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https://github.com/wassname/scikit-image.git
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Luv colorspace conversions
Adding XYZ <--> Luv, RGB <--> Adding test functions to mirror Lab tests
This commit is contained in:
Matthew Trentacoste
@@ -13,6 +13,10 @@ from .colorconv import (convert_colorspace,
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lab2xyz,
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lab2rgb,
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rgb2lab,
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xyz2luv,
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luv2xyz,
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luv2rgb,
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rgb2luv,
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rgb2hed,
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hed2rgb,
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lab2lch,
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@@ -849,6 +849,197 @@ def lab2rgb(lab):
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return xyz2rgb(lab2xyz(lab))
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def xyz2luv(xyz):
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"""XYZ to CIE-Luv color space conversion.
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Parameters
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----------
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xyz : array_like
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The image in XYZ format, in a 3- or 4-D array of shape
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(.., ..,[ ..,] 3).
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Returns
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-------
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out : ndarray
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The image in CIE-Luv format, in a 3- or 4-D array of shape
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(.., ..,[ ..,] 3).
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Raises
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------
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ValueError
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If `xyz` is not a 3-D array of shape (.., ..,[ ..,] 3).
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Notes
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-----
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Observer= 2A, Illuminant= D65
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CIE XYZ tristimulus values x_ref = 95.047, y_ref = 100., z_ref = 108.883
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References
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----------
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.. [1] http://www.easyrgb.com/index.php?X=MATH&H=16#text16
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.. [2] http://en.wikipedia.org/wiki/CIELUV
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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 rgb2xyz, xyz2luv
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>>> lena = data.lena()
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>>> lena_xyz = rgb2xyz(lena)
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>>> lena_luv = xyz2luv(lena_xyz)
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"""
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arr = _prepare_colorarray(xyz)
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# extract channels
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x, y, z = arr[..., 0], arr[..., 1], arr[..., 2]
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machineEps = np.finfo(np.float).eps
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# compute y_r and L
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L = y / lab_ref_white[1]
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mask = L > 0.008856
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L[mask] = 116. * np.power( L[mask], 1. / 3. ) - 16.
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L[~mask] = 903.3 * L[~mask]
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u0 = 4*lab_ref_white[0] / np.dot( [1, 15, 3], lab_ref_white )
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v0 = 9*lab_ref_white[1] / np.dot( [1, 15, 3], lab_ref_white )
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def div_nan_check( n, d ):
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out = n / d
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mask = np.isnan( out )
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out[mask] = 0.
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return out
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# u' and v' helper functions
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def fu( X, Y, Z ):
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return ( 4.*X ) / ( X + 15.*Y + 3.*Z + machineEps )
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def fv( X, Y, Z ):
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return ( 9.*Y ) / ( X + 15.*Y + 3.*Z + machineEps )
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# compute u and v using helper functions
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u = 13.*L * ( fu(x,y,z) - u0 )
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v = 13.*L * ( fv(x,y,z) - v0 )
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return np.concatenate([x[..., np.newaxis] for x in [L, u, v]], axis=-1)
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def luv2xyz(luv):
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"""CIE-LAB to XYZcolor space conversion.
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Parameters
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----------
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lab : array_like
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The image in 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 XYZ 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 `lab` is not a 3-D array of shape (.., .., 3).
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Notes
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-----
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Observer= 2A, Illuminant= D65
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CIE XYZ tristimulus values x_ref = 95.047, y_ref = 100., z_ref = 108.883
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References
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----------
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.. [1] http://www.easyrgb.com/index.php?X=MATH&H=16#text16
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.. [2] http://en.wikipedia.org/wiki/CIELUV
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"""
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arr = _prepare_colorarray(luv).copy()
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L, u, v = arr[:, :, 0], arr[:, :, 1], arr[:, :, 2]
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machineEps = np.finfo(np.float).eps
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# compute y
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y = L.copy()
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mask = y > 7.999625
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y[mask] = np.power( (y[mask]+16.) / 116., 3.)
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y[~mask] = y[~mask] / 903.3
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y *= lab_ref_white[1]
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# reference white x,z
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uv_weights = [1, 15, 3]
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u0 = 4*lab_ref_white[0] / np.dot( uv_weights, lab_ref_white )
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v0 = 9*lab_ref_white[1] / np.dot( uv_weights, lab_ref_white )
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def div_nan_check( n, d ):
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out = n / d
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mask = np.isnan( out )
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out[mask] = 0.
