def test_hdx_rgb_roundtrip(self):
from skimage.color.colorconv import hdx_from_rgb, rgb_from_hdx
img_rgb = self.img_rgb
conv = combine_stains(separate_stains(img_rgb, hdx_from_rgb),
rgb_from_hdx)
with expected_warnings(['precision loss']):
assert_equal(img_as_ubyte(conv), img_rgb)
def test_hdx_rgb_roundtrip(self):
from skimage.color.colorconv import hdx_from_rgb, rgb_from_hdx
img_rgb = self.img_rgb
conv = combine_stains(separate_stains(img_rgb, hdx_from_rgb),
rgb_from_hdx)
with expected_warnings('precision loss'):
assert_equal(img_as_ubyte(conv), img_rgb)
def color_deconvolution(img, sep_matrix, comb_matrix, component_idx):
separated = separate_stains(img, sep_matrix)
stains = np.zeros_like(separated)
stains[:, :, component_idx] = separated[:, :, component_idx]
combined = combine_stains(stains, comb_matrix)
return _convert(combined, img.dtype)
def test_bro_rgb_roundtrip_float(self, channel_axis):
from skimage.color.colorconv import bro_from_rgb, rgb_from_bro
img_in = self.img_stains
img_in = np.moveaxis(img_in, source=-1, destination=channel_axis)
img_out = combine_stains(img_in,
rgb_from_bro,
channel_axis=channel_axis)
img_out = separate_stains(img_out,
bro_from_rgb,
channel_axis=channel_axis)
assert_array_almost_equal(img_out, img_in)
def test_hdx_rgb_roundtrip_float(self):
from skimage.color.colorconv import hdx_from_rgb, rgb_from_hdx
img_rgb = img_as_float(self.img_rgb)
conv = combine_stains(separate_stains(img_rgb, hdx_from_rgb),
rgb_from_hdx)
assert_array_almost_equal(conv, img_rgb)
def test_hdx_rgb_roundtrip(self):
from skimage.color.colorconv import hdx_from_rgb, rgb_from_hdx
img_rgb = self.img_rgb
conv = combine_stains(separate_stains(img_rgb, hdx_from_rgb),
rgb_from_hdx)
assert_equal(img_as_ubyte(conv), img_rgb)
def test_hdx_rgb_roundtrip(self):
from skimage.color.colorconv import hdx_from_rgb, rgb_from_hdx
img_rgb = img_as_float(self.img_rgb)
conv = combine_stains(separate_stains(img_rgb, hdx_from_rgb),
rgb_from_hdx)
assert_array_almost_equal(conv, img_rgb)
def test_hdx_rgb_roundtrip(self):
from skimage.color.colorconv import hdx_from_rgb, rgb_from_hdx
img_rgb = self.img_rgb
conv = combine_stains(separate_stains(img_rgb, hdx_from_rgb),
rgb_from_hdx)
assert_equal(img_as_ubyte(conv), img_rgb)
x[...] = x/source_mean
x[...] = x * target_mean
#x[...] = x/source_stat_vector[0,0]
#x[...] = x * target_stat_vector[0,0]
return source_array
normalized_source_Hema = normalize_mapping(source_Hema_Gray_Array, np.mean(source_Hema_Gray_Array), np.mean(target_Hema_Gray_Array))
normalized_source_red = normalize_mapping(source_red_Gray_Array, np.mean(source_red_Gray_Array), np.mean(target_red_Gray_Array))
normalized_source_DAB = normalize_mapping(source_DAB_Gray_Array, np.mean(source_DAB_Gray_Array), np.mean(source_DAB_Gray_Array))
normalized_ihc_hrd = np.dstack((normalized_source_Hema, normalized_source_red, normalized_source_DAB))
normalized_ihc_rgb = combine_stains(normalized_ihc_hrd, rgb_from_hrd)
#Plot images
fig, axes = plt.subplots(2, 2, figsize=(12, 11))
#ax0 = axes.ravel()
ax0, ax1, ax2, ax3 = axes.ravel()
