE !!!
:Lw:
| 100.0, optional
| Luminance (cd/m²) of white point.
:inputtype:
| 'xyz' or 'spd', optional
| Specifies the type of input:
| tristimulus values or spectral data for the forward mode.
:relative:
| True or False, optional
| True: xyz tristimulus values are relative (Yw = 100)
:cieobs:
| _CAM_DEFAULT_CIEOBS, optional
| CMF set to use to perform calculations where spectral data
| is involved (inputtype == 'spd'; dataw = None)
| Other options: see luxpy._CMF['types']
Returns:
:dataw:
| Ndarray with default adaptation field data (spectral or xyz)
Notes:
For an example on the use, see code _simple_cam() (type: _simple_cam??)
"""
if (dataw is None):
if cie_illuminant is None:
cie_illuminant = 'D65'
if isinstance(cie_illuminant, str):
cie_illuminant = _CIE_ILLUMINANTS[cie_illuminant]
dataw = cie_illuminant.copy() # get illuminant
xyzw = spd_to_xyz(dataw, cieobs=cieobs,
relative=False) # get abs. tristimulus values
if relative == False: #input is expected to be absolute
dataw[1:] = Lw * dataw[
1:] / xyzw[:, 1:2] # dataw = Lw*dataw # make absolute
else:
dataw = dataw # make relative (Y=100)
if inputtype == 'xyz':
dataw = spd_to_xyz(dataw, cieobs=cieobs, relative=relative)
return dataw
def spd_to_fci(spd, use_cielab=True):
"""
Calculate Feeling of Contrast Index (FCI).
Args:
:spd:
| ndarray with spectral power distribution(s) of the test light source(s).
:use_cielab:
| True, optional
| True: use original formulation of FCI, which adopts a CIECAT94
| chromatic adaptation transform followed by a conversion to
| CIELAB coordinates before calculating the gamuts.
| False: use CIECAM02 coordinates and embedded CAT02 transform.
Returns:
:fci:
| ndarray with FCI values.
References:
1. `Hashimoto, K., Yano, T., Shimizu, M., & Nayatani, Y. (2007).
New method for specifying color-rendering properties of light sources
based on feeling of contrast.
Color Research and Application, 32(5), 361–371.
<http://dx.doi.org/10.1002/col.20338>`_
"""
# get xyz:
xyz, xyzw = spd_to_xyz(spd,
cieobs='1931_2',
relative=True,
rfl=_RFL_FCI,
out=2)
# set condition parameters:
D = 1
Yb = 20
La = Yb * 1000 / np.pi / 100
if use_cielab:
# apply ciecat94 chromatic adaptation transform:
xyzc = cat.apply_ciecat94(
xyz, xyzw=xyzw, E=1000, Yb=20, D=D, cat94_old=True
) # there is apparently an updated version with an alpha incomplete adaptation factor and noise = 0.1; However, FCI doesn't use that version.
# convert to cielab:
lab = xyz_to_lab(xyzc, xyzw=_XYZW_D65_REF)
labd65 = np.repeat(xyz_to_lab(_XYZ_D65_REF, xyzw=_XYZW_D65_REF),
lab.shape[1],
axis=1)
else:
f = lambda xyz, xyzw: cam.xyz_to_jabC_ciecam02(
xyz, xyzw=xyzw, La=1000 * 20 / np.pi / 100, Yb=20, surround='avg')
lab = f(xyz, xyzw)
labd65 = np.repeat(f(_XYZ_D65_REF, _XYZW_D65_REF),
lab.shape[1],
axis=1)
fci = 100 * (_polyarea3D(lab) / _polyarea3D(labd65))**1.5
return fci
def getvars(scope="module"):
# get some spectral data:
S = lx._CIE_ILLUMINANTS['F4']
spds = lx._IESTM30['S']['data'][:5]
rfls = lx._IESTM30['R']['99']['5nm'][:6]
xyz, xyzw = lx.spd_to_xyz(spds, rfl=rfls, cieobs='1931_2', out=2)
return S, spds, rfls, xyz, xyzw
def _setup_default_adaptation_field(dataw=None,
Lw=400,
inputtype='xyz',
relative=True,
cieobs='2006_10'):
"""
Setup theh default illuminant C adaptation field with Lw = 400 cd/m² for selected CIE observer.
Args:
:dataw:
| None or ndarray, optional
| Input tristimulus values or spectral data of white point.
| None defaults to the use of CIE illuminant C.
:Lw:
| 400.0, optional
| Luminance (cd/m²) of white point.
:inputtype:
| 'xyz' or 'spd', optional
| Specifies the type of input:
| tristimulus values or spectral data for the forward mode.
:relative:
| True or False, optional
| True: xyz tristimulus values are relative (Yw = 100)
:cieobs:
| '2006_10', optional
| CMF set to use to perform calculations where spectral data
is involved (inputtype == 'spd'; dataw = None)
| Other options: see luxpy._CMF['types']
Returns:
:dataw:
| Ndarray with default adaptation field data (spectral or xyz)
"""
if (dataw is None):
dataw = _CIE_ILLUMINANTS['C'].copy() # get illuminant C
xyzw = spd_to_xyz(dataw, cieobs=cieobs,
relative=False) # get abs. tristimulus values
if relative == False: #input is expected to be absolute
dataw[1:] = Lw * dataw[
1:] / xyzw[:, 1:2] # dataw = Lw*dataw # make absolute
else:
dataw = dataw # make relative (Y=100)
if inputtype == 'xyz':
dataw = spd_to_xyz(dataw, cieobs=cieobs, relative=relative)
return dataw
def normalize_to_Lw(Ill, Lw, cieobs, rflM):
xyzw = lx.spd_to_xyz(Ill, cieobs=cieobs, relative=False)
for i in range(Ill.shape[0] - 1):
Ill[i + 1] = Lw * Ill[i + 1] / xyzw[i, 1]
IllM = []
for i in range(Ill.shape[0] - 1):
IllM.append(np.vstack((Ill1[0], Ill[i + 1] * rflM[1:, :])))
IllM = np.transpose(np.array(IllM), (1, 0, 2))
return Ill, IllM
def hsi_to_rgb(hsi,
spd=None,
cieobs=_CIEOBS,
srgb=False,
linear_rgb=False,
CSF=None,
wl=[380, 780, 1]):
"""
Convert HyperSpectral Image to rgb.
Args:
:hsi:
| ndarray with hyperspectral image [M,N,L]
:spd:
| None, optional
| ndarray with illumination spectrum
:cieobs:
| _CIEOBS, optional
| CMF set to convert spectral data to xyz tristimulus values.
:srgb:
| False, optional
| If False: Use xyz_to_srgb(spd_to_xyz(...)) to convert to srgb values
| If True: use camera sensitivity functions.
:linear_rgb:
| False, optional
| If False: use gamma = 2.4 in xyz_to_srgb, if False: use gamma = 1.
:CSF:
| None, optional
| ndarray with camera sensitivity functions
| If None: use Nikon D700
:wl:
| [380,780,1], optional
| Wavelength range and spacing or ndarray with wavelengths of HSI image.
Returns:
:rgb:
| ndarray with rgb image [M,N,3]
"""
if spd is None:
spd = _CIE_E.copy()
wlr = getwlr(wl)
spd = cie_interp(spd, wl, kind='linear')
hsi_2d = np.reshape(hsi, (hsi.shape[0] * hsi.shape[1], hsi.shape[2]))
if srgb:
xyz = spd_to_xyz(spd,
cieobs=cieobs,
relative=True,
rfl=np.vstack((wlr, hsi_2d)))
gamma = 1 if linear_rgb else 2.4
rgb = xyz_to_srgb(xyz, gamma=gamma) / 255
else:
if CSF is None: CSF = _CSF_NIKON_D700
rgb = rfl_to_rgb(hsi_2d, spd=spd, CSF=CSF, wl=wl)
return np.reshape(rgb, (hsi.shape[0], hsi.shape[1], 3))
def test_cam15u(self, getvars):
# cam15u:
S, spds, rfls, xyz, xyzw = getvars
xyz, xyzw = lx.spd_to_xyz(spds, rfl=rfls, cieobs='2006_10', out=2)
qabw = lx.xyz_to_qabW_cam15u(xyzw, fov=10)
qab = lx.xyz_to_qabW_cam15u(xyz, fov=10)
xyzw_ = lx.qabW_cam15u_to_xyz(qabw, fov=10)
xyz_ = lx.qabW_cam15u_to_xyz(qab, fov=10)
assert np.isclose(xyz, xyz_).all()
assert np.isclose(xyzw, xyzw_).all()
def test_cam_sww16(self, getvars):
# cam_sww16:
S, spds, rfls, xyz, xyzw = getvars
xyz, xyzw = lx.spd_to_xyz(S, rfl=rfls, cieobs='2006_10', out=2)
jabw = lx.xyz_to_lab_cam_sww16(xyzw, xyzw=xyzw.copy())
jab = lx.xyz_to_lab_cam_sww16(xyz, xyzw=xyzw)
xyzw_ = lx.lab_cam_sww16_to_xyz(jabw, xyzw=xyzw)
xyz_ = lx.lab_cam_sww16_to_xyz(jab, xyzw=xyzw)
assert np.isclose(xyz, xyz_, atol=1e-1).all()
assert np.isclose(xyzw, xyzw_, atol=1e-1).all()
def _plot_tm30_report_bottom(axh, spd, notes = '', max_len_notes_line = 40):
"""
Print some notes, the CIE x, y, u',v' and Ra, R9 values of the source in some empty axes.
