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Refs #24840 -- Added GDALRaster Warp and transform methods 

Thanks to Tim Graham for the review.
This commit is contained in:
Daniel Wiesmann 2015-06-19 13:56:50 +01:00 committed by Claude Paroz
parent c0fff64486
commit c078021555
7 changed files with 337 additions and 5 deletions
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3
django/contrib/gis/gdal/prototypes/raster.py
View File

@ -69,3 +69,6 @@ get_band_maximum = double_output(std_call('GDALGetRasterMaximum'), [c_void_p, PO
reproject_image = void_output(std_call('GDALReprojectImage'),
[c_void_p, c_char_p, c_void_p, c_char_p, c_int, c_double, c_double, c_void_p, c_void_p, c_void_p]
)
auto_create_warped_vrt = voidptr_output(std_call('GDALAutoCreateWarpedVRT'),
[c_void_p, c_char_p, c_char_p, c_int, c_double, c_void_p]
)

11
django/contrib/gis/gdal/raster/const.py
View File

@ -32,3 +32,14 @@ GDAL_TO_CTYPES = [
None, c_byte, c_uint16, c_int16, c_uint32, c_int32,
c_float, c_double, None, None, None, None
]
# List of resampling algorithms that can be used to warp a GDALRaster.
GDAL_RESAMPLE_ALGORITHMS = {
'NearestNeighbour': 0,
'Bilinear': 1,
'Cubic': 2,
'CubicSpline': 3,
'Lanczos': 4,
'Average': 5,
'Mode': 6,
}

116
django/contrib/gis/gdal/raster/source.py
View File

@ -1,12 +1,13 @@
import json
import os
from ctypes import addressof, byref, c_double
from ctypes import addressof, byref, c_double, c_void_p
from django.contrib.gis.gdal.base import GDALBase
from django.contrib.gis.gdal.driver import Driver
from django.contrib.gis.gdal.error import GDALException
from django.contrib.gis.gdal.prototypes import raster as capi
from django.contrib.gis.gdal.raster.band import GDALBand
from django.contrib.gis.gdal.raster.const import GDAL_RESAMPLE_ALGORITHMS
from django.contrib.gis.gdal.srs import SpatialReference, SRSException
from django.contrib.gis.geometry.regex import json_regex
from django.utils import six
@ -123,6 +124,9 @@ class GDALRaster(GDALBase):
if 'skew' in ds_input:
self.skew.x, self.skew.y = ds_input['skew']
elif isinstance(ds_input, c_void_p):
# Instantiate the object using an existing pointer to a gdal raster.
self._ptr = ds_input
else:
raise GDALException('Invalid data source input type: "{}".'.format(type(ds_input)))
@ -278,3 +282,113 @@ class GDALRaster(GDALBase):
for idx in range(1, capi.get_ds_raster_count(self._ptr) + 1):
bands.append(GDALBand(self, idx))
return bands
def warp(self, ds_input, resampling='NearestNeighbour', max_error=0.0):
"""
Returns a warped GDALRaster with the given input characteristics.
The input is expected to be a dictionary containing the parameters
of the target raster. Allowed values are width, height, SRID, origin,
scale, skew, datatype, driver, and name (filename).
By default, the warp functions keeps all parameters equal to the values
of the original source raster. For the name of the target raster, the
name of the source raster will be used and appended with
_copy. + source_driver_name.
In addition, the resampling algorithm can be specified with the "resampling"
input parameter. The default is NearestNeighbor. For a list of all options
consult the GDAL_RESAMPLE_ALGORITHMS constant.
"""
# Get the parameters defining the geotransform, srid, and size of the raster
if 'width' not in ds_input:
ds_input['width'] = self.width
if 'height' not in ds_input:
ds_input['height'] = self.height
if 'srid' not in ds_input:
ds_input['srid'] = self.srs.srid
if 'origin' not in ds_input:
ds_input['origin'] = self.origin
if 'scale' not in ds_input:
ds_input['scale'] = self.scale
if 'skew' not in ds_input:
ds_input['skew'] = self.skew
# Get the driver, name, and datatype of the target raster
if 'driver' not in ds_input:
ds_input['driver'] = self.driver.name
if 'name' not in ds_input:
ds_input['name'] = self.name + '_copy.' + self.driver.name
if 'datatype' not in ds_input:
ds_input['datatype'] = self.bands[0].datatype()
# Set the number of bands
ds_input['nr_of_bands'] = len(self.bands)
# Create target raster
target = GDALRaster(ds_input, write=True)
# Copy nodata values to warped raster
for index, band in enumerate(self.bands):
target.bands[index].nodata_value = band.nodata_value
# Select resampling algorithm
algorithm = GDAL_RESAMPLE_ALGORITHMS[resampling]
# Reproject image
capi.reproject_image(
self._ptr, self.srs.wkt.encode(),
target._ptr, target.srs.wkt.encode(),
algorithm, 0.0, max_error,
c_void_p(), c_void_p(), c_void_p()
)
# Make sure all data is written to file
target._flush()
return target
def transform(self, srid, driver=None, name=None, resampling='NearestNeighbour',
max_error=0.0):
"""
Returns a copy of this raster reprojected into the given SRID.
"""
# Convert the resampling algorithm name into an algorithm id
algorithm = GDAL_RESAMPLE_ALGORITHMS[resampling]
# Instantiate target spatial reference system
target_srs = SpatialReference(srid)
# Create warped virtual dataset in the target reference system
target = capi.auto_create_warped_vrt(
self._ptr, self.srs.wkt.encode(), target_srs.wkt.encode(),
algorithm, max_error, c_void_p()
)
target = GDALRaster(target)
# Construct the target warp dictionary from the virtual raster
data = {
'srid': srid,
'width': target.width,
'height': target.height,
'origin': [target.origin.x, target.origin.y],
'scale': [target.scale.x, target.scale.y],
'skew': [target.skew.x, target.skew.y],
}
# Set the driver and filepath if provided
if driver:
data['driver'] = driver
if name:
data['name'] = name
# Warp the raster into new srid
return self.warp(data, resampling=resampling, max_error=max_error)

