import platform
if platform.uname()[1] == "RDP-M7520-JL":
base_dir = '/data/Meristems/Carlos/SuperResolution/'
elif platform.uname()[1] == "calculus":
base_dir = '/projects/SamMaps/SuperResolution/LSM Airyscan/'
else:
raise ValueError("Unknown custom path to 'base_dir' for this system...")
fname = 'SAM1-gfp-pi-stack-LSM800-Airyscan Processing-high_PI_conf_z-stack_reg.tif'
base_fname, ext = splitext(fname)
image = imread(base_dir + fname)
iso_image = isometric_resampling(image, method='min', option='cspline')
iso_image.shape
iso_image = z_slice_contrast_stretch(iso_image, pc_min=1.5)
iso_image = linear_filtering(iso_image,
method="gaussian_smoothing",
sigma=0.1,
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(iso_image, x=mid_x, z=mid_z, plane='x', zone=mid_z)
for selected_hmin in np.arange(4000, 6000, 500):
seg_im = auto_seeded_watershed(iso_image, selected_hmin, control='most')
# np.unique(seg_im)
seg_fname = base_fname + "-seg_hmin{}{}".format(selected_hmin, ext)
from timagetk.algorithms.resample import resample
from timagetk.algorithms.resample import isometric_resampling
from timagetk.algorithms.exposure import z_slice_contrast_stretch
from timagetk.algorithms.exposure import z_slice_equalize_adapthist
raw_img = imread("/data/GabriellaMosca/EM_C_140/EM_C_140- C=0.tif")
h_min = 2
sigma = 1.5
# raw_img = imread("/data/GabriellaMosca/EM_C_214/EM_C_214 C=0.tif")
print "\n - Performing z-slices adaptative histogram equalisation on the intensity image to segment..."
eq_img1 = z_slice_equalize_adapthist(raw_img)
print "\n - Performing z-slices histogram contrast stretching on the intensity image to segment..."
eq_img2 = z_slice_contrast_stretch(raw_img)
from timagetk.visu.mplt import grayscale_imshow
grayscale_imshow([
raw_img.get_z_slice(85),
eq_img1.get_z_slice(85),
eq_img2.get_z_slice(85)
],
img_title=['Original', "AHE", 'CS'])
img2seg = eq_img2
print "\n - Automatic seed detection...".format(h_min)
print " -- Gaussian smoothing with std_dev={}...".format(sigma)
smooth_img = linear_filtering(img2seg,
sigma=sigma,
def seg_pipe(img2seg,
h_min,
img2sub=None,
iso=True,
equalize=True,
stretch=False,
std_dev=0.8,
min_cell_volume=20.,
back_id=1,
to_8bits=False):
"""Define the sementation pipeline
Parameters
----------
img2seg : str
image to segment.
h_min : int
h-minima used with the h-transform function
img2sub : str, optional
image to subtract to the image to segment.
iso : bool, optional
if True (default), isometric resampling is performed after h-minima
detection and before watershed segmentation
equalize : bool, optional
if True (default), intensity adaptative equalization is performed before
h-minima detection
stretch : bool, optional
if True (default, False), intensity histogram stretching is performed
before h-minima detection
std_dev : float, optional
real unit standard deviation used for Gaussian smoothing of the image to segment
min_cell_volume : float, optional
minimal volume accepted in the segmented image
back_id : int, optional
the background label
to_8bits : bool, optional
transform the image to segment as an unsigned 8 bits image for the h-transform
and seed-labelleing steps
Returns
-------
seg_im : SpatialImage
the labelled image obtained by seeded-watershed
Notes
-----
* Both 'equalize' & 'stretch' can not be True at the same time since they work
on the intensity of the pixels;
* Signal subtraction is performed after intensity rescaling (if any);
* Linear filtering (Gaussian smoothing) is performed before h-minima transform
for local minima detection;
* Gaussian smoothing should be performed on isometric images, if the provided
image is not isometric, we resample it before smoothing, then go back to
original voxelsize;
* In any case H-Transfrom is performed on the image with its native resolution
to speed upd seed detection;
* Same goes for connexe components detection (seed labelling);
* Segmentation will be performed on the isometric images if iso is True, in
such case we resample the image of detected seeds and use the isometric
smoothed intensity image;
"""
t_start = time.time()
# - Check we have only one intensity rescaling method called:
try:
assert equalize + stretch < 2
except AssertionError:
raise ValueError(
"Both 'equalize' & 'stretch' can not be True at once!")
# - Check the standard deviation value for Gaussian smoothing is valid:
try:
assert std_dev <= 1.
except AssertionError:
raise ValueError(
"Standard deviation for Gaussian smoothing should be superior or equal to 1!"
)
ori_vxs = img2seg.voxelsize
ori_shape = img2seg.shape
if img2sub is not None:
print "\n - Performing signal substraction..."
img2seg = signal_subtraction(img2seg, img2sub)
if equalize:
print "\n - Performing z-slices adaptative histogram equalisation on the intensity image to segment..."
img2seg = z_slice_equalize_adapthist(img2seg)
if stretch:
print "\n - Performing z-slices histogram contrast stretching on the intensity image to segment..."
