transformer_3d.py 16.3 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
# -*- coding: utf-8 -*-

# sensormapgeo, Transform remote sensing images between sensor and map geometry.
#
# Copyright (C) 2020  Daniel Scheffler (GFZ Potsdam, danschef@gfz-potsdam.de)
#
# This software was developed within the context of the EnMAP project supported
# by the DLR Space Administration with funds of the German Federal Ministry of
# Economic Affairs and Energy (on the basis of a decision by the German Bundestag:
# 50 EE 1529) and contributions from DLR, GFZ and OHB System AG.
#
# 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/>.

"""Module to transform 3D arrays between sensor and map geometry (using band-wise Lon/Lat arrays)."""

Daniel Scheffler's avatar
Daniel Scheffler committed
27
from typing import Union, Tuple
28
29
30
import multiprocessing

import numpy as np
31
32
from py_tools_ds.geo.projection import WKT2EPSG, EPSG2WKT, proj4_to_WKT
from py_tools_ds.geo.coord_trafo import get_proj4info, transform_any_prj
33

34
35
36
from .transformer_2d import \
    SensorMapGeometryTransformer, _corner_coords_lonlat_to_extent, \
    _move_extent_to_coordgrid, _get_validated_tgt_res
37
from pyresample import AreaDefinition
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101


class SensorMapGeometryTransformer3D(object):
    def __init__(self,
                 lons: np.ndarray,
                 lats: np.ndarray,
                 resamp_alg: str = 'nearest',
                 radius_of_influence: int = 30,
                 mp_alg: str = 'auto',
                 **opts) -> None:
        """Get an instance of SensorMapGeometryTransformer.

        :param lons:    3D longitude array corresponding to the 3D sensor geometry array
        :param lats:    3D latitude array corresponding to the 3D sensor geometry array

        :Keyword Arguments:  (further documentation here: https://pyresample.readthedocs.io/en/latest/swath.html)
            - resamp_alg:           resampling algorithm ('nearest', 'bilinear', 'gauss', 'custom')
            - radius_of_influence:  <float> Cut off distance in meters (default: 30)
                                    NOTE: keyword is named 'radius' in case of bilinear resampling
            - mp_alg                multiprocessing algorithm
                                    'bands': parallelize over bands using multiprocessing lib
                                    'tiles': parallelize over tiles using OpenMP
                                    'auto': automatically choose the algorithm
            - sigmas:               <list of floats or float> [ONLY 'gauss'] List of sigmas to use for the gauss
                                    weighting of each channel 1 to k, w_k = exp(-dist^2/sigma_k^2). If only one channel
                                    is resampled sigmas is a single float value.
            - neighbours:           <int> [ONLY 'bilinear', 'gauss'] Number of neighbours to consider for each grid
                                    point when searching the closest corner points
            - epsilon:              <float> Allowed uncertainty in meters. Increasing uncertainty reduces execution time
            - weight_funcs:         <list of function objects or function object> [ONLY 'custom'] List of weight
                                    functions f(dist) to use for the weighting of each channel 1 to k. If only one
                                    channel is resampled weight_funcs is a single function object.
            - fill_value:           <int or None> Set undetermined pixels to this value (default: 0).
                                    If fill_value is None a masked array is returned with undetermined pixels masked
            - reduce_data:          <bool> Perform initial coarse reduction of source dataset in order to reduce
                                    execution time
            - nprocs:               <int>, Number of processor cores to be used
            - segments:             <int or None> Number of segments to use when resampling.
                                    If set to None an estimate will be calculated
            - with_uncert:          <bool> [ONLY 'gauss' and 'custom'] Calculate uncertainty estimates
                                    NOTE: resampling function has 3 return values instead of 1: result, stddev, count
        """
        # validation
        if lons.ndim != 3:
            raise ValueError('Expected a 3D longitude array. Received a %dD array.' % lons.ndim)
        if lats.ndim != 3:
            raise ValueError('Expected a 3D latitude array. Received a %dD array.' % lats.ndim)
        if lons.shape != lats.shape:
            raise ValueError((lons.shape, lats.shape), "'lons' and 'lats' are expected to have the same shape.")

