else:
print "Loading SpatialImage:\n {}".format(res_path + res_trsf_fname)
res_img = imread(res_path + res_img_fname)
list_res_img.append(res_img)
continue
# add last reference image
list_res_img.append(list_iso_img[-1]) # add last reference image
# - Apply DEFORMABLE consecutive_registration on segmented images:
list_res_seg_img_fname = []
for ind, img in enumerate(list_iso_img[:-1]):
# Apply DEFORMABLE consecutive registration to segmented image:
print "\nApplying estimated {} transformation on '{}' to segmented image:".format('deformable', ref_ch_name)
seg_path_suffix, seg_img_fname = get_nomenclature_segmentation_name(czi_base_fname.format(index2time[ind]), nom_file, ref_ch_name)
trsf = list_res_trsf[ind]
res_seg_img = apply_trsf(imread(image_dirname + seg_path_suffix + seg_img_fname), trsf, param_str_2='-nearest', template_img=list_iso_img[ind+1])
res_seg_img[res_seg_img == 0] = back_id
res_seg_img_fname = get_res_img_fname(seg_img_fname, index2time[ind+1], index2time[ind], 'iso-deformable')
print " - {}\n --> {}".format(seg_img_fname, res_seg_img_fname)
res_path = image_dirname + seg_path_suffix + '{}_registrations/'.format(trsf_type)
if not exists(res_path + res_seg_img_fname) or force:
imsave(res_path + res_seg_img_fname, res_seg_img)
list_res_seg_img_fname.append(res_path + res_seg_img_fname)
# - Create reference segmented image list:
list_ref_seg_img_fname = []
for ind, img_fname in enumerate(list_img_fname[:-1]):
seg_path_suffix, seg_img_fname = get_nomenclature_segmentation_name(czi_base_fname.format(index2time[ind+1]), nom_file, ref_ch_name)
raise ValueError("Negative minimal area!")
force = args.force
if force:
print "WARNING: any existing CSV files will be overwritten!"
else:
print "Existing files will be kept."
# - Define variables AFTER argument parsing:
czi_fname = base_fname + "-T{}.czi".format(tp)
# Get unregistered image filename:
path_suffix, PI_signal_fname = get_nomenclature_channel_fname(
czi_fname, nomenclature_file, membrane_ch_name)
path_suffix, PIN_signal_fname = get_nomenclature_channel_fname(
czi_fname, nomenclature_file, 'PIN1')
path_suffix, seg_img_fname = get_nomenclature_segmentation_name(
czi_fname, nomenclature_file, membrane_ch_name + "_raw")
# - To create a mask, edit a MaxIntensityProj with an image editor (GIMP) by adding 'black' (0 value):
# mask = image_dirname+'PIN1-GFP-CLV3-CH-MS-E1-LD-SAM4-MIP_PI-mask.png'
mask = ''
print "\n\n# - Reading PIN1 intensity image file {}...".format(
PIN_signal_fname)
PIN_signal_im = imread(image_dirname + path_suffix + PIN_signal_fname)
# PIN_signal_im = isometric_resampling(PIN_signal_im)
# world.add(PIN_signal_im, 'PIN1 intensity image', colormap='viridis', voxelsize=PIN_signal_im.voxelsize)
print "\n\n# - Reading PI intensity image file {}...".format(PI_signal_fname)
PI_signal_im = imread(image_dirname + path_suffix + PI_signal_fname)
# PI_signal_im = isometric_resampling(PI_signal_im)
# world.add(PI_signal_im, 'PI intensity image', colormap='invert_grey', voxelsize=PI_signal_im.voxelsize)
if not exists(res_path + res_x_ch_fname) or True:
print " - {}\n --> {}".format(x_ch_fname, res_x_ch_fname)
# --- Read the extra channel image file:
x_ch_img = imread(image_dirname + x_ch_path_suffix +
x_ch_fname)
# --- Apply and save registered image:
res_x_ch_img = apply_trsf(x_ch_img, res_trsf)
imsave(res_path + res_x_ch_fname, res_x_ch_img)
else:
print " - existing file: {}".format(res_x_ch_fname)
else:
print "No supplementary channels to register."