img2seg = z_slice_contrast_stretch(img2seg)
print "\n - Automatic seed detection...".format(h_min)
# morpho_radius = 1.0
# asf_img = morphology(img2seg, max_radius=morpho_radius, method='co_alternate_sequential_filter')
# ext_img = h_transform(asf_img, h=h_min, method='h_transform_min')
min_vxs = min(img2seg.voxelsize)
if std_dev < min_vxs:
print " -- Isometric resampling prior to Gaussian smoothing...".format(
std_dev)
img2seg = isometric_resampling(img2seg)
print " -- Gaussian smoothing with std_dev={}...".format(std_dev)
iso_smooth_img = linear_filtering(img2seg,
std_dev=std_dev,
method='gaussian_smoothing',
real=True)
if std_dev < min_vxs:
print " -- Down-sampling a copy back to original voxelsize (to use with `h-transform`)..."
smooth_img = resample(iso_smooth_img, ori_vxs)
if not np.allclose(ori_shape, smooth_img.shape):
print "WARNING: shape missmatch after down-sampling from isometric image:"
print " -- original image shape: {}".format(ori_shape)
print " -- down-sampled image shape: {}".format(smooth_img.shape)
else:
print " -- Copying original image (to use with `h-transform`)..."
smooth_img = iso_smooth_img
if not iso:
del iso_smooth_img # no need to keep this image after this step!
print " -- H-minima transform with h-min={}...".format(h_min)
if to_8bits:
ext_img = h_transform(smooth_img.to_8bits(),
h=h_min,
method='h_transform_min')
else:
ext_img = h_transform(smooth_img, h=h_min, method='h_transform_min')
if iso:
smooth_img = iso_smooth_img # no need to keep both images after this step!
print " -- Region labelling: connexe components detection..."
seed_img = region_labeling(ext_img,
low_threshold=1,
high_threshold=h_min,
method='connected_components')
print "Detected {} seeds!".format(len(np.unique(seed_img)) -
1) # '0' is in the list!
del ext_img # no need to keep this image after this step!
print "\n - Performing seeded watershed segmentation..."
if iso:
seed_img = isometric_resampling(seed_img, option='label')
if to_8bits:
seg_im = segmentation(smooth_img.to_8bits(),
seed_img,
method='seeded_watershed')
else:
seg_im = segmentation(smooth_img, seed_img, method='seeded_watershed')
# seg_im[seg_im == 0] = back_id
print "Detected {} labels!".format(len(np.unique(seg_im)))
if min_cell_volume > 0.:
from vplants.tissue_analysis.spatial_image_analysis import SpatialImageAnalysis
print "\n - Performing cell volume filtering..."
spia = SpatialImageAnalysis(seg_im, background=None)
vol = spia.volume()
too_small_labels = [
k for k, v in vol.items() if v < min_cell_volume and k != 0
]
if too_small_labels != []:
print "Detected {} labels with a volume < {}µm2".format(
len(too_small_labels), min_cell_volume)
print " -- Removing seeds leading to small cells..."
spia = SpatialImageAnalysis(seed_img, background=None)
seed_img = spia.get_image_without_labels(too_small_labels)
print " -- Performing final seeded watershed segmentation..."
seg_im = segmentation(smooth_img,
seed_img,
method='seeded_watershed')
# seg_im[seg_im == 0] = back_id
print "Detected {} labels!".format(len(np.unique(seg_im)))
print "\nDone in {}s".format(round(time.time() - t_start, 3))
return seg_im
from timagetk.io import imread
from timagetk.algorithms.exposure import z_slice_contrast_stretch
from timagetk.algorithms.resample import isometric_resampling
from timagetk.plugins import linear_filtering
from timagetk.plugins.segmentation import auto_seeded_watershed
from timagetk.visu.mplt import profile_hmin
from skimage import img_as_float
from skimage.color import label2rgb, gray2rgb
int_img = imread(data_path('p58-t0-a0.lsm'))
print int_img.shape
print int_img.voxelsize
int_img = isometric_resampling(int_img, method='min', option='cspline')
int_img = z_slice_contrast_stretch(int_img, pc_min=1)
int_img = linear_filtering(int_img,
method="gaussian_smoothing",
sigma=0.25,
real=True)
xsh, ysh, zsh = int_img.shape
mid_x, mid_y, mid_z = int(xsh / 2.), int(ysh / 2.), int(zsh / 2.)
h_min = profile_hmin(int_img, x=mid_x, z=mid_z, plane='x', zone=mid_z)
seg_img = auto_seeded_watershed(int_img, hmin=h_min)
print seg_img.shape
label_rgb = label2rgb(seg_img, alpha=1, bg_label=1, bg_color=(0, 0, 0))
print label_rgb.shape
else:
raise ValueError("Unknown custom path to 'base_dir' for this system...")
fname = '20180616 3-global della_Ler_LD +++.lsm'
from timagetk.io import imread
im = imread(join(base_dir, fname))
from timagetk.plugins import linear_filtering
from timagetk.algorithms.exposure import z_slice_contrast_stretch
from timagetk.algorithms.exposure import z_slice_equalize_adapthist
s = 0.6
smooth = linear_filtering(im, method="gaussian_smoothing", sigma=s, real=True)
stretch = z_slice_contrast_stretch(im)
smooth_str = z_slice_contrast_stretch(
linear_filtering(im, method="gaussian_smoothing", sigma=s, real=True))
str_smooth = linear_filtering(z_slice_contrast_stretch(im),
method="gaussian_smoothing",
sigma=s,
real=True)
eq = z_slice_equalize_adapthist(im)
smooth_eq = z_slice_equalize_adapthist(
linear_filtering(im, method="gaussian_smoothing", sigma=s, real=True))
eq_smooth = linear_filtering(z_slice_equalize_adapthist(im),
method="gaussian_smoothing",
sigma=s,
real=True)