video
sleap_io.model.video
¶
Data model for videos.
The Video class is a SLEAP data structure that stores information regarding
a video and its components used in SLEAP.
Classes:
| Name | Description |
|---|---|
HDF5Video |
Video backend for reading videos stored in HDF5 files. |
ImageVideo |
Video backend for reading videos stored as image files. |
MediaVideo |
Video backend for reading videos stored as common media files. |
Video |
|
VideoBackend |
Base class for video backends. |
VideoWriter |
Simple video writer using imageio and FFMPEG. |
Functions:
| Name | Description |
|---|---|
is_file_accessible |
Check if a file is accessible. |
Attributes:
| Name | Type | Description |
|---|---|---|
__cached__ |
str(object='') -> str |
|
__doc__ |
str(object='') -> str |
|
__file__ |
str(object='') -> str |
|
__name__ |
str(object='') -> str |
|
__package__ |
str(object='') -> str |
__cached__ = '/home/runner/work/sleap-io/sleap-io/sleap_io/model/__pycache__/video.cpython-313.pyc'
module-attribute
¶
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__doc__ = 'Data model for videos.\n\nThe `Video` class is a SLEAP data structure that stores information regarding\na video and its components used in SLEAP.\n'
module-attribute
¶
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__file__ = '/home/runner/work/sleap-io/sleap-io/sleap_io/model/video.py'
module-attribute
¶
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__name__ = 'sleap_io.model.video'
module-attribute
¶
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__package__ = 'sleap_io.model'
module-attribute
¶
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
HDF5Video
¶
Bases: sleap_io.io.video_reading.VideoBackend
Video backend for reading videos stored in HDF5 files.
This backend supports reading videos stored in HDF5 files, both in rank-4 datasets as well as in datasets with lists of binary-encoded images.
Embedded image datasets are used in SLEAP when exporting package files (.pkg.slp)
with videos embedded in them. This is useful for bundling training or inference data
without having to worry about the videos (or frame images) being moved or deleted.
It is expected that these types of datasets will be in a Group with a int8
variable length dataset called "video". This dataset must also contain an
attribute called "format" with a string describing the image format (e.g., "png" or
"jpg") which will be used to decode it appropriately.
If a frame_numbers dataset is present in the group, it will be used to map from
source video frames to the frames in the dataset. This is useful to preserve frame
indexing when exporting a subset of frames in the video. It will also be used to
populate frame_map and source_inds attributes.
Attributes:
| Name | Type | Description |
|---|---|---|
filename |
Path to HDF5 file (.h5, .hdf5 or .slp). |
|
grayscale |
Whether to force grayscale. If None, autodetect on first frame load. |
|
keep_open |
Whether to keep the video reader open between calls to read frames. If False, will close the reader after each call. If True (the default), it will keep the reader open and cache it for subsequent calls which may enhance the performance of reading multiple frames. |
|
dataset |
Name of dataset to read from. If |
|
input_format |
Format of the data in the dataset. One of "channels_last" (the
default) in |
|
frame_map |
Mapping from frame indices to indices in the dataset. This is used to translate between the frame indices of the images within their source video and the indices of the images in the dataset. This is only used when reading embedded image datasets. |
|
source_filename |
Path to the source video file. This is metadata and only used when reading embedded image datasets. |
|
source_inds |
Indices of the frames in the source video file. This is metadata and only used when reading embedded image datasets. |
|
image_format |
Format of the images in the embedded dataset. This is metadata and only used when reading embedded image datasets. |
|
channel_order |
Channel order of embedded images, either "RGB" or "BGR". This is used to ensure consistent color channel ordering when decoding embedded images. If the encoding and decoding plugins have different channel orders, the channels will be automatically flipped during decoding. |
|
plugin |
Plugin to use for decoding embedded images. One of "opencv" or "FFMPEG". If None, uses the global default or auto-detects based on available packages. Note that "pyav" is automatically mapped to "FFMPEG" since PyAV doesn't support image decoding. |
Methods:
| Name | Description |
|---|---|
__attrs_post_init__ |
Auto-detect dataset and frame map heuristically. |
__eq__ |
Method generated by attrs for class HDF5Video. |
__init__ |
Method generated by attrs for class HDF5Video. |
__replace__ |
Method generated by attrs for class HDF5Video. |
__repr__ |
Method generated by attrs for class HDF5Video. |
__setattr__ |
Method generated by attrs for class HDF5Video. |
decode_embedded |
Decode an embedded image string into a numpy array. |
get_frame_raw_bytes |
Get raw encoded bytes for a frame without decoding. |
has_frame |
Check if a frame index is contained in the video. |
read_test_frame |
Read a single frame from the video to test for grayscale. |
Source code in sleap_io/io/video_reading.py
@attrs.define
class HDF5Video(VideoBackend):
"""Video backend for reading videos stored in HDF5 files.
This backend supports reading videos stored in HDF5 files, both in rank-4 datasets
as well as in datasets with lists of binary-encoded images.
Embedded image datasets are used in SLEAP when exporting package files (`.pkg.slp`)
with videos embedded in them. This is useful for bundling training or inference data
without having to worry about the videos (or frame images) being moved or deleted.
It is expected that these types of datasets will be in a `Group` with a `int8`
variable length dataset called `"video"`. This dataset must also contain an
attribute called "format" with a string describing the image format (e.g., "png" or
"jpg") which will be used to decode it appropriately.
If a `frame_numbers` dataset is present in the group, it will be used to map from
source video frames to the frames in the dataset. This is useful to preserve frame
indexing when exporting a subset of frames in the video. It will also be used to
populate `frame_map` and `source_inds` attributes.
Attributes:
filename: Path to HDF5 file (.h5, .hdf5 or .slp).
grayscale: Whether to force grayscale. If None, autodetect on first frame load.
keep_open: Whether to keep the video reader open between calls to read frames.
If False, will close the reader after each call. If True (the default), it
will keep the reader open and cache it for subsequent calls which may
enhance the performance of reading multiple frames.
dataset: Name of dataset to read from. If `None`, will try to find a rank-4
dataset by iterating through datasets in the file. If specifying an embedded
dataset, this can be the group containing a "video" dataset or the dataset
itself (e.g., "video0" or "video0/video").
input_format: Format of the data in the dataset. One of "channels_last" (the
default) in `(frames, height, width, channels)` order or "channels_first" in
`(frames, channels, width, height)` order. Embedded datasets should use the
"channels_last" format.
frame_map: Mapping from frame indices to indices in the dataset. This is used to
translate between the frame indices of the images within their source video
and the indices of the images in the dataset. This is only used when reading
embedded image datasets.
source_filename: Path to the source video file. This is metadata and only used
when reading embedded image datasets.
source_inds: Indices of the frames in the source video file. This is metadata
and only used when reading embedded image datasets.
image_format: Format of the images in the embedded dataset. This is metadata and
only used when reading embedded image datasets.
channel_order: Channel order of embedded images, either "RGB" or "BGR". This is
used to ensure consistent color channel ordering when decoding embedded
images. If the encoding and decoding plugins have different channel orders,
the channels will be automatically flipped during decoding.
plugin: Plugin to use for decoding embedded images. One of "opencv" or
"FFMPEG". If None, uses the global default or auto-detects based on
available packages. Note that "pyav" is automatically mapped to "FFMPEG"
since PyAV doesn't support image decoding.
"""
dataset: str | None = None
input_format: str = attrs.field(
default="channels_last",
validator=attrs.validators.in_(["channels_last", "channels_first"]),
)
frame_map: dict[int, int] = attrs.field(init=False, default=attrs.Factory(dict))
source_filename: str | None = None
source_inds: np.ndarray | None = None
image_format: str = "hdf5"
channel_order: str = "RGB"
plugin: str | None = None
EXTS = ("h5", "hdf5", "slp")
def __attrs_post_init__(self):
"""Auto-detect dataset and frame map heuristically."""
# Check if the file accessible before applying heuristics.
try:
f = h5py.File(self.filename, "r")
except OSError:
return
if self.dataset is None:
# Iterate through datasets to find a rank 4 array.
def find_movies(name, obj):
if isinstance(obj, h5py.Dataset) and obj.ndim == 4:
self.dataset = name
return True
f.visititems(find_movies)
if self.dataset is None:
# Iterate through datasets to find an embedded video dataset.
def find_embedded(name, obj):
if isinstance(obj, h5py.Dataset) and name.endswith("/video"):
self.dataset = name
return True
f.visititems(find_embedded)
if self.dataset is None:
# Couldn't find video datasets.
return
if isinstance(f[self.dataset], h5py.Group):
# If this is a group, assume it's an embedded video dataset.
if "video" in f[self.dataset]:
self.dataset = f"{self.dataset}/video"
if self.dataset.split("/")[-1] == "video":
# This may be an embedded video dataset. Check for frame map.
ds = f[self.dataset]
if "format" in ds.attrs:
self.image_format = ds.attrs["format"]
# Read channel_order, with backwards compatibility
if "channel_order" in ds.attrs:
self.channel_order = ds.attrs["channel_order"]
else:
# Backwards compatibility: Check format_id for older files
# Prior to format 1.4, embedded images were primarily encoded with
# OpenCV which uses BGR, so default to BGR for older formats
if "metadata" in f and "format_id" in f["metadata"].attrs:
format_id = f["metadata"].attrs["format_id"]
if format_id < 1.4:
self.channel_order = "BGR" # Legacy default
# If no format_id found, assume BGR (safest legacy default)
# since most embedded images before this change used OpenCV
if "frame_numbers" in ds.parent:
frame_numbers = ds.parent["frame_numbers"][:].astype(int)
self.frame_map = {frame: idx for idx, frame in enumerate(frame_numbers)}
self.source_inds = frame_numbers
if "source_video" in ds.parent:
self.source_filename = json.loads(
ds.parent["source_video"].attrs["json"]
)["backend"]["filename"]
# Read FPS from attributes if present
if "fps" in ds.attrs:
self._fps = float(ds.attrs["fps"])
elif "fps" in ds.parent.attrs:
self._fps = float(ds.parent.attrs["fps"])
f.close()
# Set default plugin if not specified (use image plugin, not video plugin)
if self.plugin is None:
# Check image plugin default first (for embedded images)
if _default_image_plugin is not None:
self.plugin = _default_image_plugin
# Otherwise auto-detect (for embedded image decoding)
elif "cv2" in sys.modules:
self.plugin = "opencv"
else:
self.plugin = "imageio" # imageio fallback
@property
def num_frames(self) -> int:
"""Number of frames in the video."""
with h5py.File(self.filename, "r") as f:
return f[self.dataset].shape[0]
@property
def img_shape(self) -> tuple[int, int, int]:
"""Shape of a single frame in the video as `(height, width, channels)`."""
with h5py.File(self.filename, "r") as f:
ds = f[self.dataset]
img_shape = None
if "height" in ds.attrs:
# Try to get shape from the attributes.
img_shape = (
ds.attrs["height"],
ds.attrs["width"],
ds.attrs["channels"],
)
if img_shape[0] == 0 or img_shape[1] == 0:
# Invalidate the shape if the attributes are zero.
img_shape = None
if img_shape is None and self.image_format == "hdf5" and ds.ndim == 4:
# Use the dataset shape if just stored as a rank-4 array.
img_shape = ds.shape[1:]
if self.input_format == "channels_first":
img_shape = img_shape[::-1]
if img_shape is None:
# Fall back to reading a test frame.
return super().img_shape
return int(img_shape[0]), int(img_shape[1]), int(img_shape[2])
def read_test_frame(self) -> np.ndarray:
"""Read a single frame from the video to test for grayscale."""
if self.frame_map:
frame_idx = list(self.frame_map.keys())[0]
else:
frame_idx = 0
return self._read_frame(frame_idx)
@property
def has_embedded_images(self) -> bool:
"""Return True if the dataset contains embedded images."""
return self.image_format is not None and self.image_format != "hdf5"
@property
def embedded_frame_inds(self) -> list[int]:
"""Return the frame indices of the embedded images."""
return list(self.frame_map.keys())
def decode_embedded(self, img_string: np.ndarray) -> np.ndarray:
"""Decode an embedded image string into a numpy array.
Args:
img_string: Binary string of the image as a `int8` numpy vector with the
bytes as values corresponding to the format-encoded image.
Returns:
The decoded image as a numpy array of shape `(height, width, channels)`. If
a rank-2 image is decoded, it will be expanded such that channels will be 1.
This method does not apply grayscale conversion as per the `grayscale`
attribute. Use the `get_frame` or `get_frames` methods of the `VideoBackend`
to apply grayscale conversion rather than calling this function directly.
"""
# Decode based on plugin
if self.plugin == "opencv":
img = cv2.imdecode(img_string, cv2.IMREAD_UNCHANGED)
decoder_order = "BGR" # OpenCV decodes to BGR
else:
# Use imageio for FFMPEG or any other plugin
img = iio.imread(BytesIO(img_string), extension=f".{self.image_format}")
decoder_order = "RGB" # imageio decodes to RGB
if img.ndim == 2:
img = np.expand_dims(img, axis=-1)
# Convert channel order if needed
# If the stored order doesn't match the decoder order, flip channels
if img.shape[-1] == 3 and self.channel_order != decoder_order:
img = img[..., ::-1] # Flip RGB <-> BGR
return img
def has_frame(self, frame_idx: int) -> bool:
"""Check if a frame index is contained in the video.
Args:
frame_idx: Index of frame to check.
Returns:
`True` if the index is contained in the video, otherwise `False`.
"""
if self.frame_map:
return frame_idx in self.frame_map
else:
return frame_idx < len(self)
def get_frame_raw_bytes(self, frame_idx: int) -> np.ndarray | None:
"""Get raw encoded bytes for a frame without decoding.
This method reads the raw compressed image data (PNG/JPEG bytes) directly
from the HDF5 dataset without decoding it. This is useful for fast copying
of embedded images when the target format matches the source format.
Args:
frame_idx: Index of the frame to read.
Returns:
Raw encoded bytes as int8 numpy array, or None if:
- The backend doesn't have embedded images (including "hdf5" format which
stores raw numpy arrays, not encoded images)
- The frame index is not available
Notes:
For variable-length datasets, returns the raw bytes directly.
For fixed-length datasets, returns bytes with trailing zeros stripped.
"""
if not self.has_embedded_images:
return None
if not self.has_frame(frame_idx):
return None
# Get the internal index (handle frame_map)
internal_idx = (
self.frame_map.get(frame_idx, frame_idx) if self.frame_map else frame_idx
)
# Read directly from dataset
if self.keep_open:
if self._open_reader is None:
self._open_reader = h5py.File(self.filename, "r")
f = self._open_reader
else:
f = h5py.File(self.filename, "r")
ds = f[self.dataset]
raw_bytes = ds[internal_idx]
# Handle fixed-length padding (strip trailing zeros)
is_vlen = h5py.check_vlen_dtype(ds.dtype) is not None
if not is_vlen:
# Find last non-zero byte
non_zero_mask = raw_bytes != 0
if non_zero_mask.any():
last_non_zero = np.where(non_zero_mask)[0][-1]
raw_bytes = raw_bytes[: last_non_zero + 1]
if not self.keep_open:
f.close()
return raw_bytes
def _read_frame(self, frame_idx: int) -> np.ndarray:
"""Read a single frame from the video.
Args:
frame_idx: Index of frame to read.
Returns:
The frame as a numpy array of shape `(height, width, channels)`.
Notes:
This does not apply grayscale conversion. It is recommended to use the
`get_frame` method of the `VideoBackend` class instead.
"""
if self.keep_open:
if self._open_reader is None:
self._open_reader = h5py.File(self.filename, "r")
f = self._open_reader
else:
f = h5py.File(self.filename, "r")
ds = f[self.dataset]
if self.frame_map:
frame_idx = self.frame_map[frame_idx]
img = ds[frame_idx]
if self.has_embedded_images:
img = self.decode_embedded(img)
if self.input_format == "channels_first":
img = np.transpose(img, (2, 1, 0))
if not self.keep_open:
f.close()
return img
def _read_frames(self, frame_inds: list) -> np.ndarray:
"""Read a list of frames from the video.
Args:
frame_inds: List of indices of frames to read.
Returns:
The frame as a numpy array of shape `(frames, height, width, channels)`.
Notes:
This does not apply grayscale conversion. It is recommended to use the
`get_frames` method of the `VideoBackend` class instead.
"""
if self.keep_open:
if self._open_reader is None:
self._open_reader = h5py.File(self.filename, "r")
f = self._open_reader
else:
f = h5py.File(self.filename, "r")
if self.frame_map:
frame_inds = [self.frame_map[idx] for idx in frame_inds]
ds = f[self.dataset]
imgs = ds[frame_inds]
if "format" in ds.attrs:
imgs = np.stack(
[self.decode_embedded(img) for img in imgs],
axis=0,
)
if self.input_format == "channels_first":
imgs = np.transpose(imgs, (0, 3, 2, 1))
if not self.keep_open:
f.close()
return imgs
EXTS = ('h5', 'hdf5', 'slp')
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__annotations__ = {'dataset': 'str | None', 'input_format': 'str', 'frame_map': 'dict[int, int]', 'source_filename': 'str | None', 'source_inds': 'np.ndarray | None', 'image_format': 'str', 'channel_order': 'str', 'plugin': 'str | None'}
class-attribute
¶
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__attrs_own_setattr__ = True
class-attribute
¶
Returns True when the argument is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.
__attrs_props__ = ClassProps(is_exception=False, is_slotted=True, has_weakref_slot=True, is_frozen=False, kw_only=<KeywordOnly.NO: 'no'>, collected_fields_by_mro=True, added_init=True, added_repr=True, added_eq=True, added_ordering=False, hashability=<Hashability.UNHASHABLE: 'unhashable'>, added_match_args=True, added_str=False, added_pickling=True, on_setattr_hook=<function pipe.<locals>.wrapped_pipe at 0x7f41e68fca40>, field_transformer=None)
class-attribute
¶
Effective class properties as derived from parameters to attr.s() or
define() decorators.
This is the same data structure that attrs uses internally to decide how to construct the final class.
Warning:
This feature is currently **experimental** and is not covered by our
strict backwards-compatibility guarantees.
Attributes:
| Name | Type | Description |
|---|---|---|
is_exception |
bool
|
Whether the class is treated as an exception class. |
is_slotted |
bool
|
Whether the class is |
has_weakref_slot |
bool
|
Whether the class has a slot for weak references. |
is_frozen |
bool
|
Whether the class is frozen. |
kw_only |
KeywordOnly
|
Whether / how the class enforces keyword-only arguments on the
|
collected_fields_by_mro |
bool
|
Whether the class fields were collected by method resolution order.
