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import numpy as np
import tensorflow as tf
def min_max_norm(img: np.ndarray, out_max_val=1.):
out_img = img
max_val = np.amax(img)
min_val = np.amin(img)
if (max_val - min_val) != 0:
out_img = (img - min_val) / (max_val - min_val)
return out_img * out_max_val
def soft_threshold(x, threshold, name='soft_threshold'):
# https://www.tensorflow.org/probability/api_docs/python/tfp/math/soft_threshold
# Foucart S., Rauhut H. (2013) Basic Algorithms. In: A Mathematical Introduction to Compressive Sensing.
# Applied and Numerical Harmonic Analysis. Birkhäuser, New York, NY. https://doi.org/10.1007/978-0-8176-4948-7_3
# Chapter 3, page 72
with tf.name_scope(name):
x = tf.convert_to_tensor(x, name='x')
threshold = tf.convert_to_tensor(threshold, dtype=x.dtype, name='threshold')
return tf.sign(x) * tf.maximum(tf.abs(x) - threshold, 0.)
def hard_threshold(x, threshold, name='hard_threshold'):
with tf.name_scope(name):
threshold = tf.convert_to_tensor(threshold, dtype=x.dtype, name='threshold')
return tf.sign(tf.maximum(tf.abs(x) - threshold, 0.))
def binary_activation(x):
# https://stackoverflow.com/questions/37743574/hard-limiting-threshold-activation-function-in-tensorflow
cond = tf.less(x, tf.zeros(tf.shape(x)))
out = tf.where(cond, tf.zeros(tf.shape(x)), tf.ones(tf.shape(x)))
return out
def gaussian_kernel(kernel_size, sigma, in_ch, out_ch, dim, dtype=tf.float32):
# SRC: https://stackoverflow.com/questions/59286171/gaussian-blur-image-in-dataset-pipeline-in-tensorflow
x = tf.range(-kernel_size // 2 + 1, kernel_size // 2 + 1, dtype=dtype)
g = tf.math.exp(-(tf.pow(x, 2) / (2 * tf.pow(tf.cast(sigma, dtype), 2))))
g_kernel = tf.identity(g)
g_kernel = tf.tensordot(g_kernel, g, 0)
g_kernel = tf.tensordot(g_kernel, g, 0)
# i = tf.constant(0)
# cond = lambda i, g_kern: tf.less(i, dim - 1)
# mult_kern = lambda i, g_kern: [tf.add(i, 1), tf.tensordot(g_kern, g, 0)]
# _, g_kernel = tf.while_loop(cond, mult_kern,
# loop_vars=[i, g_kernel],
# shape_invariants=[i.get_shape(), tf.TensorShape([kernel_size, None, None])])
g_kernel = g_kernel / tf.reduce_sum(g_kernel)
g_kernel = tf.expand_dims(tf.expand_dims(g_kernel, axis=-1), axis=-1)
return tf.tile(g_kernel, (*(1,)*dim, in_ch, out_ch))
def sample_unique(population, samples, tout=tf.int32):
# src: https://github.com/tensorflow/tensorflow/issues/9260#issuecomment-437875125
z = -tf.log(-tf.log(tf.random_uniform((tf.shape(population)[0],), 0, 1)))
_, indices = tf.nn.top_k(z, samples)
ret_val = tf.gather(population, indices)
return tf.cast(ret_val, tout)
def safe_medpy_metric(prediction, reference, nb_labels, metric_fnc, fnc_args: dict={}):
vals = list()
if 'voxelspacing' in fnc_args.keys():
diag = np.power(reference.shape[:-1] * fnc_args['voxelspacing'], 2)
else:
diag = np.power(reference.shape[:-1], 2)
diag = np.sqrt(np.sum(diag))
for l in range(nb_labels):
try:
vals.append(metric_fnc(prediction[..., l], reference[..., l], **fnc_args))
except RuntimeError:
vals.append(diag)
return vals |