reproject.py 27.3 KB
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# -*- coding: utf-8 -*-
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# py_tools_ds
#
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# Copyright (C) 2016-2021  Daniel Scheffler (GFZ Potsdam, daniel.scheffler@gfz-potsdam.de)
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#
# This software was developed within the context of the GeoMultiSens project funded
# by the German Federal Ministry of Education and Research
# (project grant code: 01 IS 14 010 A-C).
#
# This program is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the Free
# Software Foundation, either version 3 of the License, or (at your option) any
# later version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
# details.
#
# You should have received a copy of the GNU Lesser General Public License along
# with this program.  If not, see <http://www.gnu.org/licenses/>.

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import numpy as np
import warnings
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import multiprocessing
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from pkgutil import find_loader
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# custom
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from pyproj import CRS
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from osgeo import gdal, gdalnumeric
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from ...dtypes.conversion import dTypeDic_NumPy2GDAL
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from ..projection import WKT2EPSG, isProjectedOrGeographic, prj_equal
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from ..coord_trafo import pixelToLatLon, transform_any_prj
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from ..coord_calc import corner_coord_to_minmax, get_corner_coordinates
from ...io.raster.gdal import get_GDAL_ds_inmem
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from ...processing.progress_mon import ProgressBar
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__author__ = "Daniel Scheffler"
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def warp_ndarray_rasterio(ndarray, in_gt, in_prj, out_prj, out_gt=None, outRowsCols=None, outUL=None, out_res=None,
                          out_extent=None, out_dtype=None, rsp_alg=0, in_nodata=None, out_nodata=None,
                          outExtent_within=True):  # pragma: no cover
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    """Reproject / warp a numpy array with given geo information to target coordinate system.

    :param ndarray:             numpy.ndarray [rows,cols,bands]
    :param in_gt:               input gdal GeoTransform
    :param in_prj:              input projection as WKT string
    :param out_prj:             output projection as WKT string
    :param out_gt:              output gdal GeoTransform as float tuple in the source coordinate system (optional)
    :param outUL:               [X,Y] output upper left coordinates as floats in the source coordinate system
                                (requires outRowsCols)
    :param outRowsCols:         [rows, cols] (optional)
    :param out_res:             output resolution as tuple of floats (x,y) in the TARGET coordinate system
    :param out_extent:          [left, bottom, right, top] as floats in the source coordinate system
    :param out_dtype:           output data type as numpy data type
    :param rsp_alg:             Resampling method to use. One of the following (int, default is 0):
                                0 = nearest neighbour, 1 = bilinear, 2 = cubic, 3 = cubic spline, 4 = lanczos,
                                5 = average, 6 = mode
    :param in_nodata:           no data value of the input image
    :param out_nodata:          no data value of the output image
    :param outExtent_within:    Ensures that the output extent is within the input extent.
                                Otherwise an exception is raised.
    :return out_arr:            warped numpy array
    :return out_gt:             warped gdal GeoTransform
    :return out_prj:            warped projection as WKT string
    """
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    if not find_loader('rasterio'):
        raise ImportError('This function requires rasterio. You need to install it manually '
                          '(conda install -c conda-forge rasterio). It is not automatically installed.')

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    # NOTE: rasterio seems to increase the number of objects with static TLS
    #       There is a maximum number and if this is exceeded an ImportError is raised:
    #       ImportError: dlopen: cannot load any more object with static TLS
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    #       - see also: https://git.gfz-potsdam.de/danschef/py_tools_ds/issues/8
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    #       - NOTE: importing rasterio AFTER pyresample (which uses pykdtree) seems to solve that too
    #       => keep the rasterio import within the function locals to avoid not needed imports
    import rasterio
    from rasterio.warp import reproject as rio_reproject
    from rasterio.warp import calculate_default_transform as rio_calc_transform
    from rasterio.warp import Resampling
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    if not ndarray.flags['OWNDATA']:
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        temp = np.empty_like(ndarray)
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        temp[:] = ndarray
        ndarray = temp  # deep copy: converts view to its own array in order to avoid wrong output

