def remove_poses(experiment_root):
experiment_annotations = load_data.read_annotations(experiment_root)
for position, position_annotations in experiment_annotations.items():
timepoint_annotations = position_annotations[1]
for timepoint, timepoint_annotation in timepoint_annotations.items():
timepoint_annotation['pose'] = (None, None)
load_data.write_annotations(experiment_root, experiment_annotations)
def replace_annotation(experiment_root,
annotation_type,
old_annotation_values,
new_annotation_value,
annotation_dir='annotations'):
reinit = input(
'press y and enter to delete annotations; press enter to exit. ')
if reinit.lower() == 'y':
if not isinstance(old_annotation_values, collections.Iterable):
old_annotation_values = list(old_annotation_values)
if isinstance(old_annotation_values, str):
old_annotation_values = [old_annotation_values]
experiment_annotations = load_data.read_annotations(
experiment_root, annotation_dir=annotation_dir)
for position, position_annotations in experiment_annotations.items():
for timepoint, timepoint_annotations in position_annotations[
1].items():
if annotation_type in timepoint_annotations and timepoint_annotations[
annotation_type] in old_annotation_values:
timepoint_annotations[
annotation_type] = new_annotation_value
load_data.write_annotations(experiment_root,
experiment_annotations,
annotation_dir=annotation_dir)
return
def compile_annotations_from_tsv(experiment_root):
if type(experiment_root) is str:
experiment_root = pathlib.Path(experiment_root.replace('\\ ', ' '))
_check_metadata_for_timepoints(experiment_root)
process_data.update_annotations(experiment_root)
with (experiment_root /
'experiment_metadata.json').open('r') as mdata_file:
experiment_metadata = json.load(mdata_file)
annotation_data = {}
with list(experiment_root.glob('*.tsv'))[0].open('r') as annotation_file:
reader = csv.reader(annotation_file, delimiter='\t')
_ = reader.__next__() # Header
for line in reader:
position = line[0][1:] # Starts with '\'
notes = line[-1]
annotation_data[position] = {}
if 'DEAD' in notes:
for field, frame_num in zip(previous_annotations, line[1:]):
annotation_data[position][field] = experiment_metadata[
'timepoints'][int(frame_num)]
annotation_data[position]['notes'] = line[-1]
annotations = load_data.read_annotations(experiment_root)
# Note: process_data.propagate_worm_stages assumes that stages are monotonically increasing.
# To make use of propagate_worm_stages, prepopulate only the timepoints where there's a transition.
for position, (position_annotations,
timepoint_annotations) in annotations.items():
if 'DEAD' in annotation_data[position]['notes']:
first_timepoint = list(timepoint_annotations.keys())[0]
timepoint_annotations[first_timepoint]['stage'] = 'egg'
for field in previous_annotations:
transition_timepoint = annotation_data[position][field]
timepoint_annotations[transition_timepoint]['stage'] = field
position_annotations['exclude'] = False
else:
position_annotations['exclude'] = True
position_annotations['notes'] = annotation_data[position]['notes']
load_data.write_annotations(experiment_root, annotations)
process_data.annotate(
experiment_root,
position_annotators=[process_data.propagate_worm_stage])
def replace_annotation(experiment_root,
annotation_type,
old_annotation_values,
new_annotation_value,
annotation_dir='annotations'):
if not isinstance(old_annotation_values, collections.Iterable):
old_annotation_values = list(old_annotation_values)
if isinstance(old_annotation_values, str):
old_annotation_values = [old_annotation_values]
experiment_annotations = load_data.read_annotations(
experiment_root, annotation_dir=annotation_dir)
for position, position_annotations in experiment_annotations.items():
for timepoint, timepoint_annotations in position_annotations[1].items(
):
if annotation_type in timepoint_annotations and timepoint_annotations[
annotation_type] in old_annotation_values:
timepoint_annotations[annotation_type] = new_annotation_value
load_data.write_annotations(experiment_root,
experiment_annotations,
annotation_dir=annotation_dir)
def propagate_stages(experiment_root, verbose=False):
'''
Modifies experiment annotations by propagating stage information forward
in time across annotated timepoints.
Somewhat deprecated by process_data.propagate_worm_stages/update_annotations;
however, useful for the case in which one wants to propagate stages
and can't assume that stages are monotonically increasing with time.
