def test_get_tissue_mask(self):
"""Test get_tissue_mask()."""
thumbnail_rgb = get_slide_thumbnail(gc, SAMPLE_SLIDE_ID)
labeled, mask = get_tissue_mask(
thumbnail_rgb, deconvolve_first=True,
n_thresholding_steps=1, sigma=1.5, min_size=30)
# # visualize result
# vals = np.random.rand(256,3)
# vals[0, ...] = [0.9, 0.9, 0.9]
# cMap = ListedColormap(1 - vals)
#
# f, ax = plt.subplots(1, 3, figsize=(20, 20))
# ax[0].imshow(thumbnail_rgb)
# ax[1].imshow(labeled, cmap=cMap)
# ax[2].imshow(mask, cmap=cMap)
# plt.show()
self.assertTupleEqual(labeled.shape, (156, 256))
self.assertEqual(len(np.unique(labeled)), 10)
# save for use in the next test
imwrite(os.path.join(
savepath, 'tissue_binmask.png'), np.uint8(0 + (labeled > 0)))
def set_slide_info_and_get_tissue_mask(self):
"""Set self.slide_info dict and self.labeled tissue mask."""
# This is a presistent dict to store information about slide
self.slide_info = self.gc.get('item/%s/tiles' % self.slide_id)
# get tissue mask
thumbnail_rgb = get_slide_thumbnail(self.gc, self.slide_id)
# color normalization if desired
if 'thumbnail' in self.cnorm_params.keys():
thumbnail_rgb = np.uint8(
reinhard(im_src=thumbnail_rgb,
target_mu=self.cnorm_params['thumbnail']['mu'],
target_sigma=self.cnorm_params['thumbnail']['sigma']))
# get labeled tissue mask -- each unique value is one tissue piece
labeled, _ = get_tissue_mask(thumbnail_rgb,
**self.get_tissue_mask_kwargs)
if len(np.unique(labeled)) < 2:
raise ValueError("No tissue detected!")
if self.visualize_tissue_boundary:
annotation_docs = get_tissue_boundary_annotation_documents(
self.gc, slide_id=self.slide_id, labeled=labeled)
for doc in annotation_docs:
_ = self.gc.post("/annotation?itemId=" + self.slide_id,
json=doc)
# Find size relative to WSI
self.slide_info[
'F_tissue'] = self.slide_info['sizeX'] / labeled.shape[1]
return labeled
def test_get_tissue_mask(self):
"""Test get_tissue_mask()."""
thumbnail_rgb = get_slide_thumbnail(cfg.gc, cfg.iteminfo['_id'])
cfg.labeled, mask = get_tissue_mask(
thumbnail_rgb, deconvolve_first=True,
n_thresholding_steps=1, sigma=1.5, min_size=30)
assert cfg.labeled.shape == (156, 256)
assert len(np.unique(cfg.labeled)) == 11
def set_slide_info_and_get_tissue_mask(self):
"""Set self.slide_info dict and self.labeled tissue mask."""
# This is a presistent dict to store information about slide
self.slide_info = self.gc.get('item/%s/tiles' % self.slide_id)
# get tissue mask
thumbnail_rgb = get_slide_thumbnail(self.gc, self.slide_id)
# get labeled tissue mask -- each unique value is one tissue piece
labeled, _ = get_tissue_mask(thumbnail_rgb,
**self.get_tissue_mask_kwargs)
if len(np.unique(labeled)) < 2:
raise ValueError("No tissue detected!")
# Find size relative to WSI
self.slide_info[
'F_tissue'] = self.slide_info['sizeX'] / labeled.shape[1]
return labeled
def test_get_tissue_mask(self):
"""Test get_tissue_mask()."""
thumbnail_rgb = get_slide_thumbnail(gc, SAMPLE_SLIDE_ID)
labeled, mask = get_tissue_mask(thumbnail_rgb,
deconvolve_first=True,
n_thresholding_steps=2,
sigma=0.,
min_size=30)
# # visualize result
# vals = np.random.rand(256,3)
# vals[0, ...] = [0.9, 0.9, 0.9]
# cMap = ListedColormap(1 - vals)
#
# f, ax = plt.subplots(1, 3, figsize=(20, 20))
# ax[0].imshow(thumbnail_rgb)
# ax[1].imshow(labeled, cmap=cMap)
# ax[2].imshow(mask, cmap=cMap)
# plt.show()
self.assertTupleEqual(labeled.shape, (152, 256))
self.assertEqual(len(np.unique(labeled)), 10)
def test_reinhard(self):
"""Test reinhard."""
