Image Processing in Python with Montage

J. Good, G. Berriman
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Abstract

he Montage image mosaic engine has found wide applicability in astronomy re- search, integration into processing environments, and is an examplar application for the development of advanced cyber-infrastructure. It is written in C to provide performance and portability. Linking C/C++ libraries to the Python kernel at run time as binary ex- tensions allows them to run under Python at compiled speeds and enables users to take advantage of all the functionality in Python. We have built Python binary extensions of the 59 ANSI-C modules that make up version 5 of the Montage toolkit. This has in- volved a turning the code into a C library, with driver code fully separated to reproduce the calling sequence of the command-line tools; and then adding Python and C linkage code with the Cython library, which acts as a bridge between general C libraries and the Python interface. We will demonstrate how to use these Python binary extensions to perform im- age processing, including reprojecting and resampling images, rectifying background emission to a common level, creation of image mosaics that preserve the calibration and astrometric fidelity of the input images, creating visualizations with an adaptive stretch algorithm, processing HEALPix images, and analyzing and managing image metadata.
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图像处理在Python与蒙太奇
蒙太奇图像拼接引擎在天文研究、集成处理环境中具有广泛的适用性,是先进网络基础设施发展的范例应用。它是用C语言编写的,以提供性能和可移植性。在运行时将C/ c++库作为二进制扩展链接到Python内核,允许它们以编译速度在Python下运行,并使用户能够利用Python中的所有功能。我们已经为组成第5版蒙太奇工具包的59个ANSI-C模块构建了Python二进制扩展。这涉及到将代码转换为C库,与驱动程序代码完全分离,以重现命令行工具的调用序列;然后使用Cython库添加Python和C链接代码,Cython库充当通用C库和Python接口之间的桥梁。我们将演示如何使用这些Python二进制扩展来执行图像处理,包括重新投影和重新采样图像,将背景发射校正到公共水平,创建图像马赛克以保持输入图像的校准和天体测量保真度,使用自适应拉伸算法创建可视化,处理HEALPix图像,以及分析和管理图像元数据。
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