Simple Video Utils

Lightweight utilities for extracting frames and metadata from videos. Built for sign language processing workflows.

Installation

pip install simple-video-utils

Usage

Extract Video Metadata

from simple_video_utils.metadata import video_metadata

meta = video_metadata("video.mp4")
print(f"{meta.width}x{meta.height} @ {meta.fps} fps, {meta.duration}s")
# Output: VideoMetadata(width=1920, height=1080, fps=30.0, nb_frames=450, time_base='1/15360', duration=15.0)

Keyframe Indices (GOP Structure)

from simple_video_utils.metadata import keyframe_indices

keys = keyframe_indices("video.mp4")
# Presentation-order frame indices of the keyframes, e.g. [0, 250, 500]
# Demux-only (no decoding) — cheap even for long videos.

Read Frames from File

from simple_video_utils.frames import read_frames_exact

# Read specific frame range (inclusive)
frames = list(read_frames_exact("video.mp4", start_frame=0, end_frame=10))
# Returns 11 frames as numpy arrays (H, W, 3) in RGB format

# Read from frame to end of video
frames = list(read_frames_exact("video.mp4", start_frame=5, end_frame=None))

# Downsample to a target frame rate (drops frames uniformly, never duplicates)
frames = list(read_frames_exact("video.mp4", fps=15))

Resize While Reading

from av.video.reformatter import Interpolation
from simple_video_utils.frames import FrameSize, read_frames_batched

# Stretch to (width, height), resizing native YUV planes before RGB conversion.
frames = read_frames_batched("video.mp4", size=FrameSize((256, 256)))

# Choose PyAV's interpolation, e.g. bicubic for enlarging frames.
frames = read_frames_batched("video.mp4", size=FrameSize((512, 512), method=Interpolation.BICUBIC))

read_frames_exact, read_frames_batched and read_frames_from_stream accept size. Resolution is in display orientation; the default method is Interpolation.AREA. Without size, each frame keeps its original dimensions. Supply size to batch across chapters with different dimensions. Native-plane resizing can produce different RGB pixels from resizing after RGB conversion, since chroma is resized at the source pixel format's resolution.

One Open, Many Reads

from simple_video_utils.metadata import open_video, video_metadata_from_container, keyframe_indices
from simple_video_utils.frames import read_frames_exact

# Metadata + windowed frame reads from a single container open —
# e.g. sampling indices from metadata, then decoding just that window.
with open_video("video.mp4") as video:
    meta = video_metadata_from_container(video)
    keys = keyframe_indices(video)
    frames = list(read_frames_exact(video, start_frame=10, end_frame=20))

Every helper rewinds the container before reading, so call order doesn't matter. Requires seekable input; consume one frame read at a time.

open_video sets the container's decode thread_type up front (default "AUTO") — PyAV forbids changing it once a stream's codec is open, which happens on first metadata probe or frame read. Pass thread_type="NONE" if you fork worker processes around decoding (e.g. a DataLoader): an inherited AUTO-threaded decoder can deadlock post-fork.

Read Frames from Stream

from simple_video_utils.frames import read_frames_from_stream

# Useful for uploaded files or in-memory video data
with open("video.mp4", "rb") as f:
    meta, frames_gen = read_frames_from_stream(f)
    for frame in frames_gen:
        # Process each frame (numpy array)
        pass

Pass return_frame_metadata=True for source-video timing and decoded indices:

from simple_video_utils.frames import FrameSize, read_frames_from_stream

with open("video.mp4", "rb") as stream:
    meta, frames = read_frames_from_stream(
        stream, fps=15, size=FrameSize((256, 256)), return_frame_metadata=True,
    )
    for frame, info in frames:
        process(frame, info.index, info.time)

FrameMetadata contains index, pts, and time_base; its time property is source-video seconds as a Fraction, translated through chapter payloads, or None when timing is unavailable. file_time is the native pts * time_base before translation. Indices count decoded frames from the start of the read, before skipping or FPS selection. Metadata describes the original frame even when pixels are resized or rotated. The default still yields arrays only. An already-open PyAV input container can also be passed; the streaming reader preserves its position and leaves it open, without seeking for FPS recovery.

Slice into Clips

from simple_video_utils.slicing import slice_video

# One MP4 (bytes) per (start, end) second range
clips = slice_video("video.mp4", [(0.0, 1.5), (2.0, 3.2)])

# Center-crop to a square and resize to 256x256 (e.g. for model input)
clips = slice_video("video.mp4", [(0.0, 1.5)], size=256)

Clips are streamable MP4 (moov first), so a consumer can demux them from a pipe or socket without seeking.