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return out
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# compute intermediate values
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a = u0 + u / ( 13.*L + machineEps )
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b = v0 + v / ( 13.*L + machineEps )
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c = 3*y * (5*b-3)
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# compute x and z
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z = ( (a-4)*c - 15*a*b*y ) / ( 12*b )
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x = -( c/b + 3.*z )
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return np.concatenate([x[..., np.newaxis] for x in [x, y, z]], axis=-1)
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def rgb2luv(rgb):
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"""RGB to luv color space conversion.
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Parameters
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----------
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rgb : array_like
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The image in RGB format, in a 3- or 4-D array of shape
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(.., ..,[ ..,] 3).
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Returns
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-------
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out : ndarray
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The image in Luv format, in a 3- or 4-D array of shape
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(.., ..,[ ..,] 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- or 4-D array of shape (.., ..,[ ..,] 3).
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Notes
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-----
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This function uses rgb2xyz and xyz2luv.
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"""
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return xyz2luv(rgb2xyz(rgb))
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def luv2rgb(luv):
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"""Luv to RGB color space conversion.
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Parameters
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----------
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rgb : array_like
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The image in Luv 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 RGB 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 `luv` is not a 3-D array of shape (.., .., 3).
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Notes
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-----
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This function uses luv2xyz and xyz2rgb.
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"""
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return xyz2rgb(luv2xyz(luv))
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def rgb2hed(rgb):
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"""RGB to Haematoxylin-Eosin-DAB (HED) color space conversion.
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@@ -33,6 +33,8 @@ from skimage.color import (rgb2hsv, hsv2rgb,
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rgb2grey, gray2rgb,
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xyz2lab, lab2xyz,
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lab2rgb, rgb2lab,
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xyz2luv, luv2xyz,
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luv2rgb, rgb2luv,
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is_rgb, is_gray,
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lab2lch, lch2lab,
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guess_spatial_dimensions
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@@ -81,6 +83,13 @@ class TestColorconv(TestCase):
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[[46.229, -51.7, 49.898]], # green
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])
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luv_array = np.array([[[53.233, 175.053, 37.751]], # red
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[[0., 0., 0.]], # black
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[[100., 0.001, -0.017]], # white
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[[32.303, -9.400, -130.358]], # blue
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[[46.228, -43.774, 56.589]], # green
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])
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# RGB to HSV
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def test_rgb2hsv_conversion(self):
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rgb = img_as_float(self.img_rgb)[::16, ::16]
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@@ -250,6 +259,39 @@ class TestColorconv(TestCase):
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img_rgb = img_as_float(self.img_rgb)
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assert_array_almost_equal(lab2rgb(rgb2lab(img_rgb)), img_rgb)
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# test matrices for xyz2luv and luv2xyz generated using http://www.easyrgb.com/index.php?X=CALC
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# Note: easyrgb website displays xyz*100
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def test_xyz2luv(self):
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assert_array_almost_equal(xyz2luv(self.xyz_array),
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self.luv_array, decimal=3)
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def test_luv2xyz(self):
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assert_array_almost_equal(luv2xyz(self.luv_array),
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self.xyz_array, decimal=3)
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def test_rgb2luv_brucelindbloom(self):
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"""
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Test the RGB->Lab conversion by comparing to the calculator on the
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authoritative Bruce Lindbloom
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[website](http://brucelindbloom.com/index.html?ColorCalculator.html).
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"""
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# Obtained with D65 white point, sRGB model and gamma
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gt_for_colbars = np.array([
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[100,0,0],
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[97.1393, 7.7056, 106.7866],
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[91.1132, -70.4773, -15.2042],
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[87.7347, -83.0776, 107.3985],
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[60.3242, 84.0714, -108.6834],
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[53.2408, 175.0151, 37.7564],
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[32.2970, -9.4054, -130.3423],
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[0,0,0]]).T
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gt_array = np.swapaxes(gt_for_colbars.reshape(3, 4, 2), 0, 2)
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assert_array_almost_equal(rgb2luv(self.colbars_array), gt_array, decimal=2)
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def test_luv_rgb_roundtrip(self):
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img_rgb = img_as_float(self.img_rgb)
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assert_array_almost_equal(luv2rgb(rgb2luv(img_rgb)), img_rgb)
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def test_lab_lch_roundtrip(self):
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rgb = img_as_float(self.img_rgb)
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lab = rgb2lab(rgb)
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