Args:
:axh:
| None, optional
| Plot on specified axes.
:spd:
| ndarray or dict
| If ndarray: single spectral power distribution.
:notes:
| string to be split
:max_len_notes_line:
| 40, optional
| Maximum length of a single line when splitting the string.
Returns:
:axh:
| handle to figure axes.
"""
ciera = spd_to_cri(spd, cri_type = 'ciera')
cierai = spd_to_cri(spd, cri_type = 'ciera-14', out = 'Rfi')
xyzw = spd_to_xyz(spd, cieobs = '1931_2', relative = True)
Yxyw = xyz_to_Yxy(xyzw)
Yuvw = xyz_to_Yuv(xyzw)
notes_ = _split_notes(notes, max_len_notes_line = max_len_notes_line)
axh.set_xticks(np.arange(10))
axh.set_xticklabels(['' for i in np.arange(10)])
axh.set_yticks(np.arange(4))
axh.set_yticklabels(['' for i in np.arange(4)])
axh.set_axis_off()
axh.set_xlabel([])
axh.text(0,2.8, 'Notes: ', fontsize = 9, horizontalalignment='left',verticalalignment='top',color = 'k')
axh.text(0.75,2.8, notes_, fontsize = 9, horizontalalignment='left',verticalalignment='top',color = 'k')
axh.text(6,2.8, "x {:1.4f}".format(Yxyw[0,1]), fontsize = 9, horizontalalignment='left',verticalalignment='top',color = 'k')
axh.text(6,2.2, "y {:1.4f}".format(Yxyw[0,2]), fontsize = 9, horizontalalignment='left',verticalalignment='top',color = 'k')
axh.text(6,1.6, "u' {:1.4f}".format(Yuvw[0,1]), fontsize = 9, horizontalalignment='left',verticalalignment='top',color = 'k')
axh.text(6,1.0, "v' {:1.4f}".format(Yuvw[0,2]), fontsize = 9, horizontalalignment='left',verticalalignment='top',color = 'k')
axh.text(7.5,2.8, "CIE 13.3-1995", fontsize = 9, horizontalalignment='left',verticalalignment='top',color = 'k')
axh.text(7.5,2.2, " (CRI) ", fontsize = 9, horizontalalignment='left',verticalalignment='top',color = 'k')
axh.text(7.5,1.6, " $R_a$ {:1.0f}".format(ciera[0,0]), fontsize = 9, horizontalalignment='left',verticalalignment='top',color = 'k')
axh.text(7.5,1.0, " $R_9$ {:1.0f}".format(cierai[9,0]), fontsize = 9, horizontalalignment='left',verticalalignment='top',color = 'k')
# Create a Rectangle patch
rect = patches.Rectangle((7.2,0.5),1.7,2.5,linewidth=1,edgecolor='k',facecolor='none')
# Add the patch to the Axes
axh.add_patch(rect)
return axh
def _spd_to_tm30(spd, cieobs = '1931_2', mixer_type = '3mixer'):
# Call function that calculates ref.illuminant and jabt & jabr only once to obtain Rf & Rg:
data = lx.cri._tm30_process_spd(spd, cri_type = _CRI_TYPE_TM30) # use 1nm samples to avoid interpolation
Rf, Rg = data['Rf'], data['Rg']
thetad = data['hue_bin_data']['gamut_ellipse_fit']['thetad']
ecc = data['hue_bin_data']['gamut_ellipse_fit']['a/b']
xyzw = lx.spd_to_xyz(spd, cieobs = cieobs, relative = False) # set K = 1 to avoid overflow when _FLOAT_TYPE = np.float16
data['xyzw'] = xyzw
return data
def plot_rfl_color_patches(rfl,
spd=None,
cieobs='1931_2',
patch_shape=(100, 100),
patch_layout=None,
ax=None,
show=True):
"""
Create (and plot) an image with colored patches representing a set of reflectance spectra illuminated by a specified illuminant.
Args:
:rfl:
| ndarray with reflectance spectra
:spd:
| None, optional
| ndarray with illuminant spectral power distribution
| If None: _CIE_D65 is used.
:cieobs:
| '1931_2', optional
| CIE standard observer to use when converting rfl to xyz.
:patch_shape:
| (100,100), optional
| shape of each of the patches in the image
:patch_layout:
| None, optional
| If None: layout is calculated automatically to give a 'good' aspect ratio
:ax:
| None, optional
| Axes to plot the image in. If None: a new axes is created.
:show:
| True, optional
| If True: plot image in axes and return axes handle; else: return ndarray with image.
Return:
:ax: or :imagae:
| Axes is returned if show == True, else: ndarray with rgb image is returned.
"""
if spd is None:
spd = _CIE_D65
xyz = spd_to_xyz(spd, rfl=rfl, cieobs=cieobs)[:, 0, :]
rgb = xyz_to_srgb(xyz).astype('uint8')
return plot_rgb_color_patches(rgb,
ax=ax,
patch_shape=patch_shape,
patch_layout=patch_layout,
show=show)
def lab_to_xyz(lab, xyzw = None, cieobs = _CIEOBS, **kwargs):
"""
Convert CIE 1976 L*a*b* (CIELAB) color coordinates to XYZ tristimulus values.
Args:
:lab:
| ndarray with CIE 1976 L*a*b* (CIELAB) color coordinates
:xyzw:
| None or ndarray with tristimulus values of white point, optional
| None defaults to xyz of CIE D65 using the :cieobs: observer.
:cieobs:
| luxpy._CIEOBS, optional
| CMF set to use when calculating xyzw.
Returns:
:xyz:
| ndarray with tristimulus values
"""
lab = np2d(lab)
if xyzw is None:
xyzw = spd_to_xyz(_CIE_ILLUMINANTS['D65'],cieobs = cieobs)
# make xyzw same shape as data:
xyzw = xyzw*np.ones(lab.shape)
# set knee point of function:
k=(24/116) #(24/116)**3**(1/3)
# get L*, a*, b* and Xw, Yw, Zw:
L,a,b = asplit(lab)
Xw,Yw,Zw = asplit(xyzw)
fy = (L + 16.0) / 116.0
fx = a / 500.0 + fy
fz = fy - b/200.0
# apply 3rd power:
X,Y,Z = [xw*(x**3.0) for (x,xw) in ((fx,Xw),(fy,Yw),(fz,Zw))]
# Now calculate T where T/Tn is below the knee point:
p,q,r = [np.where(x<k) for x in (fx,fy,fz)]
X[p],Y[q],Z[r] = [np.squeeze(xw[xp]*((x[xp] - 16.0/116.0) / (841/108))) for (x,xw,xp) in ((fx,Xw,p),(fy,Yw,q),(fz,Zw,r))]
return ajoin((X,Y,Z))
def xyz_to_lab(xyz, xyzw=None, cieobs=_CIEOBS, **kwargs):
"""
Convert XYZ tristimulus values to CIE 1976 L*a*b* (CIELAB) coordinates.
Args:
:xyz:
| ndarray with tristimulus values
:xyzw:
| None or ndarray with tristimulus values of white point, optional
| None defaults to xyz of CIE D65 using the :cieobs: observer.
:cieobs:
| luxpy._CIEOBS, optional
| CMF set to use when calculating xyzw.