        self.lats = lats
        self.lons = lons
        self.resamp_alg = resamp_alg
        self.radius_of_influence = radius_of_influence
        self.opts = opts

        # define number of CPUs to use (but avoid sub-multiprocessing)
        #   -> parallelize either over bands or over image tiles
        #      bands: multiprocessing uses multiprocessing.Pool, implemented in to_map_geometry / to_sensor_geometry
        #      tiles: multiprocessing uses OpenMP implemented in pykdtree which is used by pyresample
        self.opts['nprocs'] = opts.get('nprocs', multiprocessing.cpu_count())
        self.mp_alg = ('bands' if self.lons.shape[2] >= opts['nprocs'] else 'tiles') if mp_alg == 'auto' else mp_alg

    @staticmethod
Daniel Scheffler's avatar
Daniel Scheffler committed
102
103
    def _to_map_geometry_2D(kwargs_dict: dict
                            ) -> Tuple[np.ndarray, tuple, str, int]:
104
105
106
107
108
109
110
111
        assert [var is not None for var in (_global_shared_lons, _global_shared_lats, _global_shared_data)]

        SMGT2D = SensorMapGeometryTransformer(lons=_global_shared_lons[:, :, kwargs_dict['band_idx']],
                                              lats=_global_shared_lats[:, :, kwargs_dict['band_idx']],
                                              resamp_alg=kwargs_dict['resamp_alg'],
                                              radius_of_influence=kwargs_dict['radius_of_influence'],
                                              **kwargs_dict['init_opts'])
        data_mapgeo, out_gt, out_prj = SMGT2D.to_map_geometry(data=_global_shared_data[:, :, kwargs_dict['band_idx']],
112
                                                              area_definition=kwargs_dict['area_definition'])
113
114
115

        return data_mapgeo, out_gt, out_prj, kwargs_dict['band_idx']

116
117
118
    def _get_common_target_extent(self,
                                  tgt_epsg: int,
                                  tgt_coordgrid: Tuple[Tuple, Tuple] = None):
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
        if tgt_epsg == 4326:
            corner_coords_ll = [[self.lons[0, 0, :].min(), self.lats[0, 0, :].max()],  # common UL_xy
                                [self.lons[0, -1, :].max(), self.lats[0, -1, :].max()],  # common UR_xy
                                [self.lons[-1, 0, :].min(), self.lats[-1, 0, :].min()],  # common LL_xy
                                [self.lons[-1, -1, :].max(), self.lats[-1, -1, :].min()],  # common LR_xy
                                ]
            common_tgt_extent = _corner_coords_lonlat_to_extent(corner_coords_ll, tgt_epsg)
        else:
            # get Lon/Lat corner coordinates of geolayers
            UL_UR_LL_LR_ll = [(self.lons[y, x], self.lats[y, x]) for y, x in [(0, 0), (0, -1), (-1, 0), (-1, -1)]]

            # transform them to target projection
            UL_UR_LL_LR_prj = [transform_any_prj(EPSG2WKT(4326), EPSG2WKT(tgt_epsg), x, y) for x, y in UL_UR_LL_LR_ll]

            # separate X and Y
            X_prj, Y_prj = zip(*UL_UR_LL_LR_prj)

            # 3D geolayers, i.e., the corner coordinates have multiple values for multiple bands
            # -> use the outermost coordinates to be sure all data is included
            X_prj = (X_prj[0].min(), X_prj[1].max(), X_prj[2].min(), X_prj[3].max())
            Y_prj = (Y_prj[0].max(), Y_prj[1].max(), Y_prj[2].min(), Y_prj[3].min())

            # get the extent
            common_tgt_extent = (min(X_prj), min(Y_prj), max(X_prj), max(Y_prj))

144
145
146
        if tgt_coordgrid:
            common_tgt_extent = _move_extent_to_coordgrid(common_tgt_extent, *tgt_coordgrid)

