import numpy as np from progressbar import * import scipy.ndimage.filters as filters import theano from pylearn2.space import CompositeSpace,VectorSpace from pylearn2.models.autoencoder import * from pylearn2.models.mlp import MLP import tables import timeit from bcfind import extract_patch import os import warnings from bcfind import timer deconvolver_timer = timer.Timer('Semantic Deconvolution analysis') class VPrint(object): def __init__(self,verbose=True): self.verbose = verbose def __call__(self, *args, **kwargs): if self.verbose: argstring = " ".join([str(arg) for arg in args]) if 'end' in kwargs: print(argstring,end=kwargs['end']) else: print(argstring) sys.stdout.flush()
""" Functions for image thresholding """ from __future__ import print_function import math from bcfind import timer multi_kapur_timer = timer.Timer('Multi Kapur') try: xrange # Python 2 except NameError: xrange = range # Python 3 def _info(p): if p > 0: return -p * math.log(p) else: return 0 def kapur(histogram): """Computes binarization threshold using the maximum entropy approach Parameters ---------- histogram : array-like Image histogram Returns -------
import numpy as np from PIL import Image from PIL import ImageDraw import cPickle as pickle from scipy.spatial import cKDTree from bcfind import timer from bcfind.log import tee from bcfind.utils import mkdir_p, which from bcfind import log SHARE_DIR = os.path.dirname(log.__file__)+'/share' hi2rgb = pickle.load(open(SHARE_DIR+'/hi2rgb.pickle', 'rb')) save_vaa3d_timer = timer.Timer('Save Vaa3D') save_markers_timer = timer.Timer('Save markers') def m_load_markers(filename, from_vaa3d=False): data = pd.read_csv(filename, skipinitialspace=True, na_filter=False) if '#x' in data.keys(): # fix some Vaa3d garbage data.rename(columns={'#x': 'x'}, inplace=True) if '##x' in data.keys(): # fix some Vaa3d garbage data.rename(columns={'##x': 'x'}, inplace=True) C = [] for i in data.index: row = data.ix[i] c = Center(0, 0, 0) for k in row.keys(): setattr(c,k,row[k]) if from_vaa3d:
from torchsummary import summary import torch.nn as nn from bcfind import timer forward_time_teacher = timer.Timer('Forward Time Teacher') class FC_teacher_max_p(nn.Module): def __init__(self, n_filters, k_conv=3, k_t_conv = 2, input_channels=1): super(FC_teacher_max_p, self).__init__() self.input_channels = input_channels self.n_filters = n_filters self.k_conv = k_conv self.k_t_conv = k_t_conv self.padding = k_conv // 2 # non riduce le dimensioni spaziali, aumenta soltanto il numero di filtri self.conv1 = nn.Conv3d(input_channels, n_filters, self.k_conv, padding=self.padding) self.conv2 = nn.Conv3d(self.n_filters, self.n_filters*2, self.k_conv, padding=self.padding) self.conv3 = nn.Conv3d(self.n_filters*2, self.n_filters*4, self.k_conv, padding=self.padding)