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"author": "Martin Beroiz",
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"description": "![\"Astroalign\"/](\"docs/aa_bw.jpg\")
\n\n***\n\n[](https://travis-ci.org/toros-astro/astroalign)\n[](https://codecov.io/github/toros-astro/astroalign)\n[](http://astroalign.readthedocs.org/en/latest/?badge=latest)\n[](https://zenodo.org/badge/latestdoi/49095023)\n[](https://pyup.io/repos/github/toros-astro/astroalign/)\n[](https://pyup.io/repos/github/toros-astro/astroalign/)\n[](https://pypi.org/project/astroalign/)\n\n\n**ASTROALIGN** is a python module that will try to align two stellar astronomical images, especially when there is no WCS information available.\n\nIt does so by finding similar 3-point asterisms (triangles) in both images and deducing the affine transformation between them.\n\nGeneric registration routines try to match feature points, using corner\ndetection routines to make the point correspondence.\nThese generally fail for stellar astronomical images, since stars have very\nlittle stable structure and so, in general, indistinguishable from each other.\nAsterism matching is more robust, and closer to the human way of matching stellar images.\n\nAstroalign can match images of very different field of view, point-spread function, seeing and atmospheric conditions.\n\nIt may not work, or work with special care, on images of extended objects with few point-like sources or in very crowded fields.\n\nYou can find a Jupyter notebook example with the main features at [http://toros-astro.github.io/astroalign/](http://toros-astro.github.io/astroalign/).\n\n***\n\n## Installation\n\nUsing setuptools:\n\n $ pip install astroalign\n\nor from this distribution with\n\n $ python setup.py install\n\n***\n\n## Usage example\n\n >>> import astroalign as aa\n >>> aligned_image, footprint = aa.register(source_image, target_image)\n\nIn this example `source_image` will be interpolated by a transformation to coincide pixel to pixel with `target_image` and stored in `aligned_image`.\n\nIf we are only interested in knowing the transformation and the correspondence of control points in both images, use `find_transform` will return the transformation in a [Scikit-Image](https://scikit-image.org/) SimilarityTransform object and a list of stars in source with the corresponding stars in target.\n\n >>> transf, (s_list, t_list) = aa.find_transform(source, target)\n\n`source` and `target` can each either be the numpy array of the image, or an iterable of (x, y) pairs of star positions on the image.\n\nThe returned `transf` object is a scikit-image [`SimilarityTranform`](http://scikit-image.org/docs/dev/api/skimage.transform.html#skimage.transform.SimilarityTransform) object that contains the transformation matrix along with the scale, rotation and translation parameters.\n\n`s_list` and `t_list` are numpy arrays of (x, y) point correspondence between `source` and `target`. `transf` applied to `s_list` will approximately render `t_list`.\n\n***\n\n*This package is inspired by the [astrometry.net](http://astrometry.net) program*\n\nTOROS Dev Team\n\n\n\n\n\n",
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