loaded_infos = ImageHandler.load_image_infos()
infos = list()
# processing steps
processing_steps = [['DIL', 'y', 2], ['CONV_BIT', 16]]
for info in loaded_infos:
info_ID = info['ID']
if info_ID in selected_info_IDs:
infos.append(info)
for info in infos:
seg = Segmentation(info)
seg.load()
img_path = seg.get_results_dir().tmp + 'contact_surface.tif'
print('LOAD', info['ID'])
# go through nuclei and add to stack
nIDs = seg.nuclei.get_nIDs(only_accepted=True)
stack_contact_surface = np.zeros_like(seg.stacks.lamin)
num_nIDs = len(nIDs)
# go through nIDs
for i, nID in enumerate(nIDs):
print('PROCESS %i/%i' % (i, num_nIDs))
stack_tmp_self = np.zeros_like(seg.stacks.lamin)
img_maps = dict()
val_maps = dict()
tiling_count_map = dict()
tiling_maps = dict()
limits = dict()
limit_lists = dict()
for info in infos:
seg = Segmentation(info)
seg.load()
# preparations for params
if 'between_space' in params_to_get:
# get binary stack without nuclei
filled_minus_nuclei = ImageHandler.load_tiff_as_stack(
seg.get_results_dir().tmp + 'filled_minus_nuclei.tif')
if filled_minus_nuclei is None:
filled_stack = np.full(
(seg.stacks.lamin.shape[0], seg.stacks.lamin.shape[1],
seg.stacks.lamin.shape[2]),
1,
dtype=np.uint8)
filled_minus_nuclei = seg.nuclei.add_nuclei_to_stack(
filled_stack, nucleus_value=0, only_accepted=True)
ImageHandler.save_stack_as_tiff(
filled_minus_nuclei,
seg.get_results_dir().tmp + 'filled_minus_nuclei.tif')
count_lists = dict()
processing_steps = [['DIL', 'y', 2],
['CONV_BIT', 16]]
# calculate compactness?
calc_compactness = False
for info in loaded_infos:
info_ID = info['ID']
if info_ID in selected_info_IDs:
infos.append(info)
for info in infos:
seg = Segmentation(info)
seg.load()
img_path = seg.get_results_dir().tmp + 'compactness_examples.tif'
voxel_size = float(info['voxel_size'])
voxel_volume = voxel_size ** 3
voxel_surface = voxel_size ** 2
print('LOAD', info['ID'])
# go through nuclei and add to stack
nIDs = seg.nuclei.get_nIDs(only_accepted=True)
example_counter = np.zeros((5 * 5)).astype(np.int8)
stack_examples = list()
examples_to_show = 5
for x in range(0, len(example_counter)):
for info in loaded_infos:
info_ID = info['ID']
if info_ID in selected_info_IDs:
infos.append(info)
for info in infos:
seg = Segmentation(info)
seg.load()
# go through nuclei and add them to a stack with
# nuclei_in_direction as value
nIDs = seg.nuclei.get_nIDs(only_accepted=True)
layer_stack = np.zeros_like(seg.stacks.nuclei)
for nID in nIDs:
# get nuclei in direction
apical = seg.nuclei.get_nucleus_apical_distance(nID)
if np.isnan(apical):
apical = 0
# add to stack
seg.nuclei.add_nucleus_to_stack(nID, layer_stack, nucleus_value=apical)
# save stack as tif
ImageHandler.save_stack_as_tiff(
layer_stack,
seg.get_results_dir().tmp + 'nuclei_layers.tif')
'xtick.labelsize': cfg.fontsdict_plot['fontsize'],
'ytick.labelsize': cfg.fontsdict_plot['fontsize']}
plt.rcParams.update(params)
# open window to select nuclei criteria
infos = ImageHandler.load_image_infos()
# select a specific info
selected_info_IDs = ['N1-19-24']
for info in infos:
if info['ID'] in selected_info_IDs:
seg = Segmentation(info)
seg.load()
processing_steps = [
['EQU'],
['THR', 'OTSU', 100, 'no3D'],
['CLS', 'bin', 2],
['FILL'],
['OPN', 'bin', 2],
['CONV_BIT', 16, '3D']
]
# apply processing steps
seg.stacks.membin = ImageProcessing.apply_filters(
processing_steps, seg.stacks.membrane, verbose=cfg.general_verbose)
ImageHandler.save_stack_as_tiff(seg.stacks.membin, seg.get_results_dir().tmp + cfg.file_stack_membin)
nIDs = seg.nuclei.get_nIDs(only_accepted=True)
stack_apical_dist = np.zeros_like(seg.stacks.lamin)
stack_pseudo_layer = np.zeros_like(seg.stacks.lamin)
# get average nuclei height
all_depths = seg.nuclei.data_frames['data_params'].get_vals_from_col('depth', only_accepted=True)
# get average
avg_depth = np.mean(all_depths)
print('MEAN DEPTH', avg_depth)
for nID in nIDs:
# get layer
apical_dist = seg.nuclei.get_nucleus_apical_distance(nID)
if not math.isnan(apical_dist):
stack_apical_dist = seg.nuclei.add_nucleus_to_stack(nID, stack_apical_dist, nucleus_value=int(apical_dist))
stack_pseudo_layer = seg.nuclei.add_nucleus_to_stack(nID, stack_pseudo_layer,
nucleus_value=int(apical_dist/avg_depth))
# save stack
img_path_apical_dist = seg.get_results_dir().tmp + 'apical_dist.tif'
img_path_pseudo_layer = seg.get_results_dir().tmp + 'pseudo_layer.tif'
print('SAVE')
ImageHandler.save_stack_as_tiff(stack_apical_dist, img_path_apical_dist)
ImageHandler.save_stack_as_tiff(stack_pseudo_layer, img_path_pseudo_layer)
print('LOAD', info['ID'])
# go through nuclei and add to stack
nIDs = seg.nuclei.get_nIDs(only_accepted=True)
stack_volume = np.zeros_like(seg.stacks.lamin).astype(np.int8)
all_volumes = seg.nuclei.data_frames['data_params'].get_vals_from_col(
'volume', only_accepted=True)
min_val = float(max(all_volumes))
max_val = float(min(all_volumes))
for nID in nIDs:
volume = seg.nuclei.get_nucleus_volume(nID)
colour = int((volume - min_val) / (max_val - min_val) * 255)
seg.nuclei.set_nucleus_colour(colour, nID)
stack_volume = seg.nuclei.add_nuclei_to_stack(stack_volume,
only_accepted=True)
# save stack
img_path = seg.get_results_dir().tmp + 'volume.tif'
print('SAVE')
ImageHandler.save_stack_as_tiff(stack_volume, img_path)