That is, correctly but unlike |
added_init |
bool
|
Whether the class has an attrs-generated |
added_repr |
bool
|
Whether the class has an attrs-generated |
added_eq |
bool
|
Whether the class has attrs-generated equality methods. |
added_ordering |
bool
|
Whether the class has attrs-generated ordering methods. |
hashability |
Hashability
|
How |
added_match_args |
bool
|
Whether the class supports positional |
added_str |
bool
|
Whether the class has an attrs-generated |
added_pickling |
bool
|
Whether the class has attrs-generated |
on_setattr_hook |
Callable[[Any, Attribute[Any], Any], Any] | None
|
The class's |
field_transformer |
Callable[[Attribute[Any]], Attribute[Any]] | None
|
The class's |
.. versionadded:: 25.4.0
__doc__ = 'Video backend for reading videos stored in HDF5 files.\n\nThis backend supports reading videos stored in HDF5 files, both in rank-4 datasets\nas well as in datasets with lists of binary-encoded images.\n\nEmbedded image datasets are used in SLEAP when exporting package files (`.pkg.slp`)\nwith videos embedded in them. This is useful for bundling training or inference data\nwithout having to worry about the videos (or frame images) being moved or deleted.\nIt is expected that these types of datasets will be in a `Group` with a `int8`\nvariable length dataset called `"video"`. This dataset must also contain an\nattribute called "format" with a string describing the image format (e.g., "png" or\n"jpg") which will be used to decode it appropriately.\n\nIf a `frame_numbers` dataset is present in the group, it will be used to map from\nsource video frames to the frames in the dataset. This is useful to preserve frame\nindexing when exporting a subset of frames in the video. It will also be used to\npopulate `frame_map` and `source_inds` attributes.\n\nAttributes:\n filename: Path to HDF5 file (.h5, .hdf5 or .slp).\n grayscale: Whether to force grayscale. If None, autodetect on first frame load.\n keep_open: Whether to keep the video reader open between calls to read frames.\n If False, will close the reader after each call. If True (the default), it\n will keep the reader open and cache it for subsequent calls which may\n enhance the performance of reading multiple frames.\n dataset: Name of dataset to read from. If `None`, will try to find a rank-4\n dataset by iterating through datasets in the file. If specifying an embedded\n dataset, this can be the group containing a "video" dataset or the dataset\n itself (e.g., "video0" or "video0/video").\n input_format: Format of the data in the dataset. One of "channels_last" (the\n default) in `(frames, height, width, channels)` order or "channels_first" in\n `(frames, channels, width, height)` order. Embedded datasets should use the\n "channels_last" format.\n frame_map: Mapping from frame indices to indices in the dataset. This is used to\n translate between the frame indices of the images within their source video\n and the indices of the images in the dataset. This is only used when reading\n embedded image datasets.\n source_filename: Path to the source video file. This is metadata and only used\n when reading embedded image datasets.\n source_inds: Indices of the frames in the source video file. This is metadata\n and only used when reading embedded image datasets.\n image_format: Format of the images in the embedded dataset. This is metadata and\n only used when reading embedded image datasets.\n channel_order: Channel order of embedded images, either "RGB" or "BGR". This is\n used to ensure consistent color channel ordering when decoding embedded\n images. If the encoding and decoding plugins have different channel orders,\n the channels will be automatically flipped during decoding.\n plugin: Plugin to use for decoding embedded images. One of "opencv" or\n "FFMPEG". If None, uses the global default or auto-detects based on\n available packages. Note that "pyav" is automatically mapped to "FFMPEG"\n since PyAV doesn\'t support image decoding.\n'
class-attribute
¶
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__firstlineno__ = 883
class-attribute
¶
int([x]) -> integer int(x, base=10) -> integer
Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating-point numbers, this truncates towards zero.
If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer literal.
int('0b100', base=0) 4
__match_args__ = ('filename', 'grayscale', 'keep_open', '_cached_shape', '_open_reader', '_fps', 'dataset', 'input_format', 'source_filename', 'source_inds', 'image_format', 'channel_order', 'plugin')
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__module__ = 'sleap_io.io.video_reading'
class-attribute
¶
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__slots__ = ('dataset', 'input_format', 'frame_map', 'source_filename', 'source_inds', 'image_format', 'channel_order', 'plugin')
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__static_attributes__ = ('_fps', '_open_reader', 'channel_order', 'dataset', 'frame_map', 'image_format', 'plugin', 'source_filename', 'source_inds')
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
embedded_frame_inds
property
¶
Return the frame indices of the embedded images.
has_embedded_images
property
¶
Return True if the dataset contains embedded images.
img_shape
property
¶
Shape of a single frame in the video as (height, width, channels).
num_frames
property
¶
Number of frames in the video.
__attrs_post_init__()
¶
Auto-detect dataset and frame map heuristically.
Source code in sleap_io/io/video_reading.py
def __attrs_post_init__(self):
"""Auto-detect dataset and frame map heuristically."""
# Check if the file accessible before applying heuristics.
try:
f = h5py.File(self.filename, "r")
except OSError:
return
if self.dataset is None:
# Iterate through datasets to find a rank 4 array.
def find_movies(name, obj):
if isinstance(obj, h5py.Dataset) and obj.ndim == 4:
self.dataset = name
return True
f.visititems(find_movies)
if self.dataset is None:
# Iterate through datasets to find an embedded video dataset.
def find_embedded(name, obj):
if isinstance(obj, h5py.Dataset) and name.endswith("/video"):
self.dataset = name
return True
f.visititems(find_embedded)
if self.dataset is None:
# Couldn't find video datasets.
return
if isinstance(f[self.dataset], h5py.Group):
# If this is a group, assume it's an embedded video dataset.
if "video" in f[self.dataset]:
self.dataset = f"{self.dataset}/video"
if self.dataset.split("/")[-1] == "video":
# This may be an embedded video dataset. Check for frame map.
ds = f[self.dataset]
if "format" in ds.attrs:
self.image_format = ds.attrs["format"]
# Read channel_order, with backwards compatibility
if "channel_order" in ds.attrs:
self.channel_order = ds.attrs["channel_order"]
else:
# Backwards compatibility: Check format_id for older files
# Prior to format 1.4, embedded images were primarily encoded with
# OpenCV which uses BGR, so default to BGR for older formats
if "metadata" in f and "format_id" in f["metadata"].attrs:
format_id = f["metadata"].attrs["format_id"]
if format_id < 1.4:
self.channel_order = "BGR" # Legacy default
# If no format_id found, assume BGR (safest legacy default)
# since most embedded images before this change used OpenCV
if "frame_numbers" in ds.parent:
frame_numbers = ds.parent["frame_numbers"][:].astype(int)
self.frame_map = {frame: idx for idx, frame in enumerate(frame_numbers)}
self.source_inds = frame_numbers
if "source_video" in ds.parent:
self.source_filename = json.loads(
ds.parent["source_video"].attrs["json"]
)["backend"]["filename"]
# Read FPS from attributes if present
if "fps" in ds.attrs:
self._fps = float(ds.attrs["fps"])
elif "fps" in ds.parent.attrs:
self._fps = float(ds.parent.attrs["fps"])
f.close()
# Set default plugin if not specified (use image plugin, not video plugin)
if self.plugin is None:
# Check image plugin default first (for embedded images)
if _default_image_plugin is not None:
self.plugin = _default_image_plugin
# Otherwise auto-detect (for embedded image decoding)
elif "cv2" in sys.modules:
self.plugin = "opencv"
else:
self.plugin = "imageio" # imageio fallback
__eq__(other)
¶
Method generated by attrs for class HDF5Video.
Source code in sleap_io/io/video_reading.py
import cv2
except ImportError:
pass
try:
import imageio_ffmpeg # noqa: F401
except ImportError:
pass
try:
import av # noqa: F401
except ImportError:
pass
# Track available backends (populated on module import)
_AVAILABLE_VIDEO_BACKENDS = {
"opencv": "cv2" in sys.modules,
"FFMPEG": "imageio_ffmpeg" in sys.modules,
__init__(filename, grayscale=None, keep_open=True, cached_shape=None, open_reader=None, fps=None, dataset=None, input_format='channels_last', source_filename=None, source_inds=None, image_format='hdf5', channel_order='RGB', plugin=None)
¶
Method generated by attrs for class HDF5Video.
Source code in sleap_io/io/video_reading.py
"pyav": "av" in sys.modules,
}
_AVAILABLE_IMAGE_BACKENDS = {
"opencv": "cv2" in sys.modules,
"imageio": True, # Always available (core dependency)
}
# Global default video plugin
_default_video_plugin: str | None = None
def normalize_plugin_name(plugin: str) -> str:
"""Normalize plugin names to standard format.
Args:
plugin: Plugin name or alias (case-insensitive).
__replace__(**changes)
¶
__repr__()
¶
Method generated by attrs for class HDF5Video.
__setattr__(name, val)
¶
Method generated by attrs for class HDF5Video.
decode_embedded(img_string)
¶
Decode an embedded image string into a numpy array.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
img_string
|
ndarray
|
Binary string of the image as a |
required |
Returns:
| Type | Description |
|---|---|
ndarray
|
The decoded image as a numpy array of shape This method does not apply grayscale conversion as per the |
Source code in sleap_io/io/video_reading.py
def decode_embedded(self, img_string: np.ndarray) -> np.ndarray:
"""Decode an embedded image string into a numpy array.
Args:
img_string: Binary string of the image as a `int8` numpy vector with the
bytes as values corresponding to the format-encoded image.
Returns:
The decoded image as a numpy array of shape `(height, width, channels)`. If
a rank-2 image is decoded, it will be expanded such that channels will be 1.
This method does not apply grayscale conversion as per the `grayscale`
attribute. Use the `get_frame` or `get_frames` methods of the `VideoBackend`
to apply grayscale conversion rather than calling this function directly.
"""
# Decode based on plugin
if self.plugin == "opencv":
img = cv2.imdecode(img_string, cv2.IMREAD_UNCHANGED)
decoder_order = "BGR" # OpenCV decodes to BGR
else:
# Use imageio for FFMPEG or any other plugin
img = iio.imread(BytesIO(img_string), extension=f".{self.image_format}")
decoder_order = "RGB" # imageio decodes to RGB
if img.ndim == 2:
img = np.expand_dims(img, axis=-1)
# Convert channel order if needed
# If the stored order doesn't match the decoder order, flip channels
if img.shape[-1] == 3 and self.channel_order != decoder_order:
img = img[..., ::-1] # Flip RGB <-> BGR
return img
get_frame_raw_bytes(frame_idx)
¶
Get raw encoded bytes for a frame without decoding.
This method reads the raw compressed image data (PNG/JPEG bytes) directly from the HDF5 dataset without decoding it. This is useful for fast copying of embedded images when the target format matches the source format.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
frame_idx
|
int
|
Index of the frame to read. |
required |
Returns:
| Type | Description |
|---|---|
ndarray | None
|
Raw encoded bytes as int8 numpy array, or None if: - The backend doesn't have embedded images (including "hdf5" format which stores raw numpy arrays, not encoded images) - The frame index is not available |
Notes
For variable-length datasets, returns the raw bytes directly. For fixed-length datasets, returns bytes with trailing zeros stripped.
Source code in sleap_io/io/video_reading.py
def get_frame_raw_bytes(self, frame_idx: int) -> np.ndarray | None:
"""Get raw encoded bytes for a frame without decoding.
This method reads the raw compressed image data (PNG/JPEG bytes) directly
from the HDF5 dataset without decoding it. This is useful for fast copying
of embedded images when the target format matches the source format.
Args:
frame_idx: Index of the frame to read.
Returns:
Raw encoded bytes as int8 numpy array, or None if:
- The backend doesn't have embedded images (including "hdf5" format which
stores raw numpy arrays, not encoded images)
- The frame index is not available
Notes:
For variable-length datasets, returns the raw bytes directly.
For fixed-length datasets, returns bytes with trailing zeros stripped.
"""
if not self.has_embedded_images:
return None
if not self.has_frame(frame_idx):
return None
# Get the internal index (handle frame_map)
internal_idx = (
self.frame_map.get(frame_idx, frame_idx) if self.frame_map else frame_idx
)
# Read directly from dataset
if self.keep_open:
if self._open_reader is None:
self._open_reader = h5py.File(self.filename, "r")
f = self._open_reader
else:
f = h5py.File(self.filename, "r")
ds = f[self.dataset]
raw_bytes = ds[internal_idx]
# Handle fixed-length padding (strip trailing zeros)
is_vlen = h5py.check_vlen_dtype(ds.dtype) is not None
if not is_vlen:
# Find last non-zero byte
non_zero_mask = raw_bytes != 0
if non_zero_mask.any():
last_non_zero = np.where(non_zero_mask)[0][-1]
raw_bytes = raw_bytes[: last_non_zero + 1]
if not self.keep_open:
f.close()
return raw_bytes
has_frame(frame_idx)
¶
Check if a frame index is contained in the video.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
frame_idx
|
int
|
Index of frame to check. |
required |
Returns:
| Type | Description |
|---|---|
bool
|
|
Source code in sleap_io/io/video_reading.py
def has_frame(self, frame_idx: int) -> bool:
"""Check if a frame index is contained in the video.
Args:
frame_idx: Index of frame to check.
Returns:
`True` if the index is contained in the video, otherwise `False`.
"""
if self.frame_map:
return frame_idx in self.frame_map
else:
return frame_idx < len(self)
read_test_frame()
¶
Read a single frame from the video to test for grayscale.
ImageVideo
¶
Bases: sleap_io.io.video_reading.VideoBackend
Video backend for reading videos stored as image files.
This backend supports reading videos stored as a list of images.
Attributes:
| Name | Type | Description |
|---|---|---|
filename |
Path to image files. |
|
grayscale |
Whether to force grayscale. If None, autodetect on first frame load. |
|
plugin |
Image plugin to use for reading. One of "opencv" or "imageio". If None, uses global default from get_default_image_plugin(), or auto-detects. |
Methods:
| Name | Description |
|---|---|
__eq__ |
Method generated by attrs for class ImageVideo. |
__init__ |
Method generated by attrs for class ImageVideo. |
__replace__ |
Method generated by attrs for class ImageVideo. |
__repr__ |
Method generated by attrs for class ImageVideo. |
__setattr__ |
Method generated by attrs for class ImageVideo. |
find_images |
Find images in a folder and return a list of filenames. |
Source code in sleap_io/io/video_reading.py
@attrs.define
class ImageVideo(VideoBackend):
"""Video backend for reading videos stored as image files.
This backend supports reading videos stored as a list of images.
Attributes:
filename: Path to image files.
grayscale: Whether to force grayscale. If None, autodetect on first frame load.
plugin: Image plugin to use for reading. One of "opencv" or "imageio".
If None, uses global default from get_default_image_plugin(), or
auto-detects.
"""
EXTS = ("png", "jpg", "jpeg", "tif", "tiff", "bmp")
plugin: str = attrs.field()
@plugin.validator
def _validate_plugin(self, attribute, value):
"""Validate and normalize plugin name."""
normalized = normalize_image_plugin_name(value)
object.__setattr__(self, attribute.name, normalized)
@plugin.default
def _default_plugin(self) -> str:
"""Get default plugin, checking global default first."""
# Check global default first
if _default_image_plugin is not None:
# Warn if preferred plugin not available
if not _AVAILABLE_IMAGE_BACKENDS.get(_default_image_plugin, False):
import warnings
available = get_available_image_backends()
install_cmd = get_installation_instructions(
_default_image_plugin, "image"
)
warnings.warn(
f"Preferred image plugin '{_default_image_plugin}' is not "
f"available. Available plugins: {available}\n"
f"Install with: {install_cmd}"
)
# Fall through to auto-detection
else:
return _default_image_plugin
# Otherwise auto-detect
if "cv2" in sys.modules:
return "opencv"
else:
return "imageio"
@staticmethod
def find_images(folder: str) -> list[str]:
"""Find images in a folder and return a list of filenames."""
folder = Path(folder)
return sorted(
[f.as_posix() for f in folder.glob("*") if f.suffix[1:] in ImageVideo.EXTS]
)
@property
def num_frames(self) -> int:
"""Number of frames in the video."""
return len(self.filename)
def _read_frame(self, frame_idx: int) -> np.ndarray:
"""Read a single frame from the video.
Args:
frame_idx: Index of frame to read.
Returns:
The frame as a numpy array of shape `(height, width, channels)` in RGB
order.
Notes:
This does not apply grayscale conversion. It is recommended to use the
`get_frame` method of the `VideoBackend` class instead.
Images are always returned in RGB order regardless of plugin:
- imageio: Returns RGB natively
- opencv: Returns BGR, automatically flipped to RGB
"""
if self.plugin == "opencv":
# OpenCV reads as BGR, flip to RGB
img = cv2.imread(self.filename[frame_idx], cv2.IMREAD_UNCHANGED)
if img is None:
raise ValueError(f"Failed to read image: {self.filename[frame_idx]}")
if img.ndim == 3 and img.shape[-1] == 3:
img = img[..., ::-1] # BGR -> RGB
else: # imageio
# imageio reads as RGB natively
img = iio.imread(self.filename[frame_idx])
if img.ndim == 2:
img = np.expand_dims(img, axis=-1)
return img
EXTS = ('png', 'jpg', 'jpeg', 'tif', 'tiff', 'bmp')
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__annotations__ = {'plugin': 'str'}
class-attribute
¶
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__attrs_own_setattr__ = True
class-attribute
¶
Returns True when the argument is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.
__attrs_props__ = ClassProps(is_exception=False, is_slotted=True, has_weakref_slot=True, is_frozen=False, kw_only=<KeywordOnly.NO: 'no'>, collected_fields_by_mro=True, added_init=True, added_repr=True, added_eq=True, added_ordering=False, hashability=<Hashability.UNHASHABLE: 'unhashable'>, added_match_args=True, added_str=False, added_pickling=True, on_setattr_hook=<function pipe.<locals>.wrapped_pipe at 0x7f41e68fca40>, field_transformer=None)
class-attribute
¶
Effective class properties as derived from parameters to attr.s() or
define() decorators.
This is the same data structure that attrs uses internally to decide how to construct the final class.
Warning:
This feature is currently **experimental** and is not covered by our
strict backwards-compatibility guarantees.
Attributes:
| Name | Type | Description |
|---|---|---|
is_exception |
bool
|
Whether the class is treated as an exception class. |
is_slotted |
bool
|
Whether the class is |
has_weakref_slot |
bool
|
Whether the class has a slot for weak references. |
is_frozen |
bool
|
Whether the class is frozen. |
kw_only |
KeywordOnly
|
Whether / how the class enforces keyword-only arguments on the
|
collected_fields_by_mro |
bool
|
Whether the class fields were collected by method resolution order.
That is, correctly but unlike |
added_init |
bool
|
Whether the class has an attrs-generated |
added_repr |
bool
|
Whether the class has an attrs-generated |
added_eq |
bool
|
Whether the class has attrs-generated equality methods. |
added_ordering |
bool
|
Whether the class has attrs-generated ordering methods. |
hashability |
Hashability
|
How |
added_match_args |
bool
|
Whether the class supports positional |
added_str |
bool
|
Whether the class has an attrs-generated |
added_pickling |
bool
|
Whether the class has attrs-generated |
on_setattr_hook |
Callable[[Any, Attribute[Any], Any], Any] | None
|
The class's |
field_transformer |
Callable[[Attribute[Any]], Attribute[Any]] | None
|
The class's |
.. versionadded:: 25.4.0
__doc__ = 'Video backend for reading videos stored as image files.\n\nThis backend supports reading videos stored as a list of images.\n\nAttributes:\n filename: Path to image files.\n grayscale: Whether to force grayscale. If None, autodetect on first frame load.\n plugin: Image plugin to use for reading. One of "opencv" or "imageio".\n If None, uses global default from get_default_image_plugin(), or\n auto-detects.\n'
class-attribute
¶
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__firstlineno__ = 1275
class-attribute
¶
int([x]) -> integer int(x, base=10) -> integer
Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating-point numbers, this truncates towards zero.
