Source code for data_juicer.ops.deduplicator.video_deduplicator

import hashlib
from collections import defaultdict
from typing import Dict, Set, Tuple

from data_juicer.utils.constant import HashKeys
from data_juicer.utils.mm_utils import (close_video, load_data_with_context,
                                        load_video)

from ..base_op import OPERATORS, Deduplicator
from ..op_fusion import LOADED_VIDEOS
from .document_deduplicator import DocumentDeduplicator

OP_NAME = 'video_deduplicator'




@OPERATORS.register_module(OP_NAME)
@LOADED_VIDEOS.register_module(OP_NAME)
class VideoDeduplicator(Deduplicator):
    """
    Deduplicator to deduplicate samples at document-level using exact matching
    of videos between documents.
    """



    def __init__(self, consider_text: bool = False, *args, **kwargs):
        """
        Initialization.

        :param consider_text: whether to consider text hash together with video
            hash when applying deduplication.
        :param args: extra args
        :param kwargs: extra args
        """
        super().__init__(*args, **kwargs)
        self.consider_text = consider_text
        self.text_dedup_op = None
        if self.consider_text:
            self.text_dedup_op = DocumentDeduplicator(**kwargs)




    def compute_hash(self, sample, context=False):
        # get hash of text first
        if self.consider_text:
            sample = self.text_dedup_op.compute_hash(sample)
        # check if it's computed already
        if HashKeys.videohash in sample:
            return sample

        # there is no video in this sample
        sample[HashKeys.videohash] = ''
        if self.video_key not in sample or not sample[self.video_key]:
            return sample

        # load videos
        loaded_video_keys = sample[self.video_key]
        sample, videos = load_data_with_context(sample, context,
                                                loaded_video_keys, load_video)

        # compute hash
        md5_hash = hashlib.md5()
        for key in videos:
            # consider the multi stream of video in one container
            for packet in videos[key].demux():
                if packet.stream.type == 'video':
                    md5_hash.update(bytes(packet))

        for key in videos:
            close_video(videos[key])

        sample[HashKeys.videohash] = md5_hash.hexdigest()
        return sample




    def process(self, dataset, show_num=0):
        """
        For doc-level, dataset --> dataset.

        :param dataset: input dataset
        :param show_num: number of traced samples used when tracer is
            open.
        :return: deduplicated dataset and the sampled duplicate pairs.
        """
        # no need to deduplicate because too few samples
        if len(dataset) <= 1:
            return dataset, {}

        dup_hashes = None
        if show_num > 0:
            # sample duplicate pairs
            if self.consider_text:
                hash2ids: Dict[Tuple[int, int], Set[int]] = defaultdict(set)
                hashes = zip(dataset[HashKeys.videohash],
                             dataset[HashKeys.hash])
            else:
                hash2ids: Dict[int, Set[int]] = defaultdict(set)
                hashes = dataset[HashKeys.videohash]
            for sid, hash_val in enumerate(hashes):
                if hash_val:
                    hash2ids[hash_val].add(sid)
            dup_samples = sorted(list(hash2ids.items()),
                                 key=lambda x: len(x[1]),
                                 reverse=True)
            dup_hashes = set([
                item[0] for item in dup_samples if len(item[1]) > 1
            ][:show_num])

        def _filter_dup_helper(sample, hashes):
            if self.consider_text:
                hash = (sample[HashKeys.videohash], sample[HashKeys.hash])
            else:
                hash = sample[HashKeys.videohash]
            if not hash:
                return True
            if show_num > 0 and hash in dup_hashes \
                    and len(dup_pairs[hash]) < 2:
                # tracer is open and not enough duplicate sample pairs
                dup_pairs[hash].append(sample)
            if hash in hashes:
                return False
            else:
                hashes.add(hash)
                return True

        hashes = set()
        dup_pairs = {hash_v: [] for hash_v in dup_hashes} if dup_hashes else {}
        dataset = dataset.filter(
            _filter_dup_helper,
            fn_kwargs=dict(hashes=hashes),
            load_from_cache_file=False if show_num > 0 else True)  # num_proc=1
        return dataset, dup_pairs