def extract_tf_records_from_GEDI_tiffs():
"""Extracts data directly from GEDI tiffs and
inserts them into tf records. This allows us to
offload the normalization procedure to either
right before training (via sampling) or during
training (via normalization with a batch's max)"""
# Grab the global config
config = GEDIconfig()
# Make dirs if they do not exist
dir_list = [
config.train_directory, config.validation_directory,
config.tfrecord_dir, config.train_checkpoint
]
if 'test' in config.tvt_flags:
dir_list += [config.test_directory]
config.raw_im_dirs = [x + '_train' for x in config.raw_im_dirs]
config.raw_im_dirs += [
x.split('_train')[0] + '_test' for x in config.raw_im_dirs
]
[make_dir(d) for d in dir_list]
print('raw_im_dirs', config.raw_im_dirs)
# gather file names of images to process
im_lists = flatten_list([
glob(os.path.join(config.home_dir, r, '*' + config.raw_im_ext))
for r in config.raw_im_dirs
])
print('im_lists', im_lists[:3])
# Write labels list
label_list = os.path.join(
config.processed_image_patch_dir,
'list_of_' + '_'.join(x
for x in config.image_prefixes) + '_labels.txt')
print('label_list', label_list)
write_label_list(im_lists, label_list)
# Finally, write the labels file:
labels_to_class_names = dict(
zip(range(len(config.label_directories)), config.label_directories))
write_label_file(labels_to_class_names, config.tfrecord_dir)
# Copy data into the appropriate training/testing directories
if 'test' in config.tvt_flags:
new_files = split_files(im_lists, config.train_proportion,
config.tvt_flags)
else:
new_files = split_files(im_lists, config.train_proportion,
config.tvt_flags)
if type(config.tvt_flags) is str:
files = new_files[config.tvt_flags]
label_list = new_files[config.tvt_flags + '_labels']
output_pointer = os.path.join(config.tfrecord_dir,
config.tvt_flags + '.tfrecords')
extract_to_tf_records(files=files,
label_list=label_list,
output_pointer=output_pointer,
ratio_list=None,
config=config,
k=config.tvt_flags)
else:
for k in config.tvt_flags:
files = new_files[k]
label_list = new_files[k + '_labels']
print('GEDI label list', label_list)
output_pointer = os.path.join(config.tfrecord_dir,
k + '.tfrecords')
extract_to_tf_records(files=files,
label_list=label_list,
output_pointer=output_pointer,
ratio_list=None,
config=config,
k=k)
def extract_tf_records_from_GEDI_tiffs():
"""Extracts data directly from GEDI tiffs and
inserts them into tf records. This allows us to
offload the normalization procedure to either
right before training (via sampling) or during
training (via normalization with a batch's max)"""
# Grab the global config
config = GEDIconfig()
# If requested load in the ratio file
ratio_file = os.path.join(
config.home_dir, config.original_image_dir, config.ratio_stem,
'%s%s.csv' % (config.ratio_prefix, config.experiment_image_set))
if config.ratio_prefix is not None and os.path.exists(ratio_file):
ratio_list = read_csv(os.path.join(ratio_file))
else:
ratio_list = None
# Allow option for per-timestep image extraction
if config.timestep_delta_frames:
extraction = extract_to_tf_records
else:
extraction = vtf
# Make dirs if they do not exist
dir_list = [
config.train_directory, config.validation_directory,
config.tfrecord_dir, config.train_checkpoint
]
if 'test' in config.tvt_flags:
dir_list += [config.test_directory]
config.raw_im_dirs = [x + '_train' for x in config.raw_im_dirs]
config.raw_im_dirs += [
x.split('_train')[0] + '_test' for x in config.raw_im_dirs
]
[make_dir(d) for d in dir_list]
im_lists = get_image_dict(config)
# Sample from training for validation images
if 'val' in config.tvt_flags:
im_lists['val'], im_lists['train'] = sample_files(
im_lists['train'], config.train_proportion, config.tvt_flags)
if config.encode_time_of_death is not None:
death_timepoints = pd.read_csv(
config.encode_time_of_death)[['plate_well_neuron', 'dead_tp']]
keep_experiments = pd.read_csv(config.time_of_death_experiments)
im_labels = {}
for k, v in im_lists.items():
if k is not 'test':
proc_ims, proc_labels = find_timepoint(
images=v,
data=death_timepoints,
keep_experiments=keep_experiments,
remove_thresh=config.mask_timepoint_value)
im_labels[k] = proc_labels
im_lists[k] = proc_ims
df = pd.DataFrame(np.vstack(
(proc_ims, proc_labels)).transpose(),
columns=['image', 'timepoint'])
df.to_csv('%s.csv' % k)
else:
im_labels[k] = find_label(v)
else:
im_labels = {k: find_label(v) for k, v in im_lists.items()}
if type(config.tvt_flags) is str:
tvt_flags = [config.tvt_flags]
else:
tvt_flags = config.tvt_flags
assert len(np.concatenate(im_lists.values())), 'Could not find any files.'
label_list = [
write_labels(flag=x, im_lists=im_lists, config=config)
for x in tvt_flags
]
if config.include_GEDI_in_tfrecords > 0:
tf_flag = '_%sgedi' % config.include_GEDI_in_tfrecords
else:
tf_flag = ''
if config.extra_image:
tf_flag = '_1image'
else:
tf_flag = ''
if type(config.tvt_flags) is str:
files = im_lists[config.tvt_flags]
label_list = im_labels[config.tvt_flags]
output_pointer = os.path.join(
config.tfrecord_dir,
'%s%s.tfrecords' % (config.tvt_flags, tf_flag))
extraction(files=files,
label_list=label_list,
output_pointer=output_pointer,
ratio_list=ratio_list,
config=config,
k=config.tvt_flags)
else:
for k in config.tvt_flags:
files = im_lists[k]
label_list = im_labels[k]
output_pointer = os.path.join(
config.tfrecord_dir,
'%s%s.tfrecords' % (tf_flag, config.tf_record_names[k]))
extraction(files=files,
label_list=label_list,
output_pointer=output_pointer,
ratio_list=ratio_list,
config=config,
k=k)
