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zamba.data.video

Classes

VideoLoaderConfig (BaseModel) pydantic-model

Configuration for load_video_frames.

Parameters:

Name Type Description Default
crop_bottom_pixels int

Number of pixels to crop from the bottom of the video (prior to resizing to video_height).

required
i_frames bool

Only load the I-Frames. See https://en.wikipedia.org/wiki/Video_compression_picture_types#Intra-coded_(I)frames/slices(key_frames)

required
scene_threshold float

Only load frames that correspond to scene changes. See http://www.ffmpeg.org/ffmpeg-filters.html#select_002c-aselect

required
megadetector_lite_config MegadetectorLiteYoloXConfig

Configuration of MegadetectorLiteYoloX frame selection model.

required
frame_selection_height int

Resize the video to this height in pixels, prior to frame selection. If None, the full size video will be used for frame selection. Using full size images (setting to None) is recommended for MegadetectorLite, especially if your species of interest are smaller.

required
frame_selection_width int

Resize the video to this width in pixels, prior to frame selection.

required
total_frames int

Number of frames that should ultimately be returned.

required
ensure_total_frames bool

Selecting the number of frames by resampling may result in one more or fewer frames due to rounding. If True, ensure the requested number of frames is returned by either clipping or duplicating the final frame. Raises an error if no frames have been selected. Otherwise, return the array unchanged.

required
fps float

Resample the video evenly from the entire duration to a specific number of frames per second.

required
early_bias bool

Resamples to 24 fps and selects 16 frames biased toward the front (strategy used by competition winner).

required
frame_indices list(int)

Select specific frame numbers. Note: frame selection is done after any resampling.

required
evenly_sample_total_frames bool

Reach the total number of frames specified by evenly sampling from the duration of the video. Defaults to False.

required
pix_fmt str

ffmpeg pixel format, defaults to 'rgb24' for RGB channels; can be changed to 'bgr24' for BGR.

required
model_input_height int

After frame selection, resize the video to this height in pixels.

required
model_input_width int

After frame selection, resize the video to this width in pixels.

required
cache_dir Path

Cache directory where preprocessed videos will be saved upon first load. Alternatively, can be set with VIDEO_CACHE_DIR environment variable. Defaults to None, which means videos will not be cached. Provided there is enough space on your machine, it is highly encouraged to cache videos for training as this will speed up all subsequent epochs. If you are predicting on the same videos with the same video loader configuration, this will save time on future runs.

required
cleanup_cache bool

Whether to delete the cache dir after training or predicting ends. Defaults to False.

required
cache_dir: Path pydantic-field
cleanup_cache: bool pydantic-field
crop_bottom_pixels: int pydantic-field
early_bias: bool pydantic-field
ensure_total_frames: bool pydantic-field
evenly_sample_total_frames: bool pydantic-field
fps: float pydantic-field
frame_indices: List[int] pydantic-field
frame_selection_height: int pydantic-field
frame_selection_width: int pydantic-field
i_frames: bool pydantic-field
megadetector_lite_config: MegadetectorLiteYoloXConfig pydantic-field
model_input_height: int pydantic-field
model_input_width: int pydantic-field
pix_fmt: str pydantic-field
scene_threshold: float pydantic-field
total_frames: int pydantic-field
Config

