# MIVisionX
**Repository Path**: choumin0930/MIVisionX
## Basic Information
- **Project Name**: MIVisionX
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-11-02
- **Last Updated**: 2024-06-02
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[](https://opensource.org/licenses/MIT)
[](https://gpuopen-professionalcompute-libraries.github.io/MIVisionX/)
[](https://travis-ci.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX)
MIVisionX toolkit is a set of comprehensive computer vision and machine intelligence libraries, utilities, and applications bundled into a single toolkit. AMD MIVisionX delivers highly optimized conformant open-source implementation of the Khronos OpenVX™ and OpenVX™ Extensions along with Convolution Neural Net Model Compiler & Optimizer supporting ONNX, and Khronos NNEF™ exchange formats. The toolkit allows for rapid prototyping and deployment of optimized computer vision and machine learning inference workloads on a wide range of computer hardware, including small embedded x86 CPUs, APUs, discrete GPUs, and heterogeneous servers.
#### Latest Release
[](https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX/releases)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Documentation](#documentation)
- [AMD OpenVX™](#amd-openvx)
- [AMD OpenVX™ Extensions](#amd-openvx-extensions)
- [Applications](#applications)
- [Neural Net Model Compiler \& Optimizer](#neural-net-model-compiler--optimizer)
- [rocAL](#rocal)
- [Toolkit](#toolkit)
- [Utilities](#utilities)
- [Prerequisites](#prerequisites)
- [Hardware](#hardware)
- [Operating System \& Prerequisites](#operating-system--prerequisites)
- [Windows](#windows)
- [macOS](#macos)
- [Linux](#linux)
- [Prerequisites setup script for Linux](#prerequisites-setup-script-for-linux)
- [Prerequisites for running the script](#prerequisites-for-running-the-script)
- [Build \& Install MIVisionX](#build--install-mivisionx)
- [Windows](#windows-1)
- [Using `Visual Studio`](#using-visual-studio)
- [macOS](#macos-1)
- [Linux](#linux-1)
- [Using `apt-get` / `yum` / `zypper`](#using-apt-get--yum--zypper)
- [Using `MIVisionX-setup.py`](#using-mivisionx-setuppy)
- [Verify the Installation](#verify-the-installation)
- [Verifying on Linux / macOS](#verifying-on-linux--macos)
- [Verifying on Windows](#verifying-on-windows)
- [Docker](#docker)
- [MIVisionX Docker](#mivisionx-docker)
- [Docker Workflow on Ubuntu `20.04`/`22.04`](#docker-workflow-on-ubuntu-20042204)
- [Prerequisites](#prerequisites-1)
- [Workflow](#workflow)
- [Run docker image: Local Machine](#run-docker-image-local-machine)
- [**Option 1**: Map localhost directory on the docker image](#option-1-map-localhost-directory-on-the-docker-image)
- [**Option 2**: Display with docker](#option-2--display-with-docker)
- [Run docker image with display: Remote Server Machine](#run-docker-image-with-display-remote-server-machine)
- [Technical Support](#technical-support)
- [Release Notes](#release-notes)
- [Latest Release Version](#latest-release-version)
- [Changelog](#changelog)
- [Tested configurations](#tested-configurations)
- [Known issues](#known-issues)
- [MIVisionX Dependency Map](#mivisionx-dependency-map)
- [HIP Backend](#hip-backend)
## Documentation
Run the steps below to build documentation locally.
* sphinx documentation
```Bash
cd docs
pip3 install -r sphinx/requirements.txt
python3 -m sphinx -T -E -b html -d _build/doctrees -D language=en . _build/html
```
* Doxygen
```
doxygen .Doxyfile
```
## AMD OpenVX™
[AMD OpenVX™](amd_openvx/README.md) is a highly optimized conformant open source implementation of the Khronos OpenVX™ 1.3 computer vision specification. It allows for rapid prototyping as well as fast execution on a wide range of computer hardware, including small embedded x86 CPUs and large workstation discrete GPUs.
Khronos OpenVX™ 1.0.1 conformant implementation is available in [MIVisionX Lite](https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX/tree/openvx-1.0.1)
## AMD OpenVX™ Extensions
The OpenVX framework provides a mechanism to add new vision functionality to OpenVX by vendors. This project has below mentioned OpenVX [modules](amd_openvx_extensions/README.md) and utilities to extend [amd_openvx](amd_openvx/README.md), which contains the AMD OpenVX™ Core Engine.