    with rasterio.env.Env():
        if outUL is not None:
            assert outRowsCols is not None, 'outRowsCols must be given if outUL is given.'
        outUL = [in_gt[0], in_gt[3]] if outUL is None else outUL

        inEPSG, outEPSG = [WKT2EPSG(prj) for prj in [in_prj, out_prj]]
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        assert inEPSG, 'Could not derive input EPSG code.'
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        assert outEPSG, 'Could not derive output EPSG code.'
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        assert in_nodata is None or isinstance(in_nodata, (int, float)), \
            'Received invalid input nodata value: %s; type: %s.' % (in_nodata, type(in_nodata))
        assert out_nodata is None or isinstance(out_nodata, (int, float)), \
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            'Received invalid output nodata value: %s; type: %s.' % (out_nodata, type(out_nodata))
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        src_crs = {'init': 'EPSG:%s' % inEPSG}
        dst_crs = {'init': 'EPSG:%s' % outEPSG}

        if len(ndarray.shape) == 3:
            # convert input array axis order to rasterio axis order
            ndarray = np.swapaxes(np.swapaxes(ndarray, 0, 2), 1, 2)
            bands, rows, cols = ndarray.shape
            rows, cols = outRowsCols if outRowsCols else (rows, cols)
        else:
            rows, cols = ndarray.shape if outRowsCols is None else outRowsCols

        # set dtypes ensuring at least int16 (int8 is not supported by rasterio)
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        in_dtype = ndarray.dtype
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        out_dtype = ndarray.dtype if out_dtype is None else out_dtype
        out_dtype = np.int16 if str(out_dtype) == 'int8' else out_dtype
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        ndarray = ndarray.astype(np.int16) if str(in_dtype) == 'int8' else ndarray
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        # get dst_transform
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        def gt2bounds(gt, r, c): return [gt[0], gt[3] + r * gt[5], gt[0] + c * gt[1], gt[3]]  # left, bottom, right, top

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        if out_gt is None and out_extent is None:
            if outRowsCols:
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                outUL = [in_gt[0], in_gt[3]] if outUL is None else outUL

                def ulRC2bounds(ul, r, c):
                    return [ul[0], ul[1] + r * in_gt[5], ul[0] + c * in_gt[1], ul[1]]  # left, bottom, right, top

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                left, bottom, right, top = ulRC2bounds(outUL, rows, cols)
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            else:  # outRowsCols is None and outUL is None: use in_gt
                left, bottom, right, top = gt2bounds(in_gt, rows, cols)
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        elif out_extent:
            left, bottom, right, top = out_extent
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        else:  # out_gt is given
            left, bottom, right, top = gt2bounds(in_gt, rows, cols)

        if outExtent_within:
            # input array is only a window of the actual input array
            assert left >= in_gt[0] and right <= (in_gt[0] + (cols + 1) * in_gt[1]) and \
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                   bottom >= in_gt[3] + (rows + 1) * in_gt[5] and top <= in_gt[3], \
                   "out_extent has to be completely within the input image bounds."
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        if out_res is None:
            # get pixel resolution in target coord system
            prj_in_out = (isProjectedOrGeographic(in_prj), isProjectedOrGeographic(out_prj))
            assert None not in prj_in_out, 'prj_in_out contains None.'
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            if prj_in_out[0] == prj_in_out[1]:
                out_res = (in_gt[1], abs(in_gt[5]))
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            elif prj_in_out == ('geographic', 'projected'):
                raise NotImplementedError('Different projections are currently not supported.')
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            else:  # ('projected','geographic')
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                px_size_LatLon = np.array(pixelToLatLon([[1, 1]], geotransform=in_gt, projection=in_prj)) - \
                                 np.array(pixelToLatLon([[0, 0]], geotransform=in_gt, projection=in_prj))
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                out_res = tuple(reversed(abs(px_size_LatLon)))
                print('OUT_RES NOCHMAL CHECKEN: ', out_res)