Methods

check_early_bias_compatibility(values) classmethod
Source code in zamba/data/video.py
@root_validator(skip_on_failure=True)
def check_early_bias_compatibility(cls, values):
    if values["early_bias"] and (
        values["i_frames"]
        or values["scene_threshold"]
        or values["total_frames"]
        or values["evenly_sample_total_frames"]
        or values["fps"]
    ):
        raise ValueError(
            f"early_bias cannot be used with i_frames, scene_threshold, total_frames, evenly_sample_total_frames, or fps. Values provided are {values}."
        )
    return values
check_evenly_sample_total_frames_compatibility(values) classmethod
Source code in zamba/data/video.py
@root_validator(skip_on_failure=True)
def check_evenly_sample_total_frames_compatibility(cls, values):
    if values["evenly_sample_total_frames"] is True and values["total_frames"] is None:
        raise ValueError(
            f"total_frames must be specified if evenly_sample_total_frames is used. Values provided are {values}."
        )
    if values["evenly_sample_total_frames"] and (
        values["scene_threshold"]
        or values["i_frames"]
        or values["fps"]
        or values["early_bias"]
    ):
        raise ValueError(
            f"evenly_sample_total_frames cannot be used with scene_threshold, i_frames, fps, or early_bias. Values provided are {values}."
        )
    return values
check_fps_compatibility(values) classmethod
Source code in zamba/data/video.py
@root_validator(skip_on_failure=True)
def check_fps_compatibility(cls, values):
    if values["fps"] and (
        values["evenly_sample_total_frames"] or values["i_frames"] or values["scene_threshold"]
    ):
        raise ValueError(
            f"fps cannot be used with evenly_sample_total_frames, i_frames, or scene_threshold. Values provided are {values}."
        )
    return values
check_frame_indices_compatibility(values) classmethod
Source code in zamba/data/video.py
@root_validator(skip_on_failure=True)
def check_frame_indices_compatibility(cls, values):
    if values["frame_indices"] and (
        values["total_frames"]
        or values["scene_threshold"]
        or values["i_frames"]
        or values["early_bias"]
        or values["evenly_sample_total_frames"]
    ):
        raise ValueError(
            f"frame_indices cannot be used with total_frames, scene_threshold, i_frames, early_bias, or evenly_sample_total_frames. Values provided are {values}."
        )
    return values
check_height_and_width(values) classmethod
Source code in zamba/data/video.py
@root_validator(skip_on_failure=True)
def check_height_and_width(cls, values):
    if (values["frame_selection_height"] is None) ^ (values["frame_selection_width"] is None):
        raise ValueError(
            f"Must provide both frame_selection_height and frame_selection_width or neither. Values provided are {values}."
        )
    if (values["model_input_height"] is None) ^ (values["model_input_width"] is None):
        raise ValueError(
            f"Must provide both model_input_height and model_input_width or neither. Values provided are {values}."
        )
    return values
check_i_frame_compatibility(values) classmethod
Source code in zamba/data/video.py
@root_validator(skip_on_failure=True)
def check_i_frame_compatibility(cls, values):
    if values["scene_threshold"] and values["i_frames"]:
        raise ValueError(
            f"i_frames cannot be used with scene_threshold. Values provided are {values}."
        )
    return values
check_megadetector_lite_compatibility(values) classmethod
Source code in zamba/data/video.py
@root_validator(skip_on_failure=True)
def check_megadetector_lite_compatibility(cls, values):
    if values["megadetector_lite_config"] and (
        values["early_bias"] or values["evenly_sample_total_frames"]
    ):
        raise ValueError(
            f"megadetector_lite_config cannot be used with early_bias or evenly_sample_total_frames. Values provided are {values}."
        )
    return values
validate_total_frames(values) classmethod
Source code in zamba/data/video.py
@root_validator(skip_on_failure=True)
def validate_total_frames(cls, values):
    if values["megadetector_lite_config"] is not None:
        # set n frames for megadetector_lite_config if only specified by total_frames
        if values["megadetector_lite_config"].n_frames is None:
            values["megadetector_lite_config"].n_frames = values["total_frames"]

        # set total frames if only specified in megadetector_lite_config
        if values["total_frames"] is None:
            values["total_frames"] = values["megadetector_lite_config"].n_frames

    return values
validate_video_cache_dir(cache_dir) classmethod

Set up cache directory for preprocessed videos. Config argument takes precedence over environment variable.

Source code in zamba/data/video.py
@validator("cache_dir", always=True)
def validate_video_cache_dir(cls, cache_dir):
    """Set up cache directory for preprocessed videos. Config argument takes precedence
    over environment variable.
    """
    if cache_dir is None:
        cache_dir = os.getenv("VIDEO_CACHE_DIR", None)

    if cache_dir is not None:
        cache_dir = Path(cache_dir)
        cache_dir.mkdir(parents=True, exist_ok=True)

    return cache_dir

VideoMetadata (BaseModel) pydantic-model

duration_s: float pydantic-field required
fps: int pydantic-field required
height: int pydantic-field required
n_frames: int pydantic-field required
width: int pydantic-field required

Classes

Config inherited
Classes
getter_dict (Representation)

Hack to make object's smell just enough like dicts for validate_model.

We can't inherit from Mapping[str, Any] because it upsets cython so we have to implement all methods ourselves.

Methods
get_field_info(name: unicode) -> Dict[str, Any] classmethod

Get properties of FieldInfo from the fields property of the config class.

json_dumps(obj, *, skipkeys = False, ensure_ascii = True, check_circular = True, allow_nan = True, cls = None, indent = None, separators = None, default = None, sort_keys = False, **kw)

Serialize obj to a JSON formatted str.