* [amd_loomsl](amd_openvx_extensions/amd_loomsl/README.md): AMD Radeon Loom stitching library for live 360 degree video applications
* [amd_media](amd_openvx_extensions/amd_media/README.md): `vx_amd_media` is an OpenVX AMD media extension module for encode and decode
* [amd_migraphx](amd_openvx_extensions/amd_migraphx/README.md): amd_migraphx extension integrates the AMD's MIGraphx into an OpenVX graph. This extension allows developers to combine the vision funcions in OpenVX with the MIGraphX and build an end-to-end application for inference.
* [amd_nn](amd_openvx_extensions/amd_nn/README.md): OpenVX neural network module
* [amd_opencv](amd_openvx_extensions/amd_opencv/README.md): OpenVX module that implements a mechanism to access OpenCV functionality as OpenVX kernels
* [amd_rpp](amd_openvx_extensions/amd_rpp/README.md): OpenVX extension providing an interface to some of the [RPP](https://github.com/GPUOpen-ProfessionalCompute-Libraries/rpp)'s (ROCm Performance Primitives) functions. This extension is used to enable [rocAL](rocAL/README.md) to perform image augmentation.
* [amd_winml](amd_openvx_extensions/amd_winml/README.md): WinML extension will allow developers to import a pre-trained ONNX model into an OpenVX graph and add hundreds of different pre & post processing `vision` / `generic` / `user-defined` functions, available in OpenVX and OpenCV interop, to the input and output of the neural net model. This will allow developers to build an end to end application for inference.
## Applications
MIVisionX has several [applications](apps/README.md#applications) built on top of OpenVX modules, it uses AMD optimized libraries to build applications that can be used to prototype or use as a model to develop products.
* [Bubble Pop](apps/bubble_pop/README.md#vx-bubble-pop-sample): This sample application creates bubbles and donuts to pop using OpenVX & OpenCV functionality.
* [Cloud Inference Application](apps/cloud_inference/README.md#cloud-inference-application): This sample application does inference using a client-server system.
* [Digit Test](apps/dg_test/README.md#amd-dgtest): This sample application is used to recognize hand written digits.
* [Image Augmentation](apps/image_augmentation/README.md#image-augmentation-application): This sample application demonstrates the basic usage of rocAL's C API to load JPEG images from the disk and modify them in different possible ways and displays the output images.
* [MIVisionX Inference Analyzer](apps/mivisionx_inference_analyzer/README.md#mivisionx-python-inference-analyzer): This sample application uses pre-trained `ONNX` / `NNEF` / `Caffe` models to analyze and summarize images.
* [MIVisionX OpenVX Classification](apps/README.md#mivisionx-openvx-classsification): This sample application shows how to run supported pre-trained caffe models with MIVisionX RunTime.
* [MIVisionX Validation Tool](apps/mivisionx_validation_tool/README.md#mivisionx-python-ml-model-validation-tool): This sample application uses pre-trained `ONNX` / `NNEF` / `Caffe` models to analyze, summarize and validate models.
* [MIVisionX WinML Classification](apps/README.md#mivisionx-winml-classification): This sample application shows how to run supported ONNX models with MIVisionX RunTime on Windows.
* [MIVisionX WinML YoloV2](apps/README.md#mivisionx-winml-yolov2): This sample application shows how to run tiny yolov2(20 classes) with MIVisionX RunTime on Windows.
* [Optical Flow](apps/optical_flow/README.md#openvx-samples): This sample application creates an OpenVX graph to run Optical Flow on a video/live.
* [External Applications](apps/README.md#external-application)
## Neural Net Model Compiler & Optimizer
[Neural Net Model Compiler & Optimizer](model_compiler/README.md#neural-net-model-compiler--optimizer) converts pre-trained neural net models to MIVisionX runtime code for optimized inference.
## rocAL
The ROCm Augmentation Library - [rocAL](rocAL/README.md) is designed to efficiently decode and process images and videos from a variety of storage formats and modify them through a processing graph programmable by the user.
## Toolkit
[MIVisionX Toolkit](toolkit/README.md), is a comprehensive set of helpful tools for neural net creation, development, training, and deployment. The Toolkit provides you with helpful tools to design, develop, quantize, prune, retrain, and infer your neural network work in any framework. The Toolkit is designed to help you deploy your work to any AMD or 3rd party hardware, from embedded to servers.
MIVisionX provides you with tools for accomplishing your tasks throughout the whole neural net life-cycle, from creating a model to deploying them for your target platforms.
