Package: geoio
IntroductionGeoio provides facilities to easily interact with geospatial
data. The interactions that are supported include data retrieval, spectral
processing, metadata handling, shapefile intersection/extraction, and retrieval
of statistical information. Specific attention has been paid to accessing
DigitalGlobe data and metadata, but the same facilities in this module can be
used to access non-DigitalGlobe data or to build custom processing and
metadata handling for other satellite platforms.InstallationpipinstallgeoioDependencies will be handled at install if possible.  GDAL is not cleanly
installable via pip so should be handled separately (conda, yum, apt-get, etc.).
The run dependencies are:gdal, xmltodict, pytz, tzwhere, ephem, numpy, tinytools.
Additionally,dgsamplesis required for testing andmatplotlibmust be
available for the plotting functions to work.Note for MAC users: if pip fails for ephem, try installing it directly with conda within
the conda virtual environment, i.e.:conda install ephemImportsimportgeoioimports the main classesGeoImageandDGImageto the module root.TheGeoImageclass is a relatively thin wrapper around gdal that provides a
pythonic interface for accessing an arbitrary geospatial image format
(generally those supported by gdal plus the DigitalGlobe .TIL format).
Operations supported include reading, writing, chipping, reprojecting, and meta
data access.  The class methods are populated with reasonable defaults and
object interfaces, making image operations less painful so that you can get on
with the important stuff!TheDGImageclass inherits all the capabilites ofGeoImageand adds
DigitalGlobe meta data handling, spectral processing, and band alias data
retrieval.  Therefore, it requires that the input image be a valid DigitalGlobe
image.  This is currently either a .TIL file with the associated meta data files
(.IMD and/or .XML) present in the image directory or a .TIF files with an
identially named .IMD or .XML file.  The metadata is read into anOrderedBunchobject (inherited from the tinytools package) attached to the
instantiated object.Quick StartThe geoio classes are best used interactively from within ipython where the
relevant pretty print methods can be triggered.  Meta data information will be
reutrned regardless of the interpreter, but the readability is currently
much better in ipython.The dgsamples repo is used below.  However, all the operations below can be
run on local data by replacing the dgsamples call with a string to the image
location.  From exmaples, instead of typing`dgsamples.wv2_longmont_1k.ms`,
a local files at`/path/to/imgfile.TIF`can be used.Using the GeoImage object:importdgsamples# Instantiate an image objectimg=geoio.GeoImage(dgsamples.bayou_chip.extract_test)# a TIF file# Print useful information about the objectimg.filesimg.meta# Get numpy arraydata=img.get_data()# Process data and write to new imagenewdata=data*2img.write_img_like_this('/path/to/newfile.TIF',newdata)Using the DGImage object:importdgsamples# Instantiate an image objectimg=geoio.DGImage(dgsamples.wv2_longmont_1k.ms)# a TIL file# Can also be used directly with a DigitalGlobe TIF file if an XML and/or IMD# is available with same name as the TIF file.# Print useful information about the objectimg.filesimg.meta# Print the full IMD OrderedBunch objectimg.meta_dg.IMD# tab completeable through the OrderedBunch# Return an ImgArr (a numpy array with band meta data handling)data=img.get_data()# Convert an ImgArr to a pure numpy arraynpdata=np.asarray(data)# Return a pure numpy arraydata=img.get_data(meta=False)# Get specific bands using aliases - see geoio.constants.DG_BAND_ALIASES for# additional aliases.data=img.get_data(bands='VIS')# Get specific bands using band aliasesdata=img.get_data(bands=['C','Y'])# Get image data and convert to TOA reflectancedata=img.get_data(stype='toa')PlottingPlotting with thegeoio.plottingfunctions:importdgsamples# Instantiate an image objectimg=geoio.DGImage(dgsamples.wv2_longmont_1k.ms)# a TIF file# Plot the RGB imagegeoio.plotting.imshow(img.get_data(bands='RGB'))# Plot the near-infrared false color imagegeoio.plotting.imshow(img.get_data(bands=['N1','G','B']))# Plotting a histogram of the image bandsgeoio.plotting.hist(img.get_data())# Plotting a histogram of specific bandsgeoio.plotting.hist(img.get_data(bands='VIS'))Spatial ResamplingThe geoio module has upsampling and downsmapling code that allows the user
to easily resample two images to the same grid for easy multi-image proceesing.importdgsamples# Import wv3 imagesms=geoio.DGImage(dgsamples.wv3_longmont_1k.ms)swir=geoio.DGImage(dgsamples.wv3_longmont_1k.swir)# Upsample the swir imageswir.upsample_like_that(ms,method='nearest')# default method is bilinear# Downsample the ms imagems.downsample_like_that(swir)# default method is aggregation# Or let geoio figure it outms.resample_like_that(swir)swir.resample_like_that(ms)IteratorsThe geoio module also provides several iterators to allow easy access to
yield based portions of a raster file.importdgsamplesms=geoio.DGImage(dgsamples.wv2_longmont_1k.ms)# iterate through vector geometriesv=dgsamples.wv2_longmont_1k_vectors.poly_geojson_latlon[xforxinms.iter_vector(vector=v,bands='RGB',mask=True)]# random windows from the image[x.shapeforxinms.iter_window_random(win_size=[10,10],no_chips=20)]# iterate through image with evenly spaced windows based on requested stride[x.shapeforxinms.iter_window(win_size=[10,10],stride=[100,100])