147
        return common_tgt_extent
148

149
150
151
152
    def _get_common_area_definition(self,
                                    data: np.ndarray,
                                    tgt_prj: Union[str, int],
                                    tgt_extent: Tuple[float, float, float, float] = None,
153
                                    tgt_res: Tuple = None,
Daniel Scheffler's avatar
Daniel Scheffler committed
154
155
                                    tgt_coordgrid: Tuple[Tuple, Tuple] = None
                                    ) -> AreaDefinition:
156
157
        # get common target extent
        tgt_epsg = WKT2EPSG(proj4_to_WKT(get_proj4info(proj=tgt_prj)))
158
        tgt_extent = tgt_extent or self._get_common_target_extent(tgt_epsg, tgt_coordgrid=tgt_coordgrid)
159
160
161
162
163
164
165
166
167
168
169
170
171

        SMGT2D = SensorMapGeometryTransformer(lons=self.lons[:, :, 0],  # does NOT affect the computed area definition
                                              lats=self.lats[:, :, 0],  # -> only needed for __init__
                                              resamp_alg=self.resamp_alg,
                                              radius_of_influence=self.radius_of_influence,
                                              **self.opts)
        common_area_definition = SMGT2D.compute_areadefinition_sensor2map(data=data[:, :, 0],
                                                                          tgt_prj=tgt_prj,
                                                                          tgt_extent=tgt_extent,
                                                                          tgt_res=tgt_res)

        return common_area_definition

172
173
174
175
    def to_map_geometry(self,
                        data: np.ndarray,
                        tgt_prj: Union[str, int],
                        tgt_extent: Tuple[float, float, float, float] = None,
Daniel Scheffler's avatar
Daniel Scheffler committed
176
                        tgt_res: Tuple[float, float] = None,
177
                        tgt_coordgrid: Tuple[Tuple, Tuple] = None,
Daniel Scheffler's avatar
Daniel Scheffler committed
178
179
                        area_definition: AreaDefinition = None
                        ) -> Tuple[np.ndarray, tuple, str]:
180
181
182
183
184
185
        """Transform the input sensor geometry array into map geometry.

        :param data:            3D numpy array (representing sensor geometry) to be warped to map geometry
        :param tgt_prj:         target projection (WKT or 'epsg:1234' or <EPSG_int>)
        :param tgt_extent:      extent coordinates of output map geometry array (LL_x, LL_y, UR_x, UR_y) in the tgt_prj
        :param tgt_res:         target X/Y resolution (e.g., (30, 30))
186
187
        :param tgt_coordgrid:   target coordinate grid ((x, x), (y, y)):
                                if given, the output extent is moved to this coordinate grid
188
        :param area_definition: an instance of pyresample.geometry.AreaDefinition;
189
                                OVERRIDES tgt_prj, tgt_extent, tgt_res and tgt_coordgrid; saves computation time
190
191
192
193
194
195
196
197
        """
        if data.ndim != 3:
            raise ValueError(data.ndim, "'data' must have 3 dimensions.")

        if data.shape != self.lons.shape:
            raise ValueError(data.shape, 'Expected a sensor geometry data array with %d rows, %d columns and %d bands.'
                             % self.lons.shape)

198
199
200
        if not tgt_prj and not area_definition:
            raise ValueError(tgt_prj, 'Target projection must be given if area_definition is not given.')

201
202
203
        if tgt_coordgrid:
            tgt_res = _get_validated_tgt_res(tgt_coordgrid, tgt_res)

204
205
206
207
        init_opts = self.opts.copy()
        if self.mp_alg == 'bands':
            del init_opts['nprocs']  # avoid sub-multiprocessing

208
209
        # get common area_definition
        if not area_definition:
210
            area_definition = self._get_common_area_definition(data, tgt_prj, tgt_extent, tgt_res, tgt_coordgrid)
211

