Gippy includes 3 Python modules: gippy core, an algorithms module, and a small test module that has some convenience functions for testing.
- gippy: The main library that contains the GeoImage and GeoRaster classes.
- gippy.algorithms: Functions that operate on GeoImage objects.
- gippy.test: Utility files for testing or examples. Currently just contains the get_test_image() function.
import gippy import gippy.algorithms import gippy.test
Gippy uses several global options, each one accessible with gippy.Options.[name]() and each one has a set_[name](newvalue) function.
- defaultformat - the [GDAL format code](http://www.gdal.org/formats_list.html) used for new files if not otherwise specified.
- verbose - the verbosity level for any messages printed, from 0 (nothing) to 5 (full debug).
- chunksize - the default chunk size in MB
- cores - the number of cores to use for those parts of Gippy that utilize multi-processing. For now the only thing that uses it is warping operations.
The two main classes in GIPPY are GeoImage and GeoRaster. A GeoRaster is a single raster band, and a GeoImage is a collection of GeoRaster bands that cover the same spatial footprint and have the same resolution, although could be different datatypes.
Working with images¶
A GeoImage in Gippy is the main class used and is a collection of raster bands covering the same footprint and spatial resolution. The raster bands are most often spectral, but could be a time series. The individual raster bands could be from different files, but all are contained in a GeoImage.
GeoImage has factory functions that are used to open or create new images: GeoImage.open(), GeoImage.create(), and GeoImage.create_from().
Open existing image¶
A GeoImage can be opened from a single file or multiple files, and bands can be added with add_band() or removed by using select() to get desired bands.
from gippy import GeoImage # open an existing RGB image with a nodata value of 0 geoimg = GeoImage.open(filename, bandnames=(['red', 'green', 'blue']), nodata=0) # Open up multiple files as a single image geoimg = GeoImage.open(filenames=[filename1, filename2, filename3], bandnames=(['red', 'green', 'blue']), nodata=0) # add a band from another image geoimg.add_band(geoimg2) # get a GeoImage with just the red and nir bands rn_image = geoimg.select(['red', 'nir'])
Create new images¶
To create a new image use the create() or create_from functions. When creating from scratch a geolocated image will need to be provided a bounding box and projection (default projection is EPSG:4326). Not all keywords are required. A newly created imge will default to a dataypt of byte, and a format of the defaultformat global option.
Values such as bandnames and nodata can be set with the set_[name] functions.
# Create new int16 image bounding_box = numpy.array([xmin, ymin, width, height]) geoimg = GeoImage.create(filename, xsz=1000, ysz=1000, nb=3, proj='EPSG:3857', bbox=bounding_box, dtype="int16", format="GTiff") geoimg.set_bandnames(['red', 'green', 'blue']) # Create new image with same properties (size, metadata, SRS) as an existing GeoImage geoimg2 = GeoImage.create_from(geoimg, filename) # Create new image with same footprint as geoimg, but different datatype and 4 bands geoimg2 = GeoImage.create_from(gimage, filename, nb=4, dtype='byte')
Temporary files can be created by not providng a filename (or giving filename as an empty string), or by providing the temp keyword as True. Temporary files are created with a random filename in a temporary folder depending on the OS (on Linux it is in /tmp), and are automatically deleted when the last reference to it is gone. As an example, see the code below that tests this feature:
geoimg = GeoImage.create(filename, xsz=1000, ysz=1000, nb=5, temp=True) assertTrue(os.path.exists(filename)) # keep a band band = geoimg geoimg = None # band still references file assertTrue(os.path.exists(filename)) band = None # file should now have been deleted assertFalse(os.path.exists(filename))
Band names & Colors¶
The band names assigned when opening an image or creating a new image can be used to reference that raster band. Bands can be referenced via band index or the band name and can be iterated over as a collection. This allows algorithms to written to target specific types of bands (e.g., red, nir, lwir). The code below illustrates using band names instead of indexes and that band 0 and the red band are the same band.
from gippy import GeoImage geoimg = GeoImage.open(filename, bandnames=(['red', 'green', 'blue'])) # add band from another image geoimg.add_band( geoimg2['lwir']) arr1 = geoimg.read() arr2 = geoimg['red'].read() print(geoimg.nbands()) > 4 print(numpy.array_equal(arr1, arr2)) > True
Many algorithms can be applied to different sensors because they use standard band colors. For instance NDVI uses red and near-infrared bands and can be calculated regardless of the sensor specs as long as they have been calibrated into reflectance units. As such, the standard names below should be used for bands, where appropriate.