# get RGB image at a small magnification
slide_info = gc.get('item/%s/tiles' % SAMPLE_SLIDE_ID)
getStr = "/item/%s/tiles/region?left=%d&right=%d&top=%d&bottom=%d" % (
SAMPLE_SLIDE_ID, 0, slide_info['sizeX'], 0, slide_info['sizeY']
) + "&magnification=%.2f" % MAG
tissue_rgb = get_image_from_htk_response(
gc.get(getStr, jsonResp=False))
# # SANITY CHECK! normalize to LAB mean and std from SAME slide
# mean_lab, std_lab = lab_mean_std(tissue_rgb)
# tissue_rgb_normalized = reinhard(
# tissue_rgb, target_mu=mean_lab, target_sigma=std_lab)
#
# # we expect the images to be (almost) exactly the same
# assert np.mean(tissue_rgb - tissue_rgb_normalized) < 1
# Normalize to pre-set color standard
tissue_rgb_normalized = reinhard(
tissue_rgb, target_mu=cnorm['mu'], target_sigma=cnorm['sigma'])
# check that it matches
mean_lab, std_lab = lab_mean_std(tissue_rgb_normalized)
self.assertTrue(all(
np.abs(mean_lab - cnorm['mu']) < [0.1, 0.1, 0.1]))
self.assertTrue(all(
np.abs(std_lab - cnorm['sigma']) < [0.1, 0.1, 0.1]))
# get tissue mask
thumbnail_rgb = get_slide_thumbnail(gc, SAMPLE_SLIDE_ID)
labeled, mask = get_tissue_mask(
thumbnail_rgb, deconvolve_first=True,
n_thresholding_steps=1, sigma=1.5, min_size=30)
# # visualize result
# vals = np.random.rand(256, 3)
# vals[0, ...] = [0.9, 0.9, 0.9]
# cMap = ListedColormap(1 - vals)
#
# f, ax = plt.subplots(1, 3, figsize=(20, 20))
# ax[0].imshow(thumbnail_rgb)
# ax[1].imshow(labeled, cmap=cMap)
# ax[2].imshow(mask, cmap=cMap)
# plt.show()
# Do MASKED normalization to preset standard
mask_out = resize(
labeled == 0, output_shape=tissue_rgb.shape[:2],
order=0, preserve_range=True) == 1
tissue_rgb_normalized = reinhard(
tissue_rgb, target_mu=cnorm['mu'], target_sigma=cnorm['sigma'],
mask_out=mask_out)
# check that it matches
mean_lab, std_lab = lab_mean_std(
tissue_rgb_normalized, mask_out=mask_out)
self.assertTrue(all(
np.abs(mean_lab - cnorm['mu']) < [0.1, 0.1, 0.1]))
self.assertTrue(all(
np.abs(std_lab - cnorm['sigma']) < [0.1, 0.1, 0.1]))
[0.71681094, 0.90081588, 0.41999816],
[0.38588316, 0.42616716, -0.90380025]])
# %%===========================================================================
print("Getting images to be normalized ...")
# get RGB image at a small magnification
slide_info = gc.get('item/%s/tiles' % SAMPLE_SLIDE_ID)
getStr = "/item/%s/tiles/region?left=%d&right=%d&top=%d&bottom=%d" % (
SAMPLE_SLIDE_ID, 0, slide_info['sizeX'], 0,
slide_info['sizeY']) + "&magnification=%.2f" % MAG
tissue_rgb = get_image_from_htk_response(gc.get(getStr, jsonResp=False))
# get mask of things to ignore
thumbnail_rgb = get_slide_thumbnail(gc, SAMPLE_SLIDE_ID)
mask_out, _ = get_tissue_mask(thumbnail_rgb,
deconvolve_first=True,
n_thresholding_steps=1,
sigma=1.5,
min_size=30)
mask_out = resize(mask_out == 0,
output_shape=tissue_rgb.shape[:2],
order=0,
preserve_range=True) == 1
# since this is a unit test, just work on a small image
tissue_rgb = tissue_rgb[1000:1500, 2500:3000, :]
mask_out = mask_out[1000:1500, 2500:3000]
# for reproducibility