If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer literal.
int('0b100', base=0) 4
__match_args__ = ('filename', 'grayscale', 'keep_open', '_cached_shape', '_open_reader', '_fps', 'plugin')
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__module__ = 'sleap_io.io.video_reading'
class-attribute
¶
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__slots__ = ('plugin',)
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__static_attributes__ = ()
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
num_frames
property
¶
Number of frames in the video.
__eq__(other)
¶
__init__(filename, grayscale=None, keep_open=True, cached_shape=None, open_reader=None, fps=None, plugin=NOTHING)
¶
Method generated by attrs for class ImageVideo.
Source code in sleap_io/io/video_reading.py
pass
# Track available backends (populated on module import)
_AVAILABLE_VIDEO_BACKENDS = {
"opencv": "cv2" in sys.modules,
"FFMPEG": "imageio_ffmpeg" in sys.modules,
"pyav": "av" in sys.modules,
}
_AVAILABLE_IMAGE_BACKENDS = {
"opencv": "cv2" in sys.modules,
"imageio": True, # Always available (core dependency)
}
__replace__(**changes)
¶
__repr__()
¶
Method generated by attrs for class ImageVideo.
__setattr__(name, val)
¶
Method generated by attrs for class ImageVideo.
find_images(folder)
staticmethod
¶
Find images in a folder and return a list of filenames.
MediaVideo
¶
Bases: sleap_io.io.video_reading.VideoBackend
Video backend for reading videos stored as common media files.
This backend supports reading through FFMPEG (the default), pyav, or OpenCV. Here are their trade-offs:
- "opencv": Fastest video reader, but only supports a limited number of codecs
and may not be able to read some videos. It requires `opencv-python` to be
installed. It is the fastest because it uses the OpenCV C++ library to read
videos, but is limited by the version of FFMPEG that was linked into it at
build time as well as the OpenCV version used.
- "FFMPEG": Slowest, but most reliable. This is the default backend. It requires
`imageio-ffmpeg` and a `ffmpeg` executable on the system path (which can be
installed via conda). The `imageio` plugin for FFMPEG reads frames into raw
bytes which are communicated to Python through STDOUT on a subprocess pipe,
which can be slow. However, it is the most reliable and feature-complete. If
you install the conda-forge version of ffmpeg, it will be compiled with
support for many codecs, including GPU-accelerated codecs like NVDEC for
H264 and others.
- "pyav": Supports most codecs that FFMPEG does, but not as complete or reliable
of an implementation in `imageio` as FFMPEG for some video types. It is
faster than FFMPEG because it uses the `av` package to read frames directly
into numpy arrays in memory without the need for a subprocess pipe. These
are Python bindings for the C library libav, which is the same library that
FFMPEG uses under the hood.
Attributes:
| Name | Type | Description |
|---|---|---|
filename |
Path to video file. |
|
grayscale |
Whether to force grayscale. If None, autodetect on first frame load. |
|
keep_open |
Whether to keep the video reader open between calls to read frames. If False, will close the reader after each call. If True (the default), it will keep the reader open and cache it for subsequent calls which may enhance the performance of reading multiple frames. |
|
plugin |
Video plugin to use. One of "opencv", "FFMPEG", or "pyav". If |
Methods:
| Name | Description |
|---|---|
__eq__ |
Method generated by attrs for class MediaVideo. |
__init__ |
Method generated by attrs for class MediaVideo. |
__replace__ |
Method generated by attrs for class MediaVideo. |
__repr__ |
Method generated by attrs for class MediaVideo. |
__setattr__ |
Method generated by attrs for class MediaVideo. |
Source code in sleap_io/io/video_reading.py
@attrs.define
class MediaVideo(VideoBackend):
"""Video backend for reading videos stored as common media files.
This backend supports reading through FFMPEG (the default), pyav, or OpenCV. Here
are their trade-offs:
- "opencv": Fastest video reader, but only supports a limited number of codecs
and may not be able to read some videos. It requires `opencv-python` to be
installed. It is the fastest because it uses the OpenCV C++ library to read
videos, but is limited by the version of FFMPEG that was linked into it at
build time as well as the OpenCV version used.
- "FFMPEG": Slowest, but most reliable. This is the default backend. It requires
`imageio-ffmpeg` and a `ffmpeg` executable on the system path (which can be
installed via conda). The `imageio` plugin for FFMPEG reads frames into raw
bytes which are communicated to Python through STDOUT on a subprocess pipe,
which can be slow. However, it is the most reliable and feature-complete. If
you install the conda-forge version of ffmpeg, it will be compiled with
support for many codecs, including GPU-accelerated codecs like NVDEC for
H264 and others.
- "pyav": Supports most codecs that FFMPEG does, but not as complete or reliable
of an implementation in `imageio` as FFMPEG for some video types. It is
faster than FFMPEG because it uses the `av` package to read frames directly
into numpy arrays in memory without the need for a subprocess pipe. These
are Python bindings for the C library libav, which is the same library that
FFMPEG uses under the hood.
Attributes:
filename: Path to video file.
grayscale: Whether to force grayscale. If None, autodetect on first frame load.
keep_open: Whether to keep the video reader open between calls to read frames.
If False, will close the reader after each call. If True (the default), it
will keep the reader open and cache it for subsequent calls which may
enhance the performance of reading multiple frames.
plugin: Video plugin to use. One of "opencv", "FFMPEG", or "pyav". If `None`,
will use the first available plugin in the order listed above.
"""
plugin: str = attrs.field()
@plugin.validator
def _validate_plugin(self, attribute, value):
# Normalize the plugin name
normalized = normalize_plugin_name(value)
# Update the actual value to the normalized version
object.__setattr__(self, attribute.name, normalized)
EXTS = ("mp4", "avi", "mov", "mj2", "mkv")
@plugin.default
def _default_plugin(self) -> str:
# Check global default first
if _default_video_plugin is not None:
# Warn if preferred plugin not available
if not _AVAILABLE_VIDEO_BACKENDS.get(_default_video_plugin, False):
import warnings
available = get_available_video_backends()
install_cmd = get_installation_instructions(_default_video_plugin)
warnings.warn(
f"Preferred video plugin '{_default_video_plugin}' is not "
f"available. Available plugins: {available}\n"
f"Install with: {install_cmd}"
)
# Fall through to auto-detection
else:
return _default_video_plugin
# Auto-detect based on what's available
if "cv2" in sys.modules:
return "opencv"
elif "imageio_ffmpeg" in sys.modules:
return "FFMPEG"
elif "av" in sys.modules:
return "pyav"
else:
# Enhanced error message with installation instructions
raise ImportError(
"No video backend plugins are available.\n\n"
"The bundled imageio-ffmpeg should be available by default.\n"
"If you see this error, try reinstalling sleap-io:\n"
" pip install --force-reinstall sleap-io\n\n"
"Alternative backends:\n"
" opencv (fastest): pip install sleap-io[opencv]\n"
" pyav (balanced): pip install sleap-io[pyav]\n\n"
"For more information, see: https://io.sleap.ai"
)
@property
def reader(self) -> object:
"""Return the reader object for the video, caching if necessary."""
if self.keep_open:
if self._open_reader is None:
if self.plugin == "opencv":
self._open_reader = cv2.VideoCapture(self.filename)
elif self.plugin == "pyav" or self.plugin == "FFMPEG":
self._open_reader = iio.imopen(
self.filename, "r", plugin=self.plugin
)
return self._open_reader
else:
if self.plugin == "opencv":
return cv2.VideoCapture(self.filename)
elif self.plugin == "pyav" or self.plugin == "FFMPEG":
return iio.imopen(self.filename, "r", plugin=self.plugin)
@property
def num_frames(self) -> int:
"""Number of frames in the video."""
if self.plugin == "opencv":
return int(self.reader.get(cv2.CAP_PROP_FRAME_COUNT))
else:
props = iio.improps(self.filename, plugin=self.plugin)
n_frames = props.n_images
if np.isinf(n_frames):
legacy_reader = self.reader.legacy_get_reader()
# Note: This might be super slow for some videos, so maybe we should
# defer evaluation of this or give the user control over it.
n_frames = legacy_reader.count_frames()
return n_frames
@property
def fps(self) -> float | None:
"""Frames per second from video container metadata.
Returns:
The FPS from the video container, or None if it cannot be determined.
Notes:
This reads the FPS from the video file metadata using the appropriate
method for the current plugin:
- OpenCV: cv2.CAP_PROP_FPS
- FFMPEG/pyav: imageio metadata
"""
# Return cached/explicit value if set
if self._fps is not None:
return self._fps
# Read from container metadata
try:
if self.plugin == "opencv":
fps = self.reader.get(cv2.CAP_PROP_FPS)
return fps if fps > 0 else None
else:
# Use imageio v2 API to get metadata (v3 improps doesn't include fps)
import imageio.v2 as iio_v2
reader = iio_v2.get_reader(self.filename, format="FFMPEG")
meta = reader.get_meta_data()
reader.close()
fps = meta.get("fps")
return float(fps) if fps is not None else None
except Exception:
return None
@fps.setter
def fps(self, value: float | None) -> None:
"""Set an explicit FPS override.
Args:
value: Frames per second. Must be positive if not None.
Raises:
ValueError: If value is not positive.
Notes:
Setting FPS on MediaVideo overrides the value from container metadata.
This can be useful when the container metadata is incorrect or missing.
"""
if value is not None and value <= 0:
raise ValueError(f"FPS must be positive, got {value}")
self._fps = value
def _read_frame(self, frame_idx: int) -> np.ndarray:
"""Read a single frame from the video.
Args:
frame_idx: Index of frame to read.
Returns:
The frame as a numpy array of shape `(height, width, channels)`.
Notes:
This does not apply grayscale conversion. It is recommended to use the
`get_frame` method of the `VideoBackend` class instead.
"""
if self.plugin == "opencv":
if self.keep_open:
if self._open_reader is None:
self._open_reader = cv2.VideoCapture(self.filename)
reader = self._open_reader
else:
reader = cv2.VideoCapture(self.filename)
if reader.get(cv2.CAP_PROP_POS_FRAMES) != frame_idx:
reader.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
success, img = reader.read()
if success:
img = img[..., ::-1] # BGR -> RGB
elif self.plugin == "pyav" or self.plugin == "FFMPEG":
if self.keep_open:
img = self.reader.read(index=frame_idx)
else:
with iio.imopen(self.filename, "r", plugin=self.plugin) as reader:
img = reader.read(index=frame_idx)
success = img is not None
if not success:
raise IndexError(f"Failed to read frame index {frame_idx}.")
return img
def _read_frames(self, frame_inds: list) -> np.ndarray:
"""Read a list of frames from the video.
Args:
frame_inds: List of indices of frames to read.
Returns:
The frame as a numpy array of shape `(frames, height, width, channels)`.
Notes:
This does not apply grayscale conversion. It is recommended to use the
`get_frames` method of the `VideoBackend` class instead.
"""
if self.plugin == "opencv":
if self.keep_open:
if self._open_reader is None:
self._open_reader = cv2.VideoCapture(self.filename)
reader = self._open_reader
else:
reader = cv2.VideoCapture(self.filename)
reader.set(cv2.CAP_PROP_POS_FRAMES, frame_inds[0])
imgs = []
for idx in frame_inds:
if reader.get(cv2.CAP_PROP_POS_FRAMES) != idx:
reader.set(cv2.CAP_PROP_POS_FRAMES, idx)
_, img = reader.read()
imgs.append(img)
imgs = np.stack(imgs, axis=0)
imgs = imgs[..., ::-1] # BGR -> RGB
elif self.plugin == "pyav" or self.plugin == "FFMPEG":
if self.keep_open:
if self._open_reader is None:
self._open_reader = iio.imopen(
self.filename, "r", plugin=self.plugin
)
reader = self._open_reader
imgs = np.stack([reader.read(index=idx) for idx in frame_inds], axis=0)
else:
with iio.imopen(self.filename, "r", plugin=self.plugin) as reader:
imgs = np.stack(
[reader.read(index=idx) for idx in frame_inds], axis=0
)
return imgs
EXTS = ('mp4', 'avi', 'mov', 'mj2', 'mkv')
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__annotations__ = {'plugin': 'str'}
class-attribute
¶
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__attrs_own_setattr__ = True
class-attribute
¶
Returns True when the argument is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.
__attrs_props__ = ClassProps(is_exception=False, is_slotted=True, has_weakref_slot=True, is_frozen=False, kw_only=<KeywordOnly.NO: 'no'>, collected_fields_by_mro=True, added_init=True, added_repr=True, added_eq=True, added_ordering=False, hashability=<Hashability.UNHASHABLE: 'unhashable'>, added_match_args=True, added_str=False, added_pickling=True, on_setattr_hook=<function pipe.<locals>.wrapped_pipe at 0x7f41e68fca40>, field_transformer=None)
class-attribute
¶
Effective class properties as derived from parameters to attr.s() or
define() decorators.
This is the same data structure that attrs uses internally to decide how to construct the final class.
Warning:
This feature is currently **experimental** and is not covered by our
strict backwards-compatibility guarantees.
Attributes:
| Name | Type | Description |
|---|---|---|
is_exception |
bool
|
Whether the class is treated as an exception class. |
is_slotted |
bool
|
Whether the class is |
has_weakref_slot |
bool
|
Whether the class has a slot for weak references. |
is_frozen |
bool
|
Whether the class is frozen. |
kw_only |
KeywordOnly
|
Whether / how the class enforces keyword-only arguments on the
|
collected_fields_by_mro |
bool
|
Whether the class fields were collected by method resolution order.
That is, correctly but unlike |
added_init |
bool
|
Whether the class has an attrs-generated |
added_repr |
bool
|
Whether the class has an attrs-generated |
added_eq |
bool
|
Whether the class has attrs-generated equality methods. |
added_ordering |
bool
|
Whether the class has attrs-generated ordering methods. |
hashability |
Hashability
|
How |
added_match_args |
bool
|
Whether the class supports positional |
added_str |
bool
|
Whether the class has an attrs-generated |
added_pickling |
bool
|
Whether the class has attrs-generated |
on_setattr_hook |
Callable[[Any, Attribute[Any], Any], Any] | None
|
The class's |
field_transformer |
Callable[[Attribute[Any]], Attribute[Any]] | None
|
The class's |
.. versionadded:: 25.4.0
__doc__ = 'Video backend for reading videos stored as common media files.\n\nThis backend supports reading through FFMPEG (the default), pyav, or OpenCV. Here\nare their trade-offs:\n\n - "opencv": Fastest video reader, but only supports a limited number of codecs\n and may not be able to read some videos. It requires `opencv-python` to be\n installed. It is the fastest because it uses the OpenCV C++ library to read\n videos, but is limited by the version of FFMPEG that was linked into it at\n build time as well as the OpenCV version used.\n - "FFMPEG": Slowest, but most reliable. This is the default backend. It requires\n `imageio-ffmpeg` and a `ffmpeg` executable on the system path (which can be\n installed via conda). The `imageio` plugin for FFMPEG reads frames into raw\n bytes which are communicated to Python through STDOUT on a subprocess pipe,\n which can be slow. However, it is the most reliable and feature-complete. If\n you install the conda-forge version of ffmpeg, it will be compiled with\n support for many codecs, including GPU-accelerated codecs like NVDEC for\n H264 and others.\n - "pyav": Supports most codecs that FFMPEG does, but not as complete or reliable\n of an implementation in `imageio` as FFMPEG for some video types. It is\n faster than FFMPEG because it uses the `av` package to read frames directly\n into numpy arrays in memory without the need for a subprocess pipe. These\n are Python bindings for the C library libav, which is the same library that\n FFMPEG uses under the hood.\n\nAttributes:\n filename: Path to video file.\n grayscale: Whether to force grayscale. If None, autodetect on first frame load.\n keep_open: Whether to keep the video reader open between calls to read frames.\n If False, will close the reader after each call. If True (the default), it\n will keep the reader open and cache it for subsequent calls which may\n enhance the performance of reading multiple frames.\n plugin: Video plugin to use. One of "opencv", "FFMPEG", or "pyav". If `None`,\n will use the first available plugin in the order listed above.\n'
class-attribute
¶
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__firstlineno__ = 621
class-attribute
¶
int([x]) -> integer int(x, base=10) -> integer
Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating-point numbers, this truncates towards zero.
If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer literal.
int('0b100', base=0) 4
__match_args__ = ('filename', 'grayscale', 'keep_open', '_cached_shape', '_open_reader', '_fps', 'plugin')
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__module__ = 'sleap_io.io.video_reading'
class-attribute
¶
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__slots__ = ('plugin',)
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__static_attributes__ = ('_fps', '_open_reader')
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
fps
property
¶
Frames per second from video container metadata.
Returns:
| Type | Description |
|---|---|
|
The FPS from the video container, or None if it cannot be determined. |
Notes
This reads the FPS from the video file metadata using the appropriate method for the current plugin: - OpenCV: cv2.CAP_PROP_FPS - FFMPEG/pyav: imageio metadata
num_frames
property
¶
Number of frames in the video.
reader
property
¶
Return the reader object for the video, caching if necessary.
__eq__(other)
¶
__init__(filename, grayscale=None, keep_open=True, cached_shape=None, open_reader=None, fps=None, plugin=NOTHING)
¶
Method generated by attrs for class MediaVideo.
Source code in sleap_io/io/video_reading.py
pass
# Track available backends (populated on module import)
_AVAILABLE_VIDEO_BACKENDS = {
"opencv": "cv2" in sys.modules,
"FFMPEG": "imageio_ffmpeg" in sys.modules,
"pyav": "av" in sys.modules,
}
_AVAILABLE_IMAGE_BACKENDS = {
"opencv": "cv2" in sys.modules,
"imageio": True, # Always available (core dependency)
}
__replace__(**changes)
¶
__repr__()
¶
Method generated by attrs for class MediaVideo.
__setattr__(name, val)
¶
Method generated by attrs for class MediaVideo.
Video
¶
Video class used by sleap to represent videos and data associated with them.
This class is used to store information regarding a video and its components.
It is used to store the video's filename, shape, and the video's backend.
To create a Video object, use the from_filename method which will select the
backend appropriately.
Attributes:
| Name | Type | Description |
|---|---|---|
filename |
The filename(s) of the video. Supported extensions: "mp4", "avi", "mov", "mj2", "mkv", "h5", "hdf5", "slp", "png", "jpg", "jpeg", "tif", "tiff", "bmp". If the filename is a list, a list of image filenames are expected. If filename is a folder, it will be searched for images. |
|
backend |
An object that implements the basic methods for reading and manipulating frames of a specific video type. |
|
backend_metadata |
A dictionary of metadata specific to the backend. This is useful for storing metadata that requires an open backend (e.g., shape information) without having access to the video file itself. |
|
source_video |
The source video object if this is a proxy video. This is present when the video contains an embedded subset of frames from another video. |
|
open_backend |
Whether to open the backend when the video is available. If |
Notes
Instances of this class are hashed by identity, not by value. This means that
two Video instances with the same attributes will NOT be considered equal in a
set or dict.
