evaluation-toolkit | compute metric scores , create figures

by lightfield-analysis Python Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | evaluation-toolkit Summary

evaluation-toolkit is a Python library. evaluation-toolkit has no bugs, it has no vulnerabilities, it has build file available and it has low support. However evaluation-toolkit has a Non-SPDX License. You can download it from GitHub.

Per default, the toolkit expects the following file structure. You may adjust the settings.py if you prefer another setup. algo_results: Place the results and runtimes of our method into this directory to run the evaluation on your method. If you want to compare your results to other benchmark participants, download their disparity maps of the stratified and training scenes here (snapshot 10.07.2017) and place it next to your method. data: Contains the config files for all scenes, but no light field data. If you want to run the evaluation please download the scene data from our website and place it into this directory. The easiest way is to download and extract the "benchmark.zip" from the list of download links that you receive via email. evaluation: The target directory for scores and figures that will be created during the evaluation. source: Contains the Python 2.7 source code. Feel free to adjust and extend it according to your needs.

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Quality

Security

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Support

evaluation-toolkit has a low active ecosystem.

It has 30 star(s) with 13 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. On average issues are closed in 818 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of evaluation-toolkit is current.

Quality

evaluation-toolkit has no bugs reported.

Security

evaluation-toolkit has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

evaluation-toolkit has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

evaluation-toolkit releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed evaluation-toolkit and discovered the below as its top functions. This is intended to give you an instant insight into evaluation-toolkit implemented functionality, and help decide if they suit your requirements.

Plots Fattening transformation
Returns the background extrapolation
Calculate bin scores for each bin
Returns the fractional extrapolation
Plot the algorithm overview
Get image data
Get the center view of the image
Plot a matplotlib figure
Compute the bad pixels for each algorithm
Validate submission
Plot the algorithm overview
Collect the scores for the given algorithms
Compute the score for the given algorithm
Visualize masks
Compute the best disparity estimate
Parse command line arguments
Get the center view of the image
Plot the benchmark scene overview
Plot error vs noise
Plots ground truth normals in scene
Plots normals for a set of algorithms
Plot the similarity between two scenes
Evaluate algorithm results
Plots a scatter plot of the given algorithms
Plots the radar charts
Plots an overview of algorithms
Plot a basic overview of the given algorithms

Get all kandi verified functions for this library.

evaluation-toolkit Key Features

No Key Features are available at this moment for evaluation-toolkit.

evaluation-toolkit Examples and Code Snippets

No Code Snippets are available at this moment for evaluation-toolkit.

Community Discussions

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Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install evaluation-toolkit

You can download it from GitHub.
You can use evaluation-toolkit like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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