def DE2000(xyzt,
xyzr,
dtype='xyz',
DEtype='jab',
avg=None,
avg_axis=0,
out='DEi',
xyzwt=None,
xyzwr=None,
KLCH=None):
"""
Calculate DE2000 color difference.
Args:
:xyzt:
| ndarray with tristimulus values of test data.
:xyzr:
| ndarray with tristimulus values of reference data.
:dtype:
| 'xyz' or 'lab', 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 lx.xyz_to_lab()
:xyzwr:
| None or ndarray, optional
| Whitepoint tristimulus values of reference data
| None defaults to the one set in lx.xyz_to_lab()
: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.
:KLCH:
| None, optional
| Weigths for L, C, H
| None: default to [1,1,1]
: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.
Note:
For the other input arguments, see specific color space used.
Returns:
:returns:
| ndarray with DEi [, DEa] or other as specified by :out:
References:
1. `Sharma, G., Wu, W., & Dalal, E. N. (2005).
The CIEDE2000 color‐difference formula: Implementation notes,
supplementary test data, and mathematical observations.
Color Research & Application, 30(1), 21–30.
<https://doi.org/10.1002/col.20070>`_
"""
if KLCH is None:
KLCH = [1, 1, 1]
if dtype == 'xyz':
labt = xyz_to_lab(xyzt, xyzw=xyzwt)
labr = xyz_to_lab(xyzr, xyzw=xyzwr)
else:
labt = xyzt
labr = xyzr
Lt = labt[..., 0:1]
at = labt[..., 1:2]
bt = labt[..., 2:3]
Ct = np.sqrt(at**2 + bt**2)
#ht = cam.hue_angle(at,bt,htype = 'rad')
Lr = labr[..., 0:1]
ar = labr[..., 1:2]
br = labr[..., 2:3]
Cr = np.sqrt(ar**2 + br**2)
#hr = cam.hue_angle(at,bt,htype = 'rad')
# Step 1:
Cavg = (Ct + Cr) / 2
G = 0.5 * (1 - np.sqrt((Cavg**7.0) / ((Cavg**7.0) + (25.0**7))))
apt = (1 + G) * at
apr = (1 + G) * ar
Cpt = np.sqrt(apt**2 + bt**2)
Cpr = np.sqrt(apr**2 + br**2)
Cpprod = Cpt * Cpr
hpt = cam.hue_angle(apt, bt, htype='deg')
hpr = cam.hue_angle(apr, br, htype='deg')
hpt[(apt == 0) * (bt == 0)] = 0
hpr[(apr == 0) * (br == 0)] = 0
# Step 2:
dL = np.abs(Lr - Lt)
dCp = np.abs(Cpr - Cpt)
dhp_ = hpr - hpt
dhp = dhp_.copy()
dhp[np.where(np.abs(dhp_) > 180)] = dhp[np.where(np.abs(dhp_) > 180)] - 360
dhp[np.where(
np.abs(dhp_) < -180)] = dhp[np.where(np.abs(dhp_) < -180)] + 360
dhp[np.where(Cpprod == 0)] = 0
#dH = 2*np.sqrt(Cpprod)*np.sin(dhp/2*np.pi/180)
dH = deltaH(dhp, Cpprod, htype='deg')
# Step 3:
Lp = (Lr + Lt) / 2
Cp = (Cpr + Cpt) / 2
hps = hpt + hpr
hp = (hpt + hpr) / 2
hp[np.where((np.abs(dhp_) > 180)
& (hps < 360))] = hp[np.where((np.abs(dhp_) > 180)
& (hps < 360))] + 180
hp[np.where((np.abs(dhp_) > 180)
& (hps >= 360))] = hp[np.where((np.abs(dhp_) > 180)
& (hps >= 360))] - 180
hp[np.where(Cpprod == 0)] = 0
T = 1 - 0.17*np.cos((hp - 30)*np.pi/180) + 0.24*np.cos(2*hp*np.pi/180) +\
0.32*np.cos((3*hp + 6)*np.pi/180) - 0.20*np.cos((4*hp - 63)*np.pi/180)
dtheta = 30 * np.exp(-((hp - 275) / 25)**2)
RC = 2 * np.sqrt((Cp**7) / ((Cp**7) + (25**7)))
SL = 1 + ((0.015 * (Lp - 50)**2) / np.sqrt(20 + (Lp - 50)**2))
SC = 1 + 0.045 * Cp
SH = 1 + 0.015 * Cp * T
RT = -np.sin(2 * dtheta * np.pi / 180) * RC
kL, kC, kH = KLCH
DEi = ((dL / (kL * SL))**2, (dCp / (kC * SC))**2 + (dH / (kH * SH))**2 +
RT * (dCp / (kC * SC)) * (dH / (kH * SH)))
return _process_DEi(DEi,
DEtype=DEtype,
avg=avg,
avg_axis=avg_axis,
out=out)
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)
jabt, jabr = out2 # jabt2,jabr2=out2 # ii=0 # ht = positive_arctan(jabt[:,ii,1],jabt[:,ii,2], htype='rad') # ht2 = positive_arctan(jabt2[...,1], jabt2[...,2], htype = 'rad') # ht = np.arctan2(jabt[:,ii,2],jabt[:,ii,1]) # ht2 = np.arctan2(jabt2[...,2], jabt2[...,1]) #----------------------------------------------------------- dh = 360 / nhbins hue_bin_edges = np.arange(start_hue, 360 + 1, dh) * np.pi / 180 # get hues of jabt, jabr: ht = cam.hue_angle(jabt[..., 1], jabt[..., 2], htype='rad') hr = cam.hue_angle(jabr[..., 1], jabr[..., 2], htype='rad') # Get chroma of jabt, jabr: Ct = ((jabt[..., 1]**2 + jabt[..., 2]**2))**0.5 Cr = ((jabr[..., 1]**2 + jabr[..., 2]**2))**0.5 # Calculate DEi between jabt, jabr: DEi = ((jabt - jabr)**2).sum(axis=-1, keepdims=True)**0.5 # calculate hue-bin averages for jabt, jabr: jabt_hj = np.ones((nhbins, ht.shape[1], 3)) * np.nan jabr_hj = np.ones((nhbins, hr.shape[1], 3)) * np.nan DE_hj = np.ones((nhbins, hr.shape[1])) * np.nan ht_idx = np.ones_like((ht)) * np.nan hr_idx = np.ones_like((hr)) * np.nan