Media Video Plugin Support
For media files (mp4, avi, etc.), the following plugins are supported: - "opencv": Uses OpenCV (cv2) for video reading - "FFMPEG": Uses imageio-ffmpeg for video reading - "pyav": Uses PyAV for video reading
Plugin aliases (case-insensitive): - opencv: "opencv", "cv", "cv2", "ocv" - FFMPEG: "FFMPEG", "ffmpeg", "imageio-ffmpeg", "imageio_ffmpeg" - pyav: "pyav", "av"
Plugin selection priority: 1. Explicitly specified plugin parameter 2. Backend metadata plugin value 3. Global default (set via sio.set_default_video_plugin) 4. Auto-detection based on available packages
See Also
VideoBackend: The backend interface for reading video data. sleap_io.set_default_video_plugin: Set global default plugin. sleap_io.get_default_video_plugin: Get current default plugin.
Methods:
| Name | Description |
|---|---|
__attrs_post_init__ |
Post init syntactic sugar. |
__deepcopy__ |
Deep copy the video object. |
__getitem__ |
Return the frames of the video at the given indices. |
__init__ |
Method generated by attrs for class Video. |
__len__ |
Return the length of the video as the number of frames. |
__replace__ |
Method generated by attrs for class Video. |
__repr__ |
Informal string representation (for print or format). |
__str__ |
Informal string representation (for print or format). |
close |
Close the video backend. |
deduplicate_with |
Create a new video with duplicate images removed. |
exists |
Check if the video file exists and is accessible. |
frame_to_seconds |
Convert a frame index to timestamp in seconds. |
from_filename |
Create a Video from a filename. |
has_overlapping_images |
Check if this video has overlapping images with another video. |
matches_content |
Check if this video has the same content as another video. |
matches_path |
Check if this video has the same path as another video. |
matches_shape |
Check if this video has the same shape as another video. |
merge_with |
Merge another video's images into this one. |
open |
Open the video backend for reading. |
replace_filename |
Update the filename of the video, optionally opening the backend. |
save |
Save video frames to a new video file. |
seconds_to_frame |
Convert a timestamp in seconds to frame index. |
set_video_plugin |
Set the video plugin and reopen the video. |
Source code in sleap_io/model/video.py
@attrs.define(eq=False)
class Video:
"""`Video` class used by sleap to represent videos and data associated with them.
This class is used to store information regarding a video and its components.
It is used to store the video's `filename`, `shape`, and the video's `backend`.
To create a `Video` object, use the `from_filename` method which will select the
backend appropriately.
Attributes:
filename: The filename(s) of the video. Supported extensions: "mp4", "avi",
"mov", "mj2", "mkv", "h5", "hdf5", "slp", "png", "jpg", "jpeg", "tif",
"tiff", "bmp". If the filename is a list, a list of image filenames are
expected. If filename is a folder, it will be searched for images.
backend: An object that implements the basic methods for reading and
manipulating frames of a specific video type.
backend_metadata: A dictionary of metadata specific to the backend. This is
useful for storing metadata that requires an open backend (e.g., shape
information) without having access to the video file itself.
source_video: The source video object if this is a proxy video. This is present
when the video contains an embedded subset of frames from another video.
open_backend: Whether to open the backend when the video is available. If `True`
(the default), the backend will be automatically opened if the video exists.
Set this to `False` when you want to manually open the backend, or when the
you know the video file does not exist and you want to avoid trying to open
the file.
Notes:
Instances of this class are hashed by identity, not by value. This means that
two `Video` instances with the same attributes will NOT be considered equal in a
set or dict.
Media Video Plugin Support:
For media files (mp4, avi, etc.), the following plugins are supported:
- "opencv": Uses OpenCV (cv2) for video reading
- "FFMPEG": Uses imageio-ffmpeg for video reading
- "pyav": Uses PyAV for video reading
Plugin aliases (case-insensitive):
- opencv: "opencv", "cv", "cv2", "ocv"
- FFMPEG: "FFMPEG", "ffmpeg", "imageio-ffmpeg", "imageio_ffmpeg"
- pyav: "pyav", "av"
Plugin selection priority:
1. Explicitly specified plugin parameter
2. Backend metadata plugin value
3. Global default (set via sio.set_default_video_plugin)
4. Auto-detection based on available packages
See Also:
VideoBackend: The backend interface for reading video data.
sleap_io.set_default_video_plugin: Set global default plugin.
sleap_io.get_default_video_plugin: Get current default plugin.
"""
filename: str | list[str]
backend: VideoBackend | None = None
backend_metadata: dict[str, any] = attrs.field(factory=dict)
source_video: "Video | None" = None
open_backend: bool = True
EXTS = MediaVideo.EXTS + HDF5Video.EXTS + ImageVideo.EXTS
@property
def original_video(self) -> "Video | None":
"""The root video in the provenance chain.
For embedded videos, this returns the ultimate source video by
traversing the source_video chain. Returns None if this video
has no source_video (i.e., it IS an original).
This property is computed by following the source_video chain to find
the root. For a single-level embedding (A embeds from B), original_video
returns B. For multi-level embedding (A <- B <- C), it returns C.
"""
if self.source_video is None:
return None # This IS the original
# Traverse to root
v = self.source_video
while v.source_video is not None:
v = v.source_video
return v
def __attrs_post_init__(self):
"""Post init syntactic sugar."""
if self.open_backend and self.backend is None and self.exists():
try:
self.open()
except Exception:
# If we can't open the backend, just ignore it for now so we don't
# prevent the user from building the Video object entirely.
pass
def __deepcopy__(self, memo):
"""Deep copy the video object."""
if id(self) in memo:
return memo[id(self)]
reopen = False
if self.is_open:
reopen = True
self.close()
new_video = Video(
filename=self.filename,
backend=None,
backend_metadata=self.backend_metadata.copy(),
source_video=self.source_video,
open_backend=self.open_backend,
)
memo[id(self)] = new_video
if reopen:
self.open()
return new_video
@classmethod
def from_filename(
cls,
filename: str | list[str],
dataset: str | None = None,
grayscale: bool | None = None,
keep_open: bool = True,
source_video: "Video | None" = None,
**kwargs,
) -> VideoBackend:
"""Create a Video from a filename.
Args:
filename: The filename(s) of the video. Supported extensions: "mp4", "avi",
"mov", "mj2", "mkv", "h5", "hdf5", "slp", "png", "jpg", "jpeg", "tif",
"tiff", "bmp". If the filename is a list, a list of image filenames are
expected. If filename is a folder, it will be searched for images.
dataset: Name of dataset in HDF5 file.
grayscale: Whether to force grayscale. If None, autodetect on first frame
load.
keep_open: Whether to keep the video reader open between calls to read
frames. If False, will close the reader after each call. If True (the
default), it will keep the reader open and cache it for subsequent calls
which may enhance the performance of reading multiple frames.
source_video: The source video object if this is a proxy video. This is
present when the video contains an embedded subset of frames from
another video.
**kwargs: Additional backend-specific arguments passed to
VideoBackend.from_filename. See VideoBackend.from_filename for supported
arguments.
Returns:
Video instance with the appropriate backend instantiated.
"""
backend = VideoBackend.from_filename(
filename,
dataset=dataset,
grayscale=grayscale,
keep_open=keep_open,
**kwargs,
)
# If filename is a directory, VideoBackend.from_filename will expand it
# to a list of paths to images contained within the directory. In this
# case we want to use the expanded list as filename
return cls(
filename=backend.filename,
backend=backend,
source_video=source_video,
)
@property
def shape(self) -> tuple[int, int, int, int] | None:
"""Return the shape of the video as (num_frames, height, width, channels).
If the video backend is not set or it cannot determine the shape of the video,
this will return None.
"""
return self._get_shape()
def _get_shape(self) -> tuple[int, int, int, int] | None:
"""Return the shape of the video as (num_frames, height, width, channels).
This suppresses errors related to querying the backend for the video shape, such
as when it has not been set or when the video file is not found.
"""
try:
return self.backend.shape
except Exception:
if "shape" in self.backend_metadata:
return self.backend_metadata["shape"]
return None
@property
def grayscale(self) -> bool | None:
"""Return whether the video is grayscale.
If the video backend is not set or it cannot determine whether the video is
grayscale, this will return None.
"""
shape = self.shape
if shape is not None:
return shape[-1] == 1
else:
grayscale = None
if "grayscale" in self.backend_metadata:
grayscale = self.backend_metadata["grayscale"]
return grayscale
@grayscale.setter
def grayscale(self, value: bool):
"""Set the grayscale value and adjust the backend."""
if self.backend is not None:
self.backend.grayscale = value
self.backend._cached_shape = None
self.backend_metadata["grayscale"] = value
@property
def fps(self) -> float | None:
"""Return the frames per second of the video.
For MediaVideo backends, this reads FPS from the video container metadata.
For other backends (ImageVideo, HDF5Video, TiffVideo), this returns the
explicitly set value or None if not set.
Returns:
The FPS if known, or None if unavailable/unknown.
"""
if self.backend is not None:
return self.backend.fps
return self.backend_metadata.get("fps")
@fps.setter
def fps(self, value: float | None):
"""Set the frames per second.
Args:
value: Frames per second. Must be positive if not None.
Raises:
ValueError: If value is not positive.
Notes:
For MediaVideo backends, setting FPS overrides the value from container
metadata. For other backends, this sets the FPS directly.
"""
if value is not None and value <= 0:
raise ValueError(f"FPS must be positive, got {value}")
if self.backend is not None:
self.backend.fps = value
self.backend_metadata["fps"] = value
def frame_to_seconds(self, frame_idx: int) -> float | None:
"""Convert a frame index to timestamp in seconds.
Args:
frame_idx: Zero-indexed frame number.
Returns:
Time in seconds, or None if FPS is unknown.
Notes:
This assumes constant frame rate. For variable frame rate videos,
the returned timestamp may be approximate.
"""
if self.fps is None or self.fps <= 0:
return None
return frame_idx / self.fps
def seconds_to_frame(self, seconds: float) -> int | None:
"""Convert a timestamp in seconds to frame index.
Args:
seconds: Time in seconds from video start.
Returns:
Zero-indexed frame number (rounded down), or None if FPS unknown.
"""
if self.fps is None or self.fps <= 0:
return None
return int(seconds * self.fps)
def __len__(self) -> int:
"""Return the length of the video as the number of frames."""
shape = self.shape
return 0 if shape is None else shape[0]
def __repr__(self) -> str:
"""Informal string representation (for print or format)."""
dataset = (
f"dataset={self.backend.dataset}, "
if getattr(self.backend, "dataset", "")
else ""
)
return (
"Video("
f'filename="{self.filename}", '
f"shape={self.shape}, "
f"{dataset}"
f"backend={type(self.backend).__name__}"
")"
)
def __str__(self) -> str:
"""Informal string representation (for print or format)."""
return self.__repr__()
def __getitem__(self, inds: int | list[int] | slice) -> np.ndarray:
"""Return the frames of the video at the given indices.
Args:
inds: Index or list of indices of frames to read.
Returns:
Frame or frames as a numpy array of shape `(height, width, channels)` if a
scalar index is provided, or `(frames, height, width, channels)` if a list
of indices is provided.
See also: VideoBackend.get_frame, VideoBackend.get_frames
"""
if not self.is_open:
if self.open_backend:
self.open()
else:
raise ValueError(
"Video backend is not open. Call video.open() or set "
"video.open_backend to True to do automatically on frame read."
)
return self.backend[inds]
def exists(self, check_all: bool = False, dataset: str | None = None) -> bool:
"""Check if the video file exists and is accessible.
Args:
check_all: If `True`, check that all filenames in a list exist. If `False`
(the default), check that the first filename exists.
dataset: Name of dataset in HDF5 file. If specified, this will function will
return `False` if the dataset does not exist.
Returns:
`True` if the file exists and is accessible, `False` otherwise.
"""
if isinstance(self.filename, list):
if check_all:
for f in self.filename:
if not is_file_accessible(f):
return False
return True
else:
return is_file_accessible(self.filename[0])
file_is_accessible = is_file_accessible(self.filename)
if not file_is_accessible:
return False
if dataset is None or dataset == "":
dataset = self.backend_metadata.get("dataset", None)
if dataset is not None and dataset != "":
has_dataset = False
if (
self.backend is not None
and type(self.backend) is HDF5Video
and self.backend._open_reader is not None
):
has_dataset = dataset in self.backend._open_reader
else:
with h5py.File(self.filename, "r") as f:
has_dataset = dataset in f
return has_dataset
return True
@property
def is_open(self) -> bool:
"""Check if the video backend is open."""
return self.exists() and self.backend is not None
def open(
self,
filename: str | None = None,
dataset: str | None = None,
grayscale: str | None = None,
keep_open: bool = True,
plugin: str | None = None,
):
"""Open the video backend for reading.
Args:
filename: Filename to open. If not specified, will use the filename set on
the video object.
dataset: Name of dataset in HDF5 file.
grayscale: Whether to force grayscale. If None, autodetect on first frame
load.
keep_open: Whether to keep the video reader open between calls to read
frames. If False, will close the reader after each call. If True (the
default), it will keep the reader open and cache it for subsequent calls
which may enhance the performance of reading multiple frames.
plugin: Video plugin to use for MediaVideo files. One of "opencv",
"FFMPEG", or "pyav". Also accepts aliases (case-insensitive).
If not specified, uses the backend metadata, global default,
or auto-detection in that order.
Notes:
This is useful for opening the video backend to read frames and then closing
it after reading all the necessary frames.
If the backend was already open, it will be closed before opening a new one.
Values for the HDF5 dataset and grayscale will be remembered if not
specified.
"""
if filename is not None:
self.replace_filename(filename, open=False)
# Try to remember values from previous backend if available and not specified.
if self.backend is not None:
if dataset is None:
dataset = getattr(self.backend, "dataset", None)
if grayscale is None:
grayscale = getattr(self.backend, "grayscale", None)
else:
if dataset is None and "dataset" in self.backend_metadata:
dataset = self.backend_metadata["dataset"]
if grayscale is None:
if "grayscale" in self.backend_metadata:
grayscale = self.backend_metadata["grayscale"]
elif "shape" in self.backend_metadata:
grayscale = self.backend_metadata["shape"][-1] == 1
if not self.exists(dataset=dataset):
msg = (
f"Video does not exist or cannot be opened for reading: {self.filename}"
)
if dataset is not None:
msg += f" (dataset: {dataset})"
raise FileNotFoundError(msg)
# Close previous backend if open.
self.close()
# Handle plugin parameter
backend_kwargs = {}
if plugin is not None:
from sleap_io.io.video_reading import normalize_plugin_name
plugin = normalize_plugin_name(plugin)
self.backend_metadata["plugin"] = plugin
if "plugin" in self.backend_metadata:
backend_kwargs["plugin"] = self.backend_metadata["plugin"]
# Create new backend.
self.backend = VideoBackend.from_filename(
self.filename,
dataset=dataset,
grayscale=grayscale,
keep_open=keep_open,
**backend_kwargs,
)
def close(self):
"""Close the video backend."""
if self.backend is not None:
# Try to remember values from previous backend if available and not
# specified.
try:
self.backend_metadata["dataset"] = getattr(
self.backend, "dataset", None
)
self.backend_metadata["grayscale"] = getattr(
self.backend, "grayscale", None
)
self.backend_metadata["shape"] = getattr(self.backend, "shape", None)
self.backend_metadata["fps"] = getattr(self.backend, "fps", None)
except Exception:
pass
del self.backend
self.backend = None
def replace_filename(
self, new_filename: str | Path | list[str] | list[Path], open: bool = True
):
"""Update the filename of the video, optionally opening the backend.
Args:
new_filename: New filename to set for the video.
open: If `True` (the default), open the backend with the new filename. If
the new filename does not exist, no error is raised.
"""
if isinstance(new_filename, Path):
new_filename = new_filename.as_posix()
if isinstance(new_filename, list):
new_filename = [
p.as_posix() if isinstance(p, Path) else p for p in new_filename
]
self.filename = new_filename
self.backend_metadata["filename"] = new_filename
if open:
if self.exists():
self.open()
else:
self.close()
def matches_path(self, other: "Video", strict: bool = False) -> bool:
"""Check if this video has the same path as another video.
Args:
other: Another video to compare with.
strict: If True, require exact path match. If False, consider videos
with the same filename (basename) as matching.
Returns:
True if the videos have matching paths, False otherwise.
Notes:
For HDF5 video backends (e.g., embedded videos in .pkg.slp files),
matching prioritizes the source_filename attribute since multiple
videos can share the same HDF5 file path but reference different
source videos. Falls back to dataset name matching if source_filename
is not available.
"""
# Handle HDF5 backends specially - prioritize source_filename matching
self_is_hdf5 = isinstance(self.backend, HDF5Video)
other_is_hdf5 = isinstance(other.backend, HDF5Video)
if self_is_hdf5 and other_is_hdf5:
# Both are HDF5 videos - match by source_filename first
self_source = self.backend.source_filename
other_source = other.backend.source_filename
if self_source is not None and other_source is not None:
if strict:
return Path(self_source).resolve() == Path(other_source).resolve()
else:
return Path(self_source).name == Path(other_source).name
# Fall back to dataset name matching if source_filename is not available
self_dataset = self.backend.dataset
other_dataset = other.backend.dataset
if self_dataset is not None and other_dataset is not None:
return self_dataset == other_dataset
# If neither source_filename nor dataset available, cannot match
return False
if isinstance(self.filename, list) and isinstance(other.filename, list):
# Both are image sequences
if strict:
return self.filename == other.filename
else:
# Compare basenames
self_basenames = [Path(f).name for f in self.filename]
other_basenames = [Path(f).name for f in other.filename]
return self_basenames == other_basenames
elif isinstance(self.filename, list) or isinstance(other.filename, list):
# One is image sequence, other is single file
return False
else:
# Both are single files
if strict:
return Path(self.filename).resolve() == Path(other.filename).resolve()
else:
return Path(self.filename).name == Path(other.filename).name
def matches_content(self, other: "Video") -> bool:
"""Check if this video has the same content as another video.
Args:
other: Another video to compare with.
Returns:
True if the videos have the same shape and backend type.
Notes:
This compares metadata like shape and backend type, not actual frame data.
"""
# Compare shapes
self_shape = self.shape
other_shape = other.shape
if self_shape != other_shape:
return False
# Compare backend types
if self.backend is None and other.backend is None:
return True
elif self.backend is None or other.backend is None:
return False
return type(self.backend).__name__ == type(other.backend).__name__
def matches_shape(self, other: "Video") -> bool:
"""Check if this video has the same shape as another video.
Args:
other: Another video to compare with.
Returns:
True if the videos have the same height, width, and channels.
Notes:
This only compares spatial dimensions, not the number of frames.
"""
# Try to get shape from backend metadata first if shape is not available
if self.backend is None and "shape" in self.backend_metadata:
self_shape = self.backend_metadata["shape"]
else:
self_shape = self.shape
if other.backend is None and "shape" in other.backend_metadata:
other_shape = other.backend_metadata["shape"]
else:
other_shape = other.shape
# Handle None shapes
if self_shape is None or other_shape is None:
return False
# Compare only height, width, channels (not frames)
return self_shape[1:] == other_shape[1:]
def has_overlapping_images(self, other: "Video") -> bool:
"""Check if this video has overlapping images with another video.
This method is specifically for ImageVideo backends (image sequences).
Args:
other: Another video to compare with.
Returns:
True if both are ImageVideo instances with overlapping image files.