If skipkeys is true then dict keys that are not basic types (str, int, float, bool, None) will be skipped instead of raising a TypeError.

If ensure_ascii is false, then the return value can contain non-ASCII characters if they appear in strings contained in obj. Otherwise, all such characters are escaped in JSON strings.

If check_circular is false, then the circular reference check for container types will be skipped and a circular reference will result in an OverflowError (or worse).

If allow_nan is false, then it will be a ValueError to serialize out of range float values (nan, inf, -inf) in strict compliance of the JSON specification, instead of using the JavaScript equivalents (NaN, Infinity, -Infinity).

If indent is a non-negative integer, then JSON array elements and object members will be pretty-printed with that indent level. An indent level of 0 will only insert newlines. None is the most compact representation.

If specified, separators should be an (item_separator, key_separator) tuple. The default is (', ', ': ') if indent is None and (',', ': ') otherwise. To get the most compact JSON representation, you should specify (',', ':') to eliminate whitespace.

default(obj) is a function that should return a serializable version of obj or raise TypeError. The default simply raises TypeError.

If sort_keys is true (default: False), then the output of dictionaries will be sorted by key.

To use a custom JSONEncoder subclass (e.g. one that overrides the .default() method to serialize additional types), specify it with the cls kwarg; otherwise JSONEncoder is used.

Source code in zamba/data/video.py
def dumps(obj, *, skipkeys=False, ensure_ascii=True, check_circular=True,
        allow_nan=True, cls=None, indent=None, separators=None,
        default=None, sort_keys=False, **kw):
    """Serialize ``obj`` to a JSON formatted ``str``.

    If ``skipkeys`` is true then ``dict`` keys that are not basic types
    (``str``, ``int``, ``float``, ``bool``, ``None``) will be skipped
    instead of raising a ``TypeError``.

    If ``ensure_ascii`` is false, then the return value can contain non-ASCII
    characters if they appear in strings contained in ``obj``. Otherwise, all
    such characters are escaped in JSON strings.

    If ``check_circular`` is false, then the circular reference check
    for container types will be skipped and a circular reference will
    result in an ``OverflowError`` (or worse).

    If ``allow_nan`` is false, then it will be a ``ValueError`` to
    serialize out of range ``float`` values (``nan``, ``inf``, ``-inf``) in
    strict compliance of the JSON specification, instead of using the
    JavaScript equivalents (``NaN``, ``Infinity``, ``-Infinity``).

    If ``indent`` is a non-negative integer, then JSON array elements and
    object members will be pretty-printed with that indent level. An indent
    level of 0 will only insert newlines. ``None`` is the most compact
    representation.

    If specified, ``separators`` should be an ``(item_separator, key_separator)``
    tuple.  The default is ``(', ', ': ')`` if *indent* is ``None`` and
    ``(',', ': ')`` otherwise.  To get the most compact JSON representation,
    you should specify ``(',', ':')`` to eliminate whitespace.

    ``default(obj)`` is a function that should return a serializable version
    of obj or raise TypeError. The default simply raises TypeError.

    If *sort_keys* is true (default: ``False``), then the output of
    dictionaries will be sorted by key.

    To use a custom ``JSONEncoder`` subclass (e.g. one that overrides the
    ``.default()`` method to serialize additional types), specify it with
    the ``cls`` kwarg; otherwise ``JSONEncoder`` is used.

    """
    # cached encoder
    if (not skipkeys and ensure_ascii and
        check_circular and allow_nan and
        cls is None and indent is None and separators is None and
        default is None and not sort_keys and not kw):
        return _default_encoder.encode(obj)
    if cls is None:
        cls = JSONEncoder
    return cls(
        skipkeys=skipkeys, ensure_ascii=ensure_ascii,
        check_circular=check_circular, allow_nan=allow_nan, indent=indent,
        separators=separators, default=default, sort_keys=sort_keys,
        **kw).encode(obj)
json_loads(s, *, cls = None, object_hook = None, parse_float = None, parse_int = None, parse_constant = None, object_pairs_hook = None, **kw)

Deserialize s (a str, bytes or bytearray instance containing a JSON document) to a Python object.

object_hook is an optional function that will be called with the result of any object literal decode (a dict). The return value of object_hook will be used instead of the dict. This feature can be used to implement custom decoders (e.g. JSON-RPC class hinting).

object_pairs_hook is an optional function that will be called with the result of any object literal decoded with an ordered list of pairs. The return value of object_pairs_hook will be used instead of the dict. This feature can be used to implement custom decoders. If object_hook is also defined, the object_pairs_hook takes priority.

parse_float, if specified, will be called with the string of every JSON float to be decoded. By default this is equivalent to float(num_str). This can be used to use another datatype or parser for JSON floats (e.g. decimal.Decimal).

parse_int, if specified, will be called with the string of every JSON int to be decoded. By default this is equivalent to int(num_str). This can be used to use another datatype or parser for JSON integers (e.g. float).

parse_constant, if specified, will be called with one of the following strings: -Infinity, Infinity, NaN. This can be used to raise an exception if invalid JSON numbers are encountered.

