import copy
import re
from data_juicer.utils.constant import Fields
from data_juicer.utils.file_utils import (add_suffix_to_filename,
transfer_filename)
from data_juicer.utils.mm_utils import (SpecialTokens, close_video,
cut_video_by_seconds,
get_key_frame_seconds, load_video)
from ..base_op import OPERATORS, Mapper
from ..op_fusion import LOADED_VIDEOS
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def create_replacer(replacements):
def replacer(match):
return replacements.pop(0)
return replacer
OP_NAME = 'video_split_by_key_frame_mapper'
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@OPERATORS.register_module(OP_NAME)
@LOADED_VIDEOS.register_module(OP_NAME)
class VideoSplitByKeyFrameMapper(Mapper):
"""Mapper to split video by key frame.
"""
_batched_op = True
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def __init__(self, keep_original_sample: bool = True, *args, **kwargs):
"""
Initialization method.
:param keep_original_sample: whether to keep the original sample. If
it's set to False, there will be only split sample in the
final datasets and the original sample will be removed. It's True
in default.
:param args: extra args
:param kwargs: extra args
"""
super().__init__(*args, **kwargs)
self._init_parameters = self.remove_extra_parameters(locals())
self.keep_original_sample = keep_original_sample
self.extra_args = kwargs
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def get_split_key_frame(self, video_key, container):
timestamps = get_key_frame_seconds(container)
count = 0
split_video_keys = []
unique_video_key = transfer_filename(video_key, OP_NAME,
**self._init_parameters)
for i in range(1, len(timestamps)):
split_video_key = add_suffix_to_filename(unique_video_key,
f'_{count}')
if cut_video_by_seconds(container, split_video_key,
timestamps[i - 1], timestamps[i]):
split_video_keys.append(split_video_key)
count += 1
split_video_key = add_suffix_to_filename(unique_video_key, f'_{count}')
if cut_video_by_seconds(container, split_video_key, timestamps[-1]):
split_video_keys.append(split_video_key)
return split_video_keys
def _process_single_sample(self, sample):
# there is no video in this sample
if self.video_key not in sample or sample[
self.video_key] is None or len(sample[self.video_key]) == 0:
sample[Fields.source_file] = []
return []
if Fields.source_file not in sample or not sample[Fields.source_file]:
sample[Fields.source_file] = sample[self.video_key]
# the split results
split_sample = copy.deepcopy(sample)
split_sample[self.text_key] = ''
split_sample[Fields.source_file] = []
# load all video(s)
loaded_video_keys = sample[self.video_key]
videos = {}
for loaded_video_key in loaded_video_keys:
if loaded_video_key not in videos:
# avoid loading the same videos
video = load_video(loaded_video_key)
videos[loaded_video_key] = video
split_video_keys = []
offset = 0
# split each video chunk by chunk
for chunk in sample[self.text_key].split(SpecialTokens.eoc):
# skip empty chunks or contents after the last eoc token
if not chunk.strip():
continue
else:
video_count = chunk.count(SpecialTokens.video)
place_holders = []
for video_key in loaded_video_keys[offset:offset +
video_count]:
video = videos[video_key]
new_video_keys = self.get_split_key_frame(video_key, video)
close_video(video)
split_video_keys.extend(new_video_keys)
place_holders.append(SpecialTokens.video *
len(new_video_keys))
split_sample[Fields.source_file].extend(
[video_key] * len(new_video_keys))
# insert the generated text according to given mode
replacer_function = create_replacer(place_holders)
new_split_text_per_chunk = re.sub(SpecialTokens.video,
replacer_function, chunk)
split_sample[
self.
text_key] += f'{new_split_text_per_chunk}{SpecialTokens.eoc}' # noqa: E501
offset += video_count
split_sample[self.video_key] = split_video_keys
return [split_sample]
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def process_batched(self, samples):
# reconstruct samples from "dict of lists" to "list of dicts"
reconstructed_samples = []
for i in range(len(samples[self.text_key])):
reconstructed_samples.append(
{key: samples[key][i]
for key in samples})
samples_after_split = []
# do split for each sample within the batch
for ori_sample in reconstructed_samples:
if self.keep_original_sample:
samples_after_split.append(ori_sample)
generated_samples = self._process_single_sample(ori_sample)
if len(generated_samples) != 0:
samples_after_split.extend(generated_samples)
# reconstruct samples from "list of dicts" to "dict of lists"
keys = samples_after_split[0].keys()
res_samples = {}
for key in keys:
res_samples[key] = [s[key] for s in samples_after_split]
return res_samples