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))

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

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) encoded-pixel coordinates. 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 play consecutively, and 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. Each trim decodes its source lead-in again.

list_chapters yields native PyAV chapter dictionaries. Use read_frames_exact for frames; its generator accommodates chapters with different dimensions.

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 .