To use a custom JSONDecoder subclass, specify it with the cls kwarg; otherwise JSONDecoder is used.

The encoding argument is ignored and deprecated since Python 3.1.

Source code in zamba/data/video.py
def loads(s, *, cls=None, object_hook=None, parse_float=None,
        parse_int=None, parse_constant=None, object_pairs_hook=None, **kw):
    """Deserialize ``s`` (a ``str``, ``bytes`` or ``bytearray`` instance
    containing a JSON document) to a Python object.

    ``object_hook`` is an optional function that will be called with the
    result of any object literal decode (a ``dict``). The return value of
    ``object_hook`` will be used instead of the ``dict``. This feature
    can be used to implement custom decoders (e.g. JSON-RPC class hinting).

    ``object_pairs_hook`` is an optional function that will be called with the
    result of any object literal decoded with an ordered list of pairs.  The
    return value of ``object_pairs_hook`` will be used instead of the ``dict``.
    This feature can be used to implement custom decoders.  If ``object_hook``
    is also defined, the ``object_pairs_hook`` takes priority.

    ``parse_float``, if specified, will be called with the string
    of every JSON float to be decoded. By default this is equivalent to
    float(num_str). This can be used to use another datatype or parser
    for JSON floats (e.g. decimal.Decimal).

    ``parse_int``, if specified, will be called with the string
    of every JSON int to be decoded. By default this is equivalent to
    int(num_str). This can be used to use another datatype or parser
    for JSON integers (e.g. float).

    ``parse_constant``, if specified, will be called with one of the
    following strings: -Infinity, Infinity, NaN.
    This can be used to raise an exception if invalid JSON numbers
    are encountered.

    To use a custom ``JSONDecoder`` subclass, specify it with the ``cls``
    kwarg; otherwise ``JSONDecoder`` is used.

    The ``encoding`` argument is ignored and deprecated since Python 3.1.
    """
    if isinstance(s, str):
        if s.startswith('\ufeff'):
            raise JSONDecodeError("Unexpected UTF-8 BOM (decode using utf-8-sig)",
                                  s, 0)
    else:
        if not isinstance(s, (bytes, bytearray)):
            raise TypeError(f'the JSON object must be str, bytes or bytearray, '
                            f'not {s.__class__.__name__}')
        s = s.decode(detect_encoding(s), 'surrogatepass')

    if "encoding" in kw:
        import warnings
        warnings.warn(
            "'encoding' is ignored and deprecated. It will be removed in Python 3.9",
            DeprecationWarning,
            stacklevel=2
        )
        del kw['encoding']

    if (cls is None and object_hook is None and
            parse_int is None and parse_float is None and
            parse_constant is None and object_pairs_hook is None and not kw):
        return _default_decoder.decode(s)
    if cls is None:
        cls = JSONDecoder
    if object_hook is not None:
        kw['object_hook'] = object_hook
    if object_pairs_hook is not None:
        kw['object_pairs_hook'] = object_pairs_hook
    if parse_float is not None:
        kw['parse_float'] = parse_float
    if parse_int is not None:
        kw['parse_int'] = parse_int
    if parse_constant is not None:
        kw['parse_constant'] = parse_constant
    return cls(**kw).decode(s)
prepare_field(field: ModelField) -> None classmethod

Optional hook to check or modify fields during model creation.

from_video(path: PathLike) classmethod
Source code in zamba/data/video.py
@classmethod
def from_video(cls, path: os.PathLike):
    stream = get_video_stream(path)
    return cls(
        height=int(stream["height"]),
        width=int(stream["width"]),
        n_frames=int(stream["nb_frames"]),
        duration_s=float(stream["duration"]),
        fps=int(Fraction(stream["r_frame_rate"])),  # reported, not average
    )

npy_cache

__init__(self, cache_path: Optional[pathlib.Path] = None, cleanup: bool = False) special
Source code in zamba/data/video.py
def __init__(self, cache_path: Optional[Path] = None, cleanup: bool = False):
    self.cache_path = cache_path
    self.cleanup = cleanup

Functions

ensure_frame_number(arr, total_frames: int)

Ensures the array contains the requested number of frames either by clipping frames from the end or dulpicating the last frame.

