Read Me

Release Notes

Key Features and Enhancements

This release of PyNvVideoCodec includes the following features and enhancements:

Decode Features

• 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.

Limitations and Known Issues

• PyNvVideoCodec uses the FFmpeg binaries for demuxing of audio and video content.

  NVIDIA will not update the FFmpeg binaries included in our release package as these binaries are available, maintained and updated by the FFmpeg open-source community.

  Attention:

  NVIDIA does not provide support for FFMPEG; therefore, it is the responsibility of end users and developers, to stay informed about any vulnerabilities or quality bugs reported against FFMPEG. Users are encouraged to refer to the official FFmpeg website and community forums for the latest updates, patches, and support related to FFmpeg binaries and act as they deem necessary.

Package Contents

This package contains the following:

1. Sample applications demonstrating usage of PyNvVideoCodec APIs for encoding, decoding and transcoding use cases.
   • [.\samples\]
2. Python Bindings
   • [.src\PyNvVideoCodec]
3. Video codec helper classes and utilities
   • [.src\VideoCodecSDKUtils]
4. FFmpeg libraries and source code
   • [.external\ffmpeg]
5. Documents
   • [.docs]
6. Benchmarks contains performance benchmarking scripts for testing various PyNvVideoCodec features including segmented transcoding, decoder caching, and frame sampling capabilities.
   • [.\benchmarks\]

The sample applications provided in the package are for demonstration purposes only and may not be fully tuned for quality and performance. Hence the users are advised to do their independent evaluation for quality and/or performance.

System Requirements

Windows Subsystem for Linux (WSL) Configuration Requirements

• Add the directory /usr/lib/wsl/lib to PATH environment variable, in case it is not added by default. This is required to include path for the WSL libraries.
• Plus all the requirements under System Requirements

Installing PyNvVideoCodec Python Module

The Python module can be installed using following ways.

Installing from PyPI

1. The ready-to-use Python WHL's (Wheel) of the PyNvVideoCodec for Windows and Linux OSes are hosted on PyPI.
2. Open the bash/shell prompt and run:
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   $>pip install PyNvVideoCodec
           

3. This is the recommended way.

Upon installation of the wheel, the sample applications and benchmark scripts are placed in the Python site-packages directory. The specific location of site-packages may vary depending on the operating system and Python environment. The path can be identified by running:

            

            
import site; print(site.getsitepackages())
        

Building and Installing from Source on NVIDIA NGC

The package containing PyNvVideCodec Python module's source code, all dependencies, Python sample applications, and documents is hosted on NVIDIA NGC.

Follow these steps:

1. Download the zip file of the latest package from NVIDIA NGC .
2. Open the bash/shell prompt from the same directory where zip was downloaded and run the following command, replacing "PyNvVideoCodec.zip" with the actual name of the downloaded zip file:
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   $>pip install "PyNvVideoCodec.zip"
           

3. You can access documents and Python sample applications from the package.

Use this method if you need any customization on PyNvVideoCodec Python module e.g. enabling NVTX markers for profiling

Follow these steps to build customized version:

1. Unzip the source package to a directory.
2. Do the necessary modifications to the source.
3. On the same directory where setup.py is located, run the following commands:
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   $>pip install .
           

Running Sample Applications

PyNvVideoCodec includes several sample applications that demonstrate key features and capabilities. These samples provide practical examples of how to implement video processing workflows using the API.

Prerequisites

Before running the samples, ensure you have:

• Installed PyNvVideoCodec following the installation instructions
• NVIDIA GPU with appropriate drivers installed
• Required dependencies installed (as listed in the Read Me)

Decoder Sample Applications

Decode.py - Basic video decoding sample

            

            
python Decode.py -g 0 -i input_video.mp4 -o output_frames_dir -d 1
        

DecodePerf.py - Measures decoder performance

            

            
python DecodePerf.py -g 0 -i input_video.mp4 -d 1 -n 1
        

DecodeMultiprocessing.py - Demonstrates decoder in a multiprocessing setup

            

            
python DecodeMultiprocessing.py -g 0 -i input_video.mp4 -n 3 -f 100
        

DecodeSEIMsgExtraction.py - Demonstrates extracting SEI messages during decoding

            

            
python DecodeSEIMsgExtraction.py -g 0 -i input_video.mp4 -o output.yuv -d 1
        

DemuxFromByteArray.py - Demonstrates demuxing from byte array

            

            
python DemuxFromByteArray.py -i input.ts -o output.yuv -d 1
        

Encoder Sample Applications

Encode.py - Basic video encoding sample

            

            
python Encode.py -g 0 -i input.yuv -o output.h264 -s 1920x1080 -if nv12 -c h264 -json encode_config.json
        

EncodeFromCPUBuffer.py - Demonstrates encoding from host memory buffers

            

            
python EncodeFromCPUBuffer.py -g 0 -i input.yuv -o output.h264 -s 848x464 -if nv12 -c h264 -json encode_config.json
        

EncodeSEIMsgInsertion.py - Demonstrates inserting SEI messages during encoding

            

            
python EncodeSEIMsgInsertion.py -i input.yuv -o output.hevc -g 0 -if NV12 -c hevc -s 1920x1080
        

EncodeReconfigure.py - Demonstrates encoder reconfiguration at runtime

            

            
python EncodeReconfigure.py -i input.yuv -o output.h264 -s 848x464 -if nv12 -c h264 -json encode_config_lowlatency.json
        

EncodePerf.py - Measures encoder performance

            

            
python EncodePerf.py -g 0 -i input.yuv -s 1920x1080 -if NV12 -c h264 -n 3 -f 100
        

EncodeMultiprocessing.py - Demonstrates encoding in a multiprocessing setup

            

            
python EncodeMultiprocessing.py -g 0 -i input.yuv -s 1920x1080 -if NV12 -c h264 -n 3 -f 100
        

Transcoding and Advanced Sample Applications

EnsembleApp.py - Demonstrates various frame retrieval methods and segmented transcoding

            

            
python EnsembleApp.py -i input.mp4 -t timeListInSeconds.txt -d 1 -g 0 -json transcode_config.json -segments segments.txt -o 1 3 4 -so segmented_output.mp4
        

SubsamplingAndReconfigure.py - Demonstrates frame subsampling and decoder reconfiguration

            

            
python SubsamplingAndReconfigure.py -i fileLists.txt -fps 5 -c 1 -d 1 -v 1 -g 0
        

ObjectDetection.py - Demonstrates integration with AI model for object detection

            

            
python ObjectDetection.py -i test_video.mp4 -d -c 0.5
        

JSON Encoder Configuration Parameters

Many of the samples accept a JSON configuration file for encoder parameters. Here are the key parameters:

Notice

This document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. NVIDIA Corporation (“NVIDIA”) makes no representations or warranties, expressed or implied, as to the accuracy or completeness of the information contained in this document and assumes no responsibility for any errors contained herein. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality.

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Trademarks

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