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utils.py
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utils.py
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import os
import re
import tensorflow as tf
from os import path as osp
import numpy as np
def get_files(directory, pattern):
return [
osp.join(directory, f) for f in os.listdir(directory)
if re.match(pattern, f)
]
def validate_stack_shape(img, config):
if img.ndim != 5:
raise ValueError(
'Expecting 5 dimensions in image stack, '
'given shape = {}'.format(img.shape)
)
ncyc, nz, nch, nh, nw = img.shape
if ncyc != config.n_cycles():
raise ValueError(
'Expecting {} cycles but found {} in image stack'
.format(config.n_cycles(), ncyc)
)
if nz != config.n_z_planes():
raise ValueError(
'Expecting {} z planes but found {} in image stack'
.format(config.n_z_planes(), nz)
)
if nch != config.n_channels_per_cycle():
raise ValueError(
'Expecting {} channels but found {} in image stack'
.format(config.n_channels_per_cycle(), nch)
)
def arr_to_uint(img, dtype):
""" Convert image array to data type with no scaling """
if img.min() < 0:
raise ValueError('Expecting only positive values in array')
minv, maxv = np.iinfo(dtype).min, np.iinfo(dtype).max
return np.clip(img, minv, maxv).astype(dtype)
def disable_tf_logging():
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
tf.logging.set_verbosity(tf.logging.WARN)
def get_immersion_ri(immersion):
"""Get refractive index for an immersion type"""
if immersion == 'air':
return 1.0
elif immersion == 'water':
return 1.33
elif immersion == 'oil':
return 1.5115
else:
raise ValueError('Immersion "{}" is not valid (must be air, water, or oil)'.format(immersion))