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393 lines
14 KiB
Python
393 lines
14 KiB
Python
# Created By: Virgil Dupras
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# Created On: 2006/01/29
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# Copyright 2011 Hardcoded Software (http://www.hardcoded.net)
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#
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# This software is licensed under the "BSD" License as described in the "LICENSE" file,
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# which should be included with this package. The terms are also available at
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# http://www.hardcoded.net/licenses/bsd_license
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import difflib
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import itertools
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import logging
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import string
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from collections import defaultdict, namedtuple
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from unicodedata import normalize
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from hscommon.util import flatten, multi_replace
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from hscommon.trans import tr
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from jobprogress import job
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(WEIGHT_WORDS,
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MATCH_SIMILAR_WORDS,
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NO_FIELD_ORDER) = range(3)
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JOB_REFRESH_RATE = 100
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def getwords(s):
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# We decompose the string so that ascii letters with accents can be part of the word.
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s = normalize('NFD', s)
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s = multi_replace(s, "-_&+():;\\[]{}.,<>/?~!@#$*", ' ').lower()
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s = ''.join(c for c in s if c in string.ascii_letters + string.digits + string.whitespace)
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return [_f for _f in s.split(' ') if _f] # remove empty elements
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def getfields(s):
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fields = [getwords(field) for field in s.split(' - ')]
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return [_f for _f in fields if _f]
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def unpack_fields(fields):
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result = []
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for field in fields:
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if isinstance(field, list):
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result += field
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else:
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result.append(field)
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return result
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def compare(first, second, flags=()):
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"""Returns the % of words that match between first and second
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The result is a int in the range 0..100.
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First and second can be either a string or a list.
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"""
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if not (first and second):
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return 0
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if any(isinstance(element, list) for element in first):
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return compare_fields(first, second, flags)
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second = second[:] #We must use a copy of second because we remove items from it
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match_similar = MATCH_SIMILAR_WORDS in flags
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weight_words = WEIGHT_WORDS in flags
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joined = first + second
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total_count = (sum(len(word) for word in joined) if weight_words else len(joined))
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match_count = 0
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in_order = True
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for word in first:
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if match_similar and (word not in second):
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similar = difflib.get_close_matches(word, second, 1, 0.8)
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if similar:
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word = similar[0]
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if word in second:
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if second[0] != word:
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in_order = False
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second.remove(word)
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match_count += (len(word) if weight_words else 1)
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result = round(((match_count * 2) / total_count) * 100)
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if (result == 100) and (not in_order):
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result = 99 # We cannot consider a match exact unless the ordering is the same
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return result
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def compare_fields(first, second, flags=()):
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"""Returns the score for the lowest matching fields.
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first and second must be lists of lists of string.
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"""
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if len(first) != len(second):
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return 0
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if NO_FIELD_ORDER in flags:
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results = []
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#We don't want to remove field directly in the list. We must work on a copy.
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second = second[:]
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for field1 in first:
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max = 0
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matched_field = None
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for field2 in second:
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r = compare(field1, field2, flags)
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if r > max:
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max = r
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matched_field = field2
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results.append(max)
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if matched_field:
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second.remove(matched_field)
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else:
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results = [compare(word1, word2, flags) for word1, word2 in zip(first, second)]
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return min(results) if results else 0
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def build_word_dict(objects, j=job.nulljob):
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"""Returns a dict of objects mapped by their words.
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objects must have a 'words' attribute being a list of strings or a list of lists of strings.
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The result will be a dict with words as keys, lists of objects as values.
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"""
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result = defaultdict(set)
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for object in j.iter_with_progress(objects, 'Prepared %d/%d files', JOB_REFRESH_RATE):
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for word in unpack_fields(object.words):
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result[word].add(object)
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return result
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def merge_similar_words(word_dict):
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"""Take all keys in word_dict that are similar, and merge them together.
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"""
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keys = list(word_dict.keys())
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keys.sort(key=len)# we want the shortest word to stay
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while keys:
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key = keys.pop(0)
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similars = difflib.get_close_matches(key, keys, 100, 0.8)
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if not similars:
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continue
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objects = word_dict[key]
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for similar in similars:
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objects |= word_dict[similar]
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del word_dict[similar]
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keys.remove(similar)
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def reduce_common_words(word_dict, threshold):
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"""Remove all objects from word_dict values where the object count >= threshold
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The exception to this removal are the objects where all the words of the object are common.
