mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-13 05:17:07 +08:00
Merge pull request #665 from mrterry/deltae
Add deltaE functions and lab2lch color conversion utils
This commit is contained in:
Juan Nunez-Iglesias
@@ -145,3 +145,6 @@
|
||||
- Jostein Bø Fløystad
|
||||
Reconstruction circle mode for Radon transform
|
||||
Simultaneous Algebraic Reconstruction Technique for inverse Radon transform
|
||||
|
||||
- Matt Terry
|
||||
Color difference functions
|
||||
|
||||
@@ -15,6 +15,8 @@ from .colorconv import (convert_colorspace,
|
||||
rgb2lab,
|
||||
rgb2hed,
|
||||
hed2rgb,
|
||||
lab2lch,
|
||||
lch2lab,
|
||||
separate_stains,
|
||||
combine_stains,
|
||||
rgb_from_hed,
|
||||
@@ -44,6 +46,12 @@ from .colorconv import (convert_colorspace,
|
||||
|
||||
from .colorlabel import color_dict, label2rgb
|
||||
|
||||
from .delta_e import (deltaE_cie76,
|
||||
deltaE_ciede94,
|
||||
deltaE_ciede2000,
|
||||
deltaE_cmc,
|
||||
)
|
||||
|
||||
|
||||
__all__ = ['convert_colorspace',
|
||||
'guess_spatial_dimensions',
|
||||
@@ -62,6 +70,8 @@ __all__ = ['convert_colorspace',
|
||||
'rgb2lab',
|
||||
'rgb2hed',
|
||||
'hed2rgb',
|
||||
'lab2lch',
|
||||
'lch2lab',
|
||||
'separate_stains',
|
||||
'combine_stains',
|
||||
'rgb_from_hed',
|
||||
@@ -89,4 +99,9 @@ __all__ = ['convert_colorspace',
|
||||
'is_rgb',
|
||||
'is_gray',
|
||||
'color_dict',
|
||||
'label2rgb']
|
||||
'label2rgb',
|
||||
'deltaE_cie76',
|
||||
'deltaE_ciede94',
|
||||
'deltaE_ciede2000',
|
||||
'deltaE_cmc',
|
||||
]
|
||||
|
||||
@@ -26,10 +26,17 @@ Supported color spaces
|
||||
Derived from the RGB CIE color space. Chosen such that
|
||||
``x == y == z == 1/3`` at the whitepoint, and all color matching
|
||||
functions are greater than zero everywhere.
|
||||
* LAB CIE : Lightness, a, b
|
||||
Colorspace derived from XYZ CIE that is intended to be more
|
||||
perceptually uniform
|
||||
* LCH CIE : Lightness, Chroma, Hue
|
||||
Defined in terms of LAB CIE. C and H are the polar representation of
|
||||
a and b. The polar angle C is defined to be on (0, 2*pi)
|
||||
|
||||
:author: Nicolas Pinto (rgb2hsv)
|
||||
:author: Ralf Gommers (hsv2rgb)
|
||||
:author: Travis Oliphant (XYZ and RGB CIE functions)
|
||||
:author: Matt Terry (lab2lch)
|
||||
|
||||
:license: modified BSD
|
||||
|
||||
@@ -1026,3 +1033,105 @@ def combine_stains(stains, conv_matrix):
|
||||
logrgb2 = np.dot(-np.reshape(stains, (-1, 3)), conv_matrix)
|
||||
rgb2 = np.exp(logrgb2)
|
||||
return rescale_intensity(np.reshape(rgb2 - 2, stains.shape), in_range=(-1, 1))
|
||||
|
||||
|
||||
def lab2lch(lab):
|
||||
"""CIE-LAB to CIE-LCH color space conversion.
|
||||
|
||||
LCH is the cylindrical representation of the LAB (Cartesian) colorspace
|
||||
|
||||
Parameters
|
||||
----------
|
||||
lab : array_like
|
||||
The N-D image in CIE-LAB format. The last (`N+1`th) dimension must have
|
||||
at least 3 elements, corresponding to the ``L``, ``a``, and ``b`` color
|
||||
channels. Subsequent elements are copied.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : ndarray
|
||||
The image in LCH format, in a N-D array with same shape as input `lab`.
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
If `lch` does not have at least 3 color channels (i.e. l, a, b).
|
||||
|
||||
Notes
|
||||
-----
|
||||
The Hue is expressed as an angle between (0, 2*pi)
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from skimage import data
|
||||
>>> from skimage.color import rgb2lab, lab2lch
|
||||
>>> lena = data.lena()
|
||||
>>> lena_lab = rgb2lab(lena)
|
||||
>>> lena_lch = lab2lch(lena_lab)
|
||||
"""
|
||||
lch = _prepare_lab_array(lab)
|
||||
|
||||
a, b = lch[..., 1], lch[..., 2]
|
||||
lch[..., 1], lch[..., 2] = _cart2polar_2pi(a, b)
|
||||
return lch
|
||||
|
||||
|
||||
def _cart2polar_2pi(x, y):
|
||||
"""convert cartesian coordiantes to polar (uses non-standard theta range!)
|
||||
|
||||
NON-STANDARD RANGE! Maps to (0, 2*pi) rather than usual (-pi, +pi)
|
||||
"""
|
||||
r, t = np.hypot(x, y), np.arctan2(y, x)
|
||||
t += np.where(t < 0., 2 * np.pi, 0)
|
||||
return r, t
|
||||
|
||||
|
||||
def lch2lab(lch):
|
||||
"""CIE-LCH to CIE-LAB color space conversion.
|
||||
|
||||
LCH is the cylindrical representation of the LAB (Cartesian) colorspace
|
||||
|
||||
Parameters
|
||||
----------
|
||||
lch : array_like
|
||||
The N-D image in CIE-LCH format. The last (`N+1`th) dimension must have
|
||||
at least 3 elements, corresponding to the ``L``, ``a``, and ``b`` color
|
||||
channels. Subsequent elements are copied.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : ndarray
|
||||
The image in LAB format, with same shape as input `lch`.
