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add color distance functions, ciede2000 tests
This commit is contained in:
Matt Terry
@@ -44,6 +44,12 @@ from .colorconv import (convert_colorspace,
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from .colorlabel import color_dict, label2rgb
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from .delta_e import (deltaE_cie76,
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deltaE_ciede94,
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deltaE_ciede2000,
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deltaE_cmc,
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)
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__all__ = ['convert_colorspace',
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'guess_spatial_dimensions',
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@@ -89,4 +95,6 @@ __all__ = ['convert_colorspace',
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'is_rgb',
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'is_gray',
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'color_dict',
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'label2rgb']
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'label2rgb',
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'deltaE_ciede2000',
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]
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@@ -26,10 +26,13 @@ Supported color spaces
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Derived from the RGB CIE color space. Chosen such that
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``x == y == z == 1/3`` at the whitepoint, and all color matching
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functions are greater than zero everywhere.
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* LAB CIE : Lightness, a, b
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* LCH CIE : Lightness, Chroma, Hue
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:author: Nicolas Pinto (rgb2hsv)
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:author: Ralf Gommers (hsv2rgb)
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:author: Travis Oliphant (XYZ and RGB CIE functions)
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:author: Matt Terry (lab2lch)
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:license: modified BSD
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@@ -1026,3 +1029,94 @@ def combine_stains(stains, conv_matrix):
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logrgb2 = np.dot(-np.reshape(stains, (-1, 3)), conv_matrix)
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rgb2 = np.exp(logrgb2)
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return rescale_intensity(np.reshape(rgb2 - 2, stains.shape), in_range=(-1, 1))
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def lab2lch(lab):
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"""CIE-LAB to LCH color space conversion.
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TODO: something about cylindrical representation
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Parameters
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----------
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lab : array_like
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The image in CIE-LAB format, in a 3-D array of shape (.., .., 3).
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Returns
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-------
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out : ndarray
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The image in LCH format, in a 3-D array of shape (.., .., 3).
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Raises
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------
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ValueError
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If `rgb` is not a 3-D array of shape (.., .., 3).
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References
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----------
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Notes
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-----
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The Hue is expressed as an angle between (0, 2*pi)
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Examples
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--------
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>>> from skimage import data
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>>> from skimage.color import rgb2lab, lab2lch
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>>> lena = data.lena()
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>>> lena_lab = rgb2lab(lena)
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>>> lena_lch = lab2lch(lena_lab)
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"""
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shape = lab.shape
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if len(shape) != 3 or shape[2] != 3:
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raise ValueError("Input image expected to be LAB")
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lab = _prepare_colorarray(lab)
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lch = np.empty_like(lab)
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a, b = lab[:, :, 1], lab[:, :, 2]
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lch[:, :, 0] = lab[:, :, 0]
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lch[:, :, 1] = np.sqrt(a**2 + b**2) # C
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H = lch[:, :, 2] = np.arctan2(b, a)
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H[H < 0] += 2*np.pi # (-pi, pi) -> (0, 2*pi)
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return lch
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def lch2lab(lch):
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"""CIE-LCH to CIE-LAB color space conversion.
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TODO: something about cylindrical representation
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Parameters
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----------
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lab : array_like
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The image in CIE-LCH format, in a 3-D array of shape (.., .., 3).
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Returns
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-------
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out : ndarray
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The image in LAB format, in a 3-D array of shape (.., .., 3).
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Raises
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------
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ValueError
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If `rgb` is not a 3-D array of shape (.., .., 3).
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References
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----------
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Examples
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--------
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>>> from skimage import data
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>>> from skimage.color import rgb2lab, lch2lab
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>>> lena = data.lena()
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>>> lena_lab = rgb2lab(lena)
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>>> lena_lch = lab2lch(lena_lab)
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>>> lena_lab2 = lch2lab(lena_lch)
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"""
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lch = _prepare_colorarray(lch)
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lab = np.empty_like(lch)
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c, h = lch[:, :, 1], lch[:, :, 2]
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lab[:, :, 0] = lch[:, :, 0]
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lab[:, :, 1] = c*np.cos(h)
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lab[:, :, 2] = c*np.sin(h)
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return lab
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@@ -0,0 +1,215 @@
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"""
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Functions for calculating the "distance" between colors.
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Implicit in these definitions of "distance" is the notion of "Just Noticible
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Distance" (JND). This represents the distance between colors where a human can
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percieve different colors. Humans are more sensitive to certain colors than
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others, which different deltaE metrics correct for this with varying degrees of
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sophistication.