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        dict_rspInt_rspAlg = \
            {0: Resampling.nearest, 1: Resampling.bilinear, 2: Resampling.cubic,
             3: Resampling.cubic_spline, 4: Resampling.lanczos, 5: Resampling.average, 6: Resampling.mode}
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        var1 = True
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        if var1:
            src_transform = rasterio.transform.from_origin(in_gt[0], in_gt[3], in_gt[1], abs(in_gt[5]))
            print('calc_trafo_args')
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            for i in [src_crs, dst_crs, cols, rows, left, bottom, right, top, out_res]:
                print(i, '\n')
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            left, right, bottom, top = corner_coord_to_minmax(get_corner_coordinates(gt=in_gt, rows=rows, cols=cols))
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            dst_transform, out_cols, out_rows = rio_calc_transform(
                src_crs, dst_crs, cols, rows, left, bottom, right, top, resolution=out_res)
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            out_arr = np.zeros((bands, out_rows, out_cols), out_dtype) \
                if len(ndarray.shape) == 3 else np.zeros((out_rows, out_cols), out_dtype)
            print(out_res)
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            for i in [src_transform, src_crs, dst_transform, dst_crs]:
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                print(i, '\n')
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            rio_reproject(ndarray, out_arr, src_transform=src_transform, src_crs=src_crs, dst_transform=dst_transform,
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                          dst_crs=dst_crs, resampling=dict_rspInt_rspAlg[rsp_alg], src_nodata=in_nodata,
                          dst_nodata=out_nodata)
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            aff = list(dst_transform)
            out_gt = out_gt if out_gt else (aff[2], aff[0], aff[1], aff[5], aff[3], aff[4])
            # FIXME sometimes output dimensions are not exactly as requested (1px difference)
        else:
            dst_transform, out_cols, out_rows = rio_calc_transform(
                src_crs, dst_crs, cols, rows, left, bottom, right, top, resolution=out_res)

            # check if calculated output dimensions correspond to expected ones and correct them if neccessary
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            # rows_expected = int(round(abs(top - bottom) / out_res[1], 0))
            # cols_expected = int(round(abs(right - left) / out_res[0], 0))

            # diff_rows_exp_real, diff_cols_exp_real = abs(out_rows - rows_expected), abs(out_cols - cols_expected)
            # if diff_rows_exp_real > 0.1 or diff_cols_exp_real > 0.1:
            # assert diff_rows_exp_real < 1.1 and diff_cols_exp_real < 1.1,
            #     'warp_ndarray: The output image size calculated by rasterio is too far away from the expected output '
            #     'image size.'
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            #    out_rows, out_cols = rows_expected, cols_expected
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            # fixes an issue where rio_calc_transform() does not return quadratic output image although input parameters
            # correspond to a quadratic image and inEPSG equals outEPSG
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            aff = list(dst_transform)
            out_gt = out_gt if out_gt else (aff[2], aff[0], aff[1], aff[5], aff[3], aff[4])

            out_arr = np.zeros((bands, out_rows, out_cols), out_dtype) \
                if len(ndarray.shape) == 3 else np.zeros((out_rows, out_cols), out_dtype)

            with warnings.catch_warnings():
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                # FIXME supresses: FutureWarning:
                # FIXME: GDAL-style transforms are deprecated and will not be supported in Rasterio 1.0.
                warnings.simplefilter('ignore')
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                try:
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                    # print('INPUTS')
                    # print(ndarray.shape, ndarray.dtype, out_arr.shape, out_arr.dtype)
                    # print(in_gt)
                    # print(src_crs)
                    # print(out_gt)
                    # print(dst_crs)
                    # print(dict_rspInt_rspAlg[rsp_alg])
                    # print(in_nodata)
                    # print(out_nodata)
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                    for i in [in_gt, src_crs, out_gt, dst_crs]:
                        print(i, '\n')
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                    rio_reproject(ndarray, out_arr,
                                  src_transform=in_gt, src_crs=src_crs, dst_transform=out_gt, dst_crs=dst_crs,
                                  resampling=dict_rspInt_rspAlg[rsp_alg], src_nodata=in_nodata, dst_nodata=out_nodata)
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                    # from matplotlib import pyplot as plt
                    # print(out_arr.shape)
                    # plt.figure()
                    # plt.imshow(out_arr[:,:,1])
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                except KeyError:
                    print(in_dtype, str(in_dtype))
                    print(ndarray.dtype)
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        # convert output array axis order to GMS axis order [rows,cols,bands]
        out_arr = out_arr if len(ndarray.shape) == 2 else np.swapaxes(np.swapaxes(out_arr, 0, 1), 1, 2)