False if either video is not an ImageVideo or no overlap exists.
Notes:
Only works with ImageVideo backends where filename is a list.
Compares individual image filenames (basenames only).
"""
# Both must be image sequences
if not (isinstance(self.filename, list) and isinstance(other.filename, list)):
return False
# Get basenames for comparison
self_basenames = set(Path(f).name for f in self.filename)
other_basenames = set(Path(f).name for f in other.filename)
# Check if there's any overlap
return len(self_basenames & other_basenames) > 0
def deduplicate_with(self, other: "Video") -> "Video":
"""Create a new video with duplicate images removed.
This method is specifically for ImageVideo backends (image sequences).
Args:
other: Another video to deduplicate against. Must also be ImageVideo.
Returns:
A new Video object with duplicate images removed from this video,
or None if all images were duplicates.
Raises:
ValueError: If either video is not an ImageVideo backend.
Notes:
Only works with ImageVideo backends where filename is a list.
Images are considered duplicates if they have the same basename.
The returned video contains only images from this video that are
not present in the other video.
"""
if not isinstance(self.filename, list):
raise ValueError("deduplicate_with only works with ImageVideo backends")
if not isinstance(other.filename, list):
raise ValueError("Other video must also be ImageVideo backend")
# Get basenames from other video
other_basenames = set(Path(f).name for f in other.filename)
# Keep only non-duplicate images
deduplicated_paths = [
f for f in self.filename if Path(f).name not in other_basenames
]
if not deduplicated_paths:
# All images were duplicates
return None
# Create new video with deduplicated images
return Video.from_filename(deduplicated_paths, grayscale=self.grayscale)
def merge_with(self, other: "Video") -> "Video":
"""Merge another video's images into this one.
This method is specifically for ImageVideo backends (image sequences).
Args:
other: Another video to merge with. Must also be ImageVideo.
Returns:
A new Video object with unique images from both videos.
Raises:
ValueError: If either video is not an ImageVideo backend.
Notes:
Only works with ImageVideo backends where filename is a list.
The merged video contains all unique images from both videos,
with automatic deduplication based on image basename.
"""
if not isinstance(self.filename, list):
raise ValueError("merge_with only works with ImageVideo backends")
if not isinstance(other.filename, list):
raise ValueError("Other video must also be ImageVideo backend")
# Get all unique images (by basename) preserving order
seen_basenames = set()
merged_paths = []
for path in self.filename:
basename = Path(path).name
if basename not in seen_basenames:
merged_paths.append(path)
seen_basenames.add(basename)
for path in other.filename:
basename = Path(path).name
if basename not in seen_basenames:
merged_paths.append(path)
seen_basenames.add(basename)
# Create new video with merged images
return Video.from_filename(merged_paths, grayscale=self.grayscale)
def save(
self,
save_path: str | Path,
frame_inds: list[int] | np.ndarray | None = None,
fps: float | None = None,
video_kwargs: dict[str, Any] | None = None,
) -> "Video":
"""Save video frames to a new video file.
Args:
save_path: Path to the new video file. Should end in MP4.
frame_inds: Frame indices to save. Can be specified as a list or array of
frame integers. If not specified, saves all video frames.
fps: Frames per second for the output video. If not specified, uses the
source video's FPS if available, otherwise defaults to 30.
video_kwargs: A dictionary of keyword arguments to provide to
`sio.save_video` for video compression.
Returns:
A new `Video` object pointing to the new video file.
"""
video_kwargs = {} if video_kwargs is None else video_kwargs.copy()
frame_inds = np.arange(len(self)) if frame_inds is None else frame_inds
# Use source video FPS if not explicitly specified
if fps is None:
fps = self.fps
if fps is not None and "fps" not in video_kwargs:
video_kwargs["fps"] = fps
with VideoWriter(save_path, **video_kwargs) as vw:
for frame_ind in frame_inds:
vw(self[frame_ind])
new_video = Video.from_filename(save_path, grayscale=self.grayscale)
return new_video
def set_video_plugin(self, plugin: str) -> None:
"""Set the video plugin and reopen the video.
Args:
plugin: Video plugin to use. One of "opencv", "FFMPEG", or "pyav".
Also accepts aliases (case-insensitive).
Raises:
ValueError: If the video is not a MediaVideo type.
Examples:
>>> video.set_video_plugin("opencv")
>>> video.set_video_plugin("CV2") # Same as "opencv"
"""
from sleap_io.io.video_reading import MediaVideo, normalize_plugin_name
if not self.filename.endswith(MediaVideo.EXTS):
raise ValueError(f"Cannot set plugin for non-media video: {self.filename}")
plugin = normalize_plugin_name(plugin)
# Close current backend if open
was_open = self.is_open
if was_open:
self.close()
# Update backend metadata
self.backend_metadata["plugin"] = plugin
# Reopen with new plugin if it was open
if was_open:
self.open()
EXTS = ('mp4', 'avi', 'mov', 'mj2', 'mkv', 'h5', 'hdf5', 'slp', 'png', 'jpg', 'jpeg', 'tif', 'tiff', 'bmp')
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__annotations__ = {'filename': 'str | list[str]', 'backend': 'VideoBackend | None', 'backend_metadata': 'dict[str, any]', 'source_video': "'Video | None'", 'open_backend': 'bool'}
class-attribute
¶
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__attrs_own_setattr__ = False
class-attribute
¶
Returns True when the argument is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.
__attrs_props__ = ClassProps(is_exception=False, is_slotted=True, has_weakref_slot=True, is_frozen=False, kw_only=<KeywordOnly.NO: 'no'>, collected_fields_by_mro=True, added_init=True, added_repr=False, added_eq=False, added_ordering=False, hashability=<Hashability.LEAVE_ALONE: 'leave_alone'>, added_match_args=True, added_str=False, added_pickling=True, on_setattr_hook=<function pipe.<locals>.wrapped_pipe at 0x7f41e68fca40>, field_transformer=None)
class-attribute
¶
Effective class properties as derived from parameters to attr.s() or
define() decorators.
This is the same data structure that attrs uses internally to decide how to construct the final class.
Warning:
This feature is currently **experimental** and is not covered by our
strict backwards-compatibility guarantees.
Attributes:
| Name | Type | Description |
|---|---|---|
is_exception |
bool
|
Whether the class is treated as an exception class. |
is_slotted |
bool
|
Whether the class is |
has_weakref_slot |
bool
|
Whether the class has a slot for weak references. |
is_frozen |
bool
|
Whether the class is frozen. |
kw_only |
KeywordOnly
|
Whether / how the class enforces keyword-only arguments on the
|
collected_fields_by_mro |
bool
|
Whether the class fields were collected by method resolution order.
That is, correctly but unlike |
added_init |
bool
|
Whether the class has an attrs-generated |
added_repr |
bool
|
Whether the class has an attrs-generated |
added_eq |
bool
|
Whether the class has attrs-generated equality methods. |
added_ordering |
bool
|
Whether the class has attrs-generated ordering methods. |
hashability |
Hashability
|
How |
added_match_args |
bool
|
Whether the class supports positional |
added_str |
bool
|
Whether the class has an attrs-generated |
added_pickling |
bool
|
Whether the class has attrs-generated |
on_setattr_hook |
Callable[[Any, Attribute[Any], Any], Any] | None
|
The class's |
field_transformer |
Callable[[Attribute[Any]], Attribute[Any]] | None
|
The class's |
.. versionadded:: 25.4.0
__doc__ = '`Video` class used by sleap to represent videos and data associated with them.\n\nThis class is used to store information regarding a video and its components.\nIt is used to store the video\'s `filename`, `shape`, and the video\'s `backend`.\n\nTo create a `Video` object, use the `from_filename` method which will select the\nbackend appropriately.\n\nAttributes:\n filename: The filename(s) of the video. Supported extensions: "mp4", "avi",\n "mov", "mj2", "mkv", "h5", "hdf5", "slp", "png", "jpg", "jpeg", "tif",\n "tiff", "bmp". If the filename is a list, a list of image filenames are\n expected. If filename is a folder, it will be searched for images.\n backend: An object that implements the basic methods for reading and\n manipulating frames of a specific video type.\n backend_metadata: A dictionary of metadata specific to the backend. This is\n useful for storing metadata that requires an open backend (e.g., shape\n information) without having access to the video file itself.\n source_video: The source video object if this is a proxy video. This is present\n when the video contains an embedded subset of frames from another video.\n open_backend: Whether to open the backend when the video is available. If `True`\n (the default), the backend will be automatically opened if the video exists.\n Set this to `False` when you want to manually open the backend, or when the\n you know the video file does not exist and you want to avoid trying to open\n the file.\n\nNotes:\n Instances of this class are hashed by identity, not by value. This means that\n two `Video` instances with the same attributes will NOT be considered equal in a\n set or dict.\n\nMedia Video Plugin Support:\n For media files (mp4, avi, etc.), the following plugins are supported:\n - "opencv": Uses OpenCV (cv2) for video reading\n - "FFMPEG": Uses imageio-ffmpeg for video reading\n - "pyav": Uses PyAV for video reading\n\n Plugin aliases (case-insensitive):\n - opencv: "opencv", "cv", "cv2", "ocv"\n - FFMPEG: "FFMPEG", "ffmpeg", "imageio-ffmpeg", "imageio_ffmpeg"\n - pyav: "pyav", "av"\n\n Plugin selection priority:\n 1. Explicitly specified plugin parameter\n 2. Backend metadata plugin value\n 3. Global default (set via sio.set_default_video_plugin)\n 4. Auto-detection based on available packages\n\nSee Also:\n VideoBackend: The backend interface for reading video data.\n sleap_io.set_default_video_plugin: Set global default plugin.\n sleap_io.get_default_video_plugin: Get current default plugin.\n'
class-attribute
¶
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__firstlineno__ = 21
class-attribute
¶
int([x]) -> integer int(x, base=10) -> integer
Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating-point numbers, this truncates towards zero.
If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer literal.
int('0b100', base=0) 4
__match_args__ = ('filename', 'backend', 'backend_metadata', 'source_video', 'open_backend')
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__module__ = 'sleap_io.model.video'
class-attribute
¶
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__slots__ = ('filename', 'backend', 'backend_metadata', 'source_video', 'open_backend', '__weakref__')
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__static_attributes__ = ('backend', 'filename')
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__weakref__
property
¶
list of weak references to the object
fps
property
¶
Return the frames per second of the video.
For MediaVideo backends, this reads FPS from the video container metadata. For other backends (ImageVideo, HDF5Video, TiffVideo), this returns the explicitly set value or None if not set.
Returns:
| Type | Description |
|---|---|
|
The FPS if known, or None if unavailable/unknown. |
grayscale
property
¶
Return whether the video is grayscale.
If the video backend is not set or it cannot determine whether the video is grayscale, this will return None.
is_open
property
¶
Check if the video backend is open.
original_video
property
¶
The root video in the provenance chain.
For embedded videos, this returns the ultimate source video by traversing the source_video chain. Returns None if this video has no source_video (i.e., it IS an original).
This property is computed by following the source_video chain to find the root. For a single-level embedding (A embeds from B), original_video returns B. For multi-level embedding (A <- B <- C), it returns C.
shape
property
¶
Return the shape of the video as (num_frames, height, width, channels).
If the video backend is not set or it cannot determine the shape of the video, this will return None.
__attrs_post_init__()
¶
Post init syntactic sugar.
Source code in sleap_io/model/video.py
__deepcopy__(memo)
¶
Deep copy the video object.
Source code in sleap_io/model/video.py
def __deepcopy__(self, memo):
"""Deep copy the video object."""
if id(self) in memo:
return memo[id(self)]
reopen = False
if self.is_open:
reopen = True
self.close()
new_video = Video(
filename=self.filename,
backend=None,
backend_metadata=self.backend_metadata.copy(),
source_video=self.source_video,
open_backend=self.open_backend,
)
memo[id(self)] = new_video
if reopen:
self.open()
return new_video
__getitem__(inds)
¶
Return the frames of the video at the given indices.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
inds
|
int | list[int] | slice
|
Index or list of indices of frames to read. |
required |
Returns:
| Type | Description |
|---|---|
ndarray
|
Frame or frames as a numpy array of shape |
See also: VideoBackend.get_frame, VideoBackend.get_frames
Source code in sleap_io/model/video.py
def __getitem__(self, inds: int | list[int] | slice) -> np.ndarray:
"""Return the frames of the video at the given indices.
Args:
inds: Index or list of indices of frames to read.
Returns:
Frame or frames as a numpy array of shape `(height, width, channels)` if a
scalar index is provided, or `(frames, height, width, channels)` if a list
of indices is provided.
See also: VideoBackend.get_frame, VideoBackend.get_frames
"""
if not self.is_open:
if self.open_backend:
self.open()
else:
raise ValueError(
"Video backend is not open. Call video.open() or set "
"video.open_backend to True to do automatically on frame read."
)
return self.backend[inds]
__init__(filename, backend=None, backend_metadata=NOTHING, source_video=None, open_backend=True)
¶
Method generated by attrs for class Video.
__len__()
¶
__replace__(**changes)
¶
Method generated by attrs for class Video.
__repr__()
¶
Informal string representation (for print or format).
Source code in sleap_io/model/video.py
def __repr__(self) -> str:
"""Informal string representation (for print or format)."""
dataset = (
f"dataset={self.backend.dataset}, "
if getattr(self.backend, "dataset", "")
else ""
)
return (
"Video("
f'filename="{self.filename}", '
f"shape={self.shape}, "
f"{dataset}"
f"backend={type(self.backend).__name__}"
")"
)
__str__()
¶
close()
¶
Close the video backend.
Source code in sleap_io/model/video.py
def close(self):
"""Close the video backend."""
if self.backend is not None:
# Try to remember values from previous backend if available and not
# specified.
try:
self.backend_metadata["dataset"] = getattr(
self.backend, "dataset", None
)
self.backend_metadata["grayscale"] = getattr(
self.backend, "grayscale", None
)
self.backend_metadata["shape"] = getattr(self.backend, "shape", None)
self.backend_metadata["fps"] = getattr(self.backend, "fps", None)
except Exception:
pass
del self.backend
self.backend = None
deduplicate_with(other)
¶
Create a new video with duplicate images removed.
This method is specifically for ImageVideo backends (image sequences).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
other
|
Video
|
Another video to deduplicate against. Must also be ImageVideo. |
required |
Returns:
| Type | Description |
|---|---|
Video
|
A new Video object with duplicate images removed from this video, or None if all images were duplicates. |
Raises:
| Type | Description |
|---|---|
ValueError
|
If either video is not an ImageVideo backend. |
Notes
Only works with ImageVideo backends where filename is a list. Images are considered duplicates if they have the same basename. The returned video contains only images from this video that are not present in the other video.
Source code in sleap_io/model/video.py
def deduplicate_with(self, other: "Video") -> "Video":
"""Create a new video with duplicate images removed.
This method is specifically for ImageVideo backends (image sequences).
Args:
other: Another video to deduplicate against. Must also be ImageVideo.
Returns:
A new Video object with duplicate images removed from this video,
or None if all images were duplicates.
Raises:
ValueError: If either video is not an ImageVideo backend.
Notes:
Only works with ImageVideo backends where filename is a list.
Images are considered duplicates if they have the same basename.
The returned video contains only images from this video that are
not present in the other video.
"""
if not isinstance(self.filename, list):
raise ValueError("deduplicate_with only works with ImageVideo backends")
if not isinstance(other.filename, list):
raise ValueError("Other video must also be ImageVideo backend")
# Get basenames from other video
other_basenames = set(Path(f).name for f in other.filename)
# Keep only non-duplicate images
deduplicated_paths = [
f for f in self.filename if Path(f).name not in other_basenames
]
if not deduplicated_paths:
# All images were duplicates
return None
# Create new video with deduplicated images
return Video.from_filename(deduplicated_paths, grayscale=self.grayscale)
exists(check_all=False, dataset=None)
¶
Check if the video file exists and is accessible.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
check_all
|
bool
|
If |
False
|
dataset
|
str | None
|
Name of dataset in HDF5 file. If specified, this will function will
return |
None
|
Returns:
| Type | Description |
|---|---|
bool
|
|
Source code in sleap_io/model/video.py
def exists(self, check_all: bool = False, dataset: str | None = None) -> bool:
"""Check if the video file exists and is accessible.
Args:
check_all: If `True`, check that all filenames in a list exist. If `False`
(the default), check that the first filename exists.
dataset: Name of dataset in HDF5 file. If specified, this will function will
return `False` if the dataset does not exist.
Returns:
`True` if the file exists and is accessible, `False` otherwise.
"""
if isinstance(self.filename, list):
if check_all:
for f in self.filename:
if not is_file_accessible(f):
return False
return True
else:
return is_file_accessible(self.filename[0])
file_is_accessible = is_file_accessible(self.filename)
if not file_is_accessible:
return False
if dataset is None or dataset == "":
dataset = self.backend_metadata.get("dataset", None)
if dataset is not None and dataset != "":
has_dataset = False
if (
self.backend is not None
and type(self.backend) is HDF5Video
and self.backend._open_reader is not None
):
has_dataset = dataset in self.backend._open_reader
else:
with h5py.File(self.filename, "r") as f:
has_dataset = dataset in f
return has_dataset
return True
frame_to_seconds(frame_idx)
¶
Convert a frame index to timestamp in seconds.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
frame_idx
|
int
|
Zero-indexed frame number. |
required |
Returns:
| Type | Description |
|---|---|
float | None
|
Time in seconds, or None if FPS is unknown. |
Notes
This assumes constant frame rate. For variable frame rate videos, the returned timestamp may be approximate.
Source code in sleap_io/model/video.py
def frame_to_seconds(self, frame_idx: int) -> float | None:
"""Convert a frame index to timestamp in seconds.
Args:
frame_idx: Zero-indexed frame number.
Returns:
Time in seconds, or None if FPS is unknown.
Notes:
This assumes constant frame rate. For variable frame rate videos,
the returned timestamp may be approximate.
"""
if self.fps is None or self.fps <= 0:
return None
return frame_idx / self.fps
from_filename(filename, dataset=None, grayscale=None, keep_open=True, source_video=None, **kwargs)
classmethod
¶
Create a Video from a filename.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename
|
str | list[str]
|
The filename(s) of the video. Supported extensions: "mp4", "avi", "mov", "mj2", "mkv", "h5", "hdf5", "slp", "png", "jpg", "jpeg", "tif", "tiff", "bmp". If the filename is a list, a list of image filenames are expected. If filename is a folder, it will be searched for images. |
required |
dataset
|
str | None
|
Name of dataset in HDF5 file. |
None
|
grayscale
|
bool | None
|
Whether to force grayscale. If None, autodetect on first frame load. |
None
|
keep_open
|
bool
|
Whether to keep the video reader open between calls to read frames. If False, will close the reader after each call. If True (the default), it will keep the reader open and cache it for subsequent calls which may enhance the performance of reading multiple frames. |
True
|
source_video
|
Video | None
|
The source video object if this is a proxy video. This is present when the video contains an embedded subset of frames from another video. |
None
|
**kwargs
|
Additional backend-specific arguments passed to VideoBackend.from_filename. See VideoBackend.from_filename for supported arguments. |
required |
Returns:
| Type | Description |
|---|---|
VideoBackend
|
Video instance with the appropriate backend instantiated. |
Source code in sleap_io/model/video.py
@classmethod
def from_filename(
cls,
filename: str | list[str],
dataset: str | None = None,
grayscale: bool | None = None,
keep_open: bool = True,
source_video: "Video | None" = None,
**kwargs,
) -> VideoBackend:
"""Create a Video from a filename.