# -- Apply estimated transformation to segmented image:
if trsf_type == 'rigid':
seg_path_suffix, seg_img_fname = get_nomenclature_segmentation_name(
czi_base_fname.format(t), nomenclature_file, membrane_ch_name)
if exists(image_dirname + seg_path_suffix + seg_img_fname):
print "\nApplying estimated {} transformation on '{}' to segmented image:".format(
trsf_type.upper(), membrane_ch_name)
res_seg_img_fname = get_res_img_fname(seg_img_fname, t_ref,
t_float, trsf_type)
if not exists(res_path + seg_img_fname) or force:
print " - {}\n --> {}".format(seg_img_fname,
res_seg_img_fname)
# --- Read the segmented image file:
seg_im = imread(image_dirname + seg_path_suffix +
seg_img_fname)
res_seg_im = apply_trsf(seg_im,
res_trsf,
param_str_2=' -nearest')
# --- Apply and save registered segmented image:
float_path_suffix, float_img_fname = get_nomenclature_channel_fname(
czi_base_fname.format(t_float), nom_file, ref_ch_name)
ref_path_suffix, ref_img_fname = get_nomenclature_channel_fname(
czi_base_fname.format(t_ref), nom_file, ref_ch_name)
# - Get RIGID registered images filenames:
rig_float_path_suffix = float_path_suffix + 'rigid_registrations/'
rig_float_img_fname = get_res_img_fname(float_img_fname, t_ref, t_float,
'iso-rigid')
if t_ref != time_steps[-1]:
ref_path_suffix += 'rigid_registrations/'
ref_img_fname = get_res_img_fname(ref_img_fname,
index2time[time2index[t_ref] + 1],
t_ref, 'iso-rigid')
seg_path_suffix, seg_img_fname = get_nomenclature_segmentation_name(
czi_base_fname.format(t_float), nom_file, ref_ch_name)
rig_seg_img_fname = get_res_img_fname(seg_img_fname, t_ref, t_float,
'iso-rigid')
res_path = image_dirname + rig_float_path_suffix
try:
assert exists(res_path + rig_seg_img_fname)
except:
# - Get the result image file name & path (output path), and create it if necessary:
if not exists(res_path):
mkdir(res_path)
print "\n# - RIGID registration for t{}/t{}:".format(
time2index[t_float], time2index[t_ref])
py_hl = 3 # defines highest level of the blockmatching-pyramid
py_ll = 1 # defines lowest level of the blockmatching-pyramid
print ' - t_{}h floating fname: {}'.format(t_float, float_img_fname)
# -3- OUTPUT directory:
image_dirname = dirname + "nuclei_images/"
# -4- Define CZI channel names, the microscope orientation, nuclei and membrane channel names and extra channels that should also be registered:
channel_names = ['DIIV', 'PIN1', 'PI', 'TagBFP', 'CLV3']
microscope_orientation = -1 # inverted microscope!
back_id = 1
for tp, t in enumerate(time_steps):
raw_czi_fname = czi_base_fname.format(t)
print "\n\n# - Entering segmentation process for {}".format(raw_czi_fname)
start = time.time()
# - Defines segmented file name and path:
seg_path_suffix, seg_img_fname = get_nomenclature_segmentation_name(raw_czi_fname, nom_file, ref_ch_name)
if os.path.exists(image_dirname + seg_path_suffix + seg_img_fname) and not force:
print "A segmentation file '{}' already exists, aborting now.".format(seg_img_fname)
sys.exit(0)
# - Get the image to segment:
# -- Get the file name and path of the image to segment:
path_suffix, img2seg_fname = get_nomenclature_channel_fname(raw_czi_fname, nom_file, ref_ch_name)
print "\n - Loading image to segment: {}".format(img2seg_fname)
img2seg = imread(image_dirname + path_suffix + img2seg_fname)
vxs = np.array(img2seg.voxelsize)
ori = np.array(img2seg.origin)
# -- Get the file name and path of the channel to substract to the image to segment:
# used to clear-out the cells for better segmentation
if clearing_ch_name:
path_suffix, substract_img_fname = get_nomenclature_channel_fname(raw_czi_fname, nom_file, clearing_ch_name)