Python gdal.GRA_Bilinear方法代码示例

# 需要导入模块: from osgeo import gdal [as 别名]
# 或者: from osgeo.gdal import GRA_Bilinear [as 别名]
def test_reproject_raster_dataset(self):
        georef.reproject_raster_dataset(
            self.ds, spacing=0.005, resample=gdal.GRA_Bilinear, align=True
        )
        georef.reproject_raster_dataset(
            self.ds, size=(1000, 1000), resample=gdal.GRA_Bilinear, align=True
        )
        with pytest.raises(NameError):
            georef.reproject_raster_dataset(self.ds)
        dst = georef.epsg_to_osr(25832)
        georef.reproject_raster_dataset(
            self.ds,
            spacing=100.0,
            resample=gdal.GRA_Bilinear,
            align=True,
            projection_target=dst,
        ) 

# 需要导入模块: from osgeo import gdal [as 别名]
# 或者: from osgeo.gdal import GRA_Bilinear [as 别名]
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() 

# 需要导入模块: from osgeo import gdal [as 别名]
# 或者: from osgeo.gdal import GRA_Bilinear [as 别名]
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)) 

# 需要导入模块: from osgeo import gdal [as 别名]
# 或者: from osgeo.gdal import GRA_Bilinear [as 别名]
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 

# 需要导入模块: from osgeo import gdal [as 别名]
# 或者: from osgeo.gdal import GRA_Bilinear [as 别名]
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))