## Utilities
* [loom_shell](utilities/loom_shell/README.md#radeon-loomsh): an interpreter to prototype 360 degree video stitching applications using a script
* [mv_deploy](utilities/mv_deploy/README.md): consists of a model-compiler and necessary header/.cpp files which are required to run inference for a specific NeuralNet model
* [RunCL](utilities/runcl/README.md#amd-runcl): command-line utility to build, execute, and debug OpenCL programs
* [RunVX](utilities/runvx/README.md#amd-runvx): command-line utility to execute OpenVX graph described in GDF text file
## Prerequisites
### Hardware
* **CPU**: [AMD64](https://docs.amd.com/bundle/Hardware_and_Software_Reference_Guide/page/Hardware_and_Software_Support.html)
* **GPU**: [AMD Radeon™ Graphics](https://docs.amd.com/bundle/Hardware_and_Software_Reference_Guide/page/Hardware_and_Software_Support.html) [optional]
* **APU**: [AMD Radeon™ `Mobile`/`Embedded`](https://docs.amd.com/bundle/Hardware_and_Software_Reference_Guide/page/Hardware_and_Software_Support.html) [optional]
**Note:** Some modules in MIVisionX can be built for `CPU ONLY`. To take advantage of `Advanced Features And Modules` we recommend using `AMD GPUs` or `AMD APUs`.
### Operating System & Prerequisites
#### Windows
* Windows `10` / `11`
* Windows SDK
* Visual Studio 2019 or later
* Install the latest AMD [drivers](https://www.amd.com/en/support)
* Install [OpenCL SDK](https://github.com/GPUOpen-LibrariesAndSDKs/OCL-SDK/releases/tag/1.0)
* Install [OpenCV 4.6.0](https://github.com/opencv/opencv/releases/tag/4.6.0)
+ Set `OpenCV_DIR` environment variable to `OpenCV/build` folder
+ Add `%OpenCV_DIR%\x64\vc14\bin` or `%OpenCV_DIR%\x64\vc15\bin` to your `PATH`
#### macOS
* macOS - Ventura `13.4`
* Install [Homebrew](https://brew.sh)
* Install [CMake](https://cmake.org)
* Install OpenCV `3`/`4`
**Note:** macOS [build instructions](https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX/wiki/macOS#macos-build-instructions)
#### Linux
* Linux distribution
+ **Ubuntu** - `20.04` / `22.04`
+ **CentOS** - `7` / `8`
+ **RedHat** - `8` / `9`
+ **SLES** - `15-SP4`
* Install [ROCm](https://rocmdocs.amd.com/en/latest/deploy/linux/installer/install.html) with `--usecase=graphics,rocm`
* CMake 3.5 or later
* MIOpen for [vx_nn](amd_openvx_extensions/amd_nn/README.md#openvx-neural-network-extension-library-vx_nn) extension
* MIGraphX for `vx_migraphx` extension
* [Protobuf](https://github.com/google/protobuf)
* [OpenCV 4.6.0](https://github.com/opencv/opencv/releases/tag/4.6.0)
* [FFMPEG n4.4.2](https://github.com/FFmpeg/FFmpeg/releases/tag/n4.4.2)
* [rocAL](rocAL/README.md#prerequisites) Prerequisites
### Prerequisites setup script for Linux
For the convenience of the developer, we provide the setup script `MIVisionX-setup.py` which will install all the dependencies required by this project.
**NOTE:** This script only needs to be executed once.
#### Prerequisites for running the script
* Linux distribution
+ Ubuntu - `20.04` / `22.04`
+ CentOS - `7` / `8`
+ RedHat - `8` / `9`
+ SLES - `15-SP4`
* [ROCm supported hardware](https://rocm.docs.amd.com/en/latest/release/gpu_os_support.html)
* Install [ROCm](https://rocmdocs.amd.com/en/latest/deploy/linux/installer/install.html) with `--usecase=graphics,rocm`
**usage:**
```
python MIVisionX-setup.py --directory [setup directory - optional (default:~/)]
--opencv [OpenCV Version - optional (default:4.6.0)]
--protobuf [ProtoBuf Version - optional (default:3.12.4)]
--rpp [RPP Version - optional (default:1.0.0)]
--pybind11 [PyBind11 Version - optional (default:v2.10.4)]
--ffmpeg [FFMPEG V4.4.2 Installation - optional (default:ON) [options:ON/OFF]]
--rocal [MIVisionX rocAL Dependency Install - optional (default:ON) [options:ON/OFF]]
--neural_net[MIVisionX Neural Net Dependency Install - optional (default:ON) [options:ON/OFF]]
--inference [MIVisionX Neural Net Inference Dependency Install - optional (default:ON) [options:ON/OFF]]
--developer [Setup Developer Options - optional (default:OFF) [options:ON/OFF]]
--reinstall [Remove previous setup and reinstall (default:OFF)[options:ON/OFF]]
--backend [MIVisionX Dependency Backend - optional (default:HIP) [options:HIP/OCL/CPU]]
--rocm_path [ROCm Installation Path - optional (default:/opt/rocm) - ROCm Installation Required]
```
**Note:**
* **ROCm upgrade** requires the setup script **rerun**.