        if outRowsCols:
            out_arr = out_arr[:outRowsCols[0], :outRowsCols[1]]

    return out_arr, out_gt, out_prj
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def warp_ndarray(ndarray, in_gt, in_prj=None, out_prj=None, out_dtype=None,
                 out_gsd=(None, None), out_bounds=None, out_bounds_prj=None, out_XYdims=(None, None),
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                 rspAlg='near', in_nodata=None, out_nodata=None, in_alpha=False,
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                 out_alpha=False, targetAlignedPixels=False, gcpList=None, polynomialOrder=None, options=None,
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                 transformerOptions=None, warpOptions=None, CPUs=1, warpMemoryLimit=0, progress=True, q=False):
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    # type: () -> (np.ndarray, tuple, str)
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    r"""
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    :param ndarray:             numpy array to be warped (or a list of numpy arrays (requires lists for in_gt/in_prj))
    :param in_gt:               input GDAL geotransform (or a list of GDAL geotransforms)
    :param in_prj:              input GDAL projection or list of projections (WKT string, 'EPSG:1234', <EPSG_int>),
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                                default: "LOCAL_CS[\"MAP\"]"
    :param out_prj:             output GDAL projection (WKT string, 'EPSG:1234', <EPSG_int>),
                                default: "LOCAL_CS[\"MAP\"]"
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    :param out_dtype:           gdal.DataType
    :param out_gsd:
    :param out_bounds:          [xmin,ymin,xmax,ymax] set georeferenced extents of output file to be created,
                                e.g. [440720, 3750120, 441920, 3751320])
                                (in target SRS by default, or in the SRS specified with -te_srs)
    :param out_bounds_prj:
    :param out_XYdims:
    :param rspAlg:              <str> Resampling method to use. Available methods are:
                                near, bilinear, cubic, cubicspline, lanczos, average, mode, max, min, med, q1, q2
    :param in_nodata:
    :param out_nodata:
    :param in_alpha:            <bool> Force the last band of a source image to be considered as a source alpha band.
    :param out_alpha:           <bool> Create an output alpha band to identify nodata (unset/transparent) pixels
    :param targetAlignedPixels:   (GDAL >= 1.8.0) (target aligned pixels) align the coordinates of the extent
                                        of the output file to the values of the -tr, such that the aligned extent
                                        includes the minimum extent.
    :param gcpList:             <list> list of ground control points in the output coordinate system
                                to be used for warping, e.g. [gdal.GCP(mapX,mapY,mapZ,column,row),...]
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    :param polynomialOrder:     <int> order of polynomial GCP interpolation
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    :param options:             <str> additional GDAL options as string, e.g. '-nosrcalpha' or '-order'
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    :param transformerOptions:  <list> list of transformer options, e.g.  ['SRC_SRS=invalid']
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    :param warpOptions:         <list> list of warp options, e.g.  ['CUTLINE_ALL_TOUCHED=TRUE'],
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                                find available options here: http://www.gdal.org/doxygen/structGDALWarpOptions.html
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    :param CPUs:                <int> number of CPUs to use (default: None, which means 'all CPUs available')
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    :param warpMemoryLimit:     <int> size of working buffer in bytes (default: 0)
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    :param progress:            <bool> show progress bar (default: True)
    :param q:                   <bool> quiet mode (default: False)
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    :return:

    """
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    # TODO complete type hint
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    # TODO test if this function delivers the exact same output like console version,
    # TODO otherwise implment error_threshold=0.125
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    # how to implement:    https://svn.osgeo.org/gdal/trunk/autotest/utilities/test_gdalwarp_lib.py

