def test_od_to_rgb(self):
np.testing.assert_array_almost_equal(
od_to_rgb(np.zeros((3, 3, 3)) + 35.6158),
np.zeros((3, 3, 3)) + 116.99987889)
# check corner cases
np.testing.assert_array_almost_equal(od_to_rgb(np.zeros((3, 3, 3))),
np.zeros((3, 3, 3)) + 255.0)
np.testing.assert_array_almost_equal(
od_to_rgb(np.zeros((3, 3, 3)) + 255.0), np.zeros((3, 3, 3)))
def test_rgb_to_od_to_rgb(self):
np.random.seed(1)
im_rand = np.random.randint(0, 255, (10, 10, 3))
np.testing.assert_array_almost_equal(od_to_rgb(rgb_to_od(im_rand)),
im_rand)
def test_od_to_rgb(self):
np.testing.assert_array_almost_equal(
od_to_rgb(np.zeros((3, 3, 3)) + 35.6158),
np.zeros((3, 3, 3)) + 116.99987889
)
# check corner cases
np.testing.assert_array_almost_equal(
od_to_rgb(np.zeros((3, 3, 3))),
np.zeros((3, 3, 3)) + 255.0
)
np.testing.assert_array_almost_equal(
od_to_rgb(np.zeros((3, 3, 3)) + 255.0),
np.zeros((3, 3, 3))
)
def test_rgb_to_od_to_rgb(self):
np.random.seed(1)
im_rand = np.random.randint(0, 255, (10, 10, 3))
np.testing.assert_array_almost_equal(
od_to_rgb(rgb_to_od(im_rand)),
im_rand
)
def color_convolution(im_stains, w):
"""Performs Color Convolution
Reconstructs a color image from the stain matrix `w` and
the individual images stored as channels in `im_stains` and generated
by ColorDeconvolution.
Parameters
----------
im_stains : array_like
An RGB image where in each channel contains image of one stain
w : array_like
A 3x3 matrix containing the stain colors in its columns.
In the case of two stains, the third column is zero and will be
complemented using cross-product. The matrix should contain a
minumum two nonzero columns.
Returns
-------
im_rgb : array_like
Reconstructed RGB image with intensity values ranging from [0, 255],
suitable for display.
See Also
--------
histomicstk.preprocessing.color_deconvolution.complement_stain_matrix,
histomicstk.preprocessing.color_deconvolution.color_deconvolution
histomicstk.preprocessing.color_conversion.rgb_to_od
histomicstk.preprocessing.color_conversion.od_to_rgb
"""
# transform 3D input stain image to 2D stain matrix format
m = im_stains.shape[0]
n = im_stains.shape[1]
im_stains = np.reshape(im_stains, (m * n, 3))
# transform input stains to optical density values, convolve and
# tfm back to stain
im_stains = im_stains.astype(dtype=np.float32)
ODfwd = color_conversion.rgb_to_od(im_stains)
ODdeconv = np.dot(ODfwd, np.transpose(w))
ODinv = color_conversion.od_to_rgb(ODdeconv)
# reshape output, transform type
im_rgb = np.reshape(ODinv, (m, n, 3))
im_rgb[im_rgb > 255] = 255
im_rgb = im_rgb.astype(np.uint8)
return im_rgb
def ColorConvolution(I, W):
"""Performs Color Convolution
Reconstructs a color image from the stain matrix `W` and
the individual images stored as channels in `I` and generated
by ColorDeconvolution.
Parameters
----------
I : array_like
An RGB image where in each channel contains image of one stain
W : array_like
A 3x3 matrix containing the stain colors in its columns.
In the case of two stains, the third column is zero and will be
complemented using cross-product. The matrix should contain a
minumum two nonzero columns.
Returns
-------
IOut : array_like
Reconstructed RGB image with intensity values ranging from [0, 255],
suitable for display.
See Also
--------
histomicstk.preprocessing.color_deconvolution.ComplementStainMatrix,
histomicstk.preprocessing.color_deconvolution.ColorDeconvolution
histomicstk.preprocessing.color_conversion.rgb_to_od
histomicstk.preprocessing.color_conversion.od_to_rgb
"""
# transform 3D input stain image to 2D stain matrix format
m = I.shape[0]
n = I.shape[1]
I = np.reshape(I, (m * n, 3))
# transform input stains to optical density values, convolve and
# tfm back to stain
I = I.astype(dtype=np.float32)
ODfwd = color_conversion.rgb_to_od(I)
ODdeconv = np.dot(ODfwd, np.transpose(W))
ODinv = color_conversion.od_to_rgb(ODdeconv)
# reshape output, transform type
IOut = np.reshape(ODinv, (m, n, 3))
IOut[IOut > 255] = 255
IOut = IOut.astype(np.uint8)
return IOut
def color_deconvolution(im_rgb, w):
"""Performs color deconvolution.
The given RGB Image `I` is first first transformed into optical density
space, and then projected onto the stain vectors in the columns of the
3x3 stain matrix `W`.
For deconvolving H&E stained image use:
`w` = array([[0.650, 0.072, 0], [0.704, 0.990, 0], [0.286, 0.105, 0]])
Parameters
----------
im_rgb : array_like
Input RGB Image that needs to be deconvolved.
w : array_like
A 3x3 matrix containing the color vectors in columns.
