Abstract Acquiring the reflectance, radiance, and related structural cloud properties from repositories of historical sky images is a challenging and a computationally intensive task, especially when performed manually or by means of nonautomated approaches. In this article, a quick and efficient, self-adaptive Python tool for the acquisition, and analysis of cloud segmentation properties that is applicable to images from all-sky image repositories is presented and a case study demonstrating its usage and the overall efficacy of the technique is demonstrated. The proposed Python tool aims to build a new data extraction technique and to improve the accessibility of data to future researchers, utilizing the freely available libraries in the Python programing language with the ability to be translated into other programing languages. After development and testing of the Python tool in determining cloud and whole sky segmentation properties, over 42,000 sky images were analyzed in a relatively short time of just under 40 min, with an average execution time of about 0.06 s to complete each image analysis.

中文翻译：

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使用Python从总天空图像库中提取云分割属性

摘要 从历史天空图像库中获取反射率、辐射率和相关的结构云特性是一项具有挑战性且计算量大的任务，尤其是在手动或通过非自动化方法执行时。在本文中，介绍了一种快速、高效、自适应的 Python 工具，用于采集和分析适用于来自全天图像存储库的图像的云分割属性，并通过一个案例研究展示了它的用法和整体效果技术得到展示。提议的 Python 工具旨在构建一种新的数据提取技术，并利用 Python 编程语言中免费提供的库以及能够翻译成其他编程语言的能力，提高未来研究人员对数据的可访问性。