What is OpenCore

介绍OpenCore是什么东西的文档.

http://www.opencore.net/files/AES34-000040.pdf

 

 

下面是源码中各个模块实现的功能

OpenCORE is the multimedia framework of Android originally contributed by

PacketVideo.  It provides an extensible framework for multimedia rendering and

authoring and video telephony (3G-324M).

 

The following an overview of the directory structure which includes a list of

the top-level directories along with a brief note describing the contents.

 

__

  |-- android  [Contains the components the interface OpenCORE with 

  |             other parts of Android]   

  |-- baselibs [Contains basic libraries for data containers, MIME string

  |             handling, messaging across thread boundaries, etc]

  |-- build_config [Contains top-level build files used to build the libraries

  |                 outside of Android]

  |-- codecs_v2 [Contains the implementations of PV's audio and video 

  |              codecs as well as the OpenMax IL interface layer]

  |-- doc       [Contains the documentation required to interface with

  |              OpenCORE]

  |-- engines   [Contains the implementation of the player and author 

  |              engines as well as a utility for metadata]

  |-- extern_libs_v2 [Contains 3rd-party libraries used by OpenCORE. 

  |                   Currently this directory contains header files 

  |                   defining the Khronos OpenMax IL interface]

  |-- extern_tools_v2 [Contains 3rd-party tools used to build OpenCORE

  |                    indpendently of the Android build system]

  |-- fileformats  [Contains the libraries for parsing a variety of

  |                 fileformats including mp4/3gp,mp3,wav,aac]

  |-- modules [Contains build files for aggregating low-level libraries]

  |-- nodes     [Contains the OpenCORE framework "nodes", which is 

  |              the abstraction used to implement independent multimedia 

  |              processing units that can be connected in a flow graph]

  |-- oscl      [This is the Operating System Compatibility Layer which 

  |              provides the mapping OS APIs as well as some basic 

  |              data structures and utilities]

  |-- protocols [Contains parsers and composers for a variety of network

  |              protocols such as HTTP, RTP/RTCP, RTSP, and SDP]

  |-- pvmi     [Contains fundamental definitions that make up OpenCORE.

  |             The directory name is an abbreviation of PacketVideo 

  |             Multimedia Infrastructure]

  |-- tools_v2  [Contains tools used to build the libraries outside of Android]

 

Within each library, the following directory structure, with a few exceptions,

is implemented to organize the files:

 

__

  |-- build

    |-- make    <- makefile to build outside of Android is here       

  |-- doc       <- directory for any documentation specific to this lib

  |-- include   <- header files that are part of the external interface go here

  |-- src       <- source and internal header files of the library

  |-- test      <- test code (follows a similar structure)

    |-- build

      |-- make

    |-- include

    |-- src

### OpenVINO Visualization Tools and Resources In the context of working with Intel's OpenVINO toolkit, several resources and tools are available that support visualization tasks. The Intel® Distribution of the OpenVINO™ toolkit includes a variety of pre-trained models which can be utilized for various computer vision applications including those requiring visualization capabilities[^2]. For instance, developers often use these models alongside custom-built or third-party software to visualize inference results. To explore specific models suitable for visualization purposes such as object detection, segmentation, etc., one may utilize the model downloader script provided by OpenVINO. This tool allows users to download different types of neural network architectures from an official list file `models.lst`: ```bash python3 /opt/intel/openvino_2021.4.582/deployment_tools/tools/model_downloader/downloader.py --list models.lst ``` This command will fetch all listed models into your local environment where they could potentially serve as components within larger projects involving data presentation through graphical interfaces[^3]. Moreover, while not directly part of the core OpenVINO distribution, external libraries like **ImageProcessor**, mentioned elsewhere in documentation about real-time image manipulation solutions built upon .NET frameworks, might also prove useful when integrating advanced imaging features into web-based platforms supporting visual analytics workflows[^1]. --related questions-- 1. How does one install additional plugins required for enhanced graphics rendering? 2. What kind of output formats do OpenVINO-supported models generate during runtime execution? 3. Can you provide examples of successful implementations combining OpenVINO with other UI technologies? 4. Is there any particular focus area recommended for beginners interested in developing interactive AI-driven apps?
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