Acid Cam v2 for macOS distorts video to create art.
- glitch-art
- augmented-reality
- art
- video-processing
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Acid Cam v2 for macOS distorts video to create art.
Antialiased primitives library for GameMaker
Vertex Buffers from Blender to Game Maker Studio 2
Pixel art upscaling shader for awkward resolutions for GameMaker
HistoClean is a tool for the preprocessing and augmentation of images used in deep learning models. This easy to use application brings together the most popular image processing packages from across the python universe, meaning no more looking at documentation! HistoClean provides real time feedback to augmentations and preprocessing options. This allows users to evaluate their steps before implementation.
GID (Generic Image Decoder): a standalone, portable generic Ada package for decoding images
for GameMaker 2.3+
Adding color to real-time scene reconstruction
A Python library which extracts strokes (outlines) from an SVG file as a series of line segments appropriate for driving pen plotters
Include code from external files into your shaders.
Logi is light-weight C++ Vulkan abstraction framework, which attempts to bridge the gap between previous graphics APIs and Vulkan by making API easier to use while still preserving Vulkan semantics and introducing as little overhead as possible.
A Voronoi-pixels effect for GMS 2.3
⚡ An Augmented Reality real-world length measuring web application built by the modification of the example being provided by babylonjs - concept of using WebXR HitTarget feature.
This javascript webapp converts GLSL code from shadertoy.com to Gamemaker GLSL ES as well as output other necessary code to run.
#ZVQ - Zebrafish Vascular Quantification - https://doi.org/10.1242/dev.199720
QuimP is software for tracking cellular shape changes and dynamic distributions of fluorescent reporters at the cell membrane.
DetecDiv provides a comprehensive set of tools to analyze time microscopy images using deep learning methods. The software structure is such that data can be processed either at the command line or using a graphical user-interface. Detecdiv classification models include : image classification and regression, semantic segmentation, LSTM networks to analyze data and image timeseries.
Using a U-Net for image segmentation, blending predicted patches smoothly is a must to please the human eye.