Returns:
:lab:
| ndarray with CIE 1976 L*a*b* (CIELAB) color coordinates
"""
xyz = np2d(xyz)
if xyzw is None:
xyzw = spd_to_xyz(_CIE_ILLUMINANTS['D65'], cieobs=cieobs)
# get and normalize (X,Y,Z) to white point:
XYZr = xyz / xyzw
# Apply cube-root compression:
fXYZr = XYZr**(1.0 / 3.0)
# Check for T/Tn <= 0.008856: (Note (24/116)**3 = 0.008856)
pqr = XYZr <= (24 / 116)**3
# calculate f(T) for T/Tn <= 0.008856: (Note:(1/3)*((116/24)**2) = 841/108 = 7.787)
fXYZr[pqr] = ((841 / 108) * XYZr[pqr] + 16.0 / 116.0)
# calculate L*, a*, b*:
Lab = np.empty(xyz.shape)
Lab[..., 0] = 116.0 * (fXYZr[..., 1]) - 16.0
Lab[pqr[..., 1], 0] = 903.3 * XYZr[pqr[..., 1], 1]
Lab[..., 1] = 500.0 * (fXYZr[..., 0] - fXYZr[..., 1])
Lab[..., 2] = 200.0 * (fXYZr[..., 1] - fXYZr[..., 2])
return Lab
def luv_to_xyz(luv, xyzw = None, cieobs = _CIEOBS, **kwargs):
"""
Convert CIE 1976 L*u*v* (CIELUVB) coordinates to XYZ tristimulus values.
Args:
:luv:
| ndarray with CIE 1976 L*u*v* (CIELUV) color coordinates
:xyzw:
| None or ndarray with tristimulus values of white point, optional
| None defaults to xyz of CIE D65 using the :cieobs: observer.
:cieobs:
| luxpy._CIEOBS, optional
| CMF set to use when calculating xyzw.
Returns:
:xyz:
| ndarray with tristimulus values
"""
luv = np2d(luv)
if xyzw is None:
xyzw = spd_to_xyz(_CIE_ILLUMINANTS['D65'],cieobs = cieobs)
# Make xyzw same shape as luv:
Yuvw = todim(xyz_to_Yuv(xyzw), luv.shape, equal_shape = True)
# Get Yw, uw,vw:
Yw,uw,vw = asplit(Yuvw)
# calculate u'v' from u*,v*:
L,u,v = asplit(luv)
up,vp = [(x / (13*L)) + xw for (x,xw) in ((u,uw),(v,vw))]
up[np.where(L == 0.0)] = 0.0
vp[np.where(L == 0.0)] = 0.0
fy = (L + 16.0) / 116.0
Y = Yw*(fy**3.0)
p = np.where((Y/Yw) < ((6.0/29.0)**3.0))
Y[p] = Yw[p]*(L[p]/((29.0/3.0)**3.0))
return Yuv_to_xyz(ajoin((Y,up,vp)))
def lab_to_xyz(lab, xyzw=None, cieobs=_CIEOBS, **kwargs):
"""
Convert CIE 1976 L*a*b* (CIELAB) color coordinates to XYZ tristimulus values.
Args:
:lab:
| ndarray with CIE 1976 L*a*b* (CIELAB) color coordinates
:xyzw:
| None or ndarray with tristimulus values of white point, optional
| None defaults to xyz of CIE D65 using the :cieobs: observer.
:cieobs:
| luxpy._CIEOBS, optional
| CMF set to use when calculating xyzw.
Returns:
:xyz:
| ndarray with tristimulus values
"""
lab = np2d(lab)
if xyzw is None:
xyzw = spd_to_xyz(_CIE_ILLUMINANTS['D65'], cieobs=cieobs)
# make xyzw same shape as data:
xyzw = xyzw * np.ones(lab.shape)
# get L*, a*, b* and Xw, Yw, Zw:
fXYZ = np.empty(lab.shape)
fXYZ[..., 1] = (lab[..., 0] + 16.0) / 116.0
fXYZ[..., 0] = lab[..., 1] / 500.0 + fXYZ[..., 1]
fXYZ[..., 2] = fXYZ[..., 1] - lab[..., 2] / 200.0
# apply 3rd power:
xyz = (fXYZ**3.0) * xyzw
# Now calculate T where T/Tn is below the knee point:
pqr = fXYZ <= (24 / 116) #(24/116)**3**(1/3)
xyz[pqr] = np.squeeze(xyzw[pqr] * ((fXYZ[pqr] - 16.0 / 116.0) /
(841 / 108)))
return xyz
def _get_daylightlocus_parameters(ccts, spds, cieobs):
"""
Get daylight locus parameters for a single cieobs from daylight phase spectra
determined based on parameters for '1931_2' as reported in CIE15-20xx.
"""
# get xy coordinates for new cieobs:
xyz = spd_to_xyz(spds, cieobs=cieobs)
xy = xyz[..., :2] / xyz.sum(axis=-1, keepdims=True)
# Fit 3e order polynomal xD(1/T) [4000 K < T <= 7000 K]:
l7 = ccts <= 7000
pxT_l7 = np.polyfit((1000 / ccts[l7]), xy[l7, 0], 3)
# Fit 3e order polynomal xD(1/T) [T > 7000 K]:
L7 = ccts > 7000
pxT_L7 = np.polyfit((1000 / ccts[L7]), xy[L7, 0], 3)
# Fit 2nd order polynomal yD(xD):
pxy = np.round(np.polyfit(xy[:, 0], xy[:, 1], 2), 3)
#pxy = np.hstack((0,pxy)) # make also 3e order for easy stacking
return xy, pxy, pxT_l7, pxT_L7, l7, L7
def calculate_lut(ccts=None, cieobs=None, add_to_lut=True):
"""
Function that calculates LUT for the ccts stored in
./data/cctluts/cct_lut_cctlist.dat or given as input argument.
Calculation is performed for CMF set specified in cieobs.
Adds a new (temprorary) field to the _CCT_LUT dict.
Args:
:ccts:
| ndarray or str, optional
| list of ccts for which to (re-)calculate the LUTs.
| If str, ccts contains path/filename.dat to list.
:cieobs:
| None or str, optional
| str specifying cmf set.
Returns:
:returns:
| ndarray with cct and duv.
Note:
Function changes the global variable: _CCT_LUT!
"""
if ccts is None:
ccts = getdata('{}cct_lut_cctlist.dat'.format(_CCT_LUT_PATH))
elif isinstance(ccts, str):
ccts = getdata(ccts)
Yuv = np.ones((ccts.shape[0], 2)) * np.nan
for i, cct in enumerate(ccts):
Yuv[i, :] = xyz_to_Yuv(
spd_to_xyz(blackbody(cct, wl3=[360, 830, 1]), cieobs=cieobs))[:,
1:3]
u = Yuv[:, 0, None] # get CIE 1960 u
v = (2.0 / 3.0) * Yuv[:, 1, None] # get CIE 1960 v
cctuv = np.hstack((ccts, u, v))
if add_to_lut == True:
_CCT_LUT[cieobs] = cctuv
return cctuv
def xyz_to_luv(xyz, xyzw = None, cieobs = _CIEOBS, **kwargs):
"""
Convert XYZ tristimulus values to CIE 1976 L*u*v* (CIELUV) coordinates.
Args:
:xyz:
| ndarray with tristimulus values
:xyzw:
| None or ndarray with tristimulus values of white point, optional
| None defaults to xyz of CIE D65 using the :cieobs: observer.
:cieobs:
| luxpy._CIEOBS, optional
| CMF set to use when calculating xyzw.
Returns:
:luv:
| ndarray with CIE 1976 L*u*v* (CIELUV) color coordinates
"""
xyz = np2d(xyz)
if xyzw is None:
xyzw = spd_to_xyz(_CIE_ILLUMINANTS['D65'],cieobs = cieobs)
# make xyzw same shape as xyz:
xyzw = todim(xyzw, xyz.shape)
# Calculate u',v' of test and white:
Y,u,v = asplit(xyz_to_Yuv(xyz))
Yw,uw,vw = asplit(xyz_to_Yuv(xyzw))
#uv1976 to CIELUV
YdivYw = Y / Yw
L = 116.0*YdivYw**(1.0/3.0) - 16.0
p = np.where(YdivYw <= (6.0/29.0)**3.0)
L[p] = ((29.0/3.0)**3.0)*YdivYw[p]
u = 13.0*L*(u-uw)
v = 13.0*L*(v-vw)
return ajoin((L,u,v))
def xyz_to_luv(xyz, xyzw=None, cieobs=_CIEOBS, **kwargs):
"""
Convert XYZ tristimulus values to CIE 1976 L*u*v* (CIELUV) coordinates.
Args:
:xyz:
| ndarray with tristimulus values
:xyzw:
| None or ndarray with tristimulus values of white point, optional
| None defaults to xyz of CIE D65 using the :cieobs: observer.
:cieobs:
| luxpy._CIEOBS, optional
| CMF set to use when calculating xyzw.
