dupeguru/core/engine.py

# Created By: Virgil Dupras
# Created On: 2006/01/29
# Copyright 2012 Hardcoded Software (http://www.hardcoded.net)
# 
# This software is licensed under the "BSD" License as described in the "LICENSE" file, 
# which should be included with this package. The terms are also available at 
# http://www.hardcoded.net/licenses/bsd_license

import difflib
import itertools
import logging
import string
from collections import defaultdict, namedtuple
from unicodedata import normalize

from hscommon.util import flatten, multi_replace
from hscommon.trans import tr
from jobprogress import job

(WEIGHT_WORDS,
MATCH_SIMILAR_WORDS,
NO_FIELD_ORDER) = range(3)

JOB_REFRESH_RATE = 100

def getwords(s):
    # We decompose the string so that ascii letters with accents can be part of the word.
    s = normalize('NFD', s)
    s = multi_replace(s, "-_&+():;\\[]{}.,<>/?~!@#$*", ' ').lower()
    s = ''.join(c for c in s if c in string.ascii_letters + string.digits + string.whitespace)
    return [_f for _f in s.split(' ') if _f] # remove empty elements

def getfields(s):
    fields = [getwords(field) for field in s.split(' - ')]
    return [_f for _f in fields if _f]

def unpack_fields(fields):
    result = []
    for field in fields:
        if isinstance(field, list):
            result += field
        else:
            result.append(field)
    return result

def compare(first, second, flags=()):
    """Returns the % of words that match between first and second
    
    The result is a int in the range 0..100.
    First and second can be either a string or a list.
    """
    if not (first and second):
        return 0
    if any(isinstance(element, list) for element in first):
        return compare_fields(first, second, flags)
    second = second[:] #We must use a copy of second because we remove items from it        
    match_similar = MATCH_SIMILAR_WORDS in flags
    weight_words = WEIGHT_WORDS in flags
    joined = first + second
    total_count = (sum(len(word) for word in joined) if weight_words else len(joined))
    match_count = 0
    in_order = True
    for word in first:
        if match_similar and (word not in second):
            similar = difflib.get_close_matches(word, second, 1, 0.8)
            if similar:
                word = similar[0]
        if word in second:
            if second[0] != word:
                in_order = False
            second.remove(word)
            match_count += (len(word) if weight_words else 1)
    result = round(((match_count * 2) / total_count) * 100)
    if (result == 100) and (not in_order):
        result = 99 # We cannot consider a match exact unless the ordering is the same
    return result

def compare_fields(first, second, flags=()):
    """Returns the score for the lowest matching fields.
    
    first and second must be lists of lists of string.
    """
    if len(first) != len(second):
        return 0
    if NO_FIELD_ORDER in flags:
        results = []
        #We don't want to remove field directly in the list. We must work on a copy.
        second = second[:]
        for field1 in first:
            max = 0
            matched_field = None
            for field2 in second:
                r = compare(field1, field2, flags)
                if r > max:
                    max = r
                    matched_field = field2
            results.append(max)
            if matched_field:
                second.remove(matched_field)
    else:
        results = [compare(word1, word2, flags) for word1, word2 in zip(first, second)]
    return min(results) if results else 0

def build_word_dict(objects, j=job.nulljob):
    """Returns a dict of objects mapped by their words.
    
    objects must have a 'words' attribute being a list of strings or a list of lists of strings.
    
    The result will be a dict with words as keys, lists of objects as values.
    """
    result = defaultdict(set)
    for object in j.iter_with_progress(objects, 'Prepared %d/%d files', JOB_REFRESH_RATE):
        for word in unpack_fields(object.words):
            result[word].add(object)
    return result

def merge_similar_words(word_dict):
    """Take all keys in word_dict that are similar, and merge them together.
    """
    keys = list(word_dict.keys())
    keys.sort(key=len)# we want the shortest word to stay
    while keys:
        key = keys.pop(0)
        similars = difflib.get_close_matches(key, keys, 100, 0.8)
        if not similars:
            continue
        objects = word_dict[key]
        for similar in similars:
            objects |= word_dict[similar]
            del word_dict[similar]
            keys.remove(similar)

def reduce_common_words(word_dict, threshold):
    """Remove all objects from word_dict values where the object count >= threshold
    