* use `X Window` / `X11` for [remote GUI app control](https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX/wiki/X-Window-forwarding)
## Build & Install MIVisionX
### Windows
#### Using `Visual Studio`
* Install [Windows Prerequisites](#windows)
* Use `MIVisionX.sln` to build for x64 platform
**NOTE:** `vx_nn` is not supported on `Windows` in this release
### macOS
macOS [build instructions](https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX/wiki/macOS#macos-build-instructions)
### Linux
* [ROCm supported hardware](https://rocm.docs.amd.com/en/latest/release/gpu_os_support.html)
* Install [ROCm](https://rocmdocs.amd.com/en/latest/deploy/linux/installer/install.html) with `--usecase=graphics,rocm`
#### Using `apt-get` / `yum` / `zypper`
* On `Ubuntu`
```
sudo apt-get install mivisionx
```
* On `CentOS`/`RedHat`
```
sudo yum install mivisionx
```
* On `SLES`
```
sudo zypper install mivisionx
```
**Note:**
* `vx_winml` is not supported on `Linux`
* source code will not available with `apt-get` / `yum` / `zypper` install
* the installer will copy
+ Executables into `/opt/rocm/bin`
+ Libraries into `/opt/rocm/lib`
+ OpenVX and module header files into `/opt/rocm/include/mivisionx`
+ Model compiler, & toolkit folders into `/opt/rocm/libexec/mivisionx`
+ Apps, & samples folder into `/opt/rocm/share/mivisionx`
+ Docs folder into `/opt/rocm/share/doc/mivisionx`
* Package (.deb & .rpm) install requires `OpenCV v4.6` to execute `AMD OpenCV extensions`
#### Using `MIVisionX-setup.py`
* Clone MIVisionX git repository
```
git clone https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX.git
```
**Note:** MIVisionX has support for two GPU backends: **OPENCL** and **HIP**:
* Instructions for building MIVisionX with the **HIP** GPU backend (i.e., default GPU backend):
+ run the setup script to install all the dependencies required by the **HIP** GPU backend:
```
cd MIVisionX
python MIVisionX-setup.py
```
+ run the below commands to build MIVisionX with the **HIP** GPU backend:
```
mkdir build-hip
cd build-hip
cmake ../
make -j8
sudo cmake --build . --target PyPackageInstall
sudo make install
```
+ run tests - [test option instructions](https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX/wiki/CTest)
```
make test
```
**Note:**
+ `PyPackageInstall` used for rocal_pybind installation
+ rocal_pybind not supported on windows.
+ `sudo` required for pybind installation
* Instructions for building MIVisionX with [**OPENCL** GPU backend](https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX/wiki/OpenCL-Backend)
## Verify the Installation
### Verifying on Linux / macOS
* The installer will copy
+ Executables into `/opt/rocm/bin`
+ Libraries into `/opt/rocm/lib`
+ OpenVX and OpenVX module header files into `/opt/rocm/include/mivisionx`
+ Apps, & Samples folder into `/opt/rocm/share/mivisionx`
+ Documents folder into `/opt/rocm/share/doc/mivisionx`
+ Model Compiler, and Toolkit folder into `/opt/rocm/libexec/mivisionx`
* Run the below sample to verify the installation
**Canny Edge Detection**
```
export PATH=$PATH:/opt/rocm/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/rocm/lib
runvx /opt/rocm/share/mivisionx/samples/gdf/canny.gdf
```
**Note:** More samples are available [here](samples#samples)
**Note:** For `macOS` use `export DYLD_LIBRARY_PATH=$DYLD_LIBRARY_PATH:/opt/rocm/lib`
### Verifying on Windows
* MIVisionX.sln builds the libraries & executables in the folder `MIVisionX/x64`
* Use RunVX to test the build
```
./runvx.exe PATH_TO/MIVisionX/samples/gdf/skintonedetect.gdf
```
## Docker
MIVisionX provides developers with docker images for Ubuntu `20.04` / `22.04`. Using docker images developers can quickly prototype and build applications without having to be locked into a single system setup or lose valuable time figuring out the dependencies of the underlying software.