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
If `lch` does not have at least 3 color channels (i.e. l, c, h).
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from skimage import data
|
||||
>>> from skimage.color import rgb2lab, lch2lab
|
||||
>>> lena = data.lena()
|
||||
>>> lena_lab = rgb2lab(lena)
|
||||
>>> lena_lch = lab2lch(lena_lab)
|
||||
>>> lena_lab2 = lch2lab(lena_lch)
|
||||
"""
|
||||
lch = _prepare_lab_array(lch)
|
||||
|
||||
c, h = lch[..., 1], lch[..., 2]
|
||||
lch[..., 1], lch[..., 2] = c * np.cos(h), c * np.sin(h)
|
||||
return lch
|
||||
|
||||
|
||||
def _prepare_lab_array(arr):
|
||||
"""Ensure input for lab2lch, lch2lab are well-posed.
|
||||
|
||||
Arrays must be in floating point and have at least 3 elements in
|
||||
last dimension. Return a new array.
|
||||
"""
|
||||
arr = np.asarray(arr)
|
||||
shape = arr.shape
|
||||
if shape[-1] < 3:
|
||||
raise ValueError('Input array has less than 3 color channels')
|
||||
return dtype.img_as_float(arr, force_copy=True)
|
||||
|
||||
@@ -0,0 +1,339 @@
|
||||
"""
|
||||
Functions for calculating the "distance" between colors.
|
||||
|
||||
Implicit in these definitions of "distance" is the notion of "Just Noticeable
|
||||
Distance" (JND). This represents the distance between colors where a human can
|
||||
perceive different colors. Humans are more sensitive to certain colors than
|
||||
others, which different deltaE metrics correct for with varying degrees of
|
||||
sophistication.
|
||||
|
||||
The literature often mentions 1 as the minimum distance for visual
|
||||
differentiation, but more recent studies (Mahy 1994) peg JND at 2.3
|
||||
|
||||
The delta-E notation comes from the German word for "Sensation" (Empfindung).
|
||||
|
||||
Reference
|
||||
---------
|
||||
http://en.wikipedia.org/wiki/Color_difference
|
||||
|
||||
"""
|
||||
from __future__ import division
|
||||
|
||||
import numpy as np
|
||||
|
||||
from skimage.color.colorconv import lab2lch, _cart2polar_2pi
|
||||
|
||||
|
||||
def deltaE_cie76(lab1, lab2):
|
||||
"""Euclidean distance between two points in Lab color space
|
||||
|
||||
Parameters
|
||||
----------
|
||||
lab1 : array_like
|
||||
reference color (Lab colorspace)
|
||||
lab2 : array_like
|
||||
comparison color (Lab colorspace)
|
||||
|
||||
Returns
|
||||
-------
|
||||
dE : array_like
|
||||
distance between colors `lab1` and `lab2`
|
||||
|
||||
References
|
||||
----------
|
||||
.. [1] http://en.wikipedia.org/wiki/Color_difference
|
||||
.. [2] A. R. Robertson, "The CIE 1976 color-difference formulae,"
|
||||
Color Res. Appl. 2, 7-11 (1977).
|
||||
"""
|
||||
lab1 = np.asarray(lab1)
|
||||
lab2 = np.asarray(lab2)
|
||||
L1, a1, b1 = np.rollaxis(lab1, -1)[:3]
|
||||
L2, a2, b2 = np.rollaxis(lab2, -1)[:3]
|
||||
return np.sqrt((L2 - L1) ** 2 + (a2 - a1) ** 2 + (b2 - b1) ** 2)
|
||||
|
||||
|
||||
def deltaE_ciede94(lab1, lab2, kH=1, kC=1, kL=1, k1=0.045, k2=0.015):
|
||||
"""Color difference according to CIEDE 94 standard
|
||||
|
||||
Accommodates perceptual non-uniformities through the use of application
|
||||
specific scale factors (`kH`, `kC`, `kL`, `k1`, and `k2`).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
lab1 : array_like
|
||||
reference color (Lab colorspace)
|
||||
lab2 : array_like
|
||||
comparison color (Lab colorspace)
|
||||
kH : float, optional
|
||||
Hue scale
|
||||
kC : float, optional
|
||||
Chroma scale
|
||||
kL : float, optional
|
||||
Lightness scale
|
||||
k1 : float, optional
|
||||
first scale parameter
|
||||
k2 : float, optional
|
||||
second scale parameter
|
||||
|
||||
Returns
|
||||
-------
|
||||
dE : array_like
|
||||
color difference between `lab1` and `lab2`
|
||||
|
||||
Notes
|
||||
-----
|
||||
deltaE_ciede94 is not symmetric with respect to lab1 and lab2. CIEDE94
|
||||
defines the scales for the lightness, hue, and chroma in terms of the first
|
||||
color. Consequently, the first color should be regarded as the "reference"
|
||||
color.
