Python gdal.ReprojectImage方法代碼示例

# 需要導入模塊: from osgeo import gdal [as 別名]
# 或者: from osgeo.gdal import ReprojectImage [as 別名]
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)) 

# 需要導入模塊: from osgeo import gdal [as 別名]
# 或者: from osgeo.gdal import ReprojectImage [as 別名]
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() 

# 需要導入模塊: from osgeo import gdal [as 別名]
# 或者: from osgeo.gdal import ReprojectImage [as 別名]
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:] 

# 需要導入模塊: from osgeo import gdal [as 別名]
# 或者: from osgeo.gdal import ReprojectImage [as 別名]
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) 

# 需要導入模塊: from osgeo import gdal [as 別名]
# 或者: from osgeo.gdal import ReprojectImage [as 別名]
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) 

# 需要導入模塊: from osgeo import gdal [as 別名]
# 或者: from osgeo.gdal import ReprojectImage [as 別名]
def test_reproject_with_no_data_4(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]])
        src_ds = gdal.GetDriverByName('MEM').Create('', 5, 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) 

# 需要導入模塊: from osgeo import gdal [as 別名]
# 或者: from osgeo.gdal import ReprojectImage [as 別名]
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) 

# 需要導入模塊: from osgeo import gdal [as 別名]
# 或者: from osgeo.gdal import ReprojectImage [as 別名]
def upsample_like_that(self, ext_img, method='bilinear', no_data_value=None):
        """Use gdal.ReprojectImage to upsample the object so that it looks
        like the geoio image object passed in as the ext_img argument.  Method
        can be those listed in GeoImage.upsample.
        """

        if ext_img.meta.resolution[0]>self.meta.resolution[0]:
            raise ValueError('The requested resolution is not at or higher '
                             'than the base object.  Use downsample or '
                             'resample methods.')

        # Set up destination image in memory
        drv = gdal.GetDriverByName('MEM')
        dst = drv.Create('',
                         ext_img.shape[2],
                         ext_img.shape[1],
                         ext_img.shape[0],
                         ext_img.meta.gdal_dtype)
        dst.SetGeoTransform(ext_img.meta.geo_transform)
        dst.SetProjection(ext_img.meta.projection_string)
        if no_data_value is not None:
            blist = [x + 1 for x in range(ext_img.shape[0])]
            [dst.GetRasterBand(x).SetNoDataValue(no_data_value) for x in blist]

        # Run resample, pull data, and del the temp gdal object.
        data = self._upsample_from_gdalobj(self.get_gdal_obj(),dst,method)
        del dst

        return data 

# 需要導入模塊: from osgeo import gdal [as 別名]
# 或者: from osgeo.gdal import ReprojectImage [as 別名]
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() 

# 需要導入模塊: from osgeo import gdal [as 別名]
# 或者: from osgeo.gdal import ReprojectImage [as 別名]
def gdal_average(dst_ds, src_ds, src_ds_mem, thresh):
    """
    Perform subsampling of an image by averaging values

    :param gdal.Dataset dst_ds: Destination gdal dataset object
    :param str input_tif: Input geotif
    :param float thresh: NaN fraction threshold

    :return resampled_average: resampled image data
    :rtype: ndarray
    :return src_ds_mem: Modified in memory src_ds with nan_fraction in Band2. The nan_fraction
        is computed efficiently here in gdal in the same step as the that of
        the resampled average (band 1). This results is huge memory and
        computational efficiency
    :rtype: gdal.Dataset
    """
    src_gt = src_ds.GetGeoTransform()
    src_ds_mem.SetGeoTransform(src_gt)
    data = src_ds_mem.GetRasterBand(1).ReadAsArray()
    # update nan_matrix
    # if data==nan, then 1, else 0
    nan_matrix = np.isnan(data)  # all nans due to phase data + coh masking if used
    src_ds_mem.GetRasterBand(2).WriteArray(nan_matrix)
    gdal.ReprojectImage(src_ds_mem, dst_ds, '', '', gdal.GRA_Average)
    # dst_ds band2 average is our nan_fraction matrix
    nan_frac = dst_ds.GetRasterBand(2).ReadAsArray()
    resampled_average = dst_ds.GetRasterBand(1).ReadAsArray()
    resampled_average[nan_frac >= thresh] = np.nan
    return resampled_average, src_ds_mem 

# 需要導入模塊: from osgeo import gdal [as 別名]
# 或者: from osgeo.gdal import ReprojectImage [as 別名]
def test_reproject_average_resampling(self):

        data = np.array([[4, 7, 7, 7, 2, 7.],
                         [4, 7, 7, 7, 2, 7.],
                         [4, 7, 7, 7, 2, 7.],
                         [4, 7, 7, 2, 2, 7.],
                         [4, 7, 7, 2, 2, 7.],
                         [4, 7, 7, 10, 2, 7.]], dtype=np.float32)
        src_ds = gdal.GetDriverByName('MEM').Create('', 6, 6, 1,
                                                    gdalconst.GDT_Float32)
        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('', 3, 3, 1,
                                                    gdalconst.GDT_Float32)
        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_Average)
        got_data = dst_ds.GetRasterBand(1).ReadAsArray()
        expected_data = np.array([[5.5, 7, 7],
                                  [5.5, 7, 7],
                                  [5.5, 8, 7]])
        np.testing.assert_array_equal(got_data, expected_data) 