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    # assume local coordinates if no projections are given
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    if not in_prj and not out_prj:
        if out_bounds_prj and not out_bounds_prj.startswith('LOCAL_CS'):
            raise ValueError("'out_bounds_prj' cannot have a projection if 'in_prj' and 'out_prj' are not given.")
        in_prj = out_prj = out_bounds_prj = "LOCAL_CS[\"MAP\"]"
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    # ensure GDAL 2 only gets WKT1 strings (WKT2 requires GDAL>=3)
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    if in_prj and int(gdal.__version__[0]) < 3:
        # noinspection PyTypeChecker
        in_prj = CRS(in_prj).to_wkt(version="WKT1_GDAL")
    if out_prj and int(gdal.__version__[0]) < 3:
        # noinspection PyTypeChecker
        out_prj = CRS(out_prj).to_wkt(version="WKT1_GDAL")

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    # assertions
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    if rspAlg == 'average':
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        is_avail_rsp_average = int(gdal.VersionInfo()[0]) >= 2
        if not is_avail_rsp_average:
            warnings.warn("The GDAL version on this machine does not yet support the resampling algorithm 'average'. "
                          "'cubic' is used instead. To avoid this please update GDAL to a version above 2.0.0!")
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            rspAlg = 'cubic'

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    if not isinstance(ndarray, (list, tuple)):
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        assert str(np.dtype(ndarray.dtype)) in dTypeDic_NumPy2GDAL, "Unknown target datatype '%s'." % ndarray.dtype
    else:
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        assert str(np.dtype(ndarray[0].dtype)) in dTypeDic_NumPy2GDAL, "Unknown target datatype '%s'." \
                                                                       % ndarray[0].dtype
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        assert isinstance(in_gt, (list, tuple)), "If 'ndarray' is a list, 'in_gt' must also be a list!"
        assert isinstance(in_prj, (list, tuple)), "If 'ndarray' is a list, 'in_prj' must also be a list!"
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        assert len(list(set([arr.dtype for arr in ndarray]))) == 1,  "Data types of input ndarray list must be equal."

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    def get_SRS(prjArg):
        return prjArg if isinstance(prjArg, str) and prjArg.startswith('EPSG:') else \
            'EPSG:%s' % prjArg if isinstance(prjArg, int) else prjArg

    def get_GDT(DT): return dTypeDic_NumPy2GDAL[str(np.dtype(DT))]