Parameters:

Name Type Description Default
arr np.ndarray

Array of video frames with shape (frames, height, width, channel).

required
total_frames int

Desired number of frames in output array.

required
Source code in zamba/data/video.py
def ensure_frame_number(arr, total_frames: int):
    """Ensures the array contains the requested number of frames either by clipping frames from
    the end or dulpicating the last frame.

    Args:
        arr (np.ndarray): Array of video frames with shape (frames, height, width, channel).
        total_frames (int): Desired number of frames in output array.
    """
    if (total_frames is None) or (arr.shape[0] == total_frames):
        return arr
    elif arr.shape[0] == 0:
        logger.warning(
            "No frames selected. Returning an array in the desired shape with all zeros."
        )
        return np.zeros((total_frames, arr.shape[1], arr.shape[2], arr.shape[3]), dtype="int")
    elif arr.shape[0] > total_frames:
        logger.info(
            f"Clipping {arr.shape[0] - total_frames} frames "
            f"(original: {arr.shape[0]}, requested: {total_frames})."
        )
        return arr[:total_frames]
    elif arr.shape[0] < total_frames:
        logger.info(
            f"Duplicating last frame {total_frames - arr.shape[0]} times "
            f"(original: {arr.shape[0]}, requested: {total_frames})."
        )
        return np.concatenate(
            [arr, np.tile(arr[-1], (total_frames - arr.shape[0], 1, 1, 1))], axis=0
        )

ffprobe(path: PathLike) -> Series

Source code in zamba/data/video.py
def ffprobe(path: os.PathLike) -> pd.Series:
    def flatten_json(j, name=""):
        for k in j:
            if isinstance(j[k], dict):
                yield from flatten_json(j[k], f"{name}.{k}")
            elif isinstance(j[k], list):
                for i in range(len(j[k])):
                    yield from flatten_json(j[k][i], f"{name}.{k}[{i}]")
            else:
                yield {f"{name}.{k}".strip("."): j[k]}

    output = subprocess.check_output(
        [
            "ffprobe",
            "-v",
            "quiet",
            "-show_entries",
            "stream:format",
            "-select_streams",
            "v",
            "-of",
            "json",
            path,
        ]
    )
    output = json.loads(output)
    result = reduce(lambda a, b: {**a, **b}, flatten_json(output))
    return pd.Series(result)

get_frame_time_estimates(path: PathLike)

Source code in zamba/data/video.py
def get_frame_time_estimates(path: os.PathLike):
    probe = ffmpeg.probe(str(path), show_entries="frame=best_effort_timestamp_time")
    return [float(x["best_effort_timestamp_time"]) for x in probe["frames"]]

get_video_stream(path: Union[os.PathLike, cloudpathlib.s3.s3path.S3Path]) -> dict

Source code in zamba/data/video.py
def get_video_stream(path: Union[os.PathLike, S3Path]) -> dict:
    try:
        probe = ffmpeg.probe(str(path))
    except ffmpeg.Error as exc:
        raise ZambaFfmpegException(exc.stderr)

    return next((stream for stream in probe["streams"] if stream["codec_type"] == "video"), None)

load_video_frames(filepath: PathLike, config: Optional[zamba.data.video.VideoLoaderConfig] = None, **kwargs)

Loads frames from videos using fast ffmpeg commands.

Parameters:

Name Type Description Default
filepath os.PathLike

Path to the video.

required
config VideoLoaderConfig

Configuration for video loading.

None
**kwargs

Optionally, arguments for VideoLoaderConfig can be passed in directly.

{}

Returns:

Type Description
np.ndarray

An array of video frames with dimensions (time x height x width x channels).

Source code in zamba/data/video.py
def load_video_frames(
    filepath: os.PathLike,
    config: Optional[VideoLoaderConfig] = None,
    **kwargs,
):
    """Loads frames from videos using fast ffmpeg commands.

    Args:
        filepath (os.PathLike): Path to the video.
        config (VideoLoaderConfig, optional): Configuration for video loading.
        **kwargs: Optionally, arguments for VideoLoaderConfig can be passed in directly.