|Band Name||Band Range||Landsat 5||Landsat 7||Landsat 8||Sentinel 2||MODIS|
|Coastal||0.40 - 0.45||1||1|
|Blue||0.45 - 0.5||1||1||2||2||3|
|Green||0.5 - 0.6||2||2||3||3||4|
|Red||0.6 - 0.7||3||3||4||4||1|
|Pan||0.5 - 0.7||8||8|
|NIR||0.77 - 1.00||4||4||5||8||2|
|Cirrus||1.35 - 1.40||9||10||26|
|SWIR1||1.55 - 1.75||5||5||6||11||6|
|SWIR2||2.1 - 2.3||7||7||7||12||7|
|LWIR||10.5 - 12.5||6||8|
|LWIR1||10.5 - 11.5||10||31|
|LWIR2||11.5 - 12.5||11||32|
Gippy propogates nodata value through any processing done so that nodata pixels stay as nodata pixels (even if the actual nodata value is changed when writing an output file). Every datatype has a default value used for nodata, but it can be set for each raster band, or the entire image. Normally this is done when creating an image or explicitly setting with the one of the set_(name) commands.
geoimg = GeoImage.open(filename, bandnames=(['red', 'green', 'blue'], nodata=255) # or # geoimg.set_nodata(255) # or # geoimg['red'].set_nodata(255)
Gain and offset¶
An image gain and offset is a linear function that is applied to the data. This may be to convert digital counts to radiance or other units, or to store floating point data in the -1.0 to 1.0 range as Int16 by using a gain of 0.0001 (this takes up half the disk space and is common for storing indices). While these operations are basically the same as multiplying a band by a constant and adding another:
toa = geoimg['red'] * gain + offset
instead, the gain and offset can be set explicitly, and are applied first before any other processing:
The advantage of using the gain() and offset() functions are that they use the GDAL gain and offset functions, thereby setting them on the data source (i.e., GeoTiff) and able to be used by other programs such as QGIS which will apply the gain and offset automatically.
GeoImage and GeoRaster functions¶
GeoImage and GeoRaster both inherit from a GeoResource class, and thus share many common characteristics, such as both being aware of what their projection and affine transformation are, and containing other similar functions: xsize(), ysize(), filename(), extent(), meta(). There are two types of GeoImage and GeoRaster functions, those that return a new object of that type, and those that operate directly on itself. For instance.
rgbimg = geoimg.select(['red', 'green', 'blue'])
returns a new image, rgbimg, which is a GeoImage that contains the red, green, and blue bands, in that order. However geoimg still contains all the original bands, in whatever order they were in.
On the other hand, some functions, specifically functions starting with “set_”, “add_”, and “clear_” all operate on the calling object itself, but they also return a reference to itself, so that functions can be chained together.
# this sets nodata on all bands in geoimg geoimg.set_nodata(255) # this sets nodata and adds a metadata item to the first band geoimg.set_nodata(255).add_meta("key", "value")
Images can be saved as new files using the save() function. The resulting file will have all the same metadata and number of bands, but with all processing applied. The datatype of the new image will be the same as the old one unless the dtype keyword is provided. Note that providing the dtype keyword does not scale the values however, it is up to the user to scale values to the desired range to match the output file created. Use the GeoImage.autoscale() function to automatically scale all bands, or use the GeoRaster.scale() function on each band to specify the input and output ranges.
# scale image and save to new file geoimg.autoscale(1, 255).save(filename, dtype='byte')
Images can also be warped to a new projection, which is saved as a new file. The provided proj string can be anything recognized by GDAL: an EPSG code, a proj string, or even a filename containing WKT of the projection. Resolution is in the same units as the new projection and must be provided (they default to 1.0 which will likely not be what is wanted).
If a geometric feature (see Working with vectors) is provided, the resulting file will be clipped to that feature or, if crop set to True, it will be clipped to the intersection of the feature and current extent.
Interpolation can either be 0-nearest neighbor (default), 1-bilinear, 2-cubic convolution.
Working with bands¶
Most processing occurs on individual bands, which as seen above, can be referenced by band index (0-based) or by band name. A GeoRaster cannot be created on it’s own, it is always a member of a GeoImage. GeoRaster has a variety of processing functions, all basic mathematical operators, logical and bitwise operators, among others.