86
docs/ref/contrib/gis/gdal.txt
View File

@ -1119,7 +1119,7 @@ blue.
values defining the properties of a new raster (such as size and name). If the
input is a file path, the second parameter specifies if the raster should
be opened with write access. If the input is raw data, the parameters ``width``,
``heigth``, and ``srid`` are required. The following example shows how rasters
``height``, and ``srid`` are required. The following example shows how rasters
can be created from different input sources (using the sample data from the
GeoDjango tests, see also the :ref:`gdal_sample_data` section)::
@ -1288,6 +1288,89 @@ blue.
>>> rst.bands[1].data()
array([[ 2., 3.]], dtype=float32)
.. method:: warp(ds_input, resampling='NearestNeighbour', max_error=0.0)
.. versionadded:: 1.9
Returns a warped version of this raster.
The warping parameters can be specified through the ``ds_input``
argument. The use of ``ds_input`` is analogous to the corresponding
argument of the class constructor. It is a dictionary with the
characteristics of the target raster. Allowed dictionary key values are
width, height, SRID, origin, scale, skew, datatype, driver, and name
(filename).
By default, the warp functions keeps most parameters equal to the
values of the original source raster, so only parameters that should be
changed need to be specified. Note that this includes the driver, so
for file-based rasters the warp function will create a new raster on
disk.
The only parameter that is set differently from the source raster is the
name. The default value of the the raster name is the name of the source
raster appended with ``'_copy' + source_driver_name``. For file-based
rasters it is recommended to provide the file path of the target raster.
The resampling algorithm used for warping can be specified with the
``resampling`` argument. The default is ``NearestNeighbor``, and the
other allowed values are ``Bilinear``, ``Cubic``, ``CubicSpline``,
``Lanczos``, ``Average``, and ``Mode``.
The ``max_error`` argument can be used to specify the maximum error
measured in input pixels that is allowed in approximating the
transformation. The default is 0.0 for exact calculations.
For users familiar with ``GDAL``, this function has a similar
functionality to the ``gdalwarp`` command-line utility.
For example, the warp function can be used for aggregating a raster to
the double of its original pixel scale:
>>> rst = GDALRaster({
... "width": 6, "height": 6, "srid": 3086,
... "origin": [500000, 400000],
... "scale": [100, -100],
... "bands": [{"data": range(36), "nodata_value": 99}]
... })
>>> target = rst.warp({"scale": [200, -200], "width": 3, "height": 3})
>>> target.bands[0].data()
array([[ 7., 9., 11.],
[ 19., 21., 23.],
[ 31., 33., 35.]], dtype=float32)
.. method:: transform(srid, driver=None, name=None, resampling='NearestNeighbour', max_error=0.0)
.. versionadded:: 1.9
Returns a transformed version of this raster with the specified SRID.
This function transforms the current raster into a new spatial reference
system that can be specified with an ``srid``. It calculates the bounds
and scale of the current raster in the new spatial reference system and
warps the raster using the :attr:`~GDALRaster.warp` function.
By default, the driver of the source raster is used and the name of the
raster is the original name appended with
``'_copy' + source_driver_name``. A different driver or name can be
specified with the ``driver`` and ``name`` arguments.
The default resampling algorithm is ``NearestNeighbour`` but can be
changed using the ``resampling`` argument. The default maximum allowed
error for resampling is 0.0 and can be changed using the ``max_error``
argument. Consult the :attr:`~GDALRaster.warp` documentation for detail
on those arguments.
>>> rst = GDALRaster({
... "width": 6, "height": 6, "srid": 3086,
... "origin": [500000, 400000],
... "scale": [100, -100],
... "bands": [{"data": range(36), "nodata_value": 99}]
... })
>>> target = rst.transform(4326)
>>> target.origin
[-82.98492744885776, 27.601924753080144]
``GDALBand``
------------
@ -1385,7 +1468,6 @@ blue.
[ 8, -77, -66, 11],
[ 12, 13, 14, 15]], dtype=int8)
Settings
========

