add op video_extract_frames_mapper

configs/config_all.yaml

@@ -341,6 +341,11 @@ process:
       horizontal_flip: false                                  # flip frame image horizontally (left to right).
       vertical_flip: false                                    # flip frame image vertically (top to bottom).
       mem_required: '20GB'                                    # This operation (Op) utilizes deep neural network models that consume a significant amount of memory for computation, hence the system's available memory might constrains the maximum number of processes that can be launched
+  - video_extract_frames_mapper:                            # extract frames from video files according to specified methods
+      frame_sampling_method: 'all_keyframes'                  # sampling method of extracting frame images from the videos. Should be one of ["all_keyframes", "uniform"]. The former one extracts all key frames and the latter one extract specified number of frames uniformly from the video. Default: "all_keyframes".
+      frame_num: 3                                            # the number of frames to be extracted uniformly from the video. Only works when frame_sampling_method is "uniform". If it's 1, only the middle frame will be extracted. If it's 2, only the first and the last frames will be extracted. If it's larger than 2, in addition to the first and the last frames, other frames will be extracted uniformly within the video duration.
+      duration: 0                                             # The duration of each segment in seconds. If 0, frames are extracted from the entire video. If duration > 0, the video is segmented into multiple segments based on duration, and frames are extracted from each segment.
+      frame_dir: None                                         # Output directory to save extracted frames. If None, a default directory based on the video file path is used.
   - video_face_blur_mapper:                                 # blur faces detected in videos
       cv_classifier: ''                                       # OpenCV classifier path for face detection. By default, we will use 'haarcascade_frontalface_alt.xml'.
       blur_type: 'gaussian'                                   # type of blur kernel, including ['mean', 'box', 'gaussian']

data_juicer/ops/base_op.py

@@ -259,11 +259,22 @@ def process_batched(self, samples, *args, **kwargs):
         keys = samples.keys()
         first_key = next(iter(keys))
         num_samples = len(samples[first_key])
+
+        new_keys = {}
         for i in range(num_samples):
             this_sample = {key: samples[key][i] for key in keys}
             res_sample = self.process_single(this_sample, *args, **kwargs)
-            for key in keys:
-                samples[key][i] = res_sample[key]
+            res_keys = res_sample.keys()
+            for key in res_keys:
+                if key not in keys:
+                    if key not in new_keys:
+                        new_keys.update({key: []})
+                    new_keys[key].append(res_sample[key])
+                else:
+                    samples[key][i] = res_sample[key]
+
+        for k, v in new_keys.items():
+            samples[k] = v
 
         return samples
 

data_juicer/ops/mapper/__init__.py

@@ -52,6 +52,7 @@
 from .video_captioning_from_summarizer_mapper import \
     VideoCaptioningFromSummarizerMapper
 from .video_captioning_from_video_mapper import VideoCaptioningFromVideoMapper
+from .video_extract_frames_mapper import VideoExtractFramesMapper
 from .video_face_blur_mapper import VideoFaceBlurMapper
 from .video_ffmpeg_wrapped_mapper import VideoFFmpegWrappedMapper
 from .video_remove_watermark_mapper import VideoRemoveWatermarkMapper
@@ -84,10 +85,11 @@
     'RemoveWordsWithIncorrectSubstringsMapper', 'ReplaceContentMapper',
     'SentenceSplitMapper', 'TextChunkMapper', 'VideoCaptioningFromAudioMapper',
     'VideoCaptioningFromFramesMapper', 'VideoCaptioningFromSummarizerMapper',
-    'VideoCaptioningFromVideoMapper', 'VideoFFmpegWrappedMapper',
-    'VideoFaceBlurMapper', 'VideoRemoveWatermarkMapper',
-    'VideoResizeAspectRatioMapper', 'VideoResizeResolutionMapper',
-    'VideoSplitByDurationMapper', 'VideoSplitByKeyFrameMapper',
-    'VideoSplitBySceneMapper', 'VideoTaggingFromAudioMapper',
-    'VideoTaggingFromFramesMapper', 'WhitespaceNormalizationMapper'
+    'VideoCaptioningFromVideoMapper', 'VideoExtractFramesMapper',
+    'VideoFFmpegWrappedMapper', 'VideoFaceBlurMapper',
+    'VideoRemoveWatermarkMapper', 'VideoResizeAspectRatioMapper',
+    'VideoResizeResolutionMapper', 'VideoSplitByDurationMapper',
+    'VideoSplitByKeyFrameMapper', 'VideoSplitBySceneMapper',
+    'VideoTaggingFromAudioMapper', 'VideoTaggingFromFramesMapper',
+    'WhitespaceNormalizationMapper'
 ]