'''
annotations = load_data.read_annotations(experiment_root)
for position_name, (position_annotations,
timepoint_annotations) in annotations.items():
running_stage = None
changed = []
encountered_stages = []
for timepoint, timepoint_info in timepoint_annotations.items():
already_encountered = timepoint_info.get(
'stage') in encountered_stages
stage_set = timepoint_info.get('stage') is not None
if running_stage is None: # Either first timepoint or all the annotations up to now are null
running_stage = timepoint_info.get('stage')
elif timepoint_info.get(
'stage'
) != running_stage and stage_set and not already_encountered:
running_stage = timepoint_info['stage']
if stage_set and not already_encountered:
encountered_stages.append(timepoint_info['stage'])
if not stage_set and running_stage is not None: # Also handles the case that we are working with an excluded position
timepoint_info['stage'] = running_stage
changed.append(timepoint)
elif stage_set and timepoint_info[
'stage'] != running_stage and already_encountered:
timepoint_info['stage'] = running_stage
changed.append(timepoint)
if verbose and changed: print(f'{position_name}: {changed}')
annotations = load_data.write_annotations(experiment_root, annotations)
def process_experiment_with_filter(experiment_root,
model,
image_filter,
mask_root=None,
overwrite_existing=False,
channels='bf',
make_masks=True,
do_annotations=True):
'''
image_filter - filter for scan_experiment_dir
'''
if mask_root is None:
mask_root = pathlib.Path(experiment_root) / 'derived_data' / 'mask'
# Temporary hacks until migration to new elegant complete (while zpl-9000 no longer updates annotations automatically)
process_data.update_annotations(experiment_root)
elegant_hacks.propagate_stages(experiment_root)
start_t = time.time()
positions = load_data.scan_experiment_dir(experiment_root,
timepoint_filter=image_filter,
channels=channels)
scan_t = time.time()
print(f'scanning done after {(scan_t-start_t)} s')
if make_masks:
process = segment_images.segment_positions(positions,
model,
mask_root,
use_gpu=True,
overwrite_existing=False)
if process.stderr:
print(f'Errors during segmentation: {process.stderr}'
) #raise Exception)
#raise Exception()
segment_t = time.time()
print(f'segmenting done after {(segment_t-scan_t)} s')
with (mask_root / 'notes.txt').open('a+') as notes_file:
notes_file.write(
f'{datetime.datetime.today().strftime("%Y-%m-%dt%H%M")} These masks were segmented with model {model}\n'
)
else:
print(f'No segmenting performed')
if do_annotations:
annotations = load_data.read_annotations(experiment_root)
metadata = load_data.read_metadata(experiment_root)
age_factor = metadata.get('age_factor', 1)
width_estimator = worm_widths.WidthEstimator.from_experiment_metadata(
metadata, age_factor)
segment_images.annotate_poses_from_masks(positions, mask_root,
annotations,
overwrite_existing,
width_estimator)
load_data.write_annotations(experiment_root, annotations)
annotation_t = time.time()
print(
f'annotation done after {(annotation_t - segment_t)} s') # ~3.5 hr
else:
print('No annotations done')
Example:
positions = read_annotations('my_experiment')
position_annotations, timepoint_annotations = positions['009']
life_stage = timepoint_annotations['2017-04-23t0122']['stage']
"""
experiment_root = pathlib.Path(experiment_root)
positions = collections.OrderedDict()
for annotation_file in sorted(
experiment_root.glob(annotation_subdir + '/*.pickle')):
worm_name = annotation_file.stem
positions[worm_name] = load_data.read_annotation_file(annotation_file)
return positions
def transfer_timepoint_annotations(target_annotations, source_annotations, kw):
for position in target_annotations:
assert target_annotations[position][1].keys(
) == source_annotations[position][1].keys()
for timepoint in target_annotations[position][1]:
target_annotations[position][1][timepoint][
kw] = source_annotations[position][1][timepoint][kw]
return target_annotations
expt_dir = '/mnt/9karray/Sinha_Drew/20180518_spe-9_Run_3'
DS_annotations = read_annotations(expt_dir)
LT_annotations = read_annotations(expt_dir, 'annotations_larval')
DS_annotations = transfer_timepoint_annotations(DS_annotations, LT_annotations,
'stage')
load_data.write_annotations(expt_dir, DS_annotations)