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This document provides information on SikuliX, a screen automation tool, and Mitsuba 3, a research-oriented rendering system. SikuliX uses image recognition to automate desktop actions, while Mitsuba 3 offers high-performance rendering with Python integration and differentiability. Both projects detail installation, usage, and contributions.
Paused (not available) till July 2024
What is SikuliXSikuliX automates anything you see on the screen of your desktop computer
running Windows, Mac or some Linux/Unix. It uses image recognition powered by OpenCV to identify
GUI components and can act on them with mouse and keyboard actions.
This is handy in cases when there is no easy access to a GUI's internals or
the source code of the application or web page you want to act on. More details
Great thanks for the new logo and all the help with the new webpage to @Waleed Sadek
2.0.6 (branch release_2.0.x) preparing for release - snapshots available
Latest Upload: April 17th, 2023
Direct IDE downloads
for Windows < > for macOS Intel < > for macOS Silicon Mx < > for Linux <
You get files like sikulixidemac-2.0.6-20210708.194940-1.jar, which you can place wherever you want and rename them to whatever you want.
JAVA: must be Java 11 or later (best places to get it: Eclipse Temurin or Azul)
OCR (macOS/Linux): now using Tess4J/Tesseract 5 - have a Tesseract 5.x ready (tesseract runs on commandline)
OpenCV Support: Windows/macOS have it bundled - for Linux you have to make it ready yourself
more information coming sooner or later ;-)
2.1.0 (branch master) currently not useable - development suspended
Latest stable version is 2.0.5 (still works with Java 8, does not run on Mac mX machines)
Important: Read about changes/issues/enhancements
List of fixes
Get SikuliX ready to use
For use in Java Maven projects the dependency coordinates are:
My Development environment
Contributions are welcome and appreciated
Please respect the following rules and guidelines when contributing
example:
Mitsuba Renderer 3
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Documentation
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Tutorial videos
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PyPI
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Warning
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There currently is a large amount of undocumented and unstable work going on in
the master branch. We'd highly recommend you use our
latest release
until further notice.
If you already want to try out the upcoming changes, please have a look at
this porting guide.
It should cover most of the new features and breaking changes that are coming.
Introduction
Mitsuba 3 is a research-oriented rendering system for forward and inverse light
transport simulation developed at EPFL in Switzerland.
It consists of a core library and a set of plugins that implement functionality
ranging from materials and light sources to complete rendering algorithms.
Mitsuba 3 is retargetable: this means that the underlying implementations and
data structures can transform to accomplish various different tasks. For
example, the same code can simulate both scalar (classic one-ray-at-a-time) RGB transport
or differential spectral transport on the GPU. This all builds on
Dr.Jit, a specialized just-in-time(JIT) compiler developed specifically for this project.
Main Features
Cross-platform: Mitsuba 3 has been tested on Linux (x86_64), macOS
(aarch64, x8664), and Windows (x8664).
High performance: The underlying Dr.Jit compiler fuses rendering code
into kernels that achieve state-of-the-art performance using
an LLVM backend targeting the CPU and a CUDA/OptiX backend
targeting NVIDIA GPUs with ray tracing hardware acceleration.
Python first: Mitsuba 3 is deeply integrated with Python. Materials,
textures, and even full rendering algorithms can be developed in Python,
which the system JIT-compiles (and optionally differentiates) on the fly.
This enables the experimentation needed for research in computer graphics and
other disciplines.
Differentiation: Mitsuba 3 is a differentiable renderer, meaning that it
can compute derivatives of the entire simulation with respect to input
parameters such as camera pose, geometry, BSDFs, textures, and volumes. It
implements recent differentiable rendering algorithms developed at EPFL.
Spectral & Polarization: Mitsuba 3 can be used as a monochromatic
renderer, RGB-based renderer, or spectral renderer. Each variant can
optionally account for the effects of polarization if desired.
Tutorial videos, documentation
We've recorded several YouTube videos that provide a gentle introduction
Mitsuba 3 and Dr.Jit. Beyond this you can find complete Juypter notebooks
covering a variety of applications, how-to guides, and reference documentation
on readthedocs.
Installation
We provide pre-compiled binary wheels via PyPI. Installing Mitsuba this way is as simple as running
pip install mitsuba
on the command line. The Python package includes thirteen variants by default:
scalar_rgb
scalar_spectral
scalarspectralpolarized
llvmadrgb
llvmadmono
llvmadmono_polarized
llvmadspectral
llvmadspectral_polarized
cudaadrgb
cudaadmono
cudaadmono_polarized
cudaadspectral
cudaadspectral_polarized
The first two perform classic one-ray-at-a-time simulation using either a RGB
or spectral color representation, while the latter two can be used for inverse
rendering on the CPU or GPU. To access additional variants, you will need to
compile a custom version of Dr.Jit using CMake. Please see the
documentation
for details on this.
Requirements
Python >= 3.8
(optional) For computation on the GPU: Nvidia driver >= 495.89
(optional) For vectorized / parallel computation on the CPU: LLVM >= 11.1
Usage
Here is a simple "Hello World" example that shows how simple it is to render a
scene using Mitsuba 3 from Python:
# Import the library using the alias "mi"import mitsuba as mi# Set the variant of the renderermi.setvariant('scalarrgb')# Load a scenescene = mi.loaddict(mi.cornellbox())# Render the sceneimg = mi.render(scene)# Write the rendered image to an EXR filemi.Bitmap(img).write('cbox.exr')
Tutorials and example notebooks covering a variety of applications can be found
in the documentation.
About
This project was created by Wenzel Jakob.
Significant features and/or improvements to the code were contributed by
Sébastien Speierer,
Nicolas Roussel,
Merlin Nimier-David,
Delio Vicini,
Tizian Zeltner,
Baptiste Nicolet,
Miguel Crespo,
Vincent Leroy, and
Ziyi Zhang.
When using Mitsuba 3 in academic projects, please cite:
@software{Mitsuba3,title = {Mitsuba 3 renderer},author = {Wenzel Jakob and Sébastien Speierer and Nicolas Roussel and Merlin Nimier-David and Delio Vicini and Tizian Zeltner and Baptiste Nicolet and Miguel Crespo and Vincent Leroy and Ziyi Zhang},note = {https://mitsuba-renderer.org},version = {3.1.1},year = 2022} 