Docker files to build MIVisionX containers are [available](docker#mivisionx-docker)
### MIVisionX Docker
* [Ubuntu 20.04](https://cloud.docker.com/repository/docker/mivisionx/ubuntu-20.04)
* [Ubuntu 22.04](https://cloud.docker.com/repository/docker/mivisionx/ubuntu-22.04)
### Docker Workflow on Ubuntu `20.04`/`22.04`
#### Prerequisites
* Ubuntu `20.04`/`22.04`
* [ROCm supported hardware](https://rocm.docs.amd.com/en/latest/release/gpu_os_support.html)
* Install [ROCm](https://rocmdocs.amd.com/en/latest/deploy/linux/installer/install.html) with `--usecase=graphics,rocm`
* [Docker](https://docs.docker.com/engine/install/ubuntu/)
#### Workflow
* **Step 1** - Get latest docker image
```
sudo docker pull mivisionx/ubuntu-20.04:latest
```
* **NOTE:** Use the above command to bring in latest changes from upstream
* **Step 2** - Run docker image
### Run docker image: Local Machine
```
sudo docker run -it --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mem --cap-add=SYS_RAWIO --group-add video --shm-size=4g --ipc="host" --network=host mivisionx/ubuntu-20.04:latest
```
* **Test** - Computer Vision Workflow
```
python3 /workspace/MIVisionX/tests/vision_tests/runVisionTests.py --num_frames 1
```
* **Test** - Neural Network Workflow
```
python3 /workspace/MIVisionX/tests/neural_network_tests/runNeuralNetworkTests.py --profiler_level 1
```
* **Test** - Khronos OpenVX 1.3.0 Conformance Test
```
python3 /workspace/MIVisionX/tests/conformance_tests/runConformanceTests.py --backend_type HOST
```
#### **Option 1**: Map localhost directory on the docker image
* option to map the localhost directory with data to be accessed on the docker image
* **usage**: -v {LOCAL_HOST_DIRECTORY_PATH}:{DOCKER_DIRECTORY_PATH}
```
sudo docker run -it -v /home/:/root/hostDrive/ -privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mem --cap-add=SYS_RAWIO --group-add video --shm-size=4g --ipc="host" --network=host mivisionx/ubuntu-20.04:latest
```
#### **Option 2**: Display with docker
* Using host display for docker
```
xhost +local:root
sudo docker run -it --privileged --device=/dev/kfd --device=/dev/dri --cap-add=SYS_RAWIO --device=/dev/mem --group-add video --network host --env DISPLAY=$DISPLAY --volume="$HOME/.Xauthority:/root/.Xauthority:rw" --volume /tmp/.X11-unix/:/tmp/.X11-unix mivisionx/ubuntu-20.04:latest
```
* **Test** display with MIVisionX sample
```
runvx -v /opt/rocm/share/mivisionx/samples/gdf/canny.gdf
```
### Run docker image with display: Remote Server Machine
```
sudo docker run -it --privileged --device=/dev/kfd --device=/dev/dri --cap-add=SYS_RAWIO --device=/dev/mem --group-add video --network host --env DISPLAY=$DISPLAY --volume="$HOME/.Xauthority:/root/.Xauthority:rw" --volume /tmp/.X11-unix/:/tmp/.X11-unix mivisionx/ubuntu-20.04:latest
```
* **Test** display with MIVisionX sample
```
runvx -v /opt/rocm/share/mivisionx/samples/gdf/canny.gdf
```
## Technical Support
Please email `mivisionx.support@amd.com` for questions, and feedback on MIVisionX.
Please submit your feature requests, and bug reports on the [GitHub issues](https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX/issues) page.
