Python osgeo.gdal.GRA_NearestNeighbour() Examples
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code examples of osgeo.gdal.GRA_NearestNeighbour().
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Example #1
| Source File: gdal_python.py From PyRate with Apache License 2.0 | 6 votes |
|
def resample_nearest_neighbour(input_tif, extents, new_res, output_file):
"""
Nearest neighbor resampling and cropping of an image.
:param str input_tif: input geotiff file path
:param list extents: new extents for cropping
:param list[float] new_res: new resolution for resampling
:param str output_file: output geotiff file path
:return: dst: resampled image
:rtype: ndarray
"""
dst, resampled_proj, src, _ = _crop_resample_setup(extents, input_tif,
new_res, output_file)
# Do the work
gdal.ReprojectImage(src, dst, '', resampled_proj,
gdalconst.GRA_NearestNeighbour)
return dst.ReadAsArray()
Example #2
| Source File: gdal_python.py From PyRate with Apache License 2.0 | 6 votes |
|
def _alignment(input_tif, new_res, resampled_average, src_ds_mem,
src_gt, tmp_ds):
"""
Correction step to match python multi-look/crop output to match that of
Legacy data. Modifies the resampled_average array in place.
"""
src_ds = gdal.Open(input_tif)
data = src_ds.GetRasterBand(1).ReadAsArray()
xlooks = ylooks = int(new_res[0] / src_gt[1])
xres, yres = _get_resampled_data_size(xlooks, ylooks, data)
nrows, ncols = resampled_average.shape
# Legacy nearest neighbor resampling for the last
# [yres:nrows, xres:ncols] cells without nan_conversion
# turn off nan-conversion
src_ds_mem.GetRasterBand(1).SetNoDataValue(LOW_FLOAT32)
# nearest neighbor resapling
gdal.ReprojectImage(src_ds_mem, tmp_ds, '', '', gdal.GRA_NearestNeighbour)
# only take the [yres:nrows, xres:ncols] slice
if nrows > yres or ncols > xres:
resampled_nearest_neighbor = tmp_ds.GetRasterBand(1).ReadAsArray()
resampled_average[yres - nrows:, xres - ncols:] = \
resampled_nearest_neighbor[yres - nrows:, xres - ncols:]
Example #3
| Source File: test_gdal_python.py From PyRate with Apache License 2.0 | 6 votes |
|
def test_reproject_with_no_data(self):
data = np.array([[2, 7],
[2, 7]])
src_ds = gdal.GetDriverByName('MEM').Create('', 2, 2)
src_ds.GetRasterBand(1).WriteArray(data)
src_ds.GetRasterBand(1).SetNoDataValue(2)
src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1])
dst_ds = gdal.GetDriverByName('MEM').Create('', 1, 1)
dst_ds.GetRasterBand(1).SetNoDataValue(3)
dst_ds.GetRasterBand(1).Fill(3)
dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2])
gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_NearestNeighbour)
got_data = dst_ds.GetRasterBand(1).ReadAsArray()
expected_data = np.array([[7]])
np.testing.assert_array_equal(got_data, expected_data)
Example #4
| Source File: test_gdal_python.py From PyRate with Apache License 2.0 | 6 votes |
|
def test_reproject_with_no_data_2(self):
data = np.array([[2, 7, 7, 7],
[2, 7, 7, 2]])
height, width = data.shape
src_ds = gdal.GetDriverByName('MEM').Create('', width, height)
src_ds.GetRasterBand(1).WriteArray(data)
src_ds.GetRasterBand(1).SetNoDataValue(2)
src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1])
dst_ds = gdal.GetDriverByName('MEM').Create('', 2, 1)
dst_ds.GetRasterBand(1).SetNoDataValue(3)
dst_ds.GetRasterBand(1).Fill(3)
dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2])
gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_NearestNeighbour)
got_data = dst_ds.GetRasterBand(1).ReadAsArray()
expected_data = np.array([[7, 3]])
np.testing.assert_array_equal(got_data, expected_data)
Example #5
| Source File: test_gdal_python.py From PyRate with Apache License 2.0 | 6 votes |
|
def test_reproject_with_no_data_3(self):
data = np.array([[2, 7, 7, 7],
[2, 7, 7, 7],
[2, 7, 7, 7],
[2, 7, 7, 2],
[2, 7, 7, 2]])
src_ds = gdal.GetDriverByName('MEM').Create('', 4, 5)
src_ds.GetRasterBand(1).WriteArray(data)
src_ds.GetRasterBand(1).SetNoDataValue(2)
src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1])
dst_ds = gdal.GetDriverByName('MEM').Create('', 2, 2)
dst_ds.GetRasterBand(1).SetNoDataValue(3)
dst_ds.GetRasterBand(1).Fill(3)
dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2])
gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_NearestNeighbour)
got_data = dst_ds.GetRasterBand(1).ReadAsArray()
expected_data = np.array([[7, 7],
[7, 3]])
np.testing.assert_array_equal(got_data, expected_data)
Example #6
| Source File: test_gdal_python.py From PyRate with Apache License 2.0 | 6 votes |
|
def test_reproject_with_no_data_5(self):
data = np.array([[2, 7, 7, 7, 2],
[2, 7, 7, 7, 2],
[2, 7, 7, 7, 2],
[2, 7, 7, 2, 2],
[2, 7, 7, 2, 2],
[2, 7, 7, 2, 2]])
src_ds = gdal.GetDriverByName('MEM').Create('', 5, 6)
src_ds.GetRasterBand(1).WriteArray(data)
src_ds.GetRasterBand(1).SetNoDataValue(2)
src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1])
dst_ds = gdal.GetDriverByName('MEM').Create('', 2, 3)
dst_ds.GetRasterBand(1).SetNoDataValue(3)
dst_ds.GetRasterBand(1).Fill(3)
dst_ds.SetGeoTransform([10, 2, 0, 10, 0, -2])
gdal.ReprojectImage(src_ds, dst_ds, '', '', gdal.GRA_NearestNeighbour)
got_data = dst_ds.GetRasterBand(1).ReadAsArray()
expected_data = np.array([[7, 7],
[7, 3],
[7, 3]])
np.testing.assert_array_equal(got_data, expected_data)
Example #7
| Source File: base.py From geoio with MIT License | 5 votes |
|
def _upsample_from_gdalobj(self,src,dst,method='bilinear'):
"""Hidden to run the actual reprojection gdal code that is called
from two higher level methods."""
# Set reprojection method
if isinstance(method,int):
pass
elif method == "nearest":
method = gdal.GRA_NearestNeighbour
elif method == "bilinear":
method = gdal.GRA_Bilinear
elif method == "cubic":
method = gdal.GRA_Cubic
elif method == "average":
method = gdal.GRA_Average
else:
raise ValueError("requested method is not understood.")
# Do the reprojection
gdal.ReprojectImage(src,
dst,
self.meta.projection_string,
dst.GetProjection(),
method)
# Return data and free the temp image.
return dst.ReadAsArray()
Example #8
| Source File: gdal2tiles.py From gdal2tiles with MIT License | 4 votes |
|
def scale_query_to_tile(dsquery, dstile, tiledriver, options, tilefilename=''):
"""Scales down query dataset to the tile dataset"""
querysize = dsquery.RasterXSize
tilesize = dstile.RasterXSize
tilebands = dstile.RasterCount
if options.resampling == 'average':
# Function: gdal.RegenerateOverview()
for i in range(1, tilebands+1):
# Black border around NODATA
res = gdal.RegenerateOverview(dsquery.GetRasterBand(i), dstile.GetRasterBand(i),
'average')
if res != 0:
exit_with_error("RegenerateOverview() failed on %s, error %d" % (
tilefilename, res))
elif options.resampling == 'antialias':
# Scaling by PIL (Python Imaging Library) - improved Lanczos
array = numpy.zeros((querysize, querysize, tilebands), numpy.uint8)
for i in range(tilebands):
array[:, :, i] = gdalarray.BandReadAsArray(dsquery.GetRasterBand(i+1),
0, 0, querysize, querysize)
im = Image.fromarray(array, 'RGBA') # Always four bands
im1 = im.resize((tilesize, tilesize), Image.ANTIALIAS)
if os.path.exists(tilefilename):
im0 = Image.open(tilefilename)
im1 = Image.composite(im1, im0, im1)
im1.save(tilefilename, tiledriver)
else:
if options.resampling == 'near':
gdal_resampling = gdal.GRA_NearestNeighbour
elif options.resampling == 'bilinear':
gdal_resampling = gdal.GRA_Bilinear
elif options.resampling == 'cubic':
gdal_resampling = gdal.GRA_Cubic
elif options.resampling == 'cubicspline':
gdal_resampling = gdal.GRA_CubicSpline
elif options.resampling == 'lanczos':
gdal_resampling = gdal.GRA_Lanczos
# Other algorithms are implemented by gdal.ReprojectImage().
dsquery.SetGeoTransform((0.0, tilesize / float(querysize), 0.0, 0.0, 0.0,
tilesize / float(querysize)))
dstile.SetGeoTransform((0.0, 1.0, 0.0, 0.0, 0.0, 1.0))
res = gdal.ReprojectImage(dsquery, dstile, None, None, gdal_resampling)
if res != 0:
exit_with_error("ReprojectImage() failed on %s, error %d" % (tilefilename, res))
Example #9
| Source File: raster.py From wradlib with MIT License | 4 votes |
|
def get_raster_elevation(dataset, resample=None, **kwargs):
"""Return surface elevation corresponding to raster dataset
The resampling algorithm is chosen based on scale ratio
Parameters
----------
dataset : gdal.Dataset
raster image with georeferencing (GeoTransform at least)
resample : GDALResampleAlg
If None the best algorithm is chosen based on scales.