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Because if we remove them, we will miss some duplicates!
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"""
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uncommon_words = set(word for word, objects in word_dict.items() if len(objects) < threshold)
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for word, objects in list(word_dict.items()):
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if len(objects) < threshold:
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continue
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reduced = set()
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for o in objects:
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if not any(w in uncommon_words for w in unpack_fields(o.words)):
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reduced.add(o)
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if reduced:
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word_dict[word] = reduced
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else:
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del word_dict[word]
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Match = namedtuple('Match', 'first second percentage')
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def get_match(first, second, flags=()):
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#it is assumed here that first and second both have a "words" attribute
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percentage = compare(first.words, second.words, flags)
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return Match(first, second, percentage)
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def getmatches(objects, min_match_percentage=0, match_similar_words=False, weight_words=False,
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no_field_order=False, j=job.nulljob):
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COMMON_WORD_THRESHOLD = 50
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LIMIT = 5000000
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j = j.start_subjob(2)
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sj = j.start_subjob(2)
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for o in objects:
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if not hasattr(o, 'words'):
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o.words = getwords(o.name)
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word_dict = build_word_dict(objects, sj)
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reduce_common_words(word_dict, COMMON_WORD_THRESHOLD)
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if match_similar_words:
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merge_similar_words(word_dict)
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match_flags = []
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if weight_words:
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match_flags.append(WEIGHT_WORDS)
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if match_similar_words:
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match_flags.append(MATCH_SIMILAR_WORDS)
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if no_field_order:
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match_flags.append(NO_FIELD_ORDER)
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j.start_job(len(word_dict), tr("0 matches found"))
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compared = defaultdict(set)
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result = []
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try:
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# This whole 'popping' thing is there to avoid taking too much memory at the same time.
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while word_dict:
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items = word_dict.popitem()[1]
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while items:
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ref = items.pop()
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compared_already = compared[ref]
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to_compare = items - compared_already
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compared_already |= to_compare
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for other in to_compare:
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m = get_match(ref, other, match_flags)
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if m.percentage >= min_match_percentage:
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result.append(m)
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if len(result) >= LIMIT:
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return result
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j.add_progress(desc=tr("%d matches found") % len(result))
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except MemoryError:
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# This is the place where the memory usage is at its peak during the scan.
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# Just continue the process with an incomplete list of matches.
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del compared # This should give us enough room to call logging.
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logging.warning('Memory Overflow. Matches: %d. Word dict: %d' % (len(result), len(word_dict)))
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return result
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return result
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def getmatches_by_contents(files, sizeattr='size', partial=False, j=job.nulljob):
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j = j.start_subjob([2, 8])
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size2files = defaultdict(set)
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for file in j.iter_with_progress(files, tr("Read size of %d/%d files")):
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filesize = getattr(file, sizeattr)
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if filesize:
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size2files[filesize].add(file)
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possible_matches = [files for files in size2files.values() if len(files) > 1]
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del size2files
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result = []
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j.start_job(len(possible_matches), tr("0 matches found"))
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for group in possible_matches:
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for first, second in itertools.combinations(group, 2):
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if first.is_ref and second.is_ref:
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continue # Don't spend time comparing two ref pics together.
