nelux 0.8.7

Lightspeed video decoding directly into tensors!

NeLux

NeLux is a high-performance Python library for video processing, leveraging the power of FFmpeg with hardware acceleration (NVDEC/NVENC). It delivers some of the fastest decode times globally, enabling efficient video decoding directly into ML-ready PyTorch tensors.

The name NeLux comes from the Latin words celer (speed) and lux (light), reflecting its commitment to speed and efficiency.

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Installation

pip install nelux

Note: The package is named nelux on PyPI (not nelux).

For Linux: Download the most recent release (.whl) and install manually:

pip install ./*.whl

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Quick Start

Basic Usage

from nelux import VideoReader

# Open video with hardware acceleration
reader = VideoReader("input.mp4", decode_accelerator="nvdec")

# Read frames - automatically BCHW format!
for frame in reader:
    print(frame.shape)   # [1, 3, 1080, 1920] - BCHW
    print(frame.dtype)   # torch.float16 for 8-bit videos
    
    # Ready for ML inference immediately
    output = model(frame)

Batch Frame Reading

from nelux import VideoReader

vr = VideoReader("video.mp4")

# Get specific frames
batch = vr.get_batch([0, 10, 20])           # [3, 3, H, W]
batch = vr.get_batch(range(0, 100, 10))     # [10, 3, H, W]

# Pythonic slice notation
batch = vr[0:100:10]                        # [10, 3, H, W]
single = vr[42]                             # Single frame

# Negative indexing
batch = vr[[-3, -2, -1]]                    # Last 3 frames

# Properties
print(len(vr))                              # Total frame count
print(vr.shape)                             # (frames, 3, H, W)

Video Encoding with Audio

from nelux import VideoReader
import torch

reader = VideoReader("input.mp4")

with reader.create_encoder("output.mp4") as enc:
    # Re-encode video frames
    for frame in reader:
        enc.encode_frame(frame)
    
    # Encode audio if present
    if reader.has_audio:
        pcm = reader.audio.tensor().to(torch.int16)
        enc.encode_audio_frame(pcm)

print("Done!")

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Features

Core Features

• Hardware Acceleration: NVDEC (decode) and NVENC (encode) support
• ML-Ready Output: BCHW format with automatic dtype selection
  • FP16 for 8-bit videos (optimal for ML)
  • FP32 for 10/12/16-bit videos (higher precision)
• Zero-Copy: Direct GPU tensor output, no CPU round-trip
• Batch Decoding: Efficient multi-frame decoding with smart optimization
• Audio Support: Extract and encode audio streams

Performance Optimizations

• Fused Operations: Color conversion + format change + normalization in single CUDA kernel
• Smart Seeking: Minimizes seeks in batch operations (only seeks on backward jumps or large gaps)
• Deduplication: Duplicate frame requests decoded once and shared
• Asynchronous Decode: Non-blocking GPU operations with event-based synchronization

Supported Codecs & Formats

Feature	Support
Video Codecs	H.264, H.265/HEVC, VP9, AV1 (with NVDEC)
Pixel Formats	NV12, P010, P016, YUV444 (8/10/12/16-bit)
Audio	AAC, MP3, FLAC, PCM (extraction & encoding)
Containers	MP4, MKV, AVI, MOV, WebM

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API Reference

VideoReader

VideoReader(
    file_path: str,
    num_threads: int = 4,
    force_8bit: bool = False,
    decode_accelerator: str = "cpu",  # "cpu" or "nvdec"
    cuda_device_index: int = 0
)

Properties:

• shape: Tuple of (frames, 3, height, width)
• frame_count: Total number of frames
• fps: Frame rate
• duration: Video duration in seconds
• has_audio: Whether video has audio stream

Methods:

• get_batch(indices): Decode multiple frames efficiently
• get_batch_range(start, end, step): Decode frame range
• create_encoder(output_path): Create video encoder
• __getitem__(index): Frame access via reader[42] or reader[0:100:10]

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Documentation

• Full Usage Guide - Complete API reference
• Changelog - Version history
• Benchmarks - Performance comparisons

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Requirements

• Python: 3.8+
• PyTorch: 2.0+ (with CUDA support for GPU acceleration)
• CUDA: 11.8+ (for NVDEC/NVENC)
• OS: Windows 10/11, Linux (Ubuntu 20.04+)

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Building from Source

git clone https://github.com/NevermindNilas/NeLux.git
cd NeLux

# Install dependencies
pip install -r requirements.txt

# Build (requires CMake, CUDA toolkit, FFmpeg)
python setup.py build_ext --inplace

See BUILD.md for detailed build instructions.

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License

This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0). See the LICENSE file for details.

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Acknowledgments

• FFmpeg: The backbone of video processing in NeLux
• PyTorch: For tensor operations and CUDA integration
• libyuv: For fast CPU color conversion
• Contributors: Thanks to everyone who has contributed to NeLux!