mediaref 0.3.0

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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 with image loading support
pip install mediaref

# With video support (adds PyAV for video frame extraction)
pip install mediaref[video]

Quick Start

Basic Usage

from mediaref import MediaRef, DataURI, batch_decode
import numpy as np

# 1. Create references (lightweight, no loading yet)
ref = MediaRef(uri="image.png")                        # Local file
ref = MediaRef(uri="https://example.com/image.jpg")    # Remote URL
ref = MediaRef(uri="video.mp4", pts_ns=1_000_000_000)  # Video frame at 1.0s

# 2. Load media
rgb = ref.to_rgb_array()                               # Returns (H, W, 3) numpy array
pil = ref.to_pil_image()                               # Returns PIL.Image

# 3. Embed as data URI
data_uri = DataURI.from_image(rgb, format="png")
ref = MediaRef(uri=data_uri)                           # Self-contained reference

# 4. Batch decode video frames (opens video once, reuses handle)
refs = [MediaRef(uri="video.mp4", pts_ns=int(i*1e9)) for i in range(10)]
frames = batch_decode(refs)                            # Much faster than loading individually

Batch Decoding - Optimized Video Frame Loading

When loading multiple frames from the same video, batch_decode() opens the video file once and reuses the handle, achieving 4.9× faster throughput and 41× better I/O efficiency compared to existing methods.

Benchmark details: Measured on real ML dataloader workloads (Minecraft dataset: 64×5 min episodes, 640×360 @ 20Hz, FSLDataset with 4096 token sequences) vs baseline and TorchCodec v0.6.0. See D2E paper Section 3 and Appendix A for full methodology.

from mediaref import MediaRef, batch_decode
from mediaref.video_decoder import BatchDecodingStrategy

# Use optimized batch decoding with adaptive strategy (default, recommended)
refs = [MediaRef(uri="video.mp4", pts_ns=int(i*1e9)) for i in range(10)]
frames = batch_decode(
    refs,
    # Our optimized implementation based on PyAV
    decoder="pyav",
    # Our adaptive strategy for optimal performance
    strategy=BatchDecodingStrategy.SEQUENTIAL_PER_KEYFRAME_BLOCK
)

# Or use TorchCodec for GPU-accelerated decoding
frames = batch_decode(refs, decoder="torchcodec")  # Requires: pip install torchcodec>=0.4.0

Embedding Media Directly in MediaRef

You can embed image data directly into MediaRef objects, making them self-contained and portable (useful for serialization, caching, or sharing).

from mediaref import MediaRef, DataURI
import numpy as np

# Create embedded MediaRef from numpy array
rgb = np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)
embedded_ref = MediaRef(uri=DataURI.from_image(rgb, format="png"))

# Or from file
embedded_ref = MediaRef(uri=DataURI.from_file("image.png"))

# Or from PIL Image
from PIL import Image
pil_img = Image.open("image.png")
embedded_ref = MediaRef(uri=DataURI.from_image(pil_img, format="jpeg", quality=90))

# Use just like any other MediaRef
rgb = embedded_ref.to_rgb_array()                      # (H, W, 3) numpy array
pil = embedded_ref.to_pil_image()                      # PIL Image

# Serialize with embedded data
serialized = embedded_ref.model_dump_json()            # Contains image data
restored = MediaRef.model_validate_json(serialized)    # No external file needed!

# Properties
print(data_uri.mimetype)                               # "image/png"
print(len(data_uri))                                   # URI length in bytes
print(data_uri.is_image)                               # True for image/* types

Path Resolution & Serialization

Resolve relative paths and serialize MediaRef objects for dataset metadata and storage.

# Resolve relative paths (useful for MCAP/rosbag datasets)
ref = MediaRef(uri="relative/video.mkv", pts_ns=123456)
resolved = ref.resolve_relative_path("/data/recording.mcap")

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

API Reference

MediaRef(uri: str | DataURI, 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
• 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

DataURI (for embedding media)

Class Methods:

• from_image(image: np.ndarray | PIL.Image, format="png", quality=None) -> DataURI - Create from image
• from_file(path: str | Path, format=None) -> DataURI - Create from file
• from_uri(uri: str) -> DataURI - Parse data URI string

Methods:

• to_rgb_array() -> np.ndarray - Convert to RGB array (H, W, 3)
• to_pil_image() -> PIL.Image - Convert to PIL Image

Properties:

• uri: str - Full data URI string
• is_image: bool - True if MIME type is image/*

Functions

• batch_decode(refs, strategy=None, decoder="pyav", **kwargs) -> list[np.ndarray] - Batch decode using optimized batch decoding API
  • refs: List of MediaRef objects to decode
  • strategy: Batch decoding strategy (PyAV only): SEPARATE, SEQUENTIAL, or SEQUENTIAL_PER_KEYFRAME_BLOCK (default)
  • decoder: Decoder backend ("pyav" or "torchcodec")
• cleanup_cache() - Clear video container cache (PyAV only)

Video Decoders (requires [video] extra)

• PyAVVideoDecoder(source) - PyAV-based decoder with batch decoding strategies
  • Supports batch decoding strategies: SEPARATE, SEQUENTIAL, SEQUENTIAL_PER_KEYFRAME_BLOCK
  • CPU-based decoding using FFmpeg
  • Automatic container caching with reference counting
• TorchCodecVideoDecoder(source) - TorchCodec-based decoder for GPU acceleration
  • Requires torchcodec>=0.4.0 (install separately)
  • GPU-accelerated decoding with CUDA support
  • Does not support batch decoding strategies (parameter ignored)

Decoder Comparison:

Feature	PyAVVideoDecoder	TorchCodecVideoDecoder
Batch decoding strategies	✅ Full support	❌ Not supported (ignored)
GPU acceleration	❌ CPU only	✅ CUDA support
Backend	PyAV (FFmpeg)	TorchCodec (FFmpeg)
Installation	pip install mediaref[video]	pip install torchcodec>=0.4.0

When to use:

• Use PyAVVideoDecoder (default) for fine-grained control over batch decoding strategies
• Use TorchCodecVideoDecoder for GPU-accelerated decoding when processing large batches

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 path
• Garbage collection: Triggered every 10 PyAV operations to handle FFmpeg reference cycles
• Cache size: Configurable via AV_CACHE_SIZE environment variable
• Lazy loading: Video dependencies only imported when needed (not at module import time)

Acknowledgments

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

Dependencies

Core dependencies (automatically installed):

• pydantic>=2.0 - Data validation and serialization (requires Pydantic v2 API)
• numpy - Array operations
• opencv-python - Image loading and color conversion
• pillow>=9.4.0 - Image loading from various sources
• requests>=2.32.2 - HTTP/HTTPS URL loading
• loguru - Logging (disabled by default for library code)

Optional dependencies:

• [video] extra: av>=15.0 (PyAV for video frame extraction)
• TorchCodec: torchcodec>=0.4.0 (install separately for GPU-accelerated decoding)