data_juicer/ops/mapper/video_extract_frames_mapper.py

@@ -0,0 +1,173 @@
+import json
+import os
+import os.path as osp
+
+from pydantic import PositiveInt
+
+from data_juicer.utils.constant import Fields
+from data_juicer.utils.file_utils import dict_to_hash
+from data_juicer.utils.mm_utils import (
+    SpecialTokens, close_video, extract_key_frames,
+    extract_key_frames_by_seconds, extract_video_frames_uniformly,
+    extract_video_frames_uniformly_by_seconds, load_data_with_context,
+    load_video)
+
+from ..base_op import OPERATORS, Mapper
+from ..op_fusion import LOADED_VIDEOS
+
+OP_NAME = 'video_extract_frames_mapper'
+
+
+@OPERATORS.register_module(OP_NAME)
+@LOADED_VIDEOS.register_module(OP_NAME)
+class VideoExtractFramesMapper(Mapper):
+    """Mapper to extract frames from video files according to specified methods.
+    Extracted Frames Data Format:
+        The data format for the extracted frames is a dictionary mapping
+        video key to extracted frames directory where the extracted
+        frames are saved. The dictionary follows the structure:
+        {
+            "video_key_1": "/${frame_dir}/video_key_1_filename/",
+            "video_key_2": "/${frame_dir}/video_key_2_filename/",
+            ...
+        }
+    """
+
+    _batched_op = True
+
+    def __init__(
+        self,
+        frame_sampling_method: str = 'all_keyframes',
+        frame_num: PositiveInt = 3,
+        duration: float = 0,
+        frame_dir: str = None,
+        frame_key=Fields.video_frames,
+        *args,
+        **kwargs,
+    ):
+        """
+        Initialization method.
+        :param frame_sampling_method: sampling method of extracting frame
+            videos from the videos. Should be one of
+            ["all_keyframes", "uniform"].
+            The former one extracts all key frames (the number
+            of which depends on the duration of the video) and the latter
+            one extract specified number of frames uniformly from the video.
+            If "duration" > 0, frame_sampling_method acts on every segment.
+            Default: "all_keyframes".
+        :param frame_num: the number of frames to be extracted uniformly from
+            the video. Only works when frame_sampling_method is "uniform". If
+            it's 1, only the middle frame will be extracted. If it's 2, only
+            the first and the last frames will be extracted. If it's larger
+            than 2, in addition to the first and the last frames, other frames
+            will be extracted uniformly within the video duration.
+            If "duration" > 0, frame_num is the number of frames per segment.
+        :param duration: The duration of each segment in seconds.
+            If 0, frames are extracted from the entire video.
+            If duration > 0, the video is segmented into multiple segments
+            based on duration, and frames are extracted from each segment.
+        :param frame_dir: Output directory to save extracted frames.
+            If None, a default directory based on the video file path is used.
+        :param frame_key: The name of field to save generated frames info.
+        :param args: extra args
+        :param kwargs: extra args
+        """
+        super().__init__(*args, **kwargs)
+        self._init_parameters = self.remove_extra_parameters(locals())
+
+        if frame_sampling_method not in ['all_keyframes', 'uniform']:
+            raise ValueError(
+                f'Frame sampling method '
+                f'[{frame_sampling_method}] is not supported. '
+                f'Can only be one of ["all_keyframes", "uniform"].')
+
+        self.frame_dir = frame_dir
+        self.frame_sampling_method = frame_sampling_method
+        self.frame_num = frame_num
+        self.duration = duration
+        self.frame_key = frame_key
+        self.frame_fname_template = 'frame_{}.jpg'
+
+    def _get_default_frame_dir(self, original_filepath):
+        original_dir = os.path.dirname(original_filepath)
+        dir_token = f'/{Fields.multimodal_data_output_dir}/'
+        if dir_token in original_dir:
