pynvvideocodec 2.0.5

pynvvideocodec (PyNvVideoCodec) is NVIDIA's Python library for hardware-accelerated video encode/decode on NVIDIA GPUs.

PyNvVideoCodec

PyNvVideoCodec is NVIDIA's Python-based library that provides simple yet powerful Python APIs for hardware-accelerated video encoding and decoding on NVIDIA GPUs.

PyNvVideoCodec is built on top of the Video Codec SDK and offers encode, decode, and transcode performance on par with it.

The library is distributed under MIT license and is officially supported by NVIDIA.

Features

Current PyNvVideoCodec version supports following features:

Decode Features

• Decode statistics extraction: Enables extraction of low-level decode statistics—such as QP values, coding-unit types, and motion vectors—from H.264 and H.265 streams. These statistics provide valuable insights into video quality, content complexity, and encoder behaviour.
• Seek and frame sampling: Provides efficient and flexible methods for fetching video frames in various modes, including sequential, random, periodic, indexed, batched, and sliced, as well as at a specified target frame rate.
• Decoder caching: Optimizes decoding of short video clips through decoder caching and reconfiguration.
• Threaded decoder: Supports decoding on separate threads, delivering pre-decoded frames with near-zero latency, enabling high-performance video processing pipelines.
• Video processing from buffer: Supports video processing from memory buffers, reducing I/O overhead, enabling streaming applications.
• Low latency decode: Offers zero-latency decoding for video sequences that do not contain B-frames.
• SEI extraction: Supports the extraction of Supplemental Enhancement Information (SEI) messages, allowing access to additional information such as HDR information, timecodes, and custom user data.
• Stream metadata access: Enables access to stream metadata, including frame width, height, bit depth, and keyframe indices, to enhance content management.
• GIL handling: Improved multithreaded performance through better handling of Global Interpreter Lock (GIL) in C++ layer.
• Multi-GPU decode: Enables multi-GPU decoding to efficiently handle larger workloads.
• Extended codec support: Supports codecs H.264, HEVC, AV1, VP8, VP9, VC1, MPEG4, MPEG2, and MPEG1
• 4:2:2 decode: Supports 4:2:2 decoding for both H.264 and HEVC formats on Blackwell GPUs (NV16, P210 and P216 surface formats).
• Extended output formats : Decode to various output formats including NV12, YUV420, YUV444, NV16, P010, P016 and RGB24(interleaved and planar)

Encode Features

• Encoder reconfiguration: Supports encoder reconfiguration, enabling dynamic updating of encoding parameters without recreating encoder instances.
• SEI insertion: Allows insertion of SEI messages during encoding.
• GIL handling: Improved multithreaded performance through better handling of Global Interpreter Lock (GIL) in C++ layer.
• Multi-GPU encode: Enables multi-GPU encoding to efficiently handle larger workloads.
• Codec support: Support encoding to codec H.264, HEVC, and AV1.
• 4:2:2 encode: Supports 4:2:2 encoding for both H.264 and HEVC formats on Blackwell GPUs (NV16 and P210 surface formats).
• Extended input formats: Encode from various input formats including NV12, YV12, IYUV, YUV444, YUV420_10BIT, YUV444_10BIT, NV16, P210, ARGB, ABGR, ARGB10, and ABGR10.

Transcode Features

• Segment-based transcode: Enables transcoding of video segments based on timestamp ranges, ideal for content editing and partial processing.

What's New

v 2.1 (Latest)

• Decode statistics extraction: Enables extraction of low-level decode statistics—such as QP values, coding-unit types, and motion vectors—from H.264 and H.265 streams. These statistics provide valuable insights into video quality, content complexity, and encoder behaviour.

• Jupyter notebooks: Added two step-by-step, interactive Jupyter notebook tutorials—one demonstrating the SimpleDecoder API for easy video decoding and flexible frame sampling, and another showcasing the use of ThreadedDecoder in a real-world deep-learning object detection workflow.

• Enhanced sample applications: Simplified, restructured and enhanced sample applications along with documentation to help developers learn, experiment, and build faster.

• Enhanced Documentation: Comprehensive API reference and programming guide, enriched with practical code snippets to help developers quickly understand and use the library.

Distribution

PyNvVideoCodec library is distributed in two formats: binary distribution via PyPI and source code distribution via NVIDIA NGC. In both cases, the library and its dependencies can be installed using a single pip install command.

This package on PyPI contains Python WHLs of the PyNvVideoCodec library and sample applications that demonstrate the use of the PyNvVideoCodec API. To install these please open the shell prompt, and run the following command.

$ pip install pynvvideocodec

Sample Applications and Documents

• A package containing PyNvVideoCodec source code, Python sample applications and documentation can be downloaded from NVIDIA NGC.
• For your convenience, the documents are also accessible online at PyNvVideoCodec Online Documentation.