Python osgeo.gdal.RegenerateOverview() Examples

The following are 6 code examples of osgeo.gdal.RegenerateOverview(). 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: gdal2cesium   Author: giohappy   File: gdal2cesium.py    License: GNU General Public License v2.0 6 votes vote down vote up
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 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 #3
Source Project: gdal2tiles   Author: Luqqk   File: gdal2tiles.py    License: MIT License 4 votes vote down vote up
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 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)) 
Example #5
Source Project: gdal2tiles   Author: tehamalab   File: gdal2tiles.py    License: MIT License 4 votes vote down vote up
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 Project: geopackage-python   Author: GitHubRGI   File: gdal2tiles_parallel.py    License: GNU General Public License v3.0 4 votes vote down vote up
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))

    # -------------------------------------------------------------------------