Python osgeo.gdal.GRA_Lanczos() Examples

The following are 2 code examples of osgeo.gdal.GRA_Lanczos(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module osgeo.gdal , or try the search function .
Example #1
Source Project: gdal2tiles   Author: Luqqk   File: gdal2tiles.py    License: MIT License 4 votes vote down vote up
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 #2
Source Project: gdal2tiles   Author: tehamalab   File: gdal2tiles.py    License: MIT License 4 votes vote down vote up
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))