Search Results for "python compile source code"

... be applicable to SimpleITK. Unlike ITK's support of n-dimensional spatio-temporal images, SimpleITK supports 2D, 3D and 4D images. The dimensionality refers to spatiotemporal dimensions, the voxels can be n-dimensional vectors. Open-source multi-dimensional image analysis in Python, R, Java, C#, Lua, Ruby, TCL and C++. Developed by the Insight Toolkit community for the biomedical sciences and beyond.

scikit-image is a collection of algorithms for image processing. It is available free of charge and free of restriction. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers. scikit-image builds on scipy.ndimage to provide a versatile set of image processing routines in Python. This library is developed by its community, and contributions are most welcome! Read about our mission, vision, and values and how we govern the project. Major proposals...

This repository introduces GIMP3-ML, a set of Python plugins for the widely popular GNU Image Manipulation Program (GIMP). It enables the use of recent advances in computer vision to the conventional image editing pipeline. Applications from deep learning such as monocular depth estimation, semantic segmentation, mask generative adversarial networks, image super-resolution, de-noising and coloring have been incorporated with GIMP through Python-based plugins. Additionally, operations on images...

LaueTools is an open-source project for white beam Laue x-ray microdiffraction data analysis including tools in image processing, peaks searching & indexing, crystal structure solving (orientation & strain) and data & grain mapping visualisation. Python 3 Code and new features are now at: https://gitlab.esrf.fr/micha/lauetools

... at compile-time, used for the kernel instrumentation. It performs extra operations such as cache flushing before the kernel execution, and can set real-time scheduling to prevent OS interference. - Non-null data initialization, and live-out data dump. - Syntactic constructs to prevent any dead code elimination on the kernel. - Parametric loop bounds in the kernels, for general-purpose implementation. - Clear kernel marking, using pragma-based delimiters.