Install Noisy.py For Mac
Spectral Python (SPy) is a pure Python module for processing hyperspectral image data. It has functions for reading, displaying, manipulating, and classifying hyperspectral imagery. It can be used interactively from the Python command prompt or via Python scripts. SPy is free, open source software distributed under the.
To see some examples of how SPy can be used, you may want to jump straight to the documentation sections on. A categorized listing of the main classes and functions are in the.
You can download SPy from or the. See the section section of the documentation for details. This release supports Python 3 for all functions except. Note that for Python 3, you should use the Qt4Agg matplotlib backend. New features in this release include the Adaptive Coherence/Cosine Esimator () target detector, Pixel Purity Index (), ability to save ENVI classification files (), and linear contrast enhancement (by data limits or cumulative histogram percentiles).
The SPy function now applies a 2% histogram color stretch by default (this can be overridden in the object). Additional info is in the. 2014-06-04: SPy 0.15.0 is released. This version adds the Minimum Noise Fraction algorithm () (a.k.a., Noise-Adjusted Principal Components). The related function performs estimation of image noise from a spectrally homogeneous region of the image.
SpyFile read methods now accept an optional use_memmap argument that provides finer control over when to use (or not use) the memmap interface to read image file data. Many thanks to Don March () for improving ENVI header support (comment lines and blank parameters are now accepted) and providing several performance improvements. Additional details are. 2014-03-02: SPy has moved! • Interactive image class labeling using keyboard & mouse • Zoom windows • Class overlays with adjustable transparency • Dynamic view of changing pixel classes when modified in an ND Window.
Data/Statistic cacheing and more efficient use of numpy provides significant performance improvement in mutiple algorithms (GMLC 14x, Mahalanobis classifier 8x, kmeans 3x). Functions and are significantly faster, particularly when using common global covariance. The function computes covariance averaged over a set of classes (useful when samples are limited or global covariance is desired). Christian Mielke provided code for the function, which computes the Modified SAM score (by Oshigami et al). 2013-09-06: SPy 0.12 is released. SPy 0.12 provides an improved memmap interface that enables accessing image data using arbitrary interleaves and supports editable images (see for details).
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