Split an incoming video stream into packet-copied clips as it arrives — each clip is yielded as soon as its window has been read, without re-encoding. Each carries its start on the source timeline, which counting the clips does not give you: an empty window yields nothing while the timeline moves past it.

from simple_video_utils.slicing import slice_video_stream

with open("video.mp4", "rb") as stream:
    for start, data in slice_video_stream(stream, duration=0.5):
        process(start, data)

Join clips in order. Overlapping slices from the same encoded stream are de-duplicated and remuxed losslessly; anything else is decoded and encoded once as H.264 MP4:

from simple_video_utils.joining import join_videos

video = join_videos(clips)

Chapters: Crops and Trims in One File

import json

from simple_video_utils.chapters import ChapterClip, list_chapters, write_chapters
from simple_video_utils.frames import read_frames_exact

write_chapters("video.mp4", "chapters.mkv", [
    ChapterClip(30, 60, (100, 50, 500, 354), payload={"signer": "Alice"}),
    ChapterClip(90, 120, (200, 80, 440, 400)),
    ChapterClip(150, 180, (-20, 0, 300, 250), resolution=(256, 192)),
])

for chapter in list_chapters("chapters.mkv"):
    start_seconds = chapter["start"] * chapter["time_base"]
    tags = {key.lower(): value for key, value in chapter["metadata"].items()}
    source_clip = json.loads(tags["payload"])

for frame in read_frames_exact("chapters.mkv"):
    process(frame)  # each chapter can have a different frame shape

Pass nonempty clips from a video with a nominal frame rate and an H.264-compatible pixel format. Trims use source frame indices with an exclusive end; boxes are integer (left, top, right, bottom) display coordinates. Source frames are rotated upright before cropping and padding. Box areas outside the source frame become black. resolution=(width, height) resizes the crop to exactly that size; the default keeps the box size. Clips must be in source order without overlaps. Chapter boundaries and frame timestamps keep each clip at start_frame / fps through end_frame / fps on the source timeline. Gaps contain no encoded filler frames; a player can hold the previous frame until the next chapter starts. Chapter payload tags retain their source specs. Chapter boundaries use Matroska's millisecond clock. A source that ends before a requested trim finishes raises ValueError.

Requires PyAV 19+. Output is a new, video-only Matroska file encoded as H.264 at crf=23, using the source frame rate and pixel format. Each chapter's output width and height must be multiples of 16, otherwise ValueError is raised. The destination must not already exist. All clips share one source decode.

This replaces the previous packed storage layout. Existing packed files remain readable: SVU translates source-video times through their chapter payloads automatically. Regeneration is only needed to change playback in an ordinary media player, not for correct SVU reads.

Aligned padding keeps the source chroma samples without resampling. Unaligned crops and full-range sources retain high-depth processing for accurate offsets and black borders. Scaling preserves the source color matrix, range and color tags.

list_chapters yields native PyAV chapter dictionaries and also accepts an open container without rewinding or closing it. To process each chapter separately:

from simple_video_utils.chapters import read_chapters
from simple_video_utils.frames import FrameSize, stack_frames

for chapter, source_start, frames in read_chapters(
    "chapters.mkv", fps=15, size=FrameSize((256, 256)),
):
    process_chapter(chapter, source_start, stack_frames(frames))

read_chapters reuses read_frames_from_stream with frame metadata, yielding lazy RGB frame iterators for native half-open chapter ranges [start, end). Consume each iterator before requesting the next chapter. It supports ordinary chapter metadata in any container PyAV can read; no signer payload is required. Incoming file objects work when the chapter table precedes the media. Ranges must have positive duration and must not overlap.

FPS selection restarts per chapter, and size uses the same native-plane resizing as the frame readers. source_start is a Fraction in seconds: the native chapter start unless the writer's optional payload supplies start_frame (and optionally rate). Without chapters it yields one (None, 0, frames) entry covering the whole video. No clips are encoded or temporary files created.

Source-Video Time and Stored Time

start_time / end_time in read_frames_exact and read_frames_batched, ranges passed to slice_video, and FrameMetadata.time use source-video seconds for chaptered inputs. For example, historical clips retaining source spans 0–5s and 10–15s may store the second chapter at file time 5s. Asking SVU for 10–11s selects that chapter, and its first frame's metadata reports time 10s. The same calls work on new files that retain the timestamp gap. No inference-side offset conversion is needed.

Frame indices still count retained decoded frames. A time range entirely within a removed gap yields no frames (slice_video raises ValueError for an empty slice). FPS selection uses source-video time, and streaming slices report their window starts on that timeline. Copied and resized chapter slices preserve internal source-time gaps in their output timestamps. Native chapter dictionaries, FrameMetadata.pts, time_base, and file_time retain stored timestamps. VideoMetadata.duration describes the stored media; slice validation translates its endpoint to source time.

The shared mapping is also available directly, without decoding frames:

from simple_video_utils.timeline import Timeline

timeline = Timeline.read("chapters.mkv")  # also accepts a stream or an open PyAV container
file_second = timeline.to_file(10.5)
source_second = timeline.to_source(file_second)
saved = timeline.chapters  # (file start, file end, source start) triples
restored = Timeline(saved)

Scalar mappings return exact Fraction seconds; NumPy arrays return floating-point arrays. Within a chapter the mapping changes only the offset. Removed gaps map to the shared file boundary, which belongs to the next chapter; native gaps interpolate between boundaries. Videos without chapters, or chapters without a writer source payload, keep native timing. Reporting source times supports reordered source chapters, but inverse source-to-file mapping requires source order. Ordinary unchaptered range reads retain their existing time origin at the first video frame.

Remote Videos

from simple_video_utils.metadata import video_metadata
from simple_video_utils.frames import read_frames_exact

# Works with remote URLs
url = "https://example.com/video.mp4"
meta = video_metadata(url)
frames = list(read_frames_exact(url, 0, 5))

Development

pip install -e ".[dev]"
pytest tests/
ruff check .