def load_assoc():
global commonsense_assoc
if commonsense_assoc: return commonsense_assoc
try:
from luminoso3.background_space import get_commonsense_assoc
app.logger.info("Getting assoc space; env=%s" % os.environ.get('LUMINOSO_DATA'))
commonsense_assoc = get_commonsense_assoc('5.1', 150)
app.logger.info("Done")
except ImportError:
app.logger.info("Couldn't import luminoso3; running without similarity measures")
return commonsense_assoc
def load_assoc():
global commonsense_assoc
if commonsense_assoc: return commonsense_assoc
try:
from luminoso3.background_space import get_commonsense_assoc
app.logger.info("Getting assoc space; env=%s" % os.environ.get('LUMINOSO_DATA'))
commonsense_assoc = get_commonsense_assoc('5.1', 150)
app.logger.info("Done")
except ImportError:
app.logger.info("Couldn't import luminoso3; running without similarity measures")
return commonsense_assoc
import simplenlp
from metanl import english
import math, random
from luminoso3.background_space import get_commonsense_assoc
from colorizer.color_data import make_lab_color_data, lab_to_rgb, rgb_to_hsv
from colorizer.colorvote import weighted_elect_samples
ENGLISH = simplenlp.get('en')
ASSOC = get_commonsense_assoc('en', 100)
COLORDATA = {}
origdata = make_lab_color_data()
def importance_factor(colorname):
imp = 10000 / math.sqrt(english.word_frequency(colorname.split()[0], 1000000))
return int(imp)
for key, values in origdata.items():
subset_values = random.sample(values,
min(len(values), int(math.ceil(importance_factor(key)*math.sqrt(len(values))))))
COLORDATA[key] = subset_values
def output_colors(labcolors):
return [lab_to_rgb(c) for c in sorted(labcolors)]
class IncrementalColorizer(object):
def __init__(self, ncolors):
self.ncolors = ncolors
self.colors = [(128,128,128)] * ncolors
