VisOpt Slider

Qt-based implementation of VisOpt Slider widget [UIST 2014] for C++ & Python

This repository provides

1. a fully C++ implementation based on Qt5 and
2. a fully Python implementation based on PySide2 (Qt5).

If your applications are based on Qt, it is quite easy to integrate a VisOpt Slider widget into your applications.

Features

VisOpt Slider is a GUI widget consisting of multiple sliders. It is specifically designed for interactive exploration of a high-dimensional scalar-valued function. It has the following special features.

Visualization

VisOpt Slider visualizes the values of the target function along with the sliders in the interface using a colormap.

Optimization

Not available yet. Please refer to the original paper (Koyama et al. 2014) and its extended version (Koyama et al. 2016).

For Python Users

Install

This package can be install via pip:

pip install visoptslider

By this, the dependencies (matplotlib, numpy, PySide2, and their dependencies) will be automatically installed together.

Python Example

from PySide2.QtWidgets import QApplication
import numpy as np
import visoptslider

if __name__ == "__main__":
    app = QApplication()

    # Define a target function
    num_dimensions = 3
    def target_function(x):
        return 1.0 - np.linalg.norm(x)

    # Define a target bound
    upper_bound = np.array([+1.0, +1.0, +1.0])
    lower_bound = np.array([-1.0, -1.0, -1.0])
    maximum_value = 1.0
    minimum_value = 0.0

    # Instantiate and initialize VisOpt Slider
    sliders_widget = visoptslider.SlidersWidget()
    sliders_widget.initialize(num_dimensions=num_dimensions,
                              target_function=target_function,
                              upper_bound=upper_bound,
                              lower_bound=lower_bound,
                              maximum_value=maximum_value,
                              minimum_value=minimum_value)

    # Show VisOpt Sliders
    sliders_widget.show()

    app.exec_()

See python_test/*.py for more detailed examples.

Python Versions

This package is tested on 2.7 and 3.7 only.

For C++ Users

Build

This project is managed by using CMake. It can be built by the standard CMake cycle:

git clone https://github.com/yuki-koyama/visoptslider.git --recursive
mkdir build
cd build
cmake ../visoptslider
make

C++ Example

#include <QApplication>
#include <visoptslider/visoptslider.hpp>

int main(int argc, char *argv[])
{
    QApplication app(argc, argv);

    // Define a target function
    constexpr int num_dimensions = 3;
    constexpr auto target_function = [](const Eigen::VectorXd& x)
    {
        return 1.0 - x.norm();
    };

    // Define a target bound
    const Eigen::Vector3d upper_bound(+ 1.0, + 1.0, + 1.0);
    const Eigen::Vector3d lower_bound(- 1.0, - 1.0, - 1.0);
    constexpr double maximum_value = 1.0;
    constexpr double minimum_value = 0.0;

    // Instantiate and initialize VisOpt Slider
    visopt::SlidersWidget sliders_widget;
    sliders_widget.initialize(num_dimensions,
                              target_function,
                              upper_bound,
                              lower_bound,
                              maximum_value,
                              minimum_value);

    // Show VisOpt Slider
    sliders_widget.show();

    return app.exec();
}

See tests/*.cpp for more detailed examples.

Dependencies

Prerequisites

• Eigen (e.g., brew install eigen / apt install libeigen3-dev)
• Qt5 (e.g., brew install qt / apt install qt5-default)

Included as Submodules

• tinycolormap https://github.com/yuki-koyama/tinycolormap
• optimization-test-functions https://github.com/yuki-koyama/optimization-test-functions

References

• Yuki Koyama, Daisuke Sakamoto, and Takeo Igarashi. 2014. Crowd-Powered Parameter Analysis for Visual Design Exploration. In Proceedings of the 27th annual ACM symposium on User interface software and technology (UIST '14), pp.65-74. DOI: https://doi.org/10.1145/2642918.2647386
  • Project page: https://koyama.xyz/project/CrowdPoweredAnalysis/
• Yuki Koyama, Daisuke Sakamoto, and Takeo Igarashi. 2016. SelPh: Progressive Learning and Support of Manual Photo Color Enhancement. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16), pp.2520--2532. DOI: https://doi.org/10.1145/2858036.2858111
  • Project page: https://koyama.xyz/project/SelPh/

License

MIT License.

Contributing

Issue reports, suggestions, requests, and PRs are highly welcomed.

Many features are currently missing; for example,

• Interactive optimization functionality (Koyama et al. 2014)
• Option to make colors de-saturated (Koyama et al. 2016)
• Options for colormaps (currently, it always uses the viridis colormaps)
• Options for slider directions (currently, it always uses horizontal sliders)
• Tests for the Python package with various Python environments
• Documentation