Python gdal.GRA_Average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 GRA_Average [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 GRA_Average [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 GRA_Average [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 GRA_Average [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 GRA_Average [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 

# 需要導入模塊: from osgeo import gdal [as 別名]
# 或者: from osgeo.gdal import GRA_Average [as 別名]
def check(driver_type):

        temp_tif = tempfile.mktemp(suffix='.tif')
        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(driver_type).Create(temp_tif, 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(2).SetNoDataValue(3)
        src_ds.SetGeoTransform([10, 1, 0, 10, 0, -1])
        src_ds.FlushCache()

        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)
        band1 = dst_ds.GetRasterBand(1).ReadAsArray()
        np.testing.assert_array_equal(band1, np.array([[5.5, 7, 7],
                                                       [5.5, 7, 7],
                                                       [5.5, 8, 7]]))
        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.]]))
        if os.path.exists(temp_tif):
            try:
                os.remove(temp_tif)
            except PermissionError:
                print("File opened by another process.")