|
||||
|
||||
`kL`, `k1`, `k2` depend on the application and default to the values
|
||||
suggested for graphic arts
|
||||
|
||||
========== ============== ==========
|
||||
Parameter Graphic Arts Textiles
|
||||
========== ============== ==========
|
||||
`kL` 1.000 2.000
|
||||
`k1` 0.045 0.048
|
||||
`k2` 0.015 0.014
|
||||
========== ============== ==========
|
||||
|
||||
References
|
||||
----------
|
||||
.. [1] http://en.wikipedia.org/wiki/Color_difference
|
||||
.. [2] http://www.brucelindbloom.com/index.html?Eqn_DeltaE_CIE94.html
|
||||
"""
|
||||
L1, C1 = np.rollaxis(lab2lch(lab1), -1)[:2]
|
||||
L2, C2 = np.rollaxis(lab2lch(lab2), -1)[:2]
|
||||
|
||||
dL = L1 - L2
|
||||
dC = C1 - C2
|
||||
dH2 = get_dH2(lab1, lab2)
|
||||
|
||||
SL = 1
|
||||
SC = 1 + k1 * C1
|
||||
SH = 1 + k2 * C1
|
||||
|
||||
dE2 = (dL / (kL * SL)) ** 2
|
||||
dE2 += (dC / (kC * SC)) ** 2
|
||||
dE2 += dH2 / (kH * SH) ** 2
|
||||
return np.sqrt(dE2)
|
||||
|
||||
|
||||
def deltaE_ciede2000(lab1, lab2, kL=1, kC=1, kH=1):
|
||||
"""Color difference as given by the CIEDE 2000 standard.
|
||||
|
||||
CIEDE 2000 is a major revision of CIDE94. The perceptual calibration is
|
||||
largely based on experience with automotive paint on smooth surfaces.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
lab1 : array_like
|
||||
reference color (Lab colorspace)
|
||||
lab2 : array_like
|
||||
comparison color (Lab colorspace)
|
||||
kL : float (range), optional
|
||||
lightness scale factor, 1 for "acceptably close"; 2 for "imperceptible"
|
||||
see deltaE_cmc
|
||||
kC : float (range), optional
|
||||
chroma scale factor, usually 1
|
||||
kH : float (range), optional
|
||||
hue scale factor, usually 1
|
||||
|
||||
Returns
|
||||
-------
|
||||
deltaE : array_like
|
||||
The distance between `lab1` and `lab2`
|
||||
|
||||
Notes
|
||||
-----
|
||||
CIEDE 2000 assumes parametric weighting factors for the lightness, chroma,
|
||||
and hue (`kL`, `kC`, `kH` respectively). These default to 1.
|
||||
|
||||
References
|
||||
----------
|
||||
.. [1] http://en.wikipedia.org/wiki/Color_difference
|
||||
.. [2] http://www.ece.rochester.edu/~gsharma/ciede2000/ciede2000noteCRNA.pdf
|
||||
(doi:10.1364/AO.33.008069)
|
||||
.. [3] M. Melgosa, J. Quesada, and E. Hita, "Uniformity of some recent
|
||||
color metrics tested with an accurate color-difference tolerance
|
||||
dataset," Appl. Opt. 33, 8069-8077 (1994).
|
||||
"""
|
||||
lab1 = np.asarray(lab1)
|
||||
lab2 = np.asarray(lab2)
|
||||
unroll = False
|
||||
if lab1.ndim == 1 and lab2.ndim == 1:
|
||||
unroll = True
|
||||
if lab1.ndim == 1:
|
||||
lab1 = lab1[None, :]
|
||||
if lab2.ndim == 1:
|
||||
lab2 = lab2[None, :]
|
||||
L1, a1, b1 = np.rollaxis(lab1, -1)[:3]
|
||||
L2, a2, b2 = np.rollaxis(lab2, -1)[:3]
|
||||
|
||||
# distort `a` based on average chroma
|
||||
# then convert to lch coordines from distorted `a`
|
||||
# all subsequence calculations are in the new coordiantes
|
||||
# (often denoted "prime" in the literature)
|
||||
Cbar = 0.5 * (np.hypot(a1, b1) + np.hypot(a2, b2))
|
||||
c7 = Cbar ** 7
|
||||
G = 0.5 * (1 - np.sqrt(c7 / (c7 + 25 ** 7)))
|
||||
scale = 1 + G
|
||||
C1, h1 = _cart2polar_2pi(a1 * scale, b1)
|
||||
C2, h2 = _cart2polar_2pi(a2 * scale, b2)
|
||||
# recall that c, h are polar coordiantes. c==r, h==theta
|
||||
|
||||
# cide2000 has four terms to delta_e:
|
||||
# 1) Luminance term
|
||||
# 2) Hue term
|
||||
# 3) Chroma term
|
||||
# 4) hue Rotation term
|
||||
|
||||
# lightness term
|
||||
Lbar = 0.5 * (L1 + L2)
|
||||
tmp = (Lbar - 50) ** 2
|
||||
SL = 1 + 0.015 * tmp / np.sqrt(20 + tmp)
|
||||
L_term = (L2 - L1) / (kL * SL)
|
||||
|
||||
# chroma term
|
||||
Cbar = 0.5 * (C1 + C2) # new coordiantes
|
||||
SC = 1 + 0.045 * Cbar
|
||||
C_term = (C2 - C1) / (kC * SC)
|
||||
|
||||
# hue term
|
||||
h_diff = h2 - h1
|
||||
h_sum = h1 + h2
|
||||
CC = C1 * C2
|
||||
|
||||
dH = h_diff.copy()
|
||||
dH[h_diff > np.pi] -= 2 * np.pi
|
||||