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:author: Matt Terry
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:license: modified BSD
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Reference
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---------
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"""
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from __future__ import division
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import numpy as np
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from .colorconv import lab2lch
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DEG = np.pi/180
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def _unpack_last(x):
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x = np.asarray(x)
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shape = x.shape
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return [x[..., i] for i in range(shape[-1])]
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def _arctan2pi(b, a):
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"""np.arctan2 mapped to (0, 2*pi)"""
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ans = np.arctan2(b, a)
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ans += np.where(ans < 0, 2*np.pi, 0.)
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assert ans.max() <= 2*np.pi
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assert ans.min() >= 0.
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return ans
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def deltaE_cie76(lab1, lab2):
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"""
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"just noticible difference" ~ 2.3
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"""
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l1, a1, b1 = _unpack_last(lab1)
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l2, a2, b2 = _unpack_last(lab2)
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return np.sqrt((l2-l1)**2 + (a2-a1)**2 + (b2-b1)**2)
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def deltaE_ciede94(lab1, lab2, kC=1, kH=1, kL=1, k1=0.045, k2=0.015):
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"""
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kC, kH are weighting factors, usually unity (default)
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kL, k1, k2 depend on the application. Sample values are:
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kL, k1, k2 = 1, 0.045, 0.015 (graphic arts, default)
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kL, k1, k2 = 2, 0.048, 0.014 (textiles)
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Note: deltaE_ciede94 the defines the scales for the lightness, hue, and
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chroma in terms of the first color. Consequently
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deltaE_ciede94(lab1, lab2) != deltaE_ciede94(lab2, lab1)
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"""
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l1, a1, b1 = _unpack_last(lab1)
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l2, a2, b2 = _unpack_last(lab2)
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dl = l1 - l2
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c1 = np.sqrt(a1**2 + b1**2)
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c2 = np.sqrt(a2**2 + b2**2)
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da = a1 - a2
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db = b1 - b2
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dc = c1 - c2
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dh_ab = np.sqrt(da**2 + db**2 + dc**2)
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SL = 1
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SC = 1 + k1*c1
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SH = 1 + k2*c1
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ans = (dl/(kL*SL))**2 + (dc/(kC*SC))**2 + (dh_ab/(kH*SH))**2
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return np.sqrt(ans)
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def deltaE_ciede2000(lab1, lab2, kL=1, kC=1, kH=1):
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"""
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Parameters
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----------
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lab1 : array_like
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pass
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lab2 : array_like
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pass
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kL : float (range), optional
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pass
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kC : float (range), optional
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pass
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kH : float (range), optional
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pass
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Returns
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-------
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deltaE : array_like
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The distance between `lab1` and `lab2`
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Notes
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-----
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CIEDE 2000 assumes parametric weighting factors for the luminance, chroma,
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and hue (kL, kC, kH respectively).
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kL = 1 # graphic arts
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kL = 2 # textiles
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References
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----------
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http://www.ece.rochester.edu/~gsharma/ciede2000/ciede2000noteCRNA.pdf
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"""
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L1, a1, b1 = _unpack_last(lab1)
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L2, a2, b2 = _unpack_last(lab2)
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c1 = np.sqrt(a1**2 + b1**2)
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c2 = np.sqrt(a2**2 + b2**2)
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cbar = 0.5*(c1 + c2)
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c7 = cbar**7
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G = 0.5 * (1 - np.sqrt(c7/(c7 + 25**7)))
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dL_prime = L2 - L1
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Lbar = 0.5*(L1 + L2)
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a1_prime = a1 * (1 + G)
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a2_prime = a2 * (1 + G)
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c1_prime = np.sqrt(a1_prime**2 + b1**2)
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c2_prime = np.sqrt(a2_prime**2 + b2**2)
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cbar_prime = 0.5*(c1_prime + c2_prime)
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dC_prime = c2_prime - c1_prime
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h1_prime = _arctan2pi(b1, a1_prime)
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h2_prime = _arctan2pi(b2, a2_prime)
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dh_prime = h2_prime - h1_prime
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cc = c1_prime * c2_prime
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mask1 = cc == 0.
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mask2 = (-mask1) * (dh_prime > np.pi)
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mask3 = (-mask1) * (dh_prime < -np.pi)
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dh_prime[mask1] = 0.
