def log_line(image, model, t):
imagen = ImageCategories(model)
td = t
if not type(t) == dict:
td = { int(i): 1 for i in t }
else:
td = { int(i): v for (i, v) in t.iteritems() }
nd = { imagen.name(i): v for (i, v) in td.iteritems() }
nld = sorted(nd.items(), key=lambda x: -x[1])
row = [ str(e) for l in nld for e in l ]
row = [ image, model ] + row
return ','.join(row) + "\n"
#!/usr/bin/env python
from nltk.corpus import wordnet as wn
from neuralgae import ImageCategories
OUTFILE = "definitions_places.txt"
inet = ImageCategories("places")
def find_synsets(w):
ss = wn.synsets(w)
if ss:
return ss
ss = []
ws = w.split("_")
for w1 in ws:
ss1 = wn.synsets(w1)
if ss1:
ss += ss1
return ss
with open(OUTFILE, "w") as f:
for i in range(0, 204):
w = inet.name(i)
w_ = w.replace(" ", "_")
ss = find_synsets(w_)
if not ss:
def class_names(model, t):
imagen = ImageCategories(model)
return ', '.join([imagen.name(c) for c in t])
#!/usr/bin/env python
from nltk.corpus import wordnet as wn
from neuralgae import ImageCategories
OUTFILE = 'definitions_places.txt'
inet = ImageCategories('places')
def find_synsets(w):
ss = wn.synsets(w)
if ss:
return ss
ss = []
ws = w.split('_')
for w1 in ws:
ss1 = wn.synsets(w1)
if ss1:
ss += ss1
return ss
with open(OUTFILE, 'w') as f:
for i in range(0, 204):
w = inet.name(i)
w_ = w.replace(' ', '_')
ss = find_synsets(w_)
if not ss:
print "ERR: %d %s not found" % (i, w)
