import tensorflow as tf
import numpy as np
from scipy import ndimage
def reduce_precision_np(x, npp):
"""
Reduce the precision of image, the numpy version.
:param x: a float tensor, which has been scaled to [0, 1].
:param npp: number of possible values per pixel. E.g. it's 256 for 8-bit gray-scale image, and 2 for binarized image.
:return: a tensor representing image(s) with lower precision.
"""
# Note: 0 is a possible value too.
npp_int = npp - 1
x_int = np.rint(x * npp_int)
x_float = x_int / npp_int
return x_float
def reduce_precision_tf(x, npp):
"""
Reduce the precision of image, the tensorflow version.
"""
npp_int = npp - 1
x_int = tf.rint(tf.multiply(x, npp_int))
x_float = tf.div(x_int, npp_int)
return x_float
def binary_filter_tf(x):
"""
An efficient implementation of reduce_precision_tf(x, 2).
"""
x_bin = tf.nn.relu(tf.sign(x-0.5))
return x_bin
def binary_filter_np(x):
"""
An efficient implementation of reduce_precision_np(x, 2).
"""
x_bin = np.maximum(np.sign(x-0.5), 0)
return x_bin
def median_filter_np(x, width, height=-1):
"""
Median smoothing by Scipy.
:param x: a tensor of image(s)
:param width: the width of the sliding window (number of pixels)
:param height: the height of the window. The same as width by default.
:return: a modified tensor with the same shape as x.
"""
if height == -1:
height = width
return ndimage.filters.median_filter(x, size=(1,1,width,height), mode='reflect')