    Returns:
        np.ndarray: An array of video frames with dimensions (time x height x width x channels).
    """
    if not Path(filepath).exists():
        raise FileNotFoundError(f"No file found at {filepath}")

    if config is None:
        config = VideoLoaderConfig(**kwargs)

    video_stream = get_video_stream(filepath)
    w = int(video_stream["width"])
    h = int(video_stream["height"])

    pipeline = ffmpeg.input(str(filepath))
    pipeline_kwargs = {}

    if (config.crop_bottom_pixels is not None) and (config.crop_bottom_pixels > 0):
        # scale to ensure all frames are the same height and we can crop
        pipeline = pipeline.filter("scale", f"{w},{h}")
        pipeline = pipeline.crop("0", "0", "iw", f"ih-{config.crop_bottom_pixels}")
        h = h - config.crop_bottom_pixels

    if config.evenly_sample_total_frames:
        config.fps = config.total_frames / float(video_stream["duration"])

    if config.early_bias:
        config.fps = 24  # competition frame selection assumes 24 frames per second
        config.total_frames = 16  # used for ensure_total_frames

    if config.fps:
        pipeline = pipeline.filter("fps", fps=config.fps, round="up")

    if config.i_frames:
        pipeline = pipeline.filter("select", "eq(pict_type,PICT_TYPE_I)")

    if config.scene_threshold:
        pipeline = pipeline.filter("select", f"gt(scene,{config.scene_threshold})")

    if config.frame_selection_height and config.frame_selection_width:
        pipeline = pipeline.filter(
            "scale", f"{config.frame_selection_width},{config.frame_selection_height}"
        )
        w, h = config.frame_selection_width, config.frame_selection_height

    if config.early_bias:
        config.frame_indices = [2, 8, 12, 18, 24, 36, 48, 60, 72, 84, 96, 108, 120, 132, 144, 156]

    if config.frame_indices:
        pipeline = pipeline.filter("select", "+".join(f"eq(n,{f})" for f in config.frame_indices))
        pipeline_kwargs = {"vsync": 0}

    pipeline = pipeline.output(
        "pipe:", format="rawvideo", pix_fmt=config.pix_fmt, **pipeline_kwargs
    )

    try:
        out, err = pipeline.run(capture_stdout=True, capture_stderr=True)
    except ffmpeg.Error as exc:
        raise ZambaFfmpegException(exc.stderr)

    arr = np.frombuffer(out, np.uint8).reshape([-1, h, w, 3])

    if config.megadetector_lite_config is not None:
        mdlite = MegadetectorLiteYoloX(config=config.megadetector_lite_config)
        detection_probs = mdlite.detect_video(frames=arr)

        arr = mdlite.filter_frames(arr, detection_probs)

    if (config.model_input_height is not None) and (config.model_input_width is not None):
        resized_frames = np.zeros(
            (arr.shape[0], config.model_input_height, config.model_input_width, 3), np.uint8
        )
        for ix, f in enumerate(arr):
            if (f.shape[0] != config.model_input_height) or (
                f.shape[1] != config.model_input_width
            ):
                f = cv2.resize(
                    f,
                    (config.model_input_width, config.model_input_height),
                    # https://stackoverflow.com/a/51042104/1692709
                    interpolation=(
                        cv2.INTER_LINEAR
                        if f.shape[1] < config.model_input_width
                        else cv2.INTER_AREA
                    ),
                )
            resized_frames[ix, ...] = f
        arr = np.array(resized_frames)

    if config.ensure_total_frames:
        arr = ensure_frame_number(arr, total_frames=config.total_frames)

    return arr

num_frames(stream_or_path: Union[dict, os.PathLike, cloudpathlib.s3.s3path.S3Path]) -> Optional[int]

Source code in zamba/data/video.py
def num_frames(stream_or_path: Union[dict, os.PathLike, S3Path]) -> Optional[int]:
    if not isinstance(stream_or_path, dict):
        stream = get_video_stream(stream_or_path)
    else:
        stream = stream_or_path

    if not stream:
        return

    if "nb_frames" in stream:
        return int(stream["nb_frames"])

    if "duration" in stream:
        duration = float(stream["duration"])

        if "r_frame_rate" in stream:
            frame_rate = float(Fraction(stream["r_frame_rate"]))
        elif "avg_frame_rate" in stream:
            frame_rate = float(stream["avg_frame_rate"])
        duration -= float(stream.get("start_time", 0))

        return floor(duration * frame_rate)