GRA_NearestNeighbour = 0, GRA_Bilinear = 1, GRA_Cubic = 2,
GRA_CubicSpline = 3, GRA_Lanczos = 4, GRA_Average = 5, GRA_Mode = 6,
GRA_Max = 8, GRA_Min = 9, GRA_Med = 10, GRA_Q1 = 11, GRA_Q3 = 12
kwargs : keyword arguments
passed to wradlib.io.dem.get_strm()
Returns
-------
elevation : :class:`numpy:numpy.ndarray`
Array of shape (rows, cols, 2) containing elevation
"""
extent = get_raster_extent(dataset)
src_ds = wradlib.io.dem.get_srtm(extent, **kwargs)
driver = gdal.GetDriverByName("MEM")
dst_ds = driver.CreateCopy("ds", dataset)
if resample is None:
src_gt = src_ds.GetGeoTransform()
dst_gt = dst_ds.GetGeoTransform()
src_scale = min(abs(src_gt[1]), abs(src_gt[5]))
dst_scale = min(abs(dst_gt[1]), abs(dst_gt[5]))
ratio = dst_scale / src_scale
resample = gdal.GRA_Bilinear
if ratio > 2:
resample = gdal.GRA_Average
if ratio < 0.5:
resample = gdal.GRA_NearestNeighbour
gdal.ReprojectImage(
src_ds, dst_ds, src_ds.GetProjection(), dst_ds.GetProjection(), resample
)
elevation = read_gdal_values(dst_ds)
return elevation
Example #10
| Source File: gdal2tiles.py From gdal2tiles with MIT License | 4 votes |
|
def scale_query_to_tile(dsquery, dstile, tiledriver, options, tilefilename=''):
"""Scales down query dataset to the tile dataset"""
querysize = dsquery.RasterXSize
tilesize = dstile.RasterXSize
tilebands = dstile.RasterCount
if options.resampling == 'average':
# Function: gdal.RegenerateOverview()
for i in range(1, tilebands + 1):
# Black border around NODATA
res = gdal.RegenerateOverview(dsquery.GetRasterBand(i), dstile.GetRasterBand(i),
'average')
if res != 0:
exit_with_error("RegenerateOverview() failed on %s, error %d" % (
tilefilename, res))
elif options.resampling == 'antialias':
# Scaling by PIL (Python Imaging Library) - improved Lanczos
array = numpy.zeros((querysize, querysize, tilebands), numpy.uint8)
for i in range(tilebands):
array[:, :, i] = gdalarray.BandReadAsArray(dsquery.GetRasterBand(i + 1),
0, 0, querysize, querysize)
im = Image.fromarray(array, 'RGBA') # Always four bands
im1 = im.resize((tilesize, tilesize), Image.ANTIALIAS)
if os.path.exists(tilefilename):
im0 = Image.open(tilefilename)
im1 = Image.composite(im1, im0, im1)
im1.save(tilefilename, tiledriver)
else:
if options.resampling == 'near':
gdal_resampling = gdal.GRA_NearestNeighbour
elif options.resampling == 'bilinear':
gdal_resampling = gdal.GRA_Bilinear
elif options.resampling == 'cubic':
gdal_resampling = gdal.GRA_Cubic
elif options.resampling == 'cubicspline':
gdal_resampling = gdal.GRA_CubicSpline
elif options.resampling == 'lanczos':
gdal_resampling = gdal.GRA_Lanczos
# Other algorithms are implemented by gdal.ReprojectImage().
dsquery.SetGeoTransform((0.0, tilesize / float(querysize), 0.0, 0.0, 0.0,
tilesize / float(querysize)))
dstile.SetGeoTransform((0.0, 1.0, 0.0, 0.0, 0.0, 1.0))
res = gdal.ReprojectImage(dsquery, dstile, None, None, gdal_resampling)
if res != 0:
exit_with_error("ReprojectImage() failed on %s, error %d" % (tilefilename, res))