Bands can be treated as any other single entity, and processing applied to a raster band, as per the convention explained above, returns a new raster band. To apply processing to a band directly in an image, simply replace that band with the returned band with processing applied:
geoimg['red'] = geoimg['red'] + 7 geoimg['red'] = geoimg['red'].sqrt()
The red band is now processed by adding 7, then taking the square root. As another example, to convert a landsat-7 ETM+ image from radiance to top of the atmosphere reflectance, where theta is the solar zenith angle and sundist is the earth-sun distance:
green_toa = img[‘green’] * (1.0 / ((1812.0 * numpy.cos(theta)) / (numpy.pi * sundist * sundist)))
The 1812.0 is the exoatmospheric solar irradiance for the green band, as given in the [Landsat handbook](http://landsathandbook.gsfc.nasa.gov/data_prod/prog_sect11_3.html).
This green_toa band can then be further processed, but none of the calculations anywhere in the chain will be performed until a read is requested through the read() or save() functions.
# get a numpy array of the byte scaled green TOA reflectance green = green_toa.autoscale(1, 255).read().astype(‘byte’)
A mask is not a special entity, any GeoRaster can act like a mask where 0 is off (invalid, or masked), and non-zero is on (valid). Masks can be added to a GeoRaster, in which case any subsequent processing will respect the mask values.
# calculate the stats of the blue band where red > 0.5 stats = geoimg['blue'].add_mask(geoimg['red'] > 0.5).stats()
Multiple masks can be added using the add_mask() function, while all masks can be cleared using clear_masks(). The mask can itself have other processing applied to it, which will be done, in chunks, when it is read for the purpose of applying the mask.
GeoRaster processing functions¶
These processing functions all operator on a raster band and return a raster band. They are pointwise operations, where the output, if read, will be of the same size as the input.
- +, -, *, /
- >, >=, <, <=, ==
- bxor (XOR)
- convolve, laplacian
- pow, sqrt, log, log10, exp
- cos, sin, tan, acos, asin, atan, cosh, sinh, tang, sinc
- min (pointwise min), max (pointwise max), abs, sign
In addition to the processing functions, there are several functions for calculating band statistics.
- Calculate and return array of image statistics: min, max, mean, stddev, skewness, kurtosis
- histogram(bins=100, normalize=true, cumulative=false)
- The histogram, or cumulative histogram. If normalized the sum of all frequencies will be 1.0
- Calculate the pixel value for this percentile (uses the cumulative distribution histogram)
Reading and Chunking¶
The read function can be called on a GeoRaster, in which case a 2d array is returned, or on a GeoImage, which will return a 3d array. The read() function takes in an optional chunk, and if not provided will default to the entire image.
When using a Gippy algorithm or save() function, the image is automatically chunked up into pieces no larger than a default chunk size and the processing applied in pieces. To write code that uses numpy but also utilizes chunking, use the GeoImage.chunks() function to generate a collection of chunks that cover the image. The total number of chunks can be specified, as can a padding factor to mitigate border effects in the case of local area operations.
for ch in geoimg.chunks(numchunks=100): arr = geoimg['red'].read(chunk=ch) # do something with this chunk and write it back geoimg['red'].write(arr, chunk=ch)
When data is actually read by the user it is returned as a numpy array, and numpy arrays may be passed into the GeoImage.write() function. If the number of chunks is not specified they are calculated to be no larger than a default chunk size, given in MB. This chunk size can be set in gippy’s global options.
There are some other read type functions that also take in a chunksize. The data_mask() and nodata_mask() return an array of 0 and 1 indicating where data is and where nodata is, respectively. The read_raw() function reads the data as it is stored, but without applying any gain, offset, or processing.
Working with vectors¶
Gippy provides basic support for vector data, following the same concept as a GeoImage, where a GeoVector is a container class for GeoFeature objects. Any OGR datasource can be opened as a GeoVector, is iterable, and features can be indexed.
from gippy import GeoVector geovec = GeoVector(filename) # this is the extent of all features full_extent = geovec.extent() # this is the extent of the first feature f0_extent = geovec.extent()
The GeoVector and GeoFeature are not fully featured, and are more designed as supporting classes for raster operations (e.g., warping), rather than providing extensive vector support on their own, as there are other vector libraries for more general use.