Args:
filename: The filename(s) of the video. Supported extensions: "mp4", "avi",
"mov", "mj2", "mkv", "h5", "hdf5", "slp", "png", "jpg", "jpeg", "tif",
"tiff", "bmp". If the filename is a list, a list of image filenames are
expected. If filename is a folder, it will be searched for images.
dataset: Name of dataset in HDF5 file.
grayscale: Whether to force grayscale. If None, autodetect on first frame
load.
keep_open: Whether to keep the video reader open between calls to read
frames. If False, will close the reader after each call. If True (the
default), it will keep the reader open and cache it for subsequent calls
which may enhance the performance of reading multiple frames.
source_video: The source video object if this is a proxy video. This is
present when the video contains an embedded subset of frames from
another video.
**kwargs: Additional backend-specific arguments passed to
VideoBackend.from_filename. See VideoBackend.from_filename for supported
arguments.
Returns:
Video instance with the appropriate backend instantiated.
"""
backend = VideoBackend.from_filename(
filename,
dataset=dataset,
grayscale=grayscale,
keep_open=keep_open,
**kwargs,
)
# If filename is a directory, VideoBackend.from_filename will expand it
# to a list of paths to images contained within the directory. In this
# case we want to use the expanded list as filename
return cls(
filename=backend.filename,
backend=backend,
source_video=source_video,
)
has_overlapping_images(other)
¶
Check if this video has overlapping images with another video.
This method is specifically for ImageVideo backends (image sequences).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
other
|
Video
|
Another video to compare with. |
required |
Returns:
| Type | Description |
|---|---|
bool
|
True if both are ImageVideo instances with overlapping image files. False if either video is not an ImageVideo or no overlap exists. |
Notes
Only works with ImageVideo backends where filename is a list. Compares individual image filenames (basenames only).
Source code in sleap_io/model/video.py
def has_overlapping_images(self, other: "Video") -> bool:
"""Check if this video has overlapping images with another video.
This method is specifically for ImageVideo backends (image sequences).
Args:
other: Another video to compare with.
Returns:
True if both are ImageVideo instances with overlapping image files.
False if either video is not an ImageVideo or no overlap exists.
Notes:
Only works with ImageVideo backends where filename is a list.
Compares individual image filenames (basenames only).
"""
# Both must be image sequences
if not (isinstance(self.filename, list) and isinstance(other.filename, list)):
return False
# Get basenames for comparison
self_basenames = set(Path(f).name for f in self.filename)
other_basenames = set(Path(f).name for f in other.filename)
# Check if there's any overlap
return len(self_basenames & other_basenames) > 0
matches_content(other)
¶
Check if this video has the same content as another video.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
other
|
Video
|
Another video to compare with. |
required |
Returns:
| Type | Description |
|---|---|
bool
|
True if the videos have the same shape and backend type. |
Notes
This compares metadata like shape and backend type, not actual frame data.
Source code in sleap_io/model/video.py
def matches_content(self, other: "Video") -> bool:
"""Check if this video has the same content as another video.
Args:
other: Another video to compare with.
Returns:
True if the videos have the same shape and backend type.
Notes:
This compares metadata like shape and backend type, not actual frame data.
"""
# Compare shapes
self_shape = self.shape
other_shape = other.shape
if self_shape != other_shape:
return False
# Compare backend types
if self.backend is None and other.backend is None:
return True
elif self.backend is None or other.backend is None:
return False
return type(self.backend).__name__ == type(other.backend).__name__
matches_path(other, strict=False)
¶
Check if this video has the same path as another video.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
other
|
Video
|
Another video to compare with. |
required |
strict
|
bool
|
If True, require exact path match. If False, consider videos with the same filename (basename) as matching. |
False
|
Returns:
| Type | Description |
|---|---|
bool
|
True if the videos have matching paths, False otherwise. |
Notes
For HDF5 video backends (e.g., embedded videos in .pkg.slp files), matching prioritizes the source_filename attribute since multiple videos can share the same HDF5 file path but reference different source videos. Falls back to dataset name matching if source_filename is not available.
Source code in sleap_io/model/video.py
def matches_path(self, other: "Video", strict: bool = False) -> bool:
"""Check if this video has the same path as another video.
Args:
other: Another video to compare with.
strict: If True, require exact path match. If False, consider videos
with the same filename (basename) as matching.
Returns:
True if the videos have matching paths, False otherwise.
Notes:
For HDF5 video backends (e.g., embedded videos in .pkg.slp files),
matching prioritizes the source_filename attribute since multiple
videos can share the same HDF5 file path but reference different
source videos. Falls back to dataset name matching if source_filename
is not available.
"""
# Handle HDF5 backends specially - prioritize source_filename matching
self_is_hdf5 = isinstance(self.backend, HDF5Video)
other_is_hdf5 = isinstance(other.backend, HDF5Video)
if self_is_hdf5 and other_is_hdf5:
# Both are HDF5 videos - match by source_filename first
self_source = self.backend.source_filename
other_source = other.backend.source_filename
if self_source is not None and other_source is not None:
if strict:
return Path(self_source).resolve() == Path(other_source).resolve()
else:
return Path(self_source).name == Path(other_source).name
# Fall back to dataset name matching if source_filename is not available
self_dataset = self.backend.dataset
other_dataset = other.backend.dataset
if self_dataset is not None and other_dataset is not None:
return self_dataset == other_dataset
# If neither source_filename nor dataset available, cannot match
return False
if isinstance(self.filename, list) and isinstance(other.filename, list):
# Both are image sequences
if strict:
return self.filename == other.filename
else:
# Compare basenames
self_basenames = [Path(f).name for f in self.filename]
other_basenames = [Path(f).name for f in other.filename]
return self_basenames == other_basenames
elif isinstance(self.filename, list) or isinstance(other.filename, list):
# One is image sequence, other is single file
return False
else:
# Both are single files
if strict:
return Path(self.filename).resolve() == Path(other.filename).resolve()
else:
return Path(self.filename).name == Path(other.filename).name
matches_shape(other)
¶
Check if this video has the same shape as another video.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
other
|
Video
|
Another video to compare with. |
required |
Returns:
| Type | Description |
|---|---|
bool
|
True if the videos have the same height, width, and channels. |
Notes
This only compares spatial dimensions, not the number of frames.
Source code in sleap_io/model/video.py
def matches_shape(self, other: "Video") -> bool:
"""Check if this video has the same shape as another video.
Args:
other: Another video to compare with.
Returns:
True if the videos have the same height, width, and channels.
Notes:
This only compares spatial dimensions, not the number of frames.
"""
# Try to get shape from backend metadata first if shape is not available
if self.backend is None and "shape" in self.backend_metadata:
self_shape = self.backend_metadata["shape"]
else:
self_shape = self.shape
if other.backend is None and "shape" in other.backend_metadata:
other_shape = other.backend_metadata["shape"]
else:
other_shape = other.shape
# Handle None shapes
if self_shape is None or other_shape is None:
return False
# Compare only height, width, channels (not frames)
return self_shape[1:] == other_shape[1:]
merge_with(other)
¶
Merge another video's images into this one.
This method is specifically for ImageVideo backends (image sequences).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
other
|
Video
|
Another video to merge with. Must also be ImageVideo. |
required |
Returns:
| Type | Description |
|---|---|
Video
|
A new Video object with unique images from both videos. |
Raises:
| Type | Description |
|---|---|
ValueError
|
If either video is not an ImageVideo backend. |
Notes
Only works with ImageVideo backends where filename is a list. The merged video contains all unique images from both videos, with automatic deduplication based on image basename.
Source code in sleap_io/model/video.py
def merge_with(self, other: "Video") -> "Video":
"""Merge another video's images into this one.
This method is specifically for ImageVideo backends (image sequences).
Args:
other: Another video to merge with. Must also be ImageVideo.
Returns:
A new Video object with unique images from both videos.
Raises:
ValueError: If either video is not an ImageVideo backend.
Notes:
Only works with ImageVideo backends where filename is a list.
The merged video contains all unique images from both videos,
with automatic deduplication based on image basename.
"""
if not isinstance(self.filename, list):
raise ValueError("merge_with only works with ImageVideo backends")
if not isinstance(other.filename, list):
raise ValueError("Other video must also be ImageVideo backend")
# Get all unique images (by basename) preserving order
seen_basenames = set()
merged_paths = []
for path in self.filename:
basename = Path(path).name
if basename not in seen_basenames:
merged_paths.append(path)
seen_basenames.add(basename)
for path in other.filename:
basename = Path(path).name
if basename not in seen_basenames:
merged_paths.append(path)
seen_basenames.add(basename)
# Create new video with merged images
return Video.from_filename(merged_paths, grayscale=self.grayscale)
open(filename=None, dataset=None, grayscale=None, keep_open=True, plugin=None)
¶
Open the video backend for reading.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename
|
str | None
|
Filename to open. If not specified, will use the filename set on the video object. |
None
|
dataset
|
str | None
|
Name of dataset in HDF5 file. |
None
|
grayscale
|
str | None
|
Whether to force grayscale. If None, autodetect on first frame load. |
None
|
keep_open
|
bool
|
Whether to keep the video reader open between calls to read frames. If False, will close the reader after each call. If True (the default), it will keep the reader open and cache it for subsequent calls which may enhance the performance of reading multiple frames. |
True
|
plugin
|
str | None
|
Video plugin to use for MediaVideo files. One of "opencv", "FFMPEG", or "pyav". Also accepts aliases (case-insensitive). If not specified, uses the backend metadata, global default, or auto-detection in that order. |
None
|
Notes
This is useful for opening the video backend to read frames and then closing it after reading all the necessary frames.
If the backend was already open, it will be closed before opening a new one. Values for the HDF5 dataset and grayscale will be remembered if not specified.
Source code in sleap_io/model/video.py
def open(
self,
filename: str | None = None,
dataset: str | None = None,
grayscale: str | None = None,
keep_open: bool = True,
plugin: str | None = None,
):
"""Open the video backend for reading.
Args:
filename: Filename to open. If not specified, will use the filename set on
the video object.
dataset: Name of dataset in HDF5 file.
grayscale: Whether to force grayscale. If None, autodetect on first frame
load.
keep_open: Whether to keep the video reader open between calls to read
frames. If False, will close the reader after each call. If True (the
default), it will keep the reader open and cache it for subsequent calls
which may enhance the performance of reading multiple frames.
plugin: Video plugin to use for MediaVideo files. One of "opencv",
"FFMPEG", or "pyav". Also accepts aliases (case-insensitive).
If not specified, uses the backend metadata, global default,
or auto-detection in that order.
Notes:
This is useful for opening the video backend to read frames and then closing
it after reading all the necessary frames.
If the backend was already open, it will be closed before opening a new one.
Values for the HDF5 dataset and grayscale will be remembered if not
specified.
"""
if filename is not None:
self.replace_filename(filename, open=False)
# Try to remember values from previous backend if available and not specified.
if self.backend is not None:
if dataset is None:
dataset = getattr(self.backend, "dataset", None)
if grayscale is None:
grayscale = getattr(self.backend, "grayscale", None)
else:
if dataset is None and "dataset" in self.backend_metadata:
dataset = self.backend_metadata["dataset"]
if grayscale is None:
if "grayscale" in self.backend_metadata:
grayscale = self.backend_metadata["grayscale"]
elif "shape" in self.backend_metadata:
grayscale = self.backend_metadata["shape"][-1] == 1
if not self.exists(dataset=dataset):
msg = (
f"Video does not exist or cannot be opened for reading: {self.filename}"
)
if dataset is not None:
msg += f" (dataset: {dataset})"
raise FileNotFoundError(msg)
# Close previous backend if open.
self.close()
# Handle plugin parameter
backend_kwargs = {}
if plugin is not None:
from sleap_io.io.video_reading import normalize_plugin_name
plugin = normalize_plugin_name(plugin)
self.backend_metadata["plugin"] = plugin
if "plugin" in self.backend_metadata:
backend_kwargs["plugin"] = self.backend_metadata["plugin"]
# Create new backend.
self.backend = VideoBackend.from_filename(
self.filename,
dataset=dataset,
grayscale=grayscale,
keep_open=keep_open,
**backend_kwargs,
)
replace_filename(new_filename, open=True)
¶
Update the filename of the video, optionally opening the backend.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
new_filename
|
str | Path | list[str] | list[Path]
|
New filename to set for the video. |
required |
open
|
bool
|
If |
True
|
Source code in sleap_io/model/video.py
def replace_filename(
self, new_filename: str | Path | list[str] | list[Path], open: bool = True
):
"""Update the filename of the video, optionally opening the backend.
Args:
new_filename: New filename to set for the video.
open: If `True` (the default), open the backend with the new filename. If
the new filename does not exist, no error is raised.
"""
if isinstance(new_filename, Path):
new_filename = new_filename.as_posix()
if isinstance(new_filename, list):
new_filename = [
p.as_posix() if isinstance(p, Path) else p for p in new_filename
]
self.filename = new_filename
self.backend_metadata["filename"] = new_filename
if open:
if self.exists():
self.open()
else:
self.close()
save(save_path, frame_inds=None, fps=None, video_kwargs=None)
¶
Save video frames to a new video file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
save_path
|
str | Path
|
Path to the new video file. Should end in MP4. |
required |
frame_inds
|
list[int] | ndarray | None
|
Frame indices to save. Can be specified as a list or array of frame integers. If not specified, saves all video frames. |
None
|
fps
|
float | None
|
Frames per second for the output video. If not specified, uses the source video's FPS if available, otherwise defaults to 30. |
None
|
video_kwargs
|
dict[str, Any] | None
|
A dictionary of keyword arguments to provide to
|
None
|
Returns:
| Type | Description |
|---|---|
Video
|
A new |
Source code in sleap_io/model/video.py
def save(
self,
save_path: str | Path,
frame_inds: list[int] | np.ndarray | None = None,
fps: float | None = None,
video_kwargs: dict[str, Any] | None = None,
) -> "Video":
"""Save video frames to a new video file.
Args:
save_path: Path to the new video file. Should end in MP4.
frame_inds: Frame indices to save. Can be specified as a list or array of
frame integers. If not specified, saves all video frames.
fps: Frames per second for the output video. If not specified, uses the
source video's FPS if available, otherwise defaults to 30.
video_kwargs: A dictionary of keyword arguments to provide to
`sio.save_video` for video compression.
Returns:
A new `Video` object pointing to the new video file.
"""
video_kwargs = {} if video_kwargs is None else video_kwargs.copy()
frame_inds = np.arange(len(self)) if frame_inds is None else frame_inds
# Use source video FPS if not explicitly specified
if fps is None:
fps = self.fps
if fps is not None and "fps" not in video_kwargs:
video_kwargs["fps"] = fps
with VideoWriter(save_path, **video_kwargs) as vw:
for frame_ind in frame_inds:
vw(self[frame_ind])
new_video = Video.from_filename(save_path, grayscale=self.grayscale)
return new_video
seconds_to_frame(seconds)
¶
Convert a timestamp in seconds to frame index.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
seconds
|
float
|
Time in seconds from video start. |
required |
Returns:
| Type | Description |
|---|---|
int | None
|
Zero-indexed frame number (rounded down), or None if FPS unknown. |
Source code in sleap_io/model/video.py
def seconds_to_frame(self, seconds: float) -> int | None:
"""Convert a timestamp in seconds to frame index.
Args:
seconds: Time in seconds from video start.
Returns:
Zero-indexed frame number (rounded down), or None if FPS unknown.
"""
if self.fps is None or self.fps <= 0:
return None
return int(seconds * self.fps)
set_video_plugin(plugin)
¶
Set the video plugin and reopen the video.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
plugin
|
str
|
Video plugin to use. One of "opencv", "FFMPEG", or "pyav". Also accepts aliases (case-insensitive). |
required |
Raises:
| Type | Description |
|---|---|
ValueError
|
If the video is not a MediaVideo type. |
Examples:
Source code in sleap_io/model/video.py
def set_video_plugin(self, plugin: str) -> None:
"""Set the video plugin and reopen the video.
Args:
plugin: Video plugin to use. One of "opencv", "FFMPEG", or "pyav".
Also accepts aliases (case-insensitive).
Raises:
ValueError: If the video is not a MediaVideo type.
Examples:
>>> video.set_video_plugin("opencv")
>>> video.set_video_plugin("CV2") # Same as "opencv"
"""
from sleap_io.io.video_reading import MediaVideo, normalize_plugin_name
if not self.filename.endswith(MediaVideo.EXTS):
raise ValueError(f"Cannot set plugin for non-media video: {self.filename}")
plugin = normalize_plugin_name(plugin)
# Close current backend if open
was_open = self.is_open
if was_open:
self.close()
# Update backend metadata
self.backend_metadata["plugin"] = plugin
# Reopen with new plugin if it was open
if was_open:
self.open()
VideoBackend
¶
Base class for video backends.
This class is not meant to be used directly. Instead, use the from_filename
constructor to create a backend instance.
Attributes:
| Name | Type | Description |
|---|---|---|
filename |
Path to video file(s). |
|
grayscale |
Whether to force grayscale. If None, autodetect on first frame load. |
|
keep_open |
Whether to keep the video reader open between calls to read frames. If False, will close the reader after each call. If True (the default), it will keep the reader open and cache it for subsequent calls which may enhance the performance of reading multiple frames. |
|
fps |
Frames per second of the video. For MediaVideo, this is read from container metadata. For other backends (ImageVideo, HDF5Video, TiffVideo), this must be set explicitly or will be None. |
Methods:
| Name | Description |
|---|---|
__eq__ |
Method generated by attrs for class VideoBackend. |
__getitem__ |
Return a single frame or a list of frames from the video. |
__init__ |
Method generated by attrs for class VideoBackend. |
__len__ |
Return number of frames in the video. |
__replace__ |
Method generated by attrs for class VideoBackend. |
__repr__ |
Method generated by attrs for class VideoBackend. |
detect_grayscale |
Detect whether the video is grayscale. |
from_filename |
Create a VideoBackend from a filename. |
get_frame |
Read a single frame from the video. |
get_frames |
Read a list of frames from the video. |
has_frame |
Check if a frame index is contained in the video. |
read_test_frame |
Read a single frame from the video to test for grayscale. |
Source code in sleap_io/io/video_reading.py
@attrs.define
class VideoBackend:
"""Base class for video backends.
This class is not meant to be used directly. Instead, use the `from_filename`
constructor to create a backend instance.
Attributes:
filename: Path to video file(s).
grayscale: Whether to force grayscale. If None, autodetect on first frame load.
keep_open: Whether to keep the video reader open between calls to read frames.
If False, will close the reader after each call. If True (the default), it
will keep the reader open and cache it for subsequent calls which may
enhance the performance of reading multiple frames.
fps: Frames per second of the video. For MediaVideo, this is read from container
metadata. For other backends (ImageVideo, HDF5Video, TiffVideo), this must
be set explicitly or will be None.