For two stain images the third column is zero and will be
complemented using cross-product. Atleast two of the three
columns must be non-zero.
Returns
-------
Stains : array_like
An rgb image where in each channel contains the image of the
stain of the corresponding column in the stain matrix `W`.
The intensity range of each channel is [0, 255] suitable for
displaying.
StainsFloat : array_like
An intensity image of deconvolved stains that is unbounded,
suitable for reconstructing color images of deconvolved stains
with color_convolution.
wc : array_like
A 3x3 complemented stain matrix. Useful for color image
reconstruction with color_convolution.
See Also
--------
histomicstk.preprocessing.color_deconvolution.complement_stain_matrix,
histomicstk.preprocessing.color_deconvolution.color_convolution
histomicstk.preprocessing.color_conversion.rgb_to_od
histomicstk.preprocessing.color_conversion.od_to_rgb
"""
# complement stain matrix if needed
if numpy.linalg.norm(w[:, 2]) <= 1e-16:
wc = complement_stain_matrix(w)
else:
wc = w.copy()
# normalize stains to unit-norm
for i in range(wc.shape[1]):
Norm = numpy.linalg.norm(wc[:, i])
if Norm >= 1e-16:
wc[:, i] /= Norm
# invert stain matrix
Q = numpy.linalg.inv(wc)
# transform 3D input image to 2D RGB matrix format
m = im_rgb.shape[0]
n = im_rgb.shape[1]
if im_rgb.shape[2] == 4:
im_rgb = im_rgb[:, :, (0, 1, 2)]
im_rgb = numpy.reshape(im_rgb, (m * n, 3))
# transform input RGB to optical density values and deconvolve,
# tfm back to RGB
im_rgb = im_rgb.astype(dtype=numpy.float32)
im_rgb[im_rgb == 0] = 1e-16
ODfwd = color_conversion.rgb_to_od(im_rgb)
ODdeconv = numpy.dot(ODfwd, numpy.transpose(Q))
ODinv = color_conversion.od_to_rgb(ODdeconv)
# reshape output
StainsFloat = numpy.reshape(ODinv, (m, n, 3))
# transform type
Stains = numpy.copy(StainsFloat)
Stains[Stains > 255] = 255
Stains = Stains.astype(numpy.uint8)
# return
Unmixed = collections.namedtuple('Unmixed',
['Stains', 'StainsFloat', 'Wc'])
Output = Unmixed(Stains, StainsFloat, wc)
return Output
def ColorDeconvolution(I, W):
"""Performs color deconvolution.
The given RGB Image `I` is first first transformed into optical density
space, and then projected onto the stain vectors in the columns of the
3x3 stain matrix `W`.
For deconvolving H&E stained image use:
`W` = array([[0.650, 0.072, 0], [0.704, 0.990, 0], [0.286, 0.105, 0]])
Parameters
----------
I : array_like
Input RGB Image that needs to be deconvolved.
W : array_like
A 3x3 matrix containing the color vectors in columns.
For two stain images the third column is zero and will be
complemented using cross-product. Atleast two of the three
columns must be non-zero.
Returns
-------
Stains : array_like
An rgb image where in each channel contains the image of the
stain of the corresponding column in the stain matrix `W`.
The intensity range of each channel is [0, 255] suitable for
displaying.
StainsFloat : array_like
An intensity image of deconvolved stains that is unbounded,
suitable for reconstructing color images of deconvolved stains
with ColorConvolution.
Wc : array_like
A 3x3 complemented stain matrix. Useful for color image
reconstruction with ColorConvolution.
See Also
--------
histomicstk.preprocessing.color_deconvolution.ComplementStainMatrix,
histomicstk.preprocessing.color_deconvolution.ColorConvolution
histomicstk.preprocessing.color_conversion.rgb_to_od
histomicstk.preprocessing.color_conversion.od_to_rgb
"""
# complement stain matrix if needed
if numpy.linalg.norm(W[:, 2]) <= 1e-16:
Wc = ComplementStainMatrix(W)
else:
Wc = W.copy()
# normalize stains to unit-norm
for i in range(Wc.shape[1]):
Norm = numpy.linalg.norm(Wc[:, i])
if Norm >= 1e-16:
Wc[:, i] /= Norm
# invert stain matrix
Q = numpy.linalg.inv(Wc)
# transform 3D input image to 2D RGB matrix format
m = I.shape[0]
n = I.shape[1]
if I.shape[2] == 4:
I = I[:, :, (0, 1, 2)]
I = numpy.reshape(I, (m * n, 3))
# transform input RGB to optical density values and deconvolve,
# tfm back to RGB
I = I.astype(dtype=numpy.float32)
I[I == 0] = 1e-16
ODfwd = color_conversion.rgb_to_od(I)
ODdeconv = numpy.dot(ODfwd, numpy.transpose(Q))
ODinv = color_conversion.od_to_rgb(ODdeconv)
# reshape output
StainsFloat = numpy.reshape(ODinv, (m, n, 3))
# transform type
Stains = numpy.copy(StainsFloat)
Stains[Stains > 255] = 255
Stains = Stains.astype(numpy.uint8)
# return
Unmixed = collections.namedtuple('Unmixed',
['Stains', 'StainsFloat', 'Wc'])
Output = Unmixed(Stains, StainsFloat, Wc)
return Output
