VIAME - Video and Image Analytics for Marine Environments
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README.md

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VIAME is a computer vision library designed to integrate several image and video processing algorithms together in a common distributed processing framework, majorly targeting marine species analytics. As it contains many common algorithms and compiles several other popular repositories together as a part of its build process, VIAME is also useful as a general computer vision toolkit. The core infrastructure connecting different system components is currently the KWIVER library, which can connect C/C++, python, and matlab nodes together in a graph-like pipeline architecture. Alongside the pipelined image processing system are a number of standalone utilties for model training, output detection visualization, groundtruth annotation, detector/tracker evaluation (a.k.a. scoring), image/video search, and rapid model generation.

Example Capabilities


Search Example Tracking Example Detection Example

Measurement Example Query Example

Documentation

The VIAME manual is more comprehensive, but select entries are also listed below, which include some run examples:

Build and Install Guide <> All Examples <> Core Class and Pipeline Info <> Object Detector Examples
Stereo Measurement Examples <> Embedding Detectors in C++ Code <> How to Integrate Your Own Plugin
Example Integrations <> Example Plugin Templates <> GUIs for Visualization and Annotation <> Detector Training API
Video Search and Rapid Model Generation <> Scoring and Evaluation of Detectors <> KWIVER Overview

Pre-Built Binaries

For a full installation guide, see here. In summary, extract the binaries and place them in a directory of your choosing, for example C:\Program Files\VIAME on Windows or /opt/noaa/viame on Linux. Next, set the PYTHON_INSTALL_DIR and CUDA_INSTALL_DIR variables at the top of the setup_viame.sh (Linux) or setup_viame.bat (Windows) script in the root install folder to point to the location of your installed Anaconda and CUDA distributions. Lastly, run through some of the examples to validate the installation.

Installation Requirements:
RHEL/CentOS 7 64-Bit, Ubuntu 16.04 64-Bit, Windows 7, 8, or 10 64-Bit
Anaconda3 5.2.0 x86_64 (Note: Anaconda3 x86_64, not Anaconda2 or x86)
CUDA 8.0 GA2 x86_64 (If you use GPU support)

Installation Recommendations:
A CUDA-enabled GPU with 8 Gb or more VRAM

Linux Binaries:
VIAME v0.9.7.5 Ubuntu 16.04, 64-Bit, GPU Enabled, CUDA 8.0, Python 3.6, Mirror1
VIAME v0.9.7.5 Ubuntu 16.04, 64-Bit, GPU Enabled, CUDA 8.0, Python 3.6, Mirror2
VIAME v0.9.7.7 RHEL/CentOS 7, 64-Bit, GPU Enabled, CUDA 8.0, Python 3.6, Mirror1
VIAME v0.9.7.7 RHEL/CentOS 7, 64-Bit, GPU Enabled, CUDA 8.0, Python 3.6, Mirror2

Windows Binaries:
VIAME v0.9.7.6 Windows 7/8/10, 64-Bit, GPU Enabled, CUDA 8.0, Python 3.6, Mirror1
VIAME v0.9.7.6 Windows 7/8/10, 64-Bit, GPU Enabled, CUDA 8.0, Python 3.6, Mirror2
VIAME v0.9.7.6 Windows 7/8/10, 64-Bit, CPU Only, Python 3.6, Mirror1
VIAME v0.9.7.6 Windows 7/8/10, 64-Bit, CPU Only, Python 3.6, Mirror2

Quick Build Instructions

More in-depth build instructions can be found here, but VIAME itself can be built either as a super-build, which builds most of its dependencies alongside itself, or standalone. To build VIAME requires, at a minimum, Git, CMake, and a C++ compiler. If using the command line, run the following commands, only replacing [source-directory] and [build-directory] with locations of your choice:

git clone https://github.com/Kitware/VIAME.git [source-directory]

cd [source-directory] && git submodule update --init --recursive

Next, create a build directory and run the following cmake command (or alternatively use the cmake GUI if you are not using the command line interface):

mkdir [build-directory] && cd [build-directory]

cmake -DCMAKE_BUILD_TYPE:STRING=Release [source-directory]

Once your cmake command has completed, you can configure any build flags you want using 'ccmake' or the cmake GUI, and then build with the following command on Linux:

make -j8

Or alternatively by building it in Visual Studio or your compiler of choice on Windows. The '-j8' tells the build to run multi-threaded using 8 threads, this is useful for a faster build though if you get an error it can be difficult to know here it was, in which case running just 'make' might be more helpful. For Windows, currently VS2015 (with only some sub-versions of 2017) are supported. If using CUDA, version 8.0 or 9.0, with CUDNN 6.0 is desired. Other versions have yet to be tested extensively. On Windows it can also be beneficial to use Anaconda to get multiple python packages. Boost Python (turned on by default when Python is enabled) requires Numpy and a few other dependencies.

There are several optional arguments to viame which control which plugins get built, such as those listed below. If a plugin is enabled that depends on another dependency such as OpenCV) then the dependency flag will be forced to on.