Returns:
:luv:
| ndarray with CIE 1976 L*u*v* (CIELUV) color coordinates
"""
xyz = np2d(xyz)
if xyzw is None:
xyzw = spd_to_xyz(_CIE_ILLUMINANTS['D65'], cieobs=cieobs)
# Calculate u',v' of test and white:
Yuv = xyz_to_Yuv(xyz)
Yuvw = xyz_to_Yuv(todim(xyzw,
xyz.shape)) # todim: make xyzw same shape as xyz
#uv1976 to CIELUV
luv = np.empty(xyz.shape)
YdivYw = Yuv[..., 0] / Yuvw[..., 0]
luv[..., 0] = 116.0 * YdivYw**(1.0 / 3.0) - 16.0
p = np.where(YdivYw <= (6.0 / 29.0)**3.0)
luv[..., 0][p] = ((29.0 / 3.0)**3.0) * YdivYw[p]
luv[..., 1] = 13.0 * luv[..., 0] * (Yuv[..., 1] - Yuvw[..., 1])
luv[..., 2] = 13.0 * luv[..., 0] * (Yuv[..., 2] - Yuvw[..., 2])
return luv
def luv_to_xyz(luv, xyzw=None, cieobs=_CIEOBS, **kwargs):
"""
Convert CIE 1976 L*u*v* (CIELUVB) coordinates to XYZ tristimulus values.
Args:
:luv:
| ndarray with CIE 1976 L*u*v* (CIELUV) color coordinates
:xyzw:
| None or ndarray with tristimulus values of white point, optional
| None defaults to xyz of CIE D65 using the :cieobs: observer.
:cieobs:
| luxpy._CIEOBS, optional
| CMF set to use when calculating xyzw.
Returns:
:xyz:
| ndarray with tristimulus values
"""
luv = np2d(luv)
if xyzw is None:
xyzw = spd_to_xyz(_CIE_ILLUMINANTS['D65'], cieobs=cieobs)
# Make xyzw same shape as luv and convert to Yuv:
Yuvw = todim(xyz_to_Yuv(xyzw), luv.shape, equal_shape=True)
# calculate u'v' from u*,v*:
Yuv = np.empty(luv.shape)
Yuv[..., 1:3] = (luv[..., 1:3] / (13 * luv[..., 0])) + Yuvw[..., 1:3]
Yuv[Yuv[..., 0] == 0, 1:3] = 0
Yuv[..., 0] = Yuvw[..., 0] * (((luv[..., 0] + 16.0) / 116.0)**3.0)
p = np.where((Yuv[..., 0] / Yuvw[..., 0]) < ((6.0 / 29.0)**3.0))
Yuv[..., 0][p] = Yuvw[..., 0][p] * (luv[..., 0][p] / ((29.0 / 3.0)**3.0))
return Yuv_to_xyz(Yuv)
def spd_to_mcri(SPD, D=0.9, E=None, Yb=20.0, out='Rm', wl=None):
"""
Calculates the MCRI or Memory Color Rendition Index, Rm
Args:
:SPD:
| ndarray with spectral data (can be multiple SPDs,
first axis are the wavelengths)
:D:
| 0.9, optional
| Degree of adaptation.
:E:
| None, optional
| Illuminance in lux
| (used to calculate La = (Yb/100)*(E/pi) to then calculate D
| following the 'cat02' model).
| If None: the degree is determined by :D:
| If (:E: is not None) & (:Yb: is None): :E: is assumed to contain
the adapting field luminance La (cd/m²).
:Yb:
| 20.0, optional
| Luminance factor of background. (used when calculating La from E)
| If None, E contains La (cd/m²).
:out:
| 'Rm' or str, optional
| Specifies requested output (e.g. 'Rm,Rmi,cct,duv')
:wl:
| None, optional
| Wavelengths (or [start, end, spacing]) to interpolate the SPDs to.
| None: default to no interpolation
Returns:
:returns:
| float or ndarray with MCRI Rm for :out: 'Rm'
| Other output is also possible by changing the :out: str value.
References:
1. `K.A.G. Smet, W.R. Ryckaert, M.R. Pointer, G. Deconinck, P. Hanselaer,(2012)
“A memory colour quality metric for white light sources,”
Energy Build., vol. 49, no. C, pp. 216–225.
<http://www.sciencedirect.com/science/article/pii/S0378778812000837>`_
"""
SPD = np2d(SPD)
if wl is not None:
SPD = spd(data=SPD, interpolation=_S_INTERP_TYPE, kind='np', wl=wl)
# unpack metric default values:
avg, catf, cieobs, cri_specific_pars, cspace, ref_type, rg_pars, sampleset, scale = [
_MCRI_DEFAULTS[x] for x in sorted(_MCRI_DEFAULTS.keys())
]
similarity_ai = cri_specific_pars['similarity_ai']
Mxyz2lms = cspace['Mxyz2lms']
scale_fcn = scale['fcn']
scale_factor = scale['cfactor']
sampleset = eval(sampleset)
# A. calculate xyz:
xyzti, xyztw = spd_to_xyz(SPD, cieobs=cieobs['xyz'], rfl=sampleset, out=2)
if 'cct' in out.split(','):
cct, duv = xyz_to_cct(xyztw,
cieobs=cieobs['cct'],
out='cct,duv',
mode='lut')
# B. perform chromatic adaptation to adopted whitepoint of ipt color space, i.e. D65:
if catf is not None:
Dtype_cat, F, Yb_cat, catmode_cat, cattype_cat, mcat_cat, xyzw_cat = [
catf[x] for x in sorted(catf.keys())
]
# calculate degree of adaptationn D:
if E is not None:
if Yb is not None:
La = (Yb / 100.0) * (E / np.pi)
else:
La = E
D = cat.get_degree_of_adaptation(Dtype=Dtype_cat, F=F, La=La)
else:
Dtype_cat = None # direct input of D
if (E is None) and (D is None):
D = 1.0 # set degree of adaptation to 1 !
if D > 1.0: D = 1.0
if D < 0.6: D = 0.6 # put a limit on the lowest D
# apply cat:
xyzti = cat.apply(xyzti,
cattype=cattype_cat,
catmode=catmode_cat,
xyzw1=xyztw,
xyzw0=None,
xyzw2=xyzw_cat,
D=D,
mcat=[mcat_cat],
Dtype=Dtype_cat)
xyztw = cat.apply(xyztw,
cattype=cattype_cat,
catmode=catmode_cat,
xyzw1=xyztw,
xyzw0=None,
xyzw2=xyzw_cat,
D=D,
mcat=[mcat_cat],
Dtype=Dtype_cat)
# C. convert xyz to ipt and split:
ipt = xyz_to_ipt(
xyzti, cieobs=cieobs['xyz'], M=Mxyz2lms
) #input matrix as published in Smet et al. 2012, Energy and Buildings
I, P, T = asplit(ipt)
# D. calculate specific (hue dependent) similarity indicators, Si:
if len(xyzti.shape) == 3:
ai = np.expand_dims(similarity_ai, axis=1)
else:
ai = similarity_ai
a1, a2, a3, a4, a5 = asplit(ai)
mahalanobis_d2 = (a3 * np.power((P - a1), 2.0) + a4 * np.power(
(T - a2), 2.0) + 2.0 * a5 * (P - a1) * (T - a2))
if (len(mahalanobis_d2.shape) == 3) & (mahalanobis_d2.shape[-1] == 1):
mahalanobis_d2 = mahalanobis_d2[:, :, 0].T
Si = np.exp(-0.5 * mahalanobis_d2)
# E. calculate general similarity indicator, Sa:
Sa = avg(Si, axis=0, keepdims=True)
# F. rescale similarity indicators (Si, Sa) with a 0-1 scale to memory color rendition indices (Rmi, Rm) with a 0 - 100 scale:
Rmi = scale_fcn(np.log(Si), scale_factor=scale_factor)
Rm = np2d(scale_fcn(np.log(Sa), scale_factor=scale_factor))
# G. calculate Rg (polyarea of test / polyarea of memory colours):
if 'Rg' in out.split(','):
I = I[
...,
None] #broadcast_shape(I, target_shape = None,expand_2d_to_3d = 0)
a1 = a1[:, None] * np.ones(
I.shape
) #broadcast_shape(a1, target_shape = None,expand_2d_to_3d = 0)
a2 = a2[:, None] * np.ones(
I.shape
) #broadcast_shape(a2, target_shape = None,expand_2d_to_3d = 0)
a12 = np.concatenate(
(a1, a2), axis=2
) #broadcast_shape(np.hstack((a1,a2)), target_shape = ipt.shape,expand_2d_to_3d = 0)
ipt_mc = np.concatenate((I, a12), axis=2)
nhbins, normalize_gamut, normalized_chroma_ref, start_hue = [
rg_pars[x] for x in sorted(rg_pars.keys())
]
Rg = jab_to_rg(ipt,
ipt_mc,
ordered_and_sliced=False,
nhbins=nhbins,
start_hue=start_hue,
normalize_gamut=normalize_gamut)
if (out != 'Rm'):
return eval(out)
else:
return Rm
xyzw = lx.spd_to_xyz(Ill, cieobs=cieobs, relative=False)
for i in range(Ill.shape[0] - 1):
Ill[i + 1] = Lw * Ill[i + 1] / xyzw[i, 1]
IllM = []
for i in range(Ill.shape[0] - 1):
IllM.append(np.vstack((Ill1[0], Ill[i + 1] * rflM[1:, :])))
IllM = np.transpose(np.array(IllM), (1, 0, 2))
return Ill, IllM
Ill1, Ill1M = normalize_to_Lw(Ill1, Lw, cieobs, rflM)
Ill2, Ill2M = normalize_to_Lw(Ill2, Lw, cieobs, rflM)
n = 6
xyz1, xyzw1 = lx.spd_to_xyz(Ill1,
cieobs=cieobs,
relative=True,
rfl=rflM,
out=2)
xyz1 = xyz1[:n, 0, :]
Ill1M = Ill1M[:(n + 1), 0, :]
xyz2, xyzw2 = lx.spd_to_xyz(Ill2,
cieobs=cieobs,
relative=True,
rfl=rflM,
out=2)
xyz2 = xyz2[:n, :, :]
Ill2M = Ill2M[:(n + 1), :, :]
# Single data for sample and illuminant:
# test input to _simple_cam():
LMS_All_CatObs, var_age_CatObs, vAll_CatObs = getCatObs(n_cat=10,
fieldsize=2,
out=out)
plt.figure()
plt.plot(LMS_All_CatObs[0], LMS_All_CatObs[1], color='r', linestyle='-')
plt.plot(LMS_All_CatObs[0], LMS_All_CatObs[2], color='g', linestyle='-')
plt.plot(LMS_All_CatObs[0], LMS_All_CatObs[3], color='b', linestyle='-')
plt.title('getCatObs(...)')