212
213
214
215
        args = [dict(
            resamp_alg=self.resamp_alg,
            radius_of_influence=self.radius_of_influence,
            init_opts=init_opts,
216
            area_definition=area_definition,
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
            band_idx=band
        ) for band in range(data.shape[2])]

        if self.opts['nprocs'] > 1 and self.mp_alg == 'bands':
            with multiprocessing.Pool(self.opts['nprocs'],
                                      initializer=_initializer,
                                      initargs=(self.lats, self.lons, data)) as pool:
                result = pool.map(self._to_map_geometry_2D, args)
        else:
            _initializer(self.lats, self.lons, data)
            result = [self._to_map_geometry_2D(argsdict) for argsdict in args]

        band_inds = list(np.array(result)[:, -1])
        data_mapgeo = np.dstack([result[band_inds.index(i)][0] for i in range(data.shape[2])])
        out_gt = result[0][1]
        out_prj = result[0][2]

        return data_mapgeo, out_gt, out_prj

    @staticmethod
Daniel Scheffler's avatar
Daniel Scheffler committed
237
238
    def _to_sensor_geometry_2D(kwargs_dict: dict
                               ) -> (np.ndarray, int):
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
        assert [var is not None for var in (_global_shared_lons, _global_shared_lats, _global_shared_data)]

        SMGT2D = SensorMapGeometryTransformer(lons=_global_shared_lons[:, :, kwargs_dict['band_idx']],
                                              lats=_global_shared_lats[:, :, kwargs_dict['band_idx']],
                                              resamp_alg=kwargs_dict['resamp_alg'],
                                              radius_of_influence=kwargs_dict['radius_of_influence'],
                                              **kwargs_dict['init_opts'])
        data_sensorgeo = SMGT2D.to_sensor_geometry(data=_global_shared_data[:, :, kwargs_dict['band_idx']],
                                                   src_prj=kwargs_dict['src_prj'],
                                                   src_extent=kwargs_dict['src_extent'])

        return data_sensorgeo, kwargs_dict['band_idx']

    def to_sensor_geometry(self,
                           data: np.ndarray,
                           src_prj: Union[str, int],
Daniel Scheffler's avatar
Daniel Scheffler committed
255
256
                           src_extent: Tuple[float, float, float, float]
                           ) -> np.ndarray:
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
        """Transform the input map geometry array into sensor geometry

        :param data:        3D numpy array (representing map geometry) to be warped to sensor geometry
        :param src_prj:     projection of the input map geometry array (WKT or 'epsg:1234' or <EPSG_int>)
        :param src_extent:  extent coordinates of input map geometry array (LL_x, LL_y, UR_x, UR_y) in the src_prj
        """
        if data.ndim != 3:
            raise ValueError(data.ndim, "'data' must have 3 dimensions.")

        init_opts = self.opts.copy()
        if self.mp_alg == 'bands':
            del init_opts['nprocs']  # avoid sub-multiprocessing

        args = [dict(
            resamp_alg=self.resamp_alg,
            radius_of_influence=self.radius_of_influence,
            init_opts=init_opts,
            src_prj=src_prj,
            src_extent=src_extent,
            band_idx=band
        ) for band in range(data.shape[2])]

        if self.opts['nprocs'] > 1 and self.mp_alg == 'bands':
            with multiprocessing.Pool(self.opts['nprocs'],
                                      initializer=_initializer,
                                      initargs=(self.lats, self.lons, data)) as pool:
                result = pool.map(self._to_sensor_geometry_2D, args)
        else:
            _initializer(self.lats, self.lons, data)
            result = [self._to_sensor_geometry_2D(argsdict) for argsdict in args]

        band_inds = list(np.array(result)[:, -1])
        data_sensorgeo = np.dstack([result[band_inds.index(i)][0] for i in range(data.shape[2])])

        return data_sensorgeo


_global_shared_lats = None
_global_shared_lons = None
_global_shared_data = None


def _initializer(lats, lons, data):
    """Declare global variables needed for SensorMapGeometryTransformer3D.to_map_geometry and to_sensor_geometry.

    :param lats:
    :param lons:
    :param data:
    """
    global _global_shared_lats, _global_shared_lons, _global_shared_data
    _global_shared_lats = lats
    _global_shared_lons = lons
    _global_shared_data = data