# 需要導入模塊: from osgeo import gdal [as 別名]
# 或者: from osgeo.gdal import ReprojectImage [as 別名]
def test_reproject_average_resampling_with_2bands(self):

        data = np.array([[[4, 7, 7, 7, 2, 7.],
                         [4, 7, 7, 7, 2, 7.],
                         [4, 7, 7, 7, 2, 7.],
                         [4, 7, 7, 2, 2, 7.],
                         [4, 7, 7, 2, 2, 7.],
                         [4, 7, 7, 10, 2, 7.]],
                        [[2, 0, 0, 0, 0, 0.],
                         [2, 0, 0, 0, 0, 2.],
                         [0, 1., 0, 0, 0, 1.],
                         [0, 0, 0, 0, 0, 2],
                         [0, 0, 0, 0, 0, 0.],
                         [0, 0, 0, 0, 0, 0.]]], dtype=np.float32)
        src_ds = gdal.GetDriverByName('MEM').Create('', 6, 6, 2,
                                                    gdalconst.GDT_Float32)

        src_ds.GetRasterBand(1).WriteArray(data[0, :, :])
        src_ds.GetRasterBand(1).SetNoDataValue(2)
        src_ds.GetRasterBand(2).WriteArray(data[1, :, :])
        # src_ds.GetRasterBand(1).SetNoDataValue()
        src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1])

        dst_ds = gdal.GetDriverByName('MEM').Create('', 3, 3, 2,
                                                    gdalconst.GDT_Float32)
        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_Average)
        got_data = dst_ds.GetRasterBand(1).ReadAsArray()
        expected_data = np.array([[5.5, 7, 7],
                                  [5.5, 7, 7],
                                  [5.5, 8, 7]])
        np.testing.assert_array_equal(got_data, expected_data)
        band2 = dst_ds.GetRasterBand(2).ReadAsArray()
        np.testing.assert_array_equal(band2, np.array([[1., 0., 0.5],
                                                       [0.25, 0., 0.75],
                                                       [0., 0., 0.]])) 

# 需要導入模塊: from osgeo import gdal [as 別名]
# 或者: from osgeo.gdal import ReprojectImage [as 別名]
def intersect_rasters(
        ref_rast_defn, src_rast_defn, nodata=-9999, gdt=gdalconst.GDT_Int32):
    '''
    Projects the source raster so that its top-left corner is aligned with
    the reference raster. Then, clips or pads the source raster so that it
    has the same number of rows and columns (covers the same extent at the
    same grid resolution) as the reference raster.
    Arguments:
        ref_rast_defn   A (raster, gt, wkt) tuple for the reference raster
        src_rast_defn   A (raster, gt, wkt) tuple for the source raster;
                        the raster to be changed
        nodata          The NoData value to fill in where the reference
                        raster is larger
        gdt             The GDAL data type to use for the output raster

    NOTE: If the reference raster's top-left corner is far left and/or above
    that of the source raster, the interesect raster may contain no data
    from the original raster, i.e., an empty raster will result.
    '''
    rast_ref, gt, wkt = ref_rast_defn
    rast_src, gt0, wkt0 = src_rast_defn
    # Create a new raster with the desired attributes
    width, height = (rast_ref.RasterXSize, rast_ref.RasterYSize)
    width0, height0 = (rast_src.RasterXSize, rast_src.RasterYSize)
    rast_out = gdal.GetDriverByName('MEM').Create('temp.file',
        width, height, rast_src.RasterCount, gdt)

    # Initialize and set the NoData value
    for i in range(1, rast_src.RasterCount + 1):
        b = rast_out.GetRasterBand(i)
        b.Fill(nodata)
        b.SetNoDataValue(nodata)

    # Set the desired geotransform, and projection
    rast_out.SetGeoTransform(gt)
    rast_out.SetProjection(wkt)

    # Re-project the source image; now the top-left corners are aligned
    gdal.ReprojectImage(rast_src, rast_out, wkt0, wkt, gdalconst.GRA_Bilinear)
    arr = rast_out.ReadAsArray()
    del rast_src # Delete original raster references
    del rast_ref
    del rast_out
    # Clip the extent of the src image if ref is smaller
    if arr.ndim > 2: ch, cw = arr.shape[1:]
    else: ch, cw = arr.shape
    if (width <= width0): cw = width # Clip src to ref extents
    if (height <= height0): ch = height

    if arr.ndim > 2:
        arr_out = arr[:,0:ch,0:cw]

    else:
        arr_out = arr[0:ch,0:cw]

    return array_to_raster(arr_out, gt, wkt) 

# 需要導入模塊: from osgeo import gdal [as 別名]
# 或者: from osgeo.gdal import ReprojectImage [as 別名]
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)) 

# 需要導入模塊: from osgeo import gdal [as 別名]
# 或者: from osgeo.gdal import ReprojectImage [as 別名]
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