+            original_dir = original_dir.split(dir_token)[0]
+        saved_dir = os.path.join(
+            original_dir, f'{Fields.multimodal_data_output_dir}/{OP_NAME}')
+        original_filename = osp.splitext(osp.basename(original_filepath))[0]
+        hash_val = dict_to_hash(self._init_parameters)
+
+        return osp.join(saved_dir,
+                        f'{original_filename}__dj_hash_#{hash_val}#')
+
+    def process_single(self, sample, context=False):
+        # check if it's generated already
+        if self.frame_key in sample:
+            return sample
+
+        # there is no videos in this sample
+        if self.video_key not in sample or not sample[self.video_key]:
+            return []
+
+        # load videos
+        loaded_video_keys = sample[self.video_key]
+        sample, videos = load_data_with_context(sample, context,
+                                                loaded_video_keys, load_video)
+        video_to_frame_dir = {}
+        text = sample[self.text_key]
+        offset = 0
+
+        for chunk in text.split(SpecialTokens.eoc):
+            video_count = chunk.count(SpecialTokens.video)
+            # no video or no text
+            if video_count == 0 or len(chunk) == 0:
+                continue
+            else:
+                for video_key in loaded_video_keys[offset:offset +
+                                                   video_count]:
+                    video = videos[video_key]
+                    # extract frame videos
+                    if self.frame_sampling_method == 'all_keyframes':
+                        if self.duration:
+                            frames = extract_key_frames_by_seconds(
+                                video, self.duration)
+                        else:
+                            frames = extract_key_frames(video)
+                    elif self.frame_sampling_method == 'uniform':
+                        if self.duration:
+                            frames = extract_video_frames_uniformly_by_seconds(
+                                video, self.frame_num, duration=self.duration)
+                        else:
+                            frames = extract_video_frames_uniformly(
+                                video, self.frame_num)
+                    else:
+                        raise ValueError(f'Not support sampling method \
+                            `{self.frame_sampling_method}`.')
+                    frames = [frame.to_image() for frame in frames]
+
+                    if self.frame_dir:
+                        frame_dir = osp.join(
+                            self.frame_dir,
+                            osp.splitext(osp.basename(video_key))[0])
+                    else:
+                        # video path as frames directory
+                        frame_dir = self._get_default_frame_dir(video_key)
+                    os.makedirs(frame_dir, exist_ok=True)
+                    video_to_frame_dir[video_key] = frame_dir
+
+                    for i, frame in enumerate(frames):
+                        frame_path = osp.join(
+                            frame_dir, self.frame_fname_template.format(i))
+                        if not os.path.exists(frame_path):
+                            frame.save(frame_path)
+
+                offset += video_count
+
+        if not context:
+            for vid_key in videos:
+                close_video(videos[vid_key])
+
+        sample[self.frame_key] = json.dumps(video_to_frame_dir)
+
+        return sample

data_juicer/utils/constant.py

@@ -16,6 +16,7 @@ class Fields(object):
     context = DEFAULT_PREFIX + 'context__'
     suffix = DEFAULT_PREFIX + 'suffix__'
 
+    video_frames = DEFAULT_PREFIX + 'video_frames__'
     # video_frame_tags
     video_frame_tags = DEFAULT_PREFIX + 'video_frame_tags__'
     video_audio_tags = DEFAULT_PREFIX + 'video_audio_tags__'

data_juicer/utils/mm_utils.py

@@ -1,5 +1,6 @@
 import base64
 import datetime
+import io
 import os
 import re
 import shutil
@@ -321,7 +322,11 @@ def cut_video_by_seconds(
         container = input_video
 