    The exception to this removal are the objects where all the words of the object are common.
    Because if we remove them, we will miss some duplicates!
    """
    uncommon_words = set(word for word, objects in word_dict.items() if len(objects) < threshold)
    for word, objects in list(word_dict.items()):
        if len(objects) < threshold:
            continue
        reduced = set()
        for o in objects:
            if not any(w in uncommon_words for w in unpack_fields(o.words)):
                reduced.add(o)
        if reduced:
            word_dict[word] = reduced
        else:
            del word_dict[word]

Match = namedtuple('Match', 'first second percentage')
def get_match(first, second, flags=()):
    #it is assumed here that first and second both have a "words" attribute
    percentage = compare(first.words, second.words, flags)
    return Match(first, second, percentage)

def getmatches(objects, min_match_percentage=0, match_similar_words=False, weight_words=False, 
    no_field_order=False, j=job.nulljob):
    COMMON_WORD_THRESHOLD = 50
    LIMIT = 5000000
    j = j.start_subjob(2)
    sj = j.start_subjob(2)
    for o in objects:
        if not hasattr(o, 'words'):
            o.words = getwords(o.name)
    word_dict = build_word_dict(objects, sj)
    reduce_common_words(word_dict, COMMON_WORD_THRESHOLD)
    if match_similar_words:
        merge_similar_words(word_dict)
    match_flags = []
    if weight_words:
        match_flags.append(WEIGHT_WORDS)
    if match_similar_words:
        match_flags.append(MATCH_SIMILAR_WORDS)
    if no_field_order:
        match_flags.append(NO_FIELD_ORDER)
    j.start_job(len(word_dict), tr("0 matches found"))
    compared = defaultdict(set)
    result = []
    try:
        # This whole 'popping' thing is there to avoid taking too much memory at the same time.
        while word_dict:
            items = word_dict.popitem()[1]
            while items:
                ref = items.pop()
                compared_already = compared[ref]
                to_compare = items - compared_already
                compared_already |= to_compare
                for other in to_compare:
                    m = get_match(ref, other, match_flags)
                    if m.percentage >= min_match_percentage:
                        result.append(m)
                        if len(result) >= LIMIT:
                            return result
            j.add_progress(desc=tr("%d matches found") % len(result))
    except MemoryError:
        # This is the place where the memory usage is at its peak during the scan.
        # Just continue the process with an incomplete list of matches.
        del compared # This should give us enough room to call logging.
        logging.warning('Memory Overflow. Matches: %d. Word dict: %d' % (len(result), len(word_dict)))
        return result
    return result

def getmatches_by_contents(files, sizeattr='size', partial=False, j=job.nulljob):
    j = j.start_subjob([2, 8])
    size2files = defaultdict(set)
    for file in j.iter_with_progress(files, tr("Read size of %d/%d files")):
        filesize = getattr(file, sizeattr)
        if filesize:
            size2files[filesize].add(file)
    possible_matches = [files for files in size2files.values() if len(files) > 1]
    del size2files
    result = []
    j.start_job(len(possible_matches), tr("0 matches found"))
    for group in possible_matches:
        for first, second in itertools.combinations(group, 2):
            if first.is_ref and second.is_ref:
                continue # Don't spend time comparing two ref pics together.
            if first.md5partial == second.md5partial:
                if partial or first.md5 == second.md5:
                    result.append(Match(first, second, 100))
        j.add_progress(desc=tr("%d matches found") % len(result))
    return result

class Group:
    #---Override
    def __init__(self):
        self._clear()
    
    def __contains__(self, item):
        return item in self.unordered
    
    def __getitem__(self, key):
        return self.ordered.__getitem__(key)
    
    def __iter__(self):
        return iter(self.ordered)
    
    def __len__(self):
        return len(self.ordered)
    
    #---Private
    def _clear(self):
        self._percentage = None
        self._matches_for_ref = None
        self.matches = set()
        self.candidates = defaultdict(set)
        self.ordered = []
        self.unordered = set()
    
    def _get_matches_for_ref(self):
        if self._matches_for_ref is None:
            ref = self.ref
            self._matches_for_ref = [match for match in self.matches if ref in match]
        return self._matches_for_ref
    