dH[h_diff < -np.pi] += 2 * np.pi
|
||||
dH[CC == 0.] = 0. # if r == 0, dtheta == 0
|
||||
dH_term = 2 * np.sqrt(CC) * np.sin(dH / 2)
|
||||
|
||||
Hbar = h_sum.copy()
|
||||
mask = np.logical_and(CC != 0., np.abs(h_diff) > np.pi)
|
||||
Hbar[mask * (h_sum < 2 * np.pi)] += 2 * np.pi
|
||||
Hbar[mask * (h_sum >= 2 * np.pi)] -= 2 * np.pi
|
||||
Hbar[CC == 0.] *= 2
|
||||
Hbar *= 0.5
|
||||
|
||||
T = (1 -
|
||||
0.17 * np.cos(Hbar - np.deg2rad(30)) +
|
||||
0.24 * np.cos(2 * Hbar) +
|
||||
0.32 * np.cos(3 * Hbar + np.deg2rad(6)) -
|
||||
0.20 * np.cos(4 * Hbar - np.deg2rad(63))
|
||||
)
|
||||
SH = 1 + 0.015 * Cbar * T
|
||||
|
||||
H_term = dH_term / (kH * SH)
|
||||
|
||||
# hue rotation
|
||||
c7 = Cbar ** 7
|
||||
Rc = 2 * np.sqrt(c7 / (c7 + 25 ** 7))
|
||||
dtheta = np.deg2rad(30) * np.exp(-((np.rad2deg(Hbar) - 275) / 25) ** 2)
|
||||
R_term = -np.sin(2 * dtheta) * Rc * C_term * H_term
|
||||
|
||||
# put it all together
|
||||
dE2 = L_term ** 2
|
||||
dE2 += C_term ** 2
|
||||
dE2 += H_term ** 2
|
||||
dE2 += R_term
|
||||
ans = np.sqrt(dE2)
|
||||
if unroll:
|
||||
ans = ans[0]
|
||||
return ans
|
||||
|
||||
|
||||
def deltaE_cmc(lab1, lab2, kL=1, kC=1):
|
||||
"""Color difference from the CMC l:c standard.
|
||||
|
||||
This color difference was developed by the Colour Measurement Committee
|
||||
(CMC) of the Society of Dyers and Colourists (United Kingdom). It is
|
||||
intended for use in the textile industry.
|
||||
|
||||
The scale factors `kL`, `kC` set the weight given to differences in
|
||||
lightness and chroma relative to differences in hue. The usual values are
|
||||
``kL=2``, ``kC=1`` for "acceptability" and ``kL=1``, ``kC=1`` for
|
||||
"imperceptibility". Colors with ``dE > 1`` are "different" for the given
|
||||
scale factors.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
lab1 : array_like
|
||||
reference color (Lab colorspace)
|
||||
lab2 : array_like
|
||||
comparison color (Lab colorspace)
|
||||
|
||||
Returns
|
||||
-------
|
||||
dE : array_like
|
||||
distance between colors `lab1` and `lab2`
|
||||
|
||||
Notes
|
||||
-----
|
||||
deltaE_cmc the defines the scales for the lightness, hue, and chroma
|
||||
in terms of the first color. Consequently
|
||||
``deltaE_cmc(lab1, lab2) != deltaE_cmc(lab2, lab1)``
|
||||
|
||||
References
|
||||
----------
|
||||
.. [1] http://en.wikipedia.org/wiki/Color_difference
|
||||
.. [2] http://www.brucelindbloom.com/index.html?Eqn_DeltaE_CIE94.html
|
||||
.. [3] F. J. J. Clarke, R. McDonald, and B. Rigg, "Modification to the
|
||||
JPC79 colour-difference formula," J. Soc. Dyers Colour. 100, 128-132
|
||||
(1984).
|
||||
"""
|
||||
L1, C1, h1 = np.rollaxis(lab2lch(lab1), -1)[:3]
|
||||
L2, C2, h2 = np.rollaxis(lab2lch(lab2), -1)[:3]
|
||||
|
||||
dC = C1 - C2
|
||||
dL = L1 - L2
|
||||
dH2 = get_dH2(lab1, lab2)
|
||||
|
||||
T = np.where(np.logical_and(np.rad2deg(h1) >= 164, np.rad2deg(h1) <= 345),
|
||||
0.56 + 0.2 * np.abs(np.cos(h1 + np.deg2rad(168))),
|
||||
0.36 + 0.4 * np.abs(np.cos(h1 + np.deg2rad(35)))
|
||||
)
|
||||
c1_4 = C1 ** 4
|
||||
F = np.sqrt(c1_4 / (c1_4 + 1900))
|
||||
|
||||
SL = np.where(L1 < 16, 0.511, 0.040975 * L1 / (1. + 0.01765 * L1))
|
||||
SC = 0.638 + 0.0638 * C1 / (1. + 0.0131 * C1)
|
||||
SH = SC * (F * T + 1 - F)
|
||||
|
||||
dE2 = (dL / (kL * SL)) ** 2
|
||||
dE2 += (dC / (kC * SC)) ** 2
|
||||
dE2 += dH2 / (SH ** 2)
|
||||
return np.sqrt(dE2)
|
||||
|
||||
|
||||
def get_dH2(lab1, lab2):
|
||||
"""squared hue difference term occurring in deltaE_cmc and deltaE_ciede94
|
||||
|
||||
Despite its name "dH" is not a simple difference of hue values. We avoid
|
||||
working directly with the hue value directly since differencing angles is
|
||||
troublesome. The hue term is usually written as:
|
||||
c1 = sqrt(a1**2 + b1**2)
|
||||
c2 = sqrt(a2**2 + b2**2)
|
||||
term = (a1-a2)**2 + (b1-b2)**2 - (c1-c2)**2
|
||||
dH = sqrt(term)
|
||||
|
||||
However, this has poor roundoff properties when a or b is dominant.