plt.show()
# XYZ_All_CatObs = lmsb_to_xyzb(LMS_All_CatObs, fieldsize = 3)
# plt.figure()
# plt.plot(wl[:,None],XYZ_All_CatObs[0,:,:], color ='r', linestyle='-')
# plt.plot(wl[:,None],XYZ_All_CatObs[1,:,:], color ='g', linestyle='-')
# plt.plot(wl[:,None],XYZ_All_CatObs[2,:,:], color ='b', linestyle='-')
# plt.title('getCatObs XYZ')
# plt.show()
# Calculate new set of CMFs and calculate xyzw and cct, duv:
from luxpy import spd_to_xyz, _CIE_ILLUMINANTS, xyz_to_cct_ohno
XYZb_All_CatObs, _, _ = getCatObs(n_cat=1, fieldsize=10, out=out)
add_to_cmf_dict(bar=XYZb_All_CatObs, cieobs='CatObs1', K=683)
xyz2 = spd_to_xyz(_CIE_ILLUMINANTS['F4'], cieobs='1931_2')
xyz1 = spd_to_xyz(_CIE_ILLUMINANTS['F4'], cieobs='CatObs1')
cct2, duv2 = xyz_to_cct_ohno(xyz2, cieobs='1931_2', out='cct,duv')
cct1, duv1 = xyz_to_cct_ohno(xyz1, cieobs='CatObs1', out='cct,duv')
print('cct,duv using 1931_2: {:1.0f} K, {:1.4f}'.format(
cct2[0, 0], duv2[0, 0]))
print('cct,duv using CatObs1: {:1.0f} K, {:1.4f}'.format(
cct1[0, 0], duv1[0, 0]))
def cct_to_xyz(ccts,
duv=None,
cieobs=_CIEOBS,
wl=None,
mode='lut',
out=None,
accuracy=0.1,
force_out_of_lut=True,
upper_cct_max=10.0 * 20,
approx_cct_temp=True):
"""
Convert correlated color temperature (CCT) and Duv (distance above (>0) or
below (<0) the Planckian locus) to XYZ tristimulus values.
| Finds xyzw_estimated by minimization of:
|
| F = numpy.sqrt(((100.0*(cct_min - cct)/(cct))**2.0)
| + (((duv_min - duv)/(duv))**2.0))
|
| with cct,duv the input values and cct_min, duv_min calculated using
| luxpy.xyz_to_cct(xyzw_estimated,...).
Args:
:ccts:
| ndarray of cct values
:duv:
| None or ndarray of duv values, optional
| Note that duv can be supplied together with cct values in :ccts:
as ndarray with shape (N,2)
:cieobs:
| luxpy._CIEOBS, optional
| CMF set used to calculated xyzw.
:mode:
| 'lut' or 'search', optional
| Determines what method to use.
:out:
| None (or 1), optional
| If not None or 1: output a ndarray that contains estimated
xyz and minimization results:
| (cct_min, duv_min, F_min (objective fcn value))
:wl:
| None, optional
| Wavelengths used when calculating Planckian radiators.
:accuracy:
| float, optional
| Stop brute-force search when cct :accuracy: is reached.
:upper_cct_max:
| 10.0**20, optional
| Limit brute-force search to this cct.
:approx_cct_temp:
| True, optional
| If True: use xyz_to_cct_HA() to get a first estimate of cct to
speed up search.
:force_out_of_lut:
| True, optional
| If True and cct is out of range of the LUT, then switch to
brute-force search method, else return numpy.nan values.
Returns:
:returns:
| ndarray with estimated XYZ tristimulus values
Note:
If duv is not supplied (:ccts:.shape is (N,1) and :duv: is None),
source is assumed to be on the Planckian locus.
"""
# make ccts a min. 2d np.array:
if isinstance(ccts, list):
ccts = np2dT(np.array(ccts))
else:
ccts = np2d(ccts)
if len(ccts.shape) > 2:
raise Exception('cct_to_xyz(): Input ccts.shape must be <= 2 !')
# get cct and duv arrays from :ccts:
cct = np2d(ccts[:, 0, None])
if (duv is None) & (ccts.shape[1] == 2):
duv = np2d(ccts[:, 1, None])
elif duv is not None:
duv = np2d(duv)
#get estimates of approximate xyz values in case duv = None:
BB = cri_ref(ccts=cct, wl3=wl, ref_type=['BB'])
xyz_est = spd_to_xyz(data=BB, cieobs=cieobs, out=1)
results = np.ones([ccts.shape[0], 3]) * np.nan
if duv is not None:
# optimization/minimization setup:
def objfcn(uv_offset,
uv0,
cct,
duv,
out=1): #, cieobs = cieobs, wl = wl, mode = mode):
uv0 = np2d(uv0 + uv_offset)
Yuv0 = np.concatenate((np2d([100.0]), uv0), axis=1)
cct_min, duv_min = xyz_to_cct(Yuv_to_xyz(Yuv0),
cieobs=cieobs,
out='cct,duv',
wl=wl,
mode=mode,
accuracy=accuracy,
force_out_of_lut=force_out_of_lut,
upper_cct_max=upper_cct_max,
approx_cct_temp=approx_cct_temp)
F = np.sqrt(((100.0 * (cct_min[0] - cct[0]) / (cct[0]))**2.0) +
(((duv_min[0] - duv[0]) / (duv[0]))**2.0))
if out == 'F':
return F
else:
return np.concatenate((cct_min, duv_min, np2d(F)), axis=1)
# loop through each xyz_est:
for i in range(xyz_est.shape[0]):
xyz0 = xyz_est[i]
cct_i = cct[i]
duv_i = duv[i]
cct_min, duv_min = xyz_to_cct(xyz0,
cieobs=cieobs,
out='cct,duv',
wl=wl,
mode=mode,
accuracy=accuracy,
force_out_of_lut=force_out_of_lut,
upper_cct_max=upper_cct_max,
approx_cct_temp=approx_cct_temp)
if np.abs(duv[i]) > _EPS:
# find xyz:
Yuv0 = xyz_to_Yuv(xyz0)
uv0 = Yuv0[0][1:3]
OptimizeResult = minimize(fun=objfcn,
x0=np.zeros((1, 2)),
args=(uv0, cct_i, duv_i, 'F'),
method='Nelder-Mead',
options={
"maxiter": np.inf,
"maxfev": np.inf,
'xatol': 0.000001,
'fatol': 0.000001
})
betas = OptimizeResult['x']
#betas = np.zeros(uv0.shape)
if out is not None:
results[i] = objfcn(betas, uv0, cct_i, duv_i, out=3)
uv0 = np2d(uv0 + betas)
Yuv0 = np.concatenate((np2d([100.0]), uv0), axis=1)
xyz_est[i] = Yuv_to_xyz(Yuv0)
else:
xyz_est[i] = xyz0
if (out is None) | (out == 1):
return xyz_est
else:
# Also output results of minimization:
return np.concatenate((xyz_est, results), axis=1)
def xyz_to_cct_search(xyzw,
cieobs=_CIEOBS,
out='cct',
wl=None,
accuracy=0.1,
upper_cct_max=10.0**20,
approx_cct_temp=True):
"""
Convert XYZ tristimulus values to correlated color temperature (CCT) and
Duv(distance above (> 0) or below ( < 0) the Planckian locus) by a
brute-force search.
| The algorithm uses an approximate cct_temp (HA approx., see xyz_to_cct_HA)
as starting point or uses the middle of the allowed cct-range
(1e2 K - 1e20 K, higher causes overflow) on a log-scale, then constructs
a 4-step section of the blackbody (Planckian) locus on which to find the
minimum distance to the 1960 uv chromaticity of the test source.