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dh_prime[mask2] += 2*np.pi
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dh_prime[mask3] -= 2*np.pi
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dH_prime = 2 * np.sqrt(cc) * np.sin(dh_prime/2)
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Hbar_prime = h1_prime + h2_prime
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mask0 = np.logical_and(np.abs(h1_prime - h2_prime) > np.pi, cc != 0.)
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mask1 = np.logical_and(mask0, Hbar_prime < 2*np.pi)
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mask2 = np.logical_and(mask0, Hbar_prime >= 2*np.pi)
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Hbar_prime[mask1] += 2*np.pi
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Hbar_prime[mask2] -= 2*np.pi
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Hbar_prime[cc == 0.] *= 2
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Hbar_prime *= 0.5
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deg = np.pi/180.
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T = (1 -
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0.17 * np.cos(Hbar_prime - 30*deg) +
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0.24 * np.cos(2*Hbar_prime) +
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0.32 * np.cos(3*Hbar_prime + 6*deg) -
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0.20 * np.cos(4*Hbar_prime - 63*deg)
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)
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dTheta = 30*deg * np.exp(-((Hbar_prime/deg - 275)/25)**2)
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c7 = cbar_prime**7
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Rc = 2 * np.sqrt(c7 / (c7 + 25**7))
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term = (Lbar - 50)**2
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SL = 1 + 0.015*term/np.sqrt(20 + term)
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SC = 1 + 0.045*cbar_prime
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SH = 1 + 0.015*cbar_prime * T
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RT = -np.sin(2*dTheta) * Rc
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l_term = dL_prime / (kL * SL)
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c_term = dC_prime / (kC * SC)
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h_term = dH_prime / (kH * SH)
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r_term = RT * c_term * h_term
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dE2 = l_term**2
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dE2 += c_term**2
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dE2 += h_term**2
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dE2 += r_term
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return np.sqrt(dE2)
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def deltaE_cmc(lab1, lab2):
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"""
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indistinguishable if < 1
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usual value for "different" is > 2
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Note: deltaE_cmc the defines the scales for the lightness, hue, and chroma
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in terms of the first color. Consequently
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deltaE_cmc(lab1, lab2) != deltaE_cmc(lab2, lab1)
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"""
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l1, c1, h1 = _unpack_last(lab2lch(lab1))
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l2, c2, h2 = _unpack_last(lab2lch(lab2))
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sl = np.where(l1 < 16, 0.511, 0.040975*l1 / (1 + 0.01765*l1))
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sc = 0.638 + 0.0638*c1 / (1 + 0.0131*c1)
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c1_4 = c1**4
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f = np.sqrt(c1_4 / (c1_4 + 1900))
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t = np.where(np.logical_and(h1 >= 2.862, h1 <= 6.021),
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0.56 * 0.2 * np.abs(np.cos(h1 + 2.93)),
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0.36 + 0.4 * np.abs(np.cos(h1 + 0.611))
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)
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sh = sc * (f*t + 1-f)
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l, c = 1, 1
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ans = ((l2 - l1)/(l * sl))**2
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ans += ((c2 - c1)/(c * sc))**2
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deg = np.pi/180.
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ans += ((h2 - h1)*deg/sh)**2 # metric defines h in terms of degrees
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return np.sqrt(ans)
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@@ -0,0 +1,37 @@
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# input, intermediate, and output values for CIEDE2000 dE function
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# data taken from "The CIEDE2000 Color-Difference Formula: Implementation Notes, ..." http://www.ece.rochester.edu/~gsharma/ciede2000/ciede2000noteCRNA.pdf
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# pair 1 L1 a1 b1 ap1 cp1 hp1 hbar1 G T SL SC SH RT dE 2 L2 a2 b2 ap2 cp2 hp2
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
|
||||
@@ -0,0 +1,61 @@
|
||||
"""Test for correctness of color distance functions
|
||||
|
||||
Authors
|
||||
-------
|
||||
Matt Terry
|
||||
|
||||
:license: modified BSD
|
||||
"""
|
||||
from os.path import abspath, dirname, join as pjoin
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import assert_array_almost_equal
|
||||
|
||||
from skimage.color import deltaE_ciede2000
|
||||
|
||||
|
||||
def test_ciede2000_dE():
|
||||
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__)), 'ciede_test_data.txt')
|
||||
data = np.loadtxt(path, dtype=dtype)
|
||||
|
||||
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_array_almost_equal(dE2, data['dE'])
|
||||
Reference in New Issue
Block a user