    #---Public
    def add_match(self, match):
        def add_candidate(item, match):
            matches = self.candidates[item]
            matches.add(match)
            if self.unordered <= matches:
                self.ordered.append(item)
                self.unordered.add(item)
        
        if match in self.matches:
            return
        self.matches.add(match)
        first, second, _ = match
        if first not in self.unordered:
            add_candidate(first, second)
        if second not in self.unordered:
            add_candidate(second, first)
        self._percentage = None
        self._matches_for_ref = None
    
    def discard_matches(self):
        discarded = set(m for m in self.matches if not all(obj in self.unordered for obj in [m.first, m.second]))
        self.matches -= discarded
        self.candidates = defaultdict(set)
        return discarded
    
    def get_match_of(self, item):
        if item is self.ref:
            return
        for m in self._get_matches_for_ref():
            if item in m:
                return m
    
    def prioritize(self, key_func, tie_breaker=None):
        # tie_breaker(ref, dupe) --> True if dupe should be ref
        master_key_func = lambda x: (-x.is_ref, key_func(x))
        self.ordered.sort(key=master_key_func)
        if tie_breaker is None:
            return
        ref = self.ref
        key_value = key_func(ref)
        for dupe in self.dupes:
            if key_func(dupe) != key_value:
                break
            if tie_breaker(ref, dupe):
                ref = dupe
        if ref is not self.ref:
            self.switch_ref(ref)
    
    def remove_dupe(self, item, discard_matches=True):
        try:
            self.ordered.remove(item)
            self.unordered.remove(item)
            self._percentage = None
            self._matches_for_ref = None
            if (len(self) > 1) and any(not getattr(item, 'is_ref', False) for item in self):
                if discard_matches:
                    self.matches = set(m for m in self.matches if item not in m)
            else:
                self._clear()
        except ValueError:
            pass
    
    def switch_ref(self, with_dupe):
        if self.ref.is_ref:
            return
        try:
            self.ordered.remove(with_dupe)
            self.ordered.insert(0, with_dupe)
            self._percentage = None
            self._matches_for_ref = None
        except ValueError:
            pass
    
    dupes = property(lambda self: self[1:])
    
    @property
    def percentage(self):
        if self._percentage is None:
            if self.dupes:
                matches = self._get_matches_for_ref()
                self._percentage = sum(match.percentage for match in matches) // len(matches)
            else:
                self._percentage = 0
        return self._percentage
    
    @property
    def ref(self):
        if self:
            return self[0]
    

def get_groups(matches, j=job.nulljob):
    matches.sort(key=lambda match: -match.percentage)
    dupe2group = {}
    groups = []
    try:
        for match in j.iter_with_progress(matches, tr("Grouped %d/%d matches"), JOB_REFRESH_RATE):
            first, second, _ = match
            first_group = dupe2group.get(first)
            second_group = dupe2group.get(second)
            if first_group:
                if second_group:
                    if first_group is second_group:
                        target_group = first_group
                    else:
                        continue
                else:
                    target_group = first_group
                    dupe2group[second] = target_group
            else:
                if second_group:
                    target_group = second_group
                    dupe2group[first] = target_group
                else:
                    target_group = Group()
                    groups.append(target_group)
                    dupe2group[first] = target_group
                    dupe2group[second] = target_group
            target_group.add_match(match)
    except MemoryError:
        del dupe2group
        del matches
        # should free enough memory to continue
        logging.warning('Memory Overflow. Groups: {0}'.format(len(groups)))
    # Now that we have a group, we have to discard groups' matches and see if there're any "orphan"
    # matches, that is, matches that were candidate in a group but that none of their 2 files were
    # accepted in the group. With these orphan groups, it's safe to build additional groups
    matched_files = set(flatten(groups))
    orphan_matches = []
    for group in groups:
        orphan_matches += set(m for m in group.discard_matches() if not any(obj in matched_files for obj in [m.first, m.second]))
    if groups and orphan_matches:
        groups += get_groups(orphan_matches) # no job, as it isn't supposed to take a long time
    return groups