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import os
from pathlib import Path
from glob import glob
import unidecode
import difflib
from app.settings import types
from app.utils import logger_utils
log = logger_utils.Logger.getLogger()
# Get list of names based on Dataset type
def get_names(enum_dataset):
if enum_dataset == types.Dataset.LFW:
dir_lfw = '/data_store_hdd/datasets/people/lfw/media/original/'
names_orig = [x for x in os.listdir(dir_lfw)]
names_query = [x.replace('_', ' ') for x in names_orig]
result = {'names_orig': names_orig, 'names_query': names_query}
elif enum_dataset == types.Dataset.YOUTUBE_FACES:
names = [x for x in names if 'labeled faces.txt' not in x]
else:
log.warn(f'{enum_dataset} not yet implemented')
result = {}
return result
def similarity(a, b):
return difflib.SequenceMatcher(a=a.lower(), b=b.lower()).ratio()
def names_match(name_a, name_b, threshold=0.9, as_float=False, compound_score=False):
'''Returns boolean if names are similar enough
'''
# strip spaces and split names into list of plain text words
name_a_clean = [unidecode.unidecode(x.strip().lower()) for x in name_a.strip().split(' ')]
name_b_clean = [unidecode.unidecode(x.strip().lower()) for x in name_b.strip().split(' ')]
# assign short long vars
len_a = len(name_a_clean)
len_b = len(name_b_clean)
len_min = min(len_a, len_b)
len_max = max(len_a, len_b)
# compute scores
scores = []
for i in range(len(name_a_clean)):
word_a = name_a_clean[i]
subscores = []
for j in range(len(name_b_clean)):
word_b = name_b_clean[j]
score = similarity(word_a, word_b)
subscores.append(score)
scores.append(subscores)
# return result
ratio_similar = sum(max(x) for x in scores) / len_min
# combine with any missing letters/words
letters_a = sum(len(x) for x in name_a_clean)
letters_b = sum(len(x) for x in name_b_clean)
ratio_letters = min(letters_a, letters_b) / max(letters_a, letters_b)
if compound_score:
score = (0.8 * ratio_similar) + (0.2 * ratio_letters)
else:
score = ratio_similar
if as_float:
return score
else:
return score > threshold
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