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    in_dtype_np = ndarray.dtype if not isinstance(ndarray, (list, tuple)) else ndarray[0].dtype
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    # # not yet implemented
    # # TODO cutline from OGR datasource. => implement input shapefile or Geopandas dataframe
    # cutlineDSName = 'data/cutline.vrt'  # '/vsimem/cutline.shp'
    # cutlineLayer = 'cutline'
    # cropToCutline = False
    # cutlineSQL = 'SELECT * FROM cutline'
    # cutlineWhere = '1 = 1'
    # rpc = [
    #     "HEIGHT_OFF=1466.05894327379",
    #     "HEIGHT_SCALE=144.837606185489",
    #     "LAT_OFF=38.9266809014185",
    #     "LAT_SCALE=-0.108324009570885",
    #     "LINE_DEN_COEFF="
    #     "1 -0.000392404256440504 -0.0027925489381758 0.000501819414812054 0.00216726134806561 "
    #     "-0.00185617059201599 0.000183834173326118 -0.00290342803717354 -0.00207181007131322 -0.000900223247894285 "
    #     "-0.00132518281680544 0.00165598132063197 0.00681015244696305 0.000547865679631528 0.00516214646283021 "
    #     "0.00795287690785699 -0.000705040639059332 -0.00254360623317078 -0.000291154885056484 0.00070943440010757",
    #     "LINE_NUM_COEFF="
    #     "-0.000951099635749339 1.41709976082781 -0.939591985038569 -0.00186609235173885 0.00196881101098923 "
    #     "0.00361741523740639 -0.00282449434932066 0.0115361898794214 -0.00276027843825304 9.37913944402154e-05 "
    #     "-0.00160950221565737 0.00754053609977256 0.00461831968713819 0.00274991122620312 0.000689605203796422 "
    #     "-0.0042482778732957 -0.000123966494595151 0.00307976709897974 -0.000563274426174409 0.00049981716767074",
    #     "LINE_OFF=2199.50159296339",
    #     "LINE_SCALE=2195.852519621",
    #     "LONG_OFF=76.0381768085136",
    #     "LONG_SCALE=0.130066683772651",
    #     "SAMP_DEN_COEFF="
    #     "1 -0.000632078047521022 -0.000544107268758971 0.000172438016778527 -0.00206391734870399 "
    #     "-0.00204445747536872 -0.000715754551621987 -0.00195545265530244 -0.00168532972557267 -0.00114709980708329 "
    #     "-0.00699131177532728 0.0038551339822296 0.00283631282133365 -0.00436885468926666 -0.00381335885955994 "
    #     "0.0018742043611019 -0.0027263909314293 -0.00237054119407013 0.00246374716379501 -0.00121074576302219",
    #     "SAMP_NUM_COEFF="
    #     "0.00249293151551852 -0.581492592442025 -1.00947448466175 0.00121597346320039 -0.00552825219917498 "
    #     "-0.00194683170765094 -0.00166012459012905 -0.00338315804553888 -0.00152062885009498 -0.000214562164393127 "
    #     "-0.00219914905535387 -0.000662800177832777 -0.00118644828432841 -0.00180061222825708 -0.00364756875260519 "
    #     "-0.00287273485650089 -0.000540077934728493 -0.00166800463003749 0.000201057249109451 -8.49620129025469e-05",
    #     "SAMP_OFF=3300.34602166792",
    #     "SAMP_SCALE=3297.51222987611"
    # ]
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    """ Create a WarpOptions() object that can be passed to gdal.Warp()
        Keyword arguments are :
          options --- can be be an array of strings, a string or let empty and filled from other keywords.
          format --- output format ("GTiff", etc...)
          outputBounds --- output bounds as (minX, minY, maxX, maxY) in target SRS
          outputBoundsSRS --- SRS in which output bounds are expressed, in the case they are not expressed in dstSRS
          xRes, yRes --- output resolution in target SRS
          targetAlignedPixels --- whether to force output bounds to be multiple of output resolution
          width --- width of the output raster in pixel
          height --- height of the output raster in pixel
          srcSRS --- source SRS
          dstSRS --- output SRS
          srcAlpha --- whether to force the last band of the input dataset to be considered as an alpha band
          dstAlpha --- whether to force the creation of an output alpha band
          outputType --- output type (gdal.GDT_Byte, etc...)
          workingType --- working type (gdal.GDT_Byte, etc...)
          warpOptions --- list of warping options
          errorThreshold --- error threshold for approximation transformer (in pixels)
          warpMemoryLimit --- size of working buffer in bytes
          resampleAlg --- resampling mode
          creationOptions --- list of creation options
          srcNodata --- source nodata value(s)
          dstNodata --- output nodata value(s)
          multithread --- whether to multithread computation and I/O operations
          tps --- whether to use Thin Plate Spline GCP transformer
          rpc --- whether to use RPC transformer
          geoloc --- whether to use GeoLocation array transformer
          polynomialOrder --- order of polynomial GCP interpolation
          transformerOptions --- list of transformer options
          cutlineDSName --- cutline dataset name
          cutlineLayer --- cutline layer name
          cutlineWhere --- cutline WHERE clause
          cutlineSQL --- cutline SQL statement
          cutlineBlend --- cutline blend distance in pixels
          cropToCutline --- whether to use cutline extent for output bounds
          copyMetadata --- whether to copy source metadata
          metadataConflictValue --- metadata data conflict value
          setColorInterpretation --- whether to force color interpretation of input bands to output bands
          callback --- callback method
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          callback_data --- user data for callback  # value for last parameter of progress callback
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    """
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    # get input dataset (in-MEM)
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    if not isinstance(ndarray, (list, tuple)):
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        in_ds = get_GDAL_ds_inmem(ndarray, in_gt, in_prj)
    else:
        # list of ndarrays
        in_ds = [get_GDAL_ds_inmem(arr, gt, prj) for arr, gt, prj in zip(ndarray, in_gt, in_prj)]
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    # set RPCs
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    # if rpcList:
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    #    in_ds.SetMetadata(rpc, "RPC")
    #    transformerOptions = ['RPC_DEM=data/warp_52_dem.tif']

