Python osgeo.gdal.RegenerateOverview() Examples
The following are 6
code examples of osgeo.gdal.RegenerateOverview().
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Example #1
| Source File: gdal2cesium.py From gdal2cesium with GNU General Public License v2.0 | 6 votes |
|
def scale_query_to_tile(self, dsquery, dstile):
"""Scales down query dataset to the tile dataset"""
querysize = dsquery.RasterXSize
tilesize = dstile.RasterXSize
tilebands = dstile.RasterCount
if self.options.resampling == 'average':
for i in range(1,tilebands+1):
res = gdal.RegenerateOverview( dsquery.GetRasterBand(i),
dstile.GetRasterBand(i), 'average' )
if res != 0:
self.error("RegenerateOverview() failed")
else:
# 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, self.resampling)
if res != 0:
self.error("ReprojectImage() failed on %s, error %d" % (tilefilename, res))
Example #2
| 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 #3
| Source File: gdal2tiles.py From gdal2tiles with MIT License | 4 votes |
|
def options_post_processing(options, input_file, output_folder):
if not options.title:
options.title = os.path.basename(input_file)
if options.url and not options.url.endswith('/'):
options.url += '/'
if options.url:
out_path = output_folder
if out_path.endswith("/"):
out_path = out_path[:-1]
options.url += os.path.basename(out_path) + '/'
# Supported options
if options.resampling == 'average':
try:
if gdal.RegenerateOverview:
pass
except Exception:
exit_with_error("'average' resampling algorithm is not available.",
"Please use -r 'near' argument or upgrade to newer version of GDAL.")
elif options.resampling == 'antialias':
try:
if numpy: # pylint:disable=W0125
pass
except Exception:
exit_with_error("'antialias' resampling algorithm is not available.",
"Install PIL (Python Imaging Library) and numpy.")
try:
os.path.basename(input_file).encode('ascii')
except UnicodeEncodeError:
full_ascii = False
else:
full_ascii = True
# LC_CTYPE check
if not full_ascii and 'UTF-8' not in os.environ.get("LC_CTYPE", ""):
if not options.quiet:
print("\nWARNING: "
"You are running gdal2tiles.py with a LC_CTYPE environment variable that is "
"not UTF-8 compatible, and your input file contains non-ascii characters. "
"The generated sample googlemaps, openlayers or "
"leaflet files might contain some invalid characters as a result\n")
# Output the results
if options.verbose:
print("Options:", options)
print("Input:", input_file)
print("Output:", output_folder)
print("Cache: %s MB" % (gdal.GetCacheMax() / 1024 / 1024))
print('')
return options
Example #4
| 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 #5
| Source File: gdal2tiles.py From gdal2tiles with MIT License | 4 votes |
|
def options_post_processing(options, input_file, output_folder):
if not options.title:
options.title = os.path.basename(input_file)
if options.url and not options.url.endswith('/'):
options.url += '/'
if options.url:
out_path = output_folder
if out_path.endswith("/"):
out_path = out_path[:-1]
options.url += os.path.basename(out_path) + '/'
if isinstance(options.zoom, (list, tuple)) and len(options.zoom) < 2:
raise ValueError('Invalid zoom value')
# Supported options
if options.resampling == 'average':
try:
if gdal.RegenerateOverview:
pass
except Exception:
exit_with_error("'average' resampling algorithm is not available.",
"Please use -r 'near' argument or upgrade to newer version of GDAL.")
elif options.resampling == 'antialias':
try:
if numpy: # pylint:disable=W0125
pass
except Exception:
exit_with_error("'antialias' resampling algorithm is not available.",
"Install PIL (Python Imaging Library) and numpy.")
try:
os.path.basename(input_file).encode('ascii')
except UnicodeEncodeError:
full_ascii = False
else:
full_ascii = True
# LC_CTYPE check
if not full_ascii and 'UTF-8' not in os.environ.get("LC_CTYPE", ""):
if not options.quiet:
print("\nWARNING: "
"You are running gdal2tiles.py with a LC_CTYPE environment variable that is "
"not UTF-8 compatible, and your input file contains non-ascii characters. "
"The generated sample googlemaps, openlayers or "
"leaflet files might contain some invalid characters as a result\n")
# Output the results
if options.verbose:
print("Options:", options)
print("Input:", input_file)
print("Output:", output_folder)
print("Cache: %s MB" % (gdal.GetCacheMax() / 1024 / 1024))
print('')
return options
Example #6
| Source File: gdal2tiles_parallel.py From geopackage-python with GNU General Public License v3.0 | 4 votes |
|
def scale_query_to_tile(self, dsquery, dstile, tilefilename=''):
"""Scales down query dataset to the tile dataset"""
querysize = dsquery.RasterXSize
tilesize = dstile.RasterXSize
tilebands = dstile.RasterCount
if self.options.resampling == 'average':
# Function: gdal.RegenerateOverview()
for i in range(1, tilebands + 1):
# Black border around NODATA
#if i != 4:
# dsquery.GetRasterBand(i).SetNoDataValue(0)
res = gdal.RegenerateOverview(
dsquery.GetRasterBand(i), dstile.GetRasterBand(i),
'average')
if res != 0:
self.error("RegenerateOverview() failed on %s, error %d" %
(tilefilename, res))
elif self.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 path.exists(tilefilename):
im0 = Image.open(tilefilename)
im1 = Image.composite(im1, im0, im1)
im1.save(tilefilename, self.tiledriver)
else:
# 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,
self.resampling)
if res != 0:
self.error("ReprojectImage() failed on %s, error %d" %
(tilefilename, res))
# -------------------------------------------------------------------------