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if first.md5partial == second.md5partial:
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if partial or first.md5 == second.md5:
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result.append(Match(first, second, 100))
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j.add_progress(desc=tr("%d matches found") % len(result))
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return result
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class Group:
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#---Override
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def __init__(self):
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self._clear()
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def __contains__(self, item):
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return item in self.unordered
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def __getitem__(self, key):
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return self.ordered.__getitem__(key)
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def __iter__(self):
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return iter(self.ordered)
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def __len__(self):
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return len(self.ordered)
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#---Private
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def _clear(self):
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self._percentage = None
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self._matches_for_ref = None
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self.matches = set()
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self.candidates = defaultdict(set)
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self.ordered = []
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self.unordered = set()
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def _get_matches_for_ref(self):
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if self._matches_for_ref is None:
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ref = self.ref
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self._matches_for_ref = [match for match in self.matches if ref in match]
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return self._matches_for_ref
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#---Public
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def add_match(self, match):
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def add_candidate(item, match):
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matches = self.candidates[item]
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matches.add(match)
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if self.unordered <= matches:
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self.ordered.append(item)
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self.unordered.add(item)
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if match in self.matches:
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return
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self.matches.add(match)
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first, second, _ = match
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if first not in self.unordered:
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add_candidate(first, second)
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if second not in self.unordered:
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add_candidate(second, first)
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self._percentage = None
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self._matches_for_ref = None
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def discard_matches(self):
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discarded = set(m for m in self.matches if not all(obj in self.unordered for obj in [m.first, m.second]))
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self.matches -= discarded
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self.candidates = defaultdict(set)
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return discarded
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def get_match_of(self, item):
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if item is self.ref:
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return
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for m in self._get_matches_for_ref():
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if item in m:
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return m
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def prioritize(self, key_func, tie_breaker=None):
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# tie_breaker(ref, dupe) --> True if dupe should be ref
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master_key_func = lambda x: (-x.is_ref, key_func(x))
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self.ordered.sort(key=master_key_func)
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if tie_breaker is None:
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return
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ref = self.ref
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key_value = key_func(ref)
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for dupe in self.dupes:
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if key_func(dupe) != key_value:
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break
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if tie_breaker(ref, dupe):
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ref = dupe
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if ref is not self.ref:
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self.switch_ref(ref)
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def remove_dupe(self, item, discard_matches=True):
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try:
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self.ordered.remove(item)
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self.unordered.remove(item)
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self._percentage = None
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self._matches_for_ref = None
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if (len(self) > 1) and any(not getattr(item, 'is_ref', False) for item in self):
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if discard_matches:
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self.matches = set(m for m in self.matches if item not in m)
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else:
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self._clear()
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except ValueError:
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pass
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def switch_ref(self, with_dupe):
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if self.ref.is_ref:
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return
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try:
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self.ordered.remove(with_dupe)
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self.ordered.insert(0, with_dupe)
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self._percentage = None
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self._matches_for_ref = None
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except ValueError:
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pass
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dupes = property(lambda self: self[1:])
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@property
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def percentage(self):
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if self._percentage is None:
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if self.dupes:
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matches = self._get_matches_for_ref()
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self._percentage = sum(match.percentage for match in matches) // len(matches)
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else:
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self._percentage = 0
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return self._percentage
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@property
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def ref(self):
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if self:
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return self[0]
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def get_groups(matches, j=job.nulljob):
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matches.sort(key=lambda match: -match.percentage)
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dupe2group = {}
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groups = []
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try:
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for match in j.iter_with_progress(matches, tr("Grouped %d/%d matches"), JOB_REFRESH_RATE):
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first, second, _ = match
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first_group = dupe2group.get(first)
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second_group = dupe2group.get(second)
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if first_group:
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if second_group:
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if first_group is second_group:
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target_group = first_group
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else:
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continue
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else:
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target_group = first_group
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dupe2group[second] = target_group
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else:
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if second_group:
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target_group = second_group
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dupe2group[first] = target_group
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else:
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target_group = Group()
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groups.append(target_group)
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dupe2group[first] = target_group
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dupe2group[second] = target_group
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target_group.add_match(match)
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except MemoryError:
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del dupe2group
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del matches
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# should free enough memory to continue
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logging.warning('Memory Overflow. Groups: {0}'.format(len(groups)))
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# Now that we have a group, we have to discard groups' matches and see if there're any "orphan"
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# matches, that is, matches that were candidate in a group but that none of their 2 files were
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# accepted in the group. With these orphan groups, it's safe to build additional groups
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matched_files = set(flatten(groups))
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orphan_matches = []
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for group in groups:
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orphan_matches += set(m for m in group.discard_matches() if not any(obj in matched_files for obj in [m.first, m.second]))
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if groups and orphan_matches:
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groups += get_groups(orphan_matches) # no job, as it isn't supposed to take a long time
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return groups
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