YoelShoshan

import tensorflow as tf
from collections import defaultdict

def process_tensorboard_events_file(tensorboard_events_filename, tf_version=2, tags=None):
    assert isinstance(tags, list)
    for v in tags:
        assert isinstance(v, str)    
    it = _get_events_iterator(tensorboard_events_filename, tf_version)
    ans = defaultdict(list)
    for event in it:

YoelShoshan

#!/usr/bin/python

import SimpleITK as sitk
import vtk
import numpy as np

from vtk.util.vtkConstants import *

def numpy2VTK(img,spacing=[1.0,1.0,1.0]):
    # evolved from code from Stou S.,

YoelShoshan

import abc

import six


@six.add_metaclass(abc.ABCMeta)
class Platform(object):
    """Base class for all OpenGL platforms.

    Parameters

YoelShoshan

import cv2
import numpy as np

def base_tens_stats(tens):
    print(f'shape={tens.shape} min={tens.min()} mean={tens.mean()} max={tens.max()}')

x = np.random.randint(0,1020, (256,256,20)).astype(np.uint16)

base_tens_stats(x)

YoelShoshan

def make_parallel(fn, num_gpus, inputs_to_be_splitted, inputs_to_be_passed_as_is=None):
    '''
    Converts a function into a parallel version of it, using a simple syncronized data parallelsim
    :param fn: a tensorflow function
    :param num_gpus: number of gpus
    :param inputs_to_be_splitted: a dict with all of the tensorflow variables/placeholders that will be split before being passed to fn
    :param inputs_to_be_passed_as_is: a dict with all of the variables that will be passed as is to fn
    :return:
    '''

YoelShoshan

from PyQt4.QtGui import *
from PyQt4.QtCore import *


import networkx as nx
from networkx.drawing.nx_agraph import graphviz_layout

class ZedNode(QGraphicsEllipseItem):
    rad = 5
    def __init__(self,  key):

YoelShoshan

from keras import backend as K
import keras.optimizers
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense, Input
from keras.models import Model
import numpy as np

def make_model(input_dim_size):
    if K.image_dim_ordering() == 'tf':
        input_shape = (input_dim_size, input_dim_size,1)