论文标题
GIMP-ML：用于使用GIMP中的计算机视觉模型的Python插件
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP
论文作者
论文摘要
本文介绍了GIMP-ML V1.1，这是一套为广受欢迎的GNU图像操作程序（GIMP）的Python插件。它使计算机视觉中的最新进展用于传统的图像编辑管道。通过基于Python的插件与GIMP合并了来自深度学习的应用，例如单眼深度估计，语义分割，掩蔽生成的对抗网络，图像超分辨率，去噪声，脱落，振荡，启发性和着色。此外，还添加了对基于K均值的颜色聚类等图像的操作。 GIMP-ML依赖于标准的Python软件包，例如Numpy，Pytorch，Open-CV，Scipy。除此之外，使用这些插件的几种图像操纵技术已在YouTube频道（https://youtube.com/user/kritiksoman）中进行了编译和演示，目的是演示用于基于机器学习的图像修改的用例。此外，GIMP-ML还旨在带来使用用于计算机视觉任务的深度学习网络的好处，以例行图像处理工作流程。用于配置这些插件的代码和安装过程可在https://github.com/kritiksoman/gimp-ml上找到。
This paper introduces GIMP-ML v1.1, 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, de-hazing, matting, enlightening and coloring have been incorporated with GIMP through Python-based plugins. Additionally, operations on images such as k-means based color clustering have also been added. GIMP-ML relies on standard Python packages such as numpy, pytorch, open-cv, scipy. Apart from these, several image manipulation techniques using these plugins have been compiled and demonstrated in the YouTube channel (https://youtube.com/user/kritiksoman) with the objective of demonstrating the use-cases for machine learning based image modification. In addition, GIMP-ML also aims to bring the benefits of using deep learning networks used for computer vision tasks to routine image processing workflows. The code and installation procedure for configuring these plugins is available at https://github.com/kritiksoman/GIMP-ML.