ax0.imshow(source_ihc_rgb, cmap=plt.cm.gray, interpolation='nearest')
ax0.set_title("Original source")
ax1.imshow(target_ihc_rgb, cmap=plt.cm.gray, interpolation='nearest')
ax1.set_title("original target")
ax2.imshow(normalized_ihc_rgb, cmap=plt.cm.gray)
ax2.set_title("normalized ")
plt.plot(exposure.histogram(e)[1], exposure.histogram(e)[0])
# <codecell>
io.imshow(filter.threshold_adaptive(e, 301, offset=0.025))
# <codecell>
io.imshow(A)
# <codecell>
from skimage import data
ihc = data.immunohistochemistry()
ihc_hdx = color.separate_stains(ihc, color.hdx_from_rgb)
ihc_rgb = color.combine_stains(ihc_hdx, color.rgb_from_hdx)
io.imshow(ihc_hdx[:,:,0])
# <codecell>
print [[0.644211, .835*0.716556, 0.266844], [0.092789, .835*0.954111, 0.283111], [0.00001, 0.00001, 0.00001]]
io.imshow(color.separate_stains(A,
np.linalg.inv([[0.644211, 0.716556, 0.266844], [0.092789, 0.954111, 0.283111], [0.00001, 0.00001, 0.00001]]))[:, :, 2])
# <codecell>
qstain = np.array([[.26451728, .5205347, .81183386], [.9199094, .29797825, .25489032], [.28947765, .80015373, .5253158]])
qfrompaper = np.array([[0.644211, .835*0.716556, 0.266844], [0.092789, .835*0.954111, 0.283111], [0.75919851748933231, 0.085468001712004443, 0.92121791197468583]])
def test_bro_rgb_roundtrip(self):
from skimage.color.colorconv import bro_from_rgb, rgb_from_bro
img_in = img_as_ubyte(self.img_stains)
img_out = combine_stains(img_in, rgb_from_bro)
img_out = separate_stains(img_out, bro_from_rgb)
assert_equal(img_as_ubyte(img_out), img_in)
def test_bro_rgb_roundtrip_float(self):
from skimage.color.colorconv import bro_from_rgb, rgb_from_bro
img_in = self.img_stains
img_out = combine_stains(img_in, rgb_from_bro)
img_out = separate_stains(img_out, bro_from_rgb)
assert_array_almost_equal(img_out, img_in)
#Stain space conversion
source_ihc_hrd = separate_stains(source_ihc_rgb, hrd_from_rgb)
source_Hema_Gray_Array = stainspace_to_2d_array(source_ihc_hrd, 0)
source_red_Gray_Array = stainspace_to_2d_array(source_ihc_hrd, 1)
source_DAB_Gray_Array = stainspace_to_2d_array(source_ihc_hrd, 2)
normalized_source_Hema = normalize_mapping(source_Hema_Gray_Array, np.mean(source_Hema_Gray_Array), np.mean(target_Hema_Gray_Array))
normalized_source_red = normalize_mapping(source_red_Gray_Array, np.mean(source_red_Gray_Array), np.mean(target_red_Gray_Array))
normalized_source_DAB = normalize_mapping(source_DAB_Gray_Array, np.mean(source_DAB_Gray_Array), np.mean(source_DAB_Gray_Array))
normalized_ihc_hrd = np.dstack((normalized_source_Hema, normalized_source_red, normalized_source_DAB))
normalized_ihc_rgb = combine_stains(normalized_ihc_hrd, rgb_from_hrd)
#Plot images
fig, axes = plt.subplots(2, 2, figsize=(12, 11))
#ax0 = axes.ravel()
ax0, ax1, ax2, ax3 = axes.ravel()
ax0.imshow(source_ihc_rgb, cmap=plt.cm.gray, interpolation='nearest')
ax0.set_title("Original source")
ax1.imshow(target_ihc_rgb, cmap=plt.cm.gray, interpolation='nearest')
ax1.set_title("original target")
ax2.imshow(normalized_ihc_rgb, cmap=plt.cm.gray)
ax2.set_title("normalized source")