|
||||
Instead, ab is a vector with elements a and b. The same dH term can be
|
||||
re-written as:
|
||||
|ab1-ab2|**2 - (|ab1| - |ab2|)**2
|
||||
and then simplified to:
|
||||
2*|ab1|*|ab2| - 2*dot(ab1, ab2)
|
||||
"""
|
||||
lab1 = np.asarray(lab1)
|
||||
lab2 = np.asarray(lab2)
|
||||
a1, b1 = np.rollaxis(lab1, -1)[1:3]
|
||||
a2, b2 = np.rollaxis(lab2, -1)[1:3]
|
||||
|
||||
# magnitude of (a, b) is the chroma
|
||||
C1 = np.hypot(a1, b1)
|
||||
C2 = np.hypot(a2, b2)
|
||||
|
||||
term = (C1 * C2) - (a1 * a2 + b1 * b2)
|
||||
return 2*term
|
||||
@@ -0,0 +1,38 @@
|
||||
# input, intermediate, and output values for CIEDE2000 dE function
|
||||
# data taken from "The CIEDE2000 Color-Difference Formula: Implementation Notes, ..." http://www.ece.rochester.edu/~gsharma/ciede2000/ciede2000noteCRNA.pdf
|
||||
# tab delimited data
|
||||
# pair 1 L1 a1 b1 ap1 cp1 hp1 hbar1 G T SL SC SH RT dE 2 L2 a2 b2 ap2 cp2 hp2
|
||||
1 1 50.0000 2.6772 -79.7751 2.6774 79.8200 271.9222 270.9611 0.0001 0.6907 1.0000 4.6578 1.8421 -1.7042 2.0425 2 50.0000 0.0000 -82.7485 0.0000 82.7485 270.0000
|
||||
2 1 50.0000 3.1571 -77.2803 3.1573 77.3448 272.3395 271.1698 0.0001 0.6843 1.0000 4.6021 1.8216 -1.7070 2.8615 2 50.0000 0.0000 -82.7485 0.0000 82.7485 270.0000
|
||||
3 1 50.0000 2.8361 -74.0200 2.8363 74.0743 272.1944 271.0972 0.0001 0.6865 1.0000 4.5285 1.8074 -1.7060 3.4412 2 50.0000 0.0000 -82.7485 0.0000 82.7485 270.0000
|
||||
4 1 50.0000 -1.3802 -84.2814 -1.3803 84.2927 269.0618 269.5309 0.0001 0.7357 1.0000 4.7584 1.9217 -1.6809 1.0000 2 50.0000 0.0000 -82.7485 0.0000 82.7485 270.0000
|
||||
5 1 50.0000 -1.1848 -84.8006 -1.1849 84.8089 269.1995 269.5997 0.0001 0.7335 1.0000 4.7700 1.9218 -1.6822 1.0000 2 50.0000 0.0000 -82.7485 0.0000 82.7485 270.0000
|
||||
6 1 50.0000 -0.9009 -85.5211 -0.9009 85.5258 269.3964 269.6982 0.0001 0.7303 1.0000 4.7862 1.9217 -1.6840 1.0000 2 50.0000 0.0000 -82.7485 0.0000 82.7485 270.0000
|
||||
7 1 50.0000 0.0000 0.0000 0.0000 0.0000 0.0000 126.8697 0.5000 1.2200 1.0000 1.0562 1.0229 0.0000 2.3669 2 50.0000 -1.0000 2.0000 -1.5000 2.5000 126.8697
|
||||
8 1 50.0000 -1.0000 2.0000 -1.5000 2.5000 126.8697 126.8697 0.5000 1.2200 1.0000 1.0562 1.0229 0.0000 2.3669 2 50.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
9 1 50.0000 2.4900 -0.0010 3.7346 3.7346 359.9847 269.9854 0.4998 0.7212 1.0000 1.1681 1.0404 -0.0022 7.1792 2 50.0000 -2.4900 0.0009 -3.7346 3.7346 179.9862
|
||||
10 1 50.0000 2.4900 -0.0010 3.7346 3.7346 359.9847 269.9847 0.4998 0.7212 1.0000 1.1681 1.0404 -0.0022 7.1792 2 50.0000 -2.4900 0.0010 -3.7346 3.7346 179.9847
|
||||
11 1 50.0000 2.4900 -0.0010 3.7346 3.7346 359.9847 89.9839 0.4998 0.6175 1.0000 1.1681 1.0346 0.0000 7.2195 2 50.0000 -2.4900 0.0011 -3.7346 3.7346 179.9831
|
||||
12 1 50.0000 2.4900 -0.0010 3.7346 3.7346 359.9847 89.9831 0.4998 0.6175 1.0000 1.1681 1.0346 0.0000 7.2195 2 50.0000 -2.4900 0.0012 -3.7346 3.7346 179.9816
|
||||
13 1 50.0000 -0.0010 2.4900 -0.0015 2.4900 90.0345 180.0328 0.4998 0.9779 1.0000 1.1121 1.0365 0.0000 4.8045 2 50.0000 0.0009 -2.4900 0.0013 2.4900 270.0311
|
||||
14 1 50.0000 -0.0010 2.4900 -0.0015 2.4900 90.0345 180.0345 0.4998 0.9779 1.0000 1.1121 1.0365 0.0000 4.8045 2 50.0000 0.0010 -2.4900 0.0015 2.4900 270.0345
|
||||
15 1 50.0000 -0.0010 2.4900 -0.0015 2.4900 90.0345 0.0362 0.4998 1.3197 1.0000 1.1121 1.0493 0.0000 4.7461 2 50.0000 0.0011 -2.4900 0.0016 2.4900 270.0380