     # create the output video
-    output_container = load_video(output_video, 'w')
+    if output_video:
+        output_container = load_video(output_video, 'w')
+    else:
+        output_buffer = io.BytesIO()
+        output_container = av.open(output_buffer, mode='w', format='mp4')
 
     # add the video stream into the output video according to input video
     input_video_stream = container.streams.video[0]
@@ -390,6 +395,11 @@ def cut_video_by_seconds(
     if isinstance(input_video, str):
         close_video(container)
     close_video(output_container)
+
+    if not output_video:
+        output_buffer.seek(0)
+        return output_buffer
+
     if not os.path.exists(output_video):
         logger.warning(f'This video could not be successfully cut in '
                        f'[{start_seconds}, {end_seconds}] seconds. '
@@ -463,6 +473,39 @@ def process_each_frame(input_video: Union[str, av.container.InputContainer],
                 if isinstance(input_video, str) else input_video.name)
 
 
+def extract_key_frames_by_seconds(
+        input_video: Union[str, av.container.InputContainer],
+        duration: float = 1):
+    """Extract key frames by seconds.
+        :param input_video: input video path or av.container.InputContainer.
+        :param duration: duration of each video split in seconds.
+    """
+    # load the input video
+    if isinstance(input_video, str):
+        container = load_video(input_video)
+    elif isinstance(input_video, av.container.InputContainer):
+        container = input_video
+    else:
+        raise ValueError(f'Unsupported type of input_video. Should be one of '
+                         f'[str, av.container.InputContainer], but given '
+                         f'[{type(input_video)}].')
+
+    video_duration = get_video_duration(container)
+    timestamps = np.arange(0, video_duration, duration).tolist()
+
+    all_key_frames = []
+    for i in range(1, len(timestamps)):
+        output_buffer = cut_video_by_seconds(container, None,
+                                             timestamps[i - 1], timestamps[i])
+        if output_buffer:
+            cut_inp_container = av.open(output_buffer, format='mp4', mode='r')
+            key_frames = extract_key_frames(cut_inp_container)
+            all_key_frames.extend(key_frames)
+            close_video(cut_inp_container)
+
+    return all_key_frames
+
+
 def extract_key_frames(input_video: Union[str, av.container.InputContainer]):
     """
     Extract key frames from the input video. If there is no keyframes in the
@@ -516,6 +559,43 @@ def get_key_frame_seconds(input_video: Union[str,
     return ts
 
 
+def extract_video_frames_uniformly_by_seconds(
+        input_video: Union[str, av.container.InputContainer],
+        frame_num: PositiveInt,
+        duration: float = 1):
+    """Extract video frames uniformly by seconds.
+        :param input_video: input video path or av.container.InputContainer.
+        :param frame_num: the number of frames to be extracted uniformly from
+            each video split by duration.
+        :param duration: duration of each video split in seconds.
+    """
+    # load the input video
+    if isinstance(input_video, str):
+        container = load_video(input_video)
+    elif isinstance(input_video, av.container.InputContainer):
+        container = input_video
+    else:
+        raise ValueError(f'Unsupported type of input_video. Should be one of '
+                         f'[str, av.container.InputContainer], but given '
+                         f'[{type(input_video)}].')
+
+    video_duration = get_video_duration(container)
+    timestamps = np.arange(0, video_duration, duration).tolist()
+
+    all_frames = []
+    for i in range(1, len(timestamps)):
+        output_buffer = cut_video_by_seconds(container, None,
+                                             timestamps[i - 1], timestamps[i])
+        if output_buffer:
+            cut_inp_container = av.open(output_buffer, format='mp4', mode='r')
+            key_frames = extract_video_frames_uniformly(cut_inp_container,
+                                                        frame_num=frame_num)
+            all_frames.extend(key_frames)
+            close_video(cut_inp_container)
+
+    return all_frames
+
+
 def extract_video_frames_uniformly(
     input_video: Union[str, av.container.InputContainer],
     frame_num: PositiveInt,