"""
filename: str | Path | list[str] | list[Path]
grayscale: bool | None = None
keep_open: bool = True
_cached_shape: tuple[int, int, int, int] | None = None
_open_reader: object | None = None
_fps: float | None = None
@property
def fps(self) -> float | None:
"""Frames per second of the video.
Returns:
The FPS if known, or None if unavailable/unknown.
Notes:
For MediaVideo, this is read from container metadata.
For ImageVideo, HDF5Video, and TiffVideo, this must be set explicitly
or inherited from source_video.
"""
return self._fps
@fps.setter
def fps(self, value: float | None) -> None:
"""Set the FPS.
Args:
value: Frames per second. Must be positive if not None.
Raises:
ValueError: If value is not positive.
"""
if value is not None and value <= 0:
raise ValueError(f"FPS must be positive, got {value}")
self._fps = value
@classmethod
def from_filename(
cls,
filename: str | list[str],
dataset: str | None = None,
grayscale: bool | None = None,
keep_open: bool = True,
**kwargs,
) -> "VideoBackend":
"""Create a VideoBackend from a filename.
Args:
filename: Path to video file(s).
dataset: Name of dataset in HDF5 file.
grayscale: Whether to force grayscale. If None, autodetect on first frame
load.
keep_open: Whether to keep the video reader open between calls to read
frames. If False, will close the reader after each call. If True (the
default), it will keep the reader open and cache it for subsequent calls
which may enhance the performance of reading multiple frames.
**kwargs: Additional backend-specific arguments. These are filtered to only
include parameters that are valid for the specific backend being
created:
- For ImageVideo: plugin (str): Image plugin to use. One of "opencv"
or "imageio". Also accepts aliases (case-insensitive).
If None, uses global default if set, otherwise auto-detects.
- For MediaVideo: plugin (str): Video plugin to use. One of "opencv",
"FFMPEG", or "pyav". Also accepts aliases (case-insensitive).
If None, uses global default if set, otherwise auto-detects.
- For HDF5Video: input_format (str), frame_map (dict),
source_filename (str),
source_inds (np.ndarray), image_format (str). See HDF5Video for
details.
Returns:
VideoBackend subclass instance.
"""
if isinstance(filename, Path):
filename = filename.as_posix()
if type(filename) is str and Path(filename).is_dir():
filename = ImageVideo.find_images(filename)
if type(filename) is list:
filename = [Path(f).as_posix() for f in filename]
return ImageVideo(
filename, grayscale=grayscale, **_get_valid_kwargs(ImageVideo, kwargs)
)
elif filename.lower().endswith(("tif", "tiff")):
# Detect TIFF format
format_type, metadata = TiffVideo.detect_format(filename)
if format_type in ("multi_page", "rank3_video", "rank4_video"):
# Use TiffVideo for multi-page or multi-dimensional TIFFs
tiff_kwargs = _get_valid_kwargs(TiffVideo, kwargs)
# Add format if detected
if format_type in ("rank3_video", "rank4_video"):
tiff_kwargs["format"] = metadata.get("format")
return TiffVideo(
filename,
grayscale=grayscale,
keep_open=keep_open,
**tiff_kwargs,
)
else:
# Single-page TIFF, treat as regular image
return ImageVideo(
[filename],
grayscale=grayscale,
**_get_valid_kwargs(ImageVideo, kwargs),
)
elif filename.lower().endswith(tuple(ext.lower() for ext in ImageVideo.EXTS)):
return ImageVideo(
[filename], grayscale=grayscale, **_get_valid_kwargs(ImageVideo, kwargs)
)
elif filename.lower().endswith(tuple(ext.lower() for ext in MediaVideo.EXTS)):
return MediaVideo(
filename,
grayscale=grayscale,
keep_open=keep_open,
**_get_valid_kwargs(MediaVideo, kwargs),
)
elif filename.lower().endswith(tuple(ext.lower() for ext in HDF5Video.EXTS)):
return HDF5Video(
filename,
dataset=dataset,
grayscale=grayscale,
keep_open=keep_open,
**_get_valid_kwargs(HDF5Video, kwargs),
)
else:
raise ValueError(f"Unknown video file type: {filename}")
def _read_frame(self, frame_idx: int) -> np.ndarray:
"""Read a single frame from the video. Must be implemented in subclasses."""
raise NotImplementedError
def _read_frames(self, frame_inds: list) -> np.ndarray:
"""Read a list of frames from the video."""
return np.stack([self.get_frame(i) for i in frame_inds], axis=0)
def read_test_frame(self) -> np.ndarray:
"""Read a single frame from the video to test for grayscale.
Note:
This reads the frame at index 0. This may not be appropriate if the first
frame is not available in a given backend.
"""
return self._read_frame(0)
def detect_grayscale(self, test_img: np.ndarray | None = None) -> bool:
"""Detect whether the video is grayscale.
This works by reading in a test frame and comparing the first and last channel
for equality. It may fail in cases where, due to compression, the first and
last channels are not exactly the same.
Args:
test_img: Optional test image to use. If not provided, a test image will be
loaded via the `read_test_frame` method.
Returns:
Whether the video is grayscale. This value is also cached in the `grayscale`
attribute of the class.
"""
if test_img is None:
test_img = self.read_test_frame()
is_grayscale = np.array_equal(test_img[..., 0], test_img[..., -1])
self.grayscale = is_grayscale
return is_grayscale
@property
def num_frames(self) -> int:
"""Number of frames in the video. Must be implemented in subclasses."""
raise NotImplementedError
@property
def img_shape(self) -> tuple[int, int, int]:
"""Shape of a single frame in the video."""
height, width, channels = self.read_test_frame().shape
if self.grayscale is None:
self.detect_grayscale()
if self.grayscale is False:
channels = 3
elif self.grayscale is True:
channels = 1
return int(height), int(width), int(channels)
@property
def shape(self) -> tuple[int, int, int, int]:
"""Shape of the video as a tuple of `(frames, height, width, channels)`.
On first call, this will defer to `num_frames` and `img_shape` to determine the
full shape. This call may be expensive for some subclasses, so the result is
cached and returned on subsequent calls.
"""
if self._cached_shape is not None:
return self._cached_shape
else:
shape = (self.num_frames,) + self.img_shape
self._cached_shape = shape
return shape
@property
def frames(self) -> int:
"""Number of frames in the video."""
return self.shape[0]
def __len__(self) -> int:
"""Return number of frames in the video."""
return self.shape[0]
def has_frame(self, frame_idx: int) -> bool:
"""Check if a frame index is contained in the video.
Args:
frame_idx: Index of frame to check.
Returns:
`True` if the index is contained in the video, otherwise `False`.
"""
return frame_idx < len(self)
def get_frame(self, frame_idx: int) -> np.ndarray:
"""Read a single frame from the video.
Args:
frame_idx: Index of frame to read.
Returns:
Frame as a numpy array of shape `(height, width, channels)` where the
`channels` dimension is 1 for grayscale videos and 3 for color videos.
Notes:
If the `grayscale` attribute is set to `True`, the `channels` dimension will
be reduced to 1 if an RGB frame is loaded from the backend.
If the `grayscale` attribute is set to `None`, the `grayscale` attribute
will be automatically set based on the first frame read.
See also: `get_frames`
"""
if not self.has_frame(frame_idx):
raise IndexError(f"Frame index {frame_idx} out of range.")
img = self._read_frame(frame_idx)
if self.grayscale is None:
self.detect_grayscale(img)
if self.grayscale:
img = img[..., [0]]
return img
def get_frames(self, frame_inds: list[int]) -> np.ndarray:
"""Read a list of frames from the video.
Depending on the backend implementation, this may be faster than reading frames
individually using `get_frame`.
Args:
frame_inds: List of frame indices to read.
Returns:
Frames as a numpy array of shape `(frames, height, width, channels)` where
`channels` dimension is 1 for grayscale videos and 3 for color videos.
Notes:
If the `grayscale` attribute is set to `True`, the `channels` dimension will
be reduced to 1 if an RGB frame is loaded from the backend.
If the `grayscale` attribute is set to `None`, the `grayscale` attribute
will be automatically set based on the first frame read.
See also: `get_frame`
"""
imgs = self._read_frames(frame_inds)
if self.grayscale is None:
self.detect_grayscale(imgs[0])
if self.grayscale:
imgs = imgs[..., [0]]
return imgs
def __getitem__(self, ind: int | list[int] | slice) -> np.ndarray:
"""Return a single frame or a list of frames from the video.
Args:
ind: Index or list of indices of frames to read.
Returns:
Frame or frames as a numpy array of shape `(height, width, channels)` if a
scalar index is provided, or `(frames, height, width, channels)` if a list
of indices is provided.
See also: get_frame, get_frames
"""
if np.isscalar(ind):
return self.get_frame(ind)
else:
if type(ind) is slice:
start = (ind.start or 0) % len(self)
stop = ind.stop or len(self)
if stop < 0:
stop = len(self) + stop
step = ind.step or 1
ind = range(start, stop, step)
return self.get_frames(ind)
__annotations__ = {'filename': 'str | Path | list[str] | list[Path]', 'grayscale': 'bool | None', 'keep_open': 'bool', '_cached_shape': 'tuple[int, int, int, int] | None', '_open_reader': 'object | None', '_fps': 'float | None'}
class-attribute
¶
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__attrs_own_setattr__ = False
class-attribute
¶
Returns True when the argument is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.
__attrs_props__ = ClassProps(is_exception=False, is_slotted=True, has_weakref_slot=True, is_frozen=False, kw_only=<KeywordOnly.NO: 'no'>, collected_fields_by_mro=True, added_init=True, added_repr=True, added_eq=True, added_ordering=False, hashability=<Hashability.UNHASHABLE: 'unhashable'>, added_match_args=True, added_str=False, added_pickling=True, on_setattr_hook=<function pipe.<locals>.wrapped_pipe at 0x7f41e68fca40>, field_transformer=None)
class-attribute
¶
Effective class properties as derived from parameters to attr.s() or
define() decorators.
This is the same data structure that attrs uses internally to decide how to construct the final class.
Warning:
This feature is currently **experimental** and is not covered by our
strict backwards-compatibility guarantees.
Attributes:
| Name | Type | Description |
|---|---|---|
is_exception |
bool
|
Whether the class is treated as an exception class. |
is_slotted |
bool
|
Whether the class is |
has_weakref_slot |
bool
|
Whether the class has a slot for weak references. |
is_frozen |
bool
|
Whether the class is frozen. |
kw_only |
KeywordOnly
|
Whether / how the class enforces keyword-only arguments on the
|
collected_fields_by_mro |
bool
|
Whether the class fields were collected by method resolution order.
That is, correctly but unlike |
added_init |
bool
|
Whether the class has an attrs-generated |
added_repr |
bool
|
Whether the class has an attrs-generated |
added_eq |
bool
|
Whether the class has attrs-generated equality methods. |
added_ordering |
bool
|
Whether the class has attrs-generated ordering methods. |
hashability |
Hashability
|
How |
added_match_args |
bool
|
Whether the class supports positional |
added_str |
bool
|
Whether the class has an attrs-generated |
added_pickling |
bool
|
Whether the class has attrs-generated |
on_setattr_hook |
Callable[[Any, Attribute[Any], Any], Any] | None
|
The class's |
field_transformer |
Callable[[Attribute[Any]], Attribute[Any]] | None
|
The class's |
.. versionadded:: 25.4.0
__doc__ = 'Base class for video backends.\n\nThis class is not meant to be used directly. Instead, use the `from_filename`\nconstructor to create a backend instance.\n\nAttributes:\n filename: Path to video file(s).\n grayscale: Whether to force grayscale. If None, autodetect on first frame load.\n keep_open: Whether to keep the video reader open between calls to read frames.\n If False, will close the reader after each call. If True (the default), it\n will keep the reader open and cache it for subsequent calls which may\n enhance the performance of reading multiple frames.\n fps: Frames per second of the video. For MediaVideo, this is read from container\n metadata. For other backends (ImageVideo, HDF5Video, TiffVideo), this must\n be set explicitly or will be None.\n'
class-attribute
¶
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__firstlineno__ = 294
class-attribute
¶
int([x]) -> integer int(x, base=10) -> integer
Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating-point numbers, this truncates towards zero.
If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer literal.
int('0b100', base=0) 4
__match_args__ = ('filename', 'grayscale', 'keep_open', '_cached_shape', '_open_reader', '_fps')
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__module__ = 'sleap_io.io.video_reading'
class-attribute
¶
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__slots__ = ('filename', 'grayscale', 'keep_open', '_cached_shape', '_open_reader', '_fps', '__weakref__')
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__static_attributes__ = ('_cached_shape', '_fps', 'grayscale')
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__weakref__
property
¶
list of weak references to the object
fps
property
¶
Frames per second of the video.
Returns:
| Type | Description |
|---|---|
|
The FPS if known, or None if unavailable/unknown. |
Notes
For MediaVideo, this is read from container metadata. For ImageVideo, HDF5Video, and TiffVideo, this must be set explicitly or inherited from source_video.
frames
property
¶
Number of frames in the video.
img_shape
property
¶
Shape of a single frame in the video.
num_frames
property
¶
Number of frames in the video. Must be implemented in subclasses.
shape
property
¶
Shape of the video as a tuple of (frames, height, width, channels).
On first call, this will defer to num_frames and img_shape to determine the
full shape. This call may be expensive for some subclasses, so the result is
cached and returned on subsequent calls.
__eq__(other)
¶
__getitem__(ind)
¶
Return a single frame or a list of frames from the video.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
ind
|
int | list[int] | slice
|
Index or list of indices of frames to read. |
required |
Returns:
| Type | Description |
|---|---|
ndarray
|
Frame or frames as a numpy array of shape |
See also: get_frame, get_frames
Source code in sleap_io/io/video_reading.py
def __getitem__(self, ind: int | list[int] | slice) -> np.ndarray:
"""Return a single frame or a list of frames from the video.
Args:
ind: Index or list of indices of frames to read.
Returns:
Frame or frames as a numpy array of shape `(height, width, channels)` if a
scalar index is provided, or `(frames, height, width, channels)` if a list
of indices is provided.
See also: get_frame, get_frames
"""
if np.isscalar(ind):
return self.get_frame(ind)
else:
if type(ind) is slice:
start = (ind.start or 0) % len(self)
stop = ind.stop or len(self)
if stop < 0:
stop = len(self) + stop
step = ind.step or 1
ind = range(start, stop, step)
return self.get_frames(ind)
__init__(filename, grayscale=None, keep_open=True, cached_shape=None, open_reader=None, fps=None)
¶
__len__()
¶
__replace__(**changes)
¶
__repr__()
¶
Method generated by attrs for class VideoBackend.
detect_grayscale(test_img=None)
¶
Detect whether the video is grayscale.
This works by reading in a test frame and comparing the first and last channel for equality. It may fail in cases where, due to compression, the first and last channels are not exactly the same.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
test_img
|
ndarray | None
|
Optional test image to use. If not provided, a test image will be
loaded via the |
None
|
Returns:
| Type | Description |
|---|---|
bool
|
Whether the video is grayscale. This value is also cached in the |
Source code in sleap_io/io/video_reading.py
def detect_grayscale(self, test_img: np.ndarray | None = None) -> bool:
"""Detect whether the video is grayscale.
This works by reading in a test frame and comparing the first and last channel
for equality. It may fail in cases where, due to compression, the first and
last channels are not exactly the same.
Args:
test_img: Optional test image to use. If not provided, a test image will be
loaded via the `read_test_frame` method.
Returns:
Whether the video is grayscale. This value is also cached in the `grayscale`
attribute of the class.
"""
if test_img is None:
test_img = self.read_test_frame()
is_grayscale = np.array_equal(test_img[..., 0], test_img[..., -1])
self.grayscale = is_grayscale
return is_grayscale
from_filename(filename, dataset=None, grayscale=None, keep_open=True, **kwargs)
classmethod
¶
Create a VideoBackend from a filename.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename
|
str | list[str]
|
Path to video file(s). |
required |
dataset
|
str | None
|
Name of dataset in HDF5 file. |
None
|
grayscale
|
bool | None
|
Whether to force grayscale. If None, autodetect on first frame load. |
None
|
keep_open
|
bool
|
Whether to keep the video reader open between calls to read frames. If False, will close the reader after each call. If True (the default), it will keep the reader open and cache it for subsequent calls which may enhance the performance of reading multiple frames. |
True
|
**kwargs
|
Additional backend-specific arguments. These are filtered to only include parameters that are valid for the specific backend being created: - For ImageVideo: plugin (str): Image plugin to use. One of "opencv" or "imageio". Also accepts aliases (case-insensitive). If None, uses global default if set, otherwise auto-detects. - For MediaVideo: plugin (str): Video plugin to use. One of "opencv", "FFMPEG", or "pyav". Also accepts aliases (case-insensitive). If None, uses global default if set, otherwise auto-detects. - For HDF5Video: input_format (str), frame_map (dict), source_filename (str), source_inds (np.ndarray), image_format (str). See HDF5Video for details. |
required |
Returns:
| Type | Description |
|---|---|
VideoBackend
|
VideoBackend subclass instance. |
Source code in sleap_io/io/video_reading.py
@classmethod
def from_filename(
cls,
filename: str | list[str],
dataset: str | None = None,
grayscale: bool | None = None,
keep_open: bool = True,
**kwargs,
) -> "VideoBackend":
"""Create a VideoBackend from a filename.
Args:
filename: Path to video file(s).
dataset: Name of dataset in HDF5 file.
grayscale: Whether to force grayscale. If None, autodetect on first frame
load.
keep_open: Whether to keep the video reader open between calls to read
frames. If False, will close the reader after each call. If True (the
default), it will keep the reader open and cache it for subsequent calls
which may enhance the performance of reading multiple frames.
**kwargs: Additional backend-specific arguments. These are filtered to only
include parameters that are valid for the specific backend being
created:
- For ImageVideo: plugin (str): Image plugin to use. One of "opencv"
or "imageio". Also accepts aliases (case-insensitive).
If None, uses global default if set, otherwise auto-detects.
- For MediaVideo: plugin (str): Video plugin to use. One of "opencv",
"FFMPEG", or "pyav". Also accepts aliases (case-insensitive).
If None, uses global default if set, otherwise auto-detects.
- For HDF5Video: input_format (str), frame_map (dict),
source_filename (str),
source_inds (np.ndarray), image_format (str). See HDF5Video for
details.
Returns:
VideoBackend subclass instance.