|
||||
16 1 50.0000 2.5000 0.0000 3.7496 3.7496 0.0000 315.0000 0.4998 0.8454 1.0000 1.1406 1.0396 -0.0001 4.3065 2 50.0000 0.0000 -2.5000 0.0000 2.5000 270.0000
|
||||
17 1 50.0000 2.5000 0.0000 3.4569 3.4569 0.0000 346.2470 0.3827 1.4453 1.1608 1.9547 1.4599 -0.0003 27.1492 2 73.0000 25.0000 -18.0000 34.5687 38.9743 332.4939
|
||||
18 1 50.0000 2.5000 0.0000 3.4954 3.4954 0.0000 51.7766 0.3981 0.6447 1.0640 1.7498 1.1612 0.0000 22.8977 2 61.0000 -5.0000 29.0000 -6.9907 29.8307 103.5532
|
||||
19 1 50.0000 2.5000 0.0000 3.5514 3.5514 0.0000 272.2362 0.4206 0.6521 1.0251 1.9455 1.2055 -0.8219 31.9030 2 56.0000 -27.0000 -3.0000 -38.3556 38.4728 184.4723
|
||||
20 1 50.0000 2.5000 0.0000 3.5244 3.5244 0.0000 11.9548 0.4098 1.1031 1.0400 1.9120 1.3353 0.0000 19.4535 2 58.0000 24.0000 15.0000 33.8342 37.0102 23.9095
|
||||
21 1 50.0000 2.5000 0.0000 3.7494 3.7494 0.0000 3.5056 0.4997 1.2616 1.0000 1.1923 1.0808 0.0000 1.0000 2 50.0000 3.1736 0.5854 4.7596 4.7954 7.0113
|
||||
22 1 50.0000 2.5000 0.0000 3.7493 3.7493 0.0000 0.0000 0.4997 1.3202 1.0000 1.1956 1.0861 0.0000 1.0000 2 50.0000 3.2972 0.0000 4.9450 4.9450 0.0000
|
||||
23 1 50.0000 2.5000 0.0000 3.7497 3.7497 0.0000 5.8190 0.4999 1.2197 1.0000 1.1486 1.0604 0.0000 1.0000 2 50.0000 1.8634 0.5757 2.7949 2.8536 11.6380
|
||||
24 1 50.0000 2.5000 0.0000 3.7493 3.7493 0.0000 1.9603 0.4997 1.2883 1.0000 1.1946 1.0836 0.0000 1.0000 2 50.0000 3.2592 0.3350 4.8879 4.8994 3.9206
|
||||
25 1 60.2574 -34.0099 36.2677 -34.0678 49.7590 133.2085 132.0835 0.0017 1.3010 1.1427 3.2946 1.9951 0.0000 1.2644 2 60.4626 -34.1751 39.4387 -34.2333 52.2238 130.9584
|
||||
26 1 63.0109 -31.0961 -5.8663 -32.6194 33.1427 190.1951 188.8221 0.0490 0.9402 1.1831 2.4549 1.4560 0.0000 1.2630 2 62.8187 -29.7946 -4.0864 -31.2542 31.5202 187.4490
|
||||
27 1 61.2901 3.7196 -5.3901 5.5668 7.7487 315.9240 310.0313 0.4966 0.6952 1.1586 1.3092 1.0717 -0.0032 1.8731 2 61.4292 2.2480 -4.9620 3.3644 5.9950 304.1385
|
||||
28 1 35.0831 -44.1164 3.7933 -44.3939 44.5557 175.1161 176.4290 0.0063 1.0168 1.2148 2.9105 1.6476 0.0000 1.8645 2 35.0232 -40.0716 1.5901 -40.3237 40.3550 177.7418
|
||||
29 1 22.7233 20.0904 -46.6940 20.1424 50.8532 293.3339 291.3809 0.0026 0.3636 1.4014 3.1597 1.2617 -1.2537 2.0373 2 23.0331 14.9730 -42.5619 15.0118 45.1317 289.4279
|
||||
30 1 36.4612 47.8580 18.3852 47.9197 51.3256 20.9901 21.8781 0.0013 0.9239 1.1943 3.3888 1.7357 0.0000 1.4146 2 36.2715 50.5065 21.2231 50.5716 54.8444 22.7660
|
||||
31 1 90.8027 -2.0831 1.4410 -3.1245 3.4408 155.2410 167.1011 0.4999 1.1546 1.6110 1.1329 1.0511 0.0000 1.4441 2 91.1528 -1.6435 0.0447 -2.4651 2.4655 178.9612
|
||||
32 1 90.9257 -0.5406 -0.9208 -0.8109 1.2270 228.6315 218.4363 0.5000 1.3916 1.5930 1.0620 1.0288 0.0000 1.5381 2 88.6381 -0.8985 -0.7239 -1.3477 1.5298 208.2412
|
||||
33 1 6.7747 -0.2908 -2.4247 -0.4362 2.4636 259.8025 263.0049 0.4999 0.9556 1.6517 1.1057 1.0337 -0.0004 0.6377 2 5.8714 -0.0985 -2.2286 -0.1477 2.2335 266.2073
|
||||
34 1 2.0776 0.0795 -1.1350 0.1192 1.1412 275.9978 268.0910 0.5000 0.7826 1.7246 1.0383 1.0100 0.0000 0.9082 2 0.9033 -0.0636 -0.5514 -0.0954 0.5596 260.18421
|
||||
@@ -34,6 +34,7 @@ from skimage.color import (rgb2hsv, hsv2rgb,
|
||||
xyz2lab, lab2xyz,
|
||||
lab2rgb, rgb2lab,
|
||||
is_rgb, is_gray,
