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dupeguru/core/engine.py
Bruno Cabral 85a4557525
match all orientations (#1127)
* match all orientations

* use rotation as option

---------

Co-authored-by: Andrew Senetar <arsenetar@gmail.com>
Co-authored-by: Luke <byunghun.hyun26@gmail.com>
2024-02-19 09:19:33 -06:00

546 lines
20 KiB
Python

# Created By: Virgil Dupras
# Created On: 2006/01/29
# Copyright 2015 Hardcoded Software (http://www.hardcoded.net)
#
# This software is licensed under the "GPLv3" License as described in the "LICENSE" file,
# which should be included with this package. The terms are also available at
# http://www.gnu.org/licenses/gpl-3.0.html
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 hscommon.jobprogress import job
(
WEIGHT_WORDS,
MATCH_SIMILAR_WORDS,
NO_FIELD_ORDER,
) = range(3)
JOB_REFRESH_RATE = 100
PROGRESS_MESSAGE = tr("%d matches found from %d groups")
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()
# logging.debug(f"DEBUG chars for: {s}\n"
# f"{[c for c in s if ord(c) != 32]}\n"
# f"{[ord(c) for c in s if ord(c) != 32]}")
# HACK We shouldn't ignore non-ascii characters altogether. Any Unicode char
# above common european characters that cannot be "sanitized" (ie. stripped
# of their accents, etc.) are preserved as is. The arbitrary limit is
# obtained from this one: ord("\u037e") GREEK QUESTION MARK
s = "".join(
c
for c in s
if (ord(c) <= 894 and c in string.ascii_letters + string.digits + string.whitespace) or ord(c) > 894
)
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 (of words).
"""
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 :ref:`fields`.
``first`` and ``second`` must be lists of lists of string. Each sub-list is then compared with
:func:`compare`.
"""
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_score = 0
matched_field = None
for field2 in second:
r = compare(field1, field2, flags)
if r > max_score:
max_score = r
matched_field = field2
results.append(max_score)
if matched_field:
second.remove(matched_field)
else:
results = [compare(field1, field2, flags) for field1, field2 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
(:ref:`fields`).
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.
``word_dict`` has been built with :func:`build_word_dict`. Similarity is computed with Python's
``difflib.get_close_matches()``, which computes the number of edits that are necessary to make
a word equal to the other.
"""
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``
``word_dict`` has been built with :func:`build_word_dict`.
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 = {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]
# Writing docstrings in a namedtuple is tricky. From Python 3.3, it's possible to set __doc__, but
# some research allowed me to find a more elegant solution, which is what is done here. See
# http://stackoverflow.com/questions/1606436/adding-docstrings-to-namedtuples-in-python
class Match(namedtuple("Match", "first second percentage")):
"""Represents a match between two :class:`~core.fs.File`.
Regarless of the matching method, when two files are determined to match, a Match pair is created,
which holds, of course, the two matched files, but also their match "level".
.. attribute:: first
first file of the pair.
.. attribute:: second
second file of the pair.
.. attribute:: percentage
their match level according to the scan method which found the match. int from 1 to 100. For
exact scan methods, such as Contents scans, this will always be 100.
"""
__slots__ = ()
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,
):
"""Returns a list of :class:`Match` within ``objects`` after fuzzily matching their words.
:param objects: List of :class:`~core.fs.File` to match.
:param int min_match_percentage: minimum % of words that have to match.
:param bool match_similar_words: make similar words (see :func:`merge_similar_words`) match.
:param bool weight_words: longer words are worth more in match % computations.
:param bool no_field_order: match :ref:`fields` regardless of their order.
:param j: A :ref:`job progress instance <jobs>`.
"""
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), PROGRESS_MESSAGE % (0, 0))
compared = defaultdict(set)
result = []
try:
word_count = 0
# 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
word_count += 1
j.add_progress(desc=PROGRESS_MESSAGE % (len(result), word_count))
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, bigsize=0, j=job.nulljob):
"""Returns a list of :class:`Match` within ``files`` if their contents is the same.
:param bigsize: The size in bytes over which we consider files big enough to
justify taking samples of the file for hashing. If 0, compute digest as usual.
:param j: A :ref:`job progress instance <jobs>`.
"""
size2files = defaultdict(set)
for f in files:
size2files[f.size].add(f)
del files
possible_matches = [files for files in size2files.values() if len(files) > 1]
del size2files
result = []
j.start_job(len(possible_matches), PROGRESS_MESSAGE % (0, 0))
group_count = 0
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.size == 0 and second.size == 0:
# skip hashing for zero length files
result.append(Match(first, second, 100))
continue
# if digests are the same (and not None) then files match
if first.digest_partial is not None and first.digest_partial == second.digest_partial:
if bigsize > 0 and first.size > bigsize:
if first.digest_samples is not None and first.digest_samples == second.digest_samples:
result.append(Match(first, second, 100))
else:
if first.digest is not None and first.digest == second.digest:
result.append(Match(first, second, 100))
group_count += 1
j.add_progress(desc=PROGRESS_MESSAGE % (len(result), group_count))
return result
class Group:
"""A group of :class:`~core.fs.File` that match together.
This manages match pairs into groups and ensures that all files in the group match to each
other.
.. attribute:: ref
The "reference" file, which is the file among the group that isn't going to be deleted.
.. attribute:: ordered
Ordered list of duplicates in the group (including the :attr:`ref`).
.. attribute:: unordered
Set duplicates in the group (including the :attr:`ref`).
.. attribute:: dupes
An ordered list of the group's duplicate, without :attr:`ref`. Equivalent to
``ordered[1:]``
.. attribute:: percentage
Average match percentage of match pairs containing :attr:`ref`.
"""
# ---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):
"""Adds ``match`` to internal match list and possibly add duplicates to the group.
A duplicate can only be considered as such if it matches all other duplicates in the group.
This method registers that pair (A, B) represented in ``match`` as possible candidates and,
if A and/or B end up matching every other duplicates in the group, add these duplicates to
the group.
:param tuple match: pair of :class:`~core.fs.File` to add
"""
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):
"""Remove all recorded matches that didn't result in a duplicate being added to the group.
You can call this after the duplicate scanning process to free a bit of memory.
"""
discarded = {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):
"""Returns the match pair between ``item`` and :attr:`ref`."""
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):
"""Reorders :attr:`ordered` according to ``key_func``.
:param key_func: Key (f(x)) to be used for sorting
:param tie_breaker: function to be used to select the reference position in case the top
duplicates have the same key_func() result.
"""
# tie_breaker(ref, dupe) --> True if dupe should be ref
# Returns True if anything changed during prioritization.
new_order = sorted(self.ordered, key=lambda x: (-x.is_ref, key_func(x)))
changed = new_order != self.ordered
self.ordered = new_order
if tie_breaker is None:
return changed
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)
return True
return changed
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 = {m for m in self.matches if item not in m}
else:
self._clear()
except ValueError:
pass
def switch_ref(self, with_dupe):
"""Make the :attr:`ref` dupe of the group switch position with ``with_dupe``."""
if self.ref.is_ref:
return False
try:
self.ordered.remove(with_dupe)
self.ordered.insert(0, with_dupe)
self._percentage = None
self._matches_for_ref = None
return True
except ValueError:
return False
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):
"""Returns a list of :class:`Group` from ``matches``.
Create groups out of match pairs in the smartest way possible.
"""
matches.sort(key=lambda match: -match.percentage)
dupe2group = {}
groups = []
try:
for match in matches:
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(f"Memory Overflow. Groups: {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 += {
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