from timagetk.plugins.registration import z_stack_registration, apply_z_stack_trsf
# root_dir = '/projects/SamMaps/SuperResolution/LSM Airyscan/'
root_dir = '/data/Meristems/Carlos/SuperResolution/'
base_fname = 'SAM1-gfp-pi-stack-LSM800-Airyscan Processing-standard_{}.tif'
ref_channel = 'PI_SR'
float_channels = ['PIN_SR', 'PI_conf', 'PIN_conf']
image = imread(root_dir + base_fname.format(ref_channel))
reg_method = 'rigid'
reg_stack, z_stack_trsf = z_stack_registration(image, method=reg_method, get_trsf=True)
# - Have a look at the z-stack registration results on PI channel:
from timagetk.visu.mplt import grayscale_imshow
mid_y = int(image.get_y_dim()/2.)
grayscale_imshow([image.get_y_slice(mid_y), reg_stack.get_y_slice(mid_y)], title="Z-stack {} registration - y-slice {}/{}".format(reg_method, mid_y, image.get_y_dim()), subplot_titles=['Original', 'Registered'], range=[0, 10000])
grayscale_imshow([image, reg_stack], title="Z-stack {} registration - Contour projection".format(reg_method), subplot_titles=['Original', 'Registered'], range=[0, 10000])
from timagetk.visu.mplt import stack_browser
stack_browser(reg_stack, title="{} registered z-stack".format(reg_method))
from timagetk.visu.mplt import stack_browser_RGBA
stack_browser_RGBA([image, reg_stack], channel_names=['Original', 'Registered'], colors=['red', 'green'], title="Z-stack {} registration".format(reg_method))
imsave(root_dir + base_fname.format(ref_channel+"_z-stack_reg"), reg_stack)
# - Apply this registration to the other channels:
for float_channel in float_channels:
image = imread(root_dir + base_fname.format(float_channel))
reg_image = apply_z_stack_trsf(image, z_stack_trsf)
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,
method='gaussian_smoothing')
grayscale_imshow([
raw_img.get_z_slice(85),
eq_img2.get_z_slice(85),
smooth_img.get_z_slice(85)
],
# 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',
zone=mid_z)
selected_hmin = profile_hmin(asf_image,
x=mid_x,
z=mid_z,
plane='x',
zone=mid_z)
from timagetk.plugins import h_transform
init_trsf = BalTrsf()
init_trsf.read('/data/GabriellaMosca/T140_214.trsf')
trsf_def, img_def = registration(float_img,
ref_img,
method='deformable',
init_trsf=init_trsf)
imsave("/data/GabriellaMosca/EM_C_214/EM_C_214-masked_deformable.tif", img_def)
from timagetk.visu.mplt import grayscale_imshow
img2plot = [float_img, ref_img, img_rig, ref_img]
img_titles = ["t0", "t1", "Registered t0 on t1", "t1"]
grayscale_imshow(img2plot,
"Effect of rigid registration",
img_titles,
vmin=0,
vmax=255,
max_per_line=2)
img2plot = [
float_img.get_z_slice(40),
ref_img.get_z_slice(40),
img_rig.get_z_slice(40),
ref_img.get_z_slice(40)
]
img_titles = ["t0", "t1", "Registered t0 on t1", "t1"]
grayscale_imshow(img2plot,
"Effect of rigid registration",
img_titles,
vmin=0,
vmax=255,
control = 'most'
base_fname, ext = splitext(fname)
image = imread(base_dir + fname)
image.voxelsize
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
'20190430_multiangle_45.tif', '20190430_multiangle_90.tif',
'20190430_multiangle_135.tif', '20190430_multiangle_180.tif',
'20190430_multiangle_-45.tif', '20190430_multiangle_-90.tif',
'20190430_multiangle_-135.tif'
]
proj_list = []
for fname in filenames:
img = imread(root_dir + "/" + fname)
print "{}: shape={}, voxelsize={}".format(img.filename, img.shape,
img.voxelsize)
proj_list.append(projection(img, method='contour'))
grayscale_imshow(proj_list,
range='auto',
title='20190430_M_lti6b-gfp',
subplot_titles=filenames,
figname=root_dir + "/" + "contour_projections.png")
# filenames = ['20190430_multiangle_top.tif', '20190430_multiangle_45.tif']
# list_img = [imread(root_dir+"/"+im) for im in filenames]
# res_trsf, res_imgs = fusion_on_first(list_img, registration_method='rigid')
# from timagetk.plugins.cropping import max_percentile_profiles
# max_percentile_profiles(top_im, n_jobs=1)
#
#
# from timagetk.plugins.cropping import threshold_max_percentile_widget
# crop = threshold_max_percentile_widget(top_im, n_jobs=1)
#
xs, ys = int(sh[0] / 2.), int(sh[1] / 3.)
img_list = [im, stretch, smooth, smooth_str, str_smooth]
img_title = [
'Original', "Stretched", "Gaussian_smoothing", "Smooth + Stretching",
"Stretching + Smooth"
]
image_n_hist([img.get_array()[xs, ys:ys * 2, :] for img in img_list],
title="x_slice{}".format(xs),
img_title=img_title,
aspect_ratio=im.extent[0] / im.extent[1])
img_list = [im, eq, smooth, smooth_eq, eq_smooth]
img_title = [
'Original', "Equalize", "Gaussian_smoothing", "Smooth + Equalize",
"Equalize + Smooth"
]
image_n_hist([img.get_array()[xs, ys:ys * 2, :] for img in img_list],
title="x_slice{}".format(xs),
img_title=img_title,
aspect_ratio=im.extent[0] / im.extent[1])
image_n_hist(smooth_str.get_array()[xs, :, :],
figname=join(base_dir, "smooth-str-x_slice{}.png".format(xs)))
image_n_hist(str_smooth.get_array()[xs, :, :],
figname=join(base_dir, "str-smooth-x_slice{}.png".format(xs)))
from timagetk.visu.mplt import grayscale_imshow
grayscale_imshow([im, iso_im, st_im])