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    # use GDALs multiprocessing as long as the current process is not a child process of a multiprocessing main process
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    # - otherwise, gdal.Warp() hangs with GDAL versions from 3.2.1 and above (does not allow sub-multiprocessing)
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    if (CPUs is None or CPUs > 1) and multiprocessing.current_process().name == 'MainProcess':
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        gdal.SetConfigOption('GDAL_NUM_THREADS', str(CPUs if CPUs else multiprocessing.cpu_count()))
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        # gdal.SetConfigOption('GDAL_CACHEMAX', str(800))
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        # GDAL Translate if needed
        # if gcpList:
        #   in_ds.SetGCPs(gcpList, in_ds.GetProjection())
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    if gcpList:
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        in_ds = gdal.Translate(
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            '', in_ds, format='MEM',
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            outputSRS=get_SRS(out_prj),
            GCPs=gcpList,
            callback=ProgressBar(prefix='Translating progress', timeout=None) if progress and not q else None
        )
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        # NOTE: options = ['SPARSE_OK=YES'] ## => what is that for?

    # GDAL Warp
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    res_ds = gdal.Warp(
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        '', in_ds, format='MEM',
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        dstSRS=get_SRS(out_prj),
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        outputType=get_GDT(out_dtype) if out_dtype else get_GDT(in_dtype_np),
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        xRes=out_gsd[0],
        yRes=out_gsd[1],
        outputBounds=out_bounds,
        outputBoundsSRS=get_SRS(out_bounds_prj),
        width=out_XYdims[0],
        height=out_XYdims[1],
        resampleAlg=rspAlg,
        srcNodata=in_nodata,
        dstNodata=out_nodata,
        srcAlpha=in_alpha,
        dstAlpha=out_alpha,
        options=options if options else [],
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        warpOptions=warpOptions or [],
        transformerOptions=transformerOptions or [],
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        targetAlignedPixels=targetAlignedPixels,
        tps=True if gcpList else False,
        polynomialOrder=polynomialOrder,
        warpMemoryLimit=warpMemoryLimit,
        callback=ProgressBar(prefix='Warping progress    ', timeout=None) if progress and not q else None,
        callback_data=[0],
        errorThreshold=0.125,  # this is needed to get exactly the same output like the console version of GDAL warp
    )
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    gdal.SetConfigOption('GDAL_NUM_THREADS', None)
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    if res_ds is None:
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        raise Exception('Warping Error:  ' + gdal.GetLastErrorMsg())

    # get outputs
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    res_arr = gdalnumeric.DatasetReadAsArray(res_ds)
    if len(res_arr.shape) == 3:
        res_arr = np.swapaxes(np.swapaxes(res_arr, 0, 2), 0, 1)
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    res_gt = res_ds.GetGeoTransform()
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    res_prj = res_ds.GetProjection()
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    # cleanup
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    del in_ds, res_ds
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    # dtype check -> possibly dtype had to be changed for GDAL compatibility
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    if in_dtype_np != res_arr.dtype:
        res_arr = res_arr.astype(in_dtype_np)
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    # test output
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    if out_prj and prj_equal(out_prj, 4626):
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        assert -180 < res_gt[0] < 180 and -90 < res_gt[3] < 90, 'Testing of gdal_warp output failed.'

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    # output bounds verification
    if out_bounds:
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        if out_bounds_prj and not prj_equal(out_bounds_prj, res_prj):
            out_xmin, out_ymin = transform_any_prj(out_bounds_prj, res_prj, *out_bounds[:2])
            out_xmax, out_ymax = transform_any_prj(out_bounds_prj, res_prj, *out_bounds[2:])

        else:
            out_xmin, out_ymin, out_xmax, out_ymax = out_bounds

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        xmin, xmax, ymin, ymax = \
            corner_coord_to_minmax(get_corner_coordinates(gt=res_gt, rows=res_arr.shape[0], cols=res_arr.shape[1]))
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        if False in np.isclose((out_xmin, out_ymin, out_xmax, out_ymax), (xmin, ymin, xmax, ymax)):
            warnings.warn('The output bounds of warp_ndarray do not exactly match the requested bounds!')
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    return res_arr, res_gt, res_prj