|
||||
lab2lch, lch2lab,
|
||||
guess_spatial_dimensions
|
||||
)
|
||||
|
||||
@@ -249,6 +250,43 @@ class TestColorconv(TestCase):
|
||||
img_rgb = img_as_float(self.img_rgb)
|
||||
assert_array_almost_equal(lab2rgb(rgb2lab(img_rgb)), img_rgb)
|
||||
|
||||
def test_lab_lch_roundtrip(self):
|
||||
rgb = img_as_float(self.img_rgb)
|
||||
lab = rgb2lab(rgb)
|
||||
lab2 = lch2lab(lab2lch(lab))
|
||||
assert_array_almost_equal(lab2, lab)
|
||||
|
||||
def test_rgb_lch_roundtrip(self):
|
||||
rgb = img_as_float(self.img_rgb)
|
||||
lab = rgb2lab(rgb)
|
||||
lch = lab2lch(lab)
|
||||
lab2 = lch2lab(lch)
|
||||
rgb2 = lab2rgb(lab2)
|
||||
assert_array_almost_equal(rgb, rgb2)
|
||||
|
||||
def test_lab_lch_0d(self):
|
||||
lab0 = self._get_lab0()
|
||||
lch0 = lab2lch(lab0)
|
||||
lch2 = lab2lch(lab0[None, None, :])
|
||||
assert_array_almost_equal(lch0, lch2[0, 0, :])
|
||||
|
||||
def test_lab_lch_1d(self):
|
||||
lab0 = self._get_lab0()
|
||||
lch0 = lab2lch(lab0)
|
||||
lch1 = lab2lch(lab0[None, :])
|
||||
assert_array_almost_equal(lch0, lch1[0, :])
|
||||
|
||||
def test_lab_lch_3d(self):
|
||||
lab0 = self._get_lab0()
|
||||
lch0 = lab2lch(lab0)
|
||||
lch3 = lab2lch(lab0[None, None, None, :])
|
||||
assert_array_almost_equal(lch0, lch3[0, 0, 0, :])
|
||||
|
||||
def _get_lab0(self):
|
||||
rgb = img_as_float(self.img_rgb[:1, :1, :])
|
||||
return rgb2lab(rgb)[0, 0, :]
|
||||
|
||||
|
||||
def test_gray2rgb():
|
||||
x = np.array([0, 0.5, 1])
|
||||
assert_raises(ValueError, gray2rgb, x)
|
||||
|
||||
@@ -0,0 +1,167 @@
|
||||
"""Test for correctness of color distance functions"""
|
||||
from os.path import abspath, dirname, join as pjoin
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import assert_allclose
|
||||
|
||||
from skimage.color import (deltaE_cie76,
|
||||
deltaE_ciede94,
|
||||
deltaE_ciede2000,
|
||||
deltaE_cmc)
|
||||
|
||||
|
||||
def test_ciede2000_dE():
|
||||
data = load_ciede2000_data()
|
||||
N = len(data)
|
||||
lab1 = np.zeros((N, 3))
|
||||
lab1[:, 0] = data['L1']
|
||||
lab1[:, 1] = data['a1']
|
||||
lab1[:, 2] = data['b1']
|
||||
|
||||
lab2 = np.zeros((N, 3))
|
||||
lab2[:, 0] = data['L2']
|
||||
lab2[:, 1] = data['a2']
|
||||
lab2[:, 2] = data['b2']
|
||||
|
||||
dE2 = deltaE_ciede2000(lab1, lab2)
|
||||
|
||||
assert_allclose(dE2, data['dE'], rtol=1.e-4)
|
||||
|
||||
|
||||
def load_ciede2000_data():
|
||||
dtype = [('pair', int),
|
||||
('1', int),
|
||||
('L1', float),
|
||||
('a1', float),
|
||||
('b1', float),
|
||||
('a1_prime', float),
|
||||
('C1_prime', float),
|
||||
('h1_prime', float),
|
||||
('hbar_prime', float),
|
||||
('G', float),
|
||||
('T', float),
|
||||
('SL', float),
|
||||
('SC', float),
|
||||
('SH', float),
|
||||
('RT', float),
|
||||
('dE', float),
|
||||
('2', int),
|
||||
('L2', float),
|
||||
('a2', float),
|
||||
('b2', float),
|
||||
('a2_prime', float),
|
||||
('C2_prime', float),
|
||||
('h2_prime', float),
|
||||
]
|
||||
|
||||
# note: ciede_test_data.txt contains several intermediate quantities
|
||||
path = pjoin(dirname(abspath(__file__)), 'ciede2000_test_data.txt')
|
||||
return np.loadtxt(path, dtype=dtype)
|
||||
|
||||
|
||||
def test_cie76():
|
||||
data = load_ciede2000_data()
|
||||
N = len(data)
|
||||
lab1 = np.zeros((N, 3))
|
||||
lab1[:, 0] = data['L1']
|
||||
lab1[:, 1] = data['a1']
|
||||
lab1[:, 2] = data['b1']