"""
if isinstance(filename, Path):
filename = filename.as_posix()
if type(filename) is str and Path(filename).is_dir():
filename = ImageVideo.find_images(filename)
if type(filename) is list:
filename = [Path(f).as_posix() for f in filename]
return ImageVideo(
filename, grayscale=grayscale, **_get_valid_kwargs(ImageVideo, kwargs)
)
elif filename.lower().endswith(("tif", "tiff")):
# Detect TIFF format
format_type, metadata = TiffVideo.detect_format(filename)
if format_type in ("multi_page", "rank3_video", "rank4_video"):
# Use TiffVideo for multi-page or multi-dimensional TIFFs
tiff_kwargs = _get_valid_kwargs(TiffVideo, kwargs)
# Add format if detected
if format_type in ("rank3_video", "rank4_video"):
tiff_kwargs["format"] = metadata.get("format")
return TiffVideo(
filename,
grayscale=grayscale,
keep_open=keep_open,
**tiff_kwargs,
)
else:
# Single-page TIFF, treat as regular image
return ImageVideo(
[filename],
grayscale=grayscale,
**_get_valid_kwargs(ImageVideo, kwargs),
)
elif filename.lower().endswith(tuple(ext.lower() for ext in ImageVideo.EXTS)):
return ImageVideo(
[filename], grayscale=grayscale, **_get_valid_kwargs(ImageVideo, kwargs)
)
elif filename.lower().endswith(tuple(ext.lower() for ext in MediaVideo.EXTS)):
return MediaVideo(
filename,
grayscale=grayscale,
keep_open=keep_open,
**_get_valid_kwargs(MediaVideo, kwargs),
)
elif filename.lower().endswith(tuple(ext.lower() for ext in HDF5Video.EXTS)):
return HDF5Video(
filename,
dataset=dataset,
grayscale=grayscale,
keep_open=keep_open,
**_get_valid_kwargs(HDF5Video, kwargs),
)
else:
raise ValueError(f"Unknown video file type: {filename}")
get_frame(frame_idx)
¶
Read a single frame from the video.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
frame_idx
|
int
|
Index of frame to read. |
required |
Returns:
| Type | Description |
|---|---|
ndarray
|
Frame as a numpy array of shape |
Notes
If the grayscale attribute is set to True, the channels dimension will
be reduced to 1 if an RGB frame is loaded from the backend.
If the grayscale attribute is set to None, the grayscale attribute
will be automatically set based on the first frame read.
See also: get_frames
Source code in sleap_io/io/video_reading.py
def get_frame(self, frame_idx: int) -> np.ndarray:
"""Read a single frame from the video.
Args:
frame_idx: Index of frame to read.
Returns:
Frame as a numpy array of shape `(height, width, channels)` where the
`channels` dimension is 1 for grayscale videos and 3 for color videos.
Notes:
If the `grayscale` attribute is set to `True`, the `channels` dimension will
be reduced to 1 if an RGB frame is loaded from the backend.
If the `grayscale` attribute is set to `None`, the `grayscale` attribute
will be automatically set based on the first frame read.
See also: `get_frames`
"""
if not self.has_frame(frame_idx):
raise IndexError(f"Frame index {frame_idx} out of range.")
img = self._read_frame(frame_idx)
if self.grayscale is None:
self.detect_grayscale(img)
if self.grayscale:
img = img[..., [0]]
return img
get_frames(frame_inds)
¶
Read a list of frames from the video.
Depending on the backend implementation, this may be faster than reading frames
individually using get_frame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
frame_inds
|
list[int]
|
List of frame indices to read. |
required |
Returns:
| Type | Description |
|---|---|
ndarray
|
Frames as a numpy array of shape |
Notes
If the grayscale attribute is set to True, the channels dimension will
be reduced to 1 if an RGB frame is loaded from the backend.
If the grayscale attribute is set to None, the grayscale attribute
will be automatically set based on the first frame read.
See also: get_frame
Source code in sleap_io/io/video_reading.py
def get_frames(self, frame_inds: list[int]) -> np.ndarray:
"""Read a list of frames from the video.
Depending on the backend implementation, this may be faster than reading frames
individually using `get_frame`.
Args:
frame_inds: List of frame indices to read.
Returns:
Frames as a numpy array of shape `(frames, height, width, channels)` where
`channels` dimension is 1 for grayscale videos and 3 for color videos.
Notes:
If the `grayscale` attribute is set to `True`, the `channels` dimension will
be reduced to 1 if an RGB frame is loaded from the backend.
If the `grayscale` attribute is set to `None`, the `grayscale` attribute
will be automatically set based on the first frame read.
See also: `get_frame`
"""
imgs = self._read_frames(frame_inds)
if self.grayscale is None:
self.detect_grayscale(imgs[0])
if self.grayscale:
imgs = imgs[..., [0]]
return imgs
has_frame(frame_idx)
¶
Check if a frame index is contained in the video.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
frame_idx
|
int
|
Index of frame to check. |
required |
Returns:
| Type | Description |
|---|---|
bool
|
|
read_test_frame()
¶
Read a single frame from the video to test for grayscale.
Note
This reads the frame at index 0. This may not be appropriate if the first frame is not available in a given backend.
VideoWriter
¶
Simple video writer using imageio and FFMPEG.
Attributes:
| Name | Type | Description |
|---|---|---|
filename |
Path to output video file. |
|
fps |
Frames per second. Defaults to 30. |
|
pixelformat |
Pixel format for video. Defaults to "yuv420p". |
|
codec |
Codec to use for encoding. Defaults to "libx264". |
|
crf |
Constant rate factor to control lossiness of video. Values go from 2 to 32, with numbers in the 18 to 30 range being most common. Lower values mean less compressed/higher quality. Defaults to 25. No effect if codec is not "libx264". |
|
preset |
H264 encoding preset. Defaults to "superfast". No effect if codec is not "libx264". |
|
keyframe_interval |
Interval between keyframes in seconds. If None, uses encoder default. Lower values improve seeking but increase file size. Defaults to None. |
|
no_audio |
If True, strips audio from the output. Defaults to False. |
|
output_params |
Additional output parameters for FFMPEG. This should be a list of
strings corresponding to command line arguments for FFMPEG and libx264. Use
|
Notes
This class can be used as a context manager to ensure the video is properly closed after writing. For example:
Methods:
| Name | Description |
|---|---|
__call__ |
Write a frame to the video. |
__enter__ |
Context manager entry. |
__eq__ |
Method generated by attrs for class VideoWriter. |
__exit__ |
Context manager exit. |
__init__ |
Method generated by attrs for class VideoWriter. |
__replace__ |
Method generated by attrs for class VideoWriter. |
__repr__ |
Method generated by attrs for class VideoWriter. |
__setattr__ |
Method generated by attrs for class VideoWriter. |
build_output_params |
Build the output parameters for FFMPEG. |
close |
Close the video writer. |
open |
Open the video writer. |
write_frame |
Write a frame to the video. |
Source code in sleap_io/io/video_writing.py
@attrs.define
class VideoWriter:
"""Simple video writer using imageio and FFMPEG.
Attributes:
filename: Path to output video file.
fps: Frames per second. Defaults to 30.
pixelformat: Pixel format for video. Defaults to "yuv420p".
codec: Codec to use for encoding. Defaults to "libx264".
crf: Constant rate factor to control lossiness of video. Values go from 2 to 32,
with numbers in the 18 to 30 range being most common. Lower values mean less
compressed/higher quality. Defaults to 25. No effect if codec is not
"libx264".
preset: H264 encoding preset. Defaults to "superfast". No effect if codec is not
"libx264".
keyframe_interval: Interval between keyframes in seconds. If None, uses encoder
default. Lower values improve seeking but increase file size. Defaults to
None.
no_audio: If True, strips audio from the output. Defaults to False.
output_params: Additional output parameters for FFMPEG. This should be a list of
strings corresponding to command line arguments for FFMPEG and libx264. Use
`ffmpeg -h encoder=libx264` to see all options for libx264 output_params.
Notes:
This class can be used as a context manager to ensure the video is properly
closed after writing. For example:
```python
with VideoWriter("output.mp4") as writer:
for frame in frames:
writer(frame)
```
"""
filename: Path = attrs.field(converter=Path)
fps: float = 30
pixelformat: str = "yuv420p"
codec: str = "libx264"
crf: int = 25
preset: str = "superfast"
keyframe_interval: float | None = None
no_audio: bool = False
output_params: list[str] = attrs.field(factory=list)
_writer: "imageio.plugins.ffmpeg.FfmpegFormat.Writer | None" = None
def build_output_params(self) -> list[str]:
"""Build the output parameters for FFMPEG."""
output_params = []
if self.codec == "libx264":
output_params.extend(
[
"-crf",
str(self.crf),
"-preset",
self.preset,
]
)
# Add keyframe interval (GOP size)
if self.keyframe_interval is not None:
gop_size = max(1, int(self.fps * self.keyframe_interval))
output_params.extend(["-g", str(gop_size)])
# Strip audio if requested
if self.no_audio:
output_params.extend(["-an"])
return output_params + self.output_params
def open(self):
"""Open the video writer."""
self.close()
self.filename.parent.mkdir(parents=True, exist_ok=True)
self._writer = iio_v2.get_writer(
self.filename.as_posix(),
format="FFMPEG",
fps=self.fps,
codec=self.codec,
pixelformat=self.pixelformat,
output_params=self.build_output_params(),
# Disable imageio's auto-scaling for non-divisible frame sizes.
# We handle padding manually in write_frame() to preserve coordinates.
macro_block_size=1,
)
def close(self):
"""Close the video writer."""
if self._writer is not None:
self._writer.close()
self._writer = None
def write_frame(self, frame: np.ndarray):
"""Write a frame to the video.
Args:
frame: Frame to write to video. Should be a 2D or 3D numpy array with
dimensions (height, width) or (height, width, channels).
Notes:
For libx264 codec, frames are automatically padded to dimensions divisible
by 16 (the macro block size). Padding is only added to the bottom and right
edges to preserve coordinate alignment.
"""
if self._writer is None:
self.open()
if self.codec == "libx264":
frame = _pad_to_macro_block(frame, macro_block_size=16)
self._writer.append_data(frame)
def __enter__(self):
"""Context manager entry."""
return self
def __exit__(
self,
exc_type: type[BaseException] | None,
exc_value: BaseException | None,
traceback: TracebackType | None,
) -> bool | None:
"""Context manager exit."""
self.close()
return False
def __call__(self, frame: np.ndarray):
"""Write a frame to the video.
Args:
frame: Frame to write to video. Should be a 2D or 3D numpy array with
dimensions (height, width) or (height, width, channels).
"""
self.write_frame(frame)
__annotations__ = {'filename': 'Path', 'fps': 'float', 'pixelformat': 'str', 'codec': 'str', 'crf': 'int', 'preset': 'str', 'keyframe_interval': 'float | None', 'no_audio': 'bool', 'output_params': 'list[str]', '_writer': "'imageio.plugins.ffmpeg.FfmpegFormat.Writer | None'"}
class-attribute
¶
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
__attrs_own_setattr__ = True
class-attribute
¶
Returns True when the argument is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.
__attrs_props__ = ClassProps(is_exception=False, is_slotted=True, has_weakref_slot=True, is_frozen=False, kw_only=<KeywordOnly.NO: 'no'>, collected_fields_by_mro=True, added_init=True, added_repr=True, added_eq=True, added_ordering=False, hashability=<Hashability.UNHASHABLE: 'unhashable'>, added_match_args=True, added_str=False, added_pickling=True, on_setattr_hook=<function pipe.<locals>.wrapped_pipe at 0x7f41e68fca40>, field_transformer=None)
class-attribute
¶
Effective class properties as derived from parameters to attr.s() or
define() decorators.
This is the same data structure that attrs uses internally to decide how to construct the final class.
Warning:
This feature is currently **experimental** and is not covered by our
strict backwards-compatibility guarantees.
Attributes:
| Name | Type | Description |
|---|---|---|
is_exception |
bool
|
Whether the class is treated as an exception class. |
is_slotted |
bool
|
Whether the class is |
has_weakref_slot |
bool
|
Whether the class has a slot for weak references. |
is_frozen |
bool
|
Whether the class is frozen. |
kw_only |
KeywordOnly
|
Whether / how the class enforces keyword-only arguments on the
|
collected_fields_by_mro |
bool
|
Whether the class fields were collected by method resolution order.
That is, correctly but unlike |
added_init |
bool
|
Whether the class has an attrs-generated |
added_repr |
bool
|
Whether the class has an attrs-generated |
added_eq |
bool
|
Whether the class has attrs-generated equality methods. |
added_ordering |
bool
|
Whether the class has attrs-generated ordering methods. |
hashability |
Hashability
|
How |
added_match_args |
bool
|
Whether the class supports positional |
added_str |
bool
|
Whether the class has an attrs-generated |
added_pickling |
bool
|
Whether the class has attrs-generated |
on_setattr_hook |
Callable[[Any, Attribute[Any], Any], Any] | None
|
The class's |
field_transformer |
Callable[[Attribute[Any]], Attribute[Any]] | None
|
The class's |
.. versionadded:: 25.4.0
__doc__ = 'Simple video writer using imageio and FFMPEG.\n\nAttributes:\n filename: Path to output video file.\n fps: Frames per second. Defaults to 30.\n pixelformat: Pixel format for video. Defaults to "yuv420p".\n codec: Codec to use for encoding. Defaults to "libx264".\n crf: Constant rate factor to control lossiness of video. Values go from 2 to 32,\n with numbers in the 18 to 30 range being most common. Lower values mean less\n compressed/higher quality. Defaults to 25. No effect if codec is not\n "libx264".\n preset: H264 encoding preset. Defaults to "superfast". No effect if codec is not\n "libx264".\n keyframe_interval: Interval between keyframes in seconds. If None, uses encoder\n default. Lower values improve seeking but increase file size. Defaults to\n None.\n no_audio: If True, strips audio from the output. Defaults to False.\n output_params: Additional output parameters for FFMPEG. This should be a list of\n strings corresponding to command line arguments for FFMPEG and libx264. Use\n `ffmpeg -h encoder=libx264` to see all options for libx264 output_params.\n\nNotes:\n This class can be used as a context manager to ensure the video is properly\n closed after writing. For example:\n\n ```python\n with VideoWriter("output.mp4") as writer:\n for frame in frames:\n writer(frame)\n ```\n'
class-attribute
¶
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__firstlineno__ = 46
class-attribute
¶
int([x]) -> integer int(x, base=10) -> integer
Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating-point numbers, this truncates towards zero.
If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer literal.
int('0b100', base=0) 4
__match_args__ = ('filename', 'fps', 'pixelformat', 'codec', 'crf', 'preset', 'keyframe_interval', 'no_audio', 'output_params', '_writer')
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__module__ = 'sleap_io.io.video_writing'
class-attribute
¶
str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.
__slots__ = ('filename', 'fps', 'pixelformat', 'codec', 'crf', 'preset', 'keyframe_interval', 'no_audio', 'output_params', '_writer', '__weakref__')
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__static_attributes__ = ('_writer',)
class-attribute
¶
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
__weakref__
property
¶
list of weak references to the object
__call__(frame)
¶
Write a frame to the video.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
frame
|
ndarray
|
Frame to write to video. Should be a 2D or 3D numpy array with dimensions (height, width) or (height, width, channels). |
required |
__enter__()
¶
__eq__(other)
¶
Method generated by attrs for class VideoWriter.
Source code in sleap_io/io/video_writing.py
This preserves coordinate alignment by only adding padding to the bottom and right
edges of the frame. Without this, encoding with x264 may scale or pad symmetrically,
causing coordinate shifts.
Args:
frame: Frame to pad. Should be a 2D or 3D numpy array with dimensions
(height, width) or (height, width, channels).
macro_block_size: Block size to align to. Defaults to 16 for x264.
Returns:
Padded frame with dimensions divisible by macro_block_size, or the original
frame if no padding is needed.
"""
h, w = frame.shape[:2]
__exit__(exc_type, exc_value, traceback)
¶
__init__(filename, fps=30, pixelformat='yuv420p', codec='libx264', crf=25, preset='superfast', keyframe_interval=None, no_audio=False, output_params=NOTHING, writer=None)
¶
Method generated by attrs for class VideoWriter.
Source code in sleap_io/io/video_writing.py
# Calculate padding needed (only bottom/right)
pad_h = (macro_block_size - (h % macro_block_size)) % macro_block_size
pad_w = (macro_block_size - (w % macro_block_size)) % macro_block_size
if pad_h == 0 and pad_w == 0:
return frame
# Pad only bottom and right
if frame.ndim == 2:
return np.pad(frame, ((0, pad_h), (0, pad_w)), mode="constant")
else:
return np.pad(frame, ((0, pad_h), (0, pad_w), (0, 0)), mode="constant")
__replace__(**changes)
¶
Method generated by attrs for class VideoWriter.
__repr__()
¶
Method generated by attrs for class VideoWriter.
Source code in sleap_io/io/video_writing.py
"""Utilities for writing videos."""
from __future__ import annotations
from pathlib import Path
from types import TracebackType
import attrs
import imageio
import imageio.v2 as iio_v2
import numpy as np
def _pad_to_macro_block(frame: np.ndarray, macro_block_size: int = 16) -> np.ndarray:
"""Pad frame to be divisible by macro_block_size, padding only bottom/right.
__setattr__(name, val)
¶
Method generated by attrs for class VideoWriter.
build_output_params()
¶
Build the output parameters for FFMPEG.
Source code in sleap_io/io/video_writing.py
def build_output_params(self) -> list[str]:
"""Build the output parameters for FFMPEG."""
output_params = []
if self.codec == "libx264":
output_params.extend(
[
"-crf",
str(self.crf),
"-preset",
self.preset,
]
)
# Add keyframe interval (GOP size)
if self.keyframe_interval is not None:
gop_size = max(1, int(self.fps * self.keyframe_interval))
output_params.extend(["-g", str(gop_size)])
# Strip audio if requested
if self.no_audio:
output_params.extend(["-an"])
return output_params + self.output_params
close()
¶
open()
¶
Open the video writer.
Source code in sleap_io/io/video_writing.py
def open(self):
"""Open the video writer."""
self.close()
self.filename.parent.mkdir(parents=True, exist_ok=True)
self._writer = iio_v2.get_writer(
self.filename.as_posix(),
format="FFMPEG",
fps=self.fps,
codec=self.codec,
pixelformat=self.pixelformat,
output_params=self.build_output_params(),
# Disable imageio's auto-scaling for non-divisible frame sizes.
# We handle padding manually in write_frame() to preserve coordinates.
macro_block_size=1,
)
write_frame(frame)
¶
Write a frame to the video.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
frame
|
ndarray
|
Frame to write to video. Should be a 2D or 3D numpy array with dimensions (height, width) or (height, width, channels). |
required |
Notes
For libx264 codec, frames are automatically padded to dimensions divisible by 16 (the macro block size). Padding is only added to the bottom and right edges to preserve coordinate alignment.
Source code in sleap_io/io/video_writing.py
def write_frame(self, frame: np.ndarray):
"""Write a frame to the video.
Args:
frame: Frame to write to video. Should be a 2D or 3D numpy array with
dimensions (height, width) or (height, width, channels).
Notes:
For libx264 codec, frames are automatically padded to dimensions divisible
by 16 (the macro block size). Padding is only added to the bottom and right
edges to preserve coordinate alignment.
"""
if self._writer is None:
self.open()
if self.codec == "libx264":
frame = _pad_to_macro_block(frame, macro_block_size=16)
self._writer.append_data(frame)
is_file_accessible(filename)
¶
Check if a file is accessible.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filename
|
str | Path
|
Path to a file. |
required |
Returns:
| Type | Description |
|---|---|
bool
|
|
Notes
This checks if the file readable by the current user by reading one byte from the file.
Source code in sleap_io/io/utils.py
def is_file_accessible(filename: str | Path) -> bool:
"""Check if a file is accessible.
Args:
filename: Path to a file.
Returns:
`True` if the file is accessible, `False` otherwise.
Notes:
This checks if the file readable by the current user by reading one byte from
the file.
"""
filename = Path(filename)
try:
with open(filename, "rb") as f:
f.read(1)
return True
except (FileNotFoundError, PermissionError, OSError, ValueError):
return False