## Release Notes
### Latest Release Version
[](https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX/releases)
### Changelog
Review all notable [changes](CHANGELOG.md#changelog) with the latest release
### Tested configurations
* Windows `10` / `11`
* Linux distribution
+ Ubuntu - `20.04` / `22.04`
+ CentOS - `7` / `8`
+ RHEL - `8` / `9`
+ SLES - `15-SP4`
* ROCm: rocm-core - `5.7.0.50700-6`
* miopen-hip - `2.20.0.50700-63`
* migraphx - `2.7.0.50700-63`
* Protobuf - [V3.12.4](https://github.com/protocolbuffers/protobuf/releases/tag/v3.12.4)
* OpenCV - [4.6.0](https://github.com/opencv/opencv/releases/tag/4.6.0)
* RPP - [1.4.0](https://github.com/GPUOpen-ProfessionalCompute-Libraries/rpp/releases/tag/1.4.0)
* FFMPEG - [n4.4.2](https://github.com/FFmpeg/FFmpeg/releases/tag/n4.4.2)
* Dependencies for all the above packages
* MIVisionX Setup Script - `V2.5.6`
### Known issues
* OpenCV 4.X support for some apps missing
* MIVisionX Package install requires manual prerequisites installation
## MIVisionX Dependency Map
### HIP Backend
**Docker Image:** `sudo docker build -f docker/ubuntu20/{DOCKER_LEVEL_FILE_NAME}.dockerfile -t {mivisionx-level-NUMBER} .`
-  `new component added to the level`
-  `existing component from the previous level`
| Build Level | MIVisionX Dependencies | Modules | Libraries and Executables | Docker Tag |
| ----------- | -------------------------------------------------- | ------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `Level_1` | cmake
gcc
g++ | amd_openvx
utilities |  `libopenvx.so` - OpenVX™ Lib - CPU
 `libvxu.so` - OpenVX™ immediate node Lib - CPU
 `runvx` - OpenVX™ Graph Executor - CPU with Display OFF | [](https://hub.docker.com/repository/docker/kiritigowda/ubuntu-18.04) |
| `Level_2` | ROCm HIP
+Level 1 | amd_openvx
amd_openvx_extensions
utilities |  `libopenvx.so` - OpenVX™ Lib - CPU/GPU
 `libvxu.so` - OpenVX™ immediate node Lib - CPU/GPU
 `runvx` - OpenVX™ Graph Executor - Display OFF | [](https://hub.docker.com/repository/docker/kiritigowda/ubuntu-18.04) |
| `Level_3` | OpenCV
FFMPEG
+Level 2 | amd_openvx
amd_openvx_extensions
utilities |  `libopenvx.so` - OpenVX™ Lib
 `libvxu.so` - OpenVX™ immediate node Lib
 `libvx_amd_media.so` - OpenVX™ Media Extension
 `libvx_opencv.so` - OpenVX™ OpenCV InterOp Extension
 `mv_compile` - Neural Net Model Compile
 `runvx` - OpenVX™ Graph Executor - Display ON | [](https://hub.docker.com/repository/docker/kiritigowda/ubuntu-18.04) |
| `Level_4` | MIOpenGEMM
MIOpen
ProtoBuf
+Level 3 | amd_openvx
amd_openvx_extensions
apps
utilities |  `libopenvx.so` - OpenVX™ Lib
 `libvxu.so` - OpenVX™ immediate node Lib
 `libvx_amd_media.so` - OpenVX™ Media Extension
 `libvx_opencv.so` - OpenVX™ OpenCV InterOp Extension
 `mv_compile` - Neural Net Model Compile
 `runvx` - OpenVX™ Graph Executor - Display ON
 `libvx_nn.so` - OpenVX™ Neural Net Extension | [](https://hub.docker.com/repository/docker/kiritigowda/ubuntu-18.04) |
| `Level_5` | AMD_RPP
rocAL deps
+Level 4 | amd_openvx
amd_openvx_extensions
apps
rocAL
utilities |  `libopenvx.so` - OpenVX™ Lib
 `libvxu.so` - OpenVX™ immediate node Lib
 `libvx_amd_media.so` - OpenVX™ Media Extension
 `libvx_opencv.so` - OpenVX™ OpenCV InterOp Extension
 `mv_compile` - Neural Net Model Compile
 `runvx` - OpenVX™ Graph Executor - Display ON
 `libvx_nn.so` - OpenVX™ Neural Net Extension
 `libvx_rpp.so` - OpenVX™ RPP Extension
 `librocal.so` - Radeon Augmentation Library
 `rocal_pybind.so` - rocAL Pybind Lib | [](https://hub.docker.com/repository/docker/kiritigowda/ubuntu-18.04) |
**NOTE:** OpenVX and the OpenVX logo are trademarks of the Khronos Group Inc.