mediaref 0.1.0

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MediaRef

Pydantic-based media reference for images and video frames. Supports file paths, URLs, data URIs, and video timestamps. Designed for dataset metadata and lazy loading.

Installation

# Core package (MediaRef definition only)
pip install -e .

# With loader support (includes batch loading, video decoders)
pip install -e ".[loader]"

Usage

from mediaref import MediaRef, load_batch

# Reference creation - supports multiple URI schemes
MediaRef(uri="image.png")                              # Local file
MediaRef(uri="https://example.com/image.jpg")          # Remote URL
MediaRef(uri="video.mp4", pts_ns=1_000_000_000)        # Video frame at 1.0s
MediaRef(uri="data:image/png;base64,...")              # Embedded data URI

# Loading
ref.to_rgb_array()                                     # Returns (H, W, 3) numpy array
ref.to_pil_image()                                     # Returns PIL.Image

# Batch loading with automatic caching (default: PyAV decoder)
refs = [MediaRef(uri="video.mp4", pts_ns=i*1e9) for i in range(10)]
frames = load_batch(refs)                              # Reuses video container

# Use TorchCodec decoder for GPU acceleration (requires torchcodec>=0.4.0)
frames = load_batch(refs, decoder="torchcodec")

# Embedding
data_uri = ref.embed_as_data_uri(format="png")         # Encode to data URI
MediaRef(uri=data_uri)                                 # Create from data URI

# Path resolution for MCAP/rosbag datasets
ref = MediaRef(uri="relative/video.mkv", pts_ns=123456)
ref.resolve_relative_path("/data/recording.mcap")      # Returns absolute path

# Serialization (Pydantic-based)
ref.model_dump()                                       # {'uri': '...', 'pts_ns': ...}
ref.model_dump_json()                                  # '{"uri":"...","pts_ns":...}'
MediaRef.model_validate(data)                          # From dict
MediaRef.model_validate_json(json_str)                 # From JSON string

API Reference

MediaRef(uri: str, pts_ns: int | None = None)

Properties: is_embedded, is_video, is_remote, is_local, is_relative_path

Methods:

• to_rgb_array(**kwargs) -> np.ndarray - Load as RGB array (H, W, 3)
• to_pil_image(**kwargs) -> PIL.Image - Load as PIL Image
• embed_as_data_uri(format="png", quality=None) -> str - Encode to data URI
• resolve_relative_path(base_path, allow_nonlocal=False) -> MediaRef - Resolve relative paths
• validate_uri() -> bool - Check if URI exists (local files only)
• model_dump() -> dict - Serialize to dict
• model_dump_json() -> str - Serialize to JSON
• model_validate(data) -> MediaRef - Deserialize from dict
• model_validate_json(json_str) -> MediaRef - Deserialize from JSON

Functions

• load_batch(refs: list[MediaRef], **kwargs) -> list[np.ndarray] - Batch load with caching
• cleanup_cache() - Clear video container cache

Video Decoders (requires [loader] extra)

• PyAVVideoDecoder(video_path) - PyAV-based decoder with TorchCodec-compatible interface
• TorchCodecVideoDecoder(video_path) - TorchCodec-based decoder (requires torchcodec>=0.4.0)

Design Notes

• Video container caching uses reference counting with LRU eviction (default: 10 containers)
• MCAP file path resolution: detects .mcap suffix and uses parent directory as base
• Garbage collection triggered every 10 PyAV operations to handle reference cycles
• Cache size configurable via AV_CACHE_SIZE environment variable

Acknowledgments

The video decoder interface design references TorchCodec's API design.

Dependencies

Core: pydantic>=2.0 (requires Pydantic v2 API)

Loader (optional): numpy, opencv-python, pillow, av, requests

The loader dependencies use stable APIs with no version constraints. Install with pip install mediaref[loader] to enable batch loading and video decoding.