Flag Description
VIAME_ENABLE_OPENCV Builds OpenCV and basic OpenCV processes (video readers, simple GUIs)
VIAME_ENABLE_VXL Builds VXL and basic VXL processes (video readers, image filters)
VIAME_ENABLE_CAFFE Builds Caffe and basic Caffe processes (pixel classifiers, FRCNN dependency)
VIAME_ENABLE_PYTHON Turns on support for using python processes
VIAME_ENABLE_PYTORCH Installs all pytorch processes (detectors, classifiers)
VIAME_ENABLE_MATLAB Turns on support for and installs all matlab processes
VIAME_ENABLE_SCALLOP_TK Builds Scallop-TK based object detector plugin
VIAME_ENABLE_YOLO Builds YOLO (Darknet) object detector plugin
VIAME_ENABLE_FASTER_RCNN Builds Faster-RCNN based object detector plugin
VIAME_ENABLE_BURNOUT Builds Burn-Out based pixel classifier plugin
VIAME_ENABLE_UW_CLASSIFIER Builds UW fish classifier plugin

And a number of flags which control which system utilities and optimizations are built, e.g.:

Flag Description
VIAME_ENABLE_CUDA Enables CUDA (GPU) optimizations across all processes (OpenCV, Caffe, etc...)
VIAME_ENABLE_CUDNN Enables CUDNN (GPU) optimizations across all processes
VIAME_ENABLE_VIVIA Builds VIVIA GUIs (tools for making annotations and viewing detections)
VIAME_ENABLE_KWANT Builds KWANT detection and track evaluation (scoring) tools
VIAME_ENABLE_DOCS Builds Doxygen class-level documentation for projects (puts in install share tree)
VIAME_BUILD_DEPENDENCIES Build VIAME as a super-build, building all dependencies (default behavior)
VIAME_INSTALL_EXAMPLES Installs examples for the above modules into install/examples tree
VIAME_DOWNLOAD_MODELS Downloads pre-trained models for use with the examples and training new models

Update Instructions

If you already have a checkout of VIAME and want to switch branches or update your code, it is important to re-run:

git submodule update --init --recursive

After switching branches to ensure that you have on the correct hashes of sub-packages within the build (e.g. fletch or KWIVER). Very rarely you may also need to run:

git submodule sync

Just in case the address of submodules has changed. You only need to run this command if you get a "cannot fetch hash #hashid" error.

Quick Run Instructions

If building from the source, all final compiled binaries are placed in the [build-directory]/install directory, which is the same as the root directory in the pre-built binaries. This will hereby be refered to as the [install-directory].

One way to test the system is to see if you can run the examples in the [install-directory]/examples folder, for example, the pipelined object detectors. There are some environment variables that need to be set up before you can run on Linux or Mac, which are all in the install/setup_viame.sh script. This script is sourced in all of the example run scripts, and similar paths are added in the generated windows .bat example scripts.

Another good initial test is to run the [install-directory]/bin/plugin_explorer program. It will generate a prodigious number of log messages and then list all the loadable algorithms. The output should look as follows:

---- Algorithm search path

Factories that create type "image_object_detector"
---------------------------------------------------------------
Info on algorithm type "image_object_detector" implementation "darknet"
  Plugin name: darknet      Version: 1.0
    Description:        Image object detector using darknet
    Creates concrete type: kwiver::arrows::darknet::darknet_detector
    Plugin loaded from file: /user/viame/build/install/lib/modules/kwiver_algo_darknet_plugin.so
    Plugin module name: arrows.darknet

Factories that create type "track_features"
---------------------------------------------------------------
Info on algorithm type "track_features" implementation "core"
  Plugin name: core      Version: 1.0
    Description:        Track features from frame to frame using feature detection, matching, and
    loop closure.
    Creates concrete type: kwiver::arrows::core::track_features_core
    Plugin loaded from file: /user/viame/build/install/lib/modules/kwiver_algo_core_plugin.so
    Plugin module name: arrows.core

Factories that create type "video_input"
---------------------------------------------------------------
Info on algorithm type "video_input" implementation "vxl"
  Plugin name: vxl      Version: 1.0
    Description:        Use VXL (vidl with FFMPEG) to read video files as a sequence of images.
    Creates concrete type: kwiver::arrows::vxl::vidl_ffmpeg_video_input
    Plugin loaded from file: /user/viame/build/install/lib/modules/kwiver_algo_vxl_plugin.so
    Plugin module name: arrows.vxl

etc...

The plugin loaded line represents the shared objects that have been detected and loaded. Each shared object can contain multiple algorithms. The algorithm list shows each concrete algorithm that could be loaded and declared in pipeline files. Check the log messages to see if there are any libraries that could not be located.

Each algorithm listed consists of two names. The first name is the type of algorithm and the second is the actual implementation type. For example the entry image_object_detector:hough_circle_detector indicates that it implements the image_object_detector interface and it is a hough_circle_detector.

Algorithms can be instantiated in any program and use a configuration based approach to select which concrete implementation to instantiate.

For a simple pipeline test, go to -

cd [install-directory]/examples/hello_world_pipeline/

or

cd [install-directory]/examples/detector_pipelines/

In those directories, run one of the detector pipelines. Which ENABLE_FLAGS you enabled will control which detector pipelines you can run, and only run scripts with all required dependencies enabled will show up in the install tree. Each script is just performing a call to pipeline runner under the hood, e.g.:

pipeline_runner -p [pipeline-file].pipe

Output detections can then be viewed in the GUI, e.g., see:

[install-directory]/examples/annotation_and_visualization/

License and Citation

VIAME is released under a BSD-3 license.

A system paper summarizing VIAME was published in IEEE WACV 2017 which is available here.