9
docs/releases/1.9.txt
View File

@ -177,6 +177,15 @@ Minor features
It supports automatic spatial index creation and reprojection when saving a
model. It does not yet support spatial querying.
* The new :meth:`GDALRaster.warp() <django.contrib.gis.gdal.GDALRaster.warp>`
method allows warping a raster by specifying target raster properties such as
origin, width, height, or pixel size (amongst others).
* The new :meth:`GDALRaster.transform()
<django.contrib.gis.gdal.GDALRaster.transform>` method allows transforming a
raster into a different spatial reference system by specifying a target
``srid``.
:mod:`django.contrib.messages`
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

1
docs/spelling_wordlist
View File

@ -660,6 +660,7 @@ repo
reportable
reprojection
reraise
resampling
reST
reStructuredText
reupload

116
tests/gis_tests/gdal_tests/test_raster.py
View File

@ -139,8 +139,8 @@ class GDALRasterTests(unittest.TestCase):
'skew': (self.rs.skew.x, self.rs.skew.y),
'bands': [{
'data': self.rs.bands[0].data(),
'nodata_value': self.rs.bands[0].nodata_value
}]
'nodata_value': self.rs.bands[0].nodata_value,
}],
})
# Reload newly created raster from file
@ -155,6 +155,118 @@ class GDALRasterTests(unittest.TestCase):
else:
self.assertEqual(restored_raster.bands[0].data(), self.rs.bands[0].data())
def test_raster_warp(self):
# Create in memory raster
source = GDALRaster({
'datatype': 1,
'driver': 'MEM',
'name': 'sourceraster',
'width': 4,
'height': 4,
'nr_of_bands': 1,
'srid': 3086,
'origin': (500000, 400000),
'scale': (100, -100),
'skew': (0, 0),
'bands': [{
'data': range(16),
'nodata_value': 255,
}],
})
# Test altering the scale, width, and height of a raster
data = {
'scale': [200, -200],
'width': 2,
'height': 2,
}
target = source.warp(data)
self.assertEqual(target.width, data['width'])
self.assertEqual(target.height, data['height'])
self.assertEqual(target.scale, data['scale'])
self.assertEqual(target.bands[0].datatype(), source.bands[0].datatype())
self.assertEqual(target.name, 'sourceraster_copy.MEM')
result = target.bands[0].data()
if numpy:
result = result.flatten().tolist()
self.assertEqual(result, [5, 7, 13, 15])
# Test altering the name and datatype (to float)
data = {
'name': '/path/to/targetraster.tif',
'datatype': 6,
}
target = source.warp(data)
self.assertEqual(target.bands[0].datatype(), 6)
self.assertEqual(target.name, '/path/to/targetraster.tif')
self.assertEqual(target.driver.name, 'MEM')
result = target.bands[0].data()
if numpy:
result = result.flatten().tolist()
self.assertEqual(
result,
[0.0, 1.0, 2.0, 3.0,
4.0, 5.0, 6.0, 7.0,
8.0, 9.0, 10.0, 11.0,
12.0, 13.0, 14.0, 15.0]
)
def test_raster_transform(self):
# Prepare tempfile and nodata value
rstfile = tempfile.NamedTemporaryFile(suffix='.tif')
ndv = 99
# Create in file based raster
source = GDALRaster({
'datatype': 1,
'driver': 'tif',
'name': rstfile.name,
'width': 5,
'height': 5,
'nr_of_bands': 1,
'srid': 4326,
'origin': (-5, 5),
'scale': (2, -2),
'skew': (0, 0),
'bands': [{
'data': range(25),
'nodata_value': ndv,
}],
})
# Transform raster into srid 4326.
target = source.transform(3086)
# Reload data from disk
target = GDALRaster(target.name)
self.assertEqual(target.srs.srid, 3086)
self.assertEqual(target.width, 7)
self.assertEqual(target.height, 7)
self.assertEqual(target.bands[0].datatype(), source.bands[0].datatype())
self.assertEqual(target.origin, [9124842.791079799, 1589911.6476407414])
self.assertEqual(target.scale, [223824.82664250192, -223824.82664250192])
self.assertEqual(target.skew, [0, 0])
result = target.bands[0].data()
if numpy:
result = result.flatten().tolist()
# The reprojection of a raster that spans over a large area
# skews the data matrix and might introduce nodata values.
self.assertEqual(
result,
[
ndv, ndv, ndv, ndv, 4, ndv, ndv,
ndv, ndv, 2, 3, 9, ndv, ndv,
ndv, 1, 2, 8, 13, 19, ndv,
0, 6, 6, 12, 18, 18, 24,
ndv, 10, 11, 16, 22, 23, ndv,
ndv, ndv, 15, 21, 22, ndv, ndv,
ndv, ndv, 20, ndv, ndv, ndv, ndv,
]
)
@unittest.skipUnless(HAS_GDAL, "GDAL is required")
class GDALBandTests(unittest.TestCase):