print "\n# - Creating seed image from EXPERT seed positions..."
xp_seed_pos = expert_topomesh.wisp_property('barycenter', 0)
xp_seed_pos = {
k: v * microscope_orientation
for k, v in xp_seed_pos.items()
}
# --- Create the seed image:
seed_img = seed_image_from_points(size, voxelsize, xp_seed_pos, 2., 0)
# --- Add background position:
background_threshold = 2000.
smooth_img_bck = linear_filtering(img,
std_dev=3.0,
method='gaussian_smoothing')
background_img = (smooth_img_bck < background_threshold).astype(np.uint16)
for it in xrange(15):
background_img = morphology(
background_img, param_str_2='-operation erosion -iterations 10')
# ---- Detect small regions defined as background and remove them:
connected_background_components, n_components = nd.label(background_img)
components_area = nd.sum(np.ones_like(connected_background_components),
connected_background_components,
index=np.arange(n_components) + 1)
largest_component = (np.arange(n_components) +
1)[np.argmax(components_area)]
background_img = (
connected_background_components == largest_component).astype(np.uint16)
# ---- Finaly add the background and make a SpatialImage:
seed_img[background_img == 1] = 1
del smooth_img_bck, background_img
seed_img = SpatialImage(seed_img, voxelsize=voxelsize)
# world.add(seed_img,"seed_image", colormap="glasbey", alphamap="constant",voxelsize=microscope_orientation*voxelsize, bg_id=0)
print " --> Adaptative Histogram Equalization..."
img = z_slice_equalize_adapthist(img)
if rescaling == 'ContrastStretch':
print " --> Contrast Stretching..."
img = z_slice_contrast_stretch(img)
if std_dev != 0:
print " --> 'gaussian_smoothing' with std_dev={}...".format(
std_dev)
img = linear_filtering(img,
'gaussian_smoothing',
std_dev=std_dev)
# - Performs seed detection:
print " --> Closing-Opening Alternate Sequencial Filter..."
asf_img = morphology(
img,
max_radius=morpho_radius,
method='co_alternate_sequential_filter')
print " --> H-transform: local minima detection..."
ext_img = h_transform(asf_img,
h=h_min,
method='h_transform_min')
print " --> Components labelling..."
con_img = region_labeling(ext_img,
low_threshold=1,
high_threshold=h_min,
method='connected_components')
nb_connectec_comp = len(np.unique(con_img.get_array()))
print nb_connectec_comp, "seeds detected!"
if nb_connectec_comp <= 3:
print "Not enough seeds, aborting!"
continue
positions = array_dict(microscope_orientation * positions.values(),
positions.keys())
# Create a seed image fro the nuclei barycenters:
seed_img = seed_image_from_points(membrane_img.shape,
membrane_img.voxelsize,
positions,
background_label=0)
# Add the "background seed":
background_threshold = 2000.
smooth_img_bck = linearfilter(membrane_img,
param_str_2='-x 0 -y 0 -z 0 -sigma 3.0')
background_img = (smooth_img_bck < background_threshold).astype(np.uint16)
for it in xrange(10):
background_img = morphology(
background_img, param_str_2='-operation erosion -iterations 10')
seed_img += background_img
seed_img = SpatialImage(seed_img, voxelsize=membrane_img.voxelsize)
#world.add(seed_img,'seed_image',colormap='glasbey',alphamap='constant',bg_id=0)
segmented_filename = image_dirname + "/" + nomenclature_names[
filename] + "/" + nomenclature_names[
filename] + "_corrected_nuclei_seed.inr"
imsave(segmented_filename, seed_img)
seed_img = isometric_resampling(seed_img, option='label')
std_dev = 2.0
membrane_img = isometric_resampling(membrane_img)
vxs = membrane_img.voxelsize
try:
base_fname, ext = splitext(fname)
image = imread(base_dir + fname)
image.voxelsize
iso_image = isometric_resampling(image, method='min', option='cspline')
iso_image.shape
iso_image = global_contrast_stretch(iso_image)
# iso_image = z_slice_contrast_stretch(iso_image, pc_min=1.5)
smooth_image = linear_filtering(iso_image,
method="gaussian_smoothing",
sigma=1.,
real=True)
asf_image = morphology(iso_image,
method="oc_alternate_sequential_filter",
max_radius=1)
xsh, ysh, zsh = iso_image.shape
mid_x, mid_y, mid_z = int(xsh / 2.), int(ysh / 2.), int(zsh / 2.)
from timagetk.visu.mplt import grayscale_imshow
from timagetk.visu.stack import stack_browser
grayscale_imshow([iso_image, smooth_image, asf_image], slice_id=mid_z)
stack_browser(asf_image)
selected_hmin = profile_hmin(smooth_image,
x=mid_x,
z=mid_z,
plane='x',
iso_image = isometric_resampling(image, method='min', option='cspline')
iso_image.shape
st_img1 = global_contrast_stretch(iso_image)
st_img2 = z_slice_equalize_adapthist(iso_image)
from timagetk.visu.mplt import grayscale_imshow
grayscale_imshow([iso_image, st_img1, st_img2],
100,
title=[
"Original", "global_contrast_stretch",
"z_slice_equalize_adapthist"
])
seed_detect_img = morphology(st_img2, "erosion", radius=1)
seed_detect_img = morphology(seed_detect_img,
"coc_alternate_sequential_filter",
max_radius=1,
iterations=2)
seed_detect_img = linear_filtering(seed_detect_img,
method="gaussian_smoothing",
sigma=1.,
real=True)
xsh, ysh, zsh = iso_image.shape
mid_x, mid_y, mid_z = int(xsh / 2.), int(ysh / 2.), int(zsh / 2.)
selected_hmin = profile_hmin(seed_detect_img,
x=mid_x,
z=mid_z,
from timagetk.plugins import linear_filtering, morphology
from timagetk.plugins import h_transform
from timagetk.plugins import region_labeling, segmentation
from timagetk.plugins import labels_post_processing
except ImportError:
raise ImportError('Import Error')
out_path = './results/' # to save results
# we consider an input image
# SpatialImage instance
input_img = imread(data_path('input.tif'))
# optional denoising block
smooth_img = linear_filtering(input_img, std_dev=2.0,
method='gaussian_smoothing')
asf_img = morphology(smooth_img, max_radius=3,
method='co_alternate_sequential_filter')
ext_img = h_transform(asf_img, h=150,
method='h_transform_min')
con_img = region_labeling(ext_img, low_threshold=1,
high_threshold=150,
method='connected_components')
seg_img = segmentation(smooth_img, con_img, control='first',
method='seeded_watershed')
# optional post processig block
pp_img = labels_post_processing(seg_img, radius=1,
iterations=1,
method='labels_erosion')
res_name = 'example_segmentation.tif'
def test_plugin_2():
im = imread(data_path('filtering_src.inr'))
im_ref = imread(data_path('morpho_erosion_default.inr'))
output = morphology(im, method='erosion')
np.testing.assert_array_equal(output, im_ref)