Args:
:xyzw:
| ndarray of tristimulus values
:cieobs:
| luxpy._CIEOBS, optional
| CMF set used to calculated xyzw.
:out:
| 'cct' (or 1), optional
| Determines what to return.
| Other options: 'duv' (or -1), 'cct,duv'(or 2), "[cct,duv]" (or -2)
:wl:
| None, optional
| Wavelengths used when calculating Planckian radiators.
:accuracy:
| float, optional
| Stop brute-force search when cct :accuracy: is reached.
:upper_cct_max:
| 10.0**20, optional
| Limit brute-force search to this cct.
:approx_cct_temp:
| True, optional
| If True: use xyz_to_cct_HA() to get a first estimate of cct to
speed up search.
Returns:
:returns:
| ndarray with:
| cct: out == 'cct' (or 1)
| duv: out == 'duv' (or -1)
| cct, duv: out == 'cct,duv' (or 2)
| [cct,duv]: out == "[cct,duv]" (or -2)
Notes:
This program is more accurate, but slower than xyz_to_cct_ohno!
Note that cct must be between 1e3 K - 1e20 K
(very large cct take a long time!!!)
"""
xyzw = np2d(xyzw)
if len(xyzw.shape) > 2:
raise Exception('xyz_to_cct_search(): Input xyzw.shape must be <= 2 !')
# get 1960 u,v of test source:
Yuvt = xyz_to_Yuv(np.squeeze(
xyzw)) # remove possible 1-dim + convert xyzw to CIE 1976 u',v'
#axis_of_v3t = len(Yuvt.shape)-1 # axis containing color components
ut = Yuvt[:, 1, None] #.take([1],axis = axis_of_v3t) # get CIE 1960 u
vt = (2 / 3) * Yuvt[:, 2,
None] #.take([2],axis = axis_of_v3t) # get CIE 1960 v
# Initialize arrays:
ccts = np.ones((xyzw.shape[0], 1)) * np.nan
duvs = ccts.copy()
#calculate preliminary solution(s):
if (approx_cct_temp == True):
ccts_est = xyz_to_cct_HA(xyzw)
procent_estimates = np.array([[3000.0, 100000.0, 0.05],
[100000.0, 200000.0, 0.1],
[200000.0, 300000.0, 0.25],
[300000.0, 400000.0, 0.4],
[400000.0, 600000.0, 0.4],
[600000.0, 800000.0, 0.4],
[800000.0, np.inf, 0.25]])
else:
upper_cct = np.array(upper_cct_max)
lower_cct = np.array(10.0**2)
cct_scale_fun = lambda x: np.log10(x)
cct_scale_ifun = lambda x: np.power(10.0, x)
dT = (cct_scale_fun(upper_cct) - cct_scale_fun(lower_cct)) / 2
ccttemp = np.array([cct_scale_ifun(cct_scale_fun(lower_cct) + dT)])
ccts_est = np2d(ccttemp * np.ones((xyzw.shape[0], 1)))
dT_approx_cct_False = dT.copy()
# Loop through all ccts:
for i in range(xyzw.shape[0]):
#initialize CCT search parameters:
cct = np.nan
duv = np.nan
ccttemp = ccts_est[i].copy()
# Take care of (-1, NaN)'s from xyz_to_cct_HA signifying (CCT < lower, CCT > upper) bounds:
approx_cct_temp_temp = approx_cct_temp
if (approx_cct_temp == True):
cct_scale_fun = lambda x: x
cct_scale_ifun = lambda x: x
if (ccttemp != -1) & (
np.isnan(ccttemp) == False
): # within validity range of CCT estimator-function
for ii in range(procent_estimates.shape[0]):
if (ccttemp >=
(1.0 - 0.05 *
(ii == 0)) * procent_estimates[ii, 0]) & (
ccttemp < (1.0 + 0.05 *
(ii == 0)) * procent_estimates[ii, 1]):
procent_estimate = procent_estimates[ii, 2]
break
dT = np.multiply(
ccttemp, procent_estimate
) # determines range around CCTtemp (25% around estimate) or 100 K
elif (ccttemp == -1) & (np.isnan(ccttemp) == False):
ccttemp = np.array([procent_estimates[0, 0] / 2])
procent_estimate = 1 # cover 0 K to min_CCT of estimator
dT = np.multiply(ccttemp, procent_estimate)
elif (np.isnan(ccttemp) == True):
upper_cct = np.array(upper_cct_max)
lower_cct = np.array(10.0**2)
cct_scale_fun = lambda x: np.log10(x)
cct_scale_ifun = lambda x: np.power(10.0, x)
dT = (cct_scale_fun(upper_cct) - cct_scale_fun(lower_cct)) / 2
ccttemp = np.array(
[cct_scale_ifun(cct_scale_fun(lower_cct) + dT)])
approx_cct_temp = False
else:
dT = dT_approx_cct_False
nsteps = 3
signduv = 1.0
ccttemp = ccttemp[0]
delta_cct = dT
while ((delta_cct > accuracy)): # keep converging on CCT
#generate range of ccts:
ccts_i = cct_scale_ifun(
np.linspace(
cct_scale_fun(ccttemp) - dT,
cct_scale_fun(ccttemp) + dT, nsteps + 1))
ccts_i[ccts_i < 100.0] = 100.0 # avoid nan's in calculation
# Generate BB:
BB = cri_ref(ccts_i, wl3=wl, ref_type=['BB'], cieobs=cieobs)
# Calculate xyz:
xyz = spd_to_xyz(BB, cieobs=cieobs)
# Convert to CIE 1960 u,v:
Yuv = xyz_to_Yuv(np.squeeze(
xyz)) # remove possible 1-dim + convert xyz to CIE 1976 u',v'
#axis_of_v3 = len(Yuv.shape)-1 # axis containing color components
u = Yuv[:, 1, None] # get CIE 1960 u
v = (2.0 / 3.0) * Yuv[:, 2, None] # get CIE 1960 v
# Calculate distance between list of uv's and uv of test source:
dc = ((ut[i] - u)**2 + (vt[i] - v)**2)**0.5
if np.isnan(dc.min()) == False:
#eps = _EPS
q = dc.argmin()
if np.size(
q
) > 1: #to minimize calculation time: only calculate median when necessary
cct = np.median(ccts[q])
duv = np.median(dc[q])
q = np.median(q)
q = int(q) #must be able to serve as index
else:
cct = ccts_i[q]
duv = dc[q]
if (q == 0):
ccttemp = cct_scale_ifun(
np.array(cct_scale_fun([cct])) + 2 * dT / nsteps)
#dT = 2.0*dT/nsteps
continue # look in higher section of planckian locus
if (q == np.size(ccts_i)):
ccttemp = cct_scale_ifun(
np.array(cct_scale_fun([cct])) - 2 * dT / nsteps)
#dT = 2.0*dT/nsteps
continue # look in lower section of planckian locus
if (q > 0) & (q < np.size(ccts_i) - 1):
dT = 2 * dT / nsteps
# get Duv sign:
d_p1m1 = ((u[q + 1] - u[q - 1])**2.0 +
(v[q + 1] - v[q - 1])**2.0)**0.5
x = (dc[q - 1]**2.0 - dc[q + 1]**2.0 +
d_p1m1**2.0) / 2.0 * d_p1m1
vBB = v[q - 1] + ((v[q + 1] - v[q - 1]) * (x / d_p1m1))
signduv = np.sign(vt[i] - vBB)
#calculate difference with previous intermediate solution:
delta_cct = abs(cct - ccttemp)
ccttemp = np.array(cct) #%set new intermediate CCT
approx_cct_temp = approx_cct_temp_temp
else:
ccttemp = np.nan
cct = np.nan
duv = np.nan
duvs[i] = signduv * abs(duv)
ccts[i] = cct
# Regulate output:
if (out == 'cct') | (out == 1):
return np2d(ccts)
elif (out == 'duv') | (out == -1):
return np2d(duvs)
elif (out == 'cct,duv') | (out == 2):
return np2d(ccts), np2d(duvs)
elif (out == "[cct,duv]") | (out == -2):
return np.vstack((ccts, duvs)).T
def cam18sl(data, datab = None, Lb = [100], fov = 10.0, inputtype = 'xyz', direction = 'forward', outin = 'Q,aW,bW', parameters = None):
"""
Convert between CIE 2006 10° XYZ tristimulus values (or spectral data)
and CAM18sl color appearance correlates.
Args:
:data:
| ndarray of CIE 2006 10° absolute XYZ tristimulus values or spectral data
or color appearance attributes of stimulus
:datab:
| ndarray of CIE 2006 10° absolute XYZ tristimulus values or spectral data
of stimulus background
:Lb:
| [100], optional
| Luminance (cd/m²) value(s) of background(s) calculated using the CIE 2006 10° CMFs
| (only used in case datab == None and the background is assumed to be an Equal-Energy-White)
:fov:
| 10.0, optional
| Field-of-view of stimulus (for size effect on brightness)
:inputtpe:
| 'xyz' or 'spd', optional
| Specifies the type of input:
| tristimulus values or spectral data for the forward mode.
:direction:
| 'forward' or 'inverse', optional
| -'forward': xyz -> cam18sl
| -'inverse': cam18sl -> xyz
:outin:
| 'Q,aW,bW' or str, optional
| 'Q,aW,bW' (brightness and opponent signals for amount-of-neutral)
| other options: 'Q,aM,bM' (colorfulness) and 'Q,aS,bS' (saturation)
| Str specifying the type of
| input (:direction: == 'inverse') and
| output (:direction: == 'forward')
:parameters:
| None or dict, optional
| Set of model parameters.
| - None: defaults to luxpy.cam._CAM18SL_PARAMETERS
| (see references below)
Returns:
:returns:
| ndarray with color appearance correlates (:direction: == 'forward')
| or
| XYZ tristimulus values (:direction: == 'inverse')
Notes:
| * Instead of using the CIE 1964 10° CMFs in some places of the model,
| the CIE 2006 10° CMFs are used througout, making it more self_consistent.
| This has an effect on the k scaling factors (now different those in CAM15u)
| and the illuminant E normalization for use in the chromatic adaptation transform.
| (see future erratum to Hermans et al., 2018)
| * The paper also used an equation for the amount of white W, which is
| based on a Q value not expressed in 'bright' ('cA' = 0.937 instead of 123).
| This has been corrected for in the luxpy version of the model, i.e.
| _CAM18SL_PARAMETERS['cW'][0] has been changed from 2.29 to 1/11672.
| (see future erratum to Hermans et al., 2018)
References:
1. `Hermans, S., Smet, K. A. G., & Hanselaer, P. (2018).
"Color appearance model for self-luminous stimuli."
Journal of the Optical Society of America A, 35(12), 2000–2009.
<https://doi.org/10.1364/JOSAA.35.002000>`_
"""
if parameters is None:
parameters = _CAM18SL_PARAMETERS
outin = outin.split(',')
#unpack model parameters:
cA, cAlms, cHK, cM, cW, ca, calms, cb, cblms, cfov, k, naka, unique_hue_data = [parameters[x] for x in sorted(parameters.keys())]
# precomputations:
Mlms2xyz = np.linalg.inv(_CMF['2006_10']['M'])
MAab = np.array([cAlms,calms,cblms])
invMAab = np.linalg.inv(MAab)
#-------------------------------------------------
# setup EEW reference field and default background field (Lr should be equal to Lb):
# Get Lb values:
if datab is not None:
if inputtype != 'xyz':
Lb = spd_to_xyz(datab, cieobs = '2006_10', relative = False)[...,1:2]
else:
Lb = datab[...,1:2]
else:
if isinstance(Lb,list):
Lb = np2dT(Lb)
# Setup EEW ref of same luminance as datab:
if inputtype == 'xyz':
wlr = getwlr(_CAM18SL_WL3)
else:
if datab is None:
wlr = data[0] # use wlr of stimulus data
else:
wlr = datab[0] # use wlr of background data
datar = np.vstack((wlr,np.ones((Lb.shape[0], wlr.shape[0])))) # create eew
xyzr = spd_to_xyz(datar, cieobs = '2006_10', relative = False) # get abs. tristimulus values
datar[1:] = datar[1:]/xyzr[...,1:2]*Lb
# Create datab if None:
if (datab is None):
if inputtype != 'xyz':
datab = datar.copy()
else:
datab = spd_to_xyz(datar, cieobs = '2006_10', relative = False)
datar = datab.copy()
# prepare data and datab for loop over backgrounds:
# make axis 1 of datab have 'same' dimensions as data:
if (data.ndim == 2):
data = np.expand_dims(data, axis = 1) # add light source axis 1
if inputtype == 'xyz':
if datab.shape[0] == 1: #make datab and datar have same lights source dimension (used to store different backgrounds) size as data
datab = np.repeat(datab,data.shape[1],axis=0)
datar = np.repeat(datar,data.shape[1],axis=0)
else:
if datab.shape[0] == 2:
datab = np.vstack((datab[0],np.repeat(datab[1:], data.shape[1], axis = 0)))
if datar.shape[0] == 2:
datar = np.vstack((datar[0],np.repeat(datar[1:], data.shape[1], axis = 0)))
# Flip light source/ background dim to axis 0:
data = np.transpose(data, axes = (1,0,2))
#-------------------------------------------------
#initialize camout:
dshape = list(data.shape)
dshape[-1] = len(outin) # requested number of correlates
if (inputtype != 'xyz') & (direction == 'forward'):
dshape[-2] = dshape[-2] - 1 # wavelength row doesn't count & only with forward can the input data be spectral
camout = np.nan*np.ones(dshape)
for i in range(data.shape[0]):
# get rho, gamma, beta of background and reference white:
if (inputtype != 'xyz'):
xyzb = spd_to_xyz(np.vstack((datab[0], datab[i+1:i+2,:])), cieobs = '2006_10', relative = False)
xyzr = spd_to_xyz(np.vstack((datar[0], datar[i+1:i+2,:])), cieobs = '2006_10', relative = False)
else:
xyzb = datab[i:i+1,:]
xyzr = datar[i:i+1,:]
lmsb = np.dot(_CMF['2006_10']['M'],xyzb.T).T # convert to l,m,s
rgbb = (lmsb / _CMF['2006_10']['K']) * k # convert to rho, gamma, beta
#lmsr = np.dot(_CMF['2006_10']['M'],xyzr.T).T # convert to l,m,s
#rgbr = (lmsr / _CMF['2006_10']['K']) * k # convert to rho, gamma, beta
#rgbr = rgbr/rgbr[...,1:2]*Lb[i] # calculated EEW cone excitations at same luminance values as background
rgbr = np.ones(xyzr.shape)*Lb[i] # explicitely equal EEW cone excitations at same luminance values as background
if direction == 'forward':
# get rho, gamma, beta of stimulus:
if (inputtype != 'xyz'):
xyz = spd_to_xyz(data[i], cieobs = '2006_10', relative = False)
elif (inputtype == 'xyz'):
xyz = data[i]
lms = np.dot(_CMF['2006_10']['M'],xyz.T).T # convert to l,m,s
rgb = (lms / _CMF['2006_10']['K']) * k # convert to rho, gamma, beta
# apply von-kries cat with D = 1:
if (rgbb == 0).any():
Mcat = np.eye(3)
else:
Mcat = np.diag((rgbr/rgbb)[0])
rgba = np.dot(Mcat,rgb.T).T
# apply naka-rushton compression:
rgbc = naka_rushton(rgba, n = naka['n'], sig = naka['sig'](rgbr.mean()), noise = naka['noise'], scaling = naka['scaling'])
#rgbc = np.ones(rgbc.shape)*rgbc.mean() # test if eew ends up at origin
# calculate achromatic and color difference signals, A, a, b:
Aab = np.dot(MAab, rgbc.T).T
A,a,b = asplit(Aab)
a = ca*a
b = cb*b
# calculate colorfullness like signal M:
M = cM*((a**2.0 + b**2.0)**0.5)
# calculate brightness Q:
Q = cA*(A + cHK[0]*M**cHK[1]) # last term is contribution of Helmholtz-Kohlrausch effect on brightness
# calculate saturation, s:
s = M / Q
# calculate amount of white, W:
W = 1 / (1.0 + cW[0]*(s**cW[1]))
# adjust Q for size (fov) of stimulus (matter of debate whether to do this before or after calculation of s or W, there was no data on s, M or W for different sized stimuli: after)
Q = Q*(fov/10.0)**cfov
# calculate hue, h and Hue quadrature, H:
h = hue_angle(a,b, htype = 'deg')
if 'H' in outin:
H = hue_quadrature(h, unique_hue_data = unique_hue_data)
else:
H = None
# calculate cart. co.:
if 'aM' in outin:
aM = M*np.cos(h*np.pi/180.0)
bM = M*np.sin(h*np.pi/180.0)
if 'aS' in outin:
aS = s*np.cos(h*np.pi/180.0)
bS = s*np.sin(h*np.pi/180.0)
if 'aW' in outin:
aW = W*np.cos(h*np.pi/180.0)
bW = W*np.sin(h*np.pi/180.0)
if (outin != ['Q','aW','bW']):
camout[i] = eval('ajoin(('+','.join(outin)+'))')
else:
camout[i] = ajoin((Q,aW,bW))
elif direction == 'inverse':
# get Q, M and a, b depending on input type:
if 'aW' in outin:
Q,a,b = asplit(data[i])
Q = Q / ((fov/10.0)**cfov) #adjust Q for size (fov) of stimulus back to that 10° ref
W = (a**2.0 + b**2.0)**0.5
s = (((1.0 / W) - 1.0)/cW[0])**(1.0/cW[1])
M = s*Q
if 'aM' in outin:
Q,a,b = asplit(data[i])
Q = Q / ((fov/10.0)**cfov) #adjust Q for size (fov) of stimulus back to that 10° ref
M = (a**2.0 + b**2.0)**0.5
if 'aS' in outin:
Q,a,b = asplit(data[i])
Q = Q / ((fov/10.0)**cfov) #adjust Q for size (fov) of stimulus back to that 10° ref
s = (a**2.0 + b**2.0)**0.5
M = s*Q
if 'h' in outin:
Q, WsM, h = asplit(data[i])
Q = Q / ((fov/10.0)**cfov) #adjust Q for size (fov) of stimulus back to that 10° ref
if 'W' in outin:
s = (((1.0 / WsM) - 1.0)/cW[0])**(1.0/cW[1])
M = s*Q
elif 's' in outin:
M = WsM*Q
elif 'M' in outin:
M = WsM
# calculate achromatic signal, A from Q and M:
A = Q/cA - cHK[0]*M**cHK[1]
# calculate hue angle:
h = hue_angle(a,b, htype = 'rad')
# calculate a,b from M and h:
a = (M/cM)*np.cos(h)
b = (M/cM)*np.sin(h)
a = a/ca
b = b/cb
# create Aab:
Aab = ajoin((A,a,b))
# calculate rgbc:
rgbc = np.dot(invMAab, Aab.T).T
# decompress rgbc to (adapted) rgba :
rgba = naka_rushton(rgbc, n = naka['n'], sig = naka['sig'](rgbr.mean()), noise = naka['noise'], scaling = naka['scaling'], direction = 'inverse')
# apply inverse von-kries cat with D = 1:
rgb = np.dot(np.diag((rgbb/rgbr)[0]),rgba.T).T
# convert rgb to lms to xyz:
lms = rgb/k*_CMF['2006_10']['K']
xyz = np.dot(Mlms2xyz,lms.T).T
camout[i] = xyz
if camout.shape[0] == 1:
camout = np.squeeze(camout,axis = 0)
return camout
| For help on parameter details: ?luxpy.cam.cam18sl
"""
return cam18sl(qab, datab = xyzb, Lb = Lb, fov = fov, direction = 'inverse', inputtype = 'xyz', outin = 'Q,aS,bS', parameters = parameters)
#------------------------------------------------------------------------------
if __name__ == '__main__':
C = _CIE_ILLUMINANTS['C'].copy()
C = np.vstack((C,cie_interp(_CIE_ILLUMINANTS['D65'],C[0],kind='spd')[1:]))
M = _MUNSELL.copy()
rflM = M['R']
cieobs = '2006_10'
# Normalize to Lw:
Lw = 100
xyzw2 = spd_to_xyz(C, cieobs = cieobs, relative = False)
for i in range(C.shape[0]-1):
C[i+1] = Lw*C[i+1]/xyzw2[i,1]
xyz, xyzw = spd_to_xyz(C, cieobs = cieobs, relative = True, rfl = rflM, out = 2)
qab = xyz_to_qabW_cam18sl(xyzw, xyzb = None, Lb = [100], fov = 10.0)
print('qab: ',qab)
qab2 = cam18sl(C, datab = None, Lb = [100], fov = 10.0, direction = 'forward', inputtype = 'spd', outin = 'Q,aW,bW', parameters = None)
print('qab2: ',qab2)
xyz_ = qabW_cam18sl_to_xyz(qab, xyzb = None, Lb = [100], fov = 10.0)
print('delta: ', xyzw-xyz_)
# test 2:
cieobs = '2006_10'
Lb = np2d([100])
def _get_absolute_xyz_xyzw(data,
dataw,
i=0,
Lw=100,
direction='forward',
cieobs=_CAM_DEFAULT_CIEOBS,
inputtype='xyz',
relative=True):
"""
Calculate absolute xyz tristimulus values of stimulus and white point
from spectral input or convert relative xyz values to absolute ones.
Args:
:data:
| ndarray with input tristimulus values
| or spectral data
| or input color appearance correlates
| Can be of shape: (N [, xM], x 3), whereby:
| N refers to samples and M refers to light sources.
| Note that for spectral input shape is (N x (M+1) x wl)
:dataw:
| None or ndarray, optional
| Input tristimulus values or spectral data of white point.
| None defaults to the use of CIE illuminant C.
:i:
| 0, optional
| row number in data and dataw ndarrays
| (for loops across illuminant dimension after dimension reshape
| with _massage_output_data_to_original_shape).
:Lw:
| 100.0, optional
| Luminance (cd/m²) of white point.
:inputtype:
| 'xyz' or 'spd', optional
| Specifies the type of input:
| tristimulus values or spectral data for the forward mode.
:direction:
| 'forward' or 'inverse', optional
| -'forward': xyz -> cam
| -'inverse': cam -> xyz
:relative:
| True or False, optional
| True: xyz tristimulus values are relative (Yw = 100)
:cieobs:
| _CAM_DEFAULT_CIEOBS, optional
| CMF set to use to perform calculations where spectral data
is involved (inputtype == 'spd'; dataw = None)
| Other options: see luxpy._CMF['types']
Returns:
:xyzti:
| in forward mode : ndarray with relative or absolute sample xyz for data[i]
| in inverse mode: None
:xyzwi:
| ndarray with relative or absolute white point for dataw[i]
:xyzw_abs:
| ndarray with absolute xyz for white point for dataw[i]
Notes:
For an example on the use, see code _simple_cam() (type: _simple_cam??)
"""
xyzw_abs = None
# Spectral input:
if (inputtype != 'xyz'):
# make spectral data in `dataw` absolute:
if relative == True:
xyzw_abs = spd_to_xyz(dataw[i], cieobs=cieobs, relative=False)
dataw[i, 1:, :] = Lw * dataw[i, 1:, :] / xyzw_abs[0, 1]
# Calculate absolute xyzw:
xyzwi = spd_to_xyz(dataw[i], cieobs=cieobs, relative=False)
# make spectral data in `data` absolute:
if (direction == 'forward'
): # no xyz data or spectra in data if == 'inverse'!!!
if relative == True:
data[i, 1:, :] = Lw * data[i, 1:, :] / xyzw_abs[0, 1]
# Calculate absolute xyz of test field:
xyzti = spd_to_xyz(data[i, ...], cieobs=cieobs, relative=False)
else:
xyzti = None
# XYZ input:
elif (inputtype == 'xyz'):
# make xyz data in `dataw` absolute:
if relative == True:
xyzw_abs = dataw[i].copy()
dataw[i] = Lw * dataw[i] / xyzw_abs[:, 1]
xyzwi = dataw[i]
if (direction == 'forward'):
if relative == True:
# make xyz data in `data` absolute:
data[i] = Lw * data[i] / xyzw_abs[:, 1] # make absolute
xyzti = data[i]
else:
xyzti = None # not needed in inverse model
return xyzti, xyzwi, xyzw_abs