|
||||
|
||||
lab2 = np.zeros((N, 3))
|
||||
lab2[:, 0] = data['L2']
|
||||
lab2[:, 1] = data['a2']
|
||||
lab2[:, 2] = data['b2']
|
||||
|
||||
dE2 = deltaE_cie76(lab1, lab2)
|
||||
oracle = np.array([
|
||||
4.00106328, 6.31415011, 9.1776999, 2.06270077, 2.36957073,
|
||||
2.91529271, 2.23606798, 2.23606798, 4.98000036, 4.9800004,
|
||||
4.98000044, 4.98000049, 4.98000036, 4.9800004, 4.98000044,
|
||||
3.53553391, 36.86800781, 31.91002977, 30.25309901, 27.40894015,
|
||||
0.89242934, 0.7972, 0.8583065, 0.82982507, 3.1819238,
|
||||
2.21334297, 1.53890382, 4.60630929, 6.58467989, 3.88641412,
|
||||
1.50514845, 2.3237848, 0.94413208, 1.31910843
|
||||
])
|
||||
assert_allclose(dE2, oracle, rtol=1.e-8)
|
||||
|
||||
|
||||
def test_ciede94():
|
||||
data = load_ciede2000_data()
|
||||
N = len(data)
|
||||
lab1 = np.zeros((N, 3))
|
||||
lab1[:, 0] = data['L1']
|
||||
lab1[:, 1] = data['a1']
|
||||
lab1[:, 2] = data['b1']
|
||||
|
||||
lab2 = np.zeros((N, 3))
|
||||
lab2[:, 0] = data['L2']
|
||||
lab2[:, 1] = data['a2']
|
||||
lab2[:, 2] = data['b2']
|
||||
|
||||
dE2 = deltaE_ciede94(lab1, lab2)
|
||||
oracle = np.array([
|
||||
1.39503887, 1.93410055, 2.45433566, 0.68449187, 0.6695627,
|
||||
0.69194527, 2.23606798, 2.03163832, 4.80069441, 4.80069445,
|
||||
4.80069449, 4.80069453, 4.80069441, 4.80069445, 4.80069449,
|
||||
3.40774352, 34.6891632, 29.44137328, 27.91408781, 24.93766082,
|
||||
0.82213163, 0.71658427, 0.8048753, 0.75284394, 1.39099471,
|
||||
1.24808929, 1.29795787, 1.82045088, 2.55613309, 1.42491303,
|
||||
1.41945261, 2.3225685, 0.93853308, 1.30654464
|
||||
])
|
||||
assert_allclose(dE2, oracle, rtol=1.e-8)
|
||||
|
||||
|
||||
def test_cmc():
|
||||
data = load_ciede2000_data()
|
||||
N = len(data)
|
||||
lab1 = np.zeros((N, 3))
|
||||
lab1[:, 0] = data['L1']
|
||||
lab1[:, 1] = data['a1']
|
||||
lab1[:, 2] = data['b1']
|
||||
|
||||
lab2 = np.zeros((N, 3))
|
||||
lab2[:, 0] = data['L2']
|
||||
lab2[:, 1] = data['a2']
|
||||
lab2[:, 2] = data['b2']
|
||||
|
||||
dE2 = deltaE_cmc(lab1, lab2)
|
||||
oracle = np.array([
|
||||
1.73873611, 2.49660844, 3.30494501, 0.85735576, 0.88332927,
|
||||
0.97822692, 3.50480874, 2.87930032, 6.5783807, 6.57838075,
|
||||
6.5783808, 6.57838086, 6.67492321, 6.67492326, 6.67492331,
|
||||
4.66852997, 42.10875485, 39.45889064, 38.36005919, 33.93663807,
|
||||
1.14400168, 1.00600419, 1.11302547, 1.05335328, 1.42822951,
|
||||
1.2548143, 1.76838061, 2.02583367, 3.08695508, 1.74893533,
|
||||
1.90095165, 1.70258148, 1.80317207, 2.44934417
|
||||
])
|
||||
|
||||
assert_allclose(dE2, oracle, rtol=1.e-8)
|
||||
|
||||
|
||||
def test_single_color_cie76():
|
||||
lab1 = (0.5, 0.5, 0.5)
|
||||
lab2 = (0.4, 0.4, 0.4)
|
||||
deltaE_cie76(lab1, lab2)
|
||||
|
||||
|
||||
def test_single_color_ciede94():
|
||||
lab1 = (0.5, 0.5, 0.5)
|
||||
lab2 = (0.4, 0.4, 0.4)
|
||||
deltaE_ciede94(lab1, lab2)
|
||||
|
||||
|
||||
def test_single_color_ciede2000():
|
||||
lab1 = (0.5, 0.5, 0.5)
|
||||
lab2 = (0.4, 0.4, 0.4)
|
||||
deltaE_ciede2000(lab1, lab2)
|
||||
|
||||
|
||||
def test_single_color_cmc():
|
||||
lab1 = (0.5, 0.5, 0.5)
|
||||
lab2 = (0.4, 0.4, 0.4)
|
||||
deltaE_cmc(lab1, lab2)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from numpy.testing import run_module_suite
|
||||
run_